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2024,35(4):1585-1586, DOI: 10.13328/j.cnki.jos.007020
Abstract:
2024,35(4):1587-1600, DOI: 10.13328/j.cnki.jos.007014
Abstract:
With the development of technologies such as big data, computing, and the Internet, artificial intelligence techniques represented by machine learning and deep learning have achieved tremendous success. Particularly, the emergence of various large-scale models has greatly accelerated the application of artificial intelligence in various fields. However, the success of these techniques heavily relies on massive training data and abundant computing resources, which significantly limits their application in data or resource-scarce domains. Therefore, how to learn from limited samples, known as few-shot learning, has become a crucial research problem in the new wave of industrial transformation led by artificial intelligence. The most commonly used approach in few-shot learning is based on meta- learning. Such methods learn meta-knowledge for solving similar tasks by training on a series of related training tasks, which enables fast learning on new testing tasks using the acquired meta-knowledge. Although these methods have achieved sound results in few-shot classification tasks, they assume that the training and testing tasks come from the same distribution. This implies that a sufficient number of training tasks are required for the model to generalize the learned meta-knowledge to continuously changing testing tasks. However, in some real-world scenarios with truly limited data, ensuring an adequate number of training tasks is challenging. To address this issue, this study proposes a robust few-shot classification method based on diverse and authentic task generation (DATG). The method generates additional training tasks by applying Mixup to a small number of existing tasks, aiding the model in learning. By constraining the diversity and authenticity of the generated tasks, this method effectively improves the generalization of few-shot classification methods. Specifically, the base classes in the training set are firstly clustered to obtain different clusters and then tasks are selected from different clusters for Mixup to increase task diversity. Furthermore, performing inter-cluster tasks Mixup helps alleviate the learning of pseudo-discriminative features highly correlated with the categories. To ensure that the generated tasks do not deviate too much from the real distribution and mislead the model’s learning, the maximum mean discrepancy (MMD) between the generated tasks and real tasks is minimized, thus ensuring the authenticity of the generated tasks. Finally, it is theoretically analyzed why the inter-cluster task Mixup strategy can improve the model’s generalization performance. Experimental results on multiple datasets further demonstrate the effectiveness of the proposed method.
With the development of technologies such as big data, computing, and the Internet, artificial intelligence techniques represented by machine learning and deep learning have achieved tremendous success. Particularly, the emergence of various large-scale models has greatly accelerated the application of artificial intelligence in various fields. However, the success of these techniques heavily relies on massive training data and abundant computing resources, which significantly limits their application in data or resource-scarce domains. Therefore, how to learn from limited samples, known as few-shot learning, has become a crucial research problem in the new wave of industrial transformation led by artificial intelligence. The most commonly used approach in few-shot learning is based on meta- learning. Such methods learn meta-knowledge for solving similar tasks by training on a series of related training tasks, which enables fast learning on new testing tasks using the acquired meta-knowledge. Although these methods have achieved sound results in few-shot classification tasks, they assume that the training and testing tasks come from the same distribution. This implies that a sufficient number of training tasks are required for the model to generalize the learned meta-knowledge to continuously changing testing tasks. However, in some real-world scenarios with truly limited data, ensuring an adequate number of training tasks is challenging. To address this issue, this study proposes a robust few-shot classification method based on diverse and authentic task generation (DATG). The method generates additional training tasks by applying Mixup to a small number of existing tasks, aiding the model in learning. By constraining the diversity and authenticity of the generated tasks, this method effectively improves the generalization of few-shot classification methods. Specifically, the base classes in the training set are firstly clustered to obtain different clusters and then tasks are selected from different clusters for Mixup to increase task diversity. Furthermore, performing inter-cluster tasks Mixup helps alleviate the learning of pseudo-discriminative features highly correlated with the categories. To ensure that the generated tasks do not deviate too much from the real distribution and mislead the model’s learning, the maximum mean discrepancy (MMD) between the generated tasks and real tasks is minimized, thus ensuring the authenticity of the generated tasks. Finally, it is theoretically analyzed why the inter-cluster task Mixup strategy can improve the model’s generalization performance. Experimental results on multiple datasets further demonstrate the effectiveness of the proposed method.
2024,35(4):1601-1617, DOI: 10.13328/j.cnki.jos.007016
Abstract:
Code representation aims to extract the characteristics of source code to obtain its semantic embedding, playing a crucial role in deep learning-based code intelligence. Traditional handcrafted code representation methods mainly rely on domain expert annotations, which are time-consuming and labor-intensive. Moreover, the obtained code representations are task-specific and not easily reusable for specific downstream tasks, which contradicts the green and sustainable development concept. To this end, many large-scale pretraining models for source code representation have shown remarkable success in recent years. These methods utilize massive source code for self-supervised learning to obtain universal code representations, which are then easily fine-tuned for various downstream tasks. Based on the abstraction levels of programming languages, code representations have four level features: text level, semantic level, functional level, and structural level. Nevertheless, current models for code representation treat programming languages merely as ordinary text sequences resembling natural language. They overlook the functional-level and structural-level features, which bring performance inferior. To overcome this drawback, this study proposes a representation enhanced contrastive multimodal pretraining (REcomp) framework for code representation pretraining. REcomp has developed a novel semantic-level to structure-level feature fusion algorithm, which is employed for serializing abstract syntax trees. Through a multi-modal contrastive learning approach, this composite feature is integrated with both the textual and functional features of programming languages, enabling a more precise semantic modeling. Extensive experiments are conducted on three real-world public datasets. Experimental results clearly validate the superiority of REcomp.
Code representation aims to extract the characteristics of source code to obtain its semantic embedding, playing a crucial role in deep learning-based code intelligence. Traditional handcrafted code representation methods mainly rely on domain expert annotations, which are time-consuming and labor-intensive. Moreover, the obtained code representations are task-specific and not easily reusable for specific downstream tasks, which contradicts the green and sustainable development concept. To this end, many large-scale pretraining models for source code representation have shown remarkable success in recent years. These methods utilize massive source code for self-supervised learning to obtain universal code representations, which are then easily fine-tuned for various downstream tasks. Based on the abstraction levels of programming languages, code representations have four level features: text level, semantic level, functional level, and structural level. Nevertheless, current models for code representation treat programming languages merely as ordinary text sequences resembling natural language. They overlook the functional-level and structural-level features, which bring performance inferior. To overcome this drawback, this study proposes a representation enhanced contrastive multimodal pretraining (REcomp) framework for code representation pretraining. REcomp has developed a novel semantic-level to structure-level feature fusion algorithm, which is employed for serializing abstract syntax trees. Through a multi-modal contrastive learning approach, this composite feature is integrated with both the textual and functional features of programming languages, enabling a more precise semantic modeling. Extensive experiments are conducted on three real-world public datasets. Experimental results clearly validate the superiority of REcomp.
2024,35(4):1618-1650, DOI: 10.13328/j.cnki.jos.007011
Abstract:
In recent years, deep reinforcement learning (DRL) has achieved remarkable success in many sequential decision-making tasks. However, the current success of deep reinforcement learning heavily relies on massive learning data and computing resources. The poor sample efficiency and strategy generalization ability are the key factors restricting DRL’s further development. Meta-reinforcement learning (Meta-RL) studies to adapt to a wider range of tasks with a smaller sample size. Related researches are expected to alleviate the above limitations and promote the development of reinforcement learning. Taking the scope of research object and application range of current research works, this study comprehensively combs the research progress in the field of meta-reinforcement learning. Firstly, a basic introduction is given to deep reinforcement learning and the background of meta-reinforcement learning. Then, meta-reinforcement learning is formally defined and common scene settings are summarized, and the current research progress of meta-reinforcement learning is also introduced from the perspective of application range of the research results. Finally, the research challenges and potential future development directions are discussed.
In recent years, deep reinforcement learning (DRL) has achieved remarkable success in many sequential decision-making tasks. However, the current success of deep reinforcement learning heavily relies on massive learning data and computing resources. The poor sample efficiency and strategy generalization ability are the key factors restricting DRL’s further development. Meta-reinforcement learning (Meta-RL) studies to adapt to a wider range of tasks with a smaller sample size. Related researches are expected to alleviate the above limitations and promote the development of reinforcement learning. Taking the scope of research object and application range of current research works, this study comprehensively combs the research progress in the field of meta-reinforcement learning. Firstly, a basic introduction is given to deep reinforcement learning and the background of meta-reinforcement learning. Then, meta-reinforcement learning is formally defined and common scene settings are summarized, and the current research progress of meta-reinforcement learning is also introduced from the perspective of application range of the research results. Finally, the research challenges and potential future development directions are discussed.
2024,35(4):1651-1666, DOI: 10.13328/j.cnki.jos.007010
Abstract:
Unsupervised domain adaptation (UDA) has achieved success in solving the problem that the training set (source domain) and the test set (target domain) come from different distributions. In the low energy consumption and open dynamic task environment, with the emergence of resource constraints and public classes, existing UDA methods encounter severe challenges. Source free open-set domain adaptation (SF-ODA) aims to transfer the knowledge from the source model to the unlabeled target domain where public classes appear, thus realizing the identification of common classes and detection of public class without the source data. Existing SF-ODA methods focus on designing source models that accurately detect public class or modifying the model structures. However, they not only require extra storage space and training overhead, but also are difficult to be implemented in the strict privacy scenarios. This study proposes a more practical scenario: Active learning source free open-set domain adaptive adaptation (ASF-ODA), based on a common training source model and a small number of valuable target samples labeled by experts to achieve a robust transfer. A local consistent active learning (LCAL) algorithm is proposed to achieve this objective. First of all, LCAL includes a new proposed active selection method, local diversity selection, to select more valuable samples of target domain and promote the separation of threshold fuzzy samples by taking advantage of the feature local labels in the consistent target domain. Then, based on information entropy, LCAL initially selects possible common class set and public class set, and corrects these two sets with labeled samples obtained in the first step to obtain two corresponding reliable sets. Finally, LCAL introduces open set loss and information maximization loss to further promote the separation of common and public classes, and introduces cross entropy loss to realize the discrimination of common classes. A large number of experiments on three publicly available benchmark datasets, Office-31, Office-Home, and VisDA-C, show that with the help of a small number of valuable target samples, LCAL significantly outperforms the existing active learning methods and SF-ODA methods, with over 20% HOS improvements in some transfer tasks.
Unsupervised domain adaptation (UDA) has achieved success in solving the problem that the training set (source domain) and the test set (target domain) come from different distributions. In the low energy consumption and open dynamic task environment, with the emergence of resource constraints and public classes, existing UDA methods encounter severe challenges. Source free open-set domain adaptation (SF-ODA) aims to transfer the knowledge from the source model to the unlabeled target domain where public classes appear, thus realizing the identification of common classes and detection of public class without the source data. Existing SF-ODA methods focus on designing source models that accurately detect public class or modifying the model structures. However, they not only require extra storage space and training overhead, but also are difficult to be implemented in the strict privacy scenarios. This study proposes a more practical scenario: Active learning source free open-set domain adaptive adaptation (ASF-ODA), based on a common training source model and a small number of valuable target samples labeled by experts to achieve a robust transfer. A local consistent active learning (LCAL) algorithm is proposed to achieve this objective. First of all, LCAL includes a new proposed active selection method, local diversity selection, to select more valuable samples of target domain and promote the separation of threshold fuzzy samples by taking advantage of the feature local labels in the consistent target domain. Then, based on information entropy, LCAL initially selects possible common class set and public class set, and corrects these two sets with labeled samples obtained in the first step to obtain two corresponding reliable sets. Finally, LCAL introduces open set loss and information maximization loss to further promote the separation of common and public classes, and introduces cross entropy loss to realize the discrimination of common classes. A large number of experiments on three publicly available benchmark datasets, Office-31, Office-Home, and VisDA-C, show that with the help of a small number of valuable target samples, LCAL significantly outperforms the existing active learning methods and SF-ODA methods, with over 20% HOS improvements in some transfer tasks.
2024,35(4):1667-1681, DOI: 10.13328/j.cnki.jos.007009
Abstract:
Open-set recognition is an important issue for ensuring the efficient and robust deployment of machine learning models in the open world. It aims to address the challenge of encountering samples from unseen classes that emerge during testing, i.e., to accurately classify the seen classes while identifying and rejecting the unseen ones. Current open-set recognition studies assume that the covariate distribution of the seen classes remains constant during both training and testing. However, in practical scenarios, the covariate distribution is constantly shifting, which can cause previous methods to fail, and their performance may even be worse than the baseline method. Therefore, it is urgent to study novel open-set recognition methods that can adapt to the constantly changing covariate distribution so that they can robustly classify seen categories and identify unseen categories during testing. This novel problem adaptation in the open world (AOW) is named and a test-time adaptation method is proposed for open-set recognition called open-set test-time adaptation (OTA). OTA method only utilizes unlabeled test data to update the model with adaptive entropy loss and open-set entropy loss, maintaining the model’s ability to discriminate seen classes while further enhancing its ability to recognize unseen classes. Comprehensive experiments are conducted on multiple benchmark datasets with different covariate shift levels. The results show that the proposal is robust to covariate shift and demonstrates superior performance compared to many state-of-the-art methods.
Open-set recognition is an important issue for ensuring the efficient and robust deployment of machine learning models in the open world. It aims to address the challenge of encountering samples from unseen classes that emerge during testing, i.e., to accurately classify the seen classes while identifying and rejecting the unseen ones. Current open-set recognition studies assume that the covariate distribution of the seen classes remains constant during both training and testing. However, in practical scenarios, the covariate distribution is constantly shifting, which can cause previous methods to fail, and their performance may even be worse than the baseline method. Therefore, it is urgent to study novel open-set recognition methods that can adapt to the constantly changing covariate distribution so that they can robustly classify seen categories and identify unseen categories during testing. This novel problem adaptation in the open world (AOW) is named and a test-time adaptation method is proposed for open-set recognition called open-set test-time adaptation (OTA). OTA method only utilizes unlabeled test data to update the model with adaptive entropy loss and open-set entropy loss, maintaining the model’s ability to discriminate seen classes while further enhancing its ability to recognize unseen classes. Comprehensive experiments are conducted on multiple benchmark datasets with different covariate shift levels. The results show that the proposal is robust to covariate shift and demonstrates superior performance compared to many state-of-the-art methods.
2024,35(4):1682-1702, DOI: 10.13328/j.cnki.jos.007012
Abstract:
Neural architecture search (NAS) is an important part of automated machine learning, which has been widely used in multiple fields, including computer vision, speech recognition, etc. NAS can search the optimal deep neural network structures for specific data, scenarios, and tasks. In recent years, NAS has been increasingly applied to brain data analysis, significantly improving the performance in multiple application fields, such as brain image segment, feature extraction, brain disease auxiliary diagnosis, etc. Such researches have demonstrated the advantages of low-energy automated machine learning in the field of brain data analysis. NAS-based brain data analysis is one of the current research hotspots, and it still has certain challenges. At present, there are few review literatures available for reference in this field worldwide. This study conducts a detailed survey and analysis of relevant literature from different perspectives, including search frameworks, search space, search strategies, research tasks, and experimental data. At the same time, a systematic summary of brain data sets is also provided that can be used for NAS training. In addition, challenges and future research directions of NAS are prospected in brain data analysis.
Neural architecture search (NAS) is an important part of automated machine learning, which has been widely used in multiple fields, including computer vision, speech recognition, etc. NAS can search the optimal deep neural network structures for specific data, scenarios, and tasks. In recent years, NAS has been increasingly applied to brain data analysis, significantly improving the performance in multiple application fields, such as brain image segment, feature extraction, brain disease auxiliary diagnosis, etc. Such researches have demonstrated the advantages of low-energy automated machine learning in the field of brain data analysis. NAS-based brain data analysis is one of the current research hotspots, and it still has certain challenges. At present, there are few review literatures available for reference in this field worldwide. This study conducts a detailed survey and analysis of relevant literature from different perspectives, including search frameworks, search space, search strategies, research tasks, and experimental data. At the same time, a systematic summary of brain data sets is also provided that can be used for NAS training. In addition, challenges and future research directions of NAS are prospected in brain data analysis.
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Available online: April 12, 2024 , DOI: 10.13328/j.cnki.jos.007146
Abstract:
A function-as-a-service (FaaS) workflow, composed of multiple function services, can realize a complex business application by orchestrating and controlling the function services. The current FaaS workflow execution systems achieve data transfer among function services mainly based on centralized data storages, resulting in heavy data transmission overhead and affecting application performance significantly. In the cases of high concurrency, frequent data transmission will also cause serious contention for network bandwidth resources, resulting in application performance degradation. To address the above problems, this study analyzes the fine-grained data dependency between function services and proposes a critical path-based FaaS workflow deployment optimization method. In addition, the study designs a dependency-sensitive data access and management mechanism to effectively reduce the data transmission between function services, thereby reducing the data transmission latency and end-to-end execution latency of FaaS workflow applications. The study implements a FaaS workflow system, FineFlow, and conducts experiments based on five real-world FaaS workflow applications. The experimental results show that FineFlow can effectively reduce the data transmission latency (the highest reduction and the average reduction are 74.6% and 63.8%, respectively) compared with the FaaS workflow platform with the centralized data storing-based function interaction mechanism. On average, FineFlow reduces the latency of the end-to-end FaaS workflow executions by 19.6%. In particular, for the FaaS workflow application with fine-grained data dependencies, FineFlow can further reduce its data transmission latency and the end-to-end execution latency by 28.4% and 13.8% respectively compared with the state-of-the-art work. In addition, FineFlow can effectively alleviate the impact of network bandwidth fluctuations on application performance by reducing cross-node data transmission, improving the robustness of application performance influenced by the network bandwidth changes.
A function-as-a-service (FaaS) workflow, composed of multiple function services, can realize a complex business application by orchestrating and controlling the function services. The current FaaS workflow execution systems achieve data transfer among function services mainly based on centralized data storages, resulting in heavy data transmission overhead and affecting application performance significantly. In the cases of high concurrency, frequent data transmission will also cause serious contention for network bandwidth resources, resulting in application performance degradation. To address the above problems, this study analyzes the fine-grained data dependency between function services and proposes a critical path-based FaaS workflow deployment optimization method. In addition, the study designs a dependency-sensitive data access and management mechanism to effectively reduce the data transmission between function services, thereby reducing the data transmission latency and end-to-end execution latency of FaaS workflow applications. The study implements a FaaS workflow system, FineFlow, and conducts experiments based on five real-world FaaS workflow applications. The experimental results show that FineFlow can effectively reduce the data transmission latency (the highest reduction and the average reduction are 74.6% and 63.8%, respectively) compared with the FaaS workflow platform with the centralized data storing-based function interaction mechanism. On average, FineFlow reduces the latency of the end-to-end FaaS workflow executions by 19.6%. In particular, for the FaaS workflow application with fine-grained data dependencies, FineFlow can further reduce its data transmission latency and the end-to-end execution latency by 28.4% and 13.8% respectively compared with the state-of-the-art work. In addition, FineFlow can effectively alleviate the impact of network bandwidth fluctuations on application performance by reducing cross-node data transmission, improving the robustness of application performance influenced by the network bandwidth changes.
Available online: April 03, 2024 , DOI: 10.13328/j.cnki.jos.007054
Abstract:
Deep learning has been widely employed in many fields and yields excellent performance. However, this often requires the support of large amounts of labeled data, which usually means high costs and harsh application conditions. Therefore, with the development of deep learning, how to break through data limitations in practical scenarios has become an important research problem. Specifically, as one of the most important research directions, semi-supervised learning greatly relieves the data requirement pressure of deep learning by conducting learning with the assistance of abundant unlabeled data and a small number of labeled data. The pseudo-labeling method plays a significant role in semi-supervised learning, and the quality of its generated pseudo labels will influence the final results of semi-supervised learning. Focusing on pseudo-labeling in semi-supervised learning, this study proposes the pseudo-labeling method based on optimal transport theory, which introduces the pseudo-labeling procedure constraint with labeled data as generation process guidance. On this basis, the pseudo-labeling procedure is converted to the optimization problem of optimal transport, which offers a new form for solving pseudo-labeling. Meanwhile, to solve this problem, this study introduces the Sinkhorn-Knopp algorithm for approximate fast solutions to avoid the heavy computation burden. As an independent module, the proposed method can be combined with other semi-supervised learning tricks such as consistency regularization for complete semi-supervised learning. Finally, this study conducts experiments on four classic public image classification datasets of CIFAR-10, SVHN, MNIST, and FashionMNIST to verify the effectiveness of the proposed method. The experimental results show that compared with the state-of-the-art semi-supervised learning methods, this method yields better performance, especially under fewer labeled data.
Deep learning has been widely employed in many fields and yields excellent performance. However, this often requires the support of large amounts of labeled data, which usually means high costs and harsh application conditions. Therefore, with the development of deep learning, how to break through data limitations in practical scenarios has become an important research problem. Specifically, as one of the most important research directions, semi-supervised learning greatly relieves the data requirement pressure of deep learning by conducting learning with the assistance of abundant unlabeled data and a small number of labeled data. The pseudo-labeling method plays a significant role in semi-supervised learning, and the quality of its generated pseudo labels will influence the final results of semi-supervised learning. Focusing on pseudo-labeling in semi-supervised learning, this study proposes the pseudo-labeling method based on optimal transport theory, which introduces the pseudo-labeling procedure constraint with labeled data as generation process guidance. On this basis, the pseudo-labeling procedure is converted to the optimization problem of optimal transport, which offers a new form for solving pseudo-labeling. Meanwhile, to solve this problem, this study introduces the Sinkhorn-Knopp algorithm for approximate fast solutions to avoid the heavy computation burden. As an independent module, the proposed method can be combined with other semi-supervised learning tricks such as consistency regularization for complete semi-supervised learning. Finally, this study conducts experiments on four classic public image classification datasets of CIFAR-10, SVHN, MNIST, and FashionMNIST to verify the effectiveness of the proposed method. The experimental results show that compared with the state-of-the-art semi-supervised learning methods, this method yields better performance, especially under fewer labeled data.
Available online: March 27, 2024 , DOI: 10.13328/j.cnki.jos.007084
Abstract:
The homegrown Shenwei AI acceleration card is equipped with the Shenwei many-core processor based on systolic array enhancement, and although its intelligent computing power can be comparable to the mainstream GPU, there is still a lack of basic software support. To lower the utilization threshold of the Shenwei AI acceleration card and effectively support the development of AI applications, this study designs a runtime system SDAA for the Shenwei AI acceleration card, whose semantics is consistent with the mainstream CUDA. For key paths such as memory management, data transmission, and kernel function launch, the software and hardware co-design method is adopted to realize the multi-level memory allocation algorithm with segment and paged memory combined on the card, pageable memory transmission model of multiple threads and channels, adaptive data transmission algorithm with multi-heterogeneous components, and fast kernel function launch method based on on-chip array communication. As a result, the runtime performance of SDAA is better than that of the mainstream GPU. The experimental results indicate that the memory allocation speed of SDAA is 120 times the corresponding interface of NVIDIA V100, the memory transmission overhead is 1/2 of the corresponding interface, and the data transmission bandwidth is 1.7 times the corresponding interface. Additionally, the launch time of the kernel function is equivalent to the corresponding interface, and thus the SDAA runtime system can support the efficient operation of mainstream frameworks and actual model training on the Shenwei AI acceleration card.
The homegrown Shenwei AI acceleration card is equipped with the Shenwei many-core processor based on systolic array enhancement, and although its intelligent computing power can be comparable to the mainstream GPU, there is still a lack of basic software support. To lower the utilization threshold of the Shenwei AI acceleration card and effectively support the development of AI applications, this study designs a runtime system SDAA for the Shenwei AI acceleration card, whose semantics is consistent with the mainstream CUDA. For key paths such as memory management, data transmission, and kernel function launch, the software and hardware co-design method is adopted to realize the multi-level memory allocation algorithm with segment and paged memory combined on the card, pageable memory transmission model of multiple threads and channels, adaptive data transmission algorithm with multi-heterogeneous components, and fast kernel function launch method based on on-chip array communication. As a result, the runtime performance of SDAA is better than that of the mainstream GPU. The experimental results indicate that the memory allocation speed of SDAA is 120 times the corresponding interface of NVIDIA V100, the memory transmission overhead is 1/2 of the corresponding interface, and the data transmission bandwidth is 1.7 times the corresponding interface. Additionally, the launch time of the kernel function is equivalent to the corresponding interface, and thus the SDAA runtime system can support the efficient operation of mainstream frameworks and actual model training on the Shenwei AI acceleration card.
Available online: March 27, 2024 , DOI: 10.13328/j.cnki.jos.007081
Abstract:
Embedded systems are becoming increasingly complex, and the requirements analysis of their software systems has become a bottleneck in embedded system development. Device dependency and interleaving execution logic are typical characteristics of embedded software systems, necessitating effective requirement analysis methods to decouple the requirements based on device dependencies. Starting from the idea of environment-based modeling in requirement engineering, this study proposes a projection-based requirement analysis approach from system requirements to software requirements for embedded software systems, helping requirement engineers to effectively decouple the requirements. The study first summarizes the system requirement and software requirement descriptions of embedded software systems, defines the requirement decoupling strategies of embedded software systems based on interactive environment characteristics, and designs the specification process from system requirements to software requirements. A real case study is carried out in the spacecraft sun search system, and five representative case scenarios are quantitatively evaluated through two metrics of coupling and cohesion, which demonstrate the effectiveness of the proposed approach.
Embedded systems are becoming increasingly complex, and the requirements analysis of their software systems has become a bottleneck in embedded system development. Device dependency and interleaving execution logic are typical characteristics of embedded software systems, necessitating effective requirement analysis methods to decouple the requirements based on device dependencies. Starting from the idea of environment-based modeling in requirement engineering, this study proposes a projection-based requirement analysis approach from system requirements to software requirements for embedded software systems, helping requirement engineers to effectively decouple the requirements. The study first summarizes the system requirement and software requirement descriptions of embedded software systems, defines the requirement decoupling strategies of embedded software systems based on interactive environment characteristics, and designs the specification process from system requirements to software requirements. A real case study is carried out in the spacecraft sun search system, and five representative case scenarios are quantitatively evaluated through two metrics of coupling and cohesion, which demonstrate the effectiveness of the proposed approach.
Available online: March 27, 2024 , DOI: 10.13328/j.cnki.jos.007090
Abstract:
Elephant flow identification is a fundamental task in network measurements. Currently, the mainstream methods generally employ sketch data structure Sketch to quickly count network traffic and efficiently find elephant flows. However, the rapid influx of numerous packets will significantly decrease the identification accuracy of elephant flows under network traffic jitters. To this end, this study proposes an elastic identification method for elephant flows supporting network traffic jitters, which is named RobustSketch. This method first designs a stretchable mice flow filter based on the cyclic Sketch chain, and adaptively increases and reduces the number of Sketch in real-time packet arrival rates. As a result, it always completely records all arrived packets within the current period to ensure accurate mice flow filtering even under network traffic jitters. Subsequently, this study designs a scalable elephant flow record table based on dynamic segmented hashing, which adaptively increases and reduces segments according to the number of candidate elephant flows filtered out by the mice flow filter. Finally, this can fully record all candidate elephant flows and keep high storage space utilization. Furthermore, the error bounds of the proposed mice flow filter and elephant flow recording table are provided by theoretical analysis. Finally, experimental evaluation is conducted on the proposed elephant flow identification method RobustSketch with real network traffic samples. Experimental results indicate that the identification accuracy of elephant flows of the proposed method is significantly higher than that of the existing methods, and can stably keep high accuracy of over 99% even under network traffic jitters. Meanwhile, its average relative error is reduced by more than 2.7 times, which enhances the accuracy and robustness of elephant flow identification.
Elephant flow identification is a fundamental task in network measurements. Currently, the mainstream methods generally employ sketch data structure Sketch to quickly count network traffic and efficiently find elephant flows. However, the rapid influx of numerous packets will significantly decrease the identification accuracy of elephant flows under network traffic jitters. To this end, this study proposes an elastic identification method for elephant flows supporting network traffic jitters, which is named RobustSketch. This method first designs a stretchable mice flow filter based on the cyclic Sketch chain, and adaptively increases and reduces the number of Sketch in real-time packet arrival rates. As a result, it always completely records all arrived packets within the current period to ensure accurate mice flow filtering even under network traffic jitters. Subsequently, this study designs a scalable elephant flow record table based on dynamic segmented hashing, which adaptively increases and reduces segments according to the number of candidate elephant flows filtered out by the mice flow filter. Finally, this can fully record all candidate elephant flows and keep high storage space utilization. Furthermore, the error bounds of the proposed mice flow filter and elephant flow recording table are provided by theoretical analysis. Finally, experimental evaluation is conducted on the proposed elephant flow identification method RobustSketch with real network traffic samples. Experimental results indicate that the identification accuracy of elephant flows of the proposed method is significantly higher than that of the existing methods, and can stably keep high accuracy of over 99% even under network traffic jitters. Meanwhile, its average relative error is reduced by more than 2.7 times, which enhances the accuracy and robustness of elephant flow identification.
Available online: March 27, 2024 , DOI: 10.13328/j.cnki.jos.007091
Abstract:
Internet service providers employ routing protection algorithms to meet real-time, low-latency, and high-availability application needs. However, existing routing protection algorithms have the following three problems. (1) The failure protection ratio is generally low under the premise of not changing the traditional routing protocol forwarding mechanism. (2) The traditional routing protocol forwarding mechanism should be changed to pursue a high failure protection ratio, which is difficult to deploy in practice. (3) The optimal next hop and backup next hop cannot be utilized simultaneously, which causes poor network load balancing capability. For the three problems, this study proposes a routing protection algorithm based on the shortest path serialization graph, which does not need to change the forwarding mechanism, supports incremental deployment and adopts both optimal next hop and backup next hop without routing loops, with a high failure protection ratio. The proposed algorithm mainly includes the following two steps. (1) A sequence number for each node is calculated, and the shortest path sequencing graph is generated. (2) The shortest path serialization graph is generated based on the node sequence number and reverse order search rules, and the next hop set between node pairs is calculated according to the backup next hop calculation rules. Tests on real and simulated network topologies show that the proposed scheme has significant advantages over other routing protection schemes in the average number of backup next hops, failure protection ratio, and path stretch.
Internet service providers employ routing protection algorithms to meet real-time, low-latency, and high-availability application needs. However, existing routing protection algorithms have the following three problems. (1) The failure protection ratio is generally low under the premise of not changing the traditional routing protocol forwarding mechanism. (2) The traditional routing protocol forwarding mechanism should be changed to pursue a high failure protection ratio, which is difficult to deploy in practice. (3) The optimal next hop and backup next hop cannot be utilized simultaneously, which causes poor network load balancing capability. For the three problems, this study proposes a routing protection algorithm based on the shortest path serialization graph, which does not need to change the forwarding mechanism, supports incremental deployment and adopts both optimal next hop and backup next hop without routing loops, with a high failure protection ratio. The proposed algorithm mainly includes the following two steps. (1) A sequence number for each node is calculated, and the shortest path sequencing graph is generated. (2) The shortest path serialization graph is generated based on the node sequence number and reverse order search rules, and the next hop set between node pairs is calculated according to the backup next hop calculation rules. Tests on real and simulated network topologies show that the proposed scheme has significant advantages over other routing protection schemes in the average number of backup next hops, failure protection ratio, and path stretch.
Available online: March 20, 2024 , DOI: 10.13328/j.cnki.jos.007095
Abstract:
In recent years, with the popularity of cloud services, increasingly more enterprises and individuals have stored their data in cloud databases. However, enjoying the convenience of cloud services also brings about data security issues. One of the crucial problems is data confidentiality protection, which is to safeguard the sensitive data of users from being spied on or leaked. Fully encrypted databases have emerged to face this challenge. Compared with traditional databases, fully encrypted databases can encrypt data in the entire lifecycle of data transmission, storage, and computation, thereby ensuring data confidentiality. Currently, there are still many challenges in encrypting data while supporting all SQL functionalities and maintaining high performance. This study comprehensively investigates the key techniques of encrypted computing in fully encrypted databases, summarizes the techniques according to the types, and compares and sums up them based on functionality, security, and performance. Firstly, it introduces the architecture of fully encrypted databases, including crypto-based architecture, trusted execution environment (TEE)-based architecture, and hybrid architecture. Then, the key techniques of each architecture are summarized. Finally, the challenges and opportunities of current research are discussed, with some open problems provided for future research.
In recent years, with the popularity of cloud services, increasingly more enterprises and individuals have stored their data in cloud databases. However, enjoying the convenience of cloud services also brings about data security issues. One of the crucial problems is data confidentiality protection, which is to safeguard the sensitive data of users from being spied on or leaked. Fully encrypted databases have emerged to face this challenge. Compared with traditional databases, fully encrypted databases can encrypt data in the entire lifecycle of data transmission, storage, and computation, thereby ensuring data confidentiality. Currently, there are still many challenges in encrypting data while supporting all SQL functionalities and maintaining high performance. This study comprehensively investigates the key techniques of encrypted computing in fully encrypted databases, summarizes the techniques according to the types, and compares and sums up them based on functionality, security, and performance. Firstly, it introduces the architecture of fully encrypted databases, including crypto-based architecture, trusted execution environment (TEE)-based architecture, and hybrid architecture. Then, the key techniques of each architecture are summarized. Finally, the challenges and opportunities of current research are discussed, with some open problems provided for future research.
Available online: March 20, 2024 , DOI: 10.13328/j.cnki.jos.007085
Abstract:
As a new type of distributed machine learning paradigm, federated learning makes full use of the computing power of many distributed clients and their local data to jointly train a machine learning model under the premise of meeting user privacy and data confidentiality requirements. In cross-device federated learning scenarios, the client usually consists of thousands or even tens of thousands of mobile devices or terminal devices. Due to the limitations of communication and computing costs, the aggregation server only selects few clients for the training during each round of training. Meanwhile, several widely employed federated optimization algorithms adopt a completely random client selection algorithm, which has been proven to have a huge optimization space. In recent years, how to efficiently and reliably select a suitable set from massive heterogeneous clients to participate in training and thus optimize the resource consumption and model performance of federated learning protocols has been extensively studied, but there is still no comprehensive investigation on the key issue. Therefore, this study conducts a comprehensive survey of client selection algorithms for cross-device federated learning. Specifically, it provides a formal description of the client selection problem, then gives the classification of selection algorithms, and discusses and analyzes the algorithms one by one. Finally, some future research directions for client selection algorithms are explored.
As a new type of distributed machine learning paradigm, federated learning makes full use of the computing power of many distributed clients and their local data to jointly train a machine learning model under the premise of meeting user privacy and data confidentiality requirements. In cross-device federated learning scenarios, the client usually consists of thousands or even tens of thousands of mobile devices or terminal devices. Due to the limitations of communication and computing costs, the aggregation server only selects few clients for the training during each round of training. Meanwhile, several widely employed federated optimization algorithms adopt a completely random client selection algorithm, which has been proven to have a huge optimization space. In recent years, how to efficiently and reliably select a suitable set from massive heterogeneous clients to participate in training and thus optimize the resource consumption and model performance of federated learning protocols has been extensively studied, but there is still no comprehensive investigation on the key issue. Therefore, this study conducts a comprehensive survey of client selection algorithms for cross-device federated learning. Specifically, it provides a formal description of the client selection problem, then gives the classification of selection algorithms, and discusses and analyzes the algorithms one by one. Finally, some future research directions for client selection algorithms are explored.
Available online: March 20, 2024 , DOI: 10.13328/j.cnki.jos.007087
Abstract:
Financial risk prediction plays an important role in financial market regulation and financial investment, and has become a research hotspot in artificial intelligence and financial technology in recent years. Due to the complex investment, supply and other relationships among financial event entities, existing research on financial risk prediction often employs various static and dynamic graph structures to model the relationship among financial entities. Meanwhile, convolutional graph neural networks and other methods are adopted to embed relevant graph structure information into the feature representation of financial entities, which enables the representation of both semantic and structural information related to financial risks. However, previous reviews of financial risk prediction only focus on studies based on static graph structures, but ignore the characteristics that the relationship among entities in financial events will change dynamically over time, which reduces the accuracy of risk prediction results. With the development of temporal graph neural networks, increasingly more studies have begun to pay attention to financial risk prediction based on dynamic graph structures, and a systematic and comprehensive review of these studies will help learners foster a complete understanding of financial risk prediction research. According to different methods to extract temporal information from dynamic graphs, this study first reviews three different neural network models for temporal graphs. Then, based on different graph learning tasks, it introduces the research on financial risk prediction in four areas, including stock price trend risk prediction, loan default risk prediction, fraud transaction risk prediction, and money laundering and tax evasion risk prediction. Finally, the difficulties and challenges facing the existing temporal graph neural network models in financial risk prediction are summarized, and potential directions for future research are prospected.
Financial risk prediction plays an important role in financial market regulation and financial investment, and has become a research hotspot in artificial intelligence and financial technology in recent years. Due to the complex investment, supply and other relationships among financial event entities, existing research on financial risk prediction often employs various static and dynamic graph structures to model the relationship among financial entities. Meanwhile, convolutional graph neural networks and other methods are adopted to embed relevant graph structure information into the feature representation of financial entities, which enables the representation of both semantic and structural information related to financial risks. However, previous reviews of financial risk prediction only focus on studies based on static graph structures, but ignore the characteristics that the relationship among entities in financial events will change dynamically over time, which reduces the accuracy of risk prediction results. With the development of temporal graph neural networks, increasingly more studies have begun to pay attention to financial risk prediction based on dynamic graph structures, and a systematic and comprehensive review of these studies will help learners foster a complete understanding of financial risk prediction research. According to different methods to extract temporal information from dynamic graphs, this study first reviews three different neural network models for temporal graphs. Then, based on different graph learning tasks, it introduces the research on financial risk prediction in four areas, including stock price trend risk prediction, loan default risk prediction, fraud transaction risk prediction, and money laundering and tax evasion risk prediction. Finally, the difficulties and challenges facing the existing temporal graph neural network models in financial risk prediction are summarized, and potential directions for future research are prospected.
Available online: March 20, 2024 , DOI: 10.13328/j.cnki.jos.007093
Abstract:
As a research hotspot in artificial intelligence in recent years, knowledge graphs have been applied to many fields in reality. However, with the increasingly diversified application scenarios of knowledge graphs, people gradually find that static knowledge graphs which do not change with time cannot fully adapt to the scenarios of high-frequency knowledge update. To this end, researchers propose the concept of temporal knowledge graphs containing temporal information. This study organizes all existing temporal knowledge graph representation and reasoning models and summarizes and constructs a theoretical framework for these models. Then, on this basis, it briefly introduces and analyzes the current research progress of temporal representation reasoning, and carries out the future trend prediction to help researchers develop and design better models.
As a research hotspot in artificial intelligence in recent years, knowledge graphs have been applied to many fields in reality. However, with the increasingly diversified application scenarios of knowledge graphs, people gradually find that static knowledge graphs which do not change with time cannot fully adapt to the scenarios of high-frequency knowledge update. To this end, researchers propose the concept of temporal knowledge graphs containing temporal information. This study organizes all existing temporal knowledge graph representation and reasoning models and summarizes and constructs a theoretical framework for these models. Then, on this basis, it briefly introduces and analyzes the current research progress of temporal representation reasoning, and carries out the future trend prediction to help researchers develop and design better models.
Available online: March 13, 2024 , DOI: 10.13328/j.cnki.jos.007082
Abstract:
In recent years, service-oriented IoT architectures have received a lot of attention from academia and industry. By encapsulating IoT resources into intelligent IoT services, interconnecting and collaborating these resource-constrained and capacity-evolving IoT services to facilitate IoT applications has become a widely adopted and flexible mechanism. Upon capacity-fluctuating and resource-varying edge devices, IoT services may experience QoS degradations or resource mismatches during their execution, making it difficult for IoT applications to continue and possibly inducing failures. Therefore, quantitative monitoring of IoT services at runtime has become the key to guaranteeing the robustness of IoT applications. Different monitoring mechanisms have been proposed in recent literature, but they are inadequate in formal interpretation with strong domain relevance and empirical subjectivity. Based on formal methods, such as signal temporal logic (STL), the problem of IoT service monitoring can be formulated as a temporal logic task to achieve runtime quantitative monitoring. However, STL and its extensions suffer from issues of non-differentiability, loss of soundness, and inapplicability in dynamic environments. Moreover, existing works are inadequate for the monitoring of composite services, with a lack of integrity, linkage, and dynamics. To solve these problems, this study proposes a compositional signal temporal logic (CSTL) to achieve quantitative monitoring of different QoS constraints and time constraints upon intra-, inter-, and composite services. Specifically, CSTL extends an accumulative operator based on positively and negatively biased Riemann sums to emphasize the robust satisfaction of all sub-formulae over their entire time domains and to evaluate qualitative and quantitative constraint satisfaction for IoT service monitoring. Besides, CSTL extends a compositional operator based on constraint types and composite structures, as well as dynamic variables that can vary with the dynamic environment, to effectively monitor QoS variations and temporal violations of composite services. As a result, temporal and QoS constraints upon intra-, inter-, and composite services, can be specified by CSTL formulae, and formally interpreted with qualitative and quantitative satisfaction at runtime. Extensive evaluations show that the proposed CSTL performs better than baseline techniques in terms of expressiveness, applicability, and robustness.
In recent years, service-oriented IoT architectures have received a lot of attention from academia and industry. By encapsulating IoT resources into intelligent IoT services, interconnecting and collaborating these resource-constrained and capacity-evolving IoT services to facilitate IoT applications has become a widely adopted and flexible mechanism. Upon capacity-fluctuating and resource-varying edge devices, IoT services may experience QoS degradations or resource mismatches during their execution, making it difficult for IoT applications to continue and possibly inducing failures. Therefore, quantitative monitoring of IoT services at runtime has become the key to guaranteeing the robustness of IoT applications. Different monitoring mechanisms have been proposed in recent literature, but they are inadequate in formal interpretation with strong domain relevance and empirical subjectivity. Based on formal methods, such as signal temporal logic (STL), the problem of IoT service monitoring can be formulated as a temporal logic task to achieve runtime quantitative monitoring. However, STL and its extensions suffer from issues of non-differentiability, loss of soundness, and inapplicability in dynamic environments. Moreover, existing works are inadequate for the monitoring of composite services, with a lack of integrity, linkage, and dynamics. To solve these problems, this study proposes a compositional signal temporal logic (CSTL) to achieve quantitative monitoring of different QoS constraints and time constraints upon intra-, inter-, and composite services. Specifically, CSTL extends an accumulative operator based on positively and negatively biased Riemann sums to emphasize the robust satisfaction of all sub-formulae over their entire time domains and to evaluate qualitative and quantitative constraint satisfaction for IoT service monitoring. Besides, CSTL extends a compositional operator based on constraint types and composite structures, as well as dynamic variables that can vary with the dynamic environment, to effectively monitor QoS variations and temporal violations of composite services. As a result, temporal and QoS constraints upon intra-, inter-, and composite services, can be specified by CSTL formulae, and formally interpreted with qualitative and quantitative satisfaction at runtime. Extensive evaluations show that the proposed CSTL performs better than baseline techniques in terms of expressiveness, applicability, and robustness.
Available online: March 13, 2024 , DOI: 10.13328/j.cnki.jos.007089
Abstract:
As the core foundation for ensuring network security, cryptography plays a crucial role in data protection, identity verification, encrypted communication, and other aspects. With the rapid popularization of 5G and the Internet of Things technology, network security is facing unprecedented challenges, and the demand for cryptographic performance is showing explosive growth. GPU can utilize thousands of parallel computing cores to accelerate complex computing problems, which is very suitable for the computationally intensive nature of cryptographic algorithms. Therefore, researchers have extensively explored methods to accelerate various cryptographic algorithms on GPU platforms. Compared with platforms such as CPU and FPGA, GPU has significant performance advantages. This study discusses the classification of various cryptographic algorithms and GPU platform architecture, and provides a detailed analysis of current research on various ciphers on GPU heterogeneous platforms. Additionally, it summarizes the current technical challenges confronted by high-performance cryptography based on GPU platforms and provides prospects for future technological development. Finally, comprehensive references can be provided for practitioners in cryptography engineering research on the latest research progress and application practices of high-performance cryptography based on GPU by in-depth studies and summaries.
As the core foundation for ensuring network security, cryptography plays a crucial role in data protection, identity verification, encrypted communication, and other aspects. With the rapid popularization of 5G and the Internet of Things technology, network security is facing unprecedented challenges, and the demand for cryptographic performance is showing explosive growth. GPU can utilize thousands of parallel computing cores to accelerate complex computing problems, which is very suitable for the computationally intensive nature of cryptographic algorithms. Therefore, researchers have extensively explored methods to accelerate various cryptographic algorithms on GPU platforms. Compared with platforms such as CPU and FPGA, GPU has significant performance advantages. This study discusses the classification of various cryptographic algorithms and GPU platform architecture, and provides a detailed analysis of current research on various ciphers on GPU heterogeneous platforms. Additionally, it summarizes the current technical challenges confronted by high-performance cryptography based on GPU platforms and provides prospects for future technological development. Finally, comprehensive references can be provided for practitioners in cryptography engineering research on the latest research progress and application practices of high-performance cryptography based on GPU by in-depth studies and summaries.
Available online: March 06, 2024 , DOI: 10.13328/j.cnki.jos.007083
Abstract:
Multi-modal medical image fusion provides a more comprehensive and accurate medical image description for medical diagnosis, surgical navigation, and other clinical applications by effectively combining human tissue structure and lesion information reflected by different modal datasets. This study aims to address partial spectral degradation, lack of edges and details and insufficient color reproduction of adhesion lesion-invaded regions in current fusion methods. It proposes a novel multi-modal medical image fusion method to achieve multi-feature enhancement and color preservation in the multi-scale feature frequency domain decomposition filter domain. This method decomposes the source image into four parts: smoothing, texture, contour, and edge feature layers, which employ specific fusion rules and generate fusion results by image reconstruction. In particular, given the potential feature information contained in the smoothing layer, the study proposes a visual saliency decomposition strategy to explore the energy and partial fiber texture features with multi-scale and multi-dimensionality, enhancing the utilization of source image information. In the texture layer, the study introduces a texture enhancement operator to extract details and hierarchical information through spatial structure and information measurement, addressing the issue of distinguishing the invasion status of adherent lesion areas in current fusion methods. In addition, due to the lack of a public abdominal dataset, 403 sets of abdominal images are registered in this study for public access and download. Experiments conducted on public dataset Atlas and abdominal datasets are compared with six baseline methods. Compared to the most advanced methods, the results show that the similarity between the fused image and the source image is improved by 22.92%, the edge retention, spatial frequency, and contrast ratio of fused images are improved by 35.79%, 28.79%, and 32.92%, respectively. In addition, the visual and computing efficiency of the proposed method are better than those of other methods.
Multi-modal medical image fusion provides a more comprehensive and accurate medical image description for medical diagnosis, surgical navigation, and other clinical applications by effectively combining human tissue structure and lesion information reflected by different modal datasets. This study aims to address partial spectral degradation, lack of edges and details and insufficient color reproduction of adhesion lesion-invaded regions in current fusion methods. It proposes a novel multi-modal medical image fusion method to achieve multi-feature enhancement and color preservation in the multi-scale feature frequency domain decomposition filter domain. This method decomposes the source image into four parts: smoothing, texture, contour, and edge feature layers, which employ specific fusion rules and generate fusion results by image reconstruction. In particular, given the potential feature information contained in the smoothing layer, the study proposes a visual saliency decomposition strategy to explore the energy and partial fiber texture features with multi-scale and multi-dimensionality, enhancing the utilization of source image information. In the texture layer, the study introduces a texture enhancement operator to extract details and hierarchical information through spatial structure and information measurement, addressing the issue of distinguishing the invasion status of adherent lesion areas in current fusion methods. In addition, due to the lack of a public abdominal dataset, 403 sets of abdominal images are registered in this study for public access and download. Experiments conducted on public dataset Atlas and abdominal datasets are compared with six baseline methods. Compared to the most advanced methods, the results show that the similarity between the fused image and the source image is improved by 22.92%, the edge retention, spatial frequency, and contrast ratio of fused images are improved by 35.79%, 28.79%, and 32.92%, respectively. In addition, the visual and computing efficiency of the proposed method are better than those of other methods.
Available online: February 28, 2024 , DOI: 10.13328/j.cnki.jos.007065
Abstract:
Graph data is ubiquitous in real-world applications, and graph neural networks (GNNs) have been widely used in graph data analysis. However, the performance of GNNs can be severely impacted by adversarial attacks on graph structures. Existing defense methods against adversarial attacks generally rely on low-rank graph structure reconstruction based on graph community preservation priors. However, existing graph structure adversarial defense methods cannot adaptively seek the true low-rank value for graph structure reconstruction, and low-rank graph structures are semantically mismatched with downstream tasks. To address these problems, this study proposes the over-parameterized graph neural network (OPGNN) method based on the implicit regularization effect of over-parameterization. In addition, it formally proves that this method can adaptively solve the low-rank graph structure problem and also proves that over-parameterized residual links on node deep representations can effectively address semantic mismatch. Experimental results on real datasets demonstrate that the OPGNN method is more robust than existing baseline methods, and the OPGNN framework is notably effective on different graph neural network backbones such as GCN, APPNP, and GPRGNN.
Graph data is ubiquitous in real-world applications, and graph neural networks (GNNs) have been widely used in graph data analysis. However, the performance of GNNs can be severely impacted by adversarial attacks on graph structures. Existing defense methods against adversarial attacks generally rely on low-rank graph structure reconstruction based on graph community preservation priors. However, existing graph structure adversarial defense methods cannot adaptively seek the true low-rank value for graph structure reconstruction, and low-rank graph structures are semantically mismatched with downstream tasks. To address these problems, this study proposes the over-parameterized graph neural network (OPGNN) method based on the implicit regularization effect of over-parameterization. In addition, it formally proves that this method can adaptively solve the low-rank graph structure problem and also proves that over-parameterized residual links on node deep representations can effectively address semantic mismatch. Experimental results on real datasets demonstrate that the OPGNN method is more robust than existing baseline methods, and the OPGNN framework is notably effective on different graph neural network backbones such as GCN, APPNP, and GPRGNN.
Available online: February 05, 2024 , DOI: 10.13328/j.cnki.jos.007064
Abstract:
Temporal graph is a type of graph where each edge is associated with a timestamp. Seasonal-bursting subgraph is a dense subgraph characterized by burstiness over multiple time periods, which can applied for activity discovery and group relationship analysis in social networks. Unfortunately, most previous studies for subgraph mining in temporal networks ignore the seasonal or bursting features of subgraphs. To this end, this study proposes a maximal ($\omega,\theta $)-dense subgraph model to represent a seasonal-bursting subgraph in temporal networks. Specially, the maximal ($\omega,\theta $)-dense subgraph is a subgraph that accumulates its density at the fastest speed during at least $ \omega $ particular periods of length no less than $ \theta $ on the temporal graph. To compute all seasonal bursting subgraphs efficiently, the study first models the mining problem as a mixed integer programming problem, which consists of finding the densest subgraph and the maximum burstiness segment. Then corresponding solutions are given for each subproblem, respectively. The study further conceives two optimization strategies by exploiting key-core and dynamic programming algorithms to boost performance. The results of experiments show that the proposed model is indeed able to identify many seasonal-bursting subgraphs. The efficiency, scalability, and effectiveness of the proposed algorithms are also verified on five real-life datasets.
Temporal graph is a type of graph where each edge is associated with a timestamp. Seasonal-bursting subgraph is a dense subgraph characterized by burstiness over multiple time periods, which can applied for activity discovery and group relationship analysis in social networks. Unfortunately, most previous studies for subgraph mining in temporal networks ignore the seasonal or bursting features of subgraphs. To this end, this study proposes a maximal ($\omega,\theta $)-dense subgraph model to represent a seasonal-bursting subgraph in temporal networks. Specially, the maximal ($\omega,\theta $)-dense subgraph is a subgraph that accumulates its density at the fastest speed during at least $ \omega $ particular periods of length no less than $ \theta $ on the temporal graph. To compute all seasonal bursting subgraphs efficiently, the study first models the mining problem as a mixed integer programming problem, which consists of finding the densest subgraph and the maximum burstiness segment. Then corresponding solutions are given for each subproblem, respectively. The study further conceives two optimization strategies by exploiting key-core and dynamic programming algorithms to boost performance. The results of experiments show that the proposed model is indeed able to identify many seasonal-bursting subgraphs. The efficiency, scalability, and effectiveness of the proposed algorithms are also verified on five real-life datasets.
Available online: February 05, 2024 , DOI: 10.13328/j.cnki.jos.007062
Abstract:
Spoken language understanding is a key task in task-based dialogue systems, mainly composed of two sub-tasks: slot filling and intent detection. Currently, the mainstream method is to jointly model slot filling and intent detection. Although this method has achieved good results in both slot filling and intent detection, there are still issues with error propagation in the interaction process between intent detection and slot filling in joint modeling, as well as the incorrect correspondence between multi-intent information and slot information in multi-intent scenarios. In response to these problems, this study proposes a joint model for multi-intent detection and slot filling based on graph attention networks (WISM). The WISM established a word-level one-to-one mapping relationship between fine-grained intentions and slots to correct incorrect correspondence between multi-intent information and slots. By constructing an interaction graph of word-intent-semantic slots and utilizing a fine-grained graph attention network to establish bidirectional connections between the two tasks, the problem of error propagation during the interaction process can be reduced. Experimental results on the MixSINPS and MixATIS datasets showed that, compared with the latest existing models, WISM has improved semantic accuracy by 2.58% and 3.53%, respectively. This model not only improves accuracy but also verifies the one-to-one correspondence between multi-intent and semantic slots.
Spoken language understanding is a key task in task-based dialogue systems, mainly composed of two sub-tasks: slot filling and intent detection. Currently, the mainstream method is to jointly model slot filling and intent detection. Although this method has achieved good results in both slot filling and intent detection, there are still issues with error propagation in the interaction process between intent detection and slot filling in joint modeling, as well as the incorrect correspondence between multi-intent information and slot information in multi-intent scenarios. In response to these problems, this study proposes a joint model for multi-intent detection and slot filling based on graph attention networks (WISM). The WISM established a word-level one-to-one mapping relationship between fine-grained intentions and slots to correct incorrect correspondence between multi-intent information and slots. By constructing an interaction graph of word-intent-semantic slots and utilizing a fine-grained graph attention network to establish bidirectional connections between the two tasks, the problem of error propagation during the interaction process can be reduced. Experimental results on the MixSINPS and MixATIS datasets showed that, compared with the latest existing models, WISM has improved semantic accuracy by 2.58% and 3.53%, respectively. This model not only improves accuracy but also verifies the one-to-one correspondence between multi-intent and semantic slots.
Available online: February 05, 2024 , DOI: 10.13328/j.cnki.jos.007067
Abstract:
The code search method based on deep learning realizes the code search task by calculating the similarity of the corresponding representation of the code and the description statement. However, this manner does not consider the real probability distribution of relevance between the code and the description. To solve this problem, this study proposes a code search method based on a generative adversarial game that combines the correlation between the code and the description in the classical probability model with the feature extraction in the vector space model. Then the generative adversarial game is adopted to apply the probability distribution between the code and the description to the alternate training of the generator and discriminator. Meanwhile, the code encoder and the description encoder are optimized, and high-quality code representation and description statement representation are generated for the code search task. Finally, experimental verification is carried out on the public dataset, and the results show that the proposed method improves the Recall@10, MRR@10, and NDCG@10 metrics by 8.4%, 32.5%, and 24.3% respectively compared to the DeepCS method.
The code search method based on deep learning realizes the code search task by calculating the similarity of the corresponding representation of the code and the description statement. However, this manner does not consider the real probability distribution of relevance between the code and the description. To solve this problem, this study proposes a code search method based on a generative adversarial game that combines the correlation between the code and the description in the classical probability model with the feature extraction in the vector space model. Then the generative adversarial game is adopted to apply the probability distribution between the code and the description to the alternate training of the generator and discriminator. Meanwhile, the code encoder and the description encoder are optimized, and high-quality code representation and description statement representation are generated for the code search task. Finally, experimental verification is carried out on the public dataset, and the results show that the proposed method improves the Recall@10, MRR@10, and NDCG@10 metrics by 8.4%, 32.5%, and 24.3% respectively compared to the DeepCS method.
Available online: February 05, 2024 , DOI: 10.13328/j.cnki.jos.007055
Abstract:
Detecting aligned double joint photographic experts group (JPEG) compression is a challenging task in digital image forensics. Previous studies have proposed methods that can effectively detect aligned double JPEG compression, but these methods mostly rely on features extracted during the JPEG decompression process. If the aligned double compressed JPEG image is saved in BMP format, these methods may be difficult to be directly applied. To address this issue, this study proposes a quantization step estimation method based on dual thresholds, which allows for the acquisition of quantization tables and the extraction of features. Furthermore, the study defines a minimum error based on the unique properties of JPEG compression with a quality factor of 100, and by removing the minimum error from the features, the feature detection performance of the proposed method is further improved. Finally, the study extracts first-order relative error features based on the convergence properties of the de-quantized JPEG coefficients, which further enhances the detection performance of the proposed method at lower quality factors. Experimental results demonstrate that the proposed method outperforms current state-of-the-art algorithms at different quality factors.
Detecting aligned double joint photographic experts group (JPEG) compression is a challenging task in digital image forensics. Previous studies have proposed methods that can effectively detect aligned double JPEG compression, but these methods mostly rely on features extracted during the JPEG decompression process. If the aligned double compressed JPEG image is saved in BMP format, these methods may be difficult to be directly applied. To address this issue, this study proposes a quantization step estimation method based on dual thresholds, which allows for the acquisition of quantization tables and the extraction of features. Furthermore, the study defines a minimum error based on the unique properties of JPEG compression with a quality factor of 100, and by removing the minimum error from the features, the feature detection performance of the proposed method is further improved. Finally, the study extracts first-order relative error features based on the convergence properties of the de-quantized JPEG coefficients, which further enhances the detection performance of the proposed method at lower quality factors. Experimental results demonstrate that the proposed method outperforms current state-of-the-art algorithms at different quality factors.
Available online: February 05, 2024 , DOI: 10.13328/j.cnki.jos.007045
Abstract:
Network congestion control algorithms are the key factor indetermining network transport performance. In recent years, the spreading network, the growing network bandwidth, and the increasing user requirements for network performance have brought challenges to the design of congestion control algorithms. To adapt to different network environments, many novel design ideas of congestion control algorithms have been proposed recently, which have greatly improved the performance of networks and user experience. This study reviews innovative congestion control algorithm design ideas and classifies them into four major categories: reservation scheduling, direct measurement, machine learning-based learning, and iterative detection. It introduces the corresponding representative congestion control algorithms, and further compares and analyzes the advantages and disadvantages of various congestion control ideas and methods. Finally, the study looks forward to future development direction on congestion control to inspire research in this field.
Network congestion control algorithms are the key factor indetermining network transport performance. In recent years, the spreading network, the growing network bandwidth, and the increasing user requirements for network performance have brought challenges to the design of congestion control algorithms. To adapt to different network environments, many novel design ideas of congestion control algorithms have been proposed recently, which have greatly improved the performance of networks and user experience. This study reviews innovative congestion control algorithm design ideas and classifies them into four major categories: reservation scheduling, direct measurement, machine learning-based learning, and iterative detection. It introduces the corresponding representative congestion control algorithms, and further compares and analyzes the advantages and disadvantages of various congestion control ideas and methods. Finally, the study looks forward to future development direction on congestion control to inspire research in this field.
Available online: February 05, 2024 , DOI: 10.13328/j.cnki.jos.007031
Abstract:
The utilization range of Internet of Things (IoT) devices is expanding. Model checking is an effective approach to improve the reliability and security of such devices. However, the commonly adopted model checking methods cannot well describe the cross-space movement and communication behavior common in such devices. To this end, this study proposes a modeling and verification method for the mobile and communication behavior of IoT devices to verify their spatio-temporal properties. Additionally, push/pull action and global communication mechanism are integrated into ambient calculus to propose the ambient calculus with global communication (ACGC) and provide a model checking algorithm for ACGC against the ambient logic. Then, the modeling language for mobility and communication (MLMC) is put forward to describe mobile and communication behavior of IoT devices. Additionally, a method to convert the MLMC-based description into an ACGC model is given. Furthermore, a model checking tool ACGCCk is implemented to verify whether the properties of IoT devices are satisfied. Meanwhile, some optimizations are conducted to accelerate the checking. Finally, the effectiveness of the proposed method is demonstrated by case study and experimental analysis.
The utilization range of Internet of Things (IoT) devices is expanding. Model checking is an effective approach to improve the reliability and security of such devices. However, the commonly adopted model checking methods cannot well describe the cross-space movement and communication behavior common in such devices. To this end, this study proposes a modeling and verification method for the mobile and communication behavior of IoT devices to verify their spatio-temporal properties. Additionally, push/pull action and global communication mechanism are integrated into ambient calculus to propose the ambient calculus with global communication (ACGC) and provide a model checking algorithm for ACGC against the ambient logic. Then, the modeling language for mobility and communication (MLMC) is put forward to describe mobile and communication behavior of IoT devices. Additionally, a method to convert the MLMC-based description into an ACGC model is given. Furthermore, a model checking tool ACGCCk is implemented to verify whether the properties of IoT devices are satisfied. Meanwhile, some optimizations are conducted to accelerate the checking. Finally, the effectiveness of the proposed method is demonstrated by case study and experimental analysis.
Available online: January 31, 2024 , DOI: 10.13328/j.cnki.jos.007056
Abstract:
Time series forecasting models have been widely used in various domains of daily life, and the attack against these models is related to the security of data in applications. At present, adversarial attacks on time series mostly perform large-scale perturbation at the global level, which leads to the easy perception of adversarial samples. At the same time, the effectiveness of adversarial attacks decreases significantly with the magnitude shrinkage of the perturbation. Therefore, how to generate imperceptible adversarial samples while maintaining a competitive performance of attack is an urgent problem that needs to be solved in the current adversarial attack field of time series forecasting. This study first proposes a local perturbation strategy based on sliding windows to narrow the perturbation interval of the adversarial sample. Second, it employs the differential evolutionary algorithm to find the optimal attack points and combine the segmentation function to partition the perturbation interval to further reduce the perturbation range and complete the semi-white-box attack. The comparison experiments with existing adversarial attack methods on several different deep learning models show that the proposed method can generate less perceptible adversarial samples and effectively change the prediction trend of the model. The proposed method achieves sound attack results in four challenging tasks, namely stock trading, electricity consumption, sunspot observation, and temperature prediction.
Time series forecasting models have been widely used in various domains of daily life, and the attack against these models is related to the security of data in applications. At present, adversarial attacks on time series mostly perform large-scale perturbation at the global level, which leads to the easy perception of adversarial samples. At the same time, the effectiveness of adversarial attacks decreases significantly with the magnitude shrinkage of the perturbation. Therefore, how to generate imperceptible adversarial samples while maintaining a competitive performance of attack is an urgent problem that needs to be solved in the current adversarial attack field of time series forecasting. This study first proposes a local perturbation strategy based on sliding windows to narrow the perturbation interval of the adversarial sample. Second, it employs the differential evolutionary algorithm to find the optimal attack points and combine the segmentation function to partition the perturbation interval to further reduce the perturbation range and complete the semi-white-box attack. The comparison experiments with existing adversarial attack methods on several different deep learning models show that the proposed method can generate less perceptible adversarial samples and effectively change the prediction trend of the model. The proposed method achieves sound attack results in four challenging tasks, namely stock trading, electricity consumption, sunspot observation, and temperature prediction.
Available online: January 31, 2024 , DOI: 10.13328/j.cnki.jos.007052
Abstract:
The performance of image classification algorithms is limited by the diversity of visual information and the influence of background noise. Existing works usually apply cross-modal constraints or heterogeneous feature alignment algorithms to learn visual representations with strong discrimination. However, the difference in feature distribution caused by modal heterogeneity limits the effective learning of visual representations. To address this problem, this study proposes an image classification framework (CMIF) based on cross-modal semantic information inference and fusion and introduces the semantic description of images and statistical knowledge as privileged information. The study uses the privileged information learning paradigm to guide the mapping of image features from visual space to semantic space in the training stage, and a class-aware information selection (CIS) algorithm is proposed to learn the cross-modal enhanced representation of images. In view of the heterogeneous feature differences in representation learning, the partial heterogeneous alignment (PHA) algorithm is used to achieve cross-modal alignment of visual features and semantic features extracted from privileged information. In order to further suppress the interference caused by visual noise in semantic space, the CIS algorithm based on graph fusion is selected to reconstruct the key information in the semantic representation, so as to form an effective supplement to the visual prediction information. Experiments on the cross-modal classification datasets VireoFood-172 and NUS-WIDE show that CMIF can learn robust semantic features of images, and it has achieved stable performance improvement on the convolution-based ResNet-50 and Transform-based ViT image classification models as a general framework.
The performance of image classification algorithms is limited by the diversity of visual information and the influence of background noise. Existing works usually apply cross-modal constraints or heterogeneous feature alignment algorithms to learn visual representations with strong discrimination. However, the difference in feature distribution caused by modal heterogeneity limits the effective learning of visual representations. To address this problem, this study proposes an image classification framework (CMIF) based on cross-modal semantic information inference and fusion and introduces the semantic description of images and statistical knowledge as privileged information. The study uses the privileged information learning paradigm to guide the mapping of image features from visual space to semantic space in the training stage, and a class-aware information selection (CIS) algorithm is proposed to learn the cross-modal enhanced representation of images. In view of the heterogeneous feature differences in representation learning, the partial heterogeneous alignment (PHA) algorithm is used to achieve cross-modal alignment of visual features and semantic features extracted from privileged information. In order to further suppress the interference caused by visual noise in semantic space, the CIS algorithm based on graph fusion is selected to reconstruct the key information in the semantic representation, so as to form an effective supplement to the visual prediction information. Experiments on the cross-modal classification datasets VireoFood-172 and NUS-WIDE show that CMIF can learn robust semantic features of images, and it has achieved stable performance improvement on the convolution-based ResNet-50 and Transform-based ViT image classification models as a general framework.
Available online: January 31, 2024 , DOI: 10.13328/j.cnki.jos.007053
Abstract:
Freehand sketches can intuitively present users’ creative intention by drawing simple lines and enable users to express their thinking process and design inspiration or produce target images or videos. With the development of deep learning methods, sketch-based visual content generation performs cross-domain feature mapping by learning the feature distribution between sketches and visual objects (images and videos), enabling the automated generation of sketches from images and the automated generation of images or videos from sketches. Compared with traditional artificial creation, it effectively improves the efficiency and diversity of generation, which has become one of the most important research directions in computer vision and graphics and plays an important role in design, visual creation, etc. Therefore, this study presents an overview of the research progress and future development of deep learning methods for sketch-based visual content generation. The study classifies the existing work into sketch-based image generation and sketch-based video generation according to different visual objects and analyzes the generation models in detail with a combination of specific tasks including cross-domain generation between sketch and visual content, style transfer, and editing of visual content. Then, it summarizes and compares the commonly used datasets and points out sketch propagation methods to address in sufficient sketch data and evaluation methods of generated models. Furthermore, the study prospects the research trend based on the challenges faced by the sketch in the application of visual content generation and the future development direction of generated models.
Freehand sketches can intuitively present users’ creative intention by drawing simple lines and enable users to express their thinking process and design inspiration or produce target images or videos. With the development of deep learning methods, sketch-based visual content generation performs cross-domain feature mapping by learning the feature distribution between sketches and visual objects (images and videos), enabling the automated generation of sketches from images and the automated generation of images or videos from sketches. Compared with traditional artificial creation, it effectively improves the efficiency and diversity of generation, which has become one of the most important research directions in computer vision and graphics and plays an important role in design, visual creation, etc. Therefore, this study presents an overview of the research progress and future development of deep learning methods for sketch-based visual content generation. The study classifies the existing work into sketch-based image generation and sketch-based video generation according to different visual objects and analyzes the generation models in detail with a combination of specific tasks including cross-domain generation between sketch and visual content, style transfer, and editing of visual content. Then, it summarizes and compares the commonly used datasets and points out sketch propagation methods to address in sufficient sketch data and evaluation methods of generated models. Furthermore, the study prospects the research trend based on the challenges faced by the sketch in the application of visual content generation and the future development direction of generated models.
Available online: January 31, 2024 , DOI: 10.13328/j.cnki.jos.007063
Abstract:
With the development of Internet information technology, large-scale graphs have widely emerged in social networks, computer networks, and biological information networks. In view of the storage and performance limitations of traditional graph data management technology when dealing with large-scale graphs, distributed management technology has become a hotspot in industry and academia fields. The core decomposition is adopted to get core numbers of vertices in a graph and plays a key role in many applications, including community search, protein structure analysis, and network structure visualization. The existing distributed core decomposition algorithm applied a broadcast message delivery mechanism based on the vertex-centric mode, which may generate a large amount of redundant communication and computation overhead and lead to memory overflow when processing large-scale graphs. To address these issues, this study proposes novel distributed core decomposition algorithms based on global activation and peeling calculation frameworks, respectively. In addition, there are several strategies designed to improve algorithm performance. Based on the locality of the vertex core number, the study proposes a new message-pruning strategy and a new worker-centric computing mode, thereby improving the efficiency of our algorithms. To verify those strategies, this study deploys the proposed models and algorithms on the distributed cluster of the National Supercomputing Center in Changsha, and the effectiveness and efficiency of the proposed methods are evaluated through a large number of experiments on real and synthetic data sets. The total time performance of the algorithm is improved by 37% to 98%.
With the development of Internet information technology, large-scale graphs have widely emerged in social networks, computer networks, and biological information networks. In view of the storage and performance limitations of traditional graph data management technology when dealing with large-scale graphs, distributed management technology has become a hotspot in industry and academia fields. The core decomposition is adopted to get core numbers of vertices in a graph and plays a key role in many applications, including community search, protein structure analysis, and network structure visualization. The existing distributed core decomposition algorithm applied a broadcast message delivery mechanism based on the vertex-centric mode, which may generate a large amount of redundant communication and computation overhead and lead to memory overflow when processing large-scale graphs. To address these issues, this study proposes novel distributed core decomposition algorithms based on global activation and peeling calculation frameworks, respectively. In addition, there are several strategies designed to improve algorithm performance. Based on the locality of the vertex core number, the study proposes a new message-pruning strategy and a new worker-centric computing mode, thereby improving the efficiency of our algorithms. To verify those strategies, this study deploys the proposed models and algorithms on the distributed cluster of the National Supercomputing Center in Changsha, and the effectiveness and efficiency of the proposed methods are evaluated through a large number of experiments on real and synthetic data sets. The total time performance of the algorithm is improved by 37% to 98%.
Available online: January 31, 2024 , DOI: 10.13328/j.cnki.jos.007066
Abstract:
Raft is one of the most popular distributed consensus protocols. Since it was proposed in 2014, Raft and its variants have been widely used in different kinds of distributed systems. To prove the correctness of the Raft protocol, developers use the TLA+ formal specification to model and verify its design. However, due to the gap between the abstract formal specification and practical implementation, distributed systems that implement the Raft protocol can still violate the protocol design and introduce intricate bugs. This study proposes a novel testing technique based on TLA+ formal specification to unearth bugs in Raft implementations. To be specific, the study maps the formal specification to the corresponding system implementation and then uses the specification-defined state space to guide the testing in the implementations. To evaluate the feasibility and effectiveness of the proposed approach, the study applies it on two different Raft implementations and finds 3 previously unknown bugs.
Raft is one of the most popular distributed consensus protocols. Since it was proposed in 2014, Raft and its variants have been widely used in different kinds of distributed systems. To prove the correctness of the Raft protocol, developers use the TLA+ formal specification to model and verify its design. However, due to the gap between the abstract formal specification and practical implementation, distributed systems that implement the Raft protocol can still violate the protocol design and introduce intricate bugs. This study proposes a novel testing technique based on TLA+ formal specification to unearth bugs in Raft implementations. To be specific, the study maps the formal specification to the corresponding system implementation and then uses the specification-defined state space to guide the testing in the implementations. To evaluate the feasibility and effectiveness of the proposed approach, the study applies it on two different Raft implementations and finds 3 previously unknown bugs.
Available online: January 24, 2024 , DOI: 10.13328/j.cnki.jos.007051
Abstract:
A heterogeneous graph is a graph with multiple types of nodes and edges, also known as a heterogeneous information network, which is often used to model systems with rich features and association patterns in the real world. Link prediction between heterogeneous nodes is a fundamental task in network analysis. In recent years, the development of heterogeneous graph neural network (HGNN) has greatly advanced the task of link prediction, which is usually regarded as a feature similarity analysis between nodes or a binary classification problem based on paired node features. However, when learning node feature representations, existing HGNNs usually only focus on the associations between adjacent nodes or the meta-path-based structural information. This not only makes these HGNNs difficult to capture the semantic information of the ring structure inherent in heterogeneous graphs but also ignores the complementarity of structural information at different levels. To solve the above issues, this study proposes a cascade graph convolution network based on multi-level graph structures (CGCN-MGS), which is composed of graph neural networks based on three graph structures of different levels: neighboring, meta-path, and ring structures. CGCN-MGS can mine rich and complementary information from multi-level features and improve the representation ability of the learned node features on the semantics and structure information of nodes. Experimental results on several benchmark datasets show that CGCN-MGS can achieve state-of-the-art performance on the link prediction of heterogeneous graphs.
A heterogeneous graph is a graph with multiple types of nodes and edges, also known as a heterogeneous information network, which is often used to model systems with rich features and association patterns in the real world. Link prediction between heterogeneous nodes is a fundamental task in network analysis. In recent years, the development of heterogeneous graph neural network (HGNN) has greatly advanced the task of link prediction, which is usually regarded as a feature similarity analysis between nodes or a binary classification problem based on paired node features. However, when learning node feature representations, existing HGNNs usually only focus on the associations between adjacent nodes or the meta-path-based structural information. This not only makes these HGNNs difficult to capture the semantic information of the ring structure inherent in heterogeneous graphs but also ignores the complementarity of structural information at different levels. To solve the above issues, this study proposes a cascade graph convolution network based on multi-level graph structures (CGCN-MGS), which is composed of graph neural networks based on three graph structures of different levels: neighboring, meta-path, and ring structures. CGCN-MGS can mine rich and complementary information from multi-level features and improve the representation ability of the learned node features on the semantics and structure information of nodes. Experimental results on several benchmark datasets show that CGCN-MGS can achieve state-of-the-art performance on the link prediction of heterogeneous graphs.
Available online: January 24, 2024 , DOI: 10.13328/j.cnki.jos.007042
Abstract:
In recent years, machine learning has always been a research hotspot, and has been applied to various fields with an important role played. However, as the data amount continues to increase, the training time of machine learning algorithms is getting longer. Meanwhile, quantum computers demonstrate a powerful computing ability. Therefore, researchers try to solve the problem of long machine learning training time, which leads to the emergence of quantum machine learning. Quantum machine learning algorithms have been proposed, including quantum principal component analysis, quantum support vector machine, and quantum deep learning. Additionally, experiments have proven that quantum machine learning algorithms have a significant acceleration effect, leading to a gradual upward trend in research on quantum machine learning. This study reviews research on quantum machine learning algorithms. First, the fundamental concepts of quantum computing are introduced. Then, five quantum machine learning algorithms are presented, including quantum supervised learning, quantum unsupervised learning, quantum semi-supervised learning, quantum reinforcement learning, and quantum deep learning. Next, related applications of quantum machine learning are demonstrated with the algorithm experiments provided. Finally, the relevant summary and prospect of future study are discussed.
In recent years, machine learning has always been a research hotspot, and has been applied to various fields with an important role played. However, as the data amount continues to increase, the training time of machine learning algorithms is getting longer. Meanwhile, quantum computers demonstrate a powerful computing ability. Therefore, researchers try to solve the problem of long machine learning training time, which leads to the emergence of quantum machine learning. Quantum machine learning algorithms have been proposed, including quantum principal component analysis, quantum support vector machine, and quantum deep learning. Additionally, experiments have proven that quantum machine learning algorithms have a significant acceleration effect, leading to a gradual upward trend in research on quantum machine learning. This study reviews research on quantum machine learning algorithms. First, the fundamental concepts of quantum computing are introduced. Then, five quantum machine learning algorithms are presented, including quantum supervised learning, quantum unsupervised learning, quantum semi-supervised learning, quantum reinforcement learning, and quantum deep learning. Next, related applications of quantum machine learning are demonstrated with the algorithm experiments provided. Finally, the relevant summary and prospect of future study are discussed.
Available online: January 24, 2024 , DOI: 10.13328/j.cnki.jos.007080
Abstract:
Malware detection is a hotspot of cyberspace security research, such as Windows malware detection and Android malware detection. With the development of machine learning and deep learning, some outstanding algorithms in the fields of image recognition and natural language processing have been applied to malware detection. These algorithms have shown excellent learning performance with a large amount of data. However, there are some challenging problems in malware detection that have not been solved effectively. For instance, conventional learning methods cannot achieve effective detection based on a few novel malware. Therefore, few-shot learning (FSL) is adopted to solve the few-shot for malware detection (FSMD) problems. This study extracts the problem definition and the general process of FSMD by the related research. According to the principle of the method, FSMD methods are divided into methods based on data augmentation, methods based on meta-learning, and hybrid methods combining multiple technologies. Then, the study discusses the characteristics of each FSMD method. Finally, the background, technology, and application prospects of FSMD are proposed.
Malware detection is a hotspot of cyberspace security research, such as Windows malware detection and Android malware detection. With the development of machine learning and deep learning, some outstanding algorithms in the fields of image recognition and natural language processing have been applied to malware detection. These algorithms have shown excellent learning performance with a large amount of data. However, there are some challenging problems in malware detection that have not been solved effectively. For instance, conventional learning methods cannot achieve effective detection based on a few novel malware. Therefore, few-shot learning (FSL) is adopted to solve the few-shot for malware detection (FSMD) problems. This study extracts the problem definition and the general process of FSMD by the related research. According to the principle of the method, FSMD methods are divided into methods based on data augmentation, methods based on meta-learning, and hybrid methods combining multiple technologies. Then, the study discusses the characteristics of each FSMD method. Finally, the background, technology, and application prospects of FSMD are proposed.
Available online: January 24, 2024 , DOI: 10.13328/j.cnki.jos.007068
Abstract:
Currently, most of the published image steganalysis methods are designed for grayscale images, which cannot effectively detect color images widely used in social media. To solve this problem, this study proposes a color image steganalysis method based on central difference convolution and attention enhancement. The proposed method first designs a backbone flow consisting of three stages: preprocessing, feature extraction, and feature classification. In the preprocessing stage, the input color image is color channel-separated, and the residual images after SRM filtering are concatenated through each channel. In the feature extraction stage, the study constructs three convolutional blocks based on central difference convolution to extract deeper steganalysis feature maps. In the classification stage, the study uses global covariance pooling and two fully connected layers with dropout operation to classify the cover and stego images. Additionally, to further enhance the feature expression ability of the backbone flow at different stages, it introduces a residual spatial attention enhancement module and a channel attention enhancement module at the early and late stages of the backbone flow, respectively. Specifically, the residual spatial attention enhancement module first uses Gabor filter kernels to perform channel-separated convolution on the input image and then obtains the effective information of the residual feature map through the spatial attention mechanism. The channel attention enhancement module enhances the final feature classification ability of the model by obtaining the dependence relationship between channels. A large number of comparative experiments have been conducted, and the results show that the proposed method can significantly improve the detection performance of color image steganography and achieve the best results currently. In addition, the study also conducts corresponding ablation experiments to verify the rationality of the proposed network architecture.
Currently, most of the published image steganalysis methods are designed for grayscale images, which cannot effectively detect color images widely used in social media. To solve this problem, this study proposes a color image steganalysis method based on central difference convolution and attention enhancement. The proposed method first designs a backbone flow consisting of three stages: preprocessing, feature extraction, and feature classification. In the preprocessing stage, the input color image is color channel-separated, and the residual images after SRM filtering are concatenated through each channel. In the feature extraction stage, the study constructs three convolutional blocks based on central difference convolution to extract deeper steganalysis feature maps. In the classification stage, the study uses global covariance pooling and two fully connected layers with dropout operation to classify the cover and stego images. Additionally, to further enhance the feature expression ability of the backbone flow at different stages, it introduces a residual spatial attention enhancement module and a channel attention enhancement module at the early and late stages of the backbone flow, respectively. Specifically, the residual spatial attention enhancement module first uses Gabor filter kernels to perform channel-separated convolution on the input image and then obtains the effective information of the residual feature map through the spatial attention mechanism. The channel attention enhancement module enhances the final feature classification ability of the model by obtaining the dependence relationship between channels. A large number of comparative experiments have been conducted, and the results show that the proposed method can significantly improve the detection performance of color image steganography and achieve the best results currently. In addition, the study also conducts corresponding ablation experiments to verify the rationality of the proposed network architecture.
Available online: January 24, 2024 , DOI: 10.13328/j.cnki.jos.007059
Abstract:
As big data and computing power rapidly develop, deep learning has made significant breakthroughs and rapidly become a field with numerous practical application scenarios and active research topics. In response to the growing demand for the development of deep learning tasks, deep learning frameworks have arisen. Acting as an intermediate component between application scenarios and hardware platforms, deep learning frameworks facilitate the development of deep learning applications, enabling users to efficiently construct diverse deep neural network (DNN) models, and deeply adapt to various computing hardware, meeting the computational needs across different computing architectures and environments. Any issues that arise within deep learning frameworks, which serve as the fundamental software in the realm of artificial intelligence, can have severe consequences. Even a single bug in the code can trigger widespread failures within models built upon the framework, thereby posing a serious threat to the safety of deep learning systems. As the first review exclusively focuses on the testing of deep learning frameworks, this study initially introduces the developmental history and basic architectures of deep learning frameworks. Subsequently, by systematically examining 55 academic papers directly related to the testing of deep learning frameworks, the study systematically analyzes and summarizes bug characteristics, key technologies for testing, and methods based on various input forms for testing. The study explores how to combine key technologies to address research problems. Lastly, it summarizes the unresolved difficulties in the testing of deep learning frameworks and provides insights into promising research directions for the future. This study can offer valuable references and guidance to individuals involved in the research field of deep learning framework testing, ultimately promoting the sustained development and maturity of deep learning frameworks.
As big data and computing power rapidly develop, deep learning has made significant breakthroughs and rapidly become a field with numerous practical application scenarios and active research topics. In response to the growing demand for the development of deep learning tasks, deep learning frameworks have arisen. Acting as an intermediate component between application scenarios and hardware platforms, deep learning frameworks facilitate the development of deep learning applications, enabling users to efficiently construct diverse deep neural network (DNN) models, and deeply adapt to various computing hardware, meeting the computational needs across different computing architectures and environments. Any issues that arise within deep learning frameworks, which serve as the fundamental software in the realm of artificial intelligence, can have severe consequences. Even a single bug in the code can trigger widespread failures within models built upon the framework, thereby posing a serious threat to the safety of deep learning systems. As the first review exclusively focuses on the testing of deep learning frameworks, this study initially introduces the developmental history and basic architectures of deep learning frameworks. Subsequently, by systematically examining 55 academic papers directly related to the testing of deep learning frameworks, the study systematically analyzes and summarizes bug characteristics, key technologies for testing, and methods based on various input forms for testing. The study explores how to combine key technologies to address research problems. Lastly, it summarizes the unresolved difficulties in the testing of deep learning frameworks and provides insights into promising research directions for the future. This study can offer valuable references and guidance to individuals involved in the research field of deep learning framework testing, ultimately promoting the sustained development and maturity of deep learning frameworks.
Available online: January 24, 2024 , DOI: 10.13328/j.cnki.jos.007050
Abstract:
A directed acyclic graph (DAG)-based blockchain adopts a parallel topology and can significantly improve system performance compared with conventional chain-based blockchains with a serial topology. As a result, it has attracted wide attention from the industry. However, the storage model and the consensus protocol of the existing DAG-based blockchains are highly coupled, which lacks the flexibility to meet diversified application demands. Furthermore, most DAG-based blockchains lack flexibility at the consensus protocol level and are limited to probabilistic consensus protocols, which is difficult to take into account confirmation latency and security and is especially unfriendly to delay-sensitive applications. Therefore, this study presents the elastic DAG-based blockchain, namely ElasticDAG. The core idea is to decouple the storage model and the consensus protocol, enabling them to proceed in parallel and independently, so as to flexibly adapt to diversified applications. In order to improve the throughput and activity of the system, an adaptive block confirmation strategy and an epoch-based block ordering algorithm are designed for the storage model. In response to the need to reduce transaction confirmation latency, a low-latency DAG blockchain hybrid consensus protocol is designed. Experimental results demonstrate that the ElasticDAG prototype in WAN can achieve a throughput exceeding 11 Mb/s, and it yields a confirmation latency of tens of seconds. Compared with OHIE and Haootia, ElasticDAG can reduce confirmation latency by 17 times and improve security from 91.04% to 99.999 914% while maintaining the same throughput and consensus latency.
A directed acyclic graph (DAG)-based blockchain adopts a parallel topology and can significantly improve system performance compared with conventional chain-based blockchains with a serial topology. As a result, it has attracted wide attention from the industry. However, the storage model and the consensus protocol of the existing DAG-based blockchains are highly coupled, which lacks the flexibility to meet diversified application demands. Furthermore, most DAG-based blockchains lack flexibility at the consensus protocol level and are limited to probabilistic consensus protocols, which is difficult to take into account confirmation latency and security and is especially unfriendly to delay-sensitive applications. Therefore, this study presents the elastic DAG-based blockchain, namely ElasticDAG. The core idea is to decouple the storage model and the consensus protocol, enabling them to proceed in parallel and independently, so as to flexibly adapt to diversified applications. In order to improve the throughput and activity of the system, an adaptive block confirmation strategy and an epoch-based block ordering algorithm are designed for the storage model. In response to the need to reduce transaction confirmation latency, a low-latency DAG blockchain hybrid consensus protocol is designed. Experimental results demonstrate that the ElasticDAG prototype in WAN can achieve a throughput exceeding 11 Mb/s, and it yields a confirmation latency of tens of seconds. Compared with OHIE and Haootia, ElasticDAG can reduce confirmation latency by 17 times and improve security from 91.04% to 99.999 914% while maintaining the same throughput and consensus latency.
Available online: January 17, 2024 , DOI: 10.13328/j.cnki.jos.007043
Abstract:
The graphical user interface (GUI/UI) provides a visual bridge between the application and its end users, and users can use the application through interactive operations. With the development of mobile applications, GUI, which combines aesthetics and interaction design, has become more and more complex, and users are increasingly concerned about the accessibility and availability of applications. However, the complexity of GUI also brings great challenges to its design and implementation. Due to user-defined settings for mobile devices and different device models and screen resolutions, UI display issues frequently occur. For example, due to software or hardware compatibility, when rendering interfaces on different devices, there will always be display issues such as text overlap, component masking, and image loss. They have a negative impact on the availability and accessibility of applications, resulting in poor user experience. Unfortunately, little is known about the causes of UI display issues of mobile applications. In order to cope with this challenge, this study collects6729 screenshots of applications with UI display issues from Baidu crowdtesting platform and 1016 screenshots of applications provided by issue reports in GitHub and identifies nine types of UI display issues using the theme analysis method. Through the analysis of 1061 UI issue reports from GitHub and the corresponding defective code, the essence and causes of UI display issues are summarized. The research found that (1) 62.1% of the total screenshots in crowdtesting dataset are defective screenshots displayed on the UI; (2) the reason for the UI display issues is that the font scaling setting does not match the adaptive setting of components to a great extent; (3) the layout setting of the interface will lead to display issues; (4) If the hardware acceleration is not turned on, the normal display of the interface will be affected.
The graphical user interface (GUI/UI) provides a visual bridge between the application and its end users, and users can use the application through interactive operations. With the development of mobile applications, GUI, which combines aesthetics and interaction design, has become more and more complex, and users are increasingly concerned about the accessibility and availability of applications. However, the complexity of GUI also brings great challenges to its design and implementation. Due to user-defined settings for mobile devices and different device models and screen resolutions, UI display issues frequently occur. For example, due to software or hardware compatibility, when rendering interfaces on different devices, there will always be display issues such as text overlap, component masking, and image loss. They have a negative impact on the availability and accessibility of applications, resulting in poor user experience. Unfortunately, little is known about the causes of UI display issues of mobile applications. In order to cope with this challenge, this study collects
Available online: January 17, 2024 , DOI: 10.13328/j.cnki.jos.007049
Abstract:
In the white-box attack context, an attacker can access the implementation process of the cryptographic algorithm, observe the dynamic execution and internal details of the algorithm, and modify it arbitrarily. In 2002, Chow et al. proposed the concept of white-box cipher and pointed out the white-box implementation of the AES algorithm and DES algorithm by using lookup table technology, which is called the CEJO framework. The white-box implementation obfuscates the existing cryptographic algorithms, protects the key in the form of software under white-box attack, and ensures the correctness of the algorithm results. SIMON is a lightweight block cipher algorithm, which is widely used in Internet of Things devices because of its great software and hardware performance. It is of great practical significance to study the white-box implementation of this algorithm. This study presents two white-box implementations of the SIMON algorithm. The first scheme (SIMON-CEJO) uses the classical CEJO framework to protect the lookup tables by using network codings, so as to confuse the key. In this scheme, the occupied memory space is 369.016 KB. The security analysis shows that the SIMON-CEJO scheme can resist BGE attack and affine equivalent algorithm attack, but it fails to resist differential computing analysis. The second scheme (SIMON-Masking) uses the encoding method proposed by Battistello et al. to encode the plaintext information and key information, and it uses the homomorphism of encoding to convert the XOR operation and AND operation into modular multiplication and table lookup operation. Finally, the corresponding ciphertext result is obtained by decoding. During the operation of the algorithm, the Boolean mask is added to the AND operation. The randomness of the codings protects the real key information and improves the ability of the scheme to resist differential computing analysis and other attacks. SIMON-Masking occupies 655.81 KB of memory space, and the time complexity of the second-order differential computing based on the Legendre symbol is O(n2klog2p). The comparison results of the two schemes show that the classical CEJO framework cannot effectively defend against differential computing analysis, but using new coding and adding masks are effective white-box implementation methods.
In the white-box attack context, an attacker can access the implementation process of the cryptographic algorithm, observe the dynamic execution and internal details of the algorithm, and modify it arbitrarily. In 2002, Chow et al. proposed the concept of white-box cipher and pointed out the white-box implementation of the AES algorithm and DES algorithm by using lookup table technology, which is called the CEJO framework. The white-box implementation obfuscates the existing cryptographic algorithms, protects the key in the form of software under white-box attack, and ensures the correctness of the algorithm results. SIMON is a lightweight block cipher algorithm, which is widely used in Internet of Things devices because of its great software and hardware performance. It is of great practical significance to study the white-box implementation of this algorithm. This study presents two white-box implementations of the SIMON algorithm. The first scheme (SIMON-CEJO) uses the classical CEJO framework to protect the lookup tables by using network codings, so as to confuse the key. In this scheme, the occupied memory space is 369.016 KB. The security analysis shows that the SIMON-CEJO scheme can resist BGE attack and affine equivalent algorithm attack, but it fails to resist differential computing analysis. The second scheme (SIMON-Masking) uses the encoding method proposed by Battistello et al. to encode the plaintext information and key information, and it uses the homomorphism of encoding to convert the XOR operation and AND operation into modular multiplication and table lookup operation. Finally, the corresponding ciphertext result is obtained by decoding. During the operation of the algorithm, the Boolean mask is added to the AND operation. The randomness of the codings protects the real key information and improves the ability of the scheme to resist differential computing analysis and other attacks. SIMON-Masking occupies 655.81 KB of memory space, and the time complexity of the second-order differential computing based on the Legendre symbol is O(n2klog2p). The comparison results of the two schemes show that the classical CEJO framework cannot effectively defend against differential computing analysis, but using new coding and adding masks are effective white-box implementation methods.
Available online: January 17, 2024 , DOI: 10.13328/j.cnki.jos.007058
Abstract:
Due to the continuous advancements in the field of deep learning, there is growing interest in extending relational databases with collaborative query processing (CQP) techniques to handle advanced analytical queries involving structured and unstructured data. State-of-the-art CQP methods employ user-defined functions (UDFs) to implement deep neural network (NN) models for processing unstructured data while utilizing relational operations for structured data. UDF-based approaches simplify query composition, allowing users to submit analytical queries with a single SQL statement. However, they require manual selection of appropriate and efficient models based on desired performance metrics during ad-hoc data analysis, posing significant challenges to users. To address this issue, this research proposes a CQP technique based on declarative inference functions (DIF), which constructs a complete CQP framework by optimizing model selection, execution strategies, and device bindings across multiple query execution paths. Leveraging the cost model and optimization rules designed in this study, the query processor is capable of estimating the cost of different query plans and automatically selecting the optimal physical query plan. Experimental results on four datasets validate the effectiveness and efficiency of the proposed DIF-based CQP approach.
Due to the continuous advancements in the field of deep learning, there is growing interest in extending relational databases with collaborative query processing (CQP) techniques to handle advanced analytical queries involving structured and unstructured data. State-of-the-art CQP methods employ user-defined functions (UDFs) to implement deep neural network (NN) models for processing unstructured data while utilizing relational operations for structured data. UDF-based approaches simplify query composition, allowing users to submit analytical queries with a single SQL statement. However, they require manual selection of appropriate and efficient models based on desired performance metrics during ad-hoc data analysis, posing significant challenges to users. To address this issue, this research proposes a CQP technique based on declarative inference functions (DIF), which constructs a complete CQP framework by optimizing model selection, execution strategies, and device bindings across multiple query execution paths. Leveraging the cost model and optimization rules designed in this study, the query processor is capable of estimating the cost of different query plans and automatically selecting the optimal physical query plan. Experimental results on four datasets validate the effectiveness and efficiency of the proposed DIF-based CQP approach.
Available online: January 10, 2024 , DOI: 10.13328/j.cnki.jos.007060
Abstract:
To solve the problems of users’ private key security, this study proposes a user-oriented and practical private key protection framework by combining secret sharing and edge computing mode. Based on this framework, it designs a private key protection scheme for the SM2 public-key cryptographic system. In this scheme, a user’s SM2 private key is divided into two shares via a secret sharing scheme and kept by the user’s device and the edge server respectively. The public-key cryptographic task requested by Web3 applications is executed cooperatively by the user’s device and the edge server without having to recover the original private key. After the user’s device or the edge server is attacked, a key updating protocol will be executed among them to update the private key shares and scrap the one that may have been leaked. Experiment results show that the computing time of the new scheme is acceptable for common devices (smartphones, laptops, etc.) in the real world.
To solve the problems of users’ private key security, this study proposes a user-oriented and practical private key protection framework by combining secret sharing and edge computing mode. Based on this framework, it designs a private key protection scheme for the SM2 public-key cryptographic system. In this scheme, a user’s SM2 private key is divided into two shares via a secret sharing scheme and kept by the user’s device and the edge server respectively. The public-key cryptographic task requested by Web3 applications is executed cooperatively by the user’s device and the edge server without having to recover the original private key. After the user’s device or the edge server is attacked, a key updating protocol will be executed among them to update the private key shares and scrap the one that may have been leaked. Experiment results show that the computing time of the new scheme is acceptable for common devices (smartphones, laptops, etc.) in the real world.
Available online: January 10, 2024 , DOI: 10.13328/j.cnki.jos.007048
Abstract:
Database management systems (DBMSs) are the infrastructure for efficient storage, management, and analysis of data, playing a pivotal role in modern data-intensive applications. Vulnerabilities in DBMSs pose a great threat to the security of data and the operation of applications. Fuzzing is one of the most popular dynamic vulnerability detection techniques and has been applied to analyze DBMSs, uncovering many vulnerabilities. This study analyzes the requirements and the difficulties involved in testing a DBMS and proposes a foundational framework for DBMS fuzzing. It also analyzes the challenges encountered by DBMS fuzzers and identifies the dimensions that necessitate support. It introduces typical DBMS fuzzers from the perspective of discovering different types of vulnerabilities and summarizes key techniques in DBMS fuzzing, including SQL statement synthesis, code coverage tracking, and test oracle construction. Several popular DBMS fuzzers are evaluated in terms of coverage, syntax and semantic correctness of the generated test cases, and the ability to find vulnerabilities. Finally, it presents the problems faced by current DBMS fuzzing research and practices and prospects for future research directions in DBMS fuzzing.
Database management systems (DBMSs) are the infrastructure for efficient storage, management, and analysis of data, playing a pivotal role in modern data-intensive applications. Vulnerabilities in DBMSs pose a great threat to the security of data and the operation of applications. Fuzzing is one of the most popular dynamic vulnerability detection techniques and has been applied to analyze DBMSs, uncovering many vulnerabilities. This study analyzes the requirements and the difficulties involved in testing a DBMS and proposes a foundational framework for DBMS fuzzing. It also analyzes the challenges encountered by DBMS fuzzers and identifies the dimensions that necessitate support. It introduces typical DBMS fuzzers from the perspective of discovering different types of vulnerabilities and summarizes key techniques in DBMS fuzzing, including SQL statement synthesis, code coverage tracking, and test oracle construction. Several popular DBMS fuzzers are evaluated in terms of coverage, syntax and semantic correctness of the generated test cases, and the ability to find vulnerabilities. Finally, it presents the problems faced by current DBMS fuzzing research and practices and prospects for future research directions in DBMS fuzzing.
Available online: January 10, 2024 , DOI: 10.13328/j.cnki.jos.007039
Abstract:
Graph neural network (GNN) is a framework for directly characterizing graph structured data by deep learning, and has caught increasing attention in recent years. However, the traditional GNN based on message passing aggregation (MP-GNN) ignores the smoothing speed of different nodes and aggregates the neighbor information indiscriminately, which is prone to the over-smoothing phenomenon. Thus, this study proposes a graph kernel neural network classification method KENN based on linear structural entropy. KENN firstly adopts the graph kernel method to encode node subgraph structure, determines isomorphism among subgraphs, and then utilizes the isomorphism coefficient to define the smoothing coefficient among different neighbors. Secondly, it extracts the graph structural information based on the low-complexity linear structural entropy to deepen and enrich the structural expression capability of the graph data. This study puts forward a graph kernel neural network classification method by deeply integrating linear structural entropy, graph kernel and GNN, which can solve the sparse node features of biomolecular data and information redundancy generated by leveraging node degree as features in social network data. It also enables the GNN to adaptively adjust its ability to characterize the graph structural features and makes GNN beyond the upper bound of MP-GNN (WL test). Finally, experiments on seven public graph classification datasets verify that the proposed model outperforms other benchmark models.
Graph neural network (GNN) is a framework for directly characterizing graph structured data by deep learning, and has caught increasing attention in recent years. However, the traditional GNN based on message passing aggregation (MP-GNN) ignores the smoothing speed of different nodes and aggregates the neighbor information indiscriminately, which is prone to the over-smoothing phenomenon. Thus, this study proposes a graph kernel neural network classification method KENN based on linear structural entropy. KENN firstly adopts the graph kernel method to encode node subgraph structure, determines isomorphism among subgraphs, and then utilizes the isomorphism coefficient to define the smoothing coefficient among different neighbors. Secondly, it extracts the graph structural information based on the low-complexity linear structural entropy to deepen and enrich the structural expression capability of the graph data. This study puts forward a graph kernel neural network classification method by deeply integrating linear structural entropy, graph kernel and GNN, which can solve the sparse node features of biomolecular data and information redundancy generated by leveraging node degree as features in social network data. It also enables the GNN to adaptively adjust its ability to characterize the graph structural features and makes GNN beyond the upper bound of MP-GNN (WL test). Finally, experiments on seven public graph classification datasets verify that the proposed model outperforms other benchmark models.
Available online: January 10, 2024 , DOI: 10.13328/j.cnki.jos.007040
Abstract:
Named entity recognition (NER) is a fundamental task in information extraction and aims to locate the boundaries of entities in a sentence and classify them. In response to the fuzzy boundaries of nested entities based on span detection models, this study proposes a nested NER model based on span boundary perception. Firstly, it utilizes a bidirectional affine attention mechanism to capture the semantic relevance among word tokens and then generates a span semantic representation matrix. Secondly, it designs a second-order diagonal neighborhood difference operator and establishes a span semantic difference mechanism to extract semantic difference information among spans. Additionally, a span boundary perception mechanism is introduced to employ the local feature extraction ability of sliding windows to enhance the span boundary semantic differences, thereby accurately locating the entity span. The model is validated on three benchmark datasets of ACE04, ACE05, and Genia. The experimental results show that the proposed model outperforms related work in entity recognition accuracy. Additionally, the study conducts ablation experiments and case studies to verify the effectiveness of the proposed semantic difference mechanism and span boundary perception mechanism, providing new ideas and empirical evidence for further research on NER.
Named entity recognition (NER) is a fundamental task in information extraction and aims to locate the boundaries of entities in a sentence and classify them. In response to the fuzzy boundaries of nested entities based on span detection models, this study proposes a nested NER model based on span boundary perception. Firstly, it utilizes a bidirectional affine attention mechanism to capture the semantic relevance among word tokens and then generates a span semantic representation matrix. Secondly, it designs a second-order diagonal neighborhood difference operator and establishes a span semantic difference mechanism to extract semantic difference information among spans. Additionally, a span boundary perception mechanism is introduced to employ the local feature extraction ability of sliding windows to enhance the span boundary semantic differences, thereby accurately locating the entity span. The model is validated on three benchmark datasets of ACE04, ACE05, and Genia. The experimental results show that the proposed model outperforms related work in entity recognition accuracy. Additionally, the study conducts ablation experiments and case studies to verify the effectiveness of the proposed semantic difference mechanism and span boundary perception mechanism, providing new ideas and empirical evidence for further research on NER.
Available online: January 10, 2024 , DOI: 10.13328/j.cnki.jos.007037
Abstract:
Ubiquitous computing for human-cyber-physical integration is becoming a new requirement and trend in software development. Based on this new computing paradigm, human-cyber-physical applications further extend software technology to the effective utilization of offline resources, including physical devices and human resources. As a typical human-cyber-physical scenario, the collaboration between the device and human resources in the physical world features resource selectivity, high task frequency, and worker dynamics. Traditional resource scheduling techniques cannot meet the scheduling requirements of this task type (referred to as DHRC task). Thus, this study proposes an optimal scheduling method for collaborative tasks between device and human resources. This method includes two stages of device resource scheduling and human resource scheduling. In the device resource scheduling stage, a device resource scheduling algorithm based on NSGA-II is proposed to optimize task resource selection by comprehensively considering such factors as task distance, device load, and the worker number around the device location. In the human resource scheduling stage, a human resource scheduling algorithm based on DPSO is put forward to optimize the worker selection and corresponding path planning according to such factors as worker location and collaboration dependency. Experiments in a simulated environment show that the algorithm in the first stage is equivalent in efficiency and superior in utility to the compared algorithm (discrete particle swarm optimization algorithm). The algorithm in the second stage is superior in efficiency and utility to the compared algorithm (the genetic algorithm improved by the tournament mechanism).
Ubiquitous computing for human-cyber-physical integration is becoming a new requirement and trend in software development. Based on this new computing paradigm, human-cyber-physical applications further extend software technology to the effective utilization of offline resources, including physical devices and human resources. As a typical human-cyber-physical scenario, the collaboration between the device and human resources in the physical world features resource selectivity, high task frequency, and worker dynamics. Traditional resource scheduling techniques cannot meet the scheduling requirements of this task type (referred to as DHRC task). Thus, this study proposes an optimal scheduling method for collaborative tasks between device and human resources. This method includes two stages of device resource scheduling and human resource scheduling. In the device resource scheduling stage, a device resource scheduling algorithm based on NSGA-II is proposed to optimize task resource selection by comprehensively considering such factors as task distance, device load, and the worker number around the device location. In the human resource scheduling stage, a human resource scheduling algorithm based on DPSO is put forward to optimize the worker selection and corresponding path planning according to such factors as worker location and collaboration dependency. Experiments in a simulated environment show that the algorithm in the first stage is equivalent in efficiency and superior in utility to the compared algorithm (discrete particle swarm optimization algorithm). The algorithm in the second stage is superior in efficiency and utility to the compared algorithm (the genetic algorithm improved by the tournament mechanism).
Available online: January 03, 2024 , DOI: 10.13328/j.cnki.jos.007044
Abstract:
Due to the complex features of multi-view data, multi-view outlier detection has become a very challenging research topic in outlier detection. There are three types of outliers in multi-view data, namely class outliers, attribute outliers, and class-attribute outliers. Most of the early multi-view outlier detection methods are based on the assumption of clustering, which makes it difficult to detect outliers when there is no clustering structure in the data. In recent years, many multi-view outlier detection methods use the multi-view consistent nearest neighbor assumption instead of the clustering assumption, but they still suffer from the problem of inefficient detection of new data. In addition, most existing multi-view outlier detection methods are unsupervised, which are affected by outliers during model learning and do not work well when dealing with datasets with high outlier rates. To address these issues, this study proposes an intra-view reconstruction and cross-view generation network for effective multi-view outlier detection to detect the three types of outliers, which consists of two modules: intra-view reconstruction and cross-view generation. By training with normal data, the proposed method can fully capture the features of each view in the normal data and reconstruct and generate the corresponding views better. In addition, a new outlier calculation method is proposed to calculate the corresponding outlier scores for each sample to efficiently detect new data. Extensive experimental results show that the proposed method significantly outperforms existing methods. It is known that this is the first work to apply a deep model based on generative adversarial networks to multi-view outlier detection.
Due to the complex features of multi-view data, multi-view outlier detection has become a very challenging research topic in outlier detection. There are three types of outliers in multi-view data, namely class outliers, attribute outliers, and class-attribute outliers. Most of the early multi-view outlier detection methods are based on the assumption of clustering, which makes it difficult to detect outliers when there is no clustering structure in the data. In recent years, many multi-view outlier detection methods use the multi-view consistent nearest neighbor assumption instead of the clustering assumption, but they still suffer from the problem of inefficient detection of new data. In addition, most existing multi-view outlier detection methods are unsupervised, which are affected by outliers during model learning and do not work well when dealing with datasets with high outlier rates. To address these issues, this study proposes an intra-view reconstruction and cross-view generation network for effective multi-view outlier detection to detect the three types of outliers, which consists of two modules: intra-view reconstruction and cross-view generation. By training with normal data, the proposed method can fully capture the features of each view in the normal data and reconstruct and generate the corresponding views better. In addition, a new outlier calculation method is proposed to calculate the corresponding outlier scores for each sample to efficiently detect new data. Extensive experimental results show that the proposed method significantly outperforms existing methods. It is known that this is the first work to apply a deep model based on generative adversarial networks to multi-view outlier detection.
Available online: January 03, 2024 , DOI: 10.13328/j.cnki.jos.007046
Abstract:
Smart contracts are computer programs running in the contract layer of the blockchain, which can be used to manage cryptocurrencies and data on the blockchain, realize diverse business logic, and expand the application of the blockchain. A large number of assets are stored in smart contracts, which attract attackers to steal the assets and obtain economic benefits via security vulnerabilities. In recent years, with the frequent occurrence of smart contract security incidents (such as TheDAO and Parity security incidents), the security vulnerability detection technique for smart contracts has become a hot research topic. This study proposes a research framework for detecting security vulnerabilities of smart contracts and analyzes the research progress of existing vulnerability detection techniques from three aspects: vulnerability discovery and identification, vulnerability analysis and detection, and dataset and evaluation indicators. Firstly, the basic process of collecting security vulnerability information is sorted out, and the security vulnerabilities are classified into 13 types according to their basic characteristics. A classification framework for security vulnerabilities of smart contracts is proposed. Secondly, existing techniques are studied in terms of symbolic execution, fuzzing testing, machine learning, formal verification, and static analysis, and the advantages and limitations of each technique are analyzed. Thirdly, the commonly used datasets and evaluation indicators are summarized. Finally, potential research directions for security vulnerability detection of smart contracts in the future are discussed.
Smart contracts are computer programs running in the contract layer of the blockchain, which can be used to manage cryptocurrencies and data on the blockchain, realize diverse business logic, and expand the application of the blockchain. A large number of assets are stored in smart contracts, which attract attackers to steal the assets and obtain economic benefits via security vulnerabilities. In recent years, with the frequent occurrence of smart contract security incidents (such as TheDAO and Parity security incidents), the security vulnerability detection technique for smart contracts has become a hot research topic. This study proposes a research framework for detecting security vulnerabilities of smart contracts and analyzes the research progress of existing vulnerability detection techniques from three aspects: vulnerability discovery and identification, vulnerability analysis and detection, and dataset and evaluation indicators. Firstly, the basic process of collecting security vulnerability information is sorted out, and the security vulnerabilities are classified into 13 types according to their basic characteristics. A classification framework for security vulnerabilities of smart contracts is proposed. Secondly, existing techniques are studied in terms of symbolic execution, fuzzing testing, machine learning, formal verification, and static analysis, and the advantages and limitations of each technique are analyzed. Thirdly, the commonly used datasets and evaluation indicators are summarized. Finally, potential research directions for security vulnerability detection of smart contracts in the future are discussed.
Available online: January 03, 2024 , DOI: 10.13328/j.cnki.jos.007057
Abstract:
Currently, sentiment analysis research is generally based on big data-driven models, which heavily rely on expensive annotation and computational costs. Therefore, research on sentiment analysis in low-resource scenarios is particularly urgent. However, existing research on sentiment analysis in low-resource scenarios mainly focuses on a single task, making it difficult for models to acquire external task knowledge. Therefore, this study constructs successive sentiment analysis in low-resource scenarios, aiming to allow models to learn multiple sentiment analysis tasks over time by continual learning methods. This can make full use of data from different tasks and learn sentiment information from different tasks, thus alleviating the problem of insufficient training data for a single task. There are two core problems with successive sentiment analysis in low-resource scenarios. One is preserving sentiment information for a single task, and the other is fusing sentiment information between different tasks. To solve these two problems, this study proposes continual attention modeling for successive sentiment analysis in low-resource scenarios. Sentiment masked Adapter (SMA) is first constructed, which is used to generate hard attention emotion masks for different tasks. This can preserve sentiment information for different tasks and mitigate catastrophic forgetting. Secondly, dynamic sentiment attention (DSA) is proposed, which dynamically fuses features extracted by different Adapters based on the current time step and task similarity. This can fuse sentiment information between different tasks. Experimental results on multiple datasets show that the proposed approach significantly outperforms the state-of-the-art benchmark approaches. Additionally, experimental analysis indicates that the proposed approach has the best sentiment information retention ability and sentiment information fusion ability compared to other benchmark approaches while maintaining high operational efficiency.
Currently, sentiment analysis research is generally based on big data-driven models, which heavily rely on expensive annotation and computational costs. Therefore, research on sentiment analysis in low-resource scenarios is particularly urgent. However, existing research on sentiment analysis in low-resource scenarios mainly focuses on a single task, making it difficult for models to acquire external task knowledge. Therefore, this study constructs successive sentiment analysis in low-resource scenarios, aiming to allow models to learn multiple sentiment analysis tasks over time by continual learning methods. This can make full use of data from different tasks and learn sentiment information from different tasks, thus alleviating the problem of insufficient training data for a single task. There are two core problems with successive sentiment analysis in low-resource scenarios. One is preserving sentiment information for a single task, and the other is fusing sentiment information between different tasks. To solve these two problems, this study proposes continual attention modeling for successive sentiment analysis in low-resource scenarios. Sentiment masked Adapter (SMA) is first constructed, which is used to generate hard attention emotion masks for different tasks. This can preserve sentiment information for different tasks and mitigate catastrophic forgetting. Secondly, dynamic sentiment attention (DSA) is proposed, which dynamically fuses features extracted by different Adapters based on the current time step and task similarity. This can fuse sentiment information between different tasks. Experimental results on multiple datasets show that the proposed approach significantly outperforms the state-of-the-art benchmark approaches. Additionally, experimental analysis indicates that the proposed approach has the best sentiment information retention ability and sentiment information fusion ability compared to other benchmark approaches while maintaining high operational efficiency.
Available online: December 27, 2023 , DOI: 10.13328/j.cnki.jos.007032
Abstract:
Federated learning has caught much attention because it can solve data islands. However, it also faces challenges such as the risk of privacy leakage and performance degradation due to model heterogeneity under non-independent and identically distributed data. To this end, this study proposes a personalized federated learning method based on Bregman divergence and differential privacy (FedBDP). This method employs Bregman divergence to measure the differences between local and global parameters and adopt it as a regularization term to update the loss function, thereby reducing model differences to improve model accuracy. Meanwhile, adaptive differential privacy technology is utilized to perturb local model parameters, and the attenuation coefficient is defined to dynamically adjust the level of the differential privacy noise in each round, and thus reasonably allocate the privacy noise level and improve the model availability. Theoretical analysis shows that FedBDP satisfies convergence conditions under both strongly convex and non-convex smooth functions. Experimental results demonstrate that the FedBDP method can guarantee accuracy in the MNIST and CIFAR10 datasets on the premise of satisfying differential privacy.
Federated learning has caught much attention because it can solve data islands. However, it also faces challenges such as the risk of privacy leakage and performance degradation due to model heterogeneity under non-independent and identically distributed data. To this end, this study proposes a personalized federated learning method based on Bregman divergence and differential privacy (FedBDP). This method employs Bregman divergence to measure the differences between local and global parameters and adopt it as a regularization term to update the loss function, thereby reducing model differences to improve model accuracy. Meanwhile, adaptive differential privacy technology is utilized to perturb local model parameters, and the attenuation coefficient is defined to dynamically adjust the level of the differential privacy noise in each round, and thus reasonably allocate the privacy noise level and improve the model availability. Theoretical analysis shows that FedBDP satisfies convergence conditions under both strongly convex and non-convex smooth functions. Experimental results demonstrate that the FedBDP method can guarantee accuracy in the MNIST and CIFAR10 datasets on the premise of satisfying differential privacy.
Available online: December 27, 2023 , DOI: 10.13328/j.cnki.jos.007038
Abstract:
Penetration testing is an important means to discover the weaknesses of significant network information systems and protect network security. Traditional penetration testing relies heavily on manual labor and has high technical requirements for testers, limiting the popularization depth and breadth. By introducing artificial intelligence technology into the whole penetration testing process, automated penetration testing lowers the technical threshold of penetration testing based on greatly solving the problem of heavy dependence on manual labor. Automated penetration testing can be mainly divided into model-based and rule-based automated penetration testing, and the research of the two has their respective focuses. The former utilizes model algorithms to simulate hacker attacks with attention paid to attack scene perception and attack decision-making models. The latter concentrates on how to efficiently adapt attack rules and attack scenarios. This study mainly analyzes the implementation principles of automated penetration testing from three aspects of attack scenario modeling, penetration testing modeling, and decision-making reasoning model. Finally, the future development direction of automated penetration is explored from the dimensions of attack-defense confrontation and vulnerability combination utilization.
Penetration testing is an important means to discover the weaknesses of significant network information systems and protect network security. Traditional penetration testing relies heavily on manual labor and has high technical requirements for testers, limiting the popularization depth and breadth. By introducing artificial intelligence technology into the whole penetration testing process, automated penetration testing lowers the technical threshold of penetration testing based on greatly solving the problem of heavy dependence on manual labor. Automated penetration testing can be mainly divided into model-based and rule-based automated penetration testing, and the research of the two has their respective focuses. The former utilizes model algorithms to simulate hacker attacks with attention paid to attack scene perception and attack decision-making models. The latter concentrates on how to efficiently adapt attack rules and attack scenarios. This study mainly analyzes the implementation principles of automated penetration testing from three aspects of attack scenario modeling, penetration testing modeling, and decision-making reasoning model. Finally, the future development direction of automated penetration is explored from the dimensions of attack-defense confrontation and vulnerability combination utilization.
Available online: December 27, 2023 , DOI: 10.13328/j.cnki.jos.007036
Abstract:
Partitioned DM (deadline-monotonic) scheduling of sporadic real-time tasks is a classic research problem. This study proposes a partitioned scheduling algorithm PDM-FFD (partitioned deadline-monotonic first-fit decrease) with higher processor utilization for constrained-deadline sporadic tasks. In PDM-FFD, firstly tasks are sorted in non-decreasing order according to the relative deadline, then the first-fit strategy is utilized to select the processor core to allocate tasks, and each core adopts DM scheduling policy. Finally, a tighter schedulability determination method is obtained by analyzing the task interference time to determine the task schedulability. This study proves that the speedup factor of PDM-FFD is $3 - (3\Delta + 1)/(m + \Delta )$ and the time complexity is ${\rm{O}}({n^2}) + {\rm{O}}(nm)$. $\Delta =\displaystyle{\sum }_{{\tau }_{j}\in \tau }{C}_{j} \times {u}_{j}/{D}_{{\rm{max}}}$ where ${\tau _j}$ belongs to the task set $\tau $, ${C_j}$is the worst-case execution time, ${u_j}$is the utilization, ${D_{{\rm{max}}}}$ is the maximum relative deadline, n is the task number, and m is the processor core number. The speedup factor of PDM-FFD is strictly less than $3 - 1/m$, which outperforms the existing multi-core partitioned scheduling algorithm FBB-FFD. Experiments show that PDM-FFD improves processor utilization by 18.5% compared to other available algorithms on a four-core processor. The PDM-FFD performance improves with the increasing processor core number, task set utilization, and task number. Due to high performance, PDM-FFD can be widely utilized in typical real-time systems such as resource-constrained spacecraft, autonomous vehicles, and industrial robots.
Partitioned DM (deadline-monotonic) scheduling of sporadic real-time tasks is a classic research problem. This study proposes a partitioned scheduling algorithm PDM-FFD (partitioned deadline-monotonic first-fit decrease) with higher processor utilization for constrained-deadline sporadic tasks. In PDM-FFD, firstly tasks are sorted in non-decreasing order according to the relative deadline, then the first-fit strategy is utilized to select the processor core to allocate tasks, and each core adopts DM scheduling policy. Finally, a tighter schedulability determination method is obtained by analyzing the task interference time to determine the task schedulability. This study proves that the speedup factor of PDM-FFD is $3 - (3\Delta + 1)/(m + \Delta )$ and the time complexity is ${\rm{O}}({n^2}) + {\rm{O}}(nm)$. $\Delta =\displaystyle{\sum }_{{\tau }_{j}\in \tau }{C}_{j} \times {u}_{j}/{D}_{{\rm{max}}}$ where ${\tau _j}$ belongs to the task set $\tau $, ${C_j}$is the worst-case execution time, ${u_j}$is the utilization, ${D_{{\rm{max}}}}$ is the maximum relative deadline, n is the task number, and m is the processor core number. The speedup factor of PDM-FFD is strictly less than $3 - 1/m$, which outperforms the existing multi-core partitioned scheduling algorithm FBB-FFD. Experiments show that PDM-FFD improves processor utilization by 18.5% compared to other available algorithms on a four-core processor. The PDM-FFD performance improves with the increasing processor core number, task set utilization, and task number. Due to high performance, PDM-FFD can be widely utilized in typical real-time systems such as resource-constrained spacecraft, autonomous vehicles, and industrial robots.
Available online: December 20, 2023 , DOI: 10.13328/j.cnki.jos.007047
Abstract:
As the scale of open-source artificial intelligence (AI) systems expands, software development and maintenance become difficult. GitHub is one of the most important hosting platforms for open-source projects in the open-source community. Developers can easily participate in the development of open-source projects through pull request systems provided by GitHub. The description of pull requests can help the core teams of the project understand the content of the pull requests and the intention of the developers and promote the acceptance of the pull request. At present, a considerable proportion of developers do not provide a description for the pull request, which not only increases the workload of the core team but also is not conducive to the maintenance of the project in the future. This study proposes a method named PRSim to automatically generate descriptions for pull requests. This method extracts features including commit messages, comment updates, and code changes from pull requests, builds a syntax modification tree, and uses a tree-structured autoencoder to find other pull requests with similar code changes. Then, with the help of the description of a similar pull request, it summarizes commit messages and comment updates through an encoder-decoder network to generate the description of a new pull request. The experimental results show that the generation effect of PRSim reaches 36.47%, 27.69%, and 35.37% in terms of the F1 score of metrics Rouge-1, Rouge-2, and Rouge-L, respectively, which is 34.3%, 75.2%, and 55.3% higher than LeadCM, 16.2%, 22.9%, and 16.8% higher than Attn+PG+RL, and 23.5%, 72.0%, and 24.8% higher than PRHAN.
As the scale of open-source artificial intelligence (AI) systems expands, software development and maintenance become difficult. GitHub is one of the most important hosting platforms for open-source projects in the open-source community. Developers can easily participate in the development of open-source projects through pull request systems provided by GitHub. The description of pull requests can help the core teams of the project understand the content of the pull requests and the intention of the developers and promote the acceptance of the pull request. At present, a considerable proportion of developers do not provide a description for the pull request, which not only increases the workload of the core team but also is not conducive to the maintenance of the project in the future. This study proposes a method named PRSim to automatically generate descriptions for pull requests. This method extracts features including commit messages, comment updates, and code changes from pull requests, builds a syntax modification tree, and uses a tree-structured autoencoder to find other pull requests with similar code changes. Then, with the help of the description of a similar pull request, it summarizes commit messages and comment updates through an encoder-decoder network to generate the description of a new pull request. The experimental results show that the generation effect of PRSim reaches 36.47%, 27.69%, and 35.37% in terms of the F1 score of metrics Rouge-1, Rouge-2, and Rouge-L, respectively, which is 34.3%, 75.2%, and 55.3% higher than LeadCM, 16.2%, 22.9%, and 16.8% higher than Attn+PG+RL, and 23.5%, 72.0%, and 24.8% higher than PRHAN.
Available online: December 06, 2023 , DOI: 10.13328/j.cnki.jos.007033
Abstract:
In natural scenes, logos such as trademarks and traffic signs are susceptible to shooting angle, carrier deformation, and scale changes, which reduces logo detection accuracy. Thus, this study proposes an attention guided logo detection and recognition network (AGLDN) to jointly optimize the model robustness for multi-scale and complex deformation. First, a logo synthesis dataset is established by image collection and mask generation of logo templates, image selection of logo background, and logo image generation. Then, based on RetinaNet and FPN, multi-scale features are extracted and high-level semantic feature mapping is formed. Finally, the attention mechanism guided network is employed to focus on the logo area, and the influence of logo deformation on feature robustness is suppressed to improve logo detection and recognition. Experimental results show that the proposed method can reduce the influence of scale changes and non-rigid deformation, and improve detection accuracy.
In natural scenes, logos such as trademarks and traffic signs are susceptible to shooting angle, carrier deformation, and scale changes, which reduces logo detection accuracy. Thus, this study proposes an attention guided logo detection and recognition network (AGLDN) to jointly optimize the model robustness for multi-scale and complex deformation. First, a logo synthesis dataset is established by image collection and mask generation of logo templates, image selection of logo background, and logo image generation. Then, based on RetinaNet and FPN, multi-scale features are extracted and high-level semantic feature mapping is formed. Finally, the attention mechanism guided network is employed to focus on the logo area, and the influence of logo deformation on feature robustness is suppressed to improve logo detection and recognition. Experimental results show that the proposed method can reduce the influence of scale changes and non-rigid deformation, and improve detection accuracy.
Available online: December 06, 2023 , DOI: 10.13328/j.cnki.jos.007034
Abstract:
Unlimited by the state and space, the formal verification technology based on mechanized theorem proof is an important method to ensure software correctness and avoid serious loss from potential software bugs. LLRB (left-leaning red-black trees) is a variant of binary search trees, and its structure has an additional left-leaning constraint over the traditional red-black trees. During verification, conventional proof strategies cannot be employed, which requires more manual intervention and effort. Thus, the LLRB correctness verification is widely acknowledged as a challenging problem. To this end, based on the Isabelle verification framework for the binary search tree algorithm, this study refines the additional property part of the framework and provides a concrete verification scheme. The LLRB insertion and deletion operations are functionally modeled in Isabelle, with modular treatment of the LLRB invariants. Subsequently, the function correctness is verified. This is the first mechanized verification of functional LLRB insertion and deletion algorithms in Isabelle. Compared to the current Dafny verification of the LLRB algorithm, the theorem number is reduced from 158 to 84, and it is unnecessary for constructing intermediate assertions, which alleviates the verification burden. Meanwhile, this study provides references for functional modeling and verification of complex tree structure algorithms.
Unlimited by the state and space, the formal verification technology based on mechanized theorem proof is an important method to ensure software correctness and avoid serious loss from potential software bugs. LLRB (left-leaning red-black trees) is a variant of binary search trees, and its structure has an additional left-leaning constraint over the traditional red-black trees. During verification, conventional proof strategies cannot be employed, which requires more manual intervention and effort. Thus, the LLRB correctness verification is widely acknowledged as a challenging problem. To this end, based on the Isabelle verification framework for the binary search tree algorithm, this study refines the additional property part of the framework and provides a concrete verification scheme. The LLRB insertion and deletion operations are functionally modeled in Isabelle, with modular treatment of the LLRB invariants. Subsequently, the function correctness is verified. This is the first mechanized verification of functional LLRB insertion and deletion algorithms in Isabelle. Compared to the current Dafny verification of the LLRB algorithm, the theorem number is reduced from 158 to 84, and it is unnecessary for constructing intermediate assertions, which alleviates the verification burden. Meanwhile, this study provides references for functional modeling and verification of complex tree structure algorithms.
Available online: December 06, 2023 , DOI: 10.13328/j.cnki.jos.007035
Abstract:
Detecting latent topics in social media texts is a meaningful task, and the short and informal posts will cause serious data sparsity. Additionally, models based on variational auto-encoders (VAEs) ignore the social relationships among users during topic inference and VAE assumes that each input data point is independent. This results in the lack of correlation information between the inferred latent topic variables and incoherent topics. Social network structure information can not only provide clues for aggregating contextual messages but also indicate topic correlation among users. Therefore, this study proposes to utilize the microblog topic model based on message passing and graph prior distribution. This model can encode richer context information by graph convolution network (GCN) and integrate the interactive relationship of users by graph prior distribution during VAE topic inference to better understand the complex correlation among multiple data points and mine social media topic information. The experiments on three actual datasets validate the effectiveness of the proposed model.
Detecting latent topics in social media texts is a meaningful task, and the short and informal posts will cause serious data sparsity. Additionally, models based on variational auto-encoders (VAEs) ignore the social relationships among users during topic inference and VAE assumes that each input data point is independent. This results in the lack of correlation information between the inferred latent topic variables and incoherent topics. Social network structure information can not only provide clues for aggregating contextual messages but also indicate topic correlation among users. Therefore, this study proposes to utilize the microblog topic model based on message passing and graph prior distribution. This model can encode richer context information by graph convolution network (GCN) and integrate the interactive relationship of users by graph prior distribution during VAE topic inference to better understand the complex correlation among multiple data points and mine social media topic information. The experiments on three actual datasets validate the effectiveness of the proposed model.
Available online: November 29, 2023 , DOI: 10.13328/j.cnki.jos.007003
Abstract:
In the field of cyber security, the mendacious domains generated by the domain generation algorithm (DGA) are called DGA domains. Similar to real domains, they are usually a random combination of characters or numbers, which makes DGA domains highly camouflaged. Hackers take advantage of the disguised nature of DGA domains to carry out cyber attacks, so as to bypass security detection. How to effectively detect DGA domains has become a research hotspot. Traditional statistical machine learning detection methods require the manual construction of domain feature sets. However, the quality of domain features constructed manually or semi-automatically varies, which affects the accuracy of detection. In view of the powerful automatic feature extraction and representation capability of deep neural networks, a DGA domain detection method based on multi-view contrastive learning (MCL4DGA) is proposed. Different from existing methods, it incorporates attentional neural networks, convolutional neural networks, and recurrent neural networks to effectively capture global, local, and bidirectional multi-view feature dependencies of domain sequences. Besides, the self-supervision signals derived by contrastive learning can enhance the expressiveness between multi-view feature learning encoders and thus improve the accuracy of detection. The effectiveness of the proposed method is verified by experimental comparison with current methods on a real dataset.
In the field of cyber security, the mendacious domains generated by the domain generation algorithm (DGA) are called DGA domains. Similar to real domains, they are usually a random combination of characters or numbers, which makes DGA domains highly camouflaged. Hackers take advantage of the disguised nature of DGA domains to carry out cyber attacks, so as to bypass security detection. How to effectively detect DGA domains has become a research hotspot. Traditional statistical machine learning detection methods require the manual construction of domain feature sets. However, the quality of domain features constructed manually or semi-automatically varies, which affects the accuracy of detection. In view of the powerful automatic feature extraction and representation capability of deep neural networks, a DGA domain detection method based on multi-view contrastive learning (MCL4DGA) is proposed. Different from existing methods, it incorporates attentional neural networks, convolutional neural networks, and recurrent neural networks to effectively capture global, local, and bidirectional multi-view feature dependencies of domain sequences. Besides, the self-supervision signals derived by contrastive learning can enhance the expressiveness between multi-view feature learning encoders and thus improve the accuracy of detection. The effectiveness of the proposed method is verified by experimental comparison with current methods on a real dataset.
Available online: November 29, 2023 , DOI: 10.13328/j.cnki.jos.007005
Abstract:
Nowadays, deep neural network (DNN) is widely used in autonomous driving, medical diagnosis, speech recognition, face recognition, and other safety-critical fields. Therefore, DNN testing is critical to ensure the quality of DNN. However, labeling test cases to judge whether the DNN model predictions are correct is costly. Therefore, selecting test cases that reveal incorrect behavior of DNN models and labeling them earlier can help developers debug DNN models as soon as possible, thus improving the efficiency of DNN testing and ensuring the quality of DNN models. This study proposes a test case selection method based on data mutation, namely DMS. In this method, a data mutation operator is designed and implemented to generate a mutation model to simulate model defects and capture the dynamic pattern of test case bug-revealing, so as to evaluate the ability of test case bug-revealing. Experiments are conducted on the combination of 25 deep learning test sets and models. The results show that DMS is significantly better than the existing test case selection methods in terms of both the proportion of bug-revealing and the diversity of bug-revealing directions in the selected samples. Specifically, taking the original test set as the candidate set, DMS can filter out 53.85%–99.22% of all bug-revealing test cases when selecting 10% of the test cases. Moreover, when 5% of the test cases are selected, the selected cases by DMS can cover almost all bug-revealing directions. Compared with the eight comparison methods, DMS finds 12.38%–71.81% more bug-revealing cases on average, which proves the significant effectiveness of DMS in the task of test case selection.
Nowadays, deep neural network (DNN) is widely used in autonomous driving, medical diagnosis, speech recognition, face recognition, and other safety-critical fields. Therefore, DNN testing is critical to ensure the quality of DNN. However, labeling test cases to judge whether the DNN model predictions are correct is costly. Therefore, selecting test cases that reveal incorrect behavior of DNN models and labeling them earlier can help developers debug DNN models as soon as possible, thus improving the efficiency of DNN testing and ensuring the quality of DNN models. This study proposes a test case selection method based on data mutation, namely DMS. In this method, a data mutation operator is designed and implemented to generate a mutation model to simulate model defects and capture the dynamic pattern of test case bug-revealing, so as to evaluate the ability of test case bug-revealing. Experiments are conducted on the combination of 25 deep learning test sets and models. The results show that DMS is significantly better than the existing test case selection methods in terms of both the proportion of bug-revealing and the diversity of bug-revealing directions in the selected samples. Specifically, taking the original test set as the candidate set, DMS can filter out 53.85%–99.22% of all bug-revealing test cases when selecting 10% of the test cases. Moreover, when 5% of the test cases are selected, the selected cases by DMS can cover almost all bug-revealing directions. Compared with the eight comparison methods, DMS finds 12.38%–71.81% more bug-revealing cases on average, which proves the significant effectiveness of DMS in the task of test case selection.
Available online: November 29, 2023 , DOI: 10.13328/j.cnki.jos.007007
Abstract:
In current real life where data sources are diverse, and manual labeling is difficult, semi-supervised multi-view classification algorithms have important research significance in various fields. In recent years, graph neural networks-based semi-supervised multi-view classification algorithms have achieved great progress. However, most of the existing graph neural networks carry out multi-view information fusion only in the classification stage, while neglecting the multi-view information interaction between the same sample during the training stage. To solve the above issue, this study proposes a model for semi-supervised classification, named multi-view interaction graph convolutional network (MIGCN). The Transformer Encoder module is introduced to the graph convolution layer trained on different views, which aims to adaptively acquire complementary information between different views for the same sample during the training stage. More importantly, the study introduces the consistency constraint loss to make the similar relationship of the final feature expressions of different views as similar as possible. This operation can make graph convolutional neural networks during the classification stage better utilize the consistency and complementarity information between different views reasonably, and then it can further improve the robust performance of the multi-view fusion feature. Extensive experiments on several real-world multi-view datasets demonstrate that compared with the graph-based semi-supervised multi-view classification model, MIGCN can better learn the essential features of multi-view data, thereby improving the accuracy of semi-supervised multi-view classification.
In current real life where data sources are diverse, and manual labeling is difficult, semi-supervised multi-view classification algorithms have important research significance in various fields. In recent years, graph neural networks-based semi-supervised multi-view classification algorithms have achieved great progress. However, most of the existing graph neural networks carry out multi-view information fusion only in the classification stage, while neglecting the multi-view information interaction between the same sample during the training stage. To solve the above issue, this study proposes a model for semi-supervised classification, named multi-view interaction graph convolutional network (MIGCN). The Transformer Encoder module is introduced to the graph convolution layer trained on different views, which aims to adaptively acquire complementary information between different views for the same sample during the training stage. More importantly, the study introduces the consistency constraint loss to make the similar relationship of the final feature expressions of different views as similar as possible. This operation can make graph convolutional neural networks during the classification stage better utilize the consistency and complementarity information between different views reasonably, and then it can further improve the robust performance of the multi-view fusion feature. Extensive experiments on several real-world multi-view datasets demonstrate that compared with the graph-based semi-supervised multi-view classification model, MIGCN can better learn the essential features of multi-view data, thereby improving the accuracy of semi-supervised multi-view classification.
Available online: November 22, 2023 , DOI: 10.13328/j.cnki.jos.006968
Abstract:
Apache Flink is one of the most popular stream computing platforms and has many applications in industry. Complex event processing (CEP) is one of the important usage scenarios of stream computation. Apache Flink defines and implements a language for complex event processing (referred to as FlinkCEP). FlinkCEP includes rich syntactic features, not only the usual features of filtering, connecting, and looping, but also the advanced features of iterative conditions and after-match skip strategies. The semantics of FlinkCEP is complex, no language specification of FlinkCEP defines its semantics precisely, so it can only be understood by checking the implementation details. This motivates the definition of formal semantics for FlinkCEP so that the developers could understand its semantics precisely. This study proposes an automaton model called data stream transducers (DST) for FlinkCEP, where the data variables are applied to capture the iterative conditions, the data stream variables are adopted to store the outputs, and transition priorities are introduced to capture the after-match skip strategies. DST is leveraged to define the formal semantics of FlinkCEP and design the query evaluation algorithms based on the formal semantics. Moreover, a prototype of the CEP engine is implemented. Finally, test case sets are generated, which cover the syntactic features of FlinkCEP more comprehensively. They are utilized to conduct comparison experiments against the actual results of FlinkCEP on the Flink platform. The experimental results show that the proposed formal semantics of FlinkCEP conforms to the actual semantics of FlinkCEP in the vast majority of the cases. Furthermore, the inconsistencies between the formal and the actual semantics are analyzed and it is discovered that the Flink implementation of FlinkCEP may not deal with the group patterns correctly.
Apache Flink is one of the most popular stream computing platforms and has many applications in industry. Complex event processing (CEP) is one of the important usage scenarios of stream computation. Apache Flink defines and implements a language for complex event processing (referred to as FlinkCEP). FlinkCEP includes rich syntactic features, not only the usual features of filtering, connecting, and looping, but also the advanced features of iterative conditions and after-match skip strategies. The semantics of FlinkCEP is complex, no language specification of FlinkCEP defines its semantics precisely, so it can only be understood by checking the implementation details. This motivates the definition of formal semantics for FlinkCEP so that the developers could understand its semantics precisely. This study proposes an automaton model called data stream transducers (DST) for FlinkCEP, where the data variables are applied to capture the iterative conditions, the data stream variables are adopted to store the outputs, and transition priorities are introduced to capture the after-match skip strategies. DST is leveraged to define the formal semantics of FlinkCEP and design the query evaluation algorithms based on the formal semantics. Moreover, a prototype of the CEP engine is implemented. Finally, test case sets are generated, which cover the syntactic features of FlinkCEP more comprehensively. They are utilized to conduct comparison experiments against the actual results of FlinkCEP on the Flink platform. The experimental results show that the proposed formal semantics of FlinkCEP conforms to the actual semantics of FlinkCEP in the vast majority of the cases. Furthermore, the inconsistencies between the formal and the actual semantics are analyzed and it is discovered that the Flink implementation of FlinkCEP may not deal with the group patterns correctly.
Available online: November 15, 2023 , DOI: 10.13328/j.cnki.jos.006993
Abstract:
Text-based person retrieval is a developing downstream task of cross-modal retrieval and derives from conventional person re-identification, which plays a vital role in public safety and person search. In view of the problem of lacking query images in traditional person re-identification, the main challenge of this task is that it combines two different modalities and requires that the model have the capability of learning both image content and textual semantics. To narrow the semantic gap between pedestrian images and text descriptions, the traditional methods usually split image features and text features mechanically and only focus on cross-modal alignment, which ignores the potential relations between the person image and description and leads to inaccurate cross-modal alignment. To address the above issues, this study proposes a novel relation alignment-based cross-modal person retrieval network. First, the attention mechanism is used to construct the self-attention matrix and the cross-modal attention matrix, in which the attention matrix is regarded as the distribution of response values between different feature sequences. Then, two different matrix construction methods are used to reconstruct the intra-modal attention matrix and the cross-modal attention matrix respectively. Among them, the element-by-element reconstruction of the intra-modal attention matrix can well excavate the potential relationships of intra-modal. Moreover, by taking the cross-modal information as a bridge, the holistic reconstruction of the cross-modal attention matrix can fully excavate the potential information from different modalities and narrow the semantic gap. Finally, the model is jointly trained with a cross-modal projection matching loss and a KL divergence loss, which helps achieve the mutual promotion between modalities. Quantitative and qualitative results on a public text-based person search dataset (CUHK-PEDES) demonstrate that the proposed method performs favorably against state-of-the-art text-based person search methods.
Text-based person retrieval is a developing downstream task of cross-modal retrieval and derives from conventional person re-identification, which plays a vital role in public safety and person search. In view of the problem of lacking query images in traditional person re-identification, the main challenge of this task is that it combines two different modalities and requires that the model have the capability of learning both image content and textual semantics. To narrow the semantic gap between pedestrian images and text descriptions, the traditional methods usually split image features and text features mechanically and only focus on cross-modal alignment, which ignores the potential relations between the person image and description and leads to inaccurate cross-modal alignment. To address the above issues, this study proposes a novel relation alignment-based cross-modal person retrieval network. First, the attention mechanism is used to construct the self-attention matrix and the cross-modal attention matrix, in which the attention matrix is regarded as the distribution of response values between different feature sequences. Then, two different matrix construction methods are used to reconstruct the intra-modal attention matrix and the cross-modal attention matrix respectively. Among them, the element-by-element reconstruction of the intra-modal attention matrix can well excavate the potential relationships of intra-modal. Moreover, by taking the cross-modal information as a bridge, the holistic reconstruction of the cross-modal attention matrix can fully excavate the potential information from different modalities and narrow the semantic gap. Finally, the model is jointly trained with a cross-modal projection matching loss and a KL divergence loss, which helps achieve the mutual promotion between modalities. Quantitative and qualitative results on a public text-based person search dataset (CUHK-PEDES) demonstrate that the proposed method performs favorably against state-of-the-art text-based person search methods.
Available online: November 15, 2023 , DOI: 10.13328/j.cnki.jos.006997
Abstract:
Safety-critical embedded software usually has rigorous time constraints over the runtime behaviors, raising additional requirements for enforcing security properties. To protect the information flow security of embedded software and mitigate the limitations of the existing simplex verification approaches and their potential false positives, this study first proposes a new timed noninterference property, i.e., timed SIR-NNI, based on the security requirement of a realistic scenario. Then the study presents an information flow security verification approach that unifies the verification of multiple timed noninterference properties, i.e., timed BNNI, timed BSNNI, and timed SIR-NNI. Based on the different timed noninterference requirements, the approach constructs the refined automata and test automata from the timed automata under verification. The study uses UPPAAL’s reachability analysis to implement the refinement relation check and the security verification. The verification tool, i.e., TINIVER, extracts timed automata from SysML’s sequential diagrams or C++ source code to conduct the verification procedure. The verification results of TINIVER on existing timed automata models and security properties justify the usability of the proposed approach. The security verifications on the typical flight-mode switch models of the UAV flight control systems ArduPilot and PX4 demonstrate the practicability and scalability of the proposed approach. Besides, the approach is effective in mitigating the false positives of a state-of-the-art verification approach.
Safety-critical embedded software usually has rigorous time constraints over the runtime behaviors, raising additional requirements for enforcing security properties. To protect the information flow security of embedded software and mitigate the limitations of the existing simplex verification approaches and their potential false positives, this study first proposes a new timed noninterference property, i.e., timed SIR-NNI, based on the security requirement of a realistic scenario. Then the study presents an information flow security verification approach that unifies the verification of multiple timed noninterference properties, i.e., timed BNNI, timed BSNNI, and timed SIR-NNI. Based on the different timed noninterference requirements, the approach constructs the refined automata and test automata from the timed automata under verification. The study uses UPPAAL’s reachability analysis to implement the refinement relation check and the security verification. The verification tool, i.e., TINIVER, extracts timed automata from SysML’s sequential diagrams or C++ source code to conduct the verification procedure. The verification results of TINIVER on existing timed automata models and security properties justify the usability of the proposed approach. The security verifications on the typical flight-mode switch models of the UAV flight control systems ArduPilot and PX4 demonstrate the practicability and scalability of the proposed approach. Besides, the approach is effective in mitigating the false positives of a state-of-the-art verification approach.
Available online: November 15, 2023 , DOI: 10.13328/j.cnki.jos.007002
Abstract:
Temporal knowledge graph reasoning aims to fill in missing links or facts in knowledge graphs, where each fact is associated with a specific timestamp. The dynamic variational framework based on variational autoencoder is particularly effective for this task. By jointly modeling entities and relations using Gaussian distributions, this method not only offers high interpretability but also solves complex probability distribution problems. However, traditional variational autoencoder-based methods often suffer from overfitting during training, which limits their ability to accurately capture the semantic evolution of entities over time. To address this challenge, this study proposes a new temporal knowledge graph reasoning model based on a diffusion probability distribution approach. Specifically, the model uses a bi-directional iterative process to divide the entity semantic modeling process into multiple sub-modules. Each sub-module uses a forward noisy transformation and a backward Gaussian sampling to model a small-scale evolution process of entity semantics. Compared with the variational autoencoder-based method, this study can obtain more accurate modeling by learning the dynamic representation of entity semantics in the metric space over time through the joint modeling of multiple submodules. Compared with the variational autoencoder-based method, the model improves by 4.18% and 1.87% on the Yago11k dataset and Wikidata12k dataset for evaluating the MRR of the indicator and by 1.63% and 2.48% on the ICEWS14 and ICEWS05-15 datasets, respectively.
Temporal knowledge graph reasoning aims to fill in missing links or facts in knowledge graphs, where each fact is associated with a specific timestamp. The dynamic variational framework based on variational autoencoder is particularly effective for this task. By jointly modeling entities and relations using Gaussian distributions, this method not only offers high interpretability but also solves complex probability distribution problems. However, traditional variational autoencoder-based methods often suffer from overfitting during training, which limits their ability to accurately capture the semantic evolution of entities over time. To address this challenge, this study proposes a new temporal knowledge graph reasoning model based on a diffusion probability distribution approach. Specifically, the model uses a bi-directional iterative process to divide the entity semantic modeling process into multiple sub-modules. Each sub-module uses a forward noisy transformation and a backward Gaussian sampling to model a small-scale evolution process of entity semantics. Compared with the variational autoencoder-based method, this study can obtain more accurate modeling by learning the dynamic representation of entity semantics in the metric space over time through the joint modeling of multiple submodules. Compared with the variational autoencoder-based method, the model improves by 4.18% and 1.87% on the Yago11k dataset and Wikidata12k dataset for evaluating the MRR of the indicator and by 1.63% and 2.48% on the ICEWS14 and ICEWS05-15 datasets, respectively.
Available online: November 15, 2023 , DOI: 10.13328/j.cnki.jos.006995
Abstract:
Multi-view clustering has attracted more and more attention in the fields of image processing, data mining, and machine learning. Existing multi-view clustering algorithms have two shortcomings. One is that in the process of graph construction, only the pairwise relationship between each view data is considered to generate an affinity matrix, which lacks the characterization of neighborhood relationships; the second is that existing methods separate the process of multi-view information fusion and clustering, thereby reducing the clustering performance of the algorithm. Therefore, this study proposes a more accurate and robust joint spectral embedding multi-view clustering algorithm based on bipartite graphs. Firstly, based on the multi-view subspace clustering idea,bipartite graphs are constructed, and similar graphs are generated.Then the spectral embedding matrix of similar graphs is used to perform graph fusion. Secondly, by considering the importance of each view during the fusion process, weight constraints are applied, and an indicator matrix is introduced to obtain the final clustering result. A model is proposed to optimize the bipartite graph, embedding matrix, and clustering indicator matrix within a single framework. In addition, a fast optimization strategy for solving the model is provided, which decomposes the optimization problem into small module subproblems and efficiently solves them through iterative steps. The proposed algorithm and existing multi-view clustering algorithms have been experimentally analyzed on real data sets. Experimental results show that the proposed algorithm is more effective and robust in dealing with multi-view clustering problems compared with existing methods.
Multi-view clustering has attracted more and more attention in the fields of image processing, data mining, and machine learning. Existing multi-view clustering algorithms have two shortcomings. One is that in the process of graph construction, only the pairwise relationship between each view data is considered to generate an affinity matrix, which lacks the characterization of neighborhood relationships; the second is that existing methods separate the process of multi-view information fusion and clustering, thereby reducing the clustering performance of the algorithm. Therefore, this study proposes a more accurate and robust joint spectral embedding multi-view clustering algorithm based on bipartite graphs. Firstly, based on the multi-view subspace clustering idea,bipartite graphs are constructed, and similar graphs are generated.Then the spectral embedding matrix of similar graphs is used to perform graph fusion. Secondly, by considering the importance of each view during the fusion process, weight constraints are applied, and an indicator matrix is introduced to obtain the final clustering result. A model is proposed to optimize the bipartite graph, embedding matrix, and clustering indicator matrix within a single framework. In addition, a fast optimization strategy for solving the model is provided, which decomposes the optimization problem into small module subproblems and efficiently solves them through iterative steps. The proposed algorithm and existing multi-view clustering algorithms have been experimentally analyzed on real data sets. Experimental results show that the proposed algorithm is more effective and robust in dealing with multi-view clustering problems compared with existing methods.
Available online: November 08, 2023 , DOI: 10.13328/j.cnki.jos.007001
Abstract:
As an important production factor, data need to be exchanged between different entities to create value. In this process, data integrity needs to be ensured, or in other words, data cannot be tampered without authorization, or otherwise, it may lead to extremely serious consequences. The existing work realizes data evidence preservation by combining distributed ledger with data encryption and verification technology to ensure the integrity of data to be exchanged in transmission, storage, and other related data processing phrases. However, such work is difficult to confirm the integrity of the data provided by the data supplier. Once the data supplier provides forged data, all subsequent integrity assurance will be meaningless. Therefore, this study proposes a method for verifying the integrity of data services based on remote attestation. By using the trusted execution environment as the trust anchor, this method can measure and verify the integrity of the static code, execution process, and execution result of a specific data service. It also optimizes the integrity verification of a specific data service through program slicing, thus extending the scope of data integrity assurance to the time point when the data supplier provides data. A series of experiments are carried out on 25 data services of three real Java information systems to validate the proposed method.
As an important production factor, data need to be exchanged between different entities to create value. In this process, data integrity needs to be ensured, or in other words, data cannot be tampered without authorization, or otherwise, it may lead to extremely serious consequences. The existing work realizes data evidence preservation by combining distributed ledger with data encryption and verification technology to ensure the integrity of data to be exchanged in transmission, storage, and other related data processing phrases. However, such work is difficult to confirm the integrity of the data provided by the data supplier. Once the data supplier provides forged data, all subsequent integrity assurance will be meaningless. Therefore, this study proposes a method for verifying the integrity of data services based on remote attestation. By using the trusted execution environment as the trust anchor, this method can measure and verify the integrity of the static code, execution process, and execution result of a specific data service. It also optimizes the integrity verification of a specific data service through program slicing, thus extending the scope of data integrity assurance to the time point when the data supplier provides data. A series of experiments are carried out on 25 data services of three real Java information systems to validate the proposed method.
Available online: November 08, 2023 , DOI: 10.13328/j.cnki.jos.006994
Abstract:
With the development of mobile services’ computing and sensing abilities, spatial crowdsourcing, which is based on location information, comes into being. There are many challenges to improving the performance of task assignments, one of which is how to assign workers the tasks that they are interested in. Existing research methods only focus on workers’ temporal preference but ignore the impact of spatial factors on workers’ preference, and they only focus on long-term preference but ignore workers’ short-term preference and face the problem of inaccurate predictions caused by sparse historical data. This study analyzes the task assignment problem based on long-term and short-term spatio-temporal preference. By comprehensively considering workers’ preferences from both long-term and short-term perspectives, as well as temporal and spatial dimensions, the quality of task assignment is improved in task assignment success rate and completion efficiency. In order to improve the accuracy of spatio-temporal preference prediction, the study proposes a sliced imputation-based context-aware tensor decomposition algorithm (SICTD) to reduce the proportion of missing values in preference tensors and calculates short-term spatio-temporal preference through the ST-HITS algorithm and short-term active range of workers under spatio-temporal constraints. In order to maximize the total task reward and the workers’ average preference for completing tasks, the study designs a spatio-temporal preference-aware greedy and Kuhn-Munkres (KM) algorithm to optimize the results of task assignment. Extensive experimental results on real datasets show the effectiveness of the long- and short-term spatio-temporal preference-aware task assignment framework. Compared with baselines, the RMSE prediction error of the proposed SICTD for temporal and spatial preferences is decreased by 22.55% and 24.17%, respectively. In terms of task assignment, the proposed preference-aware KM algorithm significantly outperforms the baseline algorithms, with the workers’ total reward and average preference for completing tasks averagely increased by 40.86% and 22.40%, respectively.
With the development of mobile services’ computing and sensing abilities, spatial crowdsourcing, which is based on location information, comes into being. There are many challenges to improving the performance of task assignments, one of which is how to assign workers the tasks that they are interested in. Existing research methods only focus on workers’ temporal preference but ignore the impact of spatial factors on workers’ preference, and they only focus on long-term preference but ignore workers’ short-term preference and face the problem of inaccurate predictions caused by sparse historical data. This study analyzes the task assignment problem based on long-term and short-term spatio-temporal preference. By comprehensively considering workers’ preferences from both long-term and short-term perspectives, as well as temporal and spatial dimensions, the quality of task assignment is improved in task assignment success rate and completion efficiency. In order to improve the accuracy of spatio-temporal preference prediction, the study proposes a sliced imputation-based context-aware tensor decomposition algorithm (SICTD) to reduce the proportion of missing values in preference tensors and calculates short-term spatio-temporal preference through the ST-HITS algorithm and short-term active range of workers under spatio-temporal constraints. In order to maximize the total task reward and the workers’ average preference for completing tasks, the study designs a spatio-temporal preference-aware greedy and Kuhn-Munkres (KM) algorithm to optimize the results of task assignment. Extensive experimental results on real datasets show the effectiveness of the long- and short-term spatio-temporal preference-aware task assignment framework. Compared with baselines, the RMSE prediction error of the proposed SICTD for temporal and spatial preferences is decreased by 22.55% and 24.17%, respectively. In terms of task assignment, the proposed preference-aware KM algorithm significantly outperforms the baseline algorithms, with the workers’ total reward and average preference for completing tasks averagely increased by 40.86% and 22.40%, respectively.
Available online: November 08, 2023 , DOI: 10.13328/j.cnki.jos.006988
Abstract:
Aiming at the growing threat of distributed denial of service (DDoS) attacks under the rapid popularization of IPv6, this study proposes a two-stage DDoS defense mechanism, including a pre-detection stage to real-time monitor the early appearance of DDoS attacks and a deep-detection stage to accurately filter DDoS traffic after an alarm. First, the IPv6 traffic format is analyzed and the hexadecimal header fields are extracted from PCAP capture files as detection elements. Then, in the pre-detection stage, a lightweight binary convolutional neural network (BCNN) model is introduced and a two-dimensional traffic matrix is designed as model input, which can sensitively perceive the malicious situation caused by mixed DDoS traffic in the network as evidence of DDoS occurrence. After the alarm, the deep-detection stage will intervene with a one-dimensional convolutional neural network (1DCNN) model, which can specifically distinguish the mixed DDoS packets with one-dimensional packet vector as input to issue blocking policies. In the experiment, an IPv6-LAN topology is built and the proposed pure IPv6-DDoS traffic is generated by replaying the CIC-DDoS2019 public set through NAT 4to6. The results show that the proposed mechanism can effectively improve response speed, detection accuracy, and traffic filtering efficiency in DDoS defense. When DDoS traffic only takes 6% and 10% of the total network, BCNN can perceive the occurrence of DDoS with 90.9% and 96.4% accuracy, and the 1DCNN model can distinguish mixed DDoS packets with 99.4% accuracy at the same time.
Aiming at the growing threat of distributed denial of service (DDoS) attacks under the rapid popularization of IPv6, this study proposes a two-stage DDoS defense mechanism, including a pre-detection stage to real-time monitor the early appearance of DDoS attacks and a deep-detection stage to accurately filter DDoS traffic after an alarm. First, the IPv6 traffic format is analyzed and the hexadecimal header fields are extracted from PCAP capture files as detection elements. Then, in the pre-detection stage, a lightweight binary convolutional neural network (BCNN) model is introduced and a two-dimensional traffic matrix is designed as model input, which can sensitively perceive the malicious situation caused by mixed DDoS traffic in the network as evidence of DDoS occurrence. After the alarm, the deep-detection stage will intervene with a one-dimensional convolutional neural network (1DCNN) model, which can specifically distinguish the mixed DDoS packets with one-dimensional packet vector as input to issue blocking policies. In the experiment, an IPv6-LAN topology is built and the proposed pure IPv6-DDoS traffic is generated by replaying the CIC-DDoS2019 public set through NAT 4to6. The results show that the proposed mechanism can effectively improve response speed, detection accuracy, and traffic filtering efficiency in DDoS defense. When DDoS traffic only takes 6% and 10% of the total network, BCNN can perceive the occurrence of DDoS with 90.9% and 96.4% accuracy, and the 1DCNN model can distinguish mixed DDoS packets with 99.4% accuracy at the same time.
Available online: November 08, 2023 , DOI: 10.13328/j.cnki.jos.006989
Abstract:
The smart contract is a decentralized application widely deployed on the blockchain platform, e.g., Ethereum. Due to the economic attributes, the vulnerabilities in smart contracts can potentially cause huge financial losses and destroy the stable ecology of Ethereum. Thus, it is crucial to detect the vulnerabilities in smart contracts before they are deployed to Ethereum. The existing smart contract vulnerability detection methods (e.g., Oyente and Secure) are mostly based on heuristic algorithms. The reusability of these methods is weak in different application scenarios. In addition, they are time-consuming and with low accuracy. In order to improve the effectiveness of vulnerability detection, this study proposes Scruple: a smart contract timestamp vulnerability detection approach based on learning data-flow path. It first obtains all possible propagation chains of timestamp vulnerabilities, then refines the propagation chains, uses a graph pre-training model to learn the relationship in the propagation chains, and finally detects whether a smart contract has timestamp vulnerabilities using the learned model. Compared with the existing detection methods, Scruple has a stronger vulnerability capture ability and generalization ability. Meanwhile, learning the propagation chain is not only well-directed but also can avoid an unnecessarily deep hierarchy of programs for the convergence of vulnerabilities. To verify the effectiveness of Scruple, this study uses real-world distinct smart contracts to compare Scruple with 13 state-of-the-art smart contract vulnerability detection methods. The experimental results show that Scruple can achieve 96% accuracy, 90% recall, and 93% F1-score in detecting timestamp vulnerabilities. In other words, the average improvement of Scruple over 13 methods using the three metrics is 59%, 46%, and 57% respectively. It means that Scruple has substantially improved in detecting timestamp vulnerabilities.
The smart contract is a decentralized application widely deployed on the blockchain platform, e.g., Ethereum. Due to the economic attributes, the vulnerabilities in smart contracts can potentially cause huge financial losses and destroy the stable ecology of Ethereum. Thus, it is crucial to detect the vulnerabilities in smart contracts before they are deployed to Ethereum. The existing smart contract vulnerability detection methods (e.g., Oyente and Secure) are mostly based on heuristic algorithms. The reusability of these methods is weak in different application scenarios. In addition, they are time-consuming and with low accuracy. In order to improve the effectiveness of vulnerability detection, this study proposes Scruple: a smart contract timestamp vulnerability detection approach based on learning data-flow path. It first obtains all possible propagation chains of timestamp vulnerabilities, then refines the propagation chains, uses a graph pre-training model to learn the relationship in the propagation chains, and finally detects whether a smart contract has timestamp vulnerabilities using the learned model. Compared with the existing detection methods, Scruple has a stronger vulnerability capture ability and generalization ability. Meanwhile, learning the propagation chain is not only well-directed but also can avoid an unnecessarily deep hierarchy of programs for the convergence of vulnerabilities. To verify the effectiveness of Scruple, this study uses real-world distinct smart contracts to compare Scruple with 13 state-of-the-art smart contract vulnerability detection methods. The experimental results show that Scruple can achieve 96% accuracy, 90% recall, and 93% F1-score in detecting timestamp vulnerabilities. In other words, the average improvement of Scruple over 13 methods using the three metrics is 59%, 46%, and 57% respectively. It means that Scruple has substantially improved in detecting timestamp vulnerabilities.
Available online: November 01, 2023 , DOI: 10.13328/j.cnki.jos.006986
Abstract:
Distributed storage system is receiving more and more attention in mobile network scenarios. Data placement, a key technology of distributed storage, is crucial to improve the success rate of distributed data storage. However, due to unstable wireless signals and fluctuating network bandwidth in mobile environments, the traditional data placement strategies, such as random placement strategy and storage-aware placement strategy, have low success rates of data transmission because both of them do not take network bandwidth into account during data placement. To solve the problem faced by mobile distributed storage systems, this study proposes a bandwidth-aware adaptive data placement strategy (BADP). The main breakthrough is that BADP adopts the group mobility model to sense the network bandwidth of nodes and takes the network bandwidth as an important factor for data placement, thus selecting nodes with good performance to achieve adaptive data placement and improve the success of data transmission. BADP consists of three design features: (1) adopting the group mobility model to sense the network bandwidth of nodes; (2) managing node information in groups to reduce communication overhead, and taking advantage of the heap to build a node selection tree; (3) selecting nodes with good performance using adaptive data placement to improve the success rate of data transmission. Experiments show that when the network changes dynamically, BADP gains at least 30.6% and 34.6% improvements in the success rate of data transmission compared with random placement strategy and storage-aware placement strategy. At the same time, it consistently keeps communication overhead low.
Distributed storage system is receiving more and more attention in mobile network scenarios. Data placement, a key technology of distributed storage, is crucial to improve the success rate of distributed data storage. However, due to unstable wireless signals and fluctuating network bandwidth in mobile environments, the traditional data placement strategies, such as random placement strategy and storage-aware placement strategy, have low success rates of data transmission because both of them do not take network bandwidth into account during data placement. To solve the problem faced by mobile distributed storage systems, this study proposes a bandwidth-aware adaptive data placement strategy (BADP). The main breakthrough is that BADP adopts the group mobility model to sense the network bandwidth of nodes and takes the network bandwidth as an important factor for data placement, thus selecting nodes with good performance to achieve adaptive data placement and improve the success of data transmission. BADP consists of three design features: (1) adopting the group mobility model to sense the network bandwidth of nodes; (2) managing node information in groups to reduce communication overhead, and taking advantage of the heap to build a node selection tree; (3) selecting nodes with good performance using adaptive data placement to improve the success rate of data transmission. Experiments show that when the network changes dynamically, BADP gains at least 30.6% and 34.6% improvements in the success rate of data transmission compared with random placement strategy and storage-aware placement strategy. At the same time, it consistently keeps communication overhead low.
Available online: November 01, 2023 , DOI: 10.13328/j.cnki.jos.006987
Abstract:
Internet users need to resolve through DNS before accessing network applications. DNS security is the first portal to ensure the normal operation of the network. If the security of DNS cannot be effectively guaranteed, even if the level of security protection measures of other network systems is high, attackers can attack the DNS system to make the network unusable. At present, DNS malignant events still have an upward trend, and the development of DNS attack detection and defense technology still cannot meet practical needs. From the perspective of recursive servers that directly serve users’ DNS requests, this study comprehensively summarizes the security problems faced in the DNS process through two classification methods, including various security events caused by attacks or system vulnerabilities, different detection methods for various security events, and various defense and protection technologies. When summarizing various security events, detection and defense protection technologies, the study analyzes the characteristics of the corresponding typical methods and prospects for the future research direction of the DNS security field.
Internet users need to resolve through DNS before accessing network applications. DNS security is the first portal to ensure the normal operation of the network. If the security of DNS cannot be effectively guaranteed, even if the level of security protection measures of other network systems is high, attackers can attack the DNS system to make the network unusable. At present, DNS malignant events still have an upward trend, and the development of DNS attack detection and defense technology still cannot meet practical needs. From the perspective of recursive servers that directly serve users’ DNS requests, this study comprehensively summarizes the security problems faced in the DNS process through two classification methods, including various security events caused by attacks or system vulnerabilities, different detection methods for various security events, and various defense and protection technologies. When summarizing various security events, detection and defense protection technologies, the study analyzes the characteristics of the corresponding typical methods and prospects for the future research direction of the DNS security field.
Available online: October 25, 2023 , DOI: 10.13328/j.cnki.jos.006992
Abstract:
GitHub is a well-known open-source software development community that supports developers using the issue tracking system in each open-source project on GitHub to address issues. During the discussion of an issue about a defect, the developer may point out issues from other projects correlated to the defect, which are called cross-project issues, so as to provide reference information for fixing the defect. However, there are more than 200 million open-source projects and 1.2 billion issues on the GitHub platform, making it time-consuming to identify and acquire cross-project issues manually. This study presents a cross-project issue recommendation method CPIRecom for open-source software defects. This study builds a pre-selection set by filtering issues based on the number of historical issue pairs and the time interval for reporting issues. Then, the study also proposes an accurate recommendation model, which extracts textual features based on the pre-trained model of BERT, analyzes features of projects, calculates the relevant probability between defects and issues from the pre-selection set based on a random forest classifier, and obtains the recommendation list according to the ranking. This study simulates the application of CPIRecom method on GitHub platform. The mean reciprocal rank of CPIRecom method reaches 0.603, and the Recall@5 reaches 0.715 on the simulative test set.
GitHub is a well-known open-source software development community that supports developers using the issue tracking system in each open-source project on GitHub to address issues. During the discussion of an issue about a defect, the developer may point out issues from other projects correlated to the defect, which are called cross-project issues, so as to provide reference information for fixing the defect. However, there are more than 200 million open-source projects and 1.2 billion issues on the GitHub platform, making it time-consuming to identify and acquire cross-project issues manually. This study presents a cross-project issue recommendation method CPIRecom for open-source software defects. This study builds a pre-selection set by filtering issues based on the number of historical issue pairs and the time interval for reporting issues. Then, the study also proposes an accurate recommendation model, which extracts textual features based on the pre-trained model of BERT, analyzes features of projects, calculates the relevant probability between defects and issues from the pre-selection set based on a random forest classifier, and obtains the recommendation list according to the ranking. This study simulates the application of CPIRecom method on GitHub platform. The mean reciprocal rank of CPIRecom method reaches 0.603, and the Recall@5 reaches 0.715 on the simulative test set.
Available online: October 25, 2023 , DOI: 10.13328/j.cnki.jos.007000
Abstract:
Fuzzy C-means (FCM) clustering algorithm has become one of the commonly used image segmentation techniques with its low learning cost and algorithm overhead. However, the conventional FCM clustering algorithm is sensitive to noise in images. Recently, many of improved FCM algorithms have been proposed to improve the noise robustness of the conventional FCM clustering algorithm, but often at a cost of detail loss on the image. This study presents an improved FCM clustering algorithm based on Lie group theory and applies it to image segmentation. The proposed algorithm constructs matrix Lie group features for the pixels of an image, which summarizes the low-level image features of each pixel and its relationship with other pixels in the neighborhood window. By doing this, the proposed method transforms the clustering problem of measuring the Euclidean distances between pixels into calculating the geodesic distances between Lie group features of pixels on the Lie group manifold. Aiming at the problem of updating the clustering center and fuzzy membership matrix on the Lie group manifold, the proposed method uses an adaptive fuzzy weighted objective function, which improves the generalization and stability of the algorithm. The effectiveness of the proposed method is verified by comparing with conventional FCM and several classic improved algorithms on the experiments of three types of medical images.
Fuzzy C-means (FCM) clustering algorithm has become one of the commonly used image segmentation techniques with its low learning cost and algorithm overhead. However, the conventional FCM clustering algorithm is sensitive to noise in images. Recently, many of improved FCM algorithms have been proposed to improve the noise robustness of the conventional FCM clustering algorithm, but often at a cost of detail loss on the image. This study presents an improved FCM clustering algorithm based on Lie group theory and applies it to image segmentation. The proposed algorithm constructs matrix Lie group features for the pixels of an image, which summarizes the low-level image features of each pixel and its relationship with other pixels in the neighborhood window. By doing this, the proposed method transforms the clustering problem of measuring the Euclidean distances between pixels into calculating the geodesic distances between Lie group features of pixels on the Lie group manifold. Aiming at the problem of updating the clustering center and fuzzy membership matrix on the Lie group manifold, the proposed method uses an adaptive fuzzy weighted objective function, which improves the generalization and stability of the algorithm. The effectiveness of the proposed method is verified by comparing with conventional FCM and several classic improved algorithms on the experiments of three types of medical images.
Available online: October 25, 2023 , DOI: 10.13328/j.cnki.jos.006966
Abstract:
The current authentication protocol based on username and password has been difficult to meet the increasing security requirements. Specifically, users choose different passwords to access different online services, which greatly increases the user’s memory burden. In addition, password authentication has low security and faces many known attacks. To solve such problems, this study proposes a user-centric two-factor authentication key agreement protocol UC-2FAKA based on the Pointcheval-Sanders signature. Firstly, to prevent the leakage of authentication factors, passwords, and biometric two-factor credentials are constructed based on the Pointcheval-Sanders signature. The identity is authenticated to the service provider (SP) in a zero-knowledge proof manner. Secondly, using a user-centric single sign-on (SSO) architecture, users can request identity credentials by registering with an identity provider (IDP) to log in different SPs to avoid IDP or SP tracking or linking users. Thirdly, the Diffie-Hellman key exchange is used to authenticate SP identities and negotiate communication keys to ensure subsequent communication security. Finally, comprehensive security analysis and performance comparison of the proposed protocol are carried out. The results show that the proposed protocol can resist various known attacks, and the proposed protocol performs better in communication overhead and computational overhead.
The current authentication protocol based on username and password has been difficult to meet the increasing security requirements. Specifically, users choose different passwords to access different online services, which greatly increases the user’s memory burden. In addition, password authentication has low security and faces many known attacks. To solve such problems, this study proposes a user-centric two-factor authentication key agreement protocol UC-2FAKA based on the Pointcheval-Sanders signature. Firstly, to prevent the leakage of authentication factors, passwords, and biometric two-factor credentials are constructed based on the Pointcheval-Sanders signature. The identity is authenticated to the service provider (SP) in a zero-knowledge proof manner. Secondly, using a user-centric single sign-on (SSO) architecture, users can request identity credentials by registering with an identity provider (IDP) to log in different SPs to avoid IDP or SP tracking or linking users. Thirdly, the Diffie-Hellman key exchange is used to authenticate SP identities and negotiate communication keys to ensure subsequent communication security. Finally, comprehensive security analysis and performance comparison of the proposed protocol are carried out. The results show that the proposed protocol can resist various known attacks, and the proposed protocol performs better in communication overhead and computational overhead.
Available online: October 25, 2023 , DOI: 10.13328/j.cnki.jos.006970
Abstract:
Existing hypergraph network representation methods need to analyze the full batch nodes and hyperedges to recursively extend the neighbors across layers, which brings huge computational costs and leads to lower generalization accuracy due to over-expansion. To solve this problem, this study proposes a hypergraph network representation method based on importance sampling. First, the method treats nodes and hyperedges as two sets of independent identically distributed samples that satisfy specific probability measures and interprets the structural feature interactions of the hypergraph in an integral form. Second, it designs a neighbor importance sampling rule with learnable parameters and calculates sampling probabilities based on the physical relations and features of nodes and hyperedges. A fixed number of objects are recursively acquired layer by layer to construct a smaller sampled adjacency matrix. Finally, the spatial features of the entire hypergraph are approximated using Monte Carlo methods. In addition, with the advantage of physically informed neural networks, the sampling variance that needs to be reduced is added to the hypergraph neural network as a physical constraint to obtain sampling rules with better generalization capability. Extensive experiments on multiple datasets show that the method proposed in this study can obtain more accurate hypergraph representation results with a faster convergence rate.
Existing hypergraph network representation methods need to analyze the full batch nodes and hyperedges to recursively extend the neighbors across layers, which brings huge computational costs and leads to lower generalization accuracy due to over-expansion. To solve this problem, this study proposes a hypergraph network representation method based on importance sampling. First, the method treats nodes and hyperedges as two sets of independent identically distributed samples that satisfy specific probability measures and interprets the structural feature interactions of the hypergraph in an integral form. Second, it designs a neighbor importance sampling rule with learnable parameters and calculates sampling probabilities based on the physical relations and features of nodes and hyperedges. A fixed number of objects are recursively acquired layer by layer to construct a smaller sampled adjacency matrix. Finally, the spatial features of the entire hypergraph are approximated using Monte Carlo methods. In addition, with the advantage of physically informed neural networks, the sampling variance that needs to be reduced is added to the hypergraph neural network as a physical constraint to obtain sampling rules with better generalization capability. Extensive experiments on multiple datasets show that the method proposed in this study can obtain more accurate hypergraph representation results with a faster convergence rate.
Available online: October 18, 2023 , DOI: 10.13328/j.cnki.jos.006971
Abstract:
Fast vulnerability root cause analysis is crucial for patching vulnerabilities and has always been a hotspot in academia and industry. The existing vulnerability root cause analysis methods based on the statistical feature analysis of a large number of test sample execution records have problems such as random noise and missing important logical correlation instructions. According to the test set measurement in this study, the proportion of random noise in the existing statistical methods reaches more than 61%. To solve the above problems, this study proposes a vulnerability root cause analysis method based on the local path graph, which extracts vulnerability-related information such as the inter-function call graph and intra-function control flow transfer graph from the execution paths. The local path graph is utilized for eliminating irrelevant instruction (i.e., noise instructions) elimination, constructing the logic relations for vulnerability root cause relevant points, and adding missing critical instructions. An automated root cause analysis system for binary software, LGBRoot, has been implemented. The effectiveness of the system has been evaluated on a dataset of 20 public CVE memory corruption vulnerabilities. The average time for single-sample root cause analysis is 12.4 seconds. The experimental data show that the system can automatically eliminate 56.2% of noise instructions, and mend as well as visualize the 20 logical structures of vulnerability root cause relevant points, speeding up the vulnerability analysis of analysts.
Fast vulnerability root cause analysis is crucial for patching vulnerabilities and has always been a hotspot in academia and industry. The existing vulnerability root cause analysis methods based on the statistical feature analysis of a large number of test sample execution records have problems such as random noise and missing important logical correlation instructions. According to the test set measurement in this study, the proportion of random noise in the existing statistical methods reaches more than 61%. To solve the above problems, this study proposes a vulnerability root cause analysis method based on the local path graph, which extracts vulnerability-related information such as the inter-function call graph and intra-function control flow transfer graph from the execution paths. The local path graph is utilized for eliminating irrelevant instruction (i.e., noise instructions) elimination, constructing the logic relations for vulnerability root cause relevant points, and adding missing critical instructions. An automated root cause analysis system for binary software, LGBRoot, has been implemented. The effectiveness of the system has been evaluated on a dataset of 20 public CVE memory corruption vulnerabilities. The average time for single-sample root cause analysis is 12.4 seconds. The experimental data show that the system can automatically eliminate 56.2% of noise instructions, and mend as well as visualize the 20 logical structures of vulnerability root cause relevant points, speeding up the vulnerability analysis of analysts.
Available online: October 18, 2023 , DOI: 10.13328/j.cnki.jos.006991
Abstract:
Conformance checking is one of the important scenarios in the field of process mining, and its goal is to determine whether the actual running business behavior is consistent with the desired behavior and then provide a basis for business process management decisions. Traditional methods of conformance checking face the problems of too many metrics and low efficiency. In addition, the existing methods for checking the conformance between process text and process model rely heavily on expert-defined knowledge. Therefore, this study proposes a process text-oriented conformance checking method. Firstly, the study generates graph traces based on the execution semantics of the process model and obtains the structural features by the word vector model from graph traces. At the same time, Hoffman trees are introduced to reduce the computational effort. Then, the word vector representation of the process text and the activities is performed. The study also uses the Siamese mechanism to improve training efficiency. Finally, all the features of the text and the model are fused, and then the consistency score between the text and the model is predicted using a fully connected layer. Experiments show that the average absolute error value of the method in this study is two percentage points lower than that of existing methods.
Conformance checking is one of the important scenarios in the field of process mining, and its goal is to determine whether the actual running business behavior is consistent with the desired behavior and then provide a basis for business process management decisions. Traditional methods of conformance checking face the problems of too many metrics and low efficiency. In addition, the existing methods for checking the conformance between process text and process model rely heavily on expert-defined knowledge. Therefore, this study proposes a process text-oriented conformance checking method. Firstly, the study generates graph traces based on the execution semantics of the process model and obtains the structural features by the word vector model from graph traces. At the same time, Hoffman trees are introduced to reduce the computational effort. Then, the word vector representation of the process text and the activities is performed. The study also uses the Siamese mechanism to improve training efficiency. Finally, all the features of the text and the model are fused, and then the consistency score between the text and the model is predicted using a fully connected layer. Experiments show that the average absolute error value of the method in this study is two percentage points lower than that of existing methods.
Available online: October 18, 2023 , DOI: 10.13328/j.cnki.jos.006976
Abstract:
Disassembly of binary codes is hard but necessary for improving the security of binary software. One of the major reasons for the difficult binary disassembly is that the compilers create many jump tables in the binary code for efficiency. In order to solve the targets of the jump table, mainstream disassembly tools use various strategies. However, the details of the implementation of these strategies and their effectiveness are not well studied. To help researchers to well understand the algorithm implementation and performance of disassembly tools, this study first systematically summarizes the strategies used by disassembly tools to solve jump tables; then the study builds an automatic framework for testing jump tables, based on which a large-scale testsuite on jump tables (2410455 jump tables) can be generated. Lastly, this study evaluates the performance of the disassembly tools in solving jump tables on the testsuite and manually analyzes the errors introduced by each strategy of the disassembly tools. In addition, this study finds six bugs in the implementation of the disassembly tools benefiting from the systematic summary of the implementation of the disassembly tool algorithm.
Disassembly of binary codes is hard but necessary for improving the security of binary software. One of the major reasons for the difficult binary disassembly is that the compilers create many jump tables in the binary code for efficiency. In order to solve the targets of the jump table, mainstream disassembly tools use various strategies. However, the details of the implementation of these strategies and their effectiveness are not well studied. To help researchers to well understand the algorithm implementation and performance of disassembly tools, this study first systematically summarizes the strategies used by disassembly tools to solve jump tables; then the study builds an automatic framework for testing jump tables, based on which a large-scale testsuite on jump tables (
Available online: October 18, 2023 , DOI: 10.13328/j.cnki.jos.006977
Abstract:
The database performance is affected by the database configuration parameters. The quality of parameter settings will directly affect the performance of the database. Therefore, the quality of the database parameter tuning method is important. However, traditional database parameter tuning methods have many limitations, such as the inability to make full use of historical parameter tuning data, wasting time and human resources, and so on. The counterfactual interpretation methods aim to change the original prediction to the expected prediction by making small modifications to the original data. The method plays a role of suggestion, and this can be used for database configuration optimization, namely, making small modifications to the database configuration to optimize the performance of the database. Therefore, this study proposes a counterfactual interpretation method for database configuration optimization. For databases with poor performance under specific load conditions, this method can modify the database configuration and generate corresponding database configuration counterfactuals to optimize database performance. This study conducts two kinds of experiments to evaluate the counterfactual interpretation method and verify the effect of optimizing the database. The experimental results show that the counterfactual interpretation methods proposed in this study are better than other typical counterfactual interpretation methods in terms of various evaluation indicators, and the generated counterfactuals can effectively improve database performance.
The database performance is affected by the database configuration parameters. The quality of parameter settings will directly affect the performance of the database. Therefore, the quality of the database parameter tuning method is important. However, traditional database parameter tuning methods have many limitations, such as the inability to make full use of historical parameter tuning data, wasting time and human resources, and so on. The counterfactual interpretation methods aim to change the original prediction to the expected prediction by making small modifications to the original data. The method plays a role of suggestion, and this can be used for database configuration optimization, namely, making small modifications to the database configuration to optimize the performance of the database. Therefore, this study proposes a counterfactual interpretation method for database configuration optimization. For databases with poor performance under specific load conditions, this method can modify the database configuration and generate corresponding database configuration counterfactuals to optimize database performance. This study conducts two kinds of experiments to evaluate the counterfactual interpretation method and verify the effect of optimizing the database. The experimental results show that the counterfactual interpretation methods proposed in this study are better than other typical counterfactual interpretation methods in terms of various evaluation indicators, and the generated counterfactuals can effectively improve database performance.
Available online: October 11, 2023 , DOI: 10.13328/j.cnki.jos.006978
Abstract:
There are a lot of two-party threshold schemes for SM2 digital signatures proposed in recent years, which can significantly enhance the security of private keys for SM2 digital signatures. According to different methods of key splitting, public schemes can be divided into two types: multiplicative key splitting and additive key splitting. Further, these public schemes can be subdivided into various two-party threshold schemes according to different constructions of the signature random number. This study proposes the framework of two-party threshold schemes for SM2 digital signature, which provides a safe basic calculation process of two-party threshold schemes and introduces the signature random number that can be constructed variously. With the proposed framework and various constructions of the random number, this study achieves the instantiation of the framework, obtaining a variety of two-party threshold schemes for SM2 digital signature. The instantiation includes 23 known two-party threshold schemes, as well as a variety of new schemes.
There are a lot of two-party threshold schemes for SM2 digital signatures proposed in recent years, which can significantly enhance the security of private keys for SM2 digital signatures. According to different methods of key splitting, public schemes can be divided into two types: multiplicative key splitting and additive key splitting. Further, these public schemes can be subdivided into various two-party threshold schemes according to different constructions of the signature random number. This study proposes the framework of two-party threshold schemes for SM2 digital signature, which provides a safe basic calculation process of two-party threshold schemes and introduces the signature random number that can be constructed variously. With the proposed framework and various constructions of the random number, this study achieves the instantiation of the framework, obtaining a variety of two-party threshold schemes for SM2 digital signature. The instantiation includes 23 known two-party threshold schemes, as well as a variety of new schemes.
Available online: October 11, 2023 , DOI: 10.13328/j.cnki.jos.006965
Abstract:
The major challenges traditional operating system (OS) design faces are the increasing number, diversity, and distribution scope of resources to be managed and the frequent changes in system state. However, the structures of existing OSs have become the biggest obstacle to solving the above problems as (1) tight coupling and centralization of the structure lead to poor flexibility and scalability and separate OS ecology; (2) contradiction between various capabilities, e.g., security and performance, due to the unitary isolation mechanism such as kernel-user isolation. Therefore, this study combines the hierarchical software bus (softbus) principles with isolation mechanisms to organize the OS and proposes a new OS model termed Yggdrasil. Yggdrasil decomposes an OS into component nodes connected by softbuses, whose communications are standardized to message passing via the softbus. To support the division of isolated states such as supervisor mode and different software hierarchies, Yggdrasil introduces bridge nodes for cascading and controlled communication between softbuses, and enhances the logical representation capability and scalability of OS through self-similar topology. Additionally, the simplicity and hierarchy of the softbus help to achieve decentralization. To verify the feasibility of Yggdrasil, the study builds hierarchical softbus model for OS (HiBuOS) and demonstrates the feasibility of developing a new OS based on Yggdrasil’s ideas through three specific designs: (1) designing and planning a hierarchical softbus structure according to the scale and requirements of the target operating system; (2) selecting specific isolation and communication mechanisms to instantiate bridge nodes and softbuses; (3) realizing OS services based on the hierarchical softbus style. Finally, the evaluation shows that HiBuOS has notable potential and advantages to enhance system scalability, security, performance, and ecological development without significant performance loss.
The major challenges traditional operating system (OS) design faces are the increasing number, diversity, and distribution scope of resources to be managed and the frequent changes in system state. However, the structures of existing OSs have become the biggest obstacle to solving the above problems as (1) tight coupling and centralization of the structure lead to poor flexibility and scalability and separate OS ecology; (2) contradiction between various capabilities, e.g., security and performance, due to the unitary isolation mechanism such as kernel-user isolation. Therefore, this study combines the hierarchical software bus (softbus) principles with isolation mechanisms to organize the OS and proposes a new OS model termed Yggdrasil. Yggdrasil decomposes an OS into component nodes connected by softbuses, whose communications are standardized to message passing via the softbus. To support the division of isolated states such as supervisor mode and different software hierarchies, Yggdrasil introduces bridge nodes for cascading and controlled communication between softbuses, and enhances the logical representation capability and scalability of OS through self-similar topology. Additionally, the simplicity and hierarchy of the softbus help to achieve decentralization. To verify the feasibility of Yggdrasil, the study builds hierarchical softbus model for OS (HiBuOS) and demonstrates the feasibility of developing a new OS based on Yggdrasil’s ideas through three specific designs: (1) designing and planning a hierarchical softbus structure according to the scale and requirements of the target operating system; (2) selecting specific isolation and communication mechanisms to instantiate bridge nodes and softbuses; (3) realizing OS services based on the hierarchical softbus style. Finally, the evaluation shows that HiBuOS has notable potential and advantages to enhance system scalability, security, performance, and ecological development without significant performance loss.
Available online: October 11, 2023 , DOI: 10.13328/j.cnki.jos.006974
Abstract:
The functions are the smallest naming unit of aggregation behavior in most traditional programming languages. The readability of function names plays a vital role in programmers’ understanding of program functions and the interaction between different modules. Low-quality function names may confuse developers, increase the smell in the code, and then result in software defects caused by API misuse. Therefore, a method of function name consistency checking and recommendation based on deep learning is proposed, which is named DMName. Firstly, for the given source code of the target function, the internal context, interactive context, sibling context, and closed context are constructed respectively, and the context information tag sequence is obtained after merging them. Then the tag sequence is converted into the context representation vector sequence by using the word embedding technology FastText and input into the encoder of the seq2seq model. The copy mechanism and coverage mechanism are utilized to solve the OOV problem and the repeated decoding problem, respectively. Finally, the vector sequence of the prediction result of the target function name is output, and the consistency of the function name is predicted with the help of the two-channel CNN classifier. If the function name is inconsistent, the recommended function name can be obtained by direct mapping according to the vector space similarity matching. The experimental results show that the F1-measure of DMName in function name consistency check and recommendation reaches 82.65% and 73.31% respectively, which is 2.01% and 2.96% higher than the current optimal DeepName. Finally, the DMName is verified in the large-scale open-source project, namely lancia in GitHub. A total of 16 function name inconsistency problems are found, and reasonable name recommendations are made, which further confirms the effectiveness of DMName.
The functions are the smallest naming unit of aggregation behavior in most traditional programming languages. The readability of function names plays a vital role in programmers’ understanding of program functions and the interaction between different modules. Low-quality function names may confuse developers, increase the smell in the code, and then result in software defects caused by API misuse. Therefore, a method of function name consistency checking and recommendation based on deep learning is proposed, which is named DMName. Firstly, for the given source code of the target function, the internal context, interactive context, sibling context, and closed context are constructed respectively, and the context information tag sequence is obtained after merging them. Then the tag sequence is converted into the context representation vector sequence by using the word embedding technology FastText and input into the encoder of the seq2seq model. The copy mechanism and coverage mechanism are utilized to solve the OOV problem and the repeated decoding problem, respectively. Finally, the vector sequence of the prediction result of the target function name is output, and the consistency of the function name is predicted with the help of the two-channel CNN classifier. If the function name is inconsistent, the recommended function name can be obtained by direct mapping according to the vector space similarity matching. The experimental results show that the F1-measure of DMName in function name consistency check and recommendation reaches 82.65% and 73.31% respectively, which is 2.01% and 2.96% higher than the current optimal DeepName. Finally, the DMName is verified in the large-scale open-source project, namely lancia in GitHub. A total of 16 function name inconsistency problems are found, and reasonable name recommendations are made, which further confirms the effectiveness of DMName.
Available online: October 11, 2023 , DOI: 10.13328/j.cnki.jos.006982
Abstract:
Static analysis tools often suffer from high false positive rates of reported alarms, despite their ability to aid developers in detecting potential defects early in the software development life cycle. To improve the availability of these tools, many automated warning identification techniques have been proposed to assist developers in classifying false positive alarms. However, existing approaches mainly focus on using hand-engineered features or statement-level abstract syntax tree token sequences to represent the defective code, failing to capture semantics from the reported alarms. To overcome the limitations of traditional approaches, this study employs deep neural networks with powerful feature extraction and representation abilities to generate code semantics from control flow graph paths for warning identification. The control flow graph abstractly represents the execution process of a given program. Thus, the generated path sequences of the control flow graph can guide the deep neural networks to learn semantic information about the potential defect more accurately. In this study, the pre-trained language model is fine-tuned to encode the path sequences and capture the semantic representations for model building. Finally, the study conducts extensive experiments on eight open-source projects to verify the effectiveness of the proposed approach by comparing it with the state-of-the-art baselines.
Static analysis tools often suffer from high false positive rates of reported alarms, despite their ability to aid developers in detecting potential defects early in the software development life cycle. To improve the availability of these tools, many automated warning identification techniques have been proposed to assist developers in classifying false positive alarms. However, existing approaches mainly focus on using hand-engineered features or statement-level abstract syntax tree token sequences to represent the defective code, failing to capture semantics from the reported alarms. To overcome the limitations of traditional approaches, this study employs deep neural networks with powerful feature extraction and representation abilities to generate code semantics from control flow graph paths for warning identification. The control flow graph abstractly represents the execution process of a given program. Thus, the generated path sequences of the control flow graph can guide the deep neural networks to learn semantic information about the potential defect more accurately. In this study, the pre-trained language model is fine-tuned to encode the path sequences and capture the semantic representations for model building. Finally, the study conducts extensive experiments on eight open-source projects to verify the effectiveness of the proposed approach by comparing it with the state-of-the-art baselines.
Available online: October 11, 2023 , DOI: 10.13328/j.cnki.jos.006990
Abstract:
The informationization 3.0 represented by deep mining and fusion applications of big data is starting, and the software in the traditional static environment is evolving into complex software in the human-cyber-physical ternary environment which is open and dynamic. How to realize the trusted, manageable, and controllable data interconnection on the untrusted and uncontrollable Internet is an urgent problem to be solved. The Internet of Data technical system represented by digital object architecture and identi?er resolution technology provides a feasible solution for these challenges. In order to solve the problems such as low transmission efficiency, high coordination cost, and security management issues in the process of data sharing on the Internet, this study proposes identi?er resolution standard specifications for human-cyber-physical ternary environments. Moreover, to meet the demands that data resources owned by different entities need to be discoverable, accessible, understandable, trustworthy, and interoperable in the human-cyber-physical ternary environment, this study designs the identi?er resolution protocol and implements the identi?er/resolution prototype system for human-cyber-physical ternary environments. At last, this study tests the performance of the prototype system, and the validity of the system is verified by applying it to application scenarios.
The informationization 3.0 represented by deep mining and fusion applications of big data is starting, and the software in the traditional static environment is evolving into complex software in the human-cyber-physical ternary environment which is open and dynamic. How to realize the trusted, manageable, and controllable data interconnection on the untrusted and uncontrollable Internet is an urgent problem to be solved. The Internet of Data technical system represented by digital object architecture and identi?er resolution technology provides a feasible solution for these challenges. In order to solve the problems such as low transmission efficiency, high coordination cost, and security management issues in the process of data sharing on the Internet, this study proposes identi?er resolution standard specifications for human-cyber-physical ternary environments. Moreover, to meet the demands that data resources owned by different entities need to be discoverable, accessible, understandable, trustworthy, and interoperable in the human-cyber-physical ternary environment, this study designs the identi?er resolution protocol and implements the identi?er/resolution prototype system for human-cyber-physical ternary environments. At last, this study tests the performance of the prototype system, and the validity of the system is verified by applying it to application scenarios.
Available online: September 27, 2023 , DOI: 10.13328/j.cnki.jos.006967
Abstract:
With the rapid development of neural network technology, neural networks have been widely applied in safety-critical fields such as autonomous driving, intelligent manufacturing, and medical diagnosis. Thus, it is crucial to ensure the trustworthiness of neural networks. However, due to the vulnerability of neural networks, slight perturbation often leads to wrong results. Therefore, it is vital to use formal verification methods to ensure the safety and trustworthiness of neural networks. Current verification methods for neural networks are mainly concerned with the accuracy of the analysis, while apt to ignore operational efficiency. When verifying the safety properties of complex networks, the large-scale state space may lead to problems such as infeasibility or unsolvability. To reduce the state space of neural networks and improve the verification efficiency, this study presents a formal verification method for neural networks based on divide and conquer considering over-approximation errors. The method uses the reachability analysis technique to calculate the upper and lower bounds of nonlinear nodes and uses an improved symbolic linear relaxation method to reduce over-approximation errors during the boundary calculation of nonlinear nodes. The constraints of nodes are refined by calculating the direct and indirect effects of their over-approximation errors. Thereby, the original verification problem is split into a set of sub-problems whose mixed integer linear programming (MILP) formulation has a smaller number of constraints. The method is implemented as a tool named NNVerifier, whose properties are verified and evaluated through experiments on four ReLU-based fully-connected benchmark networks trained on three classic datasets. The experimental results show that the verification efficiency of the NNVerifier is 37.18% higher than that of the existing complete verification methods.
With the rapid development of neural network technology, neural networks have been widely applied in safety-critical fields such as autonomous driving, intelligent manufacturing, and medical diagnosis. Thus, it is crucial to ensure the trustworthiness of neural networks. However, due to the vulnerability of neural networks, slight perturbation often leads to wrong results. Therefore, it is vital to use formal verification methods to ensure the safety and trustworthiness of neural networks. Current verification methods for neural networks are mainly concerned with the accuracy of the analysis, while apt to ignore operational efficiency. When verifying the safety properties of complex networks, the large-scale state space may lead to problems such as infeasibility or unsolvability. To reduce the state space of neural networks and improve the verification efficiency, this study presents a formal verification method for neural networks based on divide and conquer considering over-approximation errors. The method uses the reachability analysis technique to calculate the upper and lower bounds of nonlinear nodes and uses an improved symbolic linear relaxation method to reduce over-approximation errors during the boundary calculation of nonlinear nodes. The constraints of nodes are refined by calculating the direct and indirect effects of their over-approximation errors. Thereby, the original verification problem is split into a set of sub-problems whose mixed integer linear programming (MILP) formulation has a smaller number of constraints. The method is implemented as a tool named NNVerifier, whose properties are verified and evaluated through experiments on four ReLU-based fully-connected benchmark networks trained on three classic datasets. The experimental results show that the verification efficiency of the NNVerifier is 37.18% higher than that of the existing complete verification methods.
Available online: September 27, 2023 , DOI: 10.13328/j.cnki.jos.006972
Abstract:
Subset repair for inconsistent data is an important research problem in the field of data cleaning. Most of the existing methods are based on integrity constraint rules and adopt the principle of the minimum number of deleted tuples for subset repair. However, these methods take no account of the quality of deleted tuples, and the repair accuracy is low. Therefore, this study proposes a subset repair method combining rules and probabilities. The probability of inconsistent tuples is modeled so that the average probability of correct tuples is greater than that of wrong tuples, and the optimal subset repair with the smallest sum of the probability of deleted tuples is calculated. In addition, in order to reduce the time overhead of calculating the probability of inconsistent tuples, this study proposes an efficient error detection method to reduce the size of inconsistent tuples. Experimental results on real data and synthetic data verify that the proposed method outperforms the state-of-the-art subset repair method in terms of accuracy.
Subset repair for inconsistent data is an important research problem in the field of data cleaning. Most of the existing methods are based on integrity constraint rules and adopt the principle of the minimum number of deleted tuples for subset repair. However, these methods take no account of the quality of deleted tuples, and the repair accuracy is low. Therefore, this study proposes a subset repair method combining rules and probabilities. The probability of inconsistent tuples is modeled so that the average probability of correct tuples is greater than that of wrong tuples, and the optimal subset repair with the smallest sum of the probability of deleted tuples is calculated. In addition, in order to reduce the time overhead of calculating the probability of inconsistent tuples, this study proposes an efficient error detection method to reduce the size of inconsistent tuples. Experimental results on real data and synthetic data verify that the proposed method outperforms the state-of-the-art subset repair method in terms of accuracy.
Available online: September 27, 2023 , DOI: 10.13328/j.cnki.jos.006973
Abstract:
In recent years, software system security issues are attracting increasing attention. The security threats existing in systems can be easily exploited by attackers. Attackers usually attack systems by using various attacking techniques, such as password brute force cracking, phishing, and SQL injection. Threat modeling is a method of structurally analyzing, identifying, and processing threats. Traditional tests mainly focus on testing code defects, which take place in the late stage of software development. It is not able to well connect the results from early threat modeling and analysis for building secure software. Threat modeling tools in the industry lack the function of generating security tests. In order to tackle this problem, this study proposes a framework that is able to generate security test cases from threat models and designs and implements a tool prototype. In order to facilitate tests, this study improves the traditional attack tree model and performs compliance checks. Test scenarios can be automatically generated from the model. The test scenarios are evaluated according to the probabilities of attack nodes, and the scenarios of the threats with higher probabilities will be tested first. The defense nodes are evaluated, and the defense scheme with higher profit is selected to alleviate the threats, so as to improve the system’s security design. By setting parameters for attack nodes, test scenarios can be specified as test cases. In the early stage of software development, with the inputs of the threats identified by threat modeling, test cases can be generated through this framework and tool to guide subsequent security development and test design, which improves the integration of security technology in software design and development. The case study applies this framework and tool in test generation for very high security risks, which shows their effectiveness.
In recent years, software system security issues are attracting increasing attention. The security threats existing in systems can be easily exploited by attackers. Attackers usually attack systems by using various attacking techniques, such as password brute force cracking, phishing, and SQL injection. Threat modeling is a method of structurally analyzing, identifying, and processing threats. Traditional tests mainly focus on testing code defects, which take place in the late stage of software development. It is not able to well connect the results from early threat modeling and analysis for building secure software. Threat modeling tools in the industry lack the function of generating security tests. In order to tackle this problem, this study proposes a framework that is able to generate security test cases from threat models and designs and implements a tool prototype. In order to facilitate tests, this study improves the traditional attack tree model and performs compliance checks. Test scenarios can be automatically generated from the model. The test scenarios are evaluated according to the probabilities of attack nodes, and the scenarios of the threats with higher probabilities will be tested first. The defense nodes are evaluated, and the defense scheme with higher profit is selected to alleviate the threats, so as to improve the system’s security design. By setting parameters for attack nodes, test scenarios can be specified as test cases. In the early stage of software development, with the inputs of the threats identified by threat modeling, test cases can be generated through this framework and tool to guide subsequent security development and test design, which improves the integration of security technology in software design and development. The case study applies this framework and tool in test generation for very high security risks, which shows their effectiveness.
Available online: September 27, 2023 , DOI: 10.13328/j.cnki.jos.006957
Abstract:
As one of the ten block cipher algorithms selected for the second round of the 2018 National Cryptographic Algorithm Design Contest, Feistel-based block cipher (FBC) is an efficient and lightweight block cipher algorithm with a four-branch and two-fold Feistel structure. In this study, the FBC algorithm is abstracted as the FBC model, and the pseudorandomness and super-pseudorandomness of the model are studied. It is assumed that the FBC round functions are independent random functions, and a method to find the minimal number of FBC rounds is provided, which will keep FBC indistinguishable from a random permutation. Finally, the study comes to the conclusion that under the chosen-plaintext attack, four rounds of FBC are indistinguishable from random permutation, so the model has pseudorandomness; under the adaptive chosen-plaintext and ciphertext attack, five rounds of FBC are indistinguishable from random permutation, so the model has super-pseudorandomness.
As one of the ten block cipher algorithms selected for the second round of the 2018 National Cryptographic Algorithm Design Contest, Feistel-based block cipher (FBC) is an efficient and lightweight block cipher algorithm with a four-branch and two-fold Feistel structure. In this study, the FBC algorithm is abstracted as the FBC model, and the pseudorandomness and super-pseudorandomness of the model are studied. It is assumed that the FBC round functions are independent random functions, and a method to find the minimal number of FBC rounds is provided, which will keep FBC indistinguishable from a random permutation. Finally, the study comes to the conclusion that under the chosen-plaintext attack, four rounds of FBC are indistinguishable from random permutation, so the model has pseudorandomness; under the adaptive chosen-plaintext and ciphertext attack, five rounds of FBC are indistinguishable from random permutation, so the model has super-pseudorandomness.
Available online: September 27, 2023 , DOI: 10.13328/j.cnki.jos.006958
Abstract:
Few-shot learning aims at simulating the ability of human beings to quickly learn new things with only few samples, which is of great significance for deep learning tasks when samples are limited. However, in many practical tasks with limited computing resources, the model scale may still limit a wider application of few-shot learning. This study presents a realistic requirement for lightweight tasks for few-shot learning. As a widely used auxiliary strategy in deep learning, knowledge distillation transfers knowledge between models by using additional supervised information, which has practical application in both improving model accuracy and reducing model scale. This study first verifies the effectiveness of the knowledge distillation strategy in model lightweight for few-shot learning. Then according to the characteristics of few-shot learning, two new distillation methods for few-shot learning are designed: (1) distillation based on image local features; (2) distillation based on auxiliary classifiers. Experiments on miniImageNet and TieredImageNet datasets demonstrate that the new distillation methods are significantly superior to traditional knowledge distillation in few-shot learning tasks. The source code is available from https://github.com/cjy97/FSLKD.
Few-shot learning aims at simulating the ability of human beings to quickly learn new things with only few samples, which is of great significance for deep learning tasks when samples are limited. However, in many practical tasks with limited computing resources, the model scale may still limit a wider application of few-shot learning. This study presents a realistic requirement for lightweight tasks for few-shot learning. As a widely used auxiliary strategy in deep learning, knowledge distillation transfers knowledge between models by using additional supervised information, which has practical application in both improving model accuracy and reducing model scale. This study first verifies the effectiveness of the knowledge distillation strategy in model lightweight for few-shot learning. Then according to the characteristics of few-shot learning, two new distillation methods for few-shot learning are designed: (1) distillation based on image local features; (2) distillation based on auxiliary classifiers. Experiments on miniImageNet and TieredImageNet datasets demonstrate that the new distillation methods are significantly superior to traditional knowledge distillation in few-shot learning tasks. The source code is available from https://github.com/cjy97/FSLKD.
Available online: September 27, 2023 , DOI: 10.13328/j.cnki.jos.006998
Abstract:
Multimodal sentiment analysis is a task that uses subjective information from multiple modalities to analyze sentiment. Exploring how to effectively learn the interaction between modalities has always been an essential task in multimodal analysis. In recent research, it is found that the learning rate of different modalities is unbalanced, leading to the convergence of one modality while the rest of the modalities are under-fitting, which weakens the effect of multimodal collaborative decision-making. In order to combine multiple modalities more effectively and learn the multimodal sentiment features with rich expression, this study proposes a multimodal sentiment analysis method based on adaptive weight fusion. The method of adaptive weight fusion is divided into two phases. The first phase is to adaptively change the fusion weights of unimodal feature representations according to the difference of unimodal learning gradients to dynamically balance the modal learning rate. The study calls this phase balanced fusion (B-fusion). The second phase is to eliminate the impact of the fusion weights of B-fusion on task analysis, propose the modal attention to explore the contributions of modalities to the task, and dynamically allocate the fusion weight to each modality. The study calls this phase attention fusion (A-fusion). The experimental results show that the introduction of the B-fusion method into existing multimodal sentiment analysis methods can effectively improve the accuracy of sentiment analysis. The ablation experiment results show that adding the A-fusion method to B-fusion can effectively reduce the impact of B-fusion weights on the task, which is conducive to improving the analysis results of sentiment analysis. Compared with the existing multimodal sentiment analysis models, the proposed method has a simpler structure, lower computational consumption, and better task accuracy than these comparison models, which shows that the method has high efficiency and excellent performance in multimodal sentiment analysis tasks.
Multimodal sentiment analysis is a task that uses subjective information from multiple modalities to analyze sentiment. Exploring how to effectively learn the interaction between modalities has always been an essential task in multimodal analysis. In recent research, it is found that the learning rate of different modalities is unbalanced, leading to the convergence of one modality while the rest of the modalities are under-fitting, which weakens the effect of multimodal collaborative decision-making. In order to combine multiple modalities more effectively and learn the multimodal sentiment features with rich expression, this study proposes a multimodal sentiment analysis method based on adaptive weight fusion. The method of adaptive weight fusion is divided into two phases. The first phase is to adaptively change the fusion weights of unimodal feature representations according to the difference of unimodal learning gradients to dynamically balance the modal learning rate. The study calls this phase balanced fusion (B-fusion). The second phase is to eliminate the impact of the fusion weights of B-fusion on task analysis, propose the modal attention to explore the contributions of modalities to the task, and dynamically allocate the fusion weight to each modality. The study calls this phase attention fusion (A-fusion). The experimental results show that the introduction of the B-fusion method into existing multimodal sentiment analysis methods can effectively improve the accuracy of sentiment analysis. The ablation experiment results show that adding the A-fusion method to B-fusion can effectively reduce the impact of B-fusion weights on the task, which is conducive to improving the analysis results of sentiment analysis. Compared with the existing multimodal sentiment analysis models, the proposed method has a simpler structure, lower computational consumption, and better task accuracy than these comparison models, which shows that the method has high efficiency and excellent performance in multimodal sentiment analysis tasks.
Available online: September 27, 2023 , DOI: 10.13328/j.cnki.jos.006999
Abstract:
Revealing the complex relations among emotions is an important fundamental study in cognitive psychology. From the perspective of natural language processing, the key to exploring the relations among emotions lies in the embedded representation of emotional categories. Recently, there has been some interest in obtaining a category representation in the emotion space that can characterize emotion relations. However, the existing methods for emotion category representations have several drawbacks. For example, fixed dimensionality, the dimensionality of the emotion category representation, depends on the selected dataset. In order to obtain better representations for the emotion categories, this study introduces a supervised contrastive learning representation method. In the previous supervised contrastive learning, the similarity between samples depends on the similarity of the annotated labels of the samples. In order to better reflect the complex relations among different emotion categories, the study further proposes a partially similar supervised contrastive learning representation method, which suggests that samples of different emotion categories (e.g., anger and annoyance) may also be partially similar to each other. Finally, the study organizes a series of experiments to verify the ability of the proposed method and the other five benchmark methods in representing the relationship between emotion categories. The experimental results show that the proposed method achieves satisfactory results for the emotion category representations.
Revealing the complex relations among emotions is an important fundamental study in cognitive psychology. From the perspective of natural language processing, the key to exploring the relations among emotions lies in the embedded representation of emotional categories. Recently, there has been some interest in obtaining a category representation in the emotion space that can characterize emotion relations. However, the existing methods for emotion category representations have several drawbacks. For example, fixed dimensionality, the dimensionality of the emotion category representation, depends on the selected dataset. In order to obtain better representations for the emotion categories, this study introduces a supervised contrastive learning representation method. In the previous supervised contrastive learning, the similarity between samples depends on the similarity of the annotated labels of the samples. In order to better reflect the complex relations among different emotion categories, the study further proposes a partially similar supervised contrastive learning representation method, which suggests that samples of different emotion categories (e.g., anger and annoyance) may also be partially similar to each other. Finally, the study organizes a series of experiments to verify the ability of the proposed method and the other five benchmark methods in representing the relationship between emotion categories. The experimental results show that the proposed method achieves satisfactory results for the emotion category representations.
Available online: September 20, 2023 , DOI: 10.13328/j.cnki.jos.006955
Abstract:
The detection of the human respiration waveform in the sleep state is crucial for applications in intelligent health care as well as medical and healthcare in that different respiration waveform patterns can be examined to analyze sleep quality and monitor respiratory diseases. Traditional respiration sensing methods based on contact devices cause various inconveniences to users. In contrast, contactless sensing methods are more suitable for continuous monitoring. However, the randomness of the device deployment, sleep posture, and human motion during sleep severely restrict the application of contactless respiration sensing solutions in daily life. For this reason, the study proposes a detection method for the human respiration waveform in the sleep state based on impulse radio-ultra wide band (IR-UWB). On the basis of the periodic changes in the propagation path of the wireless pulse signal caused by the undulation of the human chest during respiration in the sleep state, the proposed method generates a fine-grained human respiration waveform and thereby achieves the real-time output of the respiration waveform and high-precision respiratory rate estimation. Specifically, to obtain the position of the human chest during respiration from the received wireless radio-frequency (RF) signals, this study proposes the indicator respiration energy ratio based on IR-UWB signals to estimate the target position. Then, it puts forward a vector projection method based on the in-phase/quadrature (I/Q) complex plane and a method of projection signal selection based on the circumferential position of the respiration vector to extract the characteristic human respiration waveform from the reflected signal. Finally, a variational encoder-decoder network is leveraged to achieve the fine-grained recovery of the respiratory waveform in the sleep state. Extensive experiments and tests are conducted under different conditions, and the results show that the human respiration waveforms monitored by the proposed method in the sleep state are highly similar to the actual waveforms captured by commercial respiratory belts. The average error of the proposed method in estimating the human respiratory rate is 0.229 bpm, indicating that the method can achieve high-precision detection of the human respiration waveform in the sleep state.
The detection of the human respiration waveform in the sleep state is crucial for applications in intelligent health care as well as medical and healthcare in that different respiration waveform patterns can be examined to analyze sleep quality and monitor respiratory diseases. Traditional respiration sensing methods based on contact devices cause various inconveniences to users. In contrast, contactless sensing methods are more suitable for continuous monitoring. However, the randomness of the device deployment, sleep posture, and human motion during sleep severely restrict the application of contactless respiration sensing solutions in daily life. For this reason, the study proposes a detection method for the human respiration waveform in the sleep state based on impulse radio-ultra wide band (IR-UWB). On the basis of the periodic changes in the propagation path of the wireless pulse signal caused by the undulation of the human chest during respiration in the sleep state, the proposed method generates a fine-grained human respiration waveform and thereby achieves the real-time output of the respiration waveform and high-precision respiratory rate estimation. Specifically, to obtain the position of the human chest during respiration from the received wireless radio-frequency (RF) signals, this study proposes the indicator respiration energy ratio based on IR-UWB signals to estimate the target position. Then, it puts forward a vector projection method based on the in-phase/quadrature (I/Q) complex plane and a method of projection signal selection based on the circumferential position of the respiration vector to extract the characteristic human respiration waveform from the reflected signal. Finally, a variational encoder-decoder network is leveraged to achieve the fine-grained recovery of the respiratory waveform in the sleep state. Extensive experiments and tests are conducted under different conditions, and the results show that the human respiration waveforms monitored by the proposed method in the sleep state are highly similar to the actual waveforms captured by commercial respiratory belts. The average error of the proposed method in estimating the human respiratory rate is 0.229 bpm, indicating that the method can achieve high-precision detection of the human respiration waveform in the sleep state.
Available online: September 20, 2023 , DOI: 10.13328/j.cnki.jos.006956
Abstract:
It is essential to detect out-of-distribution (OOD) training set samples for a safe and reliable machine learning system. Likelihood-based generative models are popular methods to detect OOD samples because they do not require sample labels during training. However, recent studies show that likelihoods sometimes fail to detect OOD samples, and the failure reason and solutions are under explored, especially for text data. Therefore, this study investigates the text failure reason from the views of the model and data: insufficient generalization of the generative model and prior probability bias of the text. To tackle the above problems, the study proposes a new OOD text detection method, namely Pobe. To address insufficient generalization of the generative model, the study increases the model generalization via KNN retrieval. Next, to address the prior probability bias of the text, the study designs a strategy to calibrate the bias and improve the influence of probability bias on OOD detection by a pre-trained language model and demonstrates the effectiveness of the strategy according to Bayes’ theorem. Experimental results over a wide range of datasets show the effectiveness of the proposed method. Specifically, the average AUROC is over 99%, and FPR95 is below 1% under eight datasets.
It is essential to detect out-of-distribution (OOD) training set samples for a safe and reliable machine learning system. Likelihood-based generative models are popular methods to detect OOD samples because they do not require sample labels during training. However, recent studies show that likelihoods sometimes fail to detect OOD samples, and the failure reason and solutions are under explored, especially for text data. Therefore, this study investigates the text failure reason from the views of the model and data: insufficient generalization of the generative model and prior probability bias of the text. To tackle the above problems, the study proposes a new OOD text detection method, namely Pobe. To address insufficient generalization of the generative model, the study increases the model generalization via KNN retrieval. Next, to address the prior probability bias of the text, the study designs a strategy to calibrate the bias and improve the influence of probability bias on OOD detection by a pre-trained language model and demonstrates the effectiveness of the strategy according to Bayes’ theorem. Experimental results over a wide range of datasets show the effectiveness of the proposed method. Specifically, the average AUROC is over 99%, and FPR95 is below 1% under eight datasets.
Available online: September 13, 2023 , DOI: 10.13328/j.cnki.jos.006948
Abstract:
Forgetting is the biggest problem of artificial neural networks in incremental learning and is thus called “catastrophic forgetting”. In contrast, humans can continuously acquire new knowledge and retain most of the frequently used old knowledge. This continuous “incremental learning” ability of human without extensive forgetting is related to the partitioned learning structure and memory replay ability of the human brain. To simulate this structure and ability, the study proposes an incremental learning approach of “recency bias-avoiding self-learning mask (SLM)-based partitioned incremental learning”, or ASPIL for short. ASPIL involves the two stages of regional isolation and regional integration, which are alternately iterated to accomplish continuous incremental learning. Specifically, this study proposes the “Bayesian network (BN)-based sparse regional isolation” method to isolate the new learning process from the existing knowledge and thereby avoid the interference with the existing knowledge. For regional integration, SLM and dual-branch fusion (GBF) methods are proposed. The SLM method can accurately extracts new knowledge and improves the adaptability of the network to new knowledge, while the GBF method integrates the old and new knowledge to achieve the goal of fostering unified and high-precision cognition. During training, a regularization term for Margin Loss is proposed to avoid the “recency bias”, thereby ensuring the further balance of the old knowledge and the avoidance of the bias towards the new knowledge. To evaluate the effectiveness of the proposed method, this study also presents systematic ablation experiments performed on the standard incremental learning datasets CIFAR-100 and miniImageNet and compares the proposed method with a series of well-known state-of-the-art methods. The experimental results show that the method proposed in this study improves the memory ability of the artificial neural network and outperforms the latest well-known methods by more than 5.27% in average identification rate.
Forgetting is the biggest problem of artificial neural networks in incremental learning and is thus called “catastrophic forgetting”. In contrast, humans can continuously acquire new knowledge and retain most of the frequently used old knowledge. This continuous “incremental learning” ability of human without extensive forgetting is related to the partitioned learning structure and memory replay ability of the human brain. To simulate this structure and ability, the study proposes an incremental learning approach of “recency bias-avoiding self-learning mask (SLM)-based partitioned incremental learning”, or ASPIL for short. ASPIL involves the two stages of regional isolation and regional integration, which are alternately iterated to accomplish continuous incremental learning. Specifically, this study proposes the “Bayesian network (BN)-based sparse regional isolation” method to isolate the new learning process from the existing knowledge and thereby avoid the interference with the existing knowledge. For regional integration, SLM and dual-branch fusion (GBF) methods are proposed. The SLM method can accurately extracts new knowledge and improves the adaptability of the network to new knowledge, while the GBF method integrates the old and new knowledge to achieve the goal of fostering unified and high-precision cognition. During training, a regularization term for Margin Loss is proposed to avoid the “recency bias”, thereby ensuring the further balance of the old knowledge and the avoidance of the bias towards the new knowledge. To evaluate the effectiveness of the proposed method, this study also presents systematic ablation experiments performed on the standard incremental learning datasets CIFAR-100 and miniImageNet and compares the proposed method with a series of well-known state-of-the-art methods. The experimental results show that the method proposed in this study improves the memory ability of the artificial neural network and outperforms the latest well-known methods by more than 5.27% in average identification rate.
Available online: September 13, 2023 , DOI: 10.13328/j.cnki.jos.006949
Abstract:
Deep neural networks can be affected by well-designed backdoor attacks during training. Such attacks are an attack method that controls the model output during tests by injecting data with backdoor labels into the training set. The attacked model performs normally on a clean test set but will be misclassified as the attack target class when the backdoor labels are recognized. The currently available backdoor attack methods have poor invisibility and are still expected to achieve a higher attack success rate. A backdoor attack method based on singular value decomposition is proposed to address the above limitations. The method proposed can be implemented in two ways: One is to directly set some singular values of the picture to zero, and the obtained picture is compressed to a certain extent and can be used as an effective backdoor triggering label. The other is to inject the singular vector information of the attack target class into the left and right singular vectors of the picture, which can also achieve an effective backdoor attack. The backdoor pictures obtained in the two kinds of processing ways are basically the same as the original picture from a visual point of view. According to the experiments, the proposed method proves that singular value decomposition can be effectively leveraged in backdoor attack algorithms to attack neural networks with considerably high success rates on multiple datasets.
Deep neural networks can be affected by well-designed backdoor attacks during training. Such attacks are an attack method that controls the model output during tests by injecting data with backdoor labels into the training set. The attacked model performs normally on a clean test set but will be misclassified as the attack target class when the backdoor labels are recognized. The currently available backdoor attack methods have poor invisibility and are still expected to achieve a higher attack success rate. A backdoor attack method based on singular value decomposition is proposed to address the above limitations. The method proposed can be implemented in two ways: One is to directly set some singular values of the picture to zero, and the obtained picture is compressed to a certain extent and can be used as an effective backdoor triggering label. The other is to inject the singular vector information of the attack target class into the left and right singular vectors of the picture, which can also achieve an effective backdoor attack. The backdoor pictures obtained in the two kinds of processing ways are basically the same as the original picture from a visual point of view. According to the experiments, the proposed method proves that singular value decomposition can be effectively leveraged in backdoor attack algorithms to attack neural networks with considerably high success rates on multiple datasets.
Available online: September 13, 2023 , DOI: 10.13328/j.cnki.jos.006959
Abstract:
The openness and ease-of-use of Python make it one of the most commonly used programming languages. The PyPI ecosystem formed by Python not only provides convenience for developers but also becomes an important target for attackers to launch vulnerability attacks. Thus, after discovering Python vulnerabilities, it is critical to deal with Python vulnerabilities by accurately and comprehensively assessing the impact scope of the vulnerabilities. However, the current assessment methods of Python vulnerability impact scope mainly rely on the dependency analysis of packet granularity, which will produce a large number of false positives. On the other hand, existing Python program analysis methods of function granularity have accuracy problems due to context insensitivity and produce false positives when applied to assess the impact scope of vulnerabilities. This study proposes a vulnerability impact scope assessment method for the PyPI ecosystem based on static analysis, namely PyVul++. First, it builds the index of the PyPI ecosystem, then finds the candidate packets affected by the vulnerability through vulnerability function identification, and confirms the vulnerability packets through vulnerability trigger condition. PyVul++ realizes vulnerability impact scope assessment of function granularity, improves the call analysis of function granularity for Python code, and outperforms other tools on the PyCG benchmark (accuracy of 86.71% and recall of 83.20%). PyVul++ is used to assess the impact scope of 10 Python CVE vulnerabilities on the PyPI ecosystem (385855 packets) and finds more vulnerability packets and reduces false positives compared with other tools such as pip-audit. In addition, PyVul++ newly finds that 11 packets in the current PyPI ecosystem still have security issues of referencing unpatched vulnerable functions in 10 assessment experiments of Python CVE vulnerability impact scope.
The openness and ease-of-use of Python make it one of the most commonly used programming languages. The PyPI ecosystem formed by Python not only provides convenience for developers but also becomes an important target for attackers to launch vulnerability attacks. Thus, after discovering Python vulnerabilities, it is critical to deal with Python vulnerabilities by accurately and comprehensively assessing the impact scope of the vulnerabilities. However, the current assessment methods of Python vulnerability impact scope mainly rely on the dependency analysis of packet granularity, which will produce a large number of false positives. On the other hand, existing Python program analysis methods of function granularity have accuracy problems due to context insensitivity and produce false positives when applied to assess the impact scope of vulnerabilities. This study proposes a vulnerability impact scope assessment method for the PyPI ecosystem based on static analysis, namely PyVul++. First, it builds the index of the PyPI ecosystem, then finds the candidate packets affected by the vulnerability through vulnerability function identification, and confirms the vulnerability packets through vulnerability trigger condition. PyVul++ realizes vulnerability impact scope assessment of function granularity, improves the call analysis of function granularity for Python code, and outperforms other tools on the PyCG benchmark (accuracy of 86.71% and recall of 83.20%). PyVul++ is used to assess the impact scope of 10 Python CVE vulnerabilities on the PyPI ecosystem (385855 packets) and finds more vulnerability packets and reduces false positives compared with other tools such as pip-audit. In addition, PyVul++ newly finds that 11 packets in the current PyPI ecosystem still have security issues of referencing unpatched vulnerable functions in 10 assessment experiments of Python CVE vulnerability impact scope.
Available online: September 13, 2023 , DOI: 10.13328/j.cnki.jos.006964
Abstract:
The domain name plays an important role in cybercrimes. Existing malicious domain name detection methods are not only difficult to use with rich topology and attribute information but also require a large amount of label data, resulting in limited detection effects and high costs. To address this problem, this study proposes a malicious domain name detection method based on graph contrastive learning. The domain name and IP address are taken as two types of nodes in a heterogeneous graph, and the feature matrix of corresponding nodes is established according to their attributes. Three types of meta paths are constructed based on the inclusion relationship between domain names, the measure of similarity, and the correspondence between domain names and IP addresses. In the pre-training stage, the contrast learning model based on the asymmetric encoder is applied to avoid the damage to graph structure and semantics caused by graph data augmentation operation and reduce the demand for computing resources. By using the inductive graph neural network graph encoders HeteroSAGE and HeteroGAT, a node-centric mini-batch training strategy is adopted to explore the aggregation relationship between the target node and its neighbor nodes, which solves the problem of poor applicability of the transductive graph neural networks such as GCN in dynamic scenarios. The downstream classification detection task contrastively utilizes logistic regression and random forest algorithms. Experimental results on publicly available data sets show that detection performance is improved by two to six percentage points compared with that of related works.
The domain name plays an important role in cybercrimes. Existing malicious domain name detection methods are not only difficult to use with rich topology and attribute information but also require a large amount of label data, resulting in limited detection effects and high costs. To address this problem, this study proposes a malicious domain name detection method based on graph contrastive learning. The domain name and IP address are taken as two types of nodes in a heterogeneous graph, and the feature matrix of corresponding nodes is established according to their attributes. Three types of meta paths are constructed based on the inclusion relationship between domain names, the measure of similarity, and the correspondence between domain names and IP addresses. In the pre-training stage, the contrast learning model based on the asymmetric encoder is applied to avoid the damage to graph structure and semantics caused by graph data augmentation operation and reduce the demand for computing resources. By using the inductive graph neural network graph encoders HeteroSAGE and HeteroGAT, a node-centric mini-batch training strategy is adopted to explore the aggregation relationship between the target node and its neighbor nodes, which solves the problem of poor applicability of the transductive graph neural networks such as GCN in dynamic scenarios. The downstream classification detection task contrastively utilizes logistic regression and random forest algorithms. Experimental results on publicly available data sets show that detection performance is improved by two to six percentage points compared with that of related works.
Available online: September 13, 2023 , DOI: 10.13328/j.cnki.jos.006928
Abstract:
Detecting out-of-distribution (OOD) samples outside the training set distribution is crucial for deploying deep neural network (DNN) classifiers in the open environment. OOD sample detection is a binary classification problem, which is to classify the input samples into the in-distribution (ID) or OOD categories. Then, the detector itself can be re-bypassed by malicious adversarial attacks. These OOD samples with malicious perturbations are called adversarial OOD samples. Building robust OOD detectors to detect adversarial OOD samples is more challenging. Existing methods usually train DNN through adversarial OOD samples within the neighborhood of auxiliary clean OOD samples to learn separable and robust representations to malicious perturbations. However, due to the distributional differences between the auxiliary OOD training set and original ID training set, training adversarial OOD samples is not effective enough to ensure the robustness of ID boundary against adversarial perturbations. Adversarial ID samples generated from within the neighborhood of (clean) ID samples are closer to the ID boundary and are also effective in improving the adversarial robustness of the ID boundary. This study proposes a semi-supervised adversarial training approach, DiTing, to build robust OOD detectors to detect clean and adversarial OOD samples. This approach treats the adversarial ID samples as auxiliary near-OOD samples and trains them jointly with other auxiliary clean and adversarial OOD samples to improve the robustness of OOD detection. Experiments show that DiTing has a significant advantage in detecting adversarial OOD samples generated by strong attacks while maintaining state-of-the-art performance in classifying clean ID samples and detecting clean OOD samples. Code is available at https://gitee.com/zhiyang3344/diting.
Detecting out-of-distribution (OOD) samples outside the training set distribution is crucial for deploying deep neural network (DNN) classifiers in the open environment. OOD sample detection is a binary classification problem, which is to classify the input samples into the in-distribution (ID) or OOD categories. Then, the detector itself can be re-bypassed by malicious adversarial attacks. These OOD samples with malicious perturbations are called adversarial OOD samples. Building robust OOD detectors to detect adversarial OOD samples is more challenging. Existing methods usually train DNN through adversarial OOD samples within the neighborhood of auxiliary clean OOD samples to learn separable and robust representations to malicious perturbations. However, due to the distributional differences between the auxiliary OOD training set and original ID training set, training adversarial OOD samples is not effective enough to ensure the robustness of ID boundary against adversarial perturbations. Adversarial ID samples generated from within the neighborhood of (clean) ID samples are closer to the ID boundary and are also effective in improving the adversarial robustness of the ID boundary. This study proposes a semi-supervised adversarial training approach, DiTing, to build robust OOD detectors to detect clean and adversarial OOD samples. This approach treats the adversarial ID samples as auxiliary near-OOD samples and trains them jointly with other auxiliary clean and adversarial OOD samples to improve the robustness of OOD detection. Experiments show that DiTing has a significant advantage in detecting adversarial OOD samples generated by strong attacks while maintaining state-of-the-art performance in classifying clean ID samples and detecting clean OOD samples. Code is available at https://gitee.com/zhiyang3344/diting.
Available online: September 13, 2023 , DOI: 10.13328/j.cnki.jos.006945
Abstract:
Jacobi computation is a kind of stencil computation, which has been widely applied in the field of scientific computing. The performance optimization of Jacobi computation is a classic topic, where loop tiling is an effective optimization method. The existing loop tiling methods mainly focus on the impact of tiling on parallel communication and program locality and fail to consider other factors such as load balancing and vectorization. This study analyzes and compares several tiling methods based on multi-core computing architecture and chooses an advanced hexagonal tiling as the main method to accelerate Jacobi computation. For tile size selection, this study proposes a hexagonal tile size selection algorithm called Hexagon_TSS by comprehensively considering the impact of tiling on load balancing, vectorization efficiency, and locality. The experimental results show that the L1 data cache miss rate can be reduced to 5.46% of original serial program computation in the best case by Hexagon_TSS, and the maximum speedup reaches 24.48. The proposed method also has excellent scalability.
Jacobi computation is a kind of stencil computation, which has been widely applied in the field of scientific computing. The performance optimization of Jacobi computation is a classic topic, where loop tiling is an effective optimization method. The existing loop tiling methods mainly focus on the impact of tiling on parallel communication and program locality and fail to consider other factors such as load balancing and vectorization. This study analyzes and compares several tiling methods based on multi-core computing architecture and chooses an advanced hexagonal tiling as the main method to accelerate Jacobi computation. For tile size selection, this study proposes a hexagonal tile size selection algorithm called Hexagon_TSS by comprehensively considering the impact of tiling on load balancing, vectorization efficiency, and locality. The experimental results show that the L1 data cache miss rate can be reduced to 5.46% of original serial program computation in the best case by Hexagon_TSS, and the maximum speedup reaches 24.48. The proposed method also has excellent scalability.
Available online: September 13, 2023 , DOI: 10.13328/j.cnki.jos.006947
Abstract:
Software change prediction, aimed at identifying change-prone modules, can help software managers and developers allocate resources efficiently and reduce maintenance overhead. Extracting effective features from the code plays a vital role in the construction of accurate prediction models. In recent years, researchers have shifted from traditional hand-crafted features to semantic features with powerful representation capabilities for prediction. They extracted semantic features from abstract syntax tree (AST) node sequences to build models. However, existing studies have ignored the structural information in the AST and the rich semantic information in the code. How to extract the semantic features of the code is still a challenging problem. For this reason, the study proposes a change prediction method based on hybrid graph representation. To start with, the model combines AST, control flow graph (CFG), data flow graph (DFG), and other structural information to construct the program graph representation of the code. Then, it uses the graph neural network to learn the semantic features of the program graph and the features obtained to predict change-proneness. The model can integrate various semantic information to represent the code better. The effectiveness of the proposed method is verified by comparing it with the latest change prediction methods on various change datasets.
Software change prediction, aimed at identifying change-prone modules, can help software managers and developers allocate resources efficiently and reduce maintenance overhead. Extracting effective features from the code plays a vital role in the construction of accurate prediction models. In recent years, researchers have shifted from traditional hand-crafted features to semantic features with powerful representation capabilities for prediction. They extracted semantic features from abstract syntax tree (AST) node sequences to build models. However, existing studies have ignored the structural information in the AST and the rich semantic information in the code. How to extract the semantic features of the code is still a challenging problem. For this reason, the study proposes a change prediction method based on hybrid graph representation. To start with, the model combines AST, control flow graph (CFG), data flow graph (DFG), and other structural information to construct the program graph representation of the code. Then, it uses the graph neural network to learn the semantic features of the program graph and the features obtained to predict change-proneness. The model can integrate various semantic information to represent the code better. The effectiveness of the proposed method is verified by comparing it with the latest change prediction methods on various change datasets.
Available online: September 06, 2023 , DOI: 10.13328/j.cnki.jos.006963
Abstract:
Aspect-level sentiment classification task, which aims to determine the sentiment polarity of a given aspect, has attracted increasing attention due to its broad applications. The key to this task is to identify contextual descriptions relevant to the given aspect and predict the aspect-related sentiment orientation of the author according to the context. Statistically, it is found that close to 30% of reviews convey a clear sentiment orientation without any explicit sentiment description of the given aspect, which is called implicit sentiment expression. Recent attention mechanism-based neural network methods have gained great achievement in sentiment analysis. However, this kind of method can only capture explicit aspect-related sentiment descriptions but fails to effectively explore and analyze implicit sentiment, and it often models aspect words and sentence contexts separately, which makes the expression of aspect words lack contextual semantics. To solve the above two problems, this study proposes an aspect-level sentiment classification method that integrates local aspect information and global sentence context information and improves the classification performance of the model by curriculum learning according to different classification difficulties of implicit and explicit sentiment sentences. Experimental results show that the proposed method not only has a high accuracy in identifying the aspect-related sentiment orientation of explicit sentiment sentences but also can effectively learn the sentiment categories of implicit sentiment sentences.
Aspect-level sentiment classification task, which aims to determine the sentiment polarity of a given aspect, has attracted increasing attention due to its broad applications. The key to this task is to identify contextual descriptions relevant to the given aspect and predict the aspect-related sentiment orientation of the author according to the context. Statistically, it is found that close to 30% of reviews convey a clear sentiment orientation without any explicit sentiment description of the given aspect, which is called implicit sentiment expression. Recent attention mechanism-based neural network methods have gained great achievement in sentiment analysis. However, this kind of method can only capture explicit aspect-related sentiment descriptions but fails to effectively explore and analyze implicit sentiment, and it often models aspect words and sentence contexts separately, which makes the expression of aspect words lack contextual semantics. To solve the above two problems, this study proposes an aspect-level sentiment classification method that integrates local aspect information and global sentence context information and improves the classification performance of the model by curriculum learning according to different classification difficulties of implicit and explicit sentiment sentences. Experimental results show that the proposed method not only has a high accuracy in identifying the aspect-related sentiment orientation of explicit sentiment sentences but also can effectively learn the sentiment categories of implicit sentiment sentences.
Available online: September 06, 2023 , DOI: 10.13328/j.cnki.jos.006960
Abstract:
Thanks to the low storage cost and high retrieval speed, graph-based unsupervised cross-modal hash learning has attracted much attention from academic and industrial researchers and has been an indispensable tool for cross-modal retrieval. However, the high computational complexity of graph structures prevents its application in large-scale multi-modal applications. This study mainly attempts to solve two important challenges facing graph-based unsupervised cross-modal hash learning: 1) How to efficiently construct graphs in unsupervised cross-modal hash learning? 2) How to handle the discrete optimization in cross-modal hash learning? To address such two problems, this study presents anchor-based cross-modal learning and a differentiable hash layer. To be specific, the study first randomly samples some image-text pairs from the training set as anchor sets and uses the anchor sets as the agent to compute the graph matrix of each batch of data. The graph matrix is used to guide cross-modal hash learning, thus remarkably reducing the space and time cost; second, the proposed differentiable hash layer directly adopts binary coding for computation during network forward propagation and produces gradient to update the network without continuous-value relaxation during backpropagation, thus embracing better hash encoding performance. Finally, the study introduces cross-modal ranking loss to consider the ranking results in the training process and improve the cross-modal retrieval accuracy. To verify the effectiveness of the proposed algorithm, the study compares the algorithm with 10 cross-modal hash algorithms on three general data sets.
Thanks to the low storage cost and high retrieval speed, graph-based unsupervised cross-modal hash learning has attracted much attention from academic and industrial researchers and has been an indispensable tool for cross-modal retrieval. However, the high computational complexity of graph structures prevents its application in large-scale multi-modal applications. This study mainly attempts to solve two important challenges facing graph-based unsupervised cross-modal hash learning: 1) How to efficiently construct graphs in unsupervised cross-modal hash learning? 2) How to handle the discrete optimization in cross-modal hash learning? To address such two problems, this study presents anchor-based cross-modal learning and a differentiable hash layer. To be specific, the study first randomly samples some image-text pairs from the training set as anchor sets and uses the anchor sets as the agent to compute the graph matrix of each batch of data. The graph matrix is used to guide cross-modal hash learning, thus remarkably reducing the space and time cost; second, the proposed differentiable hash layer directly adopts binary coding for computation during network forward propagation and produces gradient to update the network without continuous-value relaxation during backpropagation, thus embracing better hash encoding performance. Finally, the study introduces cross-modal ranking loss to consider the ranking results in the training process and improve the cross-modal retrieval accuracy. To verify the effectiveness of the proposed algorithm, the study compares the algorithm with 10 cross-modal hash algorithms on three general data sets.
Available online: September 06, 2023 , DOI: 10.13328/j.cnki.jos.006969
Abstract:
As an essential component of real-time system design, priority is utilized to resolve conflicts in resource sharing and design for safety. For real-time systems that introduce priorities, each task is assigned a priority, which leads to the possibility of low-priority tasks being preempted by high-priority tasks at runtime, thus creating a preemptive scheduling problem for real-time systems. Existing research on this problem lacks a modeling and automatic verification method that can visually represent the priority of tasks and the dependencies between tasks. To this end, a preemptive priority timed automata (PPTA) is proposed and a preemptive priority timed automata network (PPTAN) is introduced. First, the priority of a task is represented by adding the priority of migration to the timed automata, and then the migration is adopted to correlate tasks with dependencies so that PPTA can be applied to model real-time tasks with priority. The blocking position is also added to the timed automata, so PPTAN can be used to model the priority preemptive scheduling problem. Second, a model-based transformation method is proposed to map the PPTA to the automatic verification tool UPPAAL. Finally, by modeling an example of a multi-core multi-task real-time system and comparing it with other models, it is shown that this model is not only suitable for modeling the priority preemptive scheduling problem but also for accurately verifying and analyzing it.
As an essential component of real-time system design, priority is utilized to resolve conflicts in resource sharing and design for safety. For real-time systems that introduce priorities, each task is assigned a priority, which leads to the possibility of low-priority tasks being preempted by high-priority tasks at runtime, thus creating a preemptive scheduling problem for real-time systems. Existing research on this problem lacks a modeling and automatic verification method that can visually represent the priority of tasks and the dependencies between tasks. To this end, a preemptive priority timed automata (PPTA) is proposed and a preemptive priority timed automata network (PPTAN) is introduced. First, the priority of a task is represented by adding the priority of migration to the timed automata, and then the migration is adopted to correlate tasks with dependencies so that PPTA can be applied to model real-time tasks with priority. The blocking position is also added to the timed automata, so PPTAN can be used to model the priority preemptive scheduling problem. Second, a model-based transformation method is proposed to map the PPTA to the automatic verification tool UPPAAL. Finally, by modeling an example of a multi-core multi-task real-time system and comparing it with other models, it is shown that this model is not only suitable for modeling the priority preemptive scheduling problem but also for accurately verifying and analyzing it.
Available online: September 06, 2023 , DOI: 10.13328/j.cnki.jos.006979
Abstract:
When prototypical networks are directly applied to few-shot named entity recognition (FEW-NER), there are the following problems: Non-entities do not have strong semantic relationships with each other, and using the same way to construct the prototype for both entities and non-entities will make non-entity prototypes fail to accurately represent the semantic characteristics of non-entities; using only the average entity vector as the computing method of the prototype will make it difficult to capture similar entities with different semantic features. To address these problems, this study proposes a FEW-NER based on fine-grained prototypical networks (FNFP) to improve the annotation effect of FEW-NER. Firstly, different non-entity prototypes are constructed for different query sets to capture the key semantic features of non-entities in sentences and obtain finer-grained prototypes to improve the recognition effect of non-entities. Then, an inconsistent metric module is designed to measure the inconsistency between similar entities, and different metric functions are applied to entities and non-entities, so as to reduce the feature representation between similar samples and improve the feature representation of the prototype. Finally, a Viterbi decoder is introduced to capture the label transformation relationship and optimize the final annotation sequence. The experimental results show that the performance of the proposed method is improved compared with that of the large-scale FEW-NER dataset, namely FEW-NERD; and the generalization ability of this method in different domain scenarios is verified on the cross-domain dataset.
When prototypical networks are directly applied to few-shot named entity recognition (FEW-NER), there are the following problems: Non-entities do not have strong semantic relationships with each other, and using the same way to construct the prototype for both entities and non-entities will make non-entity prototypes fail to accurately represent the semantic characteristics of non-entities; using only the average entity vector as the computing method of the prototype will make it difficult to capture similar entities with different semantic features. To address these problems, this study proposes a FEW-NER based on fine-grained prototypical networks (FNFP) to improve the annotation effect of FEW-NER. Firstly, different non-entity prototypes are constructed for different query sets to capture the key semantic features of non-entities in sentences and obtain finer-grained prototypes to improve the recognition effect of non-entities. Then, an inconsistent metric module is designed to measure the inconsistency between similar entities, and different metric functions are applied to entities and non-entities, so as to reduce the feature representation between similar samples and improve the feature representation of the prototype. Finally, a Viterbi decoder is introduced to capture the label transformation relationship and optimize the final annotation sequence. The experimental results show that the performance of the proposed method is improved compared with that of the large-scale FEW-NER dataset, namely FEW-NERD; and the generalization ability of this method in different domain scenarios is verified on the cross-domain dataset.
Available online: September 06, 2023 , DOI: 10.13328/j.cnki.jos.006980
Abstract:
A large number of bug reports are generated during software development and maintenance, which can help developers to locate bugs. Information retrieval based bug localization (IRBL) analyzes the similarity of bug reports and source code files to locate bugs, achieving high accuracy at the file and function levels. However, a lot of labor and time costs are consumed to find bugs from suspicious files and function fragments due to the coarse location granularity of IRBL. This study proposes a statement level software bug localization method based on historical bug information retrieval, STMTLocator. Firstly, it retrieves historical bug reports which are similar to the bug report of the program under test and extracts the bug statements from the historical bug reports. Then, it retrieves the suspicious files according to the text similarity between the source code files and the bug report of the program under test, and extracts the suspicious statements from the suspicious files. Finally, it calculates the similarity between the suspicious statements and the historical bug statements, and arranges them in descending order to localize bug statements. To evaluate the bug localization performance of STMTLocator, comparative experiments are conducted on the Defects4J and JIRA dataset with Top@N, MRR, and other evaluation metrics. The experimental results show that STMTLocator is nearly four times than the static bug localization method BugLocator in terms of MRR and locates 7 more bug statements for Top@1. The average time used by STMTLocator to locate a bug version is reduced by 98.37% and 63.41% than dynamic bug localization methods Metallaxis and DStar, and STMTLocator has a significant advantage of not requiring the construction and execution of test cases.
A large number of bug reports are generated during software development and maintenance, which can help developers to locate bugs. Information retrieval based bug localization (IRBL) analyzes the similarity of bug reports and source code files to locate bugs, achieving high accuracy at the file and function levels. However, a lot of labor and time costs are consumed to find bugs from suspicious files and function fragments due to the coarse location granularity of IRBL. This study proposes a statement level software bug localization method based on historical bug information retrieval, STMTLocator. Firstly, it retrieves historical bug reports which are similar to the bug report of the program under test and extracts the bug statements from the historical bug reports. Then, it retrieves the suspicious files according to the text similarity between the source code files and the bug report of the program under test, and extracts the suspicious statements from the suspicious files. Finally, it calculates the similarity between the suspicious statements and the historical bug statements, and arranges them in descending order to localize bug statements. To evaluate the bug localization performance of STMTLocator, comparative experiments are conducted on the Defects4J and JIRA dataset with Top@N, MRR, and other evaluation metrics. The experimental results show that STMTLocator is nearly four times than the static bug localization method BugLocator in terms of MRR and locates 7 more bug statements for Top@1. The average time used by STMTLocator to locate a bug version is reduced by 98.37% and 63.41% than dynamic bug localization methods Metallaxis and DStar, and STMTLocator has a significant advantage of not requiring the construction and execution of test cases.
Available online: August 30, 2023 , DOI: 10.13328/j.cnki.jos.006961
Abstract:
Fault localization collects and analyzes the runtime information of test case sets to evaluate the suspiciousness of each statement of being faulty. Test case sets are constructed by the data from the input domain and have two types, i.e., passing test cases and failing ones. Since failing test cases generally account for a very small portion of the input domain, and their distribution is usually random, the number of failing test cases is much fewer than that of passing ones. Previous work has shown that the lack of failing test cases leads to a class-imbalanced problem of test case sets, which severely hampers fault localization effectiveness. To address this problem, this study proposes a model-domain data augmentation approach using generative adversarial networks for fault localization. Based on the model domain (i.e., spectrum information of fault localization) rather than the traditional input domain (i.e., program input), this approach uses the generative adversarial network to synthesize the model-domain failing test cases covering the minimum suspicious set, so as to address the class-imbalanced problem from the model domain. The experimental results show that the proposed approach significantly improves the effectiveness of 12 representative fault localization approaches.
Fault localization collects and analyzes the runtime information of test case sets to evaluate the suspiciousness of each statement of being faulty. Test case sets are constructed by the data from the input domain and have two types, i.e., passing test cases and failing ones. Since failing test cases generally account for a very small portion of the input domain, and their distribution is usually random, the number of failing test cases is much fewer than that of passing ones. Previous work has shown that the lack of failing test cases leads to a class-imbalanced problem of test case sets, which severely hampers fault localization effectiveness. To address this problem, this study proposes a model-domain data augmentation approach using generative adversarial networks for fault localization. Based on the model domain (i.e., spectrum information of fault localization) rather than the traditional input domain (i.e., program input), this approach uses the generative adversarial network to synthesize the model-domain failing test cases covering the minimum suspicious set, so as to address the class-imbalanced problem from the model domain. The experimental results show that the proposed approach significantly improves the effectiveness of 12 representative fault localization approaches.
Available online: August 30, 2023 , DOI: 10.13328/j.cnki.jos.006962
Abstract:
With the rapid development of Internet information technologies, the explosive growth of online learning resources has caused the problem of “information overload” and “learning disorientation”. In the absence of expert guidance, it is difficult for users to identify their learning demands and select the appropriate content from the vast amount of learning resources. Educational domain recommendation methods have received a lot of attention from researchers in recent years because they can provide personalized recommendations of learning resources based on the historical learning behaviors of users. However, the existing educational domain recommendation methods ignore the modeling of complex relationships among knowledge points in learning demand perception and fail to consider the dynamic changes of users’ learning demands, which leads to inaccurate learning resource recommendations. To address the above problems, this study proposes a knowledge point recommendation method based on static and dynamic learning demand perception, which models users’ learning behaviors under complex knowledge association by combining static perception and dynamic perception. For static learning demand perception, this study innovatively designs an attentional graph convolutional network based on the first-course-following meta-path guidance of knowledge points, which can accurately capture users’ static learning demands at the fine-grained knowledge point level by modeling the complex constraints of the first-course-following relationship between knowledge points and eliminating the interference of other non-learning demand factors. For dynamic learning demand perception, the method aggregates knowledge point embeddings to characterize users’ knowledge levels at different moments by taking courses as units and then uses a recurrent neural network to encode users’ knowledge level sequences, which can effectively explore the dynamic learning demands hidden in users’ knowledge level changes. Finally, this study fuses the obtained static and dynamic learning demands, models the compatibility between static and dynamic learning demands in the same framework, and promotes the complementarity of these two learning demands to achieve fine-grained and personalized knowledge point recommendations. Experiments show that the proposed method can effectively perceive users’ learning demands, provide personalized knowledge point recommendations on two publicly available datasets, and outperform the mainstream recommendation methods in terms of various evaluation metrics.
With the rapid development of Internet information technologies, the explosive growth of online learning resources has caused the problem of “information overload” and “learning disorientation”. In the absence of expert guidance, it is difficult for users to identify their learning demands and select the appropriate content from the vast amount of learning resources. Educational domain recommendation methods have received a lot of attention from researchers in recent years because they can provide personalized recommendations of learning resources based on the historical learning behaviors of users. However, the existing educational domain recommendation methods ignore the modeling of complex relationships among knowledge points in learning demand perception and fail to consider the dynamic changes of users’ learning demands, which leads to inaccurate learning resource recommendations. To address the above problems, this study proposes a knowledge point recommendation method based on static and dynamic learning demand perception, which models users’ learning behaviors under complex knowledge association by combining static perception and dynamic perception. For static learning demand perception, this study innovatively designs an attentional graph convolutional network based on the first-course-following meta-path guidance of knowledge points, which can accurately capture users’ static learning demands at the fine-grained knowledge point level by modeling the complex constraints of the first-course-following relationship between knowledge points and eliminating the interference of other non-learning demand factors. For dynamic learning demand perception, the method aggregates knowledge point embeddings to characterize users’ knowledge levels at different moments by taking courses as units and then uses a recurrent neural network to encode users’ knowledge level sequences, which can effectively explore the dynamic learning demands hidden in users’ knowledge level changes. Finally, this study fuses the obtained static and dynamic learning demands, models the compatibility between static and dynamic learning demands in the same framework, and promotes the complementarity of these two learning demands to achieve fine-grained and personalized knowledge point recommendations. Experiments show that the proposed method can effectively perceive users’ learning demands, provide personalized knowledge point recommendations on two publicly available datasets, and outperform the mainstream recommendation methods in terms of various evaluation metrics.
Available online: August 30, 2023 , DOI: 10.13328/j.cnki.jos.006923
Abstract:
Kernel heap vulnerability is currently one of the main threats to operating system security. User-space attackers can leak or modify sensitive kernel information, disrupt kernel control flow, and even gain root privilege by triggering a vulnerability. However, due to the rapid increase in the number and complexity of vulnerabilities, it often takes a long time from when a vulnerability is first reported to when the developer issues a patch, and kernel mitigation mechanisms currently adopted are usually steadily bypassed. Therefore, this study proposes an eBPF-based dynamic mitigation framework for kernel heap vulnerabilities, so as to reduce kernel security risks during the time window fixing. The framework adopts data object space randomization to assign random addresses to the data objects involved in vulnerability reports at each allocation. In addition, it takes full advantage of the dynamic and secure features of eBPF to inject space-randomized objects into the kernel during runtime, so the attacker cannot place any attack payload accurately, and the heap vulnerabilities are almost unexploitable. This study evaluates 40 real kernel heap vulnerabilities and collects 12 attacks that bypass the existing mitigation mechanisms for further analysis and tests. As a result, it verifies that the dynamic mitigation framework provides sufficient security. Performance tests show that even under severe conditions, the four types of data objects only cause performance loss of about 1% and negligible memory loss to the system, and there is almost no additional performance loss when the number of protected objects increases. Compared with related work, the mechanism in this study has a wider scope of application and stronger security, and it does not require vulnerability patches issued by security experts. Furthermore, it can generate mitigation procedures according to vulnerability reports and has a broad application prospect.
Kernel heap vulnerability is currently one of the main threats to operating system security. User-space attackers can leak or modify sensitive kernel information, disrupt kernel control flow, and even gain root privilege by triggering a vulnerability. However, due to the rapid increase in the number and complexity of vulnerabilities, it often takes a long time from when a vulnerability is first reported to when the developer issues a patch, and kernel mitigation mechanisms currently adopted are usually steadily bypassed. Therefore, this study proposes an eBPF-based dynamic mitigation framework for kernel heap vulnerabilities, so as to reduce kernel security risks during the time window fixing. The framework adopts data object space randomization to assign random addresses to the data objects involved in vulnerability reports at each allocation. In addition, it takes full advantage of the dynamic and secure features of eBPF to inject space-randomized objects into the kernel during runtime, so the attacker cannot place any attack payload accurately, and the heap vulnerabilities are almost unexploitable. This study evaluates 40 real kernel heap vulnerabilities and collects 12 attacks that bypass the existing mitigation mechanisms for further analysis and tests. As a result, it verifies that the dynamic mitigation framework provides sufficient security. Performance tests show that even under severe conditions, the four types of data objects only cause performance loss of about 1% and negligible memory loss to the system, and there is almost no additional performance loss when the number of protected objects increases. Compared with related work, the mechanism in this study has a wider scope of application and stronger security, and it does not require vulnerability patches issued by security experts. Furthermore, it can generate mitigation procedures according to vulnerability reports and has a broad application prospect.
Available online: August 30, 2023 , DOI: 10.13328/j.cnki.jos.006925
Abstract:
Regular expressions are widely used in various areas of computer science. However, due to the complex syntax and the use of a large number of meta-characters, regular expressions are quite error-prone when defined and used by developers. Testing is a practical and effective way to ensure the semantic correctness of regular expressions. The most common method is to generate a set of character strings according to the tested expression and check whether they comply with the intended language. Most of the existing test data generation focuses only on positive strings. However, empirical study shows that a majority of errors during actual development are manifested by the fact that the defined language is smaller than the intended one. In addition, such errors can only be detected by negative strings. This study investigates the generation of negative strings from regular expressions based on mutation. The study first obtains a set of mutants by injecting defects into the tested expression through mutation and then selects a negative character string in the complementary set of the language defined by the tested expression to reveal the error simulated by the corresponding mutant. In order to simulate complex defects and avoid the problem that the negative strings cannot be obtained due to the specialization of mutants, a second-order mutation mechanism is adopted. Meanwhile, optimization techniques such as redundant mutant elimination and mutation operator selection are used to reduce the mutants, so as to control the size of the finally generated test set. The experimental results show that the proposed algorithm can generate negative test strings with a moderate size and have strong error detection ability compared with the existing tools.
Regular expressions are widely used in various areas of computer science. However, due to the complex syntax and the use of a large number of meta-characters, regular expressions are quite error-prone when defined and used by developers. Testing is a practical and effective way to ensure the semantic correctness of regular expressions. The most common method is to generate a set of character strings according to the tested expression and check whether they comply with the intended language. Most of the existing test data generation focuses only on positive strings. However, empirical study shows that a majority of errors during actual development are manifested by the fact that the defined language is smaller than the intended one. In addition, such errors can only be detected by negative strings. This study investigates the generation of negative strings from regular expressions based on mutation. The study first obtains a set of mutants by injecting defects into the tested expression through mutation and then selects a negative character string in the complementary set of the language defined by the tested expression to reveal the error simulated by the corresponding mutant. In order to simulate complex defects and avoid the problem that the negative strings cannot be obtained due to the specialization of mutants, a second-order mutation mechanism is adopted. Meanwhile, optimization techniques such as redundant mutant elimination and mutation operator selection are used to reduce the mutants, so as to control the size of the finally generated test set. The experimental results show that the proposed algorithm can generate negative test strings with a moderate size and have strong error detection ability compared with the existing tools.
Available online: August 23, 2023 , DOI: 10.13328/j.cnki.jos.006926
Abstract:
Hypergraphs are generalized representations of ordinary graphs, which are common in many application areas, including the Internet, bioinformatics, and social networks. The independent set problem is a fundamental research problem in the field of graph analysis. Most of the traditional independent set algorithms are targeted for ordinary graph data, and how to achieve efficient maximum independent set mining on hypergraph data is an urgent problem to be solved. To address this problem, this study proposes a definition of hypergraph independent sets. Firstly, two properties of hypergraph independent set search are analyzed, and then a basic algorithm based on the greedy strategy is proposed. Then a pruning framework for hypergraph approximate maximum independent set search is proposed, i.e., a combination of exact pruning and approximate pruning, which reduces the size of the graph by the exact pruning strategy and speeds up the search by the approximate pruning strategy. In addition, four efficient pruning strategies are proposed in this study, and a theoretical proof of each pruning strategy is presented. Finally, experiments are conducted on 10 real hypergraph data sets, and the results show that the pruning algorithm can efficiently search for hypergraph maximum independent sets that are closer to the real results.
Hypergraphs are generalized representations of ordinary graphs, which are common in many application areas, including the Internet, bioinformatics, and social networks. The independent set problem is a fundamental research problem in the field of graph analysis. Most of the traditional independent set algorithms are targeted for ordinary graph data, and how to achieve efficient maximum independent set mining on hypergraph data is an urgent problem to be solved. To address this problem, this study proposes a definition of hypergraph independent sets. Firstly, two properties of hypergraph independent set search are analyzed, and then a basic algorithm based on the greedy strategy is proposed. Then a pruning framework for hypergraph approximate maximum independent set search is proposed, i.e., a combination of exact pruning and approximate pruning, which reduces the size of the graph by the exact pruning strategy and speeds up the search by the approximate pruning strategy. In addition, four efficient pruning strategies are proposed in this study, and a theoretical proof of each pruning strategy is presented. Finally, experiments are conducted on 10 real hypergraph data sets, and the results show that the pruning algorithm can efficiently search for hypergraph maximum independent sets that are closer to the real results.
Available online: August 23, 2023 , DOI: 10.13328/j.cnki.jos.006927
Abstract:
Entity recognition is a key technology for information extraction. Compared with ordinary text, the entity recognition of Chinese medical text is often faced with a large number of nested entities. Previous methods of entity recognition often ignore the entity nesting rules unique to medical text and directly use sequence annotation methods. Therefore, a Chinese entity recognition method that incorporates entity nesting rules is proposed. This method transforms the entity recognition task into a joint training task of entity boundary recognition and boundary first-tail relationship recognition in the training process and filters the results by combining the entity nesting rules summarized from actual medical text in the decoding process. In this way, the recognition results are in line with the composition law of the nested combinations of inner and outer entities in the actual text. Good results have been achieved in public experiments on entity recognition of medical text. Experiments on the dataset show that the proposed method is significantly superior to the existing methods in terms of nested-type entity recognition performance, and the overall accuracy is increased by 0.5% compared with the state-of-the-art methods.
Entity recognition is a key technology for information extraction. Compared with ordinary text, the entity recognition of Chinese medical text is often faced with a large number of nested entities. Previous methods of entity recognition often ignore the entity nesting rules unique to medical text and directly use sequence annotation methods. Therefore, a Chinese entity recognition method that incorporates entity nesting rules is proposed. This method transforms the entity recognition task into a joint training task of entity boundary recognition and boundary first-tail relationship recognition in the training process and filters the results by combining the entity nesting rules summarized from actual medical text in the decoding process. In this way, the recognition results are in line with the composition law of the nested combinations of inner and outer entities in the actual text. Good results have been achieved in public experiments on entity recognition of medical text. Experiments on the dataset show that the proposed method is significantly superior to the existing methods in terms of nested-type entity recognition performance, and the overall accuracy is increased by 0.5% compared with the state-of-the-art methods.
Available online: August 23, 2023 , DOI: 10.13328/j.cnki.jos.006929
Abstract:
As a privacy-preserving digital identity authentication technology, anonymous credentials not only authenticate the validity of the users’ digital identity but also protect the privacy of their identity. Anonymous credentials are widely applied in anonymous authentication, anonymous tokens, and decentralized digital identity systems. Existing anonymous credentials usually adopt the commitment-signature-proof paradigm, which requires that the adopted signature scheme should have the re-randomization property, such as CL signatures, PS signatures, and structure-preserving signatures (SPS). In practical applications, ECDSA, Schnorr, and SM2 are widely employed for digital identity authentication, but they lack the protection of user identity privacy. Therefore, it is of certain practical significance to construct anonymous credentials compatible with ECDSA, Schnorr, SM2, and other digital signatures, and protect identity privacy during the authentication. This study explores anonymous credentials based on SM2 digital signature. Pedersen commitment is utilized to commit the user attributes in the registration phase. Meanwhile, according to the structural characteristics of SM2, the signed message is H(m), and the equivalence between the Pedersen commitment message and the hash commitment message is proven. This study also employs ZKB++ technology to prove the equivalence of algebraic and non-algebraic statements. The commitment message is transformed to achieve the cross-domain proof and issue the users’ credentials based on the SM2 digital signature. In the showing phase of anonymous credentials, the zero-knowledge proof is combined to prove the possession of an SM2 signature and ensure the anonymity of credentials. This study provides the construction of an anonymous credential protocol based on SM2 digital signature and proves the security of this protocol. Finally, it also verifies the effectiveness and feasibility of the protocol by analyzing the computational complexity of the protocol and testing the algorithm execution efficiency.
As a privacy-preserving digital identity authentication technology, anonymous credentials not only authenticate the validity of the users’ digital identity but also protect the privacy of their identity. Anonymous credentials are widely applied in anonymous authentication, anonymous tokens, and decentralized digital identity systems. Existing anonymous credentials usually adopt the commitment-signature-proof paradigm, which requires that the adopted signature scheme should have the re-randomization property, such as CL signatures, PS signatures, and structure-preserving signatures (SPS). In practical applications, ECDSA, Schnorr, and SM2 are widely employed for digital identity authentication, but they lack the protection of user identity privacy. Therefore, it is of certain practical significance to construct anonymous credentials compatible with ECDSA, Schnorr, SM2, and other digital signatures, and protect identity privacy during the authentication. This study explores anonymous credentials based on SM2 digital signature. Pedersen commitment is utilized to commit the user attributes in the registration phase. Meanwhile, according to the structural characteristics of SM2, the signed message is H(m), and the equivalence between the Pedersen commitment message and the hash commitment message is proven. This study also employs ZKB++ technology to prove the equivalence of algebraic and non-algebraic statements. The commitment message is transformed to achieve the cross-domain proof and issue the users’ credentials based on the SM2 digital signature. In the showing phase of anonymous credentials, the zero-knowledge proof is combined to prove the possession of an SM2 signature and ensure the anonymity of credentials. This study provides the construction of an anonymous credential protocol based on SM2 digital signature and proves the security of this protocol. Finally, it also verifies the effectiveness and feasibility of the protocol by analyzing the computational complexity of the protocol and testing the algorithm execution efficiency.
Available online: August 23, 2023 , DOI: 10.13328/j.cnki.jos.006930
Abstract:
Since the Snowden revelations, threats from backdoor attacks represented by algorithm substitution attack (ASA) have been widely concerned. This kind of attack subverts the process of the algorithm that tampers with the cryptographic protocol participants in an undetectable manner, which embeds backdoors to obtain secrets. Building a cryptographic reverse firewall (CRF) for protocol participants is a well-known and feasible approach against ASA. Identity-based encryption (IBE), as a quite applicable public key infrastructure, has vital importance to be protected by appropriate CRF schemes. However, the existing work only realizes the CRF re-randomization, ignoring the security risk of sending users’ private keys directly to the third-party CRF. Given the above problem, the formal definition and security model of security properties of CRF applicable to IBE are proposed. Then, the formal definition of rerandomizable and key-malleable secure channel free IBE (RKM-SCF-IBE) and the method of transforming traditional IBE to RKM-SFC-IBE are presented. In addition, an approach to increasing anonymity is also given. Finally, a generic provably secure framework of CRF construction for IBE is proposed based on RKM-SFC-IBE, with several instantiations from classic IBE schemes in the standard model and simulation results with optimization methods. Compared with existing work, the proposed scheme is proven secure under a more complete security model with a generic approach to building CRF for IBE schemes and clarifies the basic principles when constructing CRF for more expressive encryption schemes.
Since the Snowden revelations, threats from backdoor attacks represented by algorithm substitution attack (ASA) have been widely concerned. This kind of attack subverts the process of the algorithm that tampers with the cryptographic protocol participants in an undetectable manner, which embeds backdoors to obtain secrets. Building a cryptographic reverse firewall (CRF) for protocol participants is a well-known and feasible approach against ASA. Identity-based encryption (IBE), as a quite applicable public key infrastructure, has vital importance to be protected by appropriate CRF schemes. However, the existing work only realizes the CRF re-randomization, ignoring the security risk of sending users’ private keys directly to the third-party CRF. Given the above problem, the formal definition and security model of security properties of CRF applicable to IBE are proposed. Then, the formal definition of rerandomizable and key-malleable secure channel free IBE (RKM-SCF-IBE) and the method of transforming traditional IBE to RKM-SFC-IBE are presented. In addition, an approach to increasing anonymity is also given. Finally, a generic provably secure framework of CRF construction for IBE is proposed based on RKM-SFC-IBE, with several instantiations from classic IBE schemes in the standard model and simulation results with optimization methods. Compared with existing work, the proposed scheme is proven secure under a more complete security model with a generic approach to building CRF for IBE schemes and clarifies the basic principles when constructing CRF for more expressive encryption schemes.
Available online: August 23, 2023 , DOI: 10.13328/j.cnki.jos.006931
Abstract:
Accurately extracting two types of information including elements and clauses in contract texts can effectively improve the contract review efficiency and provide facilitation services for all trading parties. However, current contract information extraction methods generally train single-task models to extract elements and clauses separately, whereas they do not dig deep into the characteristics of contract texts, ignoring the relevance among different tasks. Therefore, this study employs a deep neural network structure to study the correlation between the two tasks of element extraction and clause extraction and proposes a multitask learning method. Firstly, the primary multitask learning model is built for contract information extraction by combining the above two tasks. Then, the model is optimized and attention mechanism is adopted to further explore the correlation. Additionally, an Attention-based dynamic multitask-learning model is built. Finally, based on the above two methods, adynamic multitask learning model with lexical knowledge is proposed for the complex semantic environment in contract texts. The experimental results show that the method can fully capture the shared features among tasks and yield better information extraction results than the single-task model. It can solve the nested entity among elements and clauses in contract texts, and realize the joint information extraction of contract elements and clauses. In addition, to verify the robustness of the proposed method, this study conducts experiments on public datasets in various fields, and the results show that the proposed method is superior to baseline methods.
Accurately extracting two types of information including elements and clauses in contract texts can effectively improve the contract review efficiency and provide facilitation services for all trading parties. However, current contract information extraction methods generally train single-task models to extract elements and clauses separately, whereas they do not dig deep into the characteristics of contract texts, ignoring the relevance among different tasks. Therefore, this study employs a deep neural network structure to study the correlation between the two tasks of element extraction and clause extraction and proposes a multitask learning method. Firstly, the primary multitask learning model is built for contract information extraction by combining the above two tasks. Then, the model is optimized and attention mechanism is adopted to further explore the correlation. Additionally, an Attention-based dynamic multitask-learning model is built. Finally, based on the above two methods, adynamic multitask learning model with lexical knowledge is proposed for the complex semantic environment in contract texts. The experimental results show that the method can fully capture the shared features among tasks and yield better information extraction results than the single-task model. It can solve the nested entity among elements and clauses in contract texts, and realize the joint information extraction of contract elements and clauses. In addition, to verify the robustness of the proposed method, this study conducts experiments on public datasets in various fields, and the results show that the proposed method is superior to baseline methods.
Available online: August 23, 2023 , DOI: 10.13328/j.cnki.jos.006932
Abstract:
Adversarial texts are malicious samples that can cause deep learning classifiers to make errors. The adversary creates an adversarial text that can deceive the target model by adding subtle perturbations to the original text that are imperceptible to humans. The study of adversarial text generation methods can evaluate the robustness of deep neural networks and contribute to the subsequent robustness improvement of the model. Among the current adversarial text generation methods designed for Chinese text, few attack the robust Chinese BERT model as the target model. For Chinese text classification tasks, this study proposes an attack method against Chinese BERT, that is Chinese BERT Tricker. This method adopts a character-level word importance scoring method, important Chinese character positioning. Meanwhile, a word-level perturbation method for Chinese based on the masked language model with two types of strategies is designed to achieve the replacement of important words. Experimental results show that for the text classification tasks, the proposed method can significantly reduce the classification accuracy of the Chinese BERT model to less than 40% on two real datasets, and it outperforms other baseline methods in terms of multiple attack performance.
Adversarial texts are malicious samples that can cause deep learning classifiers to make errors. The adversary creates an adversarial text that can deceive the target model by adding subtle perturbations to the original text that are imperceptible to humans. The study of adversarial text generation methods can evaluate the robustness of deep neural networks and contribute to the subsequent robustness improvement of the model. Among the current adversarial text generation methods designed for Chinese text, few attack the robust Chinese BERT model as the target model. For Chinese text classification tasks, this study proposes an attack method against Chinese BERT, that is Chinese BERT Tricker. This method adopts a character-level word importance scoring method, important Chinese character positioning. Meanwhile, a word-level perturbation method for Chinese based on the masked language model with two types of strategies is designed to achieve the replacement of important words. Experimental results show that for the text classification tasks, the proposed method can significantly reduce the classification accuracy of the Chinese BERT model to less than 40% on two real datasets, and it outperforms other baseline methods in terms of multiple attack performance.
Available online: August 23, 2023 , DOI: 10.13328/j.cnki.jos.006936
Abstract:
As a new learning paradigm to solve the problem of label ambiguity, label distribution learning (LDL) has received much attention in recent years. To further improve the prediction performance of LDL, this study proposes an LDL based on deep forest and heterogeneous ensemble (LDLDF), which uses the cascade structure of deep forest to simulate deep learning models with multi-layer processing structure and combines multiple heterogeneous classifiers in the cascade layer to increase the diversity of ensemble. Compared with other existing LDL methods, LDLDF can process information layer by layer and learn better feature representations to mine rich semantic information in data, and it has better representation learning ability and generalization ability. In addition, by considering the degradation problem of deep models, LDLDF adopts a layer feature reuse mechanism to reduce the training error of the model, which effectively utilizes the prediction ability of each layer in the deep model. Sufficient experimental results show that LDLDF is superior to other methods.
As a new learning paradigm to solve the problem of label ambiguity, label distribution learning (LDL) has received much attention in recent years. To further improve the prediction performance of LDL, this study proposes an LDL based on deep forest and heterogeneous ensemble (LDLDF), which uses the cascade structure of deep forest to simulate deep learning models with multi-layer processing structure and combines multiple heterogeneous classifiers in the cascade layer to increase the diversity of ensemble. Compared with other existing LDL methods, LDLDF can process information layer by layer and learn better feature representations to mine rich semantic information in data, and it has better representation learning ability and generalization ability. In addition, by considering the degradation problem of deep models, LDLDF adopts a layer feature reuse mechanism to reduce the training error of the model, which effectively utilizes the prediction ability of each layer in the deep model. Sufficient experimental results show that LDLDF is superior to other methods.
Available online: August 23, 2023 , DOI: 10.13328/j.cnki.jos.006937
Abstract:
Object detection is widely used in various fields such as autonomous driving, industry, and medical care. Using the object detection algorithm to solve key tasks in different fields has gradually become the main method. However, the robustness of the object detection model based on deep learning is seriously insufficient under the attack of adversarial samples. It is easy to make the model prediction wrong by adding the adversarial samples constructed by small perturbations, which greatly limits the application of the object detection model in key security fields. In practical applications, the models are black-box models. Related research on black-box attacks against object detection models is relatively lacking, and there are many problems such as incomplete robustness evaluation, low attack success rate of black-box, and high resource consumption. To address the aforementioned issues, this study proposes a black-box object detection attack algorithm based on a generative adversarial network. The algorithm uses the generative network fused with an attention mechanism to output the adversarial perturbations and employs the alternative model loss and the category attention loss to optimize the generated network parameters, which can support two scenarios of target attack and vanish attack. A large number of experiments are conducted on the Pascal VOC and the MSCOCO datasets. The results demonstrate that the proposed method has a higher black-box transferable attack success rate and can perform transferable attacks between different datasets.
Object detection is widely used in various fields such as autonomous driving, industry, and medical care. Using the object detection algorithm to solve key tasks in different fields has gradually become the main method. However, the robustness of the object detection model based on deep learning is seriously insufficient under the attack of adversarial samples. It is easy to make the model prediction wrong by adding the adversarial samples constructed by small perturbations, which greatly limits the application of the object detection model in key security fields. In practical applications, the models are black-box models. Related research on black-box attacks against object detection models is relatively lacking, and there are many problems such as incomplete robustness evaluation, low attack success rate of black-box, and high resource consumption. To address the aforementioned issues, this study proposes a black-box object detection attack algorithm based on a generative adversarial network. The algorithm uses the generative network fused with an attention mechanism to output the adversarial perturbations and employs the alternative model loss and the category attention loss to optimize the generated network parameters, which can support two scenarios of target attack and vanish attack. A large number of experiments are conducted on the Pascal VOC and the MSCOCO datasets. The results demonstrate that the proposed method has a higher black-box transferable attack success rate and can perform transferable attacks between different datasets.
Available online: August 23, 2023 , DOI: 10.13328/j.cnki.jos.006941
Abstract:
The transport layer is a key component in the network protocol stack, which is responsible for providing end-to-end services for applications between different hosts. Existing transport layer protocols such as TCP provide users with some basic security protection mechanisms, e.g., error controls and acknowledgments, which ensures the consistency of datagrams sent and received by applications between different hosts to a certain extent. However, these security protection mechanisms of the transport layer have serious flaws. For example, the sequence number of TCP datagrams is easy to be guessed and inferred, and the calculation of the datagram’s checksum depends on the vulnerable sum of the complement algorithm. As a result, the existing transport layer security mechanisms cannot guarantee the integrity and security of the datagram, which allows a remote attacker to craft a fake datagram and inject it into the target network stream, thus poisoning the target network stream. The attack against the transport layer occurs at the basic layers of the network protocol stack, which can bypass the security protection mechanisms enforced at the upper application layer and thus cause serious damage to the network infrastructure. After investigating various attacks over network protocols and the related security vulnerabilities in recent years, this study proposes a method for enhancing the security of the transport layer? based on lightweight chain verification, namely LightCTL. Based on the hash verification, LightCTL enables both sides of a TCP connection to create a mutually verifiable consensus on transport layer datagrams, so as to prevent attackers or middlemen from stealing and forging sensitive information. As a result, LightCTL can successfully foil various attacks against the network protocol stack, including TCP connection reset attacks based on sequence number inferring, TCP hijacking attacks, SYN flooding attacks, man-in-the-middle attacks, and datagram replay attacks. Besides, LightCTL does not need to modify the protocol stack of intermediate network devices such as routers. It only needs to modify the checksum and the related parts of the end protocol stack. Therefore, LightCTL can be easily deployed and significantly improves the security of network systems.
The transport layer is a key component in the network protocol stack, which is responsible for providing end-to-end services for applications between different hosts. Existing transport layer protocols such as TCP provide users with some basic security protection mechanisms, e.g., error controls and acknowledgments, which ensures the consistency of datagrams sent and received by applications between different hosts to a certain extent. However, these security protection mechanisms of the transport layer have serious flaws. For example, the sequence number of TCP datagrams is easy to be guessed and inferred, and the calculation of the datagram’s checksum depends on the vulnerable sum of the complement algorithm. As a result, the existing transport layer security mechanisms cannot guarantee the integrity and security of the datagram, which allows a remote attacker to craft a fake datagram and inject it into the target network stream, thus poisoning the target network stream. The attack against the transport layer occurs at the basic layers of the network protocol stack, which can bypass the security protection mechanisms enforced at the upper application layer and thus cause serious damage to the network infrastructure. After investigating various attacks over network protocols and the related security vulnerabilities in recent years, this study proposes a method for enhancing the security of the transport layer? based on lightweight chain verification, namely LightCTL. Based on the hash verification, LightCTL enables both sides of a TCP connection to create a mutually verifiable consensus on transport layer datagrams, so as to prevent attackers or middlemen from stealing and forging sensitive information. As a result, LightCTL can successfully foil various attacks against the network protocol stack, including TCP connection reset attacks based on sequence number inferring, TCP hijacking attacks, SYN flooding attacks, man-in-the-middle attacks, and datagram replay attacks. Besides, LightCTL does not need to modify the protocol stack of intermediate network devices such as routers. It only needs to modify the checksum and the related parts of the end protocol stack. Therefore, LightCTL can be easily deployed and significantly improves the security of network systems.
Available online: August 23, 2023 , DOI: 10.13328/j.cnki.jos.006951
Abstract:
Fact verification is intended to check whether a textual statement is supported by a given piece of evidence. Due to the structural dependence and implicit content of tables, the task of fact verification with tables as the evidence still faces many challenges. Existing literature has either used logical expressions to parse statements based on tabular evidence or designed table-aware neural networks to encode statement-table pairs and thereby accomplish table-based fact verification tasks. However, these approaches fail to fully utilize the implicit tabular information behind the statements, which leads to the degraded inference performance of the model. Moreover, Chinese statements based on tabular evidence have more complex syntax and semantics, which also adds to the difficulties in model inference. For this reason, the study proposes a method of fact verification with Chinese tabular data based on the capsule heterogeneous graph attention network (CapsHAN). This method can fully understand the structure and semantics of statements. On this basis, the tabular information implied by the statements is mined and utilized to effectively improve the accuracy of table-based fact verification tasks. Specifically, a heterogeneous graph is constructed by performing syntactic dependency parsing and named entity recognition of statements. Subsequently, the graph is learned and understood by the heterogeneous graph attention network and the capsule graph neural network, and the obtained textual representation of the statements is sliced together with the textual representation of the encoded tables. Finally, the result is predicted. Further, this study also attempts to address the problem that the datasets of fact verification based on Chinese tables are scarce and thus unable to support the performance evaluation of table-based fact verification methods. For this purpose, the study transforms the mainstream English table-based fact verification datasets TABFACT and INFOTABS into Chinese and constructs a dataset that is based on the uniform content label (UCL) national standard and specifically tailored to the characteristics of Chinese tabular data. This dataset, namely, UCLDS, takes Wikipedia infoboxes as evidence of manually annotated natural language statements and labels them into three classes: entailed, contradictory, and neutral. UCLDS outperforms the traditional datasets TABFACT and INFOTABS in supporting both single-table and multi-table inference. The experimental results on the above three Chinese benchmark datasets show that the proposed model outperforms the baseline model invariably, demonstrating its superiority for Chinese table-based fact verification tasks.
Fact verification is intended to check whether a textual statement is supported by a given piece of evidence. Due to the structural dependence and implicit content of tables, the task of fact verification with tables as the evidence still faces many challenges. Existing literature has either used logical expressions to parse statements based on tabular evidence or designed table-aware neural networks to encode statement-table pairs and thereby accomplish table-based fact verification tasks. However, these approaches fail to fully utilize the implicit tabular information behind the statements, which leads to the degraded inference performance of the model. Moreover, Chinese statements based on tabular evidence have more complex syntax and semantics, which also adds to the difficulties in model inference. For this reason, the study proposes a method of fact verification with Chinese tabular data based on the capsule heterogeneous graph attention network (CapsHAN). This method can fully understand the structure and semantics of statements. On this basis, the tabular information implied by the statements is mined and utilized to effectively improve the accuracy of table-based fact verification tasks. Specifically, a heterogeneous graph is constructed by performing syntactic dependency parsing and named entity recognition of statements. Subsequently, the graph is learned and understood by the heterogeneous graph attention network and the capsule graph neural network, and the obtained textual representation of the statements is sliced together with the textual representation of the encoded tables. Finally, the result is predicted. Further, this study also attempts to address the problem that the datasets of fact verification based on Chinese tables are scarce and thus unable to support the performance evaluation of table-based fact verification methods. For this purpose, the study transforms the mainstream English table-based fact verification datasets TABFACT and INFOTABS into Chinese and constructs a dataset that is based on the uniform content label (UCL) national standard and specifically tailored to the characteristics of Chinese tabular data. This dataset, namely, UCLDS, takes Wikipedia infoboxes as evidence of manually annotated natural language statements and labels them into three classes: entailed, contradictory, and neutral. UCLDS outperforms the traditional datasets TABFACT and INFOTABS in supporting both single-table and multi-table inference. The experimental results on the above three Chinese benchmark datasets show that the proposed model outperforms the baseline model invariably, demonstrating its superiority for Chinese table-based fact verification tasks.
Available online: August 23, 2023 , DOI: 10.13328/j.cnki.jos.006914
Abstract:
The training of high-precision federated learning models consumes a large number of users’ local resources. The users who participate in the training can gain illegal profits by selling the jointly trained model without others’ permission. In order to protect the property rights of federated learning models, this study proposes a federated learning watermark based on backdoor (FLWB) by using the feature that deep learning backdoor technology maintains the accuracy of main tasks and only causes misclassification in a small number of trigger set samples. FLWB allows users who participate in the training to embed their own private watermarks in the local model and then map the private backdoor watermarks to the global model through the model aggregation in the cloud as the global watermark for federated learning. Then a stepwise training method is designed to enhance the expression effect of private backdoor watermarks in the global model so that FLWB can accommodate the private watermarks of the users without affecting the accuracy of the global model. Theoretical analysis proves the security of FLWB, and experiments verify that the global model can effectively accommodate the private watermarks of the users who participate in the training by only causing an accuracy loss of 1% of the main tasks through the stepwise training method. Finally, FLWB is tested by model compression and fine-tuning attacks. The results show that more than 80% of the watermarks can be retained when the model is compressed to 30% by FLWB, and more than 90% of the watermarks can be retained under four different fine-tuning attacks, which indicates the excellent robustness of FLWB.
The training of high-precision federated learning models consumes a large number of users’ local resources. The users who participate in the training can gain illegal profits by selling the jointly trained model without others’ permission. In order to protect the property rights of federated learning models, this study proposes a federated learning watermark based on backdoor (FLWB) by using the feature that deep learning backdoor technology maintains the accuracy of main tasks and only causes misclassification in a small number of trigger set samples. FLWB allows users who participate in the training to embed their own private watermarks in the local model and then map the private backdoor watermarks to the global model through the model aggregation in the cloud as the global watermark for federated learning. Then a stepwise training method is designed to enhance the expression effect of private backdoor watermarks in the global model so that FLWB can accommodate the private watermarks of the users without affecting the accuracy of the global model. Theoretical analysis proves the security of FLWB, and experiments verify that the global model can effectively accommodate the private watermarks of the users who participate in the training by only causing an accuracy loss of 1% of the main tasks through the stepwise training method. Finally, FLWB is tested by model compression and fine-tuning attacks. The results show that more than 80% of the watermarks can be retained when the model is compressed to 30% by FLWB, and more than 90% of the watermarks can be retained under four different fine-tuning attacks, which indicates the excellent robustness of FLWB.
YANG Lan-Xin, ZHANG He, XU Jin-Wei, ZHANG Yi-Fan, WANG Zi-Kuan, ZHOU Xin, LI Jing-Yue, RONG Guo-Ping
Available online: August 16, 2023 , DOI: 10.13328/j.cnki.jos.006904
Abstract:
Code review is one of the best practices widely used in modern software development, which is crucial for ensuring software quality and strengthening engineering capability. Code review comments (CRCs) are one of the main and most important outputs of code reviews. CRCs are not only the reviewers’ perceptions of code quality but also the references for authors to fix code defects and improve quality. Nowadays, although a number of software organizations have developed guidelines for performing code reviews, there are still few effective methods for evaluating the quality of CRCs. To provide an explainable and automated quality evaluation of CRCs, this study conducts a series of empirical studies such as literature reviews and case analyses. Based on the results of the empirical studies, the study proposes a multi-label learning-based approach for evaluating the quality of CRCs. Experiments are carried out by using a large software enterprise-specific dataset that includes a total of 17 000 CRCs from 34 commercial projects. The results indicate that the proposed approach can effectively evaluate the quality attributes and grades of CRCs. The study also provides some modeling experiences such as CRC labeling and verification, so as to help software organizations struggling with code reviews better implement the proposed approach.
Code review is one of the best practices widely used in modern software development, which is crucial for ensuring software quality and strengthening engineering capability. Code review comments (CRCs) are one of the main and most important outputs of code reviews. CRCs are not only the reviewers’ perceptions of code quality but also the references for authors to fix code defects and improve quality. Nowadays, although a number of software organizations have developed guidelines for performing code reviews, there are still few effective methods for evaluating the quality of CRCs. To provide an explainable and automated quality evaluation of CRCs, this study conducts a series of empirical studies such as literature reviews and case analyses. Based on the results of the empirical studies, the study proposes a multi-label learning-based approach for evaluating the quality of CRCs. Experiments are carried out by using a large software enterprise-specific dataset that includes a total of 17 000 CRCs from 34 commercial projects. The results indicate that the proposed approach can effectively evaluate the quality attributes and grades of CRCs. The study also provides some modeling experiences such as CRC labeling and verification, so as to help software organizations struggling with code reviews better implement the proposed approach.
Available online: August 09, 2023 , DOI: 10.13328/j.cnki.jos.006915
Abstract:
Driven by mature data mining technologies, the recommendation system has been able to efficiently utilize explicit and implicit information such as score data and behavior traces and then combine the information with complex and advanced deep learning technologies to achieve sound results. Meanwhile, its application requirements also drive the in-depth mining and utilization of basic data and the load reduction of technical requirements to become research hotspots. On this basis, a lightweight recommendation model, namely LG_APIF is proposed, which uses the graph convolutional network (GCN) method to deeply integrate information. According to behavior memory, the model employs Ebbinghaus forgetting curve to simulate the users’ interest change process and adopts linear regression and other relatively lightweight traditional methods to mine adaptive periods and other depth information of items. In addition, it analyzes users’ current interest distribution and calculates the interest value of the item to obtain users’ potential interest type. It further constructs the graph structure of the user-type-item triplet and uses GCN technology after load reduction to generate the final item recommendation list. The experiments have verified the effectiveness of the proposed method. Through the comparison with eight classical models on the datasets of Last.fm, Douban, Yelp, and MovieLens, it is found that the Precision, Recall, and NDCG of the proposed method are improved, with an average improvement of 2.11% on Precision, 1.01% on Recall, and 1.48% on NDCG, respectively.
Driven by mature data mining technologies, the recommendation system has been able to efficiently utilize explicit and implicit information such as score data and behavior traces and then combine the information with complex and advanced deep learning technologies to achieve sound results. Meanwhile, its application requirements also drive the in-depth mining and utilization of basic data and the load reduction of technical requirements to become research hotspots. On this basis, a lightweight recommendation model, namely LG_APIF is proposed, which uses the graph convolutional network (GCN) method to deeply integrate information. According to behavior memory, the model employs Ebbinghaus forgetting curve to simulate the users’ interest change process and adopts linear regression and other relatively lightweight traditional methods to mine adaptive periods and other depth information of items. In addition, it analyzes users’ current interest distribution and calculates the interest value of the item to obtain users’ potential interest type. It further constructs the graph structure of the user-type-item triplet and uses GCN technology after load reduction to generate the final item recommendation list. The experiments have verified the effectiveness of the proposed method. Through the comparison with eight classical models on the datasets of Last.fm, Douban, Yelp, and MovieLens, it is found that the Precision, Recall, and NDCG of the proposed method are improved, with an average improvement of 2.11% on Precision, 1.01% on Recall, and 1.48% on NDCG, respectively.
Available online: August 09, 2023 , DOI: 10.13328/j.cnki.jos.006921
Abstract:
With the development of modern information technology, people’s demand for high resolution and realistic visual perception of image display devices has increased, which has put forward higher requirements for computer software and hardware and brought many challenges to rendering technology in terms of performance and workload. Using machine learning technologies such as deep neural networks to improve the quality and performance of rendered images has become a popular research method in computer graphics, while upsampling low-resolution images through network inference to obtain clearer high-resolution images is an important way to improve image generation performance and ensure high-resolution details. The geometry buffers (G-buffers) generated by the rendering engine in the rendering process contain much semantic information, which help the network learn scene information and features effectively and then improve the quality of upsampling results. In this study, a super-resolution method for rendered contents in low resolution based on deep neural networks is designed. In addition to the color image of the current frame, the method uses high-resolution G-buffers to assist in the calculation and reconstruct the high-resolution content details. The method also leverages a new strategy to fuse the features of high-resolution buffers and low-resolution images, which implements a multi-scale fusion of different feature information in a specific fusion module. Experiments demonstrate the effectiveness of the proposed fusion strategy and module, and the proposed method shows obvious advantages, especially in maintaining high-resolution details, when compared with other image super-resolution methods.
With the development of modern information technology, people’s demand for high resolution and realistic visual perception of image display devices has increased, which has put forward higher requirements for computer software and hardware and brought many challenges to rendering technology in terms of performance and workload. Using machine learning technologies such as deep neural networks to improve the quality and performance of rendered images has become a popular research method in computer graphics, while upsampling low-resolution images through network inference to obtain clearer high-resolution images is an important way to improve image generation performance and ensure high-resolution details. The geometry buffers (G-buffers) generated by the rendering engine in the rendering process contain much semantic information, which help the network learn scene information and features effectively and then improve the quality of upsampling results. In this study, a super-resolution method for rendered contents in low resolution based on deep neural networks is designed. In addition to the color image of the current frame, the method uses high-resolution G-buffers to assist in the calculation and reconstruct the high-resolution content details. The method also leverages a new strategy to fuse the features of high-resolution buffers and low-resolution images, which implements a multi-scale fusion of different feature information in a specific fusion module. Experiments demonstrate the effectiveness of the proposed fusion strategy and module, and the proposed method shows obvious advantages, especially in maintaining high-resolution details, when compared with other image super-resolution methods.
Available online: August 09, 2023 , DOI: 10.13328/j.cnki.jos.006922
Abstract:
SMT solver is an important system software. Therefore, bugs in the SMT solver may lead to the function failure of software relying on it and even bring security incidents. However, fixing bugs in the SMT solver is time-consuming because developers need to spend a lot of effort in understanding and finding the root cause of the bugs. Although many studies on software bug localization have been proposed, there is no systematic work to automatically locate bugs in the SMT solver. Therefore, this study proposes a bug localization method for the SMT solver based on multi-source spectrums, namely SMTLOC. First, for a given bug in the SMT solver, SMTLOC proposes an enumeration-based algorithm to mutate the formula that triggers the bug by generating a set of witness formulas that will not trigger the bug but has a similar execution trace with the formula that triggers the corresponding bug. Then, according to the execution trace of the witness formulas and the source code information of the SMT solver, SMTLOC develops a technique based on the coverage spectrum and historical spectrum to calculate the suspiciousness of files, thus locating the files that contain the bug. In order to evaluate the effectiveness of SMTLOC, 60 bugs in the SMT solver are collected. Experimental results show that SMTLOC is superior to the traditional spectrum bug localization method and can locate 46.67% of the bugs in TOP-5 files, and the localization effect is improved by 133.33%.
SMT solver is an important system software. Therefore, bugs in the SMT solver may lead to the function failure of software relying on it and even bring security incidents. However, fixing bugs in the SMT solver is time-consuming because developers need to spend a lot of effort in understanding and finding the root cause of the bugs. Although many studies on software bug localization have been proposed, there is no systematic work to automatically locate bugs in the SMT solver. Therefore, this study proposes a bug localization method for the SMT solver based on multi-source spectrums, namely SMTLOC. First, for a given bug in the SMT solver, SMTLOC proposes an enumeration-based algorithm to mutate the formula that triggers the bug by generating a set of witness formulas that will not trigger the bug but has a similar execution trace with the formula that triggers the corresponding bug. Then, according to the execution trace of the witness formulas and the source code information of the SMT solver, SMTLOC develops a technique based on the coverage spectrum and historical spectrum to calculate the suspiciousness of files, thus locating the files that contain the bug. In order to evaluate the effectiveness of SMTLOC, 60 bugs in the SMT solver are collected. Experimental results show that SMTLOC is superior to the traditional spectrum bug localization method and can locate 46.67% of the bugs in TOP-5 files, and the localization effect is improved by 133.33%.
Available online: August 09, 2023 , DOI: 10.13328/j.cnki.jos.006912
Abstract:
Adaptor signature, also known as scriptless script, is an important cryptographic technique that can be used to solve the problems of poor scalability and low transaction throughput in blockchain applications such as cryptocurrency. An adaptor signature can be seen as an extension of a digital signature on hard relations, and it ties together the authorization with witness extraction and has many advantages in blockchain applications, such as (1) low on-chain cost; (2) improved fungibility of transactions; (3) advanced functionality beyond the limitation of the blockchain’s scripting language. SM2 signature is the Chinese national standard signature algorithm and has been widely used in various important information systems. This work designs an efficient SM2-based adaptor signature with batch proofs and gives security proofs under the random oracle model. The scheme avoids to generate zero-knowledge proofs used in the pre-signing phase based on the structure of SM2 signature and is more efficient than existing ECDSA/SM2-based adaptor signature. Specifically, the efficiency of pre-signature generation is increased by 4 times, and the efficiency of pre-signature verification is increased by 3 times. Then, based on distributed SM2 signature, this work develops distributed SM2-based adaptor signature which can avoid the single point of failure and improve the security of signing key. Finally, in real-world applications, this work gives a secure and efficient batch atomic swap protocol for one-to-many scenarios based on SM2-based adaptor signature.
Adaptor signature, also known as scriptless script, is an important cryptographic technique that can be used to solve the problems of poor scalability and low transaction throughput in blockchain applications such as cryptocurrency. An adaptor signature can be seen as an extension of a digital signature on hard relations, and it ties together the authorization with witness extraction and has many advantages in blockchain applications, such as (1) low on-chain cost; (2) improved fungibility of transactions; (3) advanced functionality beyond the limitation of the blockchain’s scripting language. SM2 signature is the Chinese national standard signature algorithm and has been widely used in various important information systems. This work designs an efficient SM2-based adaptor signature with batch proofs and gives security proofs under the random oracle model. The scheme avoids to generate zero-knowledge proofs used in the pre-signing phase based on the structure of SM2 signature and is more efficient than existing ECDSA/SM2-based adaptor signature. Specifically, the efficiency of pre-signature generation is increased by 4 times, and the efficiency of pre-signature verification is increased by 3 times. Then, based on distributed SM2 signature, this work develops distributed SM2-based adaptor signature which can avoid the single point of failure and improve the security of signing key. Finally, in real-world applications, this work gives a secure and efficient batch atomic swap protocol for one-to-many scenarios based on SM2-based adaptor signature.
Available online: August 09, 2023 , DOI: 10.13328/j.cnki.jos.006905
Abstract:
Machine learning methods can be well combined with software testing to enhance test effect, but few scholars have applied it to test data generation. In order to further improve the efficiency of test data generation, a chained model combining support vector machine (SVM) and extreme gradient boosting (XGBoost) is proposed, and multi-path test data generation is realized by a genetic algorithm based on the chained model. Firstly, this study uses certain samples to train several sub-models (i.e., SVM and XGBoost) for predicting the state of path nodes, filters the optimal sub-models based on the prediction accuracy value of the sub-models, and links the optimal sub-models in sequence according to the order of the path nodes, so as to form a chained model, namely chained SVM and XGBoost (C-SVMXGBoost). When using the genetic algorithm to generate test cases, the study makes use of the chained model that is trained instead of the instrumentation method to obtain the test data coverage path (i.e., predicted path), finds the path set with the predicted path similar to the target path, performs instrumentation verification on the predicted path with similar path sets, obtains accurate paths, and calculates fitness values. In the crossover and mutation process, excellent test cases with a large path level depth in the sample set are introduced for reuse to generate test data covering the target path. Finally, individuals with higher fitness during the evolutionary generation are saved, and C-SVMXGBoost is updated, so as to further improve the test efficiency. Experiments show that C-SVMXGBoost is more suitable for solving the path prediction problem and improving the test efficiency than other chained models. Moreover, compared with the existing classical methods, the proposed method can increase the coverage rate by up to 15%. The mean evolutionary algebra is also reduced, and the reduction percentage can reach 65% on programs of large size.
Machine learning methods can be well combined with software testing to enhance test effect, but few scholars have applied it to test data generation. In order to further improve the efficiency of test data generation, a chained model combining support vector machine (SVM) and extreme gradient boosting (XGBoost) is proposed, and multi-path test data generation is realized by a genetic algorithm based on the chained model. Firstly, this study uses certain samples to train several sub-models (i.e., SVM and XGBoost) for predicting the state of path nodes, filters the optimal sub-models based on the prediction accuracy value of the sub-models, and links the optimal sub-models in sequence according to the order of the path nodes, so as to form a chained model, namely chained SVM and XGBoost (C-SVMXGBoost). When using the genetic algorithm to generate test cases, the study makes use of the chained model that is trained instead of the instrumentation method to obtain the test data coverage path (i.e., predicted path), finds the path set with the predicted path similar to the target path, performs instrumentation verification on the predicted path with similar path sets, obtains accurate paths, and calculates fitness values. In the crossover and mutation process, excellent test cases with a large path level depth in the sample set are introduced for reuse to generate test data covering the target path. Finally, individuals with higher fitness during the evolutionary generation are saved, and C-SVMXGBoost is updated, so as to further improve the test efficiency. Experiments show that C-SVMXGBoost is more suitable for solving the path prediction problem and improving the test efficiency than other chained models. Moreover, compared with the existing classical methods, the proposed method can increase the coverage rate by up to 15%. The mean evolutionary algebra is also reduced, and the reduction percentage can reach 65% on programs of large size.
Available online: July 28, 2023 , DOI: 10.13328/j.cnki.jos.006892
Abstract:
The committee consensus and hybrid consensus elect the committee to replace the whole nodes for block validation, which can effectively speed up consensus and improve throughput. However, malicious attacks and bribes can easily lead to committee corruption, affect consensus results, and even cause system paralysis. Although the existing work proposes the reputation mechanism to reduce the possibility of committee corruption, it has high overhead and poor reliability and cannot reduce the impact of corruption on the system. Therefore, this study proposes a dynamic blockchain consensus with pre-validation (DBCP). DBCP realizes reliable reputation evaluation of the committee through pre-validation with little overhead, which can eliminate malicious nodes from the committee in time. If serious corruption has undermined the consensus result, DBCP will transfer the authority of block validation to the whole nodes through dynamic consensus and eliminate the committee nodes that give wrong suggestions to avoid system paralysis. When the committee iterates to the high-credibility state, DBCP will hand over the authority of block validation to the committee, and the whole nodes will accept the consensus result from the committee without verifying the block to speed up the consensus. The experimental results show that the throughput of DBCP is two orders of magnitude higher than that of Bitcoin and similar to that of Byzcoin. In addition, DBCP can quickly deal with committee corruption within a block cycle, demonstrating better security than Byzcoin.
The committee consensus and hybrid consensus elect the committee to replace the whole nodes for block validation, which can effectively speed up consensus and improve throughput. However, malicious attacks and bribes can easily lead to committee corruption, affect consensus results, and even cause system paralysis. Although the existing work proposes the reputation mechanism to reduce the possibility of committee corruption, it has high overhead and poor reliability and cannot reduce the impact of corruption on the system. Therefore, this study proposes a dynamic blockchain consensus with pre-validation (DBCP). DBCP realizes reliable reputation evaluation of the committee through pre-validation with little overhead, which can eliminate malicious nodes from the committee in time. If serious corruption has undermined the consensus result, DBCP will transfer the authority of block validation to the whole nodes through dynamic consensus and eliminate the committee nodes that give wrong suggestions to avoid system paralysis. When the committee iterates to the high-credibility state, DBCP will hand over the authority of block validation to the committee, and the whole nodes will accept the consensus result from the committee without verifying the block to speed up the consensus. The experimental results show that the throughput of DBCP is two orders of magnitude higher than that of Bitcoin and similar to that of Byzcoin. In addition, DBCP can quickly deal with committee corruption within a block cycle, demonstrating better security than Byzcoin.
Available online: July 26, 2023 , DOI: 10.13328/j.cnki.jos.006918
Abstract:
Third-party library (TPL) detection is an upstream task in the domain of Android application security analysis, and its detection accuracy has a significant impact on its downstream tasks including malware detection, repackaged application detection, and privacy leakage detection. To improve detection accuracy and efficiency, this study proposes a package structure and signature-based TPL detection method, named LibPass, by leveraging the idea of pairwise comparison. LibPass combines primary module identification, TPL candidate identification, and fine-grained detection in a streamlined way. The primary module identification aims at improving detection efficiency by distinguishing the binary code of the main program from that of the introduced TPL. On this basis, a two-stage detection method consisting of TPL candidate identification and fine-grained detection is proposed. The TPL candidate identification leverages the stability of package structure features to deal with obfuscation of applications to improve detection accuracy and identifies candidate TPLs by rapidly comparing package structure signatures to reduce the number of pairwise comparisons, so as to improve the detection efficiency. The fine-grained detection accurately identifies the TPL of a specific version by a finer-grained but more costly pairwise comparison among candidate TPLs. In order to validate the performance and the efficiency of the detection method, three benchmark datasets are built to evaluate different detection capabilities, and experiments are conducted on these datasets. The experimental results are deeply analyzed in terms of detection performance, detection efficiency, and obfuscation resistance, and it is found that LibPass has high detection accuracy and efficiency and can deal with various common obfuscation operations.
Third-party library (TPL) detection is an upstream task in the domain of Android application security analysis, and its detection accuracy has a significant impact on its downstream tasks including malware detection, repackaged application detection, and privacy leakage detection. To improve detection accuracy and efficiency, this study proposes a package structure and signature-based TPL detection method, named LibPass, by leveraging the idea of pairwise comparison. LibPass combines primary module identification, TPL candidate identification, and fine-grained detection in a streamlined way. The primary module identification aims at improving detection efficiency by distinguishing the binary code of the main program from that of the introduced TPL. On this basis, a two-stage detection method consisting of TPL candidate identification and fine-grained detection is proposed. The TPL candidate identification leverages the stability of package structure features to deal with obfuscation of applications to improve detection accuracy and identifies candidate TPLs by rapidly comparing package structure signatures to reduce the number of pairwise comparisons, so as to improve the detection efficiency. The fine-grained detection accurately identifies the TPL of a specific version by a finer-grained but more costly pairwise comparison among candidate TPLs. In order to validate the performance and the efficiency of the detection method, three benchmark datasets are built to evaluate different detection capabilities, and experiments are conducted on these datasets. The experimental results are deeply analyzed in terms of detection performance, detection efficiency, and obfuscation resistance, and it is found that LibPass has high detection accuracy and efficiency and can deal with various common obfuscation operations.
Available online: July 26, 2023 , DOI: 10.13328/j.cnki.jos.006919
Abstract:
Memory error vulnerabilities (e.g., buffer overflow) are often caused by improper use of memory copy functions. The identification of memory copy functions in binary programs is beneficial for finding memory error vulnerabilities. However, current methods for identifying memory copy functions in binary programs mainly rely on static analysis to extract functions’ features, control flow, data flow, and other information, with a high false positive and false negative. This study proposes a novel technique, namely CPSeeker, based on hybrid static and dynamic analysis to improve the effectiveness of identifying memory copy functions. CPSeeker combines the advantages of static analysis and dynamic analysis, collects the global static information and local execution information of functions in stages, and fuses the extracted information to identify memory copy functions in binary programs. The experimental results show that CPSeeker outperforms the state-of-the-art BootStomp, SaTC, CPYFinder, and Gemini in identifying memory copy functions, despite its increased runtime consumption, and its F1 value reaches 0.96. Furthermore, CPSeeker is not affected by the compilation environment (compiler version, compiler type, and compiler optimization level). In addition, CPSeeker has a better performance in actual firmware tests.
Memory error vulnerabilities (e.g., buffer overflow) are often caused by improper use of memory copy functions. The identification of memory copy functions in binary programs is beneficial for finding memory error vulnerabilities. However, current methods for identifying memory copy functions in binary programs mainly rely on static analysis to extract functions’ features, control flow, data flow, and other information, with a high false positive and false negative. This study proposes a novel technique, namely CPSeeker, based on hybrid static and dynamic analysis to improve the effectiveness of identifying memory copy functions. CPSeeker combines the advantages of static analysis and dynamic analysis, collects the global static information and local execution information of functions in stages, and fuses the extracted information to identify memory copy functions in binary programs. The experimental results show that CPSeeker outperforms the state-of-the-art BootStomp, SaTC, CPYFinder, and Gemini in identifying memory copy functions, despite its increased runtime consumption, and its F1 value reaches 0.96. Furthermore, CPSeeker is not affected by the compilation environment (compiler version, compiler type, and compiler optimization level). In addition, CPSeeker has a better performance in actual firmware tests.
Available online: July 26, 2023 , DOI: 10.13328/j.cnki.jos.006920
Abstract:
The broad-learning-based dynamic fuzzy inference system (BL-DFIS) can automatically assemble simplified fuzzy rules and achieve high accuracy in classification tasks. However, when BL-DFIS works on large and complex datasets, it may generate too many fuzzy rules to achieve satisfactory identification accuracy, which adversely affects its interpretability. In order to circumvent such a bottleneck, a fuzzy neural network called feature-augmented random vector functional-link neural network (FA-RVFLNN) is proposed in this study to achieve excellent trade-off between classification performance and interpretability. In the proposed network, the RVFLNN with original data as input is taken as its primary structure, and BL-DFIS is taken as a performance supplement, which implies that FA-RVFLNN contains direct links to boost the performance of the whole system. The inference mechanism of the primary structure can be explained by a fuzzy logic operator (I-OR), owing to the use of Sigmoid activation functions in the enhancement nodes of this structure. Moreover, the original input data with clear meaning also help to explain the inference rules of the primary structure. With the support of direct links, FA-RVFLNN can learn more useful information through enhancement nodes, feature nodes, and fuzzy nodes. The experimental results indicate that FA-RVFLNN indeed eases the problem of rule explosion caused by excessive enhancement nodes in the primary structure and improves the interpretability of BL-DFIS therein (The average number of fuzzy rules is reduced by about 50%), and is still competitive in terms of generalization performance and network size.
The broad-learning-based dynamic fuzzy inference system (BL-DFIS) can automatically assemble simplified fuzzy rules and achieve high accuracy in classification tasks. However, when BL-DFIS works on large and complex datasets, it may generate too many fuzzy rules to achieve satisfactory identification accuracy, which adversely affects its interpretability. In order to circumvent such a bottleneck, a fuzzy neural network called feature-augmented random vector functional-link neural network (FA-RVFLNN) is proposed in this study to achieve excellent trade-off between classification performance and interpretability. In the proposed network, the RVFLNN with original data as input is taken as its primary structure, and BL-DFIS is taken as a performance supplement, which implies that FA-RVFLNN contains direct links to boost the performance of the whole system. The inference mechanism of the primary structure can be explained by a fuzzy logic operator (I-OR), owing to the use of Sigmoid activation functions in the enhancement nodes of this structure. Moreover, the original input data with clear meaning also help to explain the inference rules of the primary structure. With the support of direct links, FA-RVFLNN can learn more useful information through enhancement nodes, feature nodes, and fuzzy nodes. The experimental results indicate that FA-RVFLNN indeed eases the problem of rule explosion caused by excessive enhancement nodes in the primary structure and improves the interpretability of BL-DFIS therein (The average number of fuzzy rules is reduced by about 50%), and is still competitive in terms of generalization performance and network size.
Available online: July 26, 2023 , DOI: 10.13328/j.cnki.jos.006940
Abstract:
How to improve the accuracy of matching between natural language query input and highly structured programming language source code is a fundamental concern in code search. Accurate extraction of code features is one of the key challenges to improving matching accuracy. The semantics expressed by statements in codes is not only relevant to themselves but also to their contexts. The structural model of the code provides rich contextual information for understanding code functions. This study proposes a code search method based on function multigraph embedding. By using an early fusion strategy, the study fuses the data dependencies of code statements into a control flow graph and constructs a function multigraph to represent the code. The multigraph explicitly expresses the dependency relationships of indirect predecessor and successor nodes that are lacking in the control flow graph through data dependencies and enhances the contextual information of statement nodes. At the same time, in view of the edge heterogeneity of the multigraph, this study uses the relational graph convolutional network to extract the features of the code from the function multigraph. Experiments on a public dataset show that the proposed method can improve the MRR by more than 5% compared with the existing methods based on code text and structural models. The ablation experiments also show that the control flow graph contributes more to the search accuracy than the data dependence graph.
How to improve the accuracy of matching between natural language query input and highly structured programming language source code is a fundamental concern in code search. Accurate extraction of code features is one of the key challenges to improving matching accuracy. The semantics expressed by statements in codes is not only relevant to themselves but also to their contexts. The structural model of the code provides rich contextual information for understanding code functions. This study proposes a code search method based on function multigraph embedding. By using an early fusion strategy, the study fuses the data dependencies of code statements into a control flow graph and constructs a function multigraph to represent the code. The multigraph explicitly expresses the dependency relationships of indirect predecessor and successor nodes that are lacking in the control flow graph through data dependencies and enhances the contextual information of statement nodes. At the same time, in view of the edge heterogeneity of the multigraph, this study uses the relational graph convolutional network to extract the features of the code from the function multigraph. Experiments on a public dataset show that the proposed method can improve the MRR by more than 5% compared with the existing methods based on code text and structural models. The ablation experiments also show that the control flow graph contributes more to the search accuracy than the data dependence graph.
Available online: July 12, 2023 , DOI: 10.13328/j.cnki.jos.006910
Abstract:
Code search is an important research topic in natural language processing and software engineering. Developing efficient code search algorithms can significantly improve the code reuse and the working efficiency of software developers. The task of code search is to retrieve code fragments that meet the requirements from the massive code repository by taking the natural language describing the function of the code fragments as input. Although the sequence model-based code search method, namely DeepCS has achieved promising results, it cannot capture the deep semantics of the code. GraphSearchNet, a code search method based on graph embedding, can alleviate this problem, but it does not perform fine-grained matching on codes and texts and ignores the global relationship between code graphs and text graphs. To address the above limitations, this study proposes a code search method based on a relational graph convolutional network, which encodes the constructed text graphs and code graphs, performs fine-grained matching on text query and code fragments at the node level, and applies neural tensor networks to capture their global relationship. Experimental results on two public datasets show that the proposed method achieves higher search accuracy than state-of-the-art baseline models, namely DeepCS and GraphSearchNet.
Code search is an important research topic in natural language processing and software engineering. Developing efficient code search algorithms can significantly improve the code reuse and the working efficiency of software developers. The task of code search is to retrieve code fragments that meet the requirements from the massive code repository by taking the natural language describing the function of the code fragments as input. Although the sequence model-based code search method, namely DeepCS has achieved promising results, it cannot capture the deep semantics of the code. GraphSearchNet, a code search method based on graph embedding, can alleviate this problem, but it does not perform fine-grained matching on codes and texts and ignores the global relationship between code graphs and text graphs. To address the above limitations, this study proposes a code search method based on a relational graph convolutional network, which encodes the constructed text graphs and code graphs, performs fine-grained matching on text query and code fragments at the node level, and applies neural tensor networks to capture their global relationship. Experimental results on two public datasets show that the proposed method achieves higher search accuracy than state-of-the-art baseline models, namely DeepCS and GraphSearchNet.
Available online: July 05, 2023 , DOI: 10.13328/j.cnki.jos.006901
Abstract:
Hybrid transactional/analytical processing (HTAP) database systems have gained extensive acknowledgment of users due to their full processing support of the mixed workloads in one system, i.e., transactions and analytical queries. Most HTAP database systems tend to maintain multiple data versions or additional replicas to accomplish online analytical processing (OLAP) without downgrading the write performance of online transactional processing (OLTP). This leads to a consistency problem between the data of TP and AP versions. Meanwhile, HTAP database systems face the core challenge of achieving efficient data sharing under resource isolation, and the data-sharing model integrates the trade-off between business requirements for performance and data freshness. To systematically explain the data-sharing model and optimization strategies of existing HTAP database systems, this study first utilizes the consistency models to define the data-sharing model and classify the consistency models for HTAP data sharing into three categories, namely, linear consistency, sequential consistency, and session consistency, according to the differences between TP generated versions and AP query versions. After that, it takes a deep dive into the whole process of data-sharing models from three core issues, i.e., data-version number distribution, data version synchronization, and data version tracking, and provides the implementation methods of different consistency models. Furthermore, this study takes a dozen of classic and popular HTAP database systems as examples for an in-depth interpretation of the implementation methods. Finally, it summarizes and analyzes the optimization strategies of version synchronization, tracking, and recycling modules involved in the data-sharing process and predicts the optimization directions of the data-sharing models. It is concluded that the self-adaptability of the data synchronization scope, self-tuning of the data synchronization cycle, and freshness-bound constraint control under sequential consistency are the possible means for better performance of HTAP database systems and higher freshness.
Hybrid transactional/analytical processing (HTAP) database systems have gained extensive acknowledgment of users due to their full processing support of the mixed workloads in one system, i.e., transactions and analytical queries. Most HTAP database systems tend to maintain multiple data versions or additional replicas to accomplish online analytical processing (OLAP) without downgrading the write performance of online transactional processing (OLTP). This leads to a consistency problem between the data of TP and AP versions. Meanwhile, HTAP database systems face the core challenge of achieving efficient data sharing under resource isolation, and the data-sharing model integrates the trade-off between business requirements for performance and data freshness. To systematically explain the data-sharing model and optimization strategies of existing HTAP database systems, this study first utilizes the consistency models to define the data-sharing model and classify the consistency models for HTAP data sharing into three categories, namely, linear consistency, sequential consistency, and session consistency, according to the differences between TP generated versions and AP query versions. After that, it takes a deep dive into the whole process of data-sharing models from three core issues, i.e., data-version number distribution, data version synchronization, and data version tracking, and provides the implementation methods of different consistency models. Furthermore, this study takes a dozen of classic and popular HTAP database systems as examples for an in-depth interpretation of the implementation methods. Finally, it summarizes and analyzes the optimization strategies of version synchronization, tracking, and recycling modules involved in the data-sharing process and predicts the optimization directions of the data-sharing models. It is concluded that the self-adaptability of the data synchronization scope, self-tuning of the data synchronization cycle, and freshness-bound constraint control under sequential consistency are the possible means for better performance of HTAP database systems and higher freshness.
Available online: July 05, 2023 , DOI: 10.13328/j.cnki.jos.006906
Abstract:
Software product line testing is challenging. The similarity-based testing method can improve testing coverage and fault detection rate by increasing the diversity of test suites. Due to its excellent scalability and satisfactory testing effects, the method has become one of the most important test methods for software product lines. How to generate diverse test cases and how to maintain the diversity of test suites are two key issues in this test method. To handle the above issues, this study proposes a software product line test algorithm based on diverse SAT solvers and novelty search (NS). Specifically, the algorithm simultaneously uses two types of diverse SAT solvers to generate diverse test cases. In particular, in order to improve the diversity of stochastic local search SAT solvers, the study proposes a general strategy that is based on a probability vector to generate candidate solutions. Furthermore, two archiving strategies inspired by the idea of the NS algorithm are designed and applied to maintain both the global and local diversity of the test suites. Ablation and comparison experiments on 50 real software product lines verify the effectiveness of both the diverse SAT solvers and the two archiving strategies, as well as the superiority of the proposed algorithm over other state-of-the-art algorithms.
Software product line testing is challenging. The similarity-based testing method can improve testing coverage and fault detection rate by increasing the diversity of test suites. Due to its excellent scalability and satisfactory testing effects, the method has become one of the most important test methods for software product lines. How to generate diverse test cases and how to maintain the diversity of test suites are two key issues in this test method. To handle the above issues, this study proposes a software product line test algorithm based on diverse SAT solvers and novelty search (NS). Specifically, the algorithm simultaneously uses two types of diverse SAT solvers to generate diverse test cases. In particular, in order to improve the diversity of stochastic local search SAT solvers, the study proposes a general strategy that is based on a probability vector to generate candidate solutions. Furthermore, two archiving strategies inspired by the idea of the NS algorithm are designed and applied to maintain both the global and local diversity of the test suites. Ablation and comparison experiments on 50 real software product lines verify the effectiveness of both the diverse SAT solvers and the two archiving strategies, as well as the superiority of the proposed algorithm over other state-of-the-art algorithms.
Available online: July 05, 2023 , DOI: 10.13328/j.cnki.jos.006907
Abstract:
Business?process?execution language (BPEL) is an executable web service composition language. Compared with traditional programs, BPEL programs are significantly different in terms of programming models and execution modes. These new features make it challenging to locate and fix faults of BPEL programs detected during the testing process. In addition, fault fixing techniques developed for traditional software cannot be used for BPEL programs directly. This study proposes a fault fixing technique for BPEL programs based on template matching, namely BPELRepair from the perspective of mutation analysis. In order to overcome the high computational overhead of the mutation analysis-based fault fixing technique, a set of optimization strategies are proposed from three perspectives, namely patch generation, test case selection, and termination condition. A supporting tool is developed to improve the automation and efficiency of fault fixing for BPEL programs. An empirical study is used to evaluate the effectiveness of the proposed fault fixing technique and optimization strategies. The experimental results show that the proposed technique can successfully fix about 53% of faults of BPEL programs, and the proposed optimization strategies can significantly reduce the overhead in terms of search matching, patch program verification, test case execution, and fault fixing.
Business?process?execution language (BPEL) is an executable web service composition language. Compared with traditional programs, BPEL programs are significantly different in terms of programming models and execution modes. These new features make it challenging to locate and fix faults of BPEL programs detected during the testing process. In addition, fault fixing techniques developed for traditional software cannot be used for BPEL programs directly. This study proposes a fault fixing technique for BPEL programs based on template matching, namely BPELRepair from the perspective of mutation analysis. In order to overcome the high computational overhead of the mutation analysis-based fault fixing technique, a set of optimization strategies are proposed from three perspectives, namely patch generation, test case selection, and termination condition. A supporting tool is developed to improve the automation and efficiency of fault fixing for BPEL programs. An empirical study is used to evaluate the effectiveness of the proposed fault fixing technique and optimization strategies. The experimental results show that the proposed technique can successfully fix about 53% of faults of BPEL programs, and the proposed optimization strategies can significantly reduce the overhead in terms of search matching, patch program verification, test case execution, and fault fixing.
Available online: July 04, 2023 , DOI: 10.13328/j.cnki.jos.006836
Abstract:
Autonomous driving software based on deep neural networks (DNNs) has become the most popular solution. Like traditional software, DNN can also produce incorrect output or unexpected behaviors, and DNN-based autonomous driving software has caused serious accidents, which seriously threaten life and property safety. Therefore, how to effectively test DNN-based autonomous driving software has become an urgent problem. Since it is difficult to predict and understand the behavior of DNNs, traditional software testing methods are no longer applicable. Existing autonomous driving software testing methods are implemented byadding pixel-level perturbations to original images or modifying the whole image to generate test data. The generated test data are quite different from the real images, and the perturbation-based methods are difficult to be understood. To solve the above problem, this study proposes a test data generation method, namely interpretability analysis-based test data generation (IATG). Firstly, it uses the interpretation method for DNNs to generate visual explanations of decisions made by autonomous driving software and chooses objects in the original images that have significant impacts on the decisions. Then, it generates test data by replacing the chosen objects with other objects with the same semantics. The generated test data are more similar to the real image, and the process is more understandable. As an important part of the autonomous driving software’s decision-making module, the steering angle prediction model is used to conduct experiments. Experimental results show that the introduction of the interpretation method effectively enhances the ability of IATG to mislead the steering angle prediction model. Furthermore, when the misleading angle is the same, the test data generated by IATG are more similar to the real image than DeepTest; IATG has a stronger misleading ability than semSensFuzz, and the interpretation analysis based important object selection method of IATG can effectively improve the misleading ability of semSensFuzz.
Autonomous driving software based on deep neural networks (DNNs) has become the most popular solution. Like traditional software, DNN can also produce incorrect output or unexpected behaviors, and DNN-based autonomous driving software has caused serious accidents, which seriously threaten life and property safety. Therefore, how to effectively test DNN-based autonomous driving software has become an urgent problem. Since it is difficult to predict and understand the behavior of DNNs, traditional software testing methods are no longer applicable. Existing autonomous driving software testing methods are implemented byadding pixel-level perturbations to original images or modifying the whole image to generate test data. The generated test data are quite different from the real images, and the perturbation-based methods are difficult to be understood. To solve the above problem, this study proposes a test data generation method, namely interpretability analysis-based test data generation (IATG). Firstly, it uses the interpretation method for DNNs to generate visual explanations of decisions made by autonomous driving software and chooses objects in the original images that have significant impacts on the decisions. Then, it generates test data by replacing the chosen objects with other objects with the same semantics. The generated test data are more similar to the real image, and the process is more understandable. As an important part of the autonomous driving software’s decision-making module, the steering angle prediction model is used to conduct experiments. Experimental results show that the introduction of the interpretation method effectively enhances the ability of IATG to mislead the steering angle prediction model. Furthermore, when the misleading angle is the same, the test data generated by IATG are more similar to the real image than DeepTest; IATG has a stronger misleading ability than semSensFuzz, and the interpretation analysis based important object selection method of IATG can effectively improve the misleading ability of semSensFuzz.
Available online: June 28, 2023 , DOI: 10.13328/j.cnki.jos.006899
Abstract:
Knowledge space theory, which uses mathematical language for the knowledge evaluation and learning guide of learners, belongs to the research field of mathematical psychology. Skills and problems are the two basic elements of knowledge space, and an in-depth study of the relationship between them is the inherent requirement of knowledge state description and knowledge structure analysis. In the existing knowledge space theory, no explicit bi-directional mapping between skills and problems has been established, which makes it difficult to put forward a knowledge structure analysis model under intuitive conceptual meanings. Moreover, the partial order relationship between knowledge states has not been clearly obtained, which is not conducive to depicting the differences between knowledge states and planning the learning path of learners. In addition, the existing achievements mainly focus on the classical knowledge space, without considering the uncertainties of data in practical problems. To this end, this study introduces formal concept analysis and fuzzy sets into knowledge space theory and builds the fuzzy concept lattice models for knowledge structure analysis. Specifically, fuzzy concept lattice models of knowledge space and closure space are presented. Firstly, the fuzzy concept lattice of knowledge space is constructed, and it is proved that the extents of all concepts form a knowledge space by the upper bounds of any two concepts. The idea of granule description is introduced to define the skill-induced atomic granules of problems, whose combinations can help determine whether a combination of problems is a state in the knowledge space. On this basis, a method to obtain the fuzzy concepts in the knowledge space from the problem combinations is proposed. Secondly, the fuzzy concept lattice of closure space is established, and it is proved that the extents of all concepts form the closure space by the lower bounds of any two concepts. Similarly, the problem-induced atomic granules of skills are defined, and their combinations can help determine whether a skill combination is the skills required by a knowledge state in the closure space. In this way, a method to obtain the fuzzy concepts in the closure space from the skill combinations is presented. Finally, the effects of the number of problems, the number of skills, the filling factor, and the analysis scale on the sizes of knowledge space and closure space are analyzed by some experiments. The results show that the fuzzy concepts in the knowledge space are different from any existing concept and cannot be derived from other concepts. The fuzzy concepts in the closure space are attribute-oriented one-sided fuzzy concepts in essence. In the formal context of two-valued skills, there is one-to-one correspondence between the states in knowledge space and closure space, but this relationship does not hold in the formal context of fuzzy skills.
Knowledge space theory, which uses mathematical language for the knowledge evaluation and learning guide of learners, belongs to the research field of mathematical psychology. Skills and problems are the two basic elements of knowledge space, and an in-depth study of the relationship between them is the inherent requirement of knowledge state description and knowledge structure analysis. In the existing knowledge space theory, no explicit bi-directional mapping between skills and problems has been established, which makes it difficult to put forward a knowledge structure analysis model under intuitive conceptual meanings. Moreover, the partial order relationship between knowledge states has not been clearly obtained, which is not conducive to depicting the differences between knowledge states and planning the learning path of learners. In addition, the existing achievements mainly focus on the classical knowledge space, without considering the uncertainties of data in practical problems. To this end, this study introduces formal concept analysis and fuzzy sets into knowledge space theory and builds the fuzzy concept lattice models for knowledge structure analysis. Specifically, fuzzy concept lattice models of knowledge space and closure space are presented. Firstly, the fuzzy concept lattice of knowledge space is constructed, and it is proved that the extents of all concepts form a knowledge space by the upper bounds of any two concepts. The idea of granule description is introduced to define the skill-induced atomic granules of problems, whose combinations can help determine whether a combination of problems is a state in the knowledge space. On this basis, a method to obtain the fuzzy concepts in the knowledge space from the problem combinations is proposed. Secondly, the fuzzy concept lattice of closure space is established, and it is proved that the extents of all concepts form the closure space by the lower bounds of any two concepts. Similarly, the problem-induced atomic granules of skills are defined, and their combinations can help determine whether a skill combination is the skills required by a knowledge state in the closure space. In this way, a method to obtain the fuzzy concepts in the closure space from the skill combinations is presented. Finally, the effects of the number of problems, the number of skills, the filling factor, and the analysis scale on the sizes of knowledge space and closure space are analyzed by some experiments. The results show that the fuzzy concepts in the knowledge space are different from any existing concept and cannot be derived from other concepts. The fuzzy concepts in the closure space are attribute-oriented one-sided fuzzy concepts in essence. In the formal context of two-valued skills, there is one-to-one correspondence between the states in knowledge space and closure space, but this relationship does not hold in the formal context of fuzzy skills.
Available online: June 28, 2023 , DOI: 10.13328/j.cnki.jos.006895
Abstract:
The morphological changes in retina boundaries are important indicators of retinal diseases, and the subtle changes can be captured by images obtained by optical coherence tomography (OCT). The retinal layer boundary segmentation based on OCT images can assist in the clinical judgment of related diseases. In OCT images, due to the diverse morphological changes in retina boundaries, the key boundary-related information, such as contexts and saliency boundaries, is crucial to the judgment and segmentation of layer boundaries. However, existing segmentation methods lack the consideration of the above information, which results in incomplete and discontinuous boundaries. To solve the above problems, this study proposes a coarse-to-fine method for the segmentation of retinal layer boundary in OCT images based on the end-to-end deep neural networks and graph search (GS), which avoids the phenomenon of “faults” common in non-end-to-end methods. In coarse segmentation, the attention global residual network (AGR-Net), an end-to-end deep neural network, is proposed to extract the above key information in a more sufficient and effective way. Specifically, a global feature module (GFM) is designed to capture the global context information of OCT images by scanning from four directions of the images. After that, the channel attention module (CAM) and GFM are sequentially combined and embedded in the backbone network to realize saliency modeling of context information of the retina and its boundaries. This effort effectively solves the problem of wrong segmentation caused by retina deformation and insufficient information extraction in OCT images. In fine segmentation, a GS algorithm is adopted to remove isolated areas or holes from the coarse segmentation results obtained by AGR-Net. In this way, the boundary keeps a fixed topology, and it is continuous and smooth, which further optimizes the overall segmentation results and provides a more complete reference for medical clinical diagnosis. Finally, the performance of the proposed method is evaluated from different perspectives on two public datasets, and the method is compared with the latest methods. The comparative experiments show that the proposed method outperforms the existing methods in terms of segmentation accuracy and stability.
The morphological changes in retina boundaries are important indicators of retinal diseases, and the subtle changes can be captured by images obtained by optical coherence tomography (OCT). The retinal layer boundary segmentation based on OCT images can assist in the clinical judgment of related diseases. In OCT images, due to the diverse morphological changes in retina boundaries, the key boundary-related information, such as contexts and saliency boundaries, is crucial to the judgment and segmentation of layer boundaries. However, existing segmentation methods lack the consideration of the above information, which results in incomplete and discontinuous boundaries. To solve the above problems, this study proposes a coarse-to-fine method for the segmentation of retinal layer boundary in OCT images based on the end-to-end deep neural networks and graph search (GS), which avoids the phenomenon of “faults” common in non-end-to-end methods. In coarse segmentation, the attention global residual network (AGR-Net), an end-to-end deep neural network, is proposed to extract the above key information in a more sufficient and effective way. Specifically, a global feature module (GFM) is designed to capture the global context information of OCT images by scanning from four directions of the images. After that, the channel attention module (CAM) and GFM are sequentially combined and embedded in the backbone network to realize saliency modeling of context information of the retina and its boundaries. This effort effectively solves the problem of wrong segmentation caused by retina deformation and insufficient information extraction in OCT images. In fine segmentation, a GS algorithm is adopted to remove isolated areas or holes from the coarse segmentation results obtained by AGR-Net. In this way, the boundary keeps a fixed topology, and it is continuous and smooth, which further optimizes the overall segmentation results and provides a more complete reference for medical clinical diagnosis. Finally, the performance of the proposed method is evaluated from different perspectives on two public datasets, and the method is compared with the latest methods. The comparative experiments show that the proposed method outperforms the existing methods in terms of segmentation accuracy and stability.
Available online: June 28, 2023 , DOI: 10.13328/j.cnki.jos.006893
Abstract:
Deep neural networks (DNNs) have made remarkable achievements in many fields, but related studies show that they are vulnerable to adversarial examples. The gradient-based attack is a popular adversarial attack and has attracted wide attention. This study investigates the relationship between gradient-based adversarial attacks and numerical methods for solving ordinary differential equations (ODEs). In addition, it proposes a new adversarial attack based on Runge-Kutta (RK) method, a numerical method for solving ODEs. According to the prediction idea in the RK method, perturbations are added to the original examples first to construct predicted examples, and then the gradients of the loss functions with respect to the original and predicted examples are linearly combined to determine the perturbations to be added for the generation of adversarial examples. Different from the existing adversarial attacks, the proposed adversarial attack employs the prediction idea of the RK method to obtain the future gradient information (i.e., the gradient of the loss function with respect to the predicted examples) and uses it to determine the adversarial perturbations to be added. The proposed attack features good extensibility and can be easily applied to all available gradient-based attacks. Extensive experiments demonstrate that in contrast to the state-of-the-art gradient-based attacks, the proposed RK-based attack boasts higher success rates and better transferability.
Deep neural networks (DNNs) have made remarkable achievements in many fields, but related studies show that they are vulnerable to adversarial examples. The gradient-based attack is a popular adversarial attack and has attracted wide attention. This study investigates the relationship between gradient-based adversarial attacks and numerical methods for solving ordinary differential equations (ODEs). In addition, it proposes a new adversarial attack based on Runge-Kutta (RK) method, a numerical method for solving ODEs. According to the prediction idea in the RK method, perturbations are added to the original examples first to construct predicted examples, and then the gradients of the loss functions with respect to the original and predicted examples are linearly combined to determine the perturbations to be added for the generation of adversarial examples. Different from the existing adversarial attacks, the proposed adversarial attack employs the prediction idea of the RK method to obtain the future gradient information (i.e., the gradient of the loss function with respect to the predicted examples) and uses it to determine the adversarial perturbations to be added. The proposed attack features good extensibility and can be easily applied to all available gradient-based attacks. Extensive experiments demonstrate that in contrast to the state-of-the-art gradient-based attacks, the proposed RK-based attack boasts higher success rates and better transferability.
Available online: June 14, 2023 , DOI: 10.13328/j.cnki.jos.006896
Abstract:
The heterogeneous many-core architecture with an ultra-high energy efficiency ratio has become an important development trend of supercomputer architecture. However, the complexity of heterogeneous systems puts forward higher requirements for application development and optimization, and they face many technical challenges such as usability and programmability in the development process. The independently developed new-generation Sunway supercomputer is equipped with a homegrown heterogeneous many-core processor, SW26010Pro. To take full advantage of the performance of the new-generation many-core processors and support the development and optimization of emerging scientific computing applications, this study designs and implements an optimized compiler swLLVM oriented to the SW26010Pro platform. The compiler supports Athread and SDAA dual-mode heterogeneous programming models and provides multi-level storage hierarchy description and SIMD extensions for vector-like operations. In addition, it realizes control-flow vectorization, cost-based node combination, and compiler optimization for multi-level storage hierarchy according to the architecture characteristics of SW26010Pro. The experimental results show that the compiler optimization designed and implemented in this paper achieves significant performance improvements. The average speedup of control-flow vectorization and node combination and optimization is 1.23 and 1.11, respectively, and the memory access optimization achieves a maximum performance improvement of 2.49 times. Finally, a comprehensive evaluation of swLLVM is performed from multiple dimensions on the standard test set SPEC CPU2006. The results show that swLLVM reports an average increase of 9.04% in the performance of floating-point projects, 5.25% in overall performance, and 79.1% in compilation speed and an average decline of 0.12% in the performance of integer projects and 1.15% in the code size compared to SWGCC with the same optimization level.
The heterogeneous many-core architecture with an ultra-high energy efficiency ratio has become an important development trend of supercomputer architecture. However, the complexity of heterogeneous systems puts forward higher requirements for application development and optimization, and they face many technical challenges such as usability and programmability in the development process. The independently developed new-generation Sunway supercomputer is equipped with a homegrown heterogeneous many-core processor, SW26010Pro. To take full advantage of the performance of the new-generation many-core processors and support the development and optimization of emerging scientific computing applications, this study designs and implements an optimized compiler swLLVM oriented to the SW26010Pro platform. The compiler supports Athread and SDAA dual-mode heterogeneous programming models and provides multi-level storage hierarchy description and SIMD extensions for vector-like operations. In addition, it realizes control-flow vectorization, cost-based node combination, and compiler optimization for multi-level storage hierarchy according to the architecture characteristics of SW26010Pro. The experimental results show that the compiler optimization designed and implemented in this paper achieves significant performance improvements. The average speedup of control-flow vectorization and node combination and optimization is 1.23 and 1.11, respectively, and the memory access optimization achieves a maximum performance improvement of 2.49 times. Finally, a comprehensive evaluation of swLLVM is performed from multiple dimensions on the standard test set SPEC CPU2006. The results show that swLLVM reports an average increase of 9.04% in the performance of floating-point projects, 5.25% in overall performance, and 79.1% in compilation speed and an average decline of 0.12% in the performance of integer projects and 1.15% in the code size compared to SWGCC with the same optimization level.
Available online: June 07, 2023 , DOI: 10.13328/j.cnki.jos.006897
Abstract:
Multi-behavior recommendation aims to utilize interactive data from multiple behaviors of users to improve recommendation performance. Existing multi-behavior recommendation methods generally directly exploit the multi-behavior data for the shared initialized user representations and involve the mining of user preferences and modeling of relationships among different behaviors in the tasks. However, these methods ignore the data imbalance under different interactive behaviors (the amount of interactive data varies greatly among different behaviors) and the information loss caused by the adaptation to the above two tasks. User preferences refer to the interests that users exhibit in different behaviors (e.g., browsing preferences), and the relationship among behaviors indicates a potential conversion from one behavior to another behavior (e.g., the conversion from browsing to purchasing). In multi-behavior recommendation, the mining of user preferences and the modeling of relationships among different behaviors can be regarded as a two-stage task. On the basis of the above considerations, the model of two-stage learning for multi-behavior recommendation (TSL-MBR for short) is proposed, which decouples the above two tasks with a two-stage strategy. In particular, the model retains the end-to-end structure and learns the two tasks by alternating training with fixed parameters. The first stage is to model user preferences under different behaviors. In this stage, the interactive data from all behaviors (without distinction as to behavior type) are first used to model the global preferences of users to alleviate the problem of data sparsity to the greatest extent. Then, the interactive data of each behavior are used to refine the behavior-specific user preference (local preference) and thus lessen the influence of the data imbalance among different behaviors. The second stage is to model the relationships among different behaviors. In this stage, the mining of user preferences and modeling of relationships among different behaviors are decoupled to relieve the information loss problem caused by adaptation to the two tasks. This two-stage model significantly improves the system’s ability to predict target behaviors. Extensive experimental results show that TSL-MBR can substantially outperform the state-of-the-art baseline models, achieving 103.01% and 33.87% of relative gains on average over the best baseline on the Tmall and Beibei datasets, respectively.
Multi-behavior recommendation aims to utilize interactive data from multiple behaviors of users to improve recommendation performance. Existing multi-behavior recommendation methods generally directly exploit the multi-behavior data for the shared initialized user representations and involve the mining of user preferences and modeling of relationships among different behaviors in the tasks. However, these methods ignore the data imbalance under different interactive behaviors (the amount of interactive data varies greatly among different behaviors) and the information loss caused by the adaptation to the above two tasks. User preferences refer to the interests that users exhibit in different behaviors (e.g., browsing preferences), and the relationship among behaviors indicates a potential conversion from one behavior to another behavior (e.g., the conversion from browsing to purchasing). In multi-behavior recommendation, the mining of user preferences and the modeling of relationships among different behaviors can be regarded as a two-stage task. On the basis of the above considerations, the model of two-stage learning for multi-behavior recommendation (TSL-MBR for short) is proposed, which decouples the above two tasks with a two-stage strategy. In particular, the model retains the end-to-end structure and learns the two tasks by alternating training with fixed parameters. The first stage is to model user preferences under different behaviors. In this stage, the interactive data from all behaviors (without distinction as to behavior type) are first used to model the global preferences of users to alleviate the problem of data sparsity to the greatest extent. Then, the interactive data of each behavior are used to refine the behavior-specific user preference (local preference) and thus lessen the influence of the data imbalance among different behaviors. The second stage is to model the relationships among different behaviors. In this stage, the mining of user preferences and modeling of relationships among different behaviors are decoupled to relieve the information loss problem caused by adaptation to the two tasks. This two-stage model significantly improves the system’s ability to predict target behaviors. Extensive experimental results show that TSL-MBR can substantially outperform the state-of-the-art baseline models, achieving 103.01% and 33.87% of relative gains on average over the best baseline on the Tmall and Beibei datasets, respectively.
Available online: May 24, 2023 , DOI: 10.13328/j.cnki.jos.006894
Abstract:
Deep learning has achieved great success in image classification, natural language processing, and speech recognition. Data augmentation can effectively increase the scale and diversity of training data, thereby improving the generalization of deep learning models. However, for a given dataset, a well-designed data augmentation strategy relies heavily on expert experience and domain knowledge and requires repeated attempts, which is time-consuming and labor-intensive. In recent years, automated data augmentation has attracted widespread attention from the academic community and the industry through the automated design of data augmentation strategies. To solve the problem that existing automated data augmentation algorithms cannot strike a good balance between prediction accuracy and search efficiency, this study proposes an efficient automated data augmentation algorithm SGES AA based on a self-guided evolution strategy. First, an effective continuous vector representation method is designed for the data augmentation strategy, and then the automated data augmentation problem is converted into a search problem of continuous strategy vectors. Second, a strategy vector search method based on the self-guided evolution strategy is presented. By introducing historical estimation gradient information to guide the sampling and updating of exploration points, it can effectively avoid the local optimal solution while improving the convergence of the search process. The results of extensive experiments on image, text, and speech datasets show that the proposed algorithm is superior to or matches the current optimal automated data augmentation methods without significantly increasing the time consumption of searches.
Deep learning has achieved great success in image classification, natural language processing, and speech recognition. Data augmentation can effectively increase the scale and diversity of training data, thereby improving the generalization of deep learning models. However, for a given dataset, a well-designed data augmentation strategy relies heavily on expert experience and domain knowledge and requires repeated attempts, which is time-consuming and labor-intensive. In recent years, automated data augmentation has attracted widespread attention from the academic community and the industry through the automated design of data augmentation strategies. To solve the problem that existing automated data augmentation algorithms cannot strike a good balance between prediction accuracy and search efficiency, this study proposes an efficient automated data augmentation algorithm SGES AA based on a self-guided evolution strategy. First, an effective continuous vector representation method is designed for the data augmentation strategy, and then the automated data augmentation problem is converted into a search problem of continuous strategy vectors. Second, a strategy vector search method based on the self-guided evolution strategy is presented. By introducing historical estimation gradient information to guide the sampling and updating of exploration points, it can effectively avoid the local optimal solution while improving the convergence of the search process. The results of extensive experiments on image, text, and speech datasets show that the proposed algorithm is superior to or matches the current optimal automated data augmentation methods without significantly increasing the time consumption of searches.
Available online: December 30, 2022 , DOI: 10.13328/j.cnki.jos.006804
Abstract:
Stochastic configuration network (SCN), as an emerging incremental neural network model, is different from other randomized neural network methods. It can configure the parameters of hidden layer nodes through supervision mechanisms, thereby ensuring the fast convergence performance of SCN. Due to the advantages of high learning efficiency, low human intervention, and strong generalization ability, SCN has attracted a large number of national and international scholars and developed rapidly since it was proposed in 2017. In this study, SCN research is summarized from the aspects of basic theories, typical algorithm variants, application fields, and future research directions of SCN. Firstly, the algorithm principles, universal approximation capacity, and advantages of SCN are analyzed theoretically. Secondly, typical variants of SCN are studied, such as DeepSCN, 2DSCN, Robust SCN, Ensemble SCN, Distributed SCN, Parallel SCN, and Regularized SCN. Then, the applications of SCN in different fields, including hardware implementation, computer vision, medical data analysis, fault detection and diagnosis, and system modeling and prediction are introduced. Finally, the development potential of SCN in convolutional neural network architectures, semi-supervised learning, unsupervised learning, multi-view learning, fuzzy neural network, and recurrent neural network is pointed out.
Stochastic configuration network (SCN), as an emerging incremental neural network model, is different from other randomized neural network methods. It can configure the parameters of hidden layer nodes through supervision mechanisms, thereby ensuring the fast convergence performance of SCN. Due to the advantages of high learning efficiency, low human intervention, and strong generalization ability, SCN has attracted a large number of national and international scholars and developed rapidly since it was proposed in 2017. In this study, SCN research is summarized from the aspects of basic theories, typical algorithm variants, application fields, and future research directions of SCN. Firstly, the algorithm principles, universal approximation capacity, and advantages of SCN are analyzed theoretically. Secondly, typical variants of SCN are studied, such as DeepSCN, 2DSCN, Robust SCN, Ensemble SCN, Distributed SCN, Parallel SCN, and Regularized SCN. Then, the applications of SCN in different fields, including hardware implementation, computer vision, medical data analysis, fault detection and diagnosis, and system modeling and prediction are introduced. Finally, the development potential of SCN in convolutional neural network architectures, semi-supervised learning, unsupervised learning, multi-view learning, fuzzy neural network, and recurrent neural network is pointed out.
Abstract:
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表Proceedings of the 11th Joint Meeting of the European Software Engineering Conference and the ACM SigSoft Symposium on The Foundations of Software Engineering (ESEC/FSE)，ACM，2017年9月，315-325页. 原文链接如下：https://doi.org/10.1145/3106237.3106242， 读者如需引用该文请标引原文出处。
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表Proceedings of the 11th Joint Meeting of the European Software Engineering Conference and the ACM SigSoft Symposium on The Foundations of Software Engineering (ESEC/FSE)，ACM，2017年9月，315-325页. 原文链接如下：https://doi.org/10.1145/3106237.3106242， 读者如需引用该文请标引原文出处。
Abstract:
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表Proceedings of the 11th Joint Meeting of the European Software Engineering Conference and the ACM SigSoft Symposium on The Foundations of Software Engineering (ESEC/FSE)，ACM，2017年9月，303-314页. 原文链接如下：https://doi.org/10.1145/3106237.3106239， 读者如需引用该文请标引原文出处。
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表Proceedings of the 11th Joint Meeting of the European Software Engineering Conference and the ACM SigSoft Symposium on The Foundations of Software Engineering (ESEC/FSE)，ACM，2017年9月，303-314页. 原文链接如下：https://doi.org/10.1145/3106237.3106239， 读者如需引用该文请标引原文出处。
Abstract:
GitHub, a popular social-software-development platform, has fostered a variety of software ecosystems where projects depend on one another and practitioners interact with each other. Projects within an ecosystem often have complex inter-dependencies that impose new challenges in bug reporting and fixing. In this paper, we conduct an empirical study on cross-project correlated bugs, i.e., causally related bugs reported to different projects, focusing on two aspects: 1) how developers track the root causes across projects; and 2) how the downstream developers coordinate to deal with upstream bugs. Through manual inspection of bug reports collected from the scientific Python ecosystem and an online survey with developers, this study reveals the common practices of developers and the various factors in fixing cross-project bugs. These findings provide implications for future software bug analysis in the scope of ecosystem, as well as shed light on the requirements of issue trackers for such bugs.
GitHub, a popular social-software-development platform, has fostered a variety of software ecosystems where projects depend on one another and practitioners interact with each other. Projects within an ecosystem often have complex inter-dependencies that impose new challenges in bug reporting and fixing. In this paper, we conduct an empirical study on cross-project correlated bugs, i.e., causally related bugs reported to different projects, focusing on two aspects: 1) how developers track the root causes across projects; and 2) how the downstream developers coordinate to deal with upstream bugs. Through manual inspection of bug reports collected from the scientific Python ecosystem and an online survey with developers, this study reveals the common practices of developers and the various factors in fixing cross-project bugs. These findings provide implications for future software bug analysis in the scope of ecosystem, as well as shed light on the requirements of issue trackers for such bugs.
Abstract:
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表在IEEE Transactions on Software Engineering 2017 已录用待发表. 原文链接如下：http://ieeexplore.ieee.org/document/7792694， 读者如需引用该文请标引原文出处。
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表在IEEE Transactions on Software Engineering 2017 已录用待发表. 原文链接如下：http://ieeexplore.ieee.org/document/7792694， 读者如需引用该文请标引原文出处。
Abstract:
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表在Proceedings of the 39th International Conference on Software Engineering, Pages 27-37, Buenos Aires, Argentina — May 20 - 28, 2017, IEEE Press Piscataway, NJ, USA ?2017, ISBN: 978-1-5386-3868-2 原文链接如下：http://dl.acm.org/citation.cfm?id=3097373， 读者如需引用该文请标引原文出处。
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表在Proceedings of the 39th International Conference on Software Engineering, Pages 27-37, Buenos Aires, Argentina — May 20 - 28, 2017, IEEE Press Piscataway, NJ, USA ?2017, ISBN: 978-1-5386-3868-2 原文链接如下：http://dl.acm.org/citation.cfm?id=3097373， 读者如需引用该文请标引原文出处。
Abstract:
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表在Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE 2016). ACM, New York, NY, USA, 871-882. DOI: https://doi.org/10.1145/2950290.2950364 原文链接如下：http://dl.acm.org/citation.cfm?id=2950364， 读者如需引用该文请标引原文出处。
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表在Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE 2016). ACM, New York, NY, USA, 871-882. DOI: https://doi.org/10.1145/2950290.2950364 原文链接如下：http://dl.acm.org/citation.cfm?id=2950364， 读者如需引用该文请标引原文出处。
Abstract:
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表在Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering, Pages 133—143, Seattle WA, USA, November 2016. 原文链接如下：http://dl.acm.org/citation.cfm?id=2950327， 读者如需引用该文请标引原文出处。
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表在Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering, Pages 133—143, Seattle WA, USA, November 2016. 原文链接如下：http://dl.acm.org/citation.cfm?id=2950327， 读者如需引用该文请标引原文出处。
Abstract:
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表在Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE'16), 810 – 821, November 13 - 18, 2016. 原文链接如下：https://doi.org/10.1145/2950290.2950310， 读者如需引用该文请标引原文出处。
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表在Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE'16), 810 – 821, November 13 - 18, 2016. 原文链接如下：https://doi.org/10.1145/2950290.2950310， 读者如需引用该文请标引原文出处。
Abstract:
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表在FSE 2016, 原文链接如下：http://dl.acm.org/citation.cfm?doid=2950290.2950313, 读者如需引用该文请标引原文出处。
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表在FSE 2016, 原文链接如下：http://dl.acm.org/citation.cfm?doid=2950290.2950313, 读者如需引用该文请标引原文出处。
Abstract:
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表在FSE'16会议上Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering， 原文链接如下：http://dl.acm.org/citation.cfm?id=2950340， 读者如需引用该文请标引原文出处。
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表在FSE'16会议上Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering， 原文链接如下：http://dl.acm.org/citation.cfm?id=2950340， 读者如需引用该文请标引原文出处。
Abstract:
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表在ASE2016会议上http://ase2016.org/ 原文链接如下：http://dl.acm.org/citation.cfm?id=2970366 读者如需引用该文请标引原文出处。
文章由CCF软件工程专业委员会白颖教授推荐。 文章发表在ASE2016会议上http://ase2016.org/ 原文链接如下：http://dl.acm.org/citation.cfm?id=2970366 读者如需引用该文请标引原文出处。
Abstract:
文章由CCF软件工程专业委员会白颖教授推荐。 俊杰的文章发表在ASE2016会议上，http://ase2016.org/。 原文链接如下：http://dl.acm.org/citation.cfm?doid=2970276.2970300 请读者标引时请引注原文出处。
文章由CCF软件工程专业委员会白颖教授推荐。 俊杰的文章发表在ASE2016会议上，http://ase2016.org/。 原文链接如下：http://dl.acm.org/citation.cfm?doid=2970276.2970300 请读者标引时请引注原文出处。
Abstract:
CCF 软件工程专业委员会白晓颖教授（清华大学）推荐。 原文发表在 ASE 2016 Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering。 全文链接：http://dx.doi.org/10.1145/2970276.2970307。 重要提示：读者如引用该文时请标注原文出处。
CCF 软件工程专业委员会白晓颖教授（清华大学）推荐。 原文发表在 ASE 2016 Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering。 全文链接：http://dx.doi.org/10.1145/2970276.2970307。 重要提示：读者如引用该文时请标注原文出处。
Abstract:
CCF软件工程专业委员会白晓颖教授（清华大学）推荐。 该论文发表在ACM Transactions on Software Engineering and Methodology （TOSEM, Vol. 25, No. 2, Article 13, May 2016），被ICSE 2016主会邀请为“Journal first”报告， 全文参见http://dl.acm.org/citation.cfm?id=2876443。 论文作者包括北京大学的周明辉,马秀娟,张路和梅宏，以及田纳西大学的Audris Mockus。 重要提示：读者如引用该文时请标注原文出处。
CCF软件工程专业委员会白晓颖教授（清华大学）推荐。 该论文发表在ACM Transactions on Software Engineering and Methodology （TOSEM, Vol. 25, No. 2, Article 13, May 2016），被ICSE 2016主会邀请为“Journal first”报告， 全文参见http://dl.acm.org/citation.cfm?id=2876443。 论文作者包括北京大学的周明辉,马秀娟,张路和梅宏，以及田纳西大学的Audris Mockus。 重要提示：读者如引用该文时请标注原文出处。
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Abstract:
Sensor network, which is made by the convergence of sensor, micro-electro-mechanism system and networks technologies, is a novel technology about acquiring and processing information. In this paper, the architecture of wireless sensor network is briefly introduced. Next, some valuable applications are explained and forecasted. Combining with the existing work, the hot spots including power-aware routing and media access control schemes are discussed and presented in detail. Finally, taking account of application requirements, several future research directions are put forward.
Sensor network, which is made by the convergence of sensor, micro-electro-mechanism system and networks technologies, is a novel technology about acquiring and processing information. In this paper, the architecture of wireless sensor network is briefly introduced. Next, some valuable applications are explained and forecasted. Combining with the existing work, the hot spots including power-aware routing and media access control schemes are discussed and presented in detail. Finally, taking account of application requirements, several future research directions are put forward.
Abstract:
Automatic generation of poetry has always been considered a hard nut in natural language generation.This paper reports some pioneering research on a possible generic algorithm and its automatic generation of SONGCI. In light of the characteristics of Chinese ancient poetry, this paper designed the level and oblique tones-based coding method, the syntactic and semantic weighted function of fitness, the elitism and roulette-combined selection operator, and the partially mapped crossover operator and the heuristic mutation operator. As shown by tests, the system constructed on the basis of the computing model designed in this paper is basically capable of generating Chinese SONGCI with some aesthetic merit. This work represents progress in the field of Chinese poetry automatic generation.
Automatic generation of poetry has always been considered a hard nut in natural language generation.This paper reports some pioneering research on a possible generic algorithm and its automatic generation of SONGCI. In light of the characteristics of Chinese ancient poetry, this paper designed the level and oblique tones-based coding method, the syntactic and semantic weighted function of fitness, the elitism and roulette-combined selection operator, and the partially mapped crossover operator and the heuristic mutation operator. As shown by tests, the system constructed on the basis of the computing model designed in this paper is basically capable of generating Chinese SONGCI with some aesthetic merit. This work represents progress in the field of Chinese poetry automatic generation.
2011,22(1):71-83, DOI:10.3724/SP.J.1001.2011.03958
Abstract:
Cloud Computing is the fundamental change happening in the field of Information Technology. It is a representation of a movement towards the intensive, large scale specialization. On the other hand, it brings about not only convenience and efficiency problems, but also great challenges in the field of data security and privacy protection. Currently, security has been regarded as one of the greatest problems in the development of Cloud Computing. This paper describes the great requirements in Cloud Computing, security key technology, standard and regulation etc., and provides a Cloud Computing security framework. This paper argues that the changes in the above aspects will result in a technical revolution in the field of information security.
Cloud Computing is the fundamental change happening in the field of Information Technology. It is a representation of a movement towards the intensive, large scale specialization. On the other hand, it brings about not only convenience and efficiency problems, but also great challenges in the field of data security and privacy protection. Currently, security has been regarded as one of the greatest problems in the development of Cloud Computing. This paper describes the great requirements in Cloud Computing, security key technology, standard and regulation etc., and provides a Cloud Computing security framework. This paper argues that the changes in the above aspects will result in a technical revolution in the field of information security.
2016,27(1):45-71, DOI:10.13328/j.cnki.jos.004914
Abstract:
Android is a modern and most popular software platform for smartphones. According to report, Android accounted for a huge 81% of all smartphones in 2014 and shipped over 1 billion units worldwide for the first time ever. Apple, Microsoft, Blackberry and Firefox trailed a long way behind. At the same time, increased popularity of the Android smartphones has attracted hackers, leading to massive increase of Android malware applications. This paper summarizes and analyzes the latest advances in Android security from multidimensional perspectives, covering Android architecture, design principles, security mechanisms, major security threats, classification and detection of malware, static and dynamic analyses, machine learning approaches, and security extension proposals.
Android is a modern and most popular software platform for smartphones. According to report, Android accounted for a huge 81% of all smartphones in 2014 and shipped over 1 billion units worldwide for the first time ever. Apple, Microsoft, Blackberry and Firefox trailed a long way behind. At the same time, increased popularity of the Android smartphones has attracted hackers, leading to massive increase of Android malware applications. This paper summarizes and analyzes the latest advances in Android security from multidimensional perspectives, covering Android architecture, design principles, security mechanisms, major security threats, classification and detection of malware, static and dynamic analyses, machine learning approaches, and security extension proposals.
Abstract:
The research actuality and new progress in clustering algorithm in recent years are summarized in this paper. First, the analysis and induction of some representative clustering algorithms have been made from several aspects, such as the ideas of algorithm, key technology, advantage and disadvantage. On the other hand, several typical clustering algorithms and known data sets are selected, simulation experiments are implemented from both sides of accuracy and running efficiency, and clustering condition of one algorithm with different data sets is analyzed by comparing with the same clustering of the data set under different algorithms. Finally, the research hotspot, difficulty, shortage of the data clustering and some pending problems are addressed by the integration of the aforementioned two aspects information. The above work can give a valuable reference for data clustering and data mining.
The research actuality and new progress in clustering algorithm in recent years are summarized in this paper. First, the analysis and induction of some representative clustering algorithms have been made from several aspects, such as the ideas of algorithm, key technology, advantage and disadvantage. On the other hand, several typical clustering algorithms and known data sets are selected, simulation experiments are implemented from both sides of accuracy and running efficiency, and clustering condition of one algorithm with different data sets is analyzed by comparing with the same clustering of the data set under different algorithms. Finally, the research hotspot, difficulty, shortage of the data clustering and some pending problems are addressed by the integration of the aforementioned two aspects information. The above work can give a valuable reference for data clustering and data mining.
Abstract:
This paper surveys the current technologies adopted in cloud computing as well as the systems in enterprises. Cloud computing can be viewed from two different aspects. One is about the cloud infrastructure which is the building block for the up layer cloud application. The other is of course the cloud application. This paper focuses on the cloud infrastructure including the systems and current research. Some attractive cloud applications are also discussed. Cloud computing infrastructure has three distinct characteristics. First, the infrastructure is built on top of large scale clusters which contain a large number of cheap PC servers. Second, the applications are co-designed with the fundamental infrastructure that the computing resources can be maximally utilized. Third, the reliability of the whole system is achieved by software building on top of redundant hardware instead of mere hardware. All these technologies are for the two important goals for distributed system: high scalability and high availability. Scalability means that the cloud infrastructure can be expanded to very large scale even to thousands of nodes. Availability means that the services are available even when quite a number of nodes fail. From this paper, readers will capture the current status of cloud computing as well as its future trends.
This paper surveys the current technologies adopted in cloud computing as well as the systems in enterprises. Cloud computing can be viewed from two different aspects. One is about the cloud infrastructure which is the building block for the up layer cloud application. The other is of course the cloud application. This paper focuses on the cloud infrastructure including the systems and current research. Some attractive cloud applications are also discussed. Cloud computing infrastructure has three distinct characteristics. First, the infrastructure is built on top of large scale clusters which contain a large number of cheap PC servers. Second, the applications are co-designed with the fundamental infrastructure that the computing resources can be maximally utilized. Third, the reliability of the whole system is achieved by software building on top of redundant hardware instead of mere hardware. All these technologies are for the two important goals for distributed system: high scalability and high availability. Scalability means that the cloud infrastructure can be expanded to very large scale even to thousands of nodes. Availability means that the services are available even when quite a number of nodes fail. From this paper, readers will capture the current status of cloud computing as well as its future trends.
Abstract:
Evolutionary multi-objective optimization (EMO), whose main task is to deal with multi-objective optimization problems by evolutionary computation, has become a hot topic in evolutionary computation community. After summarizing the EMO algorithms before 2003 briefly, the recent advances in EMO are discussed in details. The current research directions are concluded. On the one hand, more new evolutionary paradigms have been introduced into EMO community, such as particle swarm optimization, artificial immune systems, and estimation distribution algorithms. On the other hand, in order to deal with many-objective optimization problems, many new dominance schemes different from traditional Pareto-dominance come forth. Furthermore, the essential characteristics of multi-objective optimization problems are deeply investigated. This paper also gives experimental comparison of several representative algorithms. Finally, several viewpoints for the future research of EMO are proposed.
Evolutionary multi-objective optimization (EMO), whose main task is to deal with multi-objective optimization problems by evolutionary computation, has become a hot topic in evolutionary computation community. After summarizing the EMO algorithms before 2003 briefly, the recent advances in EMO are discussed in details. The current research directions are concluded. On the one hand, more new evolutionary paradigms have been introduced into EMO community, such as particle swarm optimization, artificial immune systems, and estimation distribution algorithms. On the other hand, in order to deal with many-objective optimization problems, many new dominance schemes different from traditional Pareto-dominance come forth. Furthermore, the essential characteristics of multi-objective optimization problems are deeply investigated. This paper also gives experimental comparison of several representative algorithms. Finally, several viewpoints for the future research of EMO are proposed.
Abstract:
The paper gives some thinking according to the following four aspects: 1) from the law of things development, revealing the development history of software engineering technology; 2) from the point of software natural characteristic, analyzing the construction of every abstraction layer of virtual machine; 3) from the point of software development, proposing the research content of software engineering discipline, and research the pattern of industrialized software production; 4) based on the appearance of Internet technology, exploring the development trend of software technology.
The paper gives some thinking according to the following four aspects: 1) from the law of things development, revealing the development history of software engineering technology; 2) from the point of software natural characteristic, analyzing the construction of every abstraction layer of virtual machine; 3) from the point of software development, proposing the research content of software engineering discipline, and research the pattern of industrialized software production; 4) based on the appearance of Internet technology, exploring the development trend of software technology.
Abstract:
This paper surveys the state of the art of sentiment analysis. First, three important tasks of sentiment analysis are summarized and analyzed in detail, including sentiment extraction, sentiment classification, sentiment retrieval and summarization. Then, the evaluation and corpus for sentiment analysis are introduced. Finally, the applications of sentiment analysis are concluded. This paper aims to take a deep insight into the mainstream methods and recent progress in this field, making detailed comparison and analysis.
This paper surveys the state of the art of sentiment analysis. First, three important tasks of sentiment analysis are summarized and analyzed in detail, including sentiment extraction, sentiment classification, sentiment retrieval and summarization. Then, the evaluation and corpus for sentiment analysis are introduced. Finally, the applications of sentiment analysis are concluded. This paper aims to take a deep insight into the mainstream methods and recent progress in this field, making detailed comparison and analysis.
Abstract:
With the rapid development of e-business, web applications based on the Web are developed from localization to globalization, from B2C(business-to-customer) to B2B(business-to-business), from centralized fashion to decentralized fashion. Web service is a new application model for decentralized computing, and it is also an effective mechanism for the data and service integration on the web. Thus, web service has become a solution to e-business. It is important and necessary to carry out the research on the new architecture of web services, on the combinations with other good techniques, and on the integration of services. In this paper, a survey presents on various aspects of the research of web services from the basic concepts to the principal research problems and the underlying techniques, including data integration in web services, web service composition, semantic web service, web service discovery, web service security, the solution to web services in the P2P (Peer-to-Peer) computing environment, and the grid service, etc. This paper also presents a summary of the current art of the state of these techniques, a discussion on the future research topics, and the challenges of the web services.
With the rapid development of e-business, web applications based on the Web are developed from localization to globalization, from B2C(business-to-customer) to B2B(business-to-business), from centralized fashion to decentralized fashion. Web service is a new application model for decentralized computing, and it is also an effective mechanism for the data and service integration on the web. Thus, web service has become a solution to e-business. It is important and necessary to carry out the research on the new architecture of web services, on the combinations with other good techniques, and on the integration of services. In this paper, a survey presents on various aspects of the research of web services from the basic concepts to the principal research problems and the underlying techniques, including data integration in web services, web service composition, semantic web service, web service discovery, web service security, the solution to web services in the P2P (Peer-to-Peer) computing environment, and the grid service, etc. This paper also presents a summary of the current art of the state of these techniques, a discussion on the future research topics, and the challenges of the web services.
Abstract:
Wireless Sensor Networks, a novel technology about acquiring and processing information, have been proposed for a multitude of diverse applications. The problem of self-localization, that is, determining where a given node is physically or relatively located in the networks, is a challenging one, and yet extremely crucial for many applications. In this paper, the evaluation criterion of the performance and the taxonomy for wireless sensor networks self-localization systems and algorithms are described, the principles and characteristics of recent representative localization approaches are discussed and presented, and the directions of research in this area are introduced.
Wireless Sensor Networks, a novel technology about acquiring and processing information, have been proposed for a multitude of diverse applications. The problem of self-localization, that is, determining where a given node is physically or relatively located in the networks, is a challenging one, and yet extremely crucial for many applications. In this paper, the evaluation criterion of the performance and the taxonomy for wireless sensor networks self-localization systems and algorithms are described, the principles and characteristics of recent representative localization approaches are discussed and presented, and the directions of research in this area are introduced.
Abstract:
Network community structure is one of the most fundamental and important topological properties of complex networks, within which the links between nodes are very dense, but between which they are quite sparse. Network clustering algorithms which aim to discover all natural network communities from given complex networks are fundamentally important for both theoretical researches and practical applications, and can be used to analyze the topological structures, understand the functions, recognize the hidden patterns, and predict the behaviors of complex networks including social networks, biological networks, World Wide Webs and so on. This paper reviews the background, the motivation, the state of arts as well as the main issues of existing works related to discovering network communities, and tries to draw a comprehensive and clear outline for this new and active research area. This work is hopefully beneficial to the researchers from the communities of complex network analysis, data mining, intelligent Web and bioinformatics.
Network community structure is one of the most fundamental and important topological properties of complex networks, within which the links between nodes are very dense, but between which they are quite sparse. Network clustering algorithms which aim to discover all natural network communities from given complex networks are fundamentally important for both theoretical researches and practical applications, and can be used to analyze the topological structures, understand the functions, recognize the hidden patterns, and predict the behaviors of complex networks including social networks, biological networks, World Wide Webs and so on. This paper reviews the background, the motivation, the state of arts as well as the main issues of existing works related to discovering network communities, and tries to draw a comprehensive and clear outline for this new and active research area. This work is hopefully beneficial to the researchers from the communities of complex network analysis, data mining, intelligent Web and bioinformatics.
2012,23(4):962-986, DOI:10.3724/SP.J.1001.2012.04175
Abstract:
Considered as the next generation computing model, cloud computing plays an important role in scientific and commercial computing area and draws great attention from both academia and industry fields. Under cloud computing environment, data center consist of a large amount of computers, usually up to millions, and stores petabyte even exabyte of data, which may easily lead to the failure of the computers or data. The large amount of computers composition not only leads to great challenges to the scalability of the data center and its storage system, but also results in high hardware infrastructure cost and power cost. Therefore, fault-tolerance, scalability, and power consumption of the distributed storage for a data center becomes key part in the technology of cloud computing, in order to ensure the data availability and reliability. In this paper, a survey is made on the state of art of the key technologies in cloud computing in the following aspects: Design of data center network, organization and arrangement of data, strategies to improve fault-tolerance, methods to save storage space, and energy. Firstly, many kinds of classical topologies of data center network are introduced and compared. Secondly, kinds of current fault-tolerant storage techniques are discussed, and data replication and erasure code strategies are especially compared. Thirdly, the main current energy saving technology is addressed and analyzed. Finally, challenges in distributed storage are reviewed as well as future research trends are predicted.
Considered as the next generation computing model, cloud computing plays an important role in scientific and commercial computing area and draws great attention from both academia and industry fields. Under cloud computing environment, data center consist of a large amount of computers, usually up to millions, and stores petabyte even exabyte of data, which may easily lead to the failure of the computers or data. The large amount of computers composition not only leads to great challenges to the scalability of the data center and its storage system, but also results in high hardware infrastructure cost and power cost. Therefore, fault-tolerance, scalability, and power consumption of the distributed storage for a data center becomes key part in the technology of cloud computing, in order to ensure the data availability and reliability. In this paper, a survey is made on the state of art of the key technologies in cloud computing in the following aspects: Design of data center network, organization and arrangement of data, strategies to improve fault-tolerance, methods to save storage space, and energy. Firstly, many kinds of classical topologies of data center network are introduced and compared. Secondly, kinds of current fault-tolerant storage techniques are discussed, and data replication and erasure code strategies are especially compared. Thirdly, the main current energy saving technology is addressed and analyzed. Finally, challenges in distributed storage are reviewed as well as future research trends are predicted.
2012,23(1):32-45, DOI:10.3724/SP.J.1001.2012.04091
Abstract:
In many areas such as science, simulation, Internet, and e-commerce, the volume of data to be analyzed grows rapidly. Parallel techniques which could be expanded cost-effectively should be invented to deal with the big data. Relational data management technique has gone through a history of nearly 40 years. Now it encounters the tough obstacle of scalability, which relational techniques can not handle large data easily. In the mean time, none relational techniques, such as MapReduce as a typical representation, emerge as a new force, and expand their application from Web search to territories that used to be occupied by relational database systems. They confront relational technique with high availability, high scalability and massive parallel processing capability. Relational technique community, after losing the big deal of Web search, begins to learn from MapReduce. MapReduce also borrows valuable ideas from relational technique community to improve performance. Relational technique and MapReduce compete with each other, and learn from each other; new data analysis platform and new data analysis eco-system are emerging. Finally the two camps of techniques will find their right places in the new eco-system of big data analysis.
In many areas such as science, simulation, Internet, and e-commerce, the volume of data to be analyzed grows rapidly. Parallel techniques which could be expanded cost-effectively should be invented to deal with the big data. Relational data management technique has gone through a history of nearly 40 years. Now it encounters the tough obstacle of scalability, which relational techniques can not handle large data easily. In the mean time, none relational techniques, such as MapReduce as a typical representation, emerge as a new force, and expand their application from Web search to territories that used to be occupied by relational database systems. They confront relational technique with high availability, high scalability and massive parallel processing capability. Relational technique community, after losing the big deal of Web search, begins to learn from MapReduce. MapReduce also borrows valuable ideas from relational technique community to improve performance. Relational technique and MapReduce compete with each other, and learn from each other; new data analysis platform and new data analysis eco-system are emerging. Finally the two camps of techniques will find their right places in the new eco-system of big data analysis.
Abstract:
Nowadays it has been widely accepted that the quality of software highly depends on the process that iscarried out in an organization. As part of the effort to support software process engineering activities, the researchon software process modeling and analysis is to provide an effective means to represent and analyze a process and,by doing so, to enhance the understanding of the modeled process. In addition, an enactable process model canprovide a direct guidance for the actual development process. Thus, the enforcement of the process model candirectly contribute to the improvement of the software quality. In this paper, a systematic review is carried out tosurvey the recent development in software process modeling. 72 papers from 20 conference proceedings and 7journals are identified as the evidence. The review aims to promote a better understanding of the literature byanswering the following three questions: 1) What kinds of paradigms are existing methods based on? 2) What kinds of purposes does the existing research have? 3) What kinds of new trends are reflected in the current research? Afterproviding the systematic review, we present our software process modeling method based on a multi-dimensionaland integration methodology that is intended to address several core issues facing the community.
Nowadays it has been widely accepted that the quality of software highly depends on the process that iscarried out in an organization. As part of the effort to support software process engineering activities, the researchon software process modeling and analysis is to provide an effective means to represent and analyze a process and,by doing so, to enhance the understanding of the modeled process. In addition, an enactable process model canprovide a direct guidance for the actual development process. Thus, the enforcement of the process model candirectly contribute to the improvement of the software quality. In this paper, a systematic review is carried out tosurvey the recent development in software process modeling. 72 papers from 20 conference proceedings and 7journals are identified as the evidence. The review aims to promote a better understanding of the literature byanswering the following three questions: 1) What kinds of paradigms are existing methods based on? 2) What kinds of purposes does the existing research have? 3) What kinds of new trends are reflected in the current research? Afterproviding the systematic review, we present our software process modeling method based on a multi-dimensionaland integration methodology that is intended to address several core issues facing the community.
Abstract:
The appearance of plenty of intelligent devices equipped for short-range wireless communications boosts the fast rise of wireless ad hoc networks application. However, in many realistic application environments, nodes form a disconnected network for most of the time due to nodal mobility, low density, lossy link, etc. Conventional communication model of mobile ad hoc network (MANET) requires at least one path existing from source to destination nodes, which results in communication failure in these scenarios. Opportunistic networks utilize the communication opportunities arising from node movement to forward messages in a hop-by-hop way, and implement communications between nodes based on the "store-carry-forward" routing pattern. This networking approach, totally different from the traditional communication model, captures great interests from researchers. This paper first introduces the conceptions and theories of opportunistic networks and some current typical applications. Then it elaborates the popular research problems including opportunistic forwarding mechanism, mobility model and opportunistic data dissemination and retrieval. Some other interesting research points such as communication middleware, cooperation and security problem and new applications are stated briefly. Finally, the paper concludes and looks forward to the possible research focuses for opportunistic networks in the future.
The appearance of plenty of intelligent devices equipped for short-range wireless communications boosts the fast rise of wireless ad hoc networks application. However, in many realistic application environments, nodes form a disconnected network for most of the time due to nodal mobility, low density, lossy link, etc. Conventional communication model of mobile ad hoc network (MANET) requires at least one path existing from source to destination nodes, which results in communication failure in these scenarios. Opportunistic networks utilize the communication opportunities arising from node movement to forward messages in a hop-by-hop way, and implement communications between nodes based on the "store-carry-forward" routing pattern. This networking approach, totally different from the traditional communication model, captures great interests from researchers. This paper first introduces the conceptions and theories of opportunistic networks and some current typical applications. Then it elaborates the popular research problems including opportunistic forwarding mechanism, mobility model and opportunistic data dissemination and retrieval. Some other interesting research points such as communication middleware, cooperation and security problem and new applications are stated briefly. Finally, the paper concludes and looks forward to the possible research focuses for opportunistic networks in the future.
Abstract:
With the explosive growth of network applications and complexity, the threat of Internet worms against network security becomes increasingly serious. Especially under the environment of Internet, the variety of the propagation ways and the complexity of the application environment result in worm with much higher frequency of outbreak, much deeper latency and more wider coverage, and Internet worms have been a primary issue faced by malicious code researchers. In this paper, the concept and research situation of Internet worms, exploration function component and execution mechanism are first presented, then the scanning strategies and propagation model are discussed, and finally the critical techniques of Internet worm prevention are given. Some major problems and research trends in this area are also addressed.
With the explosive growth of network applications and complexity, the threat of Internet worms against network security becomes increasingly serious. Especially under the environment of Internet, the variety of the propagation ways and the complexity of the application environment result in worm with much higher frequency of outbreak, much deeper latency and more wider coverage, and Internet worms have been a primary issue faced by malicious code researchers. In this paper, the concept and research situation of Internet worms, exploration function component and execution mechanism are first presented, then the scanning strategies and propagation model are discussed, and finally the critical techniques of Internet worm prevention are given. Some major problems and research trends in this area are also addressed.
Abstract:
This paper studies uncertain graph data mining and especially investigates the problem of mining frequent subgraph patterns from uncertain graph data. A data model is introduced for representing uncertainties in graphs, and an expected support is employed to evaluate the significance of subgraph patterns. By using the apriori property of expected support, a depth-first search-based mining algorithm is proposed with an efficient method for computing expected supports and a technique for pruning search space, which reduces the number of subgraph isomorphism testings needed by computing expected support from the exponential scale to the linear scale. Experimental results show that the proposed algorithm is 3 to 5 orders of magnitude faster than a na?ve depth-first search algorithm, and is efficient and scalable.
This paper studies uncertain graph data mining and especially investigates the problem of mining frequent subgraph patterns from uncertain graph data. A data model is introduced for representing uncertainties in graphs, and an expected support is employed to evaluate the significance of subgraph patterns. By using the apriori property of expected support, a depth-first search-based mining algorithm is proposed with an efficient method for computing expected supports and a technique for pruning search space, which reduces the number of subgraph isomorphism testings needed by computing expected support from the exponential scale to the linear scale. Experimental results show that the proposed algorithm is 3 to 5 orders of magnitude faster than a na?ve depth-first search algorithm, and is efficient and scalable.
Abstract:
This paper introduces the concrete details of combining the automated reasoning techniques with planning methods, which includes planning as satisfiability using propositional logic, Conformant planning using modal logic and disjunctive reasoning, planning as nonmonotonic logic, and Flexible planning as fuzzy description logic. After considering experimental results of International Planning Competition and relevant papers, it concludes that planning methods based on automated reasoning techniques is helpful and can be adopted. It also proposes the challenges and possible hotspots.
This paper introduces the concrete details of combining the automated reasoning techniques with planning methods, which includes planning as satisfiability using propositional logic, Conformant planning using modal logic and disjunctive reasoning, planning as nonmonotonic logic, and Flexible planning as fuzzy description logic. After considering experimental results of International Planning Competition and relevant papers, it concludes that planning methods based on automated reasoning techniques is helpful and can be adopted. It also proposes the challenges and possible hotspots.
Abstract:
This paper makes a comprehensive survey of the recommender system research aiming to facilitate readers to understand this field. First the research background is introduced, including commercial application demands, academic institutes, conferences and journals. After formally and informally describing the recommendation problem, a comparison study is conducted based on categorized algorithms. In addition, the commonly adopted benchmarked datasets and evaluation methods are exhibited and most difficulties and future directions are concluded.
This paper makes a comprehensive survey of the recommender system research aiming to facilitate readers to understand this field. First the research background is introduced, including commercial application demands, academic institutes, conferences and journals. After formally and informally describing the recommendation problem, a comparison study is conducted based on categorized algorithms. In addition, the commonly adopted benchmarked datasets and evaluation methods are exhibited and most difficulties and future directions are concluded.
Abstract:
Sensor networks are integration of sensor techniques, nested computation techniques, distributed computation techniques and wireless communication techniques. They can be used for testing, sensing, collecting and processing information of monitored objects and transferring the processed information to users. Sensor network is a new research area of computer science and technology and has a wide application future. Both academia and industries are very interested in it. The concepts and characteristics of the sensor networks and the data in the networks are introduced, and the issues of the sensor networks and the data management of sensor networks are discussed. The advance of the research on sensor networks and the data management of sensor networks are also presented.
Sensor networks are integration of sensor techniques, nested computation techniques, distributed computation techniques and wireless communication techniques. They can be used for testing, sensing, collecting and processing information of monitored objects and transferring the processed information to users. Sensor network is a new research area of computer science and technology and has a wide application future. Both academia and industries are very interested in it. The concepts and characteristics of the sensor networks and the data in the networks are introduced, and the issues of the sensor networks and the data management of sensor networks are discussed. The advance of the research on sensor networks and the data management of sensor networks are also presented.
2015,26(1):62-81, DOI:10.13328/j.cnki.jos.004701
Abstract:
Network abstraction brings about the naissance of software-defined networking. SDN decouples data plane and control plane, and simplifies network management. The paper starts with a discussion on the background in the naissance and developments of SDN, combing its architecture that includes data layer, control layer and application layer. Then their key technologies are elaborated according to the hierarchical architecture of SDN. The characteristics of consistency, availability, and tolerance are especially analyzed. Moreover, latest achievements for profiled scenes are introduced. The future works are summarized in the end.
Network abstraction brings about the naissance of software-defined networking. SDN decouples data plane and control plane, and simplifies network management. The paper starts with a discussion on the background in the naissance and developments of SDN, combing its architecture that includes data layer, control layer and application layer. Then their key technologies are elaborated according to the hierarchical architecture of SDN. The characteristics of consistency, availability, and tolerance are especially analyzed. Moreover, latest achievements for profiled scenes are introduced. The future works are summarized in the end.
2014,25(4):839-862, DOI:10.13328/j.cnki.jos.004558
Abstract:
Batch computing and stream computing are two important forms of big data computing. The research and discussions on batch computing in big data environment are comparatively sufficient. But how to efficiently deal with stream computing to meet many requirements, such as low latency, high throughput and continuously reliable running, and how to build efficient stream big data computing systems, are great challenges in the big data computing research. This paper provides a research of the data computing architecture and the key issues in stream computing in big data environments. Firstly, the research gives a brief summary of three application scenarios of stream computing in business intelligence, marketing and public service. It also shows distinctive features of the stream computing in big data environment, such as real time, volatility, burstiness, irregularity and infinity. A well-designed stream computing system always optimizes in system structure, data transmission, application interfaces, high-availability, and so on. Subsequently, the research offers detailed analyses and comparisons of five typical and open-source stream computing systems in big data environment. Finally, the research specifically addresses some new challenges of the stream big data systems, such as scalability, fault tolerance, consistency, load balancing and throughput.
Batch computing and stream computing are two important forms of big data computing. The research and discussions on batch computing in big data environment are comparatively sufficient. But how to efficiently deal with stream computing to meet many requirements, such as low latency, high throughput and continuously reliable running, and how to build efficient stream big data computing systems, are great challenges in the big data computing research. This paper provides a research of the data computing architecture and the key issues in stream computing in big data environments. Firstly, the research gives a brief summary of three application scenarios of stream computing in business intelligence, marketing and public service. It also shows distinctive features of the stream computing in big data environment, such as real time, volatility, burstiness, irregularity and infinity. A well-designed stream computing system always optimizes in system structure, data transmission, application interfaces, high-availability, and so on. Subsequently, the research offers detailed analyses and comparisons of five typical and open-source stream computing systems in big data environment. Finally, the research specifically addresses some new challenges of the stream big data systems, such as scalability, fault tolerance, consistency, load balancing and throughput.
2012,23(1):1-20, DOI:10.3724/SP.J.1001.2012.04100
Abstract:
Context-Aware recommender systems, aiming to further improve performance accuracy and user satisfaction by fully utilizing contextual information, have recently become one of the hottest topics in the domain of recommender systems. This paper presents an overview of the field of context-aware recommender systems from a process-oriented perspective, including system frameworks, key techniques, main models, evaluation, and typical applications. The prospects for future development and suggestions for possible extensions are also discussed.
Context-Aware recommender systems, aiming to further improve performance accuracy and user satisfaction by fully utilizing contextual information, have recently become one of the hottest topics in the domain of recommender systems. This paper presents an overview of the field of context-aware recommender systems from a process-oriented perspective, including system frameworks, key techniques, main models, evaluation, and typical applications. The prospects for future development and suggestions for possible extensions are also discussed.
Abstract:
In a multi-hop wireless sensor network (WSN), the sensors closest to the sink tend to deplete their energy faster than other sensors, which is known as an energy hole around the sink. No more data can be delivered to the sink after an energy hole appears, while a considerable amount of energy is wasted and the network lifetime ends prematurely. This paper investigates the energy hole problem, and based on the improved corona model with levels, it concludes that the assignment of transmission ranges of nodes in different coronas is an effective approach for achieving energy-efficient network. It proves that the optimal transmission ranges for all areas is a multi-objective optimization problem (MOP), which is NP hard. The paper proposes an ACO (ant colony optimization)-based distributed algorithm to prolong the network lifetime, which can help nodes in different areas to adaptively find approximate optimal transmission range based on the node distribution. Furthermore, the simulation results indicate that the network lifetime under this solution approximates to that using the optimal list. Compared with existing algorithms, this ACO-based algorithm can not only make the network lifetime be extended more than two times longer, but also have good performance in the non-uniform node distribution.
In a multi-hop wireless sensor network (WSN), the sensors closest to the sink tend to deplete their energy faster than other sensors, which is known as an energy hole around the sink. No more data can be delivered to the sink after an energy hole appears, while a considerable amount of energy is wasted and the network lifetime ends prematurely. This paper investigates the energy hole problem, and based on the improved corona model with levels, it concludes that the assignment of transmission ranges of nodes in different coronas is an effective approach for achieving energy-efficient network. It proves that the optimal transmission ranges for all areas is a multi-objective optimization problem (MOP), which is NP hard. The paper proposes an ACO (ant colony optimization)-based distributed algorithm to prolong the network lifetime, which can help nodes in different areas to adaptively find approximate optimal transmission range based on the node distribution. Furthermore, the simulation results indicate that the network lifetime under this solution approximates to that using the optimal list. Compared with existing algorithms, this ACO-based algorithm can not only make the network lifetime be extended more than two times longer, but also have good performance in the non-uniform node distribution.
2012,23(5):1148-1166, DOI:10.3724/SP.J.1001.2012.04195
Abstract:
With the recent development of cloud computing, the importance of cloud databases has been widely acknowledged. Here, the features, influence and related products of cloud databases are first discussed. Then, research issues of cloud databases are presented in detail, which include data model, architecture, consistency, programming model, data security, performance optimization, benchmark, and so on. Finally, some future trends in this area are discussed.
With the recent development of cloud computing, the importance of cloud databases has been widely acknowledged. Here, the features, influence and related products of cloud databases are first discussed. Then, research issues of cloud databases are presented in detail, which include data model, architecture, consistency, programming model, data security, performance optimization, benchmark, and so on. Finally, some future trends in this area are discussed.
Abstract:
Intrusion detection is a highlighted topic of network security research in recent years. In this paper, first the necessity o f intrusion detection is presented, and its concepts and models are described. T hen, many intrusion detection techniques and architectures are summarized. Final ly, the existing problems and the future direction in this field are discussed.
Intrusion detection is a highlighted topic of network security research in recent years. In this paper, first the necessity o f intrusion detection is presented, and its concepts and models are described. T hen, many intrusion detection techniques and architectures are summarized. Final ly, the existing problems and the future direction in this field are discussed.
2013,24(8):1786-1803, DOI:10.3724/SP.J.1001.2013.04416
Abstract:
Many specific application oriented NoSQL database systems are developed for satisfying the new requirement of big data management. This paper surveys researches on typical NoSQL database based on key-value data model. First, the characteristics of big data, and the key technique issues supporting big data management are introduced. Then frontier efforts and research challenges are given, including system architecture, data model, access mode, index, transaction, system elasticity, load balance, replica strategy, data consistency, flash cache, MapReduce based data process and new generation data management system etc. Finally, research prospects are given.
Many specific application oriented NoSQL database systems are developed for satisfying the new requirement of big data management. This paper surveys researches on typical NoSQL database based on key-value data model. First, the characteristics of big data, and the key technique issues supporting big data management are introduced. Then frontier efforts and research challenges are given, including system architecture, data model, access mode, index, transaction, system elasticity, load balance, replica strategy, data consistency, flash cache, MapReduce based data process and new generation data management system etc. Finally, research prospects are given.
Abstract:
Software architecture (SA) is emerging as one of the primary research areas in software engineering recently and one of the key technologies to the development of large-scale software-intensive system and software product line system. The history and the major direction of SA are summarized, and the concept of SA is brought up based on analyzing and comparing the several classical definitions about SA. Based on summing up the activities about SA, two categories of study about SA are extracted out, and the advancements of researches on SA are subsequently introduced from seven aspects.Additionally,some disadvantages of study on SA are discussed,and the causes are explained at the same.Finally,it is concluded with some singificantly promising tendency about research on SA.
Software architecture (SA) is emerging as one of the primary research areas in software engineering recently and one of the key technologies to the development of large-scale software-intensive system and software product line system. The history and the major direction of SA are summarized, and the concept of SA is brought up based on analyzing and comparing the several classical definitions about SA. Based on summing up the activities about SA, two categories of study about SA are extracted out, and the advancements of researches on SA are subsequently introduced from seven aspects.Additionally,some disadvantages of study on SA are discussed,and the causes are explained at the same.Finally,it is concluded with some singificantly promising tendency about research on SA.
Abstract:
For most peer-to-peer file-swapping applications, sharing is a volunteer action, and peers are not responsible for their irresponsible bartering history. This situation indicates the trust between participants can not be set up simply on the traditional trust mechanism. A reasonable trust construction approach comes from the social network analysis, in which trust relations between individuals are set up upon recommendations of other individuals. Current p2p trust model could not promise the convergence of iteration for trust computation, and takes no consideration for model security problems, such as sybil attack and slandering. This paper presents a novel recommendation-based global trust model and gives a distributed implementation method. Mathematic analyses and simulations show that, compared to the current global trust model, the proposed model is more robust on trust security problems and more complete on iteration for computing peer trust.
For most peer-to-peer file-swapping applications, sharing is a volunteer action, and peers are not responsible for their irresponsible bartering history. This situation indicates the trust between participants can not be set up simply on the traditional trust mechanism. A reasonable trust construction approach comes from the social network analysis, in which trust relations between individuals are set up upon recommendations of other individuals. Current p2p trust model could not promise the convergence of iteration for trust computation, and takes no consideration for model security problems, such as sybil attack and slandering. This paper presents a novel recommendation-based global trust model and gives a distributed implementation method. Mathematic analyses and simulations show that, compared to the current global trust model, the proposed model is more robust on trust security problems and more complete on iteration for computing peer trust.
Abstract:
Routing technology at the network layer is pivotal in the architecture of wireless sensor networks. As an active branch of routing technology, cluster-based routing protocols excel in network topology management, energy minimization, data aggregation and so on. In this paper, cluster-based routing mechanisms for wireless sensor networks are analyzed. Cluster head selection, cluster formation and data transmission are three key techniques in cluster-based routing protocols. As viewed from the three techniques, recent representative cluster-based routing protocols are presented, and their characteristics and application areas are compared. Finally, the future research issues in this area are pointed out.
Routing technology at the network layer is pivotal in the architecture of wireless sensor networks. As an active branch of routing technology, cluster-based routing protocols excel in network topology management, energy minimization, data aggregation and so on. In this paper, cluster-based routing mechanisms for wireless sensor networks are analyzed. Cluster head selection, cluster formation and data transmission are three key techniques in cluster-based routing protocols. As viewed from the three techniques, recent representative cluster-based routing protocols are presented, and their characteristics and application areas are compared. Finally, the future research issues in this area are pointed out.
2011,22(1):115-131, DOI:10.3724/SP.J.1001.2011.03950
Abstract:
The Internet traffic model is the key issue for network performance management, Quality of Service management, and admission control. The paper first summarizes the primary characteristics of Internet traffic, as well as the metrics of Internet traffic. It also illustrates the significance and classification of traffic modeling. Next, the paper chronologically categorizes the research activities of traffic modeling into three phases: 1) traditional Poisson modeling; 2) self-similar modeling; and 3) new research debates and new progress. Thorough reviews of the major research achievements of each phase are conducted. Finally, the paper identifies some open research issue and points out possible future research directions in traffic modeling area.
The Internet traffic model is the key issue for network performance management, Quality of Service management, and admission control. The paper first summarizes the primary characteristics of Internet traffic, as well as the metrics of Internet traffic. It also illustrates the significance and classification of traffic modeling. Next, the paper chronologically categorizes the research activities of traffic modeling into three phases: 1) traditional Poisson modeling; 2) self-similar modeling; and 3) new research debates and new progress. Thorough reviews of the major research achievements of each phase are conducted. Finally, the paper identifies some open research issue and points out possible future research directions in traffic modeling area.
Abstract:
Constrained optimization problems (COPs) are mathematical programming problems frequently encountered in the disciplines of science and engineering application. Solving COPs has become an important research area of evolutionary computation in recent years. In this paper, the state-of-the-art of constrained optimization evolutionary algorithms (COEAs) is surveyed from two basic aspects of COEAs (i.e., constraint-handling techniques and evolutionary algorithms). In addition, this paper discusses some important issues of COEAs. More specifically, several typical algorithms are analyzed in detail. Based on the analyses, it concluded that to obtain competitive results, a proper constraint-handling technique needs to be considered in conjunction with an appropriate search algorithm. Finally, the open research issues in this field are also pointed out.
Constrained optimization problems (COPs) are mathematical programming problems frequently encountered in the disciplines of science and engineering application. Solving COPs has become an important research area of evolutionary computation in recent years. In this paper, the state-of-the-art of constrained optimization evolutionary algorithms (COEAs) is surveyed from two basic aspects of COEAs (i.e., constraint-handling techniques and evolutionary algorithms). In addition, this paper discusses some important issues of COEAs. More specifically, several typical algorithms are analyzed in detail. Based on the analyses, it concluded that to obtain competitive results, a proper constraint-handling technique needs to be considered in conjunction with an appropriate search algorithm. Finally, the open research issues in this field are also pointed out.
2015,26(1):26-39, DOI:10.13328/j.cnki.jos.004631
Abstract:
In recent years, transfer learning has provoked vast amount of attention and research. Transfer learning is a new machine learning method that applies the knowledge from related but different domains to target domains. It relaxes the two basic assumptions in traditional machine learning: (1) the training (also referred as source domain) and test data (also referred target domain) follow the independent and identically distributed (i.i.d.) condition; (2) there are enough labeled samples to learn a good classification model, aiming to solve the problems that there are few or even not any labeled data in target domains. This paper surveys the research progress of transfer learning and introduces its own works, especially the ones in building transfer learning models by applying generative model on the concept level. Finally, the paper introduces the applications of transfer learning, such as text classification and collaborative filtering, and further suggests the future research direction of transfer learning.
In recent years, transfer learning has provoked vast amount of attention and research. Transfer learning is a new machine learning method that applies the knowledge from related but different domains to target domains. It relaxes the two basic assumptions in traditional machine learning: (1) the training (also referred as source domain) and test data (also referred target domain) follow the independent and identically distributed (i.i.d.) condition; (2) there are enough labeled samples to learn a good classification model, aiming to solve the problems that there are few or even not any labeled data in target domains. This paper surveys the research progress of transfer learning and introduces its own works, especially the ones in building transfer learning models by applying generative model on the concept level. Finally, the paper introduces the applications of transfer learning, such as text classification and collaborative filtering, and further suggests the future research direction of transfer learning.
Abstract:
An ad hoc network is a collection of wireless mobile nodes dynamically forming a temporary network without the use of any existing network infrastructure or centralized administration. Due to bandwidth constraint and dynamic topology of mobile ad hoc networks, multipath supported routing is a very important research issue. In this paper, we present an entropy-based metric to support stability multipath on-demand routing (SMDR). The key idea of SMDR protocol is to construct the new metric-entropy and select the stability multipath with the help of entropy metric to reduce the number of route reconstruction so as to provide QoS guarantee in the ad hoc network whose topology changes continuously. Simulation results show that, with the proposed multipath routing protocol, packet delivery ratio, end-to-end delay, and routing overhead ratio can be improved in most of cases. It is an available approach to multipath routing decision.
An ad hoc network is a collection of wireless mobile nodes dynamically forming a temporary network without the use of any existing network infrastructure or centralized administration. Due to bandwidth constraint and dynamic topology of mobile ad hoc networks, multipath supported routing is a very important research issue. In this paper, we present an entropy-based metric to support stability multipath on-demand routing (SMDR). The key idea of SMDR protocol is to construct the new metric-entropy and select the stability multipath with the help of entropy metric to reduce the number of route reconstruction so as to provide QoS guarantee in the ad hoc network whose topology changes continuously. Simulation results show that, with the proposed multipath routing protocol, packet delivery ratio, end-to-end delay, and routing overhead ratio can be improved in most of cases. It is an available approach to multipath routing decision.
2013,24(1):50-66, DOI:10.3724/SP.J.1001.2013.04276
Abstract:
As an important application of acceleration in the cloud, the distributed caching technology has received considerable attention in industry and academia. This paper starts with a discussion on the combination of cloud computing and distributed caching technology, giving an analysis of its characteristics, typical application scenarios, stages of development, standards, and several key elements, which have promoted its development. In order to systematically know the state of art progress and weak points of the distributed caching technology, the paper builds a multi-dimensional framework, DctAF. This framework is constituted of 6 dimensions through analyzing the characteristics of cloud computing and boundary of the caching techniques. Based on DctAF, current techniques have been analyzed and summarized; comparisons among several influential products have also been made. Finally, the paper describes and highlights the several challenges that the cache system faces and examines the current research through in-depth analysis and comparison.
As an important application of acceleration in the cloud, the distributed caching technology has received considerable attention in industry and academia. This paper starts with a discussion on the combination of cloud computing and distributed caching technology, giving an analysis of its characteristics, typical application scenarios, stages of development, standards, and several key elements, which have promoted its development. In order to systematically know the state of art progress and weak points of the distributed caching technology, the paper builds a multi-dimensional framework, DctAF. This framework is constituted of 6 dimensions through analyzing the characteristics of cloud computing and boundary of the caching techniques. Based on DctAF, current techniques have been analyzed and summarized; comparisons among several influential products have also been made. Finally, the paper describes and highlights the several challenges that the cache system faces and examines the current research through in-depth analysis and comparison.
Abstract:
Recommendation system is one of the most important technologies in E-commerce. With the development of E-commerce, the magnitudes of users and commodities grow rapidly, resulted in the extreme sparsity of user rating data. Traditional similarity measure methods work poor in this situation, make the quality of recommendation system decreased dramatically. To address this issue a novel collaborative filtering algorithm based on item rating prediction is proposed. This method predicts item ratings that users have not rated by the similarity of items, then uses a new similarity measure to find the target users?neighbors. The experimental results show that this method can efficiently improve the extreme sparsity of user rating data, and provid better recommendation results than traditional collaborative filtering algorithms.
Recommendation system is one of the most important technologies in E-commerce. With the development of E-commerce, the magnitudes of users and commodities grow rapidly, resulted in the extreme sparsity of user rating data. Traditional similarity measure methods work poor in this situation, make the quality of recommendation system decreased dramatically. To address this issue a novel collaborative filtering algorithm based on item rating prediction is proposed. This method predicts item ratings that users have not rated by the similarity of items, then uses a new similarity measure to find the target users?neighbors. The experimental results show that this method can efficiently improve the extreme sparsity of user rating data, and provid better recommendation results than traditional collaborative filtering algorithms.
Abstract:
Wide-Spread deployment for interactive information visualization is difficult. Non-Specialist users need a general development method and a toolkit to support the generic data structures suited to tree, network and multi-dimensional data, special visualization techniques and interaction techniques, and well-known generic information tasks. This paper presents a model driven development method for interactive information visualization. First, an interactive information visualization interface model (IIVM) is proposed. Then, the development method for interactive information visualization based on IIVM is presented. The Daisy toolkit is introduced, which includes Daisy model builder, Daisy IIV generator and runtime framework with Daisy library. Finally, an application example is given. Experimental results show that Daisy can provide a general solution for development for interactive information visualization.
Wide-Spread deployment for interactive information visualization is difficult. Non-Specialist users need a general development method and a toolkit to support the generic data structures suited to tree, network and multi-dimensional data, special visualization techniques and interaction techniques, and well-known generic information tasks. This paper presents a model driven development method for interactive information visualization. First, an interactive information visualization interface model (IIVM) is proposed. Then, the development method for interactive information visualization based on IIVM is presented. The Daisy toolkit is introduced, which includes Daisy model builder, Daisy IIV generator and runtime framework with Daisy library. Finally, an application example is given. Experimental results show that Daisy can provide a general solution for development for interactive information visualization.
Abstract:
Computer forensics is the technology field that attempts to prove thorough, efficient, and secure means to investigate computer crime. Computer evidence must be authentic, accurate, complete and convincing to juries. In this paper, the stages of computer forensics are presented, and the theories and the realization of the forensics software are described. An example about forensic practice is also given. The deficiency of computer forensics technique and anti-forensics are also discussed. The result comes out that it is as the improvement of computer science technology, the forensics technique will become more integrated and thorough.
Computer forensics is the technology field that attempts to prove thorough, efficient, and secure means to investigate computer crime. Computer evidence must be authentic, accurate, complete and convincing to juries. In this paper, the stages of computer forensics are presented, and the theories and the realization of the forensics software are described. An example about forensic practice is also given. The deficiency of computer forensics technique and anti-forensics are also discussed. The result comes out that it is as the improvement of computer science technology, the forensics technique will become more integrated and thorough.
Abstract:
Visual language techniques have exhibited more advantages in describing various software artifacts than one-dimensional textual languages during software development, ranging from the requirement analysis and design to testing and maintenance, as diagrammatic and graphical notations have been well applied in modeling system. In addition to an intuitive appearance, graph grammars provide a well-established foundation for defining visual languages with the power of precise modeling and verification on computers. This paper discusses the issues and techniques for a formal foundation of visual languages, reviews related practical graphical environments, presents a spatial graph grammar formalism, and applies the spatial graph grammar to defining behavioral semantics of UML diagrams and developing a style-driven framework for software architecture design.
Visual language techniques have exhibited more advantages in describing various software artifacts than one-dimensional textual languages during software development, ranging from the requirement analysis and design to testing and maintenance, as diagrammatic and graphical notations have been well applied in modeling system. In addition to an intuitive appearance, graph grammars provide a well-established foundation for defining visual languages with the power of precise modeling and verification on computers. This paper discusses the issues and techniques for a formal foundation of visual languages, reviews related practical graphical environments, presents a spatial graph grammar formalism, and applies the spatial graph grammar to defining behavioral semantics of UML diagrams and developing a style-driven framework for software architecture design.
Abstract:
The crucial technologies related to personalization are introduced in this paper, which include the representation and modification of user profile, the representation of resource, the recommendation technology, and the architecture of personalization. By comparing with some existing prototype systems, the key technologies about how to implement personalization are discussed in detail. In addition, three representative personalization systems are analyzed. At last, some research directions for personalization are presented.
The crucial technologies related to personalization are introduced in this paper, which include the representation and modification of user profile, the representation of resource, the recommendation technology, and the architecture of personalization. By comparing with some existing prototype systems, the key technologies about how to implement personalization are discussed in detail. In addition, three representative personalization systems are analyzed. At last, some research directions for personalization are presented.
2012,23(1):82-96, DOI:10.3724/SP.J.1001.2012.04101
Abstract:
Botnets are one of the most serious threats to the Internet. Researchers have done plenty of research and made significant progress. However, botnets keep evolving and have become more and more sophisticated. Due to the underlying security limitation of current system and Internet architecture, and the complexity of botnet itself, how to effectively counter the global threat of botnets is still a very challenging issue. This paper first introduces the evolving of botnet’s propagation, attack, command, and control mechanisms. Then the paper summarizes recent advances of botnet defense research and categorizes into five areas: Botnet monitoring, botnet infiltration, analysis of botnet characteristics, botnet detection and botnet disruption. The limitation of current botnet defense techniques, the evolving trend of botnet, and some possible directions for future research are also discussed.
Botnets are one of the most serious threats to the Internet. Researchers have done plenty of research and made significant progress. However, botnets keep evolving and have become more and more sophisticated. Due to the underlying security limitation of current system and Internet architecture, and the complexity of botnet itself, how to effectively counter the global threat of botnets is still a very challenging issue. This paper first introduces the evolving of botnet’s propagation, attack, command, and control mechanisms. Then the paper summarizes recent advances of botnet defense research and categorizes into five areas: Botnet monitoring, botnet infiltration, analysis of botnet characteristics, botnet detection and botnet disruption. The limitation of current botnet defense techniques, the evolving trend of botnet, and some possible directions for future research are also discussed.
Abstract:
In this paper, a framework is proposed for handling fault of service composition through analyzing fault requirements. Petri nets are used in the framework for fault detecting and its handling, which focuses on targeting the failure of available services, component failure and network failure. The corresponding fault models are given. Based on the model, the correctness criterion of fault handling is given to analyze fault handling model, and its correctness is proven. Finally, CTL (computational tree logic) is used to specify the related properties and enforcement algorithm of fault analysis. The simulation results show that this method can ensure the reliability and consistency of service composition.
In this paper, a framework is proposed for handling fault of service composition through analyzing fault requirements. Petri nets are used in the framework for fault detecting and its handling, which focuses on targeting the failure of available services, component failure and network failure. The corresponding fault models are given. Based on the model, the correctness criterion of fault handling is given to analyze fault handling model, and its correctness is proven. Finally, CTL (computational tree logic) is used to specify the related properties and enforcement algorithm of fault analysis. The simulation results show that this method can ensure the reliability and consistency of service composition.
Abstract:
Software defect prediction has been one of the active parts of software engineering since it was developed in 1970's. It plays a very important role in the analysis of software quality and balance of software cost. This paper investigates and discusses the motivation, evolvement, solutions and challenges of software defect prediction technologies, and it also categorizes, analyzes and compares the representatives of these prediction technologies. Some case studies for software defect distribution models are given to help understanding.
Software defect prediction has been one of the active parts of software engineering since it was developed in 1970's. It plays a very important role in the analysis of software quality and balance of software cost. This paper investigates and discusses the motivation, evolvement, solutions and challenges of software defect prediction technologies, and it also categorizes, analyzes and compares the representatives of these prediction technologies. Some case studies for software defect distribution models are given to help understanding.
2017,28(1):1-16, DOI:10.13328/j.cnki.jos.005139
Abstract:
Knapsack problem (KP) is a well-known combinatorial optimization problem which includes 0-1 KP, bounded KP, multi-constraint KP, multiple KP, multiple-choice KP, quadratic KP, dynamic knapsack KP, discounted KP and other types of KPs. KP can be considered as a mathematical model extracted from variety of real fields and therefore has wide applications. Evolutionary algorithms (EAs) are universally considered as an efficient tool to solve KP approximately and quickly. This paper presents a survey on solving KP by EAs over the past ten years. It not only discusses various KP encoding mechanism and the individual infeasible solution processing but also provides useful guidelines for designing new EAs to solve KPs.
Knapsack problem (KP) is a well-known combinatorial optimization problem which includes 0-1 KP, bounded KP, multi-constraint KP, multiple KP, multiple-choice KP, quadratic KP, dynamic knapsack KP, discounted KP and other types of KPs. KP can be considered as a mathematical model extracted from variety of real fields and therefore has wide applications. Evolutionary algorithms (EAs) are universally considered as an efficient tool to solve KP approximately and quickly. This paper presents a survey on solving KP by EAs over the past ten years. It not only discusses various KP encoding mechanism and the individual infeasible solution processing but also provides useful guidelines for designing new EAs to solve KPs.
Abstract:
As an application of mobile ad hoc networks (MANET) on Intelligent Transportation Information System, the most important goal of vehicular ad hoc networks (VANET) is to reduce the high number of accidents and fatal consequences dramatically. One of the most important factors that would contribute to the realization of this goal is the design of effective broadcast protocols. This paper introduces the characteristics and application fields of VANET briefly. Then, it discusses the characteristics, performance, and application areas with analysis and comparison of various categories of broadcast protocols in VANET. According to the characteristic of VANET and its application requirement, the paper proposes the ideas and breakthrough direction of information broadcast model design of inter-vehicle communication.
As an application of mobile ad hoc networks (MANET) on Intelligent Transportation Information System, the most important goal of vehicular ad hoc networks (VANET) is to reduce the high number of accidents and fatal consequences dramatically. One of the most important factors that would contribute to the realization of this goal is the design of effective broadcast protocols. This paper introduces the characteristics and application fields of VANET briefly. Then, it discusses the characteristics, performance, and application areas with analysis and comparison of various categories of broadcast protocols in VANET. According to the characteristic of VANET and its application requirement, the paper proposes the ideas and breakthrough direction of information broadcast model design of inter-vehicle communication.
Abstract:
Data deduplication technologies can be divided into two categories: a) identical data detection techniques, and b) similar data detection and encoding techniques. This paper presents a systematic survey on these two categories of data deduplication technologies and analyzes their advantages and disadvantages. Besides, since data deduplication technologies can affect the reliability and performance of storage systems, this paper also surveys various kinds of technologies proposed to cope with these two aspects of problems. Based on the analysis of the current state of research on data deduplication technologies, this paper makes several conclusions as follows: a) How to mine data characteristic information in data deduplication has not been completely solved, and how to use data characteristic information to effectively eliminate duplicate data also needs further study; b) From the perspective of storage system design, it still needs further study how to introduce proper mechanisms to overcome the reliability limitations of data deduplication techniques and reduce the additional system overheads caused by data deduplication techniques.
Data deduplication technologies can be divided into two categories: a) identical data detection techniques, and b) similar data detection and encoding techniques. This paper presents a systematic survey on these two categories of data deduplication technologies and analyzes their advantages and disadvantages. Besides, since data deduplication technologies can affect the reliability and performance of storage systems, this paper also surveys various kinds of technologies proposed to cope with these two aspects of problems. Based on the analysis of the current state of research on data deduplication technologies, this paper makes several conclusions as follows: a) How to mine data characteristic information in data deduplication has not been completely solved, and how to use data characteristic information to effectively eliminate duplicate data also needs further study; b) From the perspective of storage system design, it still needs further study how to introduce proper mechanisms to overcome the reliability limitations of data deduplication techniques and reduce the additional system overheads caused by data deduplication techniques.
Abstract:
In recent years, there have been extensive studies and rapid progresses in automatic text categorization, which is one of the hotspots and key techniques in the information retrieval and data mining field. Highlighting the state-of-art challenging issues and research trends for content information processing of Internet and other complex applications, this paper presents a survey on the up-to-date development in text categorization based on machine learning, including model, algorithm and evaluation. It is pointed out that problems such as nonlinearity, skewed data distribution, labeling bottleneck, hierarchical categorization, scalability of algorithms and categorization of Web pages are the key problems to the study of text categorization. Possible solutions to these problems are also discussed respectively. Finally, some future directions of research are given.
In recent years, there have been extensive studies and rapid progresses in automatic text categorization, which is one of the hotspots and key techniques in the information retrieval and data mining field. Highlighting the state-of-art challenging issues and research trends for content information processing of Internet and other complex applications, this paper presents a survey on the up-to-date development in text categorization based on machine learning, including model, algorithm and evaluation. It is pointed out that problems such as nonlinearity, skewed data distribution, labeling bottleneck, hierarchical categorization, scalability of algorithms and categorization of Web pages are the key problems to the study of text categorization. Possible solutions to these problems are also discussed respectively. Finally, some future directions of research are given.
Abstract:
Combinatorial testing can use a small number of test cases to test systems while preserving fault detection ability. However, the complexity of test case generation problem for combinatorial testing is NP-complete. The efficiency and complexity of this testing method have attracted many researchers from the area of combinatorics and software engineering. This paper summarizes the research works on this topic in recent years. They include: various combinatorial test criteria, the relations between the test generation problem and other NP-complete problems, the mathematical methods for constructing test cases, the computer search techniques for test generation and fault localization techniques based on combinatorial testing.
Combinatorial testing can use a small number of test cases to test systems while preserving fault detection ability. However, the complexity of test case generation problem for combinatorial testing is NP-complete. The efficiency and complexity of this testing method have attracted many researchers from the area of combinatorics and software engineering. This paper summarizes the research works on this topic in recent years. They include: various combinatorial test criteria, the relations between the test generation problem and other NP-complete problems, the mathematical methods for constructing test cases, the computer search techniques for test generation and fault localization techniques based on combinatorial testing.
Abstract:
Web search engine has become a very important tool for finding information efficiently from the massive Web data. With the explosive growth of the Web data, traditional centralized search engines become harder to catch up with the growing step of people's information needs. With the rapid development of peer-to-peer (P2P) technology, the notion of P2P Web search has been proposed and quickly becomes a research focus. The goal of this paper is to give a brief summary of current P2P Web search technologies in order to facilitate future research. First, some main challenges for P2P Web search are presented. Then, key techniques for building a feasible and efficient P2P Web search engine are reviewed, including system topology, data placement, query routing, index partitioning, collection selection, relevance ranking and Web crawling. Finally, three recently proposed novel P2P Web search prototypes are introduced.
Web search engine has become a very important tool for finding information efficiently from the massive Web data. With the explosive growth of the Web data, traditional centralized search engines become harder to catch up with the growing step of people's information needs. With the rapid development of peer-to-peer (P2P) technology, the notion of P2P Web search has been proposed and quickly becomes a research focus. The goal of this paper is to give a brief summary of current P2P Web search technologies in order to facilitate future research. First, some main challenges for P2P Web search are presented. Then, key techniques for building a feasible and efficient P2P Web search engine are reviewed, including system topology, data placement, query routing, index partitioning, collection selection, relevance ranking and Web crawling. Finally, three recently proposed novel P2P Web search prototypes are introduced.
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Abstract:
Sensor network, which is made by the convergence of sensor, micro-electro-mechanism system and networks technologies, is a novel technology about acquiring and processing information. In this paper, the architecture of wireless sensor network is briefly introduced. Next, some valuable applications are explained and forecasted. Combining with the existing work, the hot spots including power-aware routing and media access control schemes are discussed and presented in detail. Finally, taking account of application requirements, several future research directions are put forward.
Sensor network, which is made by the convergence of sensor, micro-electro-mechanism system and networks technologies, is a novel technology about acquiring and processing information. In this paper, the architecture of wireless sensor network is briefly introduced. Next, some valuable applications are explained and forecasted. Combining with the existing work, the hot spots including power-aware routing and media access control schemes are discussed and presented in detail. Finally, taking account of application requirements, several future research directions are put forward.
Abstract:
The research actuality and new progress in clustering algorithm in recent years are summarized in this paper. First, the analysis and induction of some representative clustering algorithms have been made from several aspects, such as the ideas of algorithm, key technology, advantage and disadvantage. On the other hand, several typical clustering algorithms and known data sets are selected, simulation experiments are implemented from both sides of accuracy and running efficiency, and clustering condition of one algorithm with different data sets is analyzed by comparing with the same clustering of the data set under different algorithms. Finally, the research hotspot, difficulty, shortage of the data clustering and some pending problems are addressed by the integration of the aforementioned two aspects information. The above work can give a valuable reference for data clustering and data mining.
The research actuality and new progress in clustering algorithm in recent years are summarized in this paper. First, the analysis and induction of some representative clustering algorithms have been made from several aspects, such as the ideas of algorithm, key technology, advantage and disadvantage. On the other hand, several typical clustering algorithms and known data sets are selected, simulation experiments are implemented from both sides of accuracy and running efficiency, and clustering condition of one algorithm with different data sets is analyzed by comparing with the same clustering of the data set under different algorithms. Finally, the research hotspot, difficulty, shortage of the data clustering and some pending problems are addressed by the integration of the aforementioned two aspects information. The above work can give a valuable reference for data clustering and data mining.
Abstract:
This paper surveys the state of the art of sentiment analysis. First, three important tasks of sentiment analysis are summarized and analyzed in detail, including sentiment extraction, sentiment classification, sentiment retrieval and summarization. Then, the evaluation and corpus for sentiment analysis are introduced. Finally, the applications of sentiment analysis are concluded. This paper aims to take a deep insight into the mainstream methods and recent progress in this field, making detailed comparison and analysis.
This paper surveys the state of the art of sentiment analysis. First, three important tasks of sentiment analysis are summarized and analyzed in detail, including sentiment extraction, sentiment classification, sentiment retrieval and summarization. Then, the evaluation and corpus for sentiment analysis are introduced. Finally, the applications of sentiment analysis are concluded. This paper aims to take a deep insight into the mainstream methods and recent progress in this field, making detailed comparison and analysis.
2011,22(1):71-83, DOI:10.3724/SP.J.1001.2011.03958
Abstract:
Cloud Computing is the fundamental change happening in the field of Information Technology. It is a representation of a movement towards the intensive, large scale specialization. On the other hand, it brings about not only convenience and efficiency problems, but also great challenges in the field of data security and privacy protection. Currently, security has been regarded as one of the greatest problems in the development of Cloud Computing. This paper describes the great requirements in Cloud Computing, security key technology, standard and regulation etc., and provides a Cloud Computing security framework. This paper argues that the changes in the above aspects will result in a technical revolution in the field of information security.
Cloud Computing is the fundamental change happening in the field of Information Technology. It is a representation of a movement towards the intensive, large scale specialization. On the other hand, it brings about not only convenience and efficiency problems, but also great challenges in the field of data security and privacy protection. Currently, security has been regarded as one of the greatest problems in the development of Cloud Computing. This paper describes the great requirements in Cloud Computing, security key technology, standard and regulation etc., and provides a Cloud Computing security framework. This paper argues that the changes in the above aspects will result in a technical revolution in the field of information security.
Abstract:
Network community structure is one of the most fundamental and important topological properties of complex networks, within which the links between nodes are very dense, but between which they are quite sparse. Network clustering algorithms which aim to discover all natural network communities from given complex networks are fundamentally important for both theoretical researches and practical applications, and can be used to analyze the topological structures, understand the functions, recognize the hidden patterns, and predict the behaviors of complex networks including social networks, biological networks, World Wide Webs and so on. This paper reviews the background, the motivation, the state of arts as well as the main issues of existing works related to discovering network communities, and tries to draw a comprehensive and clear outline for this new and active research area. This work is hopefully beneficial to the researchers from the communities of complex network analysis, data mining, intelligent Web and bioinformatics.
Network community structure is one of the most fundamental and important topological properties of complex networks, within which the links between nodes are very dense, but between which they are quite sparse. Network clustering algorithms which aim to discover all natural network communities from given complex networks are fundamentally important for both theoretical researches and practical applications, and can be used to analyze the topological structures, understand the functions, recognize the hidden patterns, and predict the behaviors of complex networks including social networks, biological networks, World Wide Webs and so on. This paper reviews the background, the motivation, the state of arts as well as the main issues of existing works related to discovering network communities, and tries to draw a comprehensive and clear outline for this new and active research area. This work is hopefully beneficial to the researchers from the communities of complex network analysis, data mining, intelligent Web and bioinformatics.
Abstract:
This paper surveys the current technologies adopted in cloud computing as well as the systems in enterprises. Cloud computing can be viewed from two different aspects. One is about the cloud infrastructure which is the building block for the up layer cloud application. The other is of course the cloud application. This paper focuses on the cloud infrastructure including the systems and current research. Some attractive cloud applications are also discussed. Cloud computing infrastructure has three distinct characteristics. First, the infrastructure is built on top of large scale clusters which contain a large number of cheap PC servers. Second, the applications are co-designed with the fundamental infrastructure that the computing resources can be maximally utilized. Third, the reliability of the whole system is achieved by software building on top of redundant hardware instead of mere hardware. All these technologies are for the two important goals for distributed system: high scalability and high availability. Scalability means that the cloud infrastructure can be expanded to very large scale even to thousands of nodes. Availability means that the services are available even when quite a number of nodes fail. From this paper, readers will capture the current status of cloud computing as well as its future trends.
This paper surveys the current technologies adopted in cloud computing as well as the systems in enterprises. Cloud computing can be viewed from two different aspects. One is about the cloud infrastructure which is the building block for the up layer cloud application. The other is of course the cloud application. This paper focuses on the cloud infrastructure including the systems and current research. Some attractive cloud applications are also discussed. Cloud computing infrastructure has three distinct characteristics. First, the infrastructure is built on top of large scale clusters which contain a large number of cheap PC servers. Second, the applications are co-designed with the fundamental infrastructure that the computing resources can be maximally utilized. Third, the reliability of the whole system is achieved by software building on top of redundant hardware instead of mere hardware. All these technologies are for the two important goals for distributed system: high scalability and high availability. Scalability means that the cloud infrastructure can be expanded to very large scale even to thousands of nodes. Availability means that the services are available even when quite a number of nodes fail. From this paper, readers will capture the current status of cloud computing as well as its future trends.
Abstract:
Evolutionary multi-objective optimization (EMO), whose main task is to deal with multi-objective optimization problems by evolutionary computation, has become a hot topic in evolutionary computation community. After summarizing the EMO algorithms before 2003 briefly, the recent advances in EMO are discussed in details. The current research directions are concluded. On the one hand, more new evolutionary paradigms have been introduced into EMO community, such as particle swarm optimization, artificial immune systems, and estimation distribution algorithms. On the other hand, in order to deal with many-objective optimization problems, many new dominance schemes different from traditional Pareto-dominance come forth. Furthermore, the essential characteristics of multi-objective optimization problems are deeply investigated. This paper also gives experimental comparison of several representative algorithms. Finally, several viewpoints for the future research of EMO are proposed.
Evolutionary multi-objective optimization (EMO), whose main task is to deal with multi-objective optimization problems by evolutionary computation, has become a hot topic in evolutionary computation community. After summarizing the EMO algorithms before 2003 briefly, the recent advances in EMO are discussed in details. The current research directions are concluded. On the one hand, more new evolutionary paradigms have been introduced into EMO community, such as particle swarm optimization, artificial immune systems, and estimation distribution algorithms. On the other hand, in order to deal with many-objective optimization problems, many new dominance schemes different from traditional Pareto-dominance come forth. Furthermore, the essential characteristics of multi-objective optimization problems are deeply investigated. This paper also gives experimental comparison of several representative algorithms. Finally, several viewpoints for the future research of EMO are proposed.
Abstract:
This paper makes a comprehensive survey of the recommender system research aiming to facilitate readers to understand this field. First the research background is introduced, including commercial application demands, academic institutes, conferences and journals. After formally and informally describing the recommendation problem, a comparison study is conducted based on categorized algorithms. In addition, the commonly adopted benchmarked datasets and evaluation methods are exhibited and most difficulties and future directions are concluded.
This paper makes a comprehensive survey of the recommender system research aiming to facilitate readers to understand this field. First the research background is introduced, including commercial application demands, academic institutes, conferences and journals. After formally and informally describing the recommendation problem, a comparison study is conducted based on categorized algorithms. In addition, the commonly adopted benchmarked datasets and evaluation methods are exhibited and most difficulties and future directions are concluded.
Abstract:
Graphics processing unit (GPU) has been developing rapidly in recent years at a speed over Moor抯 law, and as a result, various applications associated with computer graphics advance greatly. At the same time, the highly processing power, parallelism and programmability available nowadays on the contemporary GPU provide an ideal platform on which the general-purpose computation could be made. Starting from an introduction to the development history and the architecture of GPU, the technical fundamentals of GPU are described in the paper. Then in the main part of the paper, the development of various applications on general purpose computation on GPU is introduced, and among those applications, fluid dynamics, algebraic computation, database operations, and spectrum analysis are introduced in detail. The experience of our work on fluid dynamics has been also given, and the development of software tools in this area is introduced. Finally, a conclusion is made, and the future development and the new challenge on both hardware and software in this subject are discussed.
Graphics processing unit (GPU) has been developing rapidly in recent years at a speed over Moor抯 law, and as a result, various applications associated with computer graphics advance greatly. At the same time, the highly processing power, parallelism and programmability available nowadays on the contemporary GPU provide an ideal platform on which the general-purpose computation could be made. Starting from an introduction to the development history and the architecture of GPU, the technical fundamentals of GPU are described in the paper. Then in the main part of the paper, the development of various applications on general purpose computation on GPU is introduced, and among those applications, fluid dynamics, algebraic computation, database operations, and spectrum analysis are introduced in detail. The experience of our work on fluid dynamics has been also given, and the development of software tools in this area is introduced. Finally, a conclusion is made, and the future development and the new challenge on both hardware and software in this subject are discussed.
Abstract:
Automatic generation of poetry has always been considered a hard nut in natural language generation.This paper reports some pioneering research on a possible generic algorithm and its automatic generation of SONGCI. In light of the characteristics of Chinese ancient poetry, this paper designed the level and oblique tones-based coding method, the syntactic and semantic weighted function of fitness, the elitism and roulette-combined selection operator, and the partially mapped crossover operator and the heuristic mutation operator. As shown by tests, the system constructed on the basis of the computing model designed in this paper is basically capable of generating Chinese SONGCI with some aesthetic merit. This work represents progress in the field of Chinese poetry automatic generation.
Automatic generation of poetry has always been considered a hard nut in natural language generation.This paper reports some pioneering research on a possible generic algorithm and its automatic generation of SONGCI. In light of the characteristics of Chinese ancient poetry, this paper designed the level and oblique tones-based coding method, the syntactic and semantic weighted function of fitness, the elitism and roulette-combined selection operator, and the partially mapped crossover operator and the heuristic mutation operator. As shown by tests, the system constructed on the basis of the computing model designed in this paper is basically capable of generating Chinese SONGCI with some aesthetic merit. This work represents progress in the field of Chinese poetry automatic generation.
2014,25(9):1889-1908, DOI:10.13328/j.cnki.jos.004674
Abstract:
This paper first introduces the key features of big data in different processing modes and their typical application scenarios, as well as corresponding representative processing systems. It then summarizes three development trends of big data processing systems. Next, the paper gives a brief survey on system supported analytic technologies and applications (including deep learning, knowledge computing, social computing, and visualization), and summarizes the key roles of individual technologies in big data analysis and understanding. Finally, the paper lays out three grand challenges of big data processing and analysis, i.e., data complexity, computation complexity, and system complexity. Potential ways for dealing with each complexity are also discussed.
This paper first introduces the key features of big data in different processing modes and their typical application scenarios, as well as corresponding representative processing systems. It then summarizes three development trends of big data processing systems. Next, the paper gives a brief survey on system supported analytic technologies and applications (including deep learning, knowledge computing, social computing, and visualization), and summarizes the key roles of individual technologies in big data analysis and understanding. Finally, the paper lays out three grand challenges of big data processing and analysis, i.e., data complexity, computation complexity, and system complexity. Potential ways for dealing with each complexity are also discussed.
2013,24(11):2476-2497, DOI:10.3724/SP.J.1001.2013.04486
Abstract:
Probabilistic graphical models are powerful tools for compactly representing complex probability distributions, efficiently computing (approximate) marginal and conditional distributions, and conveniently learning parameters and hyperparameters in probabilistic models. As a result, they have been widely used in applications that require some sort of automated probabilistic reasoning, such as computer vision and natural language processing, as a formal approach to deal with uncertainty. This paper surveys the basic concepts and key results of representation, inference and learning in probabilistic graphical models, and demonstrates their uses in two important probabilistic models. It also reviews some recent advances in speeding up classic approximate inference algorithms, followed by a discussion of promising research directions.
Probabilistic graphical models are powerful tools for compactly representing complex probability distributions, efficiently computing (approximate) marginal and conditional distributions, and conveniently learning parameters and hyperparameters in probabilistic models. As a result, they have been widely used in applications that require some sort of automated probabilistic reasoning, such as computer vision and natural language processing, as a formal approach to deal with uncertainty. This paper surveys the basic concepts and key results of representation, inference and learning in probabilistic graphical models, and demonstrates their uses in two important probabilistic models. It also reviews some recent advances in speeding up classic approximate inference algorithms, followed by a discussion of promising research directions.
2012,23(4):962-986, DOI:10.3724/SP.J.1001.2012.04175
Abstract:
Considered as the next generation computing model, cloud computing plays an important role in scientific and commercial computing area and draws great attention from both academia and industry fields. Under cloud computing environment, data center consist of a large amount of computers, usually up to millions, and stores petabyte even exabyte of data, which may easily lead to the failure of the computers or data. The large amount of computers composition not only leads to great challenges to the scalability of the data center and its storage system, but also results in high hardware infrastructure cost and power cost. Therefore, fault-tolerance, scalability, and power consumption of the distributed storage for a data center becomes key part in the technology of cloud computing, in order to ensure the data availability and reliability. In this paper, a survey is made on the state of art of the key technologies in cloud computing in the following aspects: Design of data center network, organization and arrangement of data, strategies to improve fault-tolerance, methods to save storage space, and energy. Firstly, many kinds of classical topologies of data center network are introduced and compared. Secondly, kinds of current fault-tolerant storage techniques are discussed, and data replication and erasure code strategies are especially compared. Thirdly, the main current energy saving technology is addressed and analyzed. Finally, challenges in distributed storage are reviewed as well as future research trends are predicted.
Considered as the next generation computing model, cloud computing plays an important role in scientific and commercial computing area and draws great attention from both academia and industry fields. Under cloud computing environment, data center consist of a large amount of computers, usually up to millions, and stores petabyte even exabyte of data, which may easily lead to the failure of the computers or data. The large amount of computers composition not only leads to great challenges to the scalability of the data center and its storage system, but also results in high hardware infrastructure cost and power cost. Therefore, fault-tolerance, scalability, and power consumption of the distributed storage for a data center becomes key part in the technology of cloud computing, in order to ensure the data availability and reliability. In this paper, a survey is made on the state of art of the key technologies in cloud computing in the following aspects: Design of data center network, organization and arrangement of data, strategies to improve fault-tolerance, methods to save storage space, and energy. Firstly, many kinds of classical topologies of data center network are introduced and compared. Secondly, kinds of current fault-tolerant storage techniques are discussed, and data replication and erasure code strategies are especially compared. Thirdly, the main current energy saving technology is addressed and analyzed. Finally, challenges in distributed storage are reviewed as well as future research trends are predicted.
2012,23(1):1-20, DOI:10.3724/SP.J.1001.2012.04100
Abstract:
Context-Aware recommender systems, aiming to further improve performance accuracy and user satisfaction by fully utilizing contextual information, have recently become one of the hottest topics in the domain of recommender systems. This paper presents an overview of the field of context-aware recommender systems from a process-oriented perspective, including system frameworks, key techniques, main models, evaluation, and typical applications. The prospects for future development and suggestions for possible extensions are also discussed.
Context-Aware recommender systems, aiming to further improve performance accuracy and user satisfaction by fully utilizing contextual information, have recently become one of the hottest topics in the domain of recommender systems. This paper presents an overview of the field of context-aware recommender systems from a process-oriented perspective, including system frameworks, key techniques, main models, evaluation, and typical applications. The prospects for future development and suggestions for possible extensions are also discussed.
2018,29(5):1471-1514, DOI:10.13328/j.cnki.jos.005519
Abstract:
Computer aided detection/diagnosis (CAD) can improve the accuracy of diagnosis,reduce false positive,and provide decision supports for doctors.The main purpose of this paper is to analyze the latest development of computer aided diagnosis tools.Focusing on the top four fatal cancer's incidence positions,major recent publications on CAD applications in different medical imaging areas are reviewed in this survey according to different imaging techniques and diseases.Further more,multidimentional analysis is made on the researches from image data sets,algorithms and evaluation methods.Finally,existing problems,research trend and development direction in the field of medical image CAD system are discussed.
Computer aided detection/diagnosis (CAD) can improve the accuracy of diagnosis,reduce false positive,and provide decision supports for doctors.The main purpose of this paper is to analyze the latest development of computer aided diagnosis tools.Focusing on the top four fatal cancer's incidence positions,major recent publications on CAD applications in different medical imaging areas are reviewed in this survey according to different imaging techniques and diseases.Further more,multidimentional analysis is made on the researches from image data sets,algorithms and evaluation methods.Finally,existing problems,research trend and development direction in the field of medical image CAD system are discussed.
2016,27(1):45-71, DOI:10.13328/j.cnki.jos.004914
Abstract:
Android is a modern and most popular software platform for smartphones. According to report, Android accounted for a huge 81% of all smartphones in 2014 and shipped over 1 billion units worldwide for the first time ever. Apple, Microsoft, Blackberry and Firefox trailed a long way behind. At the same time, increased popularity of the Android smartphones has attracted hackers, leading to massive increase of Android malware applications. This paper summarizes and analyzes the latest advances in Android security from multidimensional perspectives, covering Android architecture, design principles, security mechanisms, major security threats, classification and detection of malware, static and dynamic analyses, machine learning approaches, and security extension proposals.
Android is a modern and most popular software platform for smartphones. According to report, Android accounted for a huge 81% of all smartphones in 2014 and shipped over 1 billion units worldwide for the first time ever. Apple, Microsoft, Blackberry and Firefox trailed a long way behind. At the same time, increased popularity of the Android smartphones has attracted hackers, leading to massive increase of Android malware applications. This paper summarizes and analyzes the latest advances in Android security from multidimensional perspectives, covering Android architecture, design principles, security mechanisms, major security threats, classification and detection of malware, static and dynamic analyses, machine learning approaches, and security extension proposals.
2012,23(1):32-45, DOI:10.3724/SP.J.1001.2012.04091
Abstract:
In many areas such as science, simulation, Internet, and e-commerce, the volume of data to be analyzed grows rapidly. Parallel techniques which could be expanded cost-effectively should be invented to deal with the big data. Relational data management technique has gone through a history of nearly 40 years. Now it encounters the tough obstacle of scalability, which relational techniques can not handle large data easily. In the mean time, none relational techniques, such as MapReduce as a typical representation, emerge as a new force, and expand their application from Web search to territories that used to be occupied by relational database systems. They confront relational technique with high availability, high scalability and massive parallel processing capability. Relational technique community, after losing the big deal of Web search, begins to learn from MapReduce. MapReduce also borrows valuable ideas from relational technique community to improve performance. Relational technique and MapReduce compete with each other, and learn from each other; new data analysis platform and new data analysis eco-system are emerging. Finally the two camps of techniques will find their right places in the new eco-system of big data analysis.
In many areas such as science, simulation, Internet, and e-commerce, the volume of data to be analyzed grows rapidly. Parallel techniques which could be expanded cost-effectively should be invented to deal with the big data. Relational data management technique has gone through a history of nearly 40 years. Now it encounters the tough obstacle of scalability, which relational techniques can not handle large data easily. In the mean time, none relational techniques, such as MapReduce as a typical representation, emerge as a new force, and expand their application from Web search to territories that used to be occupied by relational database systems. They confront relational technique with high availability, high scalability and massive parallel processing capability. Relational technique community, after losing the big deal of Web search, begins to learn from MapReduce. MapReduce also borrows valuable ideas from relational technique community to improve performance. Relational technique and MapReduce compete with each other, and learn from each other; new data analysis platform and new data analysis eco-system are emerging. Finally the two camps of techniques will find their right places in the new eco-system of big data analysis.
Abstract:
Wireless Sensor Networks, a novel technology about acquiring and processing information, have been proposed for a multitude of diverse applications. The problem of self-localization, that is, determining where a given node is physically or relatively located in the networks, is a challenging one, and yet extremely crucial for many applications. In this paper, the evaluation criterion of the performance and the taxonomy for wireless sensor networks self-localization systems and algorithms are described, the principles and characteristics of recent representative localization approaches are discussed and presented, and the directions of research in this area are introduced.
Wireless Sensor Networks, a novel technology about acquiring and processing information, have been proposed for a multitude of diverse applications. The problem of self-localization, that is, determining where a given node is physically or relatively located in the networks, is a challenging one, and yet extremely crucial for many applications. In this paper, the evaluation criterion of the performance and the taxonomy for wireless sensor networks self-localization systems and algorithms are described, the principles and characteristics of recent representative localization approaches are discussed and presented, and the directions of research in this area are introduced.
2011,22(1):115-131, DOI:10.3724/SP.J.1001.2011.03950
Abstract:
The Internet traffic model is the key issue for network performance management, Quality of Service management, and admission control. The paper first summarizes the primary characteristics of Internet traffic, as well as the metrics of Internet traffic. It also illustrates the significance and classification of traffic modeling. Next, the paper chronologically categorizes the research activities of traffic modeling into three phases: 1) traditional Poisson modeling; 2) self-similar modeling; and 3) new research debates and new progress. Thorough reviews of the major research achievements of each phase are conducted. Finally, the paper identifies some open research issue and points out possible future research directions in traffic modeling area.
The Internet traffic model is the key issue for network performance management, Quality of Service management, and admission control. The paper first summarizes the primary characteristics of Internet traffic, as well as the metrics of Internet traffic. It also illustrates the significance and classification of traffic modeling. Next, the paper chronologically categorizes the research activities of traffic modeling into three phases: 1) traditional Poisson modeling; 2) self-similar modeling; and 3) new research debates and new progress. Thorough reviews of the major research achievements of each phase are conducted. Finally, the paper identifies some open research issue and points out possible future research directions in traffic modeling area.
2021,32(2):349-369, DOI:10.13328/j.cnki.jos.006138
Abstract:
Few-shot learning is defined as learning models to solve problems from small samples. In recent years, under the trend of training model with big data, machine learning and deep learning have achieved success in many fields. However, in many application scenarios in the real world, there is not a large amount of data or labeled data for model training, and labeling a large number of unlabeled samples will cost a lot of manpower. Therefore, how to use a small number of samples for learning has become a problem that needs to be paid attention to at present. This paper systematically combs the current approaches of few-shot learning. It introduces each kind of corresponding model from the three categories: fine-tune based, data augmentation based, and transfer learning based. Then, the data augmentation based approaches are subdivided into unlabeled data based, data generation based, and feature augmentation based approaches. The transfer learning based approaches are subdivided into metric learning based, meta-learning based, and graph neural network based methods. In the following, the paper summarizes the few-shot datasets and the results in the experiments of the aforementioned models. Next, the paper summarizes the current situation and challenges in few-shot learning. Finally, the future technological development of few-shot learning is prospected.
Few-shot learning is defined as learning models to solve problems from small samples. In recent years, under the trend of training model with big data, machine learning and deep learning have achieved success in many fields. However, in many application scenarios in the real world, there is not a large amount of data or labeled data for model training, and labeling a large number of unlabeled samples will cost a lot of manpower. Therefore, how to use a small number of samples for learning has become a problem that needs to be paid attention to at present. This paper systematically combs the current approaches of few-shot learning. It introduces each kind of corresponding model from the three categories: fine-tune based, data augmentation based, and transfer learning based. Then, the data augmentation based approaches are subdivided into unlabeled data based, data generation based, and feature augmentation based approaches. The transfer learning based approaches are subdivided into metric learning based, meta-learning based, and graph neural network based methods. In the following, the paper summarizes the few-shot datasets and the results in the experiments of the aforementioned models. Next, the paper summarizes the current situation and challenges in few-shot learning. Finally, the future technological development of few-shot learning is prospected.
2013,24(1):77-90, DOI:10.3724/SP.J.1001.2013.04339
Abstract:
Task parallel programming model is a widely used parallel programming model on multi-core platforms. With the intention of simplifying parallel programming and improving the utilization of multiple cores, this paper provides an introduction to the essential programming interfaces and the supporting mechanism used in task parallel programming models and discusses issues and the latest achievements from three perspectives: Parallelism expression, data management and task scheduling. In the end, some future trends in this area are discussed.
Task parallel programming model is a widely used parallel programming model on multi-core platforms. With the intention of simplifying parallel programming and improving the utilization of multiple cores, this paper provides an introduction to the essential programming interfaces and the supporting mechanism used in task parallel programming models and discusses issues and the latest achievements from three perspectives: Parallelism expression, data management and task scheduling. In the end, some future trends in this area are discussed.
2015,26(1):62-81, DOI:10.13328/j.cnki.jos.004701
Abstract:
Network abstraction brings about the naissance of software-defined networking. SDN decouples data plane and control plane, and simplifies network management. The paper starts with a discussion on the background in the naissance and developments of SDN, combing its architecture that includes data layer, control layer and application layer. Then their key technologies are elaborated according to the hierarchical architecture of SDN. The characteristics of consistency, availability, and tolerance are especially analyzed. Moreover, latest achievements for profiled scenes are introduced. The future works are summarized in the end.
Network abstraction brings about the naissance of software-defined networking. SDN decouples data plane and control plane, and simplifies network management. The paper starts with a discussion on the background in the naissance and developments of SDN, combing its architecture that includes data layer, control layer and application layer. Then their key technologies are elaborated according to the hierarchical architecture of SDN. The characteristics of consistency, availability, and tolerance are especially analyzed. Moreover, latest achievements for profiled scenes are introduced. The future works are summarized in the end.
2017,28(4):959-992, DOI:10.13328/j.cnki.jos.005143
Abstract:
The development of mobile internet and the popularity of mobile terminals produce massive trajectory data of moving objects under the era of big data. Trajectory data has spatio-temporal characteristics and rich information. Trajectory data processing techniques can be used to mine the patterns of human activities and behaviors, the moving patterns of vehicles in the city and the changes of atmospheric environment. However, trajectory data also can be exploited to disclose moving objects' privacy information (e.g., behaviors, hobbies and social relationships). Accordingly, attackers can easily access moving objects' privacy information by digging into their trajectory data such as activities and check-in locations. In another front of research, quantum computation presents an important theoretical direction to mine big data due to its scalable and powerful storage and computing capacity. Applying quantum computing approaches to handle trajectory big data could make some complex problem solvable and achieve higher efficiency. This paper reviews the key technologies of processing trajectory data. First the concept and characteristics of trajectory data is introduced, and the pre-processing methods, including noise filtering and data compression, are summarized. Then, the trajectory indexing and querying techniques, and the current achievements of mining trajectory data, such as pattern mining and trajectory classification, are reviewed. Next, an overview of the basic theories and characteristics of privacy preserving with respect to trajectory data is provided. The supporting techniques of trajectory big data mining, such as processing framework and data visualization, are presented in detail. Some possible ways of applying quantum computation into trajectory data processing, as well as the implementation of some core trajectory mining algorithms by quantum computation are also described. Finally, the challenges of trajectory data processing and promising future research directions are discussed.
The development of mobile internet and the popularity of mobile terminals produce massive trajectory data of moving objects under the era of big data. Trajectory data has spatio-temporal characteristics and rich information. Trajectory data processing techniques can be used to mine the patterns of human activities and behaviors, the moving patterns of vehicles in the city and the changes of atmospheric environment. However, trajectory data also can be exploited to disclose moving objects' privacy information (e.g., behaviors, hobbies and social relationships). Accordingly, attackers can easily access moving objects' privacy information by digging into their trajectory data such as activities and check-in locations. In another front of research, quantum computation presents an important theoretical direction to mine big data due to its scalable and powerful storage and computing capacity. Applying quantum computing approaches to handle trajectory big data could make some complex problem solvable and achieve higher efficiency. This paper reviews the key technologies of processing trajectory data. First the concept and characteristics of trajectory data is introduced, and the pre-processing methods, including noise filtering and data compression, are summarized. Then, the trajectory indexing and querying techniques, and the current achievements of mining trajectory data, such as pattern mining and trajectory classification, are reviewed. Next, an overview of the basic theories and characteristics of privacy preserving with respect to trajectory data is provided. The supporting techniques of trajectory big data mining, such as processing framework and data visualization, are presented in detail. Some possible ways of applying quantum computation into trajectory data processing, as well as the implementation of some core trajectory mining algorithms by quantum computation are also described. Finally, the challenges of trajectory data processing and promising future research directions are discussed.
2011,22(6):1299-1315, DOI:10.3724/SP.J.1001.2011.03993
Abstract:
Attribute-Based encryption (ABE) scheme takes attributes as the public key and associates the ciphertext and user’s secret key with attributes, so that it can support expressive access control policies. This dramatically reduces the cost of network bandwidth and sending node’s operation in fine-grained access control of data sharing. Therefore, ABE has a broad prospect of application in the area of fine-grained access control. After analyzing the basic ABE system and its two variants, Key-Policy ABE (KP-ABE) and Ciphertext-Policy ABE (CP-ABE), this study elaborates the research problems relating to ABE systems, including access structure design for CP-ABE, attribute key revocation, key abuse and multi-authorities ABE with an extensive comparison of their functionality and performance. Finally, this study discusses the need-to-be solved problems and main research directions in ABE.
Attribute-Based encryption (ABE) scheme takes attributes as the public key and associates the ciphertext and user’s secret key with attributes, so that it can support expressive access control policies. This dramatically reduces the cost of network bandwidth and sending node’s operation in fine-grained access control of data sharing. Therefore, ABE has a broad prospect of application in the area of fine-grained access control. After analyzing the basic ABE system and its two variants, Key-Policy ABE (KP-ABE) and Ciphertext-Policy ABE (CP-ABE), this study elaborates the research problems relating to ABE systems, including access structure design for CP-ABE, attribute key revocation, key abuse and multi-authorities ABE with an extensive comparison of their functionality and performance. Finally, this study discusses the need-to-be solved problems and main research directions in ABE.
Abstract:
Nowadays it has been widely accepted that the quality of software highly depends on the process that iscarried out in an organization. As part of the effort to support software process engineering activities, the researchon software process modeling and analysis is to provide an effective means to represent and analyze a process and,by doing so, to enhance the understanding of the modeled process. In addition, an enactable process model canprovide a direct guidance for the actual development process. Thus, the enforcement of the process model candirectly contribute to the improvement of the software quality. In this paper, a systematic review is carried out tosurvey the recent development in software process modeling. 72 papers from 20 conference proceedings and 7journals are identified as the evidence. The review aims to promote a better understanding of the literature byanswering the following three questions: 1) What kinds of paradigms are existing methods based on? 2) What kinds of purposes does the existing research have? 3) What kinds of new trends are reflected in the current research? Afterproviding the systematic review, we present our software process modeling method based on a multi-dimensionaland integration methodology that is intended to address several core issues facing the community.
Nowadays it has been widely accepted that the quality of software highly depends on the process that iscarried out in an organization. As part of the effort to support software process engineering activities, the researchon software process modeling and analysis is to provide an effective means to represent and analyze a process and,by doing so, to enhance the understanding of the modeled process. In addition, an enactable process model canprovide a direct guidance for the actual development process. Thus, the enforcement of the process model candirectly contribute to the improvement of the software quality. In this paper, a systematic review is carried out tosurvey the recent development in software process modeling. 72 papers from 20 conference proceedings and 7journals are identified as the evidence. The review aims to promote a better understanding of the literature byanswering the following three questions: 1) What kinds of paradigms are existing methods based on? 2) What kinds of purposes does the existing research have? 3) What kinds of new trends are reflected in the current research? Afterproviding the systematic review, we present our software process modeling method based on a multi-dimensionaland integration methodology that is intended to address several core issues facing the community.
Abstract:
The appearance of plenty of intelligent devices equipped for short-range wireless communications boosts the fast rise of wireless ad hoc networks application. However, in many realistic application environments, nodes form a disconnected network for most of the time due to nodal mobility, low density, lossy link, etc. Conventional communication model of mobile ad hoc network (MANET) requires at least one path existing from source to destination nodes, which results in communication failure in these scenarios. Opportunistic networks utilize the communication opportunities arising from node movement to forward messages in a hop-by-hop way, and implement communications between nodes based on the "store-carry-forward" routing pattern. This networking approach, totally different from the traditional communication model, captures great interests from researchers. This paper first introduces the conceptions and theories of opportunistic networks and some current typical applications. Then it elaborates the popular research problems including opportunistic forwarding mechanism, mobility model and opportunistic data dissemination and retrieval. Some other interesting research points such as communication middleware, cooperation and security problem and new applications are stated briefly. Finally, the paper concludes and looks forward to the possible research focuses for opportunistic networks in the future.
The appearance of plenty of intelligent devices equipped for short-range wireless communications boosts the fast rise of wireless ad hoc networks application. However, in many realistic application environments, nodes form a disconnected network for most of the time due to nodal mobility, low density, lossy link, etc. Conventional communication model of mobile ad hoc network (MANET) requires at least one path existing from source to destination nodes, which results in communication failure in these scenarios. Opportunistic networks utilize the communication opportunities arising from node movement to forward messages in a hop-by-hop way, and implement communications between nodes based on the "store-carry-forward" routing pattern. This networking approach, totally different from the traditional communication model, captures great interests from researchers. This paper first introduces the conceptions and theories of opportunistic networks and some current typical applications. Then it elaborates the popular research problems including opportunistic forwarding mechanism, mobility model and opportunistic data dissemination and retrieval. Some other interesting research points such as communication middleware, cooperation and security problem and new applications are stated briefly. Finally, the paper concludes and looks forward to the possible research focuses for opportunistic networks in the future.
Abstract:
Uncertainty exists widely in the subjective and objective world. In all kinds of uncertainty, randomness and fuzziness are the most important and fundamental. In this paper, the relationship between randomness and fuzziness is discussed. Uncertain states and their changes can be measured by entropy and hyper-entropy respectively. Taken advantage of entropy and hyper-entropy, the uncertainty of chaos, fractal and complex networks by their various evolution and differentiation are further studied. A simple and effective way is proposed to simulate the uncertainty by means of knowledge representation which provides a basis for the automation of both logic and image thinking with uncertainty. The AI (artificial intelligence) with uncertainty is a new cross-discipline, which covers computer science, physics, mathematics, brain science, psychology, cognitive science, biology and philosophy, and results in the automation of representation, process and thinking for uncertain information and knowledge.
Uncertainty exists widely in the subjective and objective world. In all kinds of uncertainty, randomness and fuzziness are the most important and fundamental. In this paper, the relationship between randomness and fuzziness is discussed. Uncertain states and their changes can be measured by entropy and hyper-entropy respectively. Taken advantage of entropy and hyper-entropy, the uncertainty of chaos, fractal and complex networks by their various evolution and differentiation are further studied. A simple and effective way is proposed to simulate the uncertainty by means of knowledge representation which provides a basis for the automation of both logic and image thinking with uncertainty. The AI (artificial intelligence) with uncertainty is a new cross-discipline, which covers computer science, physics, mathematics, brain science, psychology, cognitive science, biology and philosophy, and results in the automation of representation, process and thinking for uncertain information and knowledge.
Abstract:
In recent years, there have been extensive studies and rapid progresses in automatic text categorization, which is one of the hotspots and key techniques in the information retrieval and data mining field. Highlighting the state-of-art challenging issues and research trends for content information processing of Internet and other complex applications, this paper presents a survey on the up-to-date development in text categorization based on machine learning, including model, algorithm and evaluation. It is pointed out that problems such as nonlinearity, skewed data distribution, labeling bottleneck, hierarchical categorization, scalability of algorithms and categorization of Web pages are the key problems to the study of text categorization. Possible solutions to these problems are also discussed respectively. Finally, some future directions of research are given.
In recent years, there have been extensive studies and rapid progresses in automatic text categorization, which is one of the hotspots and key techniques in the information retrieval and data mining field. Highlighting the state-of-art challenging issues and research trends for content information processing of Internet and other complex applications, this paper presents a survey on the up-to-date development in text categorization based on machine learning, including model, algorithm and evaluation. It is pointed out that problems such as nonlinearity, skewed data distribution, labeling bottleneck, hierarchical categorization, scalability of algorithms and categorization of Web pages are the key problems to the study of text categorization. Possible solutions to these problems are also discussed respectively. Finally, some future directions of research are given.
2014,25(1):37-50, DOI:10.13328/j.cnki.jos.004497
Abstract:
This paper surveys the state of the art of speech emotion recognition (SER), and presents an outlook on the trend of future SER technology. First, the survey summarizes and analyzes SER in detail from five perspectives, including emotion representation models, representative emotional speech corpora, emotion-related acoustic features extraction, SER methods and applications. Then, based on the survey, the challenges faced by current SER research are concluded. This paper aims to take a deep insight into the mainstream methods and recent progress in this field, and presents detailed comparison and analysis between these methods.
This paper surveys the state of the art of speech emotion recognition (SER), and presents an outlook on the trend of future SER technology. First, the survey summarizes and analyzes SER in detail from five perspectives, including emotion representation models, representative emotional speech corpora, emotion-related acoustic features extraction, SER methods and applications. Then, based on the survey, the challenges faced by current SER research are concluded. This paper aims to take a deep insight into the mainstream methods and recent progress in this field, and presents detailed comparison and analysis between these methods.
Abstract:
The paper gives some thinking according to the following four aspects: 1) from the law of things development, revealing the development history of software engineering technology; 2) from the point of software natural characteristic, analyzing the construction of every abstraction layer of virtual machine; 3) from the point of software development, proposing the research content of software engineering discipline, and research the pattern of industrialized software production; 4) based on the appearance of Internet technology, exploring the development trend of software technology.
The paper gives some thinking according to the following four aspects: 1) from the law of things development, revealing the development history of software engineering technology; 2) from the point of software natural characteristic, analyzing the construction of every abstraction layer of virtual machine; 3) from the point of software development, proposing the research content of software engineering discipline, and research the pattern of industrialized software production; 4) based on the appearance of Internet technology, exploring the development trend of software technology.
2018,29(10):2966-2994, DOI:10.13328/j.cnki.jos.005551
Abstract:
In recent years, the rapid development of Internet technology and Web applications has triggered the explosion of various data on the Internet, which generates a large amount of valuable knowledge. How to organize, represent and analyze these knowledge has attracted much attention. Knowledge graph was thus developed to organize these knowledge in a semantical and visualized manner. Knowledge reasoning over knowledge graph then becomes one of the hot research topics and plays an important role in many applications such as vertical search and intelligent question-answer. The goal of knowledge reasoning over knowledge graph is to infer new facts or identify erroneous facts according to existing ones. Unlike traditional knowledge reasoning, knowledge reasoning over knowledge graph is more diversified, due to the simplicity, intuitiveness, flexibility, and richness of knowledge representation in knowledge graph. Starting with the basic concept of knowledge reasoning, this paper presents a survey on the recently developed methods for knowledge reasoning over knowledge graph. Specifically, the research progress is reviewed in detail from two aspects:One-Step reasoning and multi-step reasoning, each including rule based reasoning, distributed embedding based reasoning, neural network based reasoning and hybrid reasoning. Finally, future research directions and outlook of knowledge reasoning over knowledge graph are discussed.
In recent years, the rapid development of Internet technology and Web applications has triggered the explosion of various data on the Internet, which generates a large amount of valuable knowledge. How to organize, represent and analyze these knowledge has attracted much attention. Knowledge graph was thus developed to organize these knowledge in a semantical and visualized manner. Knowledge reasoning over knowledge graph then becomes one of the hot research topics and plays an important role in many applications such as vertical search and intelligent question-answer. The goal of knowledge reasoning over knowledge graph is to infer new facts or identify erroneous facts according to existing ones. Unlike traditional knowledge reasoning, knowledge reasoning over knowledge graph is more diversified, due to the simplicity, intuitiveness, flexibility, and richness of knowledge representation in knowledge graph. Starting with the basic concept of knowledge reasoning, this paper presents a survey on the recently developed methods for knowledge reasoning over knowledge graph. Specifically, the research progress is reviewed in detail from two aspects:One-Step reasoning and multi-step reasoning, each including rule based reasoning, distributed embedding based reasoning, neural network based reasoning and hybrid reasoning. Finally, future research directions and outlook of knowledge reasoning over knowledge graph are discussed.
2012,23(8):2058-2072, DOI:10.3724/SP.J.1001.2012.04237
Abstract:
The Distributed denial of service (DDoS) attack is a major threat to the current network. Based on the attack packet level, the study divides DDoS attacks into network-level DDoS attacks and application-level DDoS attacks. Next, the study analyzes the detection and control methods of these two kinds of DDoS attacks in detail, and it also analyzes the drawbacks of different control methods implemented in different network positions. Finally, the study analyzes the drawbacks of the current detection and control methods, the development trend of the DDoS filter system, and corresponding technological challenges are also proposed.
The Distributed denial of service (DDoS) attack is a major threat to the current network. Based on the attack packet level, the study divides DDoS attacks into network-level DDoS attacks and application-level DDoS attacks. Next, the study analyzes the detection and control methods of these two kinds of DDoS attacks in detail, and it also analyzes the drawbacks of different control methods implemented in different network positions. Finally, the study analyzes the drawbacks of the current detection and control methods, the development trend of the DDoS filter system, and corresponding technological challenges are also proposed.
2020,31(7):2245-2282, DOI:10.13328/j.cnki.jos.006037
Abstract:
Ultrasonography is the first choice of imaging examination and preoperative evaluation for thyroid and breast cancer. However, ultrasonic characteristics of benign and malignant nodules are commonly overlapped. The diagnosis heavily relies on operator's experience other than quantitative and stable methods. In recent years, medical imaging analysis based on computer technology has developed rapidly, and a series of landmark breakthroughs have been made, which provides effective decision supports for medical imaging diagnosis. In this work, the research progress of computer vision and image recognition technologies in thyroid and breast ultrasound images is studied. A series of key technologies involved in automatic diagnosis of ultrasound images is the main lines of the work. The major algorithms in recent years are summarized and analyzed, such as ultrasound image preprocessing, lesion localization and segmentation, feature extraction and classification. Moreover, multi-dimensional analysis is made on the algorithms, data sets, and evaluation methods. Finally, existing problems related to automatic analysis of those two kinds of ultrasound imaging are discussed, research trend and development direction in the field of ultrasound images analysis are discussed.
Ultrasonography is the first choice of imaging examination and preoperative evaluation for thyroid and breast cancer. However, ultrasonic characteristics of benign and malignant nodules are commonly overlapped. The diagnosis heavily relies on operator's experience other than quantitative and stable methods. In recent years, medical imaging analysis based on computer technology has developed rapidly, and a series of landmark breakthroughs have been made, which provides effective decision supports for medical imaging diagnosis. In this work, the research progress of computer vision and image recognition technologies in thyroid and breast ultrasound images is studied. A series of key technologies involved in automatic diagnosis of ultrasound images is the main lines of the work. The major algorithms in recent years are summarized and analyzed, such as ultrasound image preprocessing, lesion localization and segmentation, feature extraction and classification. Moreover, multi-dimensional analysis is made on the algorithms, data sets, and evaluation methods. Finally, existing problems related to automatic analysis of those two kinds of ultrasound imaging are discussed, research trend and development direction in the field of ultrasound images analysis are discussed.
Abstract:
Recommendation system is one of the most important technologies in E-commerce. With the development of E-commerce, the magnitudes of users and commodities grow rapidly, resulted in the extreme sparsity of user rating data. Traditional similarity measure methods work poor in this situation, make the quality of recommendation system decreased dramatically. To address this issue a novel collaborative filtering algorithm based on item rating prediction is proposed. This method predicts item ratings that users have not rated by the similarity of items, then uses a new similarity measure to find the target users?neighbors. The experimental results show that this method can efficiently improve the extreme sparsity of user rating data, and provid better recommendation results than traditional collaborative filtering algorithms.
Recommendation system is one of the most important technologies in E-commerce. With the development of E-commerce, the magnitudes of users and commodities grow rapidly, resulted in the extreme sparsity of user rating data. Traditional similarity measure methods work poor in this situation, make the quality of recommendation system decreased dramatically. To address this issue a novel collaborative filtering algorithm based on item rating prediction is proposed. This method predicts item ratings that users have not rated by the similarity of items, then uses a new similarity measure to find the target users?neighbors. The experimental results show that this method can efficiently improve the extreme sparsity of user rating data, and provid better recommendation results than traditional collaborative filtering algorithms.
Abstract:
As an application of mobile ad hoc networks (MANET) on Intelligent Transportation Information System, the most important goal of vehicular ad hoc networks (VANET) is to reduce the high number of accidents and fatal consequences dramatically. One of the most important factors that would contribute to the realization of this goal is the design of effective broadcast protocols. This paper introduces the characteristics and application fields of VANET briefly. Then, it discusses the characteristics, performance, and application areas with analysis and comparison of various categories of broadcast protocols in VANET. According to the characteristic of VANET and its application requirement, the paper proposes the ideas and breakthrough direction of information broadcast model design of inter-vehicle communication.
As an application of mobile ad hoc networks (MANET) on Intelligent Transportation Information System, the most important goal of vehicular ad hoc networks (VANET) is to reduce the high number of accidents and fatal consequences dramatically. One of the most important factors that would contribute to the realization of this goal is the design of effective broadcast protocols. This paper introduces the characteristics and application fields of VANET briefly. Then, it discusses the characteristics, performance, and application areas with analysis and comparison of various categories of broadcast protocols in VANET. According to the characteristic of VANET and its application requirement, the paper proposes the ideas and breakthrough direction of information broadcast model design of inter-vehicle communication.
2013,24(2):295-316, DOI:10.3724/SP.J.1001.2013.04336
Abstract:
Under the new application mode, the traditional hierarchy data centers face several limitations in size, bandwidth, scalability, and cost. In order to meet the needs of new applications, data center network should fulfill the requirements with low-cost, such as high scalability, low configuration overhead, robustness and energy-saving. First, the shortcomings of the traditional data center network architecture are summarized, and new requirements are pointed out. Secondly, the existing proposals are divided into two categories, i.e. server-centric and network-centric. Then, several representative architectures of these two categories are overviewed and compared in detail. Finally, the future directions of data center network are discussed.
Under the new application mode, the traditional hierarchy data centers face several limitations in size, bandwidth, scalability, and cost. In order to meet the needs of new applications, data center network should fulfill the requirements with low-cost, such as high scalability, low configuration overhead, robustness and energy-saving. First, the shortcomings of the traditional data center network architecture are summarized, and new requirements are pointed out. Secondly, the existing proposals are divided into two categories, i.e. server-centric and network-centric. Then, several representative architectures of these two categories are overviewed and compared in detail. Finally, the future directions of data center network are discussed.
Abstract:
This paper presents a survey on the theory of provable security and its applications to the design and analysis of security protocols. It clarifies what the provable security is, explains some basic notions involved in the theory of provable security and illustrates the basic idea of random oracle model. It also reviews the development and advances of provably secure public-key encryption and digital signature schemes, in the random oracle model or the standard model, as well as the applications of provable security to the design and analysis of session-key distribution protocols and their advances.
This paper presents a survey on the theory of provable security and its applications to the design and analysis of security protocols. It clarifies what the provable security is, explains some basic notions involved in the theory of provable security and illustrates the basic idea of random oracle model. It also reviews the development and advances of provably secure public-key encryption and digital signature schemes, in the random oracle model or the standard model, as well as the applications of provable security to the design and analysis of session-key distribution protocols and their advances.
2014,25(4):839-862, DOI:10.13328/j.cnki.jos.004558
Abstract:
Batch computing and stream computing are two important forms of big data computing. The research and discussions on batch computing in big data environment are comparatively sufficient. But how to efficiently deal with stream computing to meet many requirements, such as low latency, high throughput and continuously reliable running, and how to build efficient stream big data computing systems, are great challenges in the big data computing research. This paper provides a research of the data computing architecture and the key issues in stream computing in big data environments. Firstly, the research gives a brief summary of three application scenarios of stream computing in business intelligence, marketing and public service. It also shows distinctive features of the stream computing in big data environment, such as real time, volatility, burstiness, irregularity and infinity. A well-designed stream computing system always optimizes in system structure, data transmission, application interfaces, high-availability, and so on. Subsequently, the research offers detailed analyses and comparisons of five typical and open-source stream computing systems in big data environment. Finally, the research specifically addresses some new challenges of the stream big data systems, such as scalability, fault tolerance, consistency, load balancing and throughput.
Batch computing and stream computing are two important forms of big data computing. The research and discussions on batch computing in big data environment are comparatively sufficient. But how to efficiently deal with stream computing to meet many requirements, such as low latency, high throughput and continuously reliable running, and how to build efficient stream big data computing systems, are great challenges in the big data computing research. This paper provides a research of the data computing architecture and the key issues in stream computing in big data environments. Firstly, the research gives a brief summary of three application scenarios of stream computing in business intelligence, marketing and public service. It also shows distinctive features of the stream computing in big data environment, such as real time, volatility, burstiness, irregularity and infinity. A well-designed stream computing system always optimizes in system structure, data transmission, application interfaces, high-availability, and so on. Subsequently, the research offers detailed analyses and comparisons of five typical and open-source stream computing systems in big data environment. Finally, the research specifically addresses some new challenges of the stream big data systems, such as scalability, fault tolerance, consistency, load balancing and throughput.
Abstract:
The rapid development of Internet leads to an increase in system complexity and uncertainty. Traditional network management can not meet the requirement, and it shall evolve to fusion based Cyberspace Situational Awareness (CSA). Based on the analysis of function shortage and development requirement, this paper introduces CSA as well as its origin, conception, objective and characteristics. Firstly, a CSA research framework is proposed and the research history is investigated, based on which the main aspects and the existing issues of the research are analyzed. Meanwhile, assessment methods are divided into three categories: Mathematics model, knowledge reasoning and pattern recognition. Then, this paper discusses CSA from three aspects: Model, knowledge representation and assessment methods, and then goes into detail about main idea, assessment process, merits and shortcomings of novel methods. Many typical methods are compared. The current application research of CSA in the fields of security, transmission, survivable, system evaluation and so on is presented. Finally, this paper points the development directions of CSA and offers the conclusions from issue system, technical system and application system.
The rapid development of Internet leads to an increase in system complexity and uncertainty. Traditional network management can not meet the requirement, and it shall evolve to fusion based Cyberspace Situational Awareness (CSA). Based on the analysis of function shortage and development requirement, this paper introduces CSA as well as its origin, conception, objective and characteristics. Firstly, a CSA research framework is proposed and the research history is investigated, based on which the main aspects and the existing issues of the research are analyzed. Meanwhile, assessment methods are divided into three categories: Mathematics model, knowledge reasoning and pattern recognition. Then, this paper discusses CSA from three aspects: Model, knowledge representation and assessment methods, and then goes into detail about main idea, assessment process, merits and shortcomings of novel methods. Many typical methods are compared. The current application research of CSA in the fields of security, transmission, survivable, system evaluation and so on is presented. Finally, this paper points the development directions of CSA and offers the conclusions from issue system, technical system and application system.
Abstract:
Combinatorial testing can use a small number of test cases to test systems while preserving fault detection ability. However, the complexity of test case generation problem for combinatorial testing is NP-complete. The efficiency and complexity of this testing method have attracted many researchers from the area of combinatorics and software engineering. This paper summarizes the research works on this topic in recent years. They include: various combinatorial test criteria, the relations between the test generation problem and other NP-complete problems, the mathematical methods for constructing test cases, the computer search techniques for test generation and fault localization techniques based on combinatorial testing.
Combinatorial testing can use a small number of test cases to test systems while preserving fault detection ability. However, the complexity of test case generation problem for combinatorial testing is NP-complete. The efficiency and complexity of this testing method have attracted many researchers from the area of combinatorics and software engineering. This paper summarizes the research works on this topic in recent years. They include: various combinatorial test criteria, the relations between the test generation problem and other NP-complete problems, the mathematical methods for constructing test cases, the computer search techniques for test generation and fault localization techniques based on combinatorial testing.
2017,28(1):160-183, DOI:10.13328/j.cnki.jos.005136
Abstract:
Image segmentation is the process of dividing the image into a number of regions with similar properties, and it's the preprocessing step for many image processing tasks. In recent years, domestic and foreign scholars mainly focus on the content-based image segmentation algorithms. Based on extensive research on the existing literatures and the latest achievements, this paper categorizes image segmentation algorithms into three types:graph theory based method, pixel clustering based method and semantic segmentation method. The basic ideas, advantage and disadvantage of typical algorithms belong to each category, especially the most recent image semantic segmentation algorithms based on deep neural network are analyzed, compared and summarized. Furthermore, the paper introduces the datasets which are commonly used as benchmark in image segmentation and evaluation criteria for algorithms, and compares several image segmentation algorithms with experiments as well. Finally, some potential future research work is discussed.
Image segmentation is the process of dividing the image into a number of regions with similar properties, and it's the preprocessing step for many image processing tasks. In recent years, domestic and foreign scholars mainly focus on the content-based image segmentation algorithms. Based on extensive research on the existing literatures and the latest achievements, this paper categorizes image segmentation algorithms into three types:graph theory based method, pixel clustering based method and semantic segmentation method. The basic ideas, advantage and disadvantage of typical algorithms belong to each category, especially the most recent image semantic segmentation algorithms based on deep neural network are analyzed, compared and summarized. Furthermore, the paper introduces the datasets which are commonly used as benchmark in image segmentation and evaluation criteria for algorithms, and compares several image segmentation algorithms with experiments as well. Finally, some potential future research work is discussed.
2011,22(3):381-407, DOI:10.3724/SP.J.1001.2011.03934
Abstract:
The popularity of the Internet and the boom of the World Wide Web foster innovative changes in software technology that give birth to a new form of software—networked software, which delivers diversified and personalized on-demand services to the public. With the ever-increasing expansion of applications and users, the scale and complexity of networked software are growing beyond the information processing capability of human beings, which brings software engineers a series of challenges to face. In order to come to a scientific understanding of this kind of ultra-large-scale artificial complex systems, a survey research on the infrastructure, application services, and social interactions of networked software is conducted from a three-dimensional perspective of cyberization, servicesation, and socialization. Interestingly enough, most of them have been found to share the same global characteristics of complex networks such as “Small World” and “Scale Free”. Next, the impact of the empirical study on software engineering research and practice and its implications for further investigations are systematically set forth. The convergence of software engineering and other disciplines will put forth new ideas and thoughts that will breed a new way of thinking and input new methodologies for the study of networked software. This convergence is also expected to achieve the innovations of theories, methods, and key technologies of software engineering to promote the rapid development of software service industry in China.
The popularity of the Internet and the boom of the World Wide Web foster innovative changes in software technology that give birth to a new form of software—networked software, which delivers diversified and personalized on-demand services to the public. With the ever-increasing expansion of applications and users, the scale and complexity of networked software are growing beyond the information processing capability of human beings, which brings software engineers a series of challenges to face. In order to come to a scientific understanding of this kind of ultra-large-scale artificial complex systems, a survey research on the infrastructure, application services, and social interactions of networked software is conducted from a three-dimensional perspective of cyberization, servicesation, and socialization. Interestingly enough, most of them have been found to share the same global characteristics of complex networks such as “Small World” and “Scale Free”. Next, the impact of the empirical study on software engineering research and practice and its implications for further investigations are systematically set forth. The convergence of software engineering and other disciplines will put forth new ideas and thoughts that will breed a new way of thinking and input new methodologies for the study of networked software. This convergence is also expected to achieve the innovations of theories, methods, and key technologies of software engineering to promote the rapid development of software service industry in China.
2013,24(4):825-842, DOI:10.3724/SP.J.1001.2013.04369
Abstract:
Honeypot is a proactive defense technology, introduced by the defense side to change the asymmetric situation of a network attack and defensive game. Through the deployment of the honeypots, i.e. security resources without any production purpose, the defenders can deceive attackers to illegally take advantage of the honeypots and capture and analyze the attack behaviors to understand the attack tools and methods, and to learn the intentions and motivations. Honeypot technology has won the sustained attention of the security community to make considerable progress and get wide application, and has become one of the main technical means of the Internet security threat monitoring and analysis. In this paper, the origin and evolution process of the honeypot technology are presented first. Next, the key mechanisms of honeypot technology are comprehensively analyzed, the development process of the honeypot deployment structure is also reviewed, and the latest applications of honeypot technology in the directions of Internet security threat monitoring, analysis and prevention are summarized. Finally, the problems of honeypot technology, development trends and further research directions are discussed.
Honeypot is a proactive defense technology, introduced by the defense side to change the asymmetric situation of a network attack and defensive game. Through the deployment of the honeypots, i.e. security resources without any production purpose, the defenders can deceive attackers to illegally take advantage of the honeypots and capture and analyze the attack behaviors to understand the attack tools and methods, and to learn the intentions and motivations. Honeypot technology has won the sustained attention of the security community to make considerable progress and get wide application, and has become one of the main technical means of the Internet security threat monitoring and analysis. In this paper, the origin and evolution process of the honeypot technology are presented first. Next, the key mechanisms of honeypot technology are comprehensively analyzed, the development process of the honeypot deployment structure is also reviewed, and the latest applications of honeypot technology in the directions of Internet security threat monitoring, analysis and prevention are summarized. Finally, the problems of honeypot technology, development trends and further research directions are discussed.
2013,24(5):1078-1097, DOI:10.3724/SP.J.1001.2013.04390
Abstract:
The control and data planes are decoupled in software-defined networking, which provide a new solution for research on new network applications and future Internet technologies. The development status of OpenFlow-based SDN technologies is surveyed in this paper. The research background of decoupled architecture of network control and data transmission in OpenFlow network is summarized first, and the key components and research progress including OpenFlow switch, controller, and SDN technologies are introduced. Moreover, current problems and solutions of OpenFlow-based SDN technologies are analyzed in four aspects. Combined with the development status in recent years, the applications used in campus, data center, network management and network security are summarized. Finally, future research trends are discussed.
The control and data planes are decoupled in software-defined networking, which provide a new solution for research on new network applications and future Internet technologies. The development status of OpenFlow-based SDN technologies is surveyed in this paper. The research background of decoupled architecture of network control and data transmission in OpenFlow network is summarized first, and the key components and research progress including OpenFlow switch, controller, and SDN technologies are introduced. Moreover, current problems and solutions of OpenFlow-based SDN technologies are analyzed in four aspects. Combined with the development status in recent years, the applications used in campus, data center, network management and network security are summarized. Finally, future research trends are discussed.
Abstract:
A semi-supervised clustering method based on affinity propagation (AP) algorithm is proposed in this paper. AP takes as input measures of similarity between pairs of data points. AP is an efficient and fast clustering algorithm for large dataset compared with the existing clustering algorithms, such as K-center clustering. But for the datasets with complex cluster structures, it cannot produce good clustering results. It can improve the clustering performance of AP by using the priori known labeled data or pairwise constraints to adjust the similarity matrix. Experimental results show that such method indeed reaches its goal for complex datasets, and this method outperforms the comparative methods when there are a large number of pairwise constraints.
A semi-supervised clustering method based on affinity propagation (AP) algorithm is proposed in this paper. AP takes as input measures of similarity between pairs of data points. AP is an efficient and fast clustering algorithm for large dataset compared with the existing clustering algorithms, such as K-center clustering. But for the datasets with complex cluster structures, it cannot produce good clustering results. It can improve the clustering performance of AP by using the priori known labeled data or pairwise constraints to adjust the similarity matrix. Experimental results show that such method indeed reaches its goal for complex datasets, and this method outperforms the comparative methods when there are a large number of pairwise constraints.
2023,34(2):625-654, DOI:10.13328/j.cnki.jos.006696
Abstract:
Source code bug (vulnerability) detection is a process of judging whether there are unexpected behaviors in the program code. It is widely used in software engineering tasks such as software testing and software maintenance, and plays a vital role in software functional assurance and application security. Traditional vulnerability detection research is based on program analysis, which usually requires strong domain knowledge and complex calculation rules, and faces the problem of state explosion, resulting in limited detection performance, and there is room for greater improvement in the rate of false positives and false negatives. In recent years, the open source community's vigorous development has accumulated massive amounts of data with open source code as the core. In this context, the feature learning capabilities of deep learning can automatically learn semantically rich code representations, thereby providing a new way for vulnerability detection. This study collected the latest high-level papers in this field, systematically summarized and explained the current methods from two aspects:vulnerability code dataset and deep learning vulnerability detection model. Finally, it summarizes the main challenges faced by the research in this field, and looks forward to the possible future research focus.
Source code bug (vulnerability) detection is a process of judging whether there are unexpected behaviors in the program code. It is widely used in software engineering tasks such as software testing and software maintenance, and plays a vital role in software functional assurance and application security. Traditional vulnerability detection research is based on program analysis, which usually requires strong domain knowledge and complex calculation rules, and faces the problem of state explosion, resulting in limited detection performance, and there is room for greater improvement in the rate of false positives and false negatives. In recent years, the open source community's vigorous development has accumulated massive amounts of data with open source code as the core. In this context, the feature learning capabilities of deep learning can automatically learn semantically rich code representations, thereby providing a new way for vulnerability detection. This study collected the latest high-level papers in this field, systematically summarized and explained the current methods from two aspects:vulnerability code dataset and deep learning vulnerability detection model. Finally, it summarizes the main challenges faced by the research in this field, and looks forward to the possible future research focus.
2018,29(1):42-68, DOI:10.13328/j.cnki.jos.005320
Abstract:
The Internet has penetrated into all aspects of human society and has greatly promoted social progress. At the same time, various forms of cybercrimes and network theft occur frequently, bringing great harm to our society and national security. Cyber security has become a major concern to the public and the government. As a large number of Internet functionalities and applications are implemented by software, software plays a crucial role in cyber security research and practice. In fact, almost all cyberattacks were carried out by exploiting vulnerabilities in system software or application software. It is increasingly urgent to investigate the problems of software security in the new age. This paper reviews the state of the art of malware, software vulnerabilities and software security mechanism, and analyzes the new challenges and trends that the software ecosystem is currently facing.
The Internet has penetrated into all aspects of human society and has greatly promoted social progress. At the same time, various forms of cybercrimes and network theft occur frequently, bringing great harm to our society and national security. Cyber security has become a major concern to the public and the government. As a large number of Internet functionalities and applications are implemented by software, software plays a crucial role in cyber security research and practice. In fact, almost all cyberattacks were carried out by exploiting vulnerabilities in system software or application software. It is increasingly urgent to investigate the problems of software security in the new age. This paper reviews the state of the art of malware, software vulnerabilities and software security mechanism, and analyzes the new challenges and trends that the software ecosystem is currently facing.
Abstract:
Inspired from the idea of data fields, a community discovery algorithm based on topological potential is proposed. The basic idea is that a topological potential function is introduced to analytically model the virtual interaction among all nodes in a network and, by regarding each community as a local high potential area, the community structure in the network can be uncovered by detecting all local high potential areas margined by low potential nodes. The experiments on some real-world networks show that the algorithm requires no input parameters and can discover the intrinsic or even overlapping community structure in networks. The time complexity of the algorithm is O(m+n3/γ)～O(n2), where n is the number of nodes to be explored, m is the number of edges, and 2<γ<3 is a constant.
Inspired from the idea of data fields, a community discovery algorithm based on topological potential is proposed. The basic idea is that a topological potential function is introduced to analytically model the virtual interaction among all nodes in a network and, by regarding each community as a local high potential area, the community structure in the network can be uncovered by detecting all local high potential areas margined by low potential nodes. The experiments on some real-world networks show that the algorithm requires no input parameters and can discover the intrinsic or even overlapping community structure in networks. The time complexity of the algorithm is O(m+n3/γ)～O(n2), where n is the number of nodes to be explored, m is the number of edges, and 2<γ<3 is a constant.
2016,27(3):691-713, DOI:10.13328/j.cnki.jos.004948
Abstract:
Learning to rank(L2R) techniques try to solve sorting problems using machine learning methods, and have been well studied and widely used in various fields such as information retrieval, text mining, personalized recommendation, and biomedicine.The main task of L2R based recommendation algorithms is integrating L2R techniques into recommendation algorithms, and studying how to organize a large number of users and features of items, build more suitable user models according to user preferences requirements, and improve the performance and user satisfaction of recommendation algorithms.This paper surveys L2R based recommendation algorithms in recent years, summarizes the problem definition, compares key technologies and analyzes evaluation metrics and their applications.In addition, the paper discusses the future development trend of L2R based recommendation algorithms.
Learning to rank(L2R) techniques try to solve sorting problems using machine learning methods, and have been well studied and widely used in various fields such as information retrieval, text mining, personalized recommendation, and biomedicine.The main task of L2R based recommendation algorithms is integrating L2R techniques into recommendation algorithms, and studying how to organize a large number of users and features of items, build more suitable user models according to user preferences requirements, and improve the performance and user satisfaction of recommendation algorithms.This paper surveys L2R based recommendation algorithms in recent years, summarizes the problem definition, compares key technologies and analyzes evaluation metrics and their applications.In addition, the paper discusses the future development trend of L2R based recommendation algorithms.
2019,30(2):440-468, DOI:10.13328/j.cnki.jos.005659
Abstract:
Recent years, applying Deep Learning (DL) into Image Semantic Segmentation (ISS) has been widely used due to its state-of-the-art performances and high-quality results. This paper systematically reviews the contribution of DL to the field of ISS. Different methods of ISS based on DL (ISSbDL) are summarized. These methods are divided into ISS based on the Regional Classification (ISSbRC) and ISS based on the Pixel Classification (ISSbPC) according to the image segmentation characteristics and segmentation granularity. Then, the methods of ISSbPC are surveyed from two points of view:ISS based on Fully Supervised Learning (ISSbFSL) and ISS based on Weakly Supervised Learning (ISSbWSL). The representative algorithms of each method are introduced and analyzed, as well as the basic workflow, framework, advantages and disadvantages of these methods are detailedly analyzed and compared. In addition, the related experiments of ISS are analyzed and summarized, and the common data sets and performance evaluation indexes in ISS experiments are introduced. Finally, possible research directions and trends are given and analyzed.
Recent years, applying Deep Learning (DL) into Image Semantic Segmentation (ISS) has been widely used due to its state-of-the-art performances and high-quality results. This paper systematically reviews the contribution of DL to the field of ISS. Different methods of ISS based on DL (ISSbDL) are summarized. These methods are divided into ISS based on the Regional Classification (ISSbRC) and ISS based on the Pixel Classification (ISSbPC) according to the image segmentation characteristics and segmentation granularity. Then, the methods of ISSbPC are surveyed from two points of view:ISS based on Fully Supervised Learning (ISSbFSL) and ISS based on Weakly Supervised Learning (ISSbWSL). The representative algorithms of each method are introduced and analyzed, as well as the basic workflow, framework, advantages and disadvantages of these methods are detailedly analyzed and compared. In addition, the related experiments of ISS are analyzed and summarized, and the common data sets and performance evaluation indexes in ISS experiments are introduced. Finally, possible research directions and trends are given and analyzed.
2024,35(3):1207-1230, DOI: 10.13328/j.cnki.jos.007075
Abstract:
2024,35(3):1231-1256, DOI: 10.13328/j.cnki.jos.007061
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2024,35(3):1377-1402, DOI: 10.13328/j.cnki.jos.006807
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2024,35(2):711-738, DOI: 10.13328/j.cnki.jos.007006
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2024,35(2):927-974, DOI: 10.13328/j.cnki.jos.006996
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2024,35(2):773-799, DOI: 10.13328/j.cnki.jos.006985
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2024,35(2):1028-1047, DOI: 10.13328/j.cnki.jos.006984
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2024,35(2):629-674, DOI: 10.13328/j.cnki.jos.006983
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2024,35(2):604-628, DOI: 10.13328/j.cnki.jos.006981
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2024,35(2):581-603, DOI: 10.13328/j.cnki.jos.006975
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2024,35(2):828-851, DOI: 10.13328/j.cnki.jos.006954
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2024,35(2):532-580, DOI: 10.13328/j.cnki.jos.006953
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2024,35(2):899-926, DOI: 10.13328/j.cnki.jos.006952
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2024,35(2):800-827, DOI: 10.13328/j.cnki.jos.006950
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2024,35(2):975-1009, DOI: 10.13328/j.cnki.jos.006946
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2024,35(2):513-531, DOI: 10.13328/j.cnki.jos.006944
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2024,35(2):1010-1027, DOI: 10.13328/j.cnki.jos.006943
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2024,35(2):872-898, DOI: 10.13328/j.cnki.jos.006942
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2024,35(2):852-871, DOI: 10.13328/j.cnki.jos.006938
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2024,35(2):675-710, DOI: 10.13328/j.cnki.jos.006933
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2024,35(1):19-37, DOI: 10.13328/j.cnki.jos.006935
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2024,35(1):430-454, DOI: 10.13328/j.cnki.jos.006917
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2024,35(1):405-429, DOI: 10.13328/j.cnki.jos.006916
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2024,35(1):236-265, DOI: 10.13328/j.cnki.jos.006913
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2024,35(1):356-404, DOI: 10.13328/j.cnki.jos.006911
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2024,35(1):1-18, DOI: 10.13328/j.cnki.jos.006908
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2024,35(1):87-117, DOI: 10.13328/j.cnki.jos.006902
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2024,35(1):309-332, DOI: 10.13328/j.cnki.jos.006898
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2024,35(1):333-355, DOI: 10.13328/j.cnki.jos.006891
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2024,35(1):220-235, DOI: 10.13328/j.cnki.jos.006844
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2024,35(1):63-86, DOI: 10.13328/j.cnki.jos.006835
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2024,35(1):185-219, DOI: 10.13328/j.cnki.jos.006834
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2024,35(1):136-158, DOI: 10.13328/j.cnki.jos.006828
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2024,35(1):118-135, DOI: 10.13328/j.cnki.jos.006827
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2024,35(1):455-480, DOI: 10.13328/j.cnki.jos.006822
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2024,35(1):288-308, DOI: 10.13328/j.cnki.jos.006818
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2024,35(1):38-62, DOI: 10.13328/j.cnki.jos.006810
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2024,35(1):481-512, DOI: 10.13328/j.cnki.jos.006777
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2024,35(1):159-184, DOI: 10.13328/j.cnki.jos.006758
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2024,35(1):266-287, DOI: 10.13328/j.cnki.jos.006753
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2023,34(12):5597-5613, DOI: 10.13328/j.cnki.jos.006806
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2023,34(12):5862-5886, DOI: 10.13328/j.cnki.jos.006800
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2023,34(12):5527-5551, DOI: 10.13328/j.cnki.jos.006765
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2023,34(12):5822-5847, DOI: 10.13328/j.cnki.jos.006764
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2023,34(12):5501-5526, DOI: 10.13328/j.cnki.jos.006749
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2023,34(11):5058-5083, DOI: 10.13328/j.cnki.jos.006735
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2023,34(11):4989-5007, DOI: 10.13328/j.cnki.jos.006684
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2023,34(11):5008-5028, DOI: 10.13328/j.cnki.jos.006679
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2023,34(11):5330-5354, DOI: 10.13328/j.cnki.jos.006669
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2023,34(10):4439-4462, DOI: 10.13328/j.cnki.jos.006884
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2023,34(10):4870-4915, DOI: 10.13328/j.cnki.jos.006680
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2023,34(10):4607-4635, DOI: 10.13328/j.cnki.jos.006677
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2023,34(10):4743-4771, DOI: 10.13328/j.cnki.jos.006672
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2023,34(10):4772-4803, DOI: 10.13328/j.cnki.jos.006671
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2023,34(10):4636-4660, DOI: 10.13328/j.cnki.jos.006666
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2023,34(9):4310-4335, DOI: 10.13328/j.cnki.jos.006657
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2023,34(9):4167-4194, DOI: 10.13328/j.cnki.jos.006650
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2023,34(8):3905-3923, DOI: 10.13328/j.cnki.jos.006645
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2023,34(8):3870-3890, DOI: 10.13328/j.cnki.jos.006635
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2023,34(8):3774-3792, DOI: 10.13328/j.cnki.jos.006592
Abstract:
2023,34(8):3605-3636, DOI: 10.13328/j.cnki.jos.006558
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2023,34(7):3134-3166, DOI: 10.13328/j.cnki.jos.006863
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2023,34(6):2606-2627, DOI: 10.13328/j.cnki.jos.006850
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2023,34(6):2833-2864, DOI: 10.13328/j.cnki.jos.006658
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2023,34(5):2365-2391, DOI: 10.13328/j.cnki.jos.006808
Abstract:
2023,34(5):2300-2316, DOI: 10.13328/j.cnki.jos.006485
Abstract:
2023,34(4):1944-1961, DOI: 10.13328/j.cnki.jos.006725
Abstract:
2023,34(4):1879-1906, DOI: 10.13328/j.cnki.jos.006692
Abstract:
2023,34(3):1168-1192, DOI: 10.13328/j.cnki.jos.006786
Abstract:
2023,34(3):1236-1258, DOI: 10.13328/j.cnki.jos.006785
Abstract:
2023,34(3):1470-1490, DOI: 10.13328/j.cnki.jos.006778
Abstract:
2023,34(3):1396-1424, DOI: 10.13328/j.cnki.jos.006751
Abstract:
2023,34(3):1330-1364, DOI: 10.13328/j.cnki.jos.006717
Abstract:
2023,34(2):582-624, DOI: 10.13328/j.cnki.jos.006714
Abstract:
2023,34(2):761-785, DOI: 10.13328/j.cnki.jos.006713
Abstract:
2023,34(2):565-581, DOI: 10.13328/j.cnki.jos.006711
Abstract:
2023,34(2):690-711, DOI: 10.13328/j.cnki.jos.006709
Abstract:
2023,34(2):985-1006, DOI: 10.13328/j.cnki.jos.006707
Abstract:
2023,34(2):733-760, DOI: 10.13328/j.cnki.jos.006706
Abstract:
2023,34(2):539-564, DOI: 10.13328/j.cnki.jos.006705
Abstract:
2023,34(2):509-538, DOI: 10.13328/j.cnki.jos.006704
Abstract:
2023,34(2):712-732, DOI: 10.13328/j.cnki.jos.006699
Abstract:
2023,34(2):625-654, DOI: 10.13328/j.cnki.jos.006696
Abstract:
2023,34(2):964-984, DOI: 10.13328/j.cnki.jos.006693
Abstract:
2023,34(1):404-420, DOI: 10.13328/j.cnki.jos.006513
Abstract:
2023,34(1):351-380, DOI: 10.13328/j.cnki.jos.006510
Abstract:
2023,34(1):50-102, DOI: 10.13328/j.cnki.jos.006503
Abstract:
2023,34(1):463-488, DOI: 10.13328/j.cnki.jos.006502
Abstract:
2023,34(1):381-403, DOI: 10.13328/j.cnki.jos.006501
Abstract:
2023,34(1):171-196, DOI: 10.13328/j.cnki.jos.006500
Abstract:
2023,34(1):444-462, DOI: 10.13328/j.cnki.jos.006488
Abstract:
2023,34(1):489-508, DOI: 10.13328/j.cnki.jos.006436
Abstract:
2023,34(1):334-350, DOI: 10.13328/j.cnki.jos.006435
Abstract:
2023,34(1):421-443, DOI: 10.13328/j.cnki.jos.006434
Abstract:
2023,34(1):150-170, DOI: 10.13328/j.cnki.jos.006431
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2023,34(1):277-311, DOI: 10.13328/j.cnki.jos.006429
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2023,34(1):130-149, DOI: 10.13328/j.cnki.jos.006425
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2023,34(1):33-49, DOI: 10.13328/j.cnki.jos.006421
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2023,34(1):255-276, DOI: 10.13328/j.cnki.jos.006420
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2023,34(1):230-254, DOI: 10.13328/j.cnki.jos.006415
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2023,34(1):197-229, DOI: 10.13328/j.cnki.jos.006411
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2023,34(1):103-129, DOI: 10.13328/j.cnki.jos.006409
Abstract:
2023,34(1):1-32, DOI: 10.13328/j.cnki.jos.006402
Abstract:
2023,34(1):312-333, DOI: 10.13328/j.cnki.jos.006395
Abstract:
2022,33(12):4668-4687, DOI: 10.13328/j.cnki.jos.006544
Abstract:
2022,33(12):4644-4667, DOI: 10.13328/j.cnki.jos.006522
Abstract:
2022,33(12):4616-4643, DOI: 10.13328/j.cnki.jos.006521
Abstract:
2022,33(12):4590-4615, DOI: 10.13328/j.cnki.jos.006518
Abstract:
2022,33(11):4107-4136, DOI: 10.13328/j.cnki.jos.006539
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2022,33(11):4084-4106, DOI: 10.13328/j.cnki.jos.006528
Abstract:
2022,33(11):4334-4355, DOI: 10.13328/j.cnki.jos.006524
Abstract:
2022,33(11):4217-4238, DOI: 10.13328/j.cnki.jos.006391
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2022,33(11):4173-4191, DOI: 10.13328/j.cnki.jos.006390
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2022,33(11):4192-4216, DOI: 10.13328/j.cnki.jos.006385
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2022,33(10):3635-3655, DOI: 10.13328/j.cnki.jos.006631
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2022,33(10):3582-3618, DOI: 10.13328/j.cnki.jos.006626
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2022,33(10):3673-3699, DOI: 10.13328/j.cnki.jos.006372
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2022,33(10):3732-3753, DOI: 10.13328/j.cnki.jos.006365
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2022,33(9):3370-3390, DOI: 10.13328/j.cnki.jos.006426
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2022,33(8):3059-3085, DOI: 10.13328/j.cnki.jos.006375
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2022,33(8):3015-3034, DOI: 10.13328/j.cnki.jos.006355
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2022,33(7):2411-2446, DOI: 10.13328/j.cnki.jos.006590
Abstract:
2022,33(7):2367-2410, DOI: 10.13328/j.cnki.jos.006587
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2022,33(7):2729-2752, DOI: 10.13328/j.cnki.jos.006299
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2022,33(6):2288-2311, DOI: 10.13328/j.cnki.jos.006367
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2022,33(5):1612-1634, DOI: 10.13328/j.cnki.jos.006551
Abstract:
2022,33(5):1800-1816, DOI: 10.13328/j.cnki.jos.006122
Abstract:
2022,33(4):1244-1266, DOI: 10.13328/j.cnki.jos.006481
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2022,33(4):1287-1314, DOI: 10.13328/j.cnki.jos.006476
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2022,33(3):1057-1092, DOI: 10.13328/j.cnki.jos.006446
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2022,33(2):751-769, DOI: 10.13328/j.cnki.jos.006370
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2022,33(2):598-621, DOI: 10.13328/j.cnki.jos.006357
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2022,33(2):498-523, DOI: 10.13328/j.cnki.jos.006353
Abstract:
2022,33(1):78-101, DOI: 10.13328/j.cnki.jos.006345
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2022,33(1):297-323, DOI: 10.13328/j.cnki.jos.006344
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2022,33(1):193-210, DOI: 10.13328/j.cnki.jos.006342
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2022,33(1):274-296, DOI: 10.13328/j.cnki.jos.006338
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2022,33(1):55-77, DOI: 10.13328/j.cnki.jos.006337
Abstract:
2022,33(1):356-376, DOI: 10.13328/j.cnki.jos.006334
Abstract:
2022,33(1):171-192, DOI: 10.13328/j.cnki.jos.006323
Abstract:
2022,33(1):150-170, DOI: 10.13328/j.cnki.jos.006311
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2022,33(1):129-149, DOI: 10.13328/j.cnki.jos.006306
Abstract:
2022,33(1):102-128, DOI: 10.13328/j.cnki.jos.006304
Abstract:
2022,33(1):254-273, DOI: 10.13328/j.cnki.jos.006303
Abstract:
2022,33(1):26-54, DOI: 10.13328/j.cnki.jos.006292
Abstract:
2022,33(1):324-355, DOI: 10.13328/j.cnki.jos.006280
Abstract:
2022,33(1):1-25, DOI: 10.13328/j.cnki.jos.006279
Abstract:
2022,33(1):233-253, DOI: 10.13328/j.cnki.jos.006219
Abstract:
2022,33(1):211-232, DOI: 10.13328/j.cnki.jos.006218
Abstract:
2021,32(12):3992-4024, DOI: 10.13328/j.cnki.jos.006490
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2021,32(10):3051-3067, DOI: 10.13328/j.cnki.jos.006217
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2021,32(10):3176-3202, DOI: 10.13328/j.cnki.jos.006203
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2021,32(9):2642-2664, DOI: 10.13328/j.cnki.jos.006275
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2021,32(9):2665-2690, DOI: 10.13328/j.cnki.jos.006274
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2021,32(8):2522-2544, DOI: 10.13328/j.cnki.jos.006215
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2021,32(8):2469-2504, DOI: 10.13328/j.cnki.jos.006153
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2021,32(7):2260-2286, DOI: 10.13328/j.cnki.jos.006309
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2021,32(7):2078-2102, DOI: 10.13328/j.cnki.jos.006269
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2021,32(7):1926-1956, DOI: 10.13328/j.cnki.jos.006267
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2021,32(7):2056-2077, DOI: 10.13328/j.cnki.jos.006266
Abstract:
2021,32(7):2016-2038, DOI: 10.13328/j.cnki.jos.006265
Abstract:
2021,32(7):2118-2141, DOI: 10.13328/j.cnki.jos.006258
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2021,32(5):1404-1426, DOI: 10.13328/j.cnki.jos.006214
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2021,32(5):1495-1525, DOI: 10.13328/j.cnki.jos.006213
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2021,32(5):1461-1479, DOI: 10.13328/j.cnki.jos.006210
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2021,32(5):1427-1460, DOI: 10.13328/j.cnki.jos.006205
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2021,32(5):1526-1546, DOI: 10.13328/j.cnki.jos.006204
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2021,32(5):1373-1384, DOI: 10.13328/j.cnki.jos.006200
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2021,32(4):1082-1115, DOI: 10.13328/j.cnki.jos.006169
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2021,32(4):1129-1150, DOI: 10.13328/j.cnki.jos.006168
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2021,32(4):1067-1081, DOI: 10.13328/j.cnki.jos.006167
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2021,32(4):1201-1227, DOI: 10.13328/j.cnki.jos.006166
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2021,32(4):1165-1185, DOI: 10.13328/j.cnki.jos.006165
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2021,32(4):1151-1164, DOI: 10.13328/j.cnki.jos.006116
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2021,32(3):604-621, DOI: 10.13328/j.cnki.jos.006182
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2021,32(3):859-885, DOI: 10.13328/j.cnki.jos.006175
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2021,32(2):579-600, DOI: 10.13328/j.cnki.jos.006155
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2021,32(2):300-326, DOI: 10.13328/j.cnki.jos.006151
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2021,32(2):277-299, DOI: 10.13328/j.cnki.jos.006150
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2021,32(2):406-423, DOI: 10.13328/j.cnki.jos.006147
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2021,32(2):370-405, DOI: 10.13328/j.cnki.jos.006146
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2021,32(2):424-444, DOI: 10.13328/j.cnki.jos.006145
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2021,32(2):496-518, DOI: 10.13328/j.cnki.jos.006140
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2021,32(2):349-369, DOI: 10.13328/j.cnki.jos.006138
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2021,32(2):551-578, DOI: 10.13328/j.cnki.jos.006135
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2021,32(2):475-495, DOI: 10.13328/j.cnki.jos.006134
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2021,32(2):247-276, DOI: 10.13328/j.cnki.jos.006130
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2021,32(2):327-348, DOI: 10.13328/j.cnki.jos.006125
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2021,32(2):445-474, DOI: 10.13328/j.cnki.jos.006124
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2021,32(2):519-550, DOI: 10.13328/j.cnki.jos.006104
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2021,32(1):41-67, DOI: 10.13328/j.cnki.jos.006131
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2021,32(1):21-40, DOI: 10.13328/j.cnki.jos.006121
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2021,32(1):118-136, DOI: 10.13328/j.cnki.jos.006120
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2021,32(1):1-20, DOI: 10.13328/j.cnki.jos.006111
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2021,32(1):194-217, DOI: 10.13328/j.cnki.jos.006105
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2021,32(1):218-245, DOI: 10.13328/j.cnki.jos.006103
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2021,32(1):167-193, DOI: 10.13328/j.cnki.jos.006100
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2021,32(1):68-92, DOI: 10.13328/j.cnki.jos.006096
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2021,32(1):137-166, DOI: 10.13328/j.cnki.jos.006095
Abstract:
2021,32(1):93-117, DOI: 10.13328/j.cnki.jos.006092
Abstract:
Survey of Research on Protection Mechanisms of Operating System against Board Level Physical Attacks
2020,31(10):3120-3146, DOI: 10.13328/j.cnki.jos.006067
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2020,31(10):3100-3119, DOI: 10.13328/j.cnki.jos.006066
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2020,31(10):3087-3099, DOI: 10.13328/j.cnki.jos.006065
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2020,31(9):2903-2925, DOI: 10.13328/j.cnki.jos.006091
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2020,31(9):2826-2854, DOI: 10.13328/j.cnki.jos.006087
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2020,31(9):2802-2825, DOI: 10.13328/j.cnki.jos.006085
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2020,31(9):2883-2902, DOI: 10.13328/j.cnki.jos.006083
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2020,31(9):2654-2677, DOI: 10.13328/j.cnki.jos.005946
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2020,31(9):2627-2653, DOI: 10.13328/j.cnki.jos.005942
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2020,31(8):2574-2587, DOI: 10.13328/j.cnki.jos.006088
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2020,31(8):2432-2452, DOI: 10.13328/j.cnki.jos.006081
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2020,31(8):2603-2624, DOI: 10.13328/j.cnki.jos.006080
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2020,31(8):2465-2491, DOI: 10.13328/j.cnki.jos.006078
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2020,31(8):2404-2431, DOI: 10.13328/j.cnki.jos.006076
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2020,31(7):2040-2061, DOI: 10.13328/j.cnki.jos.006055
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2020,31(7):2127-2156, DOI: 10.13328/j.cnki.jos.006052
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2020,31(7):2221-2244, DOI: 10.13328/j.cnki.jos.006050
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2020,31(7):2095-2126, DOI: 10.13328/j.cnki.jos.006048
Abstract:
2020,31(7):2019-2039, DOI: 10.13328/j.cnki.jos.006047
Abstract:
2020,31(7):2205-2220, DOI: 10.13328/j.cnki.jos.006046
Abstract:
2020,31(7):1997-2018, DOI: 10.13328/j.cnki.jos.006045
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2020,31(7):2062-2094, DOI: 10.13328/j.cnki.jos.006040
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2020,31(7):2184-2204, DOI: 10.13328/j.cnki.jos.006039
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2020,31(7):2245-2282, DOI: 10.13328/j.cnki.jos.006037
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2020,31(6):1761-1785, DOI: 10.13328/j.cnki.jos.006042
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2020,31(6):1723-1746, DOI: 10.13328/j.cnki.jos.006029
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2020,31(6):1681-1702, DOI: 10.13328/j.cnki.jos.006014
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2020,31(5):1465-1496, DOI: 10.13328/j.cnki.jos.005988
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2020,31(5):1406-1434, DOI: 10.13328/j.cnki.jos.005967
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2020,31(5):1435-1453, DOI: 10.13328/j.cnki.jos.005966
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2020,31(5):1255-1275, DOI: 10.13328/j.cnki.jos.005951
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2020,31(5):1243-1254, DOI: 10.13328/j.cnki.jos.005950
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2020,31(4):1143-1161, DOI: 10.13328/j.cnki.jos.005987
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2020,31(4):1124-1142, DOI: 10.13328/j.cnki.jos.005982
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2020,31(4):912-931, DOI: 10.13328/j.cnki.jos.005916
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2020,31(3):893-908, DOI: 10.13328/j.cnki.jos.005911
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2020,31(3):600-619, DOI: 10.13328/j.cnki.jos.005909
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2020,31(3):806-830, DOI: 10.13328/j.cnki.jos.005908
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2020,31(3):866-892, DOI: 10.13328/j.cnki.jos.005904
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2020,31(2):544-563, DOI: 10.13328/j.cnki.jos.005979
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2020,31(2):302-320, DOI: 10.13328/j.cnki.jos.005977
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2020,31(1):47-66, DOI: 10.13328/j.cnki.jos.005893
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2020,31(1):1-19, DOI: 10.13328/j.cnki.jos.005891
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2020,31(1):20-46, DOI: 10.13328/j.cnki.jos.005887
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2020,31(1):162-182, DOI: 10.13328/j.cnki.jos.005886
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2020,31(1):67-81, DOI: 10.13328/j.cnki.jos.005884
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2020,31(1):183-207, DOI: 10.13328/j.cnki.jos.005879
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2020,31(1):228-245, DOI: 10.13328/j.cnki.jos.005878
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2020,31(1):137-161, DOI: 10.13328/j.cnki.jos.005872
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2020,31(1):113-136, DOI: 10.13328/j.cnki.jos.005871
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2020,31(1):82-112, DOI: 10.13328/j.cnki.jos.005870
Abstract:
2020,31(1):208-227, DOI: 10.13328/j.cnki.jos.005867
Abstract:
2019,30(10):3206-3226, DOI: 10.13328/j.cnki.jos.005796
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2019,30(10):3090-3114, DOI: 10.13328/j.cnki.jos.005790
Abstract:
2019,30(9):2830-2856, DOI: 10.13328/j.cnki.jos.005854
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2019,30(9):2918-2924, DOI: 10.13328/j.cnki.jos.005849
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2019,30(9):2571-2592, DOI: 10.13328/j.cnki.jos.005775
Abstract:
2019,30(8):2491-2516, DOI: 10.13328/j.cnki.jos.005861
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2019,30(8):2470-2490, DOI: 10.13328/j.cnki.jos.005859
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2019,30(8):2545-2568, DOI: 10.13328/j.cnki.jos.005845
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2019,30(8):2229-2267, DOI: 10.13328/j.cnki.jos.005767
Abstract:
2019,30(7):2139-2174, DOI: 10.13328/j.cnki.jos.005841
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2019,30(7):2073-2090, DOI: 10.13328/j.cnki.jos.005840
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2019,30(7):2188-2207, DOI: 10.13328/j.cnki.jos.005837
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2019,30(7):2052-2072, DOI: 10.13328/j.cnki.jos.005834
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2019,30(6):1853-1874, DOI: 10.13328/j.cnki.jos.005832
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2019,30(6):1793-1818, DOI: 10.13328/j.cnki.jos.005817
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2019,30(6):1649-1660, DOI: 10.13328/j.cnki.jos.005741
Abstract:
2019,30(6):1632-1648, DOI: 10.13328/j.cnki.jos.005740
Abstract:
2019,30(6):1721-1741, DOI: 10.13328/j.cnki.jos.005491
Abstract:
2019,30(5):1522-1531, DOI: 10.13328/j.cnki.jos.005727
Abstract:
2019,30(5):1206-1223, DOI: 10.13328/j.cnki.jos.005717
Abstract:
2019,30(5):1288-1307, DOI: 10.13328/j.cnki.jos.005713
Abstract:
2019,30(4):1164-1190, DOI: 10.13328/j.cnki.jos.005811
Abstract:
2019,30(4):962-980, DOI: 10.13328/j.cnki.jos.005711
Abstract:
2019,30(3):799-821, DOI: 10.13328/j.cnki.jos.005698
Abstract:
2019,30(3):845-864, DOI: 10.13328/j.cnki.jos.005689
Abstract:
2019,30(2):302-322, DOI: 10.13328/j.cnki.jos.005678
Abstract:
2019,30(2):266-281, DOI: 10.13328/j.cnki.jos.005677
Abstract:
2019,30(2):440-468, DOI: 10.13328/j.cnki.jos.005659
Abstract:
2019,30(2):244-265, DOI: 10.13328/j.cnki.jos.005657
Abstract:
2019,30(2):416-439, DOI: 10.13328/j.cnki.jos.005636
Abstract:
2019,30(2):323-345, DOI: 10.13328/j.cnki.jos.005635
Abstract:
2019,30(1):33-61, DOI: 10.13328/j.cnki.jos.005652
Abstract:
2019,30(1):80-109, DOI: 10.13328/j.cnki.jos.005651
Abstract:
2019,30(1):3-21, DOI: 10.13328/j.cnki.jos.005650
Abstract:
2019,30(1):142-163, DOI: 10.13328/j.cnki.jos.005649
Abstract:
2019,30(1):22-32, DOI: 10.13328/j.cnki.jos.005648
Abstract:
2019,30(1):164-193, DOI: 10.13328/j.cnki.jos.005646
Abstract:
2019,30(1):62-79, DOI: 10.13328/j.cnki.jos.005645
Abstract:
2019,30(1):127-141, DOI: 10.13328/j.cnki.jos.005644
Abstract:
2019,30(1):110-126, DOI: 10.13328/j.cnki.jos.005643
Abstract:
2018,29(11):3400-3411, DOI: 10.13328/j.cnki.jos.005622
Abstract:
2018,29(10):3205-3222, DOI: 10.13328/j.cnki.jos.005620
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2018,29(10):3021-3050, DOI: 10.13328/j.cnki.jos.005613
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2018,29(10):3111-3133, DOI: 10.13328/j.cnki.jos.005608
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2018,29(10):3068-3090, DOI: 10.13328/j.cnki.jos.005607
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2018,29(9):2733-2752, DOI: 10.13328/j.cnki.jos.005605
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2018,29(9):2799-2820, DOI: 10.13328/j.cnki.jos.005597
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2018,29(9):2778-2798, DOI: 10.13328/j.cnki.jos.005594
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2018,29(9):2753-2777, DOI: 10.13328/j.cnki.jos.005591
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2018,29(8):2397-2426, DOI: 10.13328/j.cnki.jos.005564
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2018,29(8):2371-2396, DOI: 10.13328/j.cnki.jos.005563
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2018,29(8):2527-2544, DOI: 10.13328/j.cnki.jos.005560
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2018,29(8):2448-2469, DOI: 10.13328/j.cnki.jos.005558
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2018,29(7):2092-2115, DOI: 10.13328/j.cnki.jos.005589
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2018,29(7):2152-2176, DOI: 10.13328/j.cnki.jos.005580
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2018,29(7):2018-2045, DOI: 10.13328/j.cnki.jos.005576
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2018,29(7):2071-2091, DOI: 10.13328/j.cnki.jos.005561
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2018,29(7):2116-2132, DOI: 10.13328/j.cnki.jos.005555
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2018,29(7):2133-2151, DOI: 10.13328/j.cnki.jos.005546
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2018,29(7):2046-2070, DOI: 10.13328/j.cnki.jos.005538
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2018,29(6):1770-1791, DOI: 10.13328/j.cnki.jos.005557
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2018,29(6):1813-1825, DOI: 10.13328/j.cnki.jos.005547
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2018,29(6):1716-1738, DOI: 10.13328/j.cnki.jos.005541
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2018,29(5):1422-1450, DOI: 10.13328/j.cnki.jos.005520
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2018,29(5):1471-1514, DOI: 10.13328/j.cnki.jos.005519
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2018,29(4):1085-1093, DOI: 10.13328/j.cnki.jos.005536
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2018,29(2):320-339, DOI: 10.13328/j.cnki.jos.005430
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2018,29(2):251-266, DOI: 10.13328/j.cnki.jos.005428
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2018,29(2):225-250, DOI: 10.13328/j.cnki.jos.005424
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2018,29(2):506-523, DOI: 10.13328/j.cnki.jos.005393
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2018,29(2):340-362, DOI: 10.13328/j.cnki.jos.005391
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2018,29(2):417-441, DOI: 10.13328/j.cnki.jos.005386
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2018,29(2):299-319, DOI: 10.13328/j.cnki.jos.005383
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2018,29(2):483-505, DOI: 10.13328/j.cnki.jos.005379
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2018,29(1):150-159, DOI: 10.13328/j.cnki.jos.005434
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2018,29(1):131-149, DOI: 10.13328/j.cnki.jos.005433
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2018,29(1):69-88, DOI: 10.13328/j.cnki.jos.005377
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2018,29(1):109-130, DOI: 10.13328/j.cnki.jos.005376
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2018,29(1):1-22, DOI: 10.13328/j.cnki.jos.005375
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2018,29(1):196-224, DOI: 10.13328/j.cnki.jos.005373
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2018,29(1):23-41, DOI: 10.13328/j.cnki.jos.005323
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2018,29(1):42-68, DOI: 10.13328/j.cnki.jos.005320
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2018,29(1):89-108, DOI: 10.13328/j.cnki.jos.005319
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2018,29(1):176-195, DOI: 10.13328/j.cnki.jos.005318
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2018,29(1):160-175, DOI: 10.13328/j.cnki.jos.005316
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2017,28(9):2402-2430, DOI: 10.13328/j.cnki.jos.005306
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2017,28(9):2502-2523, DOI: 10.13328/j.cnki.jos.005305
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2017,28(9):2431-2449, DOI: 10.13328/j.cnki.jos.005301
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2017,28(8):2010-2025, DOI: 10.13328/j.cnki.jos.005272
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2017,28(8):2026-2045, DOI: 10.13328/j.cnki.jos.005270
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2017,28(6):1547-1564, DOI: 10.13328/j.cnki.jos.005260
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2017,28(6):1529-1546, DOI: 10.13328/j.cnki.jos.005259
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2017,28(6):1584-1605, DOI: 10.13328/j.cnki.jos.005256
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2017,28(6):1606-1628, DOI: 10.13328/j.cnki.jos.005254
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2017,28(6):1565-1583, DOI: 10.13328/j.cnki.jos.005249
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2017,28(1):17-34, DOI: 10.13328/j.cnki.jos.005151
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2017,28(1):59-83, DOI: 10.13328/j.cnki.jos.005141
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2017,28(1):35-58, DOI: 10.13328/j.cnki.jos.005140
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2017,28(1):1-16, DOI: 10.13328/j.cnki.jos.005139
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2017,28(1):160-183, DOI: 10.13328/j.cnki.jos.005136
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2017,28(1):135-159, DOI: 10.13328/j.cnki.jos.005131
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2017,28(1):105-134, DOI: 10.13328/j.cnki.jos.005127
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2017,28(1):84-104, DOI: 10.13328/j.cnki.jos.005115
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2016,27(9):2230-2247, DOI: 10.13328/j.cnki.jos.005068
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2016,27(8):2147-2167, DOI: 10.13328/j.cnki.jos.005103
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2016,27(8):2048-2067, DOI: 10.13328/j.cnki.jos.005060
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2016,27(8):2025-2047, DOI: 10.13328/j.cnki.jos.005049
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2016,27(8):1934-1947, DOI: 10.13328/j.cnki.jos.004936
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2016,27(4):993-1008, DOI: 10.13328/j.cnki.jos.005028
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2016,27(4):955-968, DOI: 10.13328/j.cnki.jos.005024
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2016,27(4):1026-1041, DOI: 10.13328/j.cnki.jos.005022
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2016,27(4):1009-1025, DOI: 10.13328/j.cnki.jos.005021
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2016,27(4):969-992, DOI: 10.13328/j.cnki.jos.005020
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2016,27(3):691-713, DOI: 10.13328/j.cnki.jos.004948
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2016,27(3):736-759, DOI: 10.13328/j.cnki.jos.004947
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2016,27(3):760-767, DOI: 10.13328/j.cnki.jos.004922
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2016,27(2):247-263, DOI: 10.13328/j.cnki.jos.004944
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2016,27(2):195-208, DOI: 10.13328/j.cnki.jos.004939
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2016,27(2):394-417, DOI: 10.13328/j.cnki.jos.004935
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2016,27(2):329-347, DOI: 10.13328/j.cnki.jos.004934
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2016,27(2):264-279, DOI: 10.13328/j.cnki.jos.004881
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2016,27(1):188-194, DOI: 10.13328/j.cnki.jos.005011
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2016,27(1):155-187, DOI: 10.13328/j.cnki.jos.004933
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2016,27(1):26-44, DOI: 10.13328/j.cnki.jos.004929
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2016,27(1):96-111, DOI: 10.13328/j.cnki.jos.004925
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2016,27(1):1-25, DOI: 10.13328/j.cnki.jos.004923
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2016,27(1):112-135, DOI: 10.13328/j.cnki.jos.004919
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2016,27(1):45-71, DOI: 10.13328/j.cnki.jos.004914
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2016,27(1):72-95, DOI: 10.13328/j.cnki.jos.004883
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2016,27(1):136-154, DOI: 10.13328/j.cnki.jos.004871
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2015,26(9):2356-2372, DOI: 10.13328/j.cnki.jos.004867
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2015,26(9):2373-2395, DOI: 10.13328/j.cnki.jos.004857
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2015,26(8):1853-1870, DOI: 10.13328/j.cnki.jos.004838
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2015,26(6):1356-1372, DOI: 10.13328/j.cnki.jos.004831
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2015,26(6):1285-1305, DOI: 10.13328/j.cnki.jos.004823
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2015,26(6):1265-1284, DOI: 10.13328/j.cnki.jos.004811
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2015,26(5):1213-1236, DOI: 10.13328/j.cnki.jos.004822
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2015,26(5):1151-1172, DOI: 10.13328/j.cnki.jos.004821
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2015,26(5):1129-1150, DOI: 10.13328/j.cnki.jos.004820
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2015,26(4):927-944, DOI: 10.13328/j.cnki.jos.004812
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2015,26(4):960-976, DOI: 10.13328/j.cnki.jos.004804
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2015,26(4):945-959, DOI: 10.13328/j.cnki.jos.004794
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2015,26(4):904-926, DOI: 10.13328/j.cnki.jos.004740
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2015,26(3):680-698, DOI: 10.13328/j.cnki.jos.004763
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2015,26(3):600-616, DOI: 10.13328/j.cnki.jos.004741
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2015,26(3):617-639, DOI: 10.13328/j.cnki.jos.004715
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2015,26(2):413-426, DOI: 10.13328/j.cnki.jos.004793
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2015,26(2):390-412, DOI: 10.13328/j.cnki.jos.004708
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2015,26(1):62-81, DOI: 10.13328/j.cnki.jos.004701
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2015,26(1):109-128, DOI: 10.13328/j.cnki.jos.004700
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2015,26(1):82-97, DOI: 10.13328/j.cnki.jos.004696
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2015,26(1):167-177, DOI: 10.13328/j.cnki.jos.004692
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2015,26(1):145-166, DOI: 10.13328/j.cnki.jos.004688
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2015,26(1):40-51, DOI: 10.13328/j.cnki.jos.004669
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2015,26(1):26-39, DOI: 10.13328/j.cnki.jos.004631
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2014,25(7):1527-1540, DOI: 10.13328/j.cnki.jos.004621
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2014,25(7):1505-1526, DOI: 10.13328/j.cnki.jos.004617
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2014,25(7):1459-1475, DOI: 10.13328/j.cnki.jos.004608
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2014,25(5):1085-1100, DOI: 10.13328/j.cnki.jos.004581
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2014,25(5):1061-1084, DOI: 10.13328/j.cnki.jos.004579
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2014,25(4):839-862, DOI: 10.13328/j.cnki.jos.004558
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2014,25(4):896-912, DOI: 10.13328/j.cnki.jos.004557
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2014,25(4):880-895, DOI: 10.13328/j.cnki.jos.004554
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2014,25(3):606-630, DOI: 10.13328/j.cnki.jos.004522
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2014,25(3):439-456, DOI: 10.13328/j.cnki.jos.004521
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2014,25(3):591-605, DOI: 10.13328/j.cnki.jos.004520
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2014,25(3):506-527, DOI: 10.13328/j.cnki.jos.004517
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2014,25(3):576-590, DOI: 10.13328/j.cnki.jos.004511
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2014,25(1):78-97, DOI: 10.13328/j.cnki.jos.004509
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2014,25(1):1-15, DOI: 10.13328/j.cnki.jos.004503
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2014,25(1):37-50, DOI: 10.13328/j.cnki.jos.004497
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2014,25(1):154-175, DOI: 10.13328/j.cnki.jos.004494
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2014,25(1):118-134, DOI: 10.13328/j.cnki.jos.004458
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2014,25(1):98-117, DOI: 10.13328/j.cnki.jos.004454
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2014,25(1):135-153, DOI: 10.13328/j.cnki.jos.004445
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2013,24(9):2042-2061, DOI: 10.3724/SP.J.1001.2013.04439
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2013,24(9):2001-2019, DOI: 10.3724/SP.J.1001.2013.04433
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2013,24(9):1981-2000, DOI: 10.3724/SP.J.1001.2013.04432
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2013,24(8):1804-1815, DOI: 10.3724/SP.J.1001.2013.04422
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2013,24(8):1695-1712, DOI: 10.3724/SP.J.1001.2013.04420
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2013,24(8):1786-1803, DOI: 10.3724/SP.J.1001.2013.04416
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2013,24(6):1346-1360, DOI: 10.3724/SP.J.1001.2013.04395
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2013,24(6):1390-1402, DOI: 10.3724/SP.J.1001.2013.04392
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2013,24(6):1222-1242, DOI: 10.3724/SP.J.1001.2013.04387
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2013,24(5):1078-1097, DOI: 10.3724/SP.J.1001.2013.04390
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2013,24(4):663-674, DOI: 10.3724/SP.J.1001.2013.04379
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2013,24(4):843-858, DOI: 10.3724/SP.J.1001.2013.04376
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2013,24(4):781-797, DOI: 10.3724/SP.J.1001.2013.04374
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2013,24(4):825-842, DOI: 10.3724/SP.J.1001.2013.04369
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2013,24(4):761-780, DOI: 10.3724/SP.J.1001.2013.04355
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2013,24(2):198-214, DOI: 10.3724/SP.J.1001.2013.04349
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2013,24(2):175-197, DOI: 10.3724/SP.J.1001.2013.04345
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2013,24(2):295-316, DOI: 10.3724/SP.J.1001.2013.04336
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2013,24(1):121-138, DOI: 10.3724/SP.J.1001.2013.04346
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2013,24(1):77-90, DOI: 10.3724/SP.J.1001.2013.04339
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2013,24(1):37-49, DOI: 10.3724/SP.J.1001.2013.04334
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2013,24(1):139-152, DOI: 10.3724/SP.J.1001.2013.04332
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2013,24(1):67-76, DOI: 10.3724/SP.J.1001.2013.04296
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2013,24(1):91-108, DOI: 10.3724/SP.J.1001.2013.04292
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2013,24(1):50-66, DOI: 10.3724/SP.J.1001.2013.04276
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2012,23(11):2871-2884, DOI: 10.3724/SP.J.1001.2012.04302
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2012,23(11):2885-2904, DOI: 10.3724/SP.J.1001.2012.04297
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2012,23(11):3009-3028, DOI: 10.3724/SP.J.1001.2012.04156
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2012,23(8):2058-2072, DOI: 10.3724/SP.J.1001.2012.04237
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2012,23(8):2173-2187, DOI: 10.3724/SP.J.1001.2012.04219
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2012,23(7):1824-1837, DOI: 10.3724/SP.J.1001.2012.04225
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2012,23(7):1729-1744, DOI: 10.3724/SP.J.1001.2012.04215
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2012,23(6):1542-1560, DOI: 10.3724/SP.J.1001.2012.04200
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2012,23(5):1148-1166, DOI: 10.3724/SP.J.1001.2012.04195
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2012,23(4):962-986, DOI: 10.3724/SP.J.1001.2012.04175
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2012,23(4):864-877, DOI: 10.3724/SP.J.1001.2012.04063
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2012,23(1):46-64, DOI: 10.3724/SP.J.1001.2012.04120
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2012,23(1):65-81, DOI: 10.3724/SP.J.1001.2012.04119
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2012,23(1):82-96, DOI: 10.3724/SP.J.1001.2012.04101
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2012,23(1):1-20, DOI: 10.3724/SP.J.1001.2012.04100
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2012,23(1):152-170, DOI: 10.3724/SP.J.1001.2012.04096
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2012,23(1):140-151, DOI: 10.3724/SP.J.1001.2012.04092
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2012,23(1):32-45, DOI: 10.3724/SP.J.1001.2012.04091
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2011,22(10):2317-2334, DOI: 10.3724/SP.J.1001.2011.04080
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2011,22(8):1699-1713, DOI: 10.3724/SP.J.1001.2011.04053
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2011,22(8):1838-1854, DOI: 10.3724/SP.J.1001.2011.04034
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2011,22(7):1503-1523, DOI: 10.3724/SP.J.1001.2011.04012
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2011,22(6):1316-1332, DOI: 10.3724/SP.J.1001.2011.04007
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2011,22(6):1281-1298, DOI: 10.3724/SP.J.1001.2011.03995
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2011,22(6):1299-1315, DOI: 10.3724/SP.J.1001.2011.03993
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2011,22(4):676-694, DOI: 10.3724/SP.J.1001.2011.03954
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2011,22(3):417-438, DOI: 10.3724/SP.J.1001.2011.03962
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2011,22(3):381-407, DOI: 10.3724/SP.J.1001.2011.03934
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2011,22(3):408-416, DOI: 10.3724/SP.J.1001.2011.03931
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2011,22(1):71-83, DOI: 10.3724/SP.J.1001.2011.03958
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2011,22(1):115-131, DOI: 10.3724/SP.J.1001.2011.03950
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2011,22(1):101-114, DOI: 10.3724/SP.J.1001.2011.03936
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2011,22(1):84-100, DOI: 10.3724/SP.J.1001.2011.03935
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2011,22(1):28-43, DOI: 10.3724/SP.J.1001.2011.03928
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2011,22(1):132-148, DOI: 10.3724/SP.J.1001.2011.03899
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2024,35(5):1-16, DOI:10.13328/j.cnki.jos.007029
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2023,34(9):3939-3940, DOI:10.13328/j.cnki.jos.006880
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2023,34(9):4018-4036, DOI:10.13328/j.cnki.jos.006879
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2023,34(9):3981-4002, DOI:10.13328/j.cnki.jos.006877
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2023,34(9):4069-4095, DOI:10.13328/j.cnki.jos.006876
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2023,34(9):3941-3965, DOI:10.13328/j.cnki.jos.006875
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2023,34(9):3966-3980, DOI:10.13328/j.cnki.jos.006874
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2023,34(9):4056-4068, DOI:10.13328/j.cnki.jos.006873
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2023,34(9):4037-4055, DOI:10.13328/j.cnki.jos.006872
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2023,34(8):3465-3466, DOI:10.13328/j.cnki.jos.006871
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2023,34(8):3574-3586, DOI:10.13328/j.cnki.jos.006870
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2023,34(8):3527-3548, DOI:10.13328/j.cnki.jos.006869
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2023,34(8):3549-3573, DOI:10.13328/j.cnki.jos.006868
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2023,34(8):3467-3484, DOI:10.13328/j.cnki.jos.006867
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2023,34(8):3507-3526, DOI:10.13328/j.cnki.jos.006866
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2023,34(8):3485-3506, DOI:10.13328/j.cnki.jos.006865
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2023,34(7):2979-2980, DOI:10.13328/j.cnki.jos.006864
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2023,34(7):3043-3063, DOI:10.13328/j.cnki.jos.006862
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2023,34(7):3080-3098, DOI:10.13328/j.cnki.jos.006861
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2023,34(7):3116-3133, DOI:10.13328/j.cnki.jos.006860
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2023,34(7):3099-3115, DOI:10.13328/j.cnki.jos.006859
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2023,34(7):3022-3042, DOI:10.13328/j.cnki.jos.006858
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2023,34(7):2981-3001, DOI:10.13328/j.cnki.jos.006857
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2023,34(7):3064-3079, DOI:10.13328/j.cnki.jos.006856
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2023,34(7):3002-3021, DOI:10.13328/j.cnki.jos.006855
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2023,34(6):2507-2508, DOI:10.13328/j.cnki.jos.006854
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2023,34(6):2562-2585, DOI:10.13328/j.cnki.jos.006853
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2023,34(6):2628-2640, DOI:10.13328/j.cnki.jos.006852
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2023,34(6):2586-2605, DOI:10.13328/j.cnki.jos.006851
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2023,34(6):2543-2561, DOI:10.13328/j.cnki.jos.006849
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2023,34(6):2526-2542, DOI:10.13328/j.cnki.jos.006848
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2023,34(6):2641-2668, DOI:10.13328/j.cnki.jos.006847
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2023,34(6):2509-2525, DOI:10.13328/j.cnki.jos.006846
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2023,34(5):1997-1999, DOI:10.13328/j.cnki.jos.006776
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2023,34(5):2083-2100, DOI:10.13328/j.cnki.jos.006775
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2023,34(5):2000-2023, DOI:10.13328/j.cnki.jos.006774
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2023,34(5):2152-2169, DOI:10.13328/j.cnki.jos.006773
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2023,34(5):2134-2151, DOI:10.13328/j.cnki.jos.006772
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2023,34(5):2068-2082, DOI:10.13328/j.cnki.jos.006771
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2023,34(5):2024-2034, DOI:10.13328/j.cnki.jos.006770
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2023,34(5):2116-2133, DOI:10.13328/j.cnki.jos.006769
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2023,34(5):2051-2067, DOI:10.13328/j.cnki.jos.006768
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2023,34(5):2035-2050, DOI:10.13328/j.cnki.jos.006766
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2023,34(3):1007-1009, DOI:10.13328/j.cnki.jos.006796
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2023,34(3):1109-1125, DOI:10.13328/j.cnki.jos.006795
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2023,34(3):1065-1086, DOI:10.13328/j.cnki.jos.006793
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2023,34(3):1277-1291, DOI:10.13328/j.cnki.jos.006792
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2023,34(3):1049-1064, DOI:10.13328/j.cnki.jos.006791
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2023,34(3):1292-1309, DOI:10.13328/j.cnki.jos.006790
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2023,34(3):1027-1048, DOI:10.13328/j.cnki.jos.006789
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2023,34(3):1259-1276, DOI:10.13328/j.cnki.jos.006788
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2023,34(3):1010-1026, DOI:10.13328/j.cnki.jos.006787
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2023,34(3):1148-1167, DOI:10.13328/j.cnki.jos.006784
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2023,34(3):1213-1235, DOI:10.13328/j.cnki.jos.006783
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2023,34(3):1193-1212, DOI:10.13328/j.cnki.jos.006782
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2023,34(3):1126-1147, DOI:10.13328/j.cnki.jos.006781
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2022,33(10):3529-3530, DOI:10.13328/j.cnki.jos.006633
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2022,33(10):3619-3634, DOI:10.13328/j.cnki.jos.006632
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2022,33(10):3656-3672, DOI:10.13328/j.cnki.jos.006629
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2022,33(10):3531-3545, DOI:10.13328/j.cnki.jos.006628
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2022,33(10):3546-3564, DOI:10.13328/j.cnki.jos.006627
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2022,33(9):3137-3138, DOI:10.13328/j.cnki.jos.006625
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2022,33(9):3165-3179, DOI:10.13328/j.cnki.jos.006624
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2022,33(9):3210-3222, DOI:10.13328/j.cnki.jos.006623
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2022,33(9):3139-3151, DOI:10.13328/j.cnki.jos.006622
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2022,33(9):3195-3209, DOI:10.13328/j.cnki.jos.006621
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2022,33(9):3152-3164, DOI:10.13328/j.cnki.jos.006620
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2022,33(9):3180-3194, DOI:10.13328/j.cnki.jos.006619
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2022,33(8):2753-2754, DOI:10.13328/j.cnki.jos.006611
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2022,33(8):2995-3014, DOI:10.13328/j.cnki.jos.006610
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2022,33(8):2980-2994, DOI:10.13328/j.cnki.jos.006609
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2022,33(8):2964-2979, DOI:10.13328/j.cnki.jos.006608
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2022,33(8):2947-2963, DOI:10.13328/j.cnki.jos.006607
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2022,33(8):2930-2946, DOI:10.13328/j.cnki.jos.006606
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2022,33(8):2918-2929, DOI:10.13328/j.cnki.jos.006605
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2022,33(8):2896-2917, DOI:10.13328/j.cnki.jos.006604
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2022,33(8):2875-2895, DOI:10.13328/j.cnki.jos.006603
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2022,33(8):2839-2850, DOI:10.13328/j.cnki.jos.006601
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2022,33(8):2815-2838, DOI:10.13328/j.cnki.jos.006600
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2022,33(8):2797-2814, DOI:10.13328/j.cnki.jos.006599
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2022,33(8):2782-2796, DOI:10.13328/j.cnki.jos.006598
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2022,33(8):2769-2781, DOI:10.13328/j.cnki.jos.006597
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2022,33(8):2755-2768, DOI:10.13328/j.cnki.jos.006596
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2022,33(7):2365-2366, DOI:10.13328/j.cnki.jos.006591
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2022,33(7):2525-2537, DOI:10.13328/j.cnki.jos.006589
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2022,33(7):2538-2561, DOI:10.13328/j.cnki.jos.006588
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2022,33(7):2447-2463, DOI:10.13328/j.cnki.jos.006586
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2022,33(7):2464-2481, DOI:10.13328/j.cnki.jos.006585
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2022,33(7):2482-2498, DOI:10.13328/j.cnki.jos.006584
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2022,33(7):2499-2524, DOI:10.13328/j.cnki.jos.006583
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2022,33(6):2113-2114, DOI:10.13328/j.cnki.jos.006582
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2022,33(6):2264-2287, DOI:10.13328/j.cnki.jos.006581
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2022,33(6):2246-2263, DOI:10.13328/j.cnki.jos.006580
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2022,33(6):2224-2245, DOI:10.13328/j.cnki.jos.006579
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2022,33(6):2208-2223, DOI:10.13328/j.cnki.jos.006578
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2022,33(6):2189-2207, DOI:10.13328/j.cnki.jos.006577
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2022,33(6):2172-2188, DOI:10.13328/j.cnki.jos.006576
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2022,33(6):2150-2171, DOI:10.13328/j.cnki.jos.006575
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2022,33(6):2127-2149, DOI:10.13328/j.cnki.jos.006574
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2022,33(6):2115-2126, DOI:10.13328/j.cnki.jos.006573
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2022,33(6):1959-1960, DOI:10.13328/j.cnki.jos.006572
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2022,33(6):2097-2112, DOI:10.13328/j.cnki.jos.006571
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2022,33(6):2082-2096, DOI:10.13328/j.cnki.jos.006570
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2022,33(6):2061-2081, DOI:10.13328/j.cnki.jos.006569
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2022,33(6):2047-2060, DOI:10.13328/j.cnki.jos.006568
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2022,33(6):2030-2046, DOI:10.13328/j.cnki.jos.006567
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2022,33(6):2012-2029, DOI:10.13328/j.cnki.jos.006566
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2022,33(6):1996-2011, DOI:10.13328/j.cnki.jos.006565
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2022,33(6):1978-1995, DOI:10.13328/j.cnki.jos.006564
Abstract:
2022,33(6):1961-1977, DOI:10.13328/j.cnki.jos.006563
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2022,33(5):1527-1528, DOI:10.13328/j.cnki.jos.006562
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2022,33(5):1774-1799, DOI:10.13328/j.cnki.jos.006561
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2022,33(5):1758-1773, DOI:10.13328/j.cnki.jos.006560
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2022,33(5):1736-1757, DOI:10.13328/j.cnki.jos.006559
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2022,33(5):1711-1735, DOI:10.13328/j.cnki.jos.006557
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2022,33(5):1699-1710, DOI:10.13328/j.cnki.jos.006556
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2022,33(5):1674-1698, DOI:10.13328/j.cnki.jos.006555
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2022,33(5):1652-1673, DOI:10.13328/j.cnki.jos.006554
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2022,33(5):1635-1651, DOI:10.13328/j.cnki.jos.006553
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2022,33(5):1587-1611, DOI:10.13328/j.cnki.jos.006550
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2022,33(5):1569-1586, DOI:10.13328/j.cnki.jos.006549
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2022,33(5):1551-1568, DOI:10.13328/j.cnki.jos.006548
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2022,33(5):1529-1550, DOI:10.13328/j.cnki.jos.006547
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2022,33(4):1153-1155, DOI:10.13328/j.cnki.jos.006484
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2022,33(4):1439-1450, DOI:10.13328/j.cnki.jos.006483
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2022,33(4):1516-1526, DOI:10.13328/j.cnki.jos.006482
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2022,33(4):1315-1325, DOI:10.13328/j.cnki.jos.006480
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2022,33(4):1274-1286, DOI:10.13328/j.cnki.jos.006479
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2022,33(4):1170-1182, DOI:10.13328/j.cnki.jos.006478
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2022,33(4):1218-1230, DOI:10.13328/j.cnki.jos.006477
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2022,33(4):1267-1273, DOI:10.13328/j.cnki.jos.006475
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2022,33(4):1373-1389, DOI:10.13328/j.cnki.jos.006474
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2022,33(4):1430-1438, DOI:10.13328/j.cnki.jos.006473
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2022,33(4):1477-1500, DOI:10.13328/j.cnki.jos.006472
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2022,33(4):1354-1372, DOI:10.13328/j.cnki.jos.006471
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2022,33(4):1412-1429, DOI:10.13328/j.cnki.jos.006470
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2022,33(4):1501-1515, DOI:10.13328/j.cnki.jos.006469
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2022,33(4):1156-1169, DOI:10.13328/j.cnki.jos.006468
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2022,33(4):1183-1199, DOI:10.13328/j.cnki.jos.006467
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2022,33(4):1338-1353, DOI:10.13328/j.cnki.jos.006466
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2022,33(4):1326-1337, DOI:10.13328/j.cnki.jos.006465
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2022,33(4):1231-1243, DOI:10.13328/j.cnki.jos.006464
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2022,33(4):1200-1217, DOI:10.13328/j.cnki.jos.006463
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2022,33(4):1390-1411, DOI:10.13328/j.cnki.jos.006462
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2022,33(4):1451-1476, DOI:10.13328/j.cnki.jos.006461
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2022,33(3):771-773, DOI:10.13328/j.cnki.jos.006459
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2022,33(3):1111-1127, DOI:10.13328/j.cnki.jos.006458
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2022,33(3):1043-1056, DOI:10.13328/j.cnki.jos.006457
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2022,33(3):832-848, DOI:10.13328/j.cnki.jos.006456
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2022,33(3):931-949, DOI:10.13328/j.cnki.jos.006455
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2022,33(3):867-890, DOI:10.13328/j.cnki.jos.006454
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2022,33(3):968-984, DOI:10.13328/j.cnki.jos.006453
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2022,33(3):814-831, DOI:10.13328/j.cnki.jos.006452
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2022,33(3):1005-1017, DOI:10.13328/j.cnki.jos.006451
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2022,33(3):1093-1110, DOI:10.13328/j.cnki.jos.006450
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2022,33(3):1018-1042, DOI:10.13328/j.cnki.jos.006449
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2022,33(3):797-813, DOI:10.13328/j.cnki.jos.006448
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2022,33(3):985-1004, DOI:10.13328/j.cnki.jos.006447
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2022,33(3):950-967, DOI:10.13328/j.cnki.jos.006445
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2022,33(3):849-866, DOI:10.13328/j.cnki.jos.006444
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2022,33(3):891-908, DOI:10.13328/j.cnki.jos.006443
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2022,33(3):909-930, DOI:10.13328/j.cnki.jos.006442
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2022,33(3):774-796, DOI:10.13328/j.cnki.jos.006441
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2022,33(3):1141-1152, DOI:10.13328/j.cnki.jos.006201
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2022,33(3):1128-1140, DOI:10.13328/j.cnki.jos.006099
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2021,32(8):2377-2378, DOI:10.13328/j.cnki.jos.006194
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2021,32(8):2457-2468, DOI:10.13328/j.cnki.jos.006193
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2021,32(8):2439-2456, DOI:10.13328/j.cnki.jos.006192
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2021,32(8):2425-2438, DOI:10.13328/j.cnki.jos.006191
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2021,32(8):2408-2424, DOI:10.13328/j.cnki.jos.006190
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2021,32(8):2391-2407, DOI:10.13328/j.cnki.jos.006189
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2021,32(8):2379-2390, DOI:10.13328/j.cnki.jos.006188
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2021,32(8):2287-2288, DOI:10.13328/j.cnki.jos.006009
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2021,32(8):2365-2376, DOI:10.13328/j.cnki.jos.006008
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2021,32(8):2352-2364, DOI:10.13328/j.cnki.jos.006007
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2021,32(8):2341-2351, DOI:10.13328/j.cnki.jos.006006
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2021,32(8):2329-2340, DOI:10.13328/j.cnki.jos.006005
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2021,32(8):2319-2328, DOI:10.13328/j.cnki.jos.006004
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2021,32(8):2307-2318, DOI:10.13328/j.cnki.jos.006003
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2021,32(8):2289-2306, DOI:10.13328/j.cnki.jos.006002
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2021,32(7):1923-1925, DOI:10.13328/j.cnki.jos.006273
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2021,32(7):1999-2015, DOI:10.13328/j.cnki.jos.006272
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2021,32(7):2242-2259, DOI:10.13328/j.cnki.jos.006271
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2021,32(7):2142-2165, DOI:10.13328/j.cnki.jos.006270
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2021,32(7):1978-1998, DOI:10.13328/j.cnki.jos.006268
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2021,32(7):2103-2117, DOI:10.13328/j.cnki.jos.006264
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2021,32(7):2183-2203, DOI:10.13328/j.cnki.jos.006263
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2021,32(7):2166-2182, DOI:10.13328/j.cnki.jos.006262
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2021,32(7):2219-2241, DOI:10.13328/j.cnki.jos.006261
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2021,32(7):2039-2055, DOI:10.13328/j.cnki.jos.006260
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2021,32(7):1957-1977, DOI:10.13328/j.cnki.jos.006259
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2021,32(7):2204-2218, DOI:10.13328/j.cnki.jos.006257
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2021,32(6):1579-1580, DOI:10.13328/j.cnki.jos.006256
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2021,32(6):1882-1909, DOI:10.13328/j.cnki.jos.006255
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2021,32(6):1867-1881, DOI:10.13328/j.cnki.jos.006254
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2021,32(6):1849-1866, DOI:10.13328/j.cnki.jos.006253
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2021,32(6):1830-1848, DOI:10.13328/j.cnki.jos.006252
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2021,32(6):1818-1829, DOI:10.13328/j.cnki.jos.006251
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2021,32(6):1799-1817, DOI:10.13328/j.cnki.jos.006250
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2021,32(6):1779-1798, DOI:10.13328/j.cnki.jos.006249
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2021,32(6):1748-1778, DOI:10.13328/j.cnki.jos.006248
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2021,32(6):1733-1747, DOI:10.13328/j.cnki.jos.006247
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2021,32(6):1717-1732, DOI:10.13328/j.cnki.jos.006246
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2021,32(6):1701-1716, DOI:10.13328/j.cnki.jos.006245
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2021,32(6):1682-1700, DOI:10.13328/j.cnki.jos.006244
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2021,32(6):1663-1681, DOI:10.13328/j.cnki.jos.006243
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2021,32(6):1647-1662, DOI:10.13328/j.cnki.jos.006242
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2021,32(6):1631-1646, DOI:10.13328/j.cnki.jos.006241
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2021,32(6):1612-1630, DOI:10.13328/j.cnki.jos.006240
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2021,32(6):1597-1611, DOI:10.13328/j.cnki.jos.006239
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2021,32(6):1581-1596, DOI:10.13328/j.cnki.jos.006238
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2021,32(5):1229-1230, DOI:10.13328/j.cnki.jos.006237
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2021,32(5):1341-1359, DOI:10.13328/j.cnki.jos.006236
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2021,32(5):1322-1340, DOI:10.13328/j.cnki.jos.006235
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2021,32(5):1302-1321, DOI:10.13328/j.cnki.jos.006234
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2021,32(5):1284-1301, DOI:10.13328/j.cnki.jos.006233
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2021,32(5):1256-1283, DOI:10.13328/j.cnki.jos.006232
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2021,32(5):1231-1255, DOI:10.13328/j.cnki.jos.006231
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2021,32(4):887-888, DOI:10.13328/j.cnki.jos.006230
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2021,32(4):953-972, DOI:10.13328/j.cnki.jos.006229
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2021,32(4):1023-1038, DOI:10.13328/j.cnki.jos.006228
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2021,32(4):1006-1022, DOI:10.13328/j.cnki.jos.006227
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2021,32(4):973-987, DOI:10.13328/j.cnki.jos.006226
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2021,32(4):988-1005, DOI:10.13328/j.cnki.jos.006225
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2021,32(4):934-952, DOI:10.13328/j.cnki.jos.006224
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2021,32(4):904-933, DOI:10.13328/j.cnki.jos.006223
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2021,32(4):889-903, DOI:10.13328/j.cnki.jos.006222
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2021,32(4):1051-1066, DOI:10.13328/j.cnki.jos.006221
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2021,32(4):1039-1050, DOI:10.13328/j.cnki.jos.006220
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2021,32(3):601-603, DOI:10.13328/j.cnki.jos.006187
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2021,32(3):636-649, DOI:10.13328/j.cnki.jos.006186
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2021,32(3):818-830, DOI:10.13328/j.cnki.jos.006185
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2021,32(3):753-762, DOI:10.13328/j.cnki.jos.006184
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2021,32(3):831-844, DOI:10.13328/j.cnki.jos.006183
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2021,32(3):781-804, DOI:10.13328/j.cnki.jos.006181
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2021,32(3):763-780, DOI:10.13328/j.cnki.jos.006180
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2021,32(3):622-635, DOI:10.13328/j.cnki.jos.006179
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2021,32(3):742-752, DOI:10.13328/j.cnki.jos.006178
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2021,32(3):845-858, DOI:10.13328/j.cnki.jos.006177
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2021,32(3):689-711, DOI:10.13328/j.cnki.jos.006176
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2021,32(3):669-688, DOI:10.13328/j.cnki.jos.006174
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2021,32(3):650-668, DOI:10.13328/j.cnki.jos.006173
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2021,32(3):712-725, DOI:10.13328/j.cnki.jos.006172
Abstract:
2021,32(3):805-817, DOI:10.13328/j.cnki.jos.006171
Abstract:
2021,32(3):726-741, DOI:10.13328/j.cnki.jos.006170
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2020,31(10):2981-2982, DOI:10.13328/j.cnki.jos.006072
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2020,31(10):3074-3086, DOI:10.13328/j.cnki.jos.006071
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2020,31(10):3056-3073, DOI:10.13328/j.cnki.jos.006070
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2020,31(10):3038-3055, DOI:10.13328/j.cnki.jos.006069
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2020,31(10):3019-3037, DOI:10.13328/j.cnki.jos.006068
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2020,31(10):3004-3018, DOI:10.13328/j.cnki.jos.006064
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2020,31(10):2983-3003, DOI:10.13328/j.cnki.jos.006063
Abstract:
2020,31(9):2785-2801, DOI:10.13328/j.cnki.jos.005945
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2020,31(9):2770-2784, DOI:10.13328/j.cnki.jos.005944
Abstract:
2020,31(9):2756-2769, DOI:10.13328/j.cnki.jos.005943
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2020,31(9):2723-2740, DOI:10.13328/j.cnki.jos.005941
Abstract:
2020,31(9):2709-2722, DOI:10.13328/j.cnki.jos.005940
Abstract:
2020,31(9):2741-2755, DOI:10.13328/j.cnki.jos.005939
Abstract:
2020,31(9):2691-2708, DOI:10.13328/j.cnki.jos.005938
Abstract:
2020,31(9):2678-2690, DOI:10.13328/j.cnki.jos.005937
Abstract:
2020,31(8):2336-2361, DOI:10.13328/j.cnki.jos.005964
Abstract:
2020,31(8):2309-2335, DOI:10.13328/j.cnki.jos.005963
Abstract:
2020,31(8):2285-2308, DOI:10.13328/j.cnki.jos.005962
Abstract:
2020,31(8):2375-2387, DOI:10.13328/j.cnki.jos.005961
Abstract:
2020,31(8):2362-2374, DOI:10.13328/j.cnki.jos.005960
Abstract:
2020,31(8):2388-2403, DOI:10.13328/j.cnki.jos.005959
Abstract:
2020,31(7):1969-1979, DOI:10.13328/j.cnki.jos.005935
Abstract:
2020,31(7):1933-1942, DOI:10.13328/j.cnki.jos.005934
Abstract:
2020,31(7):1959-1968, DOI:10.13328/j.cnki.jos.005933
Abstract:
2020,31(7):1943-1958, DOI:10.13328/j.cnki.jos.005932
Abstract:
2020,31(6):1672-1680, DOI:10.13328/j.cnki.jos.006000
Abstract:
2020,31(6):1654-1671, DOI:10.13328/j.cnki.jos.005999
Abstract:
2020,31(6):1638-1653, DOI:10.13328/j.cnki.jos.005997
Abstract:
2020,31(6):1619-1637, DOI:10.13328/j.cnki.jos.005996
Abstract:
2020,31(6):1600-1618, DOI:10.13328/j.cnki.jos.005994
Abstract:
2020,31(6):1587-1599, DOI:10.13328/j.cnki.jos.005993
Abstract:
2020,31(5):1241-1242, DOI:10.13328/j.cnki.jos.005958
Abstract:
2020,31(5):1353-1373, DOI:10.13328/j.cnki.jos.005957
Abstract:
2020,31(5):1332-1352, DOI:10.13328/j.cnki.jos.005956
Abstract:
2020,31(5):1314-1331, DOI:10.13328/j.cnki.jos.005955
Abstract:
2020,31(5):1294-1313, DOI:10.13328/j.cnki.jos.005954
Abstract:
2020,31(5):1392-1405, DOI:10.13328/j.cnki.jos.005953
Abstract:
2020,31(5):1374-1391, DOI:10.13328/j.cnki.jos.005952
Abstract:
2020,31(5):1276-1293, DOI:10.13328/j.cnki.jos.005949
Abstract:
2020,31(3):597-599, DOI:10.13328/j.cnki.jos.005915
Abstract:
2020,31(3):680-694, DOI:10.13328/j.cnki.jos.005914
Abstract:
2020,31(3):778-793, DOI:10.13328/j.cnki.jos.005913
Abstract:
2020,31(3):763-777, DOI:10.13328/j.cnki.jos.005912
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2020,31(3):620-633, DOI:10.13328/j.cnki.jos.005910
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2020,31(3):726-747, DOI:10.13328/j.cnki.jos.005907
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2020,31(3):634-647, DOI:10.13328/j.cnki.jos.005906
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2020,31(3):710-725, DOI:10.13328/j.cnki.jos.005905
Abstract:
2020,31(3):748-762, DOI:10.13328/j.cnki.jos.005903
Abstract:
2020,31(3):663-679, DOI:10.13328/j.cnki.jos.005902
Abstract:
2020,31(3):648-662, DOI:10.13328/j.cnki.jos.005901
Abstract:
2020,31(3):695-709, DOI:10.13328/j.cnki.jos.005900
Abstract:
2020,31(3):831-844, DOI:10.13328/j.cnki.jos.005899
Abstract:
2020,31(3):845-865, DOI:10.13328/j.cnki.jos.005898
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2020,31(3):794-805, DOI:10.13328/j.cnki.jos.005897
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2019,30(10):3054-3055, DOI:10.13328/j.cnki.jos.005798
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2019,30(10):3227-3241, DOI:10.13328/j.cnki.jos.005797
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2019,30(10):3186-3205, DOI:10.13328/j.cnki.jos.005795
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2019,30(10):3168-3185, DOI:10.13328/j.cnki.jos.005794
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2019,30(10):3148-3167, DOI:10.13328/j.cnki.jos.005793
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2019,30(10):3127-3147, DOI:10.13328/j.cnki.jos.005792
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2019,30(10):3115-3126, DOI:10.13328/j.cnki.jos.005791
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2019,30(10):3071-3089, DOI:10.13328/j.cnki.jos.005789
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2019,30(10):3056-3070, DOI:10.13328/j.cnki.jos.005788
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2019,30(10):2925-2926, DOI:10.13328/j.cnki.jos.005787
Abstract:
2019,30(10):2964-2985, DOI:10.13328/j.cnki.jos.005786
Abstract:
2019,30(10):2986-3004, DOI:10.13328/j.cnki.jos.005785
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2019,30(10):3037-3053, DOI:10.13328/j.cnki.jos.005784
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2019,30(10):3026-3036, DOI:10.13328/j.cnki.jos.005783
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2019,30(10):3005-3016, DOI:10.13328/j.cnki.jos.005782
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2019,30(10):2942-2953, DOI:10.13328/j.cnki.jos.005781
Abstract:
2019,30(10):3017-3025, DOI:10.13328/j.cnki.jos.005780
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2019,30(10):2954-2963, DOI:10.13328/j.cnki.jos.005779
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2019,30(10):2927-2941, DOI:10.13328/j.cnki.jos.005778
Abstract:
2019,30(9):2569-2570, DOI:10.13328/j.cnki.jos.005777
Abstract:
2019,30(9):2671-2685, DOI:10.13328/j.cnki.jos.005776
Abstract:
2019,30(9):2655-2670, DOI:10.13328/j.cnki.jos.005774
Abstract:
2019,30(9):2608-2619, DOI:10.13328/j.cnki.jos.005773
Abstract:
2019,30(9):2593-2607, DOI:10.13328/j.cnki.jos.005772
Abstract:
2019,30(9):2636-2654, DOI:10.13328/j.cnki.jos.005771
Abstract:
2019,30(9):2620-2635, DOI:10.13328/j.cnki.jos.005770
Abstract:
2019,30(8):2268-2286, DOI:10.13328/j.cnki.jos.005768
Abstract:
2019,30(8):2375-2391, DOI:10.13328/j.cnki.jos.005766
Abstract:
2019,30(8):2415-2427, DOI:10.13328/j.cnki.jos.005765
Abstract:
2019,30(8):2314-2336, DOI:10.13328/j.cnki.jos.005764
Abstract:
2019,30(8):2362-2374, DOI:10.13328/j.cnki.jos.005763
Abstract:
2019,30(8):2349-2361, DOI:10.13328/j.cnki.jos.005762
Abstract:
2019,30(8):2287-2313, DOI:10.13328/j.cnki.jos.005761
Abstract:
2019,30(8):2392-2414, DOI:10.13328/j.cnki.jos.005760
Abstract:
2019,30(8):2337-2348, DOI:10.13328/j.cnki.jos.005759
Abstract:
2019,30(7):2033-2051, DOI:10.13328/j.cnki.jos.005757
Abstract:
2019,30(7):2018-2032, DOI:10.13328/j.cnki.jos.005756
Abstract:
2019,30(7):2003-2017, DOI:10.13328/j.cnki.jos.005755
Abstract:
2019,30(7):1980-2002, DOI:10.13328/j.cnki.jos.005754
Abstract:
2019,30(7):1966-1979, DOI:10.13328/j.cnki.jos.005753
Abstract:
2019,30(7):1953-1965, DOI:10.13328/j.cnki.jos.005752
Abstract:
2019,30(7):1939-1952, DOI:10.13328/j.cnki.jos.005750
Abstract:
2019,30(7):1916-1938, DOI:10.13328/j.cnki.jos.005749
Abstract:
2019,30(7):1903-1915, DOI:10.13328/j.cnki.jos.005748
Abstract:
2019,30(6):1575-1576, DOI:10.13328/j.cnki.jos.005747
Abstract:
2019,30(6):1705-1720, DOI:10.13328/j.cnki.jos.005746
Abstract:
2019,30(6):1692-1704, DOI:10.13328/j.cnki.jos.005745
Abstract:
2019,30(6):1681-1691, DOI:10.13328/j.cnki.jos.005744
Abstract:
2019,30(6):1670-1680, DOI:10.13328/j.cnki.jos.005743
Abstract:
2019,30(6):1661-1669, DOI:10.13328/j.cnki.jos.005742
Abstract:
2019,30(6):1614-1631, DOI:10.13328/j.cnki.jos.005739
Abstract:
2019,30(6):1594-1613, DOI:10.13328/j.cnki.jos.005738
Abstract:
2019,30(6):1577-1593, DOI:10.13328/j.cnki.jos.005737
Abstract:
2019,30(5):1203-1205, DOI:10.13328/j.cnki.jos.005736
Abstract:
2019,30(5):1422-1437, DOI:10.13328/j.cnki.jos.005735
Abstract:
2019,30(5):1269-1287, DOI:10.13328/j.cnki.jos.005734
Abstract:
2019,30(5):1561-1574, DOI:10.13328/j.cnki.jos.005733
Abstract:
2019,30(5):1407-1421, DOI:10.13328/j.cnki.jos.005732
Abstract:
2019,30(5):1547-1560, DOI:10.13328/j.cnki.jos.005731
Abstract:
2019,30(5):1375-1385, DOI:10.13328/j.cnki.jos.005730
Abstract:
2019,30(5):1243-1255, DOI:10.13328/j.cnki.jos.005729
Abstract:
2019,30(5):1532-1546, DOI:10.13328/j.cnki.jos.005728
Abstract:
2019,30(5):1510-1521, DOI:10.13328/j.cnki.jos.005726
Abstract:
2019,30(5):1386-1406, DOI:10.13328/j.cnki.jos.005725
Abstract:
2019,30(5):1359-1374, DOI:10.13328/j.cnki.jos.005724
Abstract:
2019,30(5):1464-1480, DOI:10.13328/j.cnki.jos.005723
Abstract:
2019,30(5):1342-1358, DOI:10.13328/j.cnki.jos.005722
Abstract:
2019,30(5):1481-1497, DOI:10.13328/j.cnki.jos.005721
Abstract:
2019,30(5):1450-1463, DOI:10.13328/j.cnki.jos.005720
Abstract:
2019,30(5):1224-1242, DOI:10.13328/j.cnki.jos.005719
Abstract:
2019,30(5):1498-1509, DOI:10.13328/j.cnki.jos.005718
Abstract:
2019,30(5):1256-1268, DOI:10.13328/j.cnki.jos.005716
Abstract:
2019,30(5):1330-1341, DOI:10.13328/j.cnki.jos.005715
Abstract:
2019,30(5):1438-1449, DOI:10.13328/j.cnki.jos.005714
Abstract:
2019,30(5):1308-1329, DOI:10.13328/j.cnki.jos.005712
Abstract:
2019,30(4):865-866, DOI:10.13328/j.cnki.jos.005668
Abstract:
2019,30(4):954-961, DOI:10.13328/j.cnki.jos.005667
Abstract:
2019,30(4):896-913, DOI:10.13328/j.cnki.jos.005666
Abstract:
2019,30(4):867-883, DOI:10.13328/j.cnki.jos.005665
Abstract:
2019,30(4):884-895, DOI:10.13328/j.cnki.jos.005664
Abstract:
2019,30(4):941-953, DOI:10.13328/j.cnki.jos.005663
Abstract:
2019,30(4):927-940, DOI:10.13328/j.cnki.jos.005662
Abstract:
2019,30(4):914-926, DOI:10.13328/j.cnki.jos.005661
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2019,30(3):495-497, DOI:10.13328/j.cnki.jos.005701
Abstract:
2019,30(3):620-647, DOI:10.13328/j.cnki.jos.005700
Abstract:
2019,30(3):552-572, DOI:10.13328/j.cnki.jos.005699
Abstract:
2019,30(3):759-769, DOI:10.13328/j.cnki.jos.005697
Abstract:
2019,30(3):498-514, DOI:10.13328/j.cnki.jos.005696
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2019,30(3):738-758, DOI:10.13328/j.cnki.jos.005695
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2019,30(3):684-699, DOI:10.13328/j.cnki.jos.005694
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2019,30(3):667-683, DOI:10.13328/j.cnki.jos.005693
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2019,30(3):515-536, DOI:10.13328/j.cnki.jos.005692
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2019,30(3):604-619, DOI:10.13328/j.cnki.jos.005691
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2019,30(3):784-798, DOI:10.13328/j.cnki.jos.005690
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2019,30(3):589-603, DOI:10.13328/j.cnki.jos.005688
Abstract:
2019,30(3):700-717, DOI:10.13328/j.cnki.jos.005687
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2019,30(3):648-666, DOI:10.13328/j.cnki.jos.005686
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2019,30(3):718-737, DOI:10.13328/j.cnki.jos.005685
Abstract:
2019,30(3):537-551, DOI:10.13328/j.cnki.jos.005684
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2019,30(3):770-783, DOI:10.13328/j.cnki.jos.005683
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2019,30(3):573-588, DOI:10.13328/j.cnki.jos.005682
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2019,30(3):822-844, DOI:10.13328/j.cnki.jos.005681
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2018,29(11):3239-3240, DOI:10.13328/j.cnki.jos.005484
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2018,29(11):3340-3354, DOI:10.13328/j.cnki.jos.005483
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2018,29(11):3374-3387, DOI:10.13328/j.cnki.jos.005482
Abstract:
2018,29(11):3295-3305, DOI:10.13328/j.cnki.jos.005481
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2018,29(11):3241-3259, DOI:10.13328/j.cnki.jos.005480
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2018,29(11):3306-3325, DOI:10.13328/j.cnki.jos.005479
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2018,29(11):3278-3294, DOI:10.13328/j.cnki.jos.005478
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2018,29(11):3326-3339, DOI:10.13328/j.cnki.jos.005477
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2018,29(11):3260-3277, DOI:10.13328/j.cnki.jos.005476
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2018,29(11):3355-3373, DOI:10.13328/j.cnki.jos.005475
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2018,29(11):3388-3399, DOI:10.13328/j.cnki.jos.005474
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2018,29(10):2897-2898, DOI:10.13328/j.cnki.jos.005554
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2018,29(10):2995-3008, DOI:10.13328/j.cnki.jos.005553
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2018,29(10):2931-2947, DOI:10.13328/j.cnki.jos.005552
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2018,29(10):2966-2994, DOI:10.13328/j.cnki.jos.005551
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2018,29(10):2948-2965, DOI:10.13328/j.cnki.jos.005550
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2018,29(10):2915-2930, DOI:10.13328/j.cnki.jos.005549
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2018,29(10):2899-2914, DOI:10.13328/j.cnki.jos.005548
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2018,29(9):2545-2546, DOI:10.13328/j.cnki.jos.005401
Abstract:
2018,29(9):2580-2594, DOI:10.13328/j.cnki.jos.005400
Abstract:
2018,29(9):2632-2648, DOI:10.13328/j.cnki.jos.005399
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2018,29(9):2595-2605, DOI:10.13328/j.cnki.jos.005398
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2018,29(9):2606-2615, DOI:10.13328/j.cnki.jos.005397
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2018,29(9):2616-2631, DOI:10.13328/j.cnki.jos.005396
Abstract:
2018,29(9):2547-2558, DOI:10.13328/j.cnki.jos.005395
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2018,29(9):2559-2579, DOI:10.13328/j.cnki.jos.005394
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2018,29(8):2485-2500, DOI:10.13328/j.cnki.jos.005543
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2018,29(8):2177-2179, DOI:10.13328/j.cnki.jos.005534
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2018,29(8):2210-2225, DOI:10.13328/j.cnki.jos.005533
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2018,29(8):2322-2335, DOI:10.13328/j.cnki.jos.005532
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2018,29(8):2243-2257, DOI:10.13328/j.cnki.jos.005531
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2018,29(8):2350-2370, DOI:10.13328/j.cnki.jos.005530
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2018,29(8):2180-2197, DOI:10.13328/j.cnki.jos.005529
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2018,29(8):2226-2242, DOI:10.13328/j.cnki.jos.005528
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2018,29(8):2198-2209, DOI:10.13328/j.cnki.jos.005527
Abstract:
2018,29(8):2336-2349, DOI:10.13328/j.cnki.jos.005526
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2018,29(8):2306-2321, DOI:10.13328/j.cnki.jos.005525
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2018,29(8):2258-2271, DOI:10.13328/j.cnki.jos.005524
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2018,29(8):2294-2305, DOI:10.13328/j.cnki.jos.005523
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2018,29(8):2283-2293, DOI:10.13328/j.cnki.jos.005522
Abstract:
2018,29(8):2272-2282, DOI:10.13328/j.cnki.jos.005521
Abstract:
2018,29(7):1827-1829, DOI:10.13328/j.cnki.jos.005366
Abstract:
2018,29(7):2006-2017, DOI:10.13328/j.cnki.jos.005365
Abstract:
2018,29(7):1981-2005, DOI:10.13328/j.cnki.jos.005364
Abstract:
2018,29(7):1963-1980, DOI:10.13328/j.cnki.jos.005363
Abstract:
2018,29(7):1953-1962, DOI:10.13328/j.cnki.jos.005362
Abstract:
2018,29(7):1937-1952, DOI:10.13328/j.cnki.jos.005361
Abstract:
2018,29(7):1922-1936, DOI:10.13328/j.cnki.jos.005360
Abstract:
2018,29(7):1909-1921, DOI:10.13328/j.cnki.jos.005359
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2018,29(7):1893-1908, DOI:10.13328/j.cnki.jos.005358
Abstract:
2018,29(7):1880-1892, DOI:10.13328/j.cnki.jos.005357
Abstract:
2018,29(7):1863-1879, DOI:10.13328/j.cnki.jos.005356
Abstract:
2018,29(7):1852-1862, DOI:10.13328/j.cnki.jos.005355
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2018,29(7):1830-1851, DOI:10.13328/j.cnki.jos.005354
Abstract:
2018,29(6):1515-1516, DOI:10.13328/j.cnki.jos.005473
Abstract:
2018,29(6):1670-1680, DOI:10.13328/j.cnki.jos.005472
Abstract:
2018,29(6):1607-1621, DOI:10.13328/j.cnki.jos.005471
Abstract:
2018,29(6):1681-1698, DOI:10.13328/j.cnki.jos.005470
Abstract:
2018,29(6):1595-1606, DOI:10.13328/j.cnki.jos.005469
Abstract:
2018,29(6):1699-1715, DOI:10.13328/j.cnki.jos.005468
Abstract:
2018,29(6):1527-1543, DOI:10.13328/j.cnki.jos.005467
Abstract:
2018,29(6):1622-1634, DOI:10.13328/j.cnki.jos.005466
Abstract:
2018,29(6):1566-1581, DOI:10.13328/j.cnki.jos.005465
Abstract:
2018,29(6):1544-1565, DOI:10.13328/j.cnki.jos.005463
Abstract:
2018,29(6):1582-1594, DOI:10.13328/j.cnki.jos.005462
Abstract:
2018,29(6):1517-1526, DOI:10.13328/j.cnki.jos.005461
Abstract:
2018,29(6):1647-1669, DOI:10.13328/j.cnki.jos.005460
Abstract:
2018,29(6):1635-1646, DOI:10.13328/j.cnki.jos.005459
Abstract:
2018,29(5):1177-1178, DOI:10.13328/j.cnki.jos.005509
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2018,29(5):1360-1378, DOI:10.13328/j.cnki.jos.005508
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2018,29(5):1213-1229, DOI:10.13328/j.cnki.jos.005507
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2018,29(5):1244-1257, DOI:10.13328/j.cnki.jos.005506
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2018,29(5):1199-1212, DOI:10.13328/j.cnki.jos.005505
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2018,29(5):1179-1198, DOI:10.13328/j.cnki.jos.005504
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2018,29(5):1230-1243, DOI:10.13328/j.cnki.jos.005503
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2018,29(5):1318-1332, DOI:10.13328/j.cnki.jos.005502
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2018,29(5):1275-1287, DOI:10.13328/j.cnki.jos.005501
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2018,29(5):1348-1359, DOI:10.13328/j.cnki.jos.005500
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2018,29(5):1258-1274, DOI:10.13328/j.cnki.jos.005499
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2018,29(5):1303-1317, DOI:10.13328/j.cnki.jos.005498
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2018,29(5):1379-1391, DOI:10.13328/j.cnki.jos.005497
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2018,29(5):1333-1347, DOI:10.13328/j.cnki.jos.005496
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2018,29(5):1410-1421, DOI:10.13328/j.cnki.jos.005495
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2018,29(5):1392-1409, DOI:10.13328/j.cnki.jos.005494
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2018,29(5):1288-1302, DOI:10.13328/j.cnki.jos.005493
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2018,29(4):897-899, DOI:10.13328/j.cnki.jos.005417
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2018,29(4):945-956, DOI:10.13328/j.cnki.jos.005416
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2018,29(4):935-944, DOI:10.13328/j.cnki.jos.005415
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2018,29(4):1071-1084, DOI:10.13328/j.cnki.jos.005414
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2018,29(4):1002-1016, DOI:10.13328/j.cnki.jos.005413
Abstract:
2018,29(4):1060-1070, DOI:10.13328/j.cnki.jos.005412
Abstract:
2018,29(4):987-1001, DOI:10.13328/j.cnki.jos.005411
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2018,29(4):973-986, DOI:10.13328/j.cnki.jos.005410
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2018,29(4):1049-1059, DOI:10.13328/j.cnki.jos.005409
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2018,29(4):1039-1048, DOI:10.13328/j.cnki.jos.005408
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2018,29(4):926-934, DOI:10.13328/j.cnki.jos.005407
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2018,29(4):957-972, DOI:10.13328/j.cnki.jos.005406
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2018,29(4):914-925, DOI:10.13328/j.cnki.jos.005405
Abstract:
2018,29(4):1029-1038, DOI:10.13328/j.cnki.jos.005404
Abstract:
2018,29(4):900-913, DOI:10.13328/j.cnki.jos.005403
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2018,29(4):1017-1028, DOI:10.13328/j.cnki.jos.005402
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2018,29(3):525-527, DOI:10.13328/j.cnki.jos.005458
Abstract:
2018,29(3):853-868, DOI:10.13328/j.cnki.jos.005457
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2018,29(3):627-641, DOI:10.13328/j.cnki.jos.005456
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2018,29(3):599-613, DOI:10.13328/j.cnki.jos.005455
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2018,29(3):839-852, DOI:10.13328/j.cnki.jos.005454
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2018,29(3):824-838, DOI:10.13328/j.cnki.jos.005453
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2018,29(3):587-598, DOI:10.13328/j.cnki.jos.005452
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2018,29(3):703-720, DOI:10.13328/j.cnki.jos.005451
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2018,29(3):569-586, DOI:10.13328/j.cnki.jos.005450
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2018,29(3):689-702, DOI:10.13328/j.cnki.jos.005449
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2018,29(3):811-823, DOI:10.13328/j.cnki.jos.005448
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2018,29(3):614-626, DOI:10.13328/j.cnki.jos.005447
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2018,29(3):883-895, DOI:10.13328/j.cnki.jos.005446
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2018,29(3):721-733, DOI:10.13328/j.cnki.jos.005445
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2018,29(3):663-688, DOI:10.13328/j.cnki.jos.005444
Abstract:
2018,29(3):545-568, DOI:10.13328/j.cnki.jos.005443
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2018,29(3):642-662, DOI:10.13328/j.cnki.jos.005442
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2018,29(3):528-544, DOI:10.13328/j.cnki.jos.005441
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2018,29(3):869-882, DOI:10.13328/j.cnki.jos.005440
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2018,29(3):799-810, DOI:10.13328/j.cnki.jos.005439
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2018,29(3):734-755, DOI:10.13328/j.cnki.jos.005438
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2018,29(3):786-798, DOI:10.13328/j.cnki.jos.005437
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2018,29(3):772-785, DOI:10.13328/j.cnki.jos.005436
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2018,29(3):756-771, DOI:10.13328/j.cnki.jos.005435
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2017,28(9):2227-2228, DOI:10.13328/j.cnki.jos.005188
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2017,28(9):2281-2292, DOI:10.13328/j.cnki.jos.005187
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2017,28(9):2370-2387, DOI:10.13328/j.cnki.jos.005186
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2017,28(9):2229-2247, DOI:10.13328/j.cnki.jos.005185
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2017,28(9):2309-2322, DOI:10.13328/j.cnki.jos.005184
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2017,28(9):2293-2308, DOI:10.13328/j.cnki.jos.005183
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2017,28(9):2334-2353, DOI:10.13328/j.cnki.jos.005182
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2017,28(9):2264-2280, DOI:10.13328/j.cnki.jos.005181
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2017,28(9):2354-2369, DOI:10.13328/j.cnki.jos.005180
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2017,28(9):2388-2401, DOI:10.13328/j.cnki.jos.005179
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2017,28(9):2323-2333, DOI:10.13328/j.cnki.jos.005178
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2017,28(9):2248-2263, DOI:10.13328/j.cnki.jos.005177
Abstract:
2017,28(8):1927-1928, DOI:10.13328/j.cnki.jos.005205
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2017,28(8):1940-1951, DOI:10.13328/j.cnki.jos.005204
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2017,28(8):1999-2009, DOI:10.13328/j.cnki.jos.005203
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2017,28(8):1968-1981, DOI:10.13328/j.cnki.jos.005202
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2017,28(8):1982-1998, DOI:10.13328/j.cnki.jos.005201
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2017,28(8):1929-1939, DOI:10.13328/j.cnki.jos.005200
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2017,28(8):1952-1967, DOI:10.13328/j.cnki.jos.005199
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2017,28(6):1327-1329, DOI:10.13328/j.cnki.jos.005222
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2017,28(6):1474-1487, DOI:10.13328/j.cnki.jos.005232
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2017,28(6):1516-1528, DOI:10.13328/j.cnki.jos.005231
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2017,28(6):1357-1372, DOI:10.13328/j.cnki.jos.005230
Abstract:
2017,28(6):1488-1497, DOI:10.13328/j.cnki.jos.005229
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2017,28(6):1455-1473, DOI:10.13328/j.cnki.jos.005228
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2017,28(6):1343-1356, DOI:10.13328/j.cnki.jos.005227
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2017,28(6):1405-1417, DOI:10.13328/j.cnki.jos.005226
Abstract:
2017,28(6):1418-1434, DOI:10.13328/j.cnki.jos.005225
Abstract:
2017,28(6):1389-1404, DOI:10.13328/j.cnki.jos.005224
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2017,28(6):1435-1454, DOI:10.13328/j.cnki.jos.005223
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2017,28(6):1330-1342, DOI:10.13328/j.cnki.jos.005222
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2017,28(6):1373-1388, DOI:10.13328/j.cnki.jos.005221
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2017,28(6):1498-1515, DOI:10.13328/j.cnki.jos.005220
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2017,28(5):1049-1050, DOI:10.13328/j.cnki.jos.005219
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2017,28(5):1204-1220, DOI:10.13328/j.cnki.jos.005218
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2017,28(5):1183-1203, DOI:10.13328/j.cnki.jos.005217
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2017,28(5):1128-1143, DOI:10.13328/j.cnki.jos.005216
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2017,28(5):1167-1182, DOI:10.13328/j.cnki.jos.005215
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2017,28(5):1144-1166, DOI:10.13328/j.cnki.jos.005214
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2017,28(5):1233-1246, DOI:10.13328/j.cnki.jos.005213
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2017,28(5):1091-1106, DOI:10.13328/j.cnki.jos.005212
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2017,28(5):1051-1069, DOI:10.13328/j.cnki.jos.005211
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2017,28(5):1107-1117, DOI:10.13328/j.cnki.jos.005210
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2017,28(5):1080-1090, DOI:10.13328/j.cnki.jos.005209
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2017,28(5):1070-1079, DOI:10.13328/j.cnki.jos.005208
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2017,28(5):1118-1127, DOI:10.13328/j.cnki.jos.005207
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2017,28(5):1221-1232, DOI:10.13328/j.cnki.jos.005206
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2017,28(4):745-746, DOI:10.13328/j.cnki.jos.005198
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2017,28(4):764-785, DOI:10.13328/j.cnki.jos.005197
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2017,28(4):819-826, DOI:10.13328/j.cnki.jos.005196
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2017,28(4):786-803, DOI:10.13328/j.cnki.jos.005195
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2017,28(4):845-859, DOI:10.13328/j.cnki.jos.005194
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2017,28(4):747-763, DOI:10.13328/j.cnki.jos.005193
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2017,28(4):883-897, DOI:10.13328/j.cnki.jos.005192
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2017,28(4):804-818, DOI:10.13328/j.cnki.jos.005191
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2017,28(4):860-882, DOI:10.13328/j.cnki.jos.005190
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2017,28(4):827-844, DOI:10.13328/j.cnki.jos.005189
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2016,27(10):2459-2461, DOI:10.13328/j.cnki.jos.005091
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2016,27(10):2462-2472, DOI:10.13328/j.cnki.jos.005090
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2016,27(10):2519-2529, DOI:10.13328/j.cnki.jos.005089
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2016,27(10):2612-2621, DOI:10.13328/j.cnki.jos.005088
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2016,27(10):2499-2508, DOI:10.13328/j.cnki.jos.005087
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2016,27(10):2473-2487, DOI:10.13328/j.cnki.jos.005086
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2016,27(10):2587-2599, DOI:10.13328/j.cnki.jos.005085
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2016,27(10):2622-2631, DOI:10.13328/j.cnki.jos.005084
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2016,27(10):2661-2675, DOI:10.13328/j.cnki.jos.005083
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2016,27(10):2654-2660, DOI:10.13328/j.cnki.jos.005082
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2016,27(10):2600-2611, DOI:10.13328/j.cnki.jos.005081
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2016,27(10):2676-2690, DOI:10.13328/j.cnki.jos.005079
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2016,27(10):2642-2653, DOI:10.13328/j.cnki.jos.005078
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2016,27(10):2632-2641, DOI:10.13328/j.cnki.jos.005077
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2016,27(10):2488-2498, DOI:10.13328/j.cnki.jos.005076
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2016,27(10):2530-2541, DOI:10.13328/j.cnki.jos.005074
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2016,27(10):2509-2518, DOI:10.13328/j.cnki.jos.005073
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2016,27(10):2557-2573, DOI:10.13328/j.cnki.jos.005072
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2016,27(10):2574-2586, DOI:10.13328/j.cnki.jos.005071
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2016,27(10):2542-2556, DOI:10.13328/j.cnki.jos.005070
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2016,27(8):1889-1890, DOI:10.13328/j.cnki.jos.005097
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2016,27(8):1891-1903, DOI:10.13328/j.cnki.jos.005096
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2016,27(8):1922-1933, DOI:10.13328/j.cnki.jos.005093
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2016,27(8):1904-1921, DOI:10.13328/j.cnki.jos.005092
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2016,27(7):1685-1699, DOI:10.13328/j.cnki.jos.005041
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2016,27(7):1626-1644, DOI:10.13328/j.cnki.jos.005040
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2016,27(7):1729-1740, DOI:10.13328/j.cnki.jos.005039
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2016,27(7):1605-1625, DOI:10.13328/j.cnki.jos.005038
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2016,27(7):1671-1684, DOI:10.13328/j.cnki.jos.005037
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2016,27(7):1645-1654, DOI:10.13328/j.cnki.jos.005036
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2016,27(7):1700-1714, DOI:10.13328/j.cnki.jos.005035
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2016,27(7):1715-1728, DOI:10.13328/j.cnki.jos.005034
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2016,27(7):1655-1670, DOI:10.13328/j.cnki.jos.005033
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2016,27(6):1325-1327, DOI:10.13328/j.cnki.jos.005010
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2016,27(6):1402-1416, DOI:10.13328/j.cnki.jos.005009
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2016,27(6):1463-1474, DOI:10.13328/j.cnki.jos.005008
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2016,27(6):1592-1601, DOI:10.13328/j.cnki.jos.005007
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2016,27(6):1384-1401, DOI:10.13328/j.cnki.jos.005006
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2016,27(6):1577-1591, DOI:10.13328/j.cnki.jos.005005
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2016,27(6):1328-1348, DOI:10.13328/j.cnki.jos.005004
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2016,27(6):1432-1450, DOI:10.13328/j.cnki.jos.005003
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2016,27(6):1475-1486, DOI:10.13328/j.cnki.jos.005002
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2016,27(6):1511-1522, DOI:10.13328/j.cnki.jos.005001
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2016,27(6):1366-1383, DOI:10.13328/j.cnki.jos.005000
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2016,27(6):1451-1462, DOI:10.13328/j.cnki.jos.004999
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2016,27(6):1566-1576, DOI:10.13328/j.cnki.jos.004998
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2016,27(6):1538-1548, DOI:10.13328/j.cnki.jos.004997
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2016,27(6):1549-1565, DOI:10.13328/j.cnki.jos.004996
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2016,27(6):1417-1431, DOI:10.13328/j.cnki.jos.004995
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2016,27(6):1349-1365, DOI:10.13328/j.cnki.jos.004994
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2016,27(6):1498-1510, DOI:10.13328/j.cnki.jos.004993
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2016,27(6):1523-1537, DOI:10.13328/j.cnki.jos.004992
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2016,27(6):1487-1497, DOI:10.13328/j.cnki.jos.004991
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2016,27(5):1059-1060, DOI:10.13328/j.cnki.jos.004963
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2016,27(5):1163-1173, DOI:10.13328/j.cnki.jos.004962
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2016,27(5):1061-1073, DOI:10.13328/j.cnki.jos.004961
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2016,27(5):1188-1198, DOI:10.13328/j.cnki.jos.004960
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2016,27(5):1199-1211, DOI:10.13328/j.cnki.jos.004959
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2016,27(5):1174-1187, DOI:10.13328/j.cnki.jos.004958
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2016,27(5):1074-1090, DOI:10.13328/j.cnki.jos.004957
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2016,27(5):1091-1102, DOI:10.13328/j.cnki.jos.004956
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2016,27(5):1114-1126, DOI:10.13328/j.cnki.jos.004955
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2016,27(5):1140-1150, DOI:10.13328/j.cnki.jos.004954
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2016,27(5):1151-1162, DOI:10.13328/j.cnki.jos.004953
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2016,27(5):1103-1113, DOI:10.13328/j.cnki.jos.004952
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2016,27(5):1127-1139, DOI:10.13328/j.cnki.jos.004951
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2016,27(4):769-770, DOI:10.13328/j.cnki.jos.004976
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2016,27(4):839-854, DOI:10.13328/j.cnki.jos.004975
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2016,27(4):855-878, DOI:10.13328/j.cnki.jos.004974
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2016,27(4):828-838, DOI:10.13328/j.cnki.jos.004973
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2016,27(4):771-784, DOI:10.13328/j.cnki.jos.004972
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2016,27(4):814-827, DOI:10.13328/j.cnki.jos.004971
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2016,27(4):879-900, DOI:10.13328/j.cnki.jos.004970
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2016,27(4):901-915, DOI:10.13328/j.cnki.jos.004969
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2016,27(4):943-954, DOI:10.13328/j.cnki.jos.004968
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2016,27(4):802-813, DOI:10.13328/j.cnki.jos.004967
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2016,27(4):785-801, DOI:10.13328/j.cnki.jos.004966
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2016,27(4):916-932, DOI:10.13328/j.cnki.jos.004965
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2016,27(4):933-942, DOI:10.13328/j.cnki.jos.004964
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2016,27(3):495-496, DOI:10.13328/j.cnki.jos.004990
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2016,27(3):682-690, DOI:10.13328/j.cnki.jos.004989
Abstract:
2016,27(3):670-681, DOI:10.13328/j.cnki.jos.004988
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2016,27(3):655-669, DOI:10.13328/j.cnki.jos.004987
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2016,27(3):645-654, DOI:10.13328/j.cnki.jos.004986
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2016,27(3):633-644, DOI:10.13328/j.cnki.jos.004985
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2016,27(3):611-632, DOI:10.13328/j.cnki.jos.004984
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2016,27(3):593-610, DOI:10.13328/j.cnki.jos.004983
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2016,27(3):580-592, DOI:10.13328/j.cnki.jos.004982
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2016,27(3):562-579, DOI:10.13328/j.cnki.jos.004981
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2016,27(3):547-561, DOI:10.13328/j.cnki.jos.004980
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2016,27(3):527-546, DOI:10.13328/j.cnki.jos.004979
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2016,27(3):517-526, DOI:10.13328/j.cnki.jos.004978
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2016,27(3):497-516, DOI:10.13328/j.cnki.jos.004977
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2015,26(11):2749-2751, DOI:10.13328/j.cnki.jos.004909
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2015,26(11):2811-2819, DOI:10.13328/j.cnki.jos.004908
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2015,26(11):2951-2963, DOI:10.13328/j.cnki.jos.004907
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2015,26(11):2994-3009, DOI:10.13328/j.cnki.jos.004906
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2015,26(11):2856-2868, DOI:10.13328/j.cnki.jos.004905
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2015,26(11):2981-2993, DOI:10.13328/j.cnki.jos.004904
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2015,26(11):2820-2835, DOI:10.13328/j.cnki.jos.004902
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2015,26(11):2781-2794, DOI:10.13328/j.cnki.jos.004901
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2015,26(11):3010-3025, DOI:10.13328/j.cnki.jos.004900
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2015,26(11):2939-2950, DOI:10.13328/j.cnki.jos.004899
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2015,26(11):2930-2938, DOI:10.13328/j.cnki.jos.004898
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2015,26(11):2795-2810, DOI:10.13328/j.cnki.jos.004897
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2015,26(11):2912-2929, DOI:10.13328/j.cnki.jos.004896
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2015,26(11):2847-2855, DOI:10.13328/j.cnki.jos.004895
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2015,26(11):2897-2911, DOI:10.13328/j.cnki.jos.004894
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2015,26(11):2884-2896, DOI:10.13328/j.cnki.jos.004893
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2015,26(11):2762-2780, DOI:10.13328/j.cnki.jos.004892
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2015,26(11):2964-2980, DOI:10.13328/j.cnki.jos.004891
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2015,26(11):2752-2761, DOI:10.13328/j.cnki.jos.004890
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2015,26(11):2869-2883, DOI:10.13328/j.cnki.jos.004889
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2015,26(11):2836-2846, DOI:10.13328/j.cnki.jos.004888
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2015,26(11):3026-3042, DOI:10.13328/j.cnki.jos.004887
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2015,26(4):711-712, DOI:10.13328/j.cnki.jos.004762
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2015,26(4):886-903, DOI:10.13328/j.cnki.jos.004761
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2015,26(4):835-848, DOI:10.13328/j.cnki.jos.004760
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2015,26(4):778-789, DOI:10.13328/j.cnki.jos.004759
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2015,26(4):730-746, DOI:10.13328/j.cnki.jos.004758
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2015,26(4):760-777, DOI:10.13328/j.cnki.jos.004757
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2015,26(4):867-885, DOI:10.13328/j.cnki.jos.004756
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2015,26(4):713-729, DOI:10.13328/j.cnki.jos.004755
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2015,26(4):802-818, DOI:10.13328/j.cnki.jos.004754
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2015,26(4):790-801, DOI:10.13328/j.cnki.jos.004753
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2015,26(4):819-834, DOI:10.13328/j.cnki.jos.004752
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2015,26(4):747-759, DOI:10.13328/j.cnki.jos.004751
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2015,26(4):849-866, DOI:10.13328/j.cnki.jos.004750
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2015,26(3):447-448, DOI:10.13328/j.cnki.jos.004774
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2015,26(3):474-490, DOI:10.13328/j.cnki.jos.004773
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2015,26(3):584-599, DOI:10.13328/j.cnki.jos.004772
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2015,26(3):509-532, DOI:10.13328/j.cnki.jos.004771
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2015,26(3):550-561, DOI:10.13328/j.cnki.jos.004770
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2015,26(3):533-549, DOI:10.13328/j.cnki.jos.004769
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2015,26(3):460-473, DOI:10.13328/j.cnki.jos.004768
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2015,26(3):574-583, DOI:10.13328/j.cnki.jos.004767
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2015,26(3):562-573, DOI:10.13328/j.cnki.jos.004766
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2015,26(3):449-459, DOI:10.13328/j.cnki.jos.004765
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2015,26(3):491-508, DOI:10.13328/j.cnki.jos.004764
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2015,26(2):179-180, DOI:10.13328/j.cnki.jos.004789
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2015,26(2):380-389, DOI:10.13328/j.cnki.jos.004788
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2015,26(2):364-379, DOI:10.13328/j.cnki.jos.004787
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2015,26(2):348-363, DOI:10.13328/j.cnki.jos.004786
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2015,26(2):332-347, DOI:10.13328/j.cnki.jos.004785
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2015,26(2):321-331, DOI:10.13328/j.cnki.jos.004784
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2015,26(2):305-320, DOI:10.13328/j.cnki.jos.004783
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2015,26(2):297-304, DOI:10.13328/j.cnki.jos.004782
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2015,26(2):279-296, DOI:10.13328/j.cnki.jos.004781
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2015,26(2):269-278, DOI:10.13328/j.cnki.jos.004780
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2015,26(2):254-268, DOI:10.13328/j.cnki.jos.004779
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2015,26(2):239-253, DOI:10.13328/j.cnki.jos.004778
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2015,26(2):223-238, DOI:10.13328/j.cnki.jos.004777
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2015,26(2):202-222, DOI:10.13328/j.cnki.jos.004776
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2015,26(2):181-201, DOI:10.13328/j.cnki.jos.004775
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2014,25(12):2731-2732, DOI:10.13328/j.cnki.jos.004732
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2014,25(12):2893-2904, DOI:10.13328/j.cnki.jos.004731
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2014,25(12):2767-2776, DOI:10.13328/j.cnki.jos.004730
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2014,25(12):2777-2789, DOI:10.13328/j.cnki.jos.004729
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2014,25(12):2790-2807, DOI:10.13328/j.cnki.jos.004728
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2014,25(12):2877-2892, DOI:10.13328/j.cnki.jos.004727
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2014,25(12):2865-2876, DOI:10.13328/j.cnki.jos.004726
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2014,25(12):2852-2864, DOI:10.13328/j.cnki.jos.004725
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2014,25(12):2733-2752, DOI:10.13328/j.cnki.jos.004724
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2014,25(12):2837-2851, DOI:10.13328/j.cnki.jos.004723
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2014,25(12):2753-2766, DOI:10.13328/j.cnki.jos.004722
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2014,25(12):2824-2836, DOI:10.13328/j.cnki.jos.004721
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2014,25(12):2808-2823, DOI:10.13328/j.cnki.jos.004720
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2014,25(10):2187-2188, DOI:10.13328/j.cnki.jos.004683
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2014,25(10):2251-2265, DOI:10.13328/j.cnki.jos.004682
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2014,25(10):2206-2219, DOI:10.13328/j.cnki.jos.004681
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2014,25(10):2189-2205, DOI:10.13328/j.cnki.jos.004680
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2014,25(10):2220-2234, DOI:10.13328/j.cnki.jos.004679
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2014,25(10):2235-2250, DOI:10.13328/j.cnki.jos.004678
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2014,25(9):1889-1908, DOI:10.13328/j.cnki.jos.004674
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2014,25(9):1887-1888, DOI:10.13328/j.cnki.jos.004652
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2014,25(9):2102-2118, DOI:10.13328/j.cnki.jos.004651
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2014,25(9):2149-2159, DOI:10.13328/j.cnki.jos.004650
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2014,25(9):2018-2025, DOI:10.13328/j.cnki.jos.004649
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2014,25(9):1953-1966, DOI:10.13328/j.cnki.jos.004648
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2014,25(9):2172-2179, DOI:10.13328/j.cnki.jos.004647
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2014,25(9):2180-2186, DOI:10.13328/j.cnki.jos.004646
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2014,25(9):1909-1936, DOI:10.13328/j.cnki.jos.004645
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2014,25(9):2050-2075, DOI:10.13328/j.cnki.jos.004644
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2014,25(9):2037-2049, DOI:10.13328/j.cnki.jos.004643
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2014,25(9):2076-2087, DOI:10.13328/j.cnki.jos.004642
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2014,25(9):1992-2001, DOI:10.13328/j.cnki.jos.004641
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2014,25(9):2119-2135, DOI:10.13328/j.cnki.jos.004640
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2014,25(9):1982-1991, DOI:10.13328/j.cnki.jos.004639
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2014,25(9):2088-2101, DOI:10.13328/j.cnki.jos.004638
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2014,25(9):2136-2148, DOI:10.13328/j.cnki.jos.004637
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2014,25(9):1937-1952, DOI:10.13328/j.cnki.jos.004636
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2014,25(9):2002-2017, DOI:10.13328/j.cnki.jos.004635
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2014,25(9):1967-1981, DOI:10.13328/j.cnki.jos.004634
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2014,25(9):2160-2171, DOI:10.13328/j.cnki.jos.004633
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2014,25(9):2026-2036, DOI:10.13328/j.cnki.jos.004632
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2014,25(8):1621-1624, DOI:10.13328/j.cnki.jos.004668
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2014,25(8):1729-1742, DOI:10.13328/j.cnki.jos.004667
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2014,25(8):1713-1728, DOI:10.13328/j.cnki.jos.004666
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2014,25(8):1696-1712, DOI:10.13328/j.cnki.jos.004665
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2014,25(8):1685-1695, DOI:10.13328/j.cnki.jos.004664
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2014,25(8):1671-1684, DOI:10.13328/j.cnki.jos.004663
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2014,25(8):1659-1670, DOI:10.13328/j.cnki.jos.004662
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2014,25(8):1640-1658, DOI:10.13328/j.cnki.jos.004661
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2014,25(8):1625-1639, DOI:10.13328/j.cnki.jos.004660
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2014,25(7):1369-1370, DOI:10.13328/j.cnki.jos.004605
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2014,25(7):1371-1387, DOI:10.13328/j.cnki.jos.004604
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2014,25(7):1416-1431, DOI:10.13328/j.cnki.jos.004603
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2014,25(7):1388-1402, DOI:10.13328/j.cnki.jos.004602
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2014,25(7):1432-1447, DOI:10.13328/j.cnki.jos.004601
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2014,25(7):1403-1415, DOI:10.13328/j.cnki.jos.004600
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2014,25(7):1448-1458, DOI:10.13328/j.cnki.jos.004599
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2014,25(4):691-692, DOI:10.13328/j.cnki.jos.004572
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2014,25(4):826-838, DOI:10.13328/j.cnki.jos.004571
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2014,25(4):713-730, DOI:10.13328/j.cnki.jos.004570
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2014,25(2):254-266, DOI:10.13328/j.cnki.jos.004537
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2014,25(2):419-438, DOI:10.13328/j.cnki.jos.004536
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2014,25(2):284-297, DOI:10.13328/j.cnki.jos.004534
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2014,25(2):234-253, DOI:10.13328/j.cnki.jos.004533
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2014,25(2):357-372, DOI:10.13328/j.cnki.jos.004532
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2014,25(2):326-340, DOI:10.13328/j.cnki.jos.004531
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2014,25(2):200-218, DOI:10.13328/j.cnki.jos.004530
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2014,25(2):179-199, DOI:10.13328/j.cnki.jos.004529
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2014,25(2):314-325, DOI:10.13328/j.cnki.jos.004528]
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2014,25(2):298-313, DOI:10.13328/j.cnki.jos.004527
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2013,24(11):2473-2475, DOI:10.3724/SP.J.1001.2013.04487
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2013,24(11):2476-2497, DOI:10.3724/SP.J.1001.2013.04486
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2013,24(11):2747-2757, DOI:10.3724/SP.J.1001.2013.04484
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2013,24(11):2522-2534, DOI:10.3724/SP.J.1001.2013.04483
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2013,24(11):2558-2570, DOI:10.3724/SP.J.1001.2013.04482
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2013,24(11):2758-2766, DOI:10.3724/SP.J.1001.2013.04481
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2013,24(11):2734-2746, DOI:10.3724/SP.J.1001.2013.04480
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2013,24(11):2498-2507, DOI:10.3724/SP.J.1001.2013.04479
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2013,24(11):2571-2583, DOI:10.3724/SP.J.1001.2013.04467
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2013,24(11):2642-2655, DOI:10.3724/SP.J.1001.2013.04464
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2012,23(11):2833-2834, DOI:10.3724/SP.J.1001.2012.04309
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2012,23(11):2971-2986, DOI:10.3724/SP.J.1001.2012.04308
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2012,23(11):3000-3008, DOI:10.3724/SP.J.1001.2012.04305
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2011,22(6):1121-1122, DOI:10.3724/SP.J.1001.2011.04036
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Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-4
Address：4# South Fourth Street, Zhong Guan Cun, Beijing 100190,Postal Code：100190
Phone：010-62562563 Fax：010-62562533 Email：jos@iscas.ac.cn
Technical Support：Beijing Qinyun Technology Development Co., Ltd.
