Abstract:Concurrency and scalability are fundamental properties of most complex systems. As a widely used formalism for modeling concurrency, Petri nets have been applied across various fields. Their mathematical abstraction, the vector addition system (VAS), has become a central object of study in theoretical computer science. The reachability problem of VAS, along with its algorithmic and complexity characterizations, has been regarded as one of the most fundamental and long-standing challenges in the field over the last 50 years. This study presents a comprehensive survey of the research on the complexity lower bounds of the VAS reachability problem. The definitions of VAS, their equivalent models, and the core verification problem, the reachability problem, are introduced. Known completeness results concerning the complexity of the reachability problem are reviewed. For the fixed-dimension case, proof frameworks based on multiplication triples and amplifiers are outlined, and the state-of-the-art achievements as well as core proof techniques are summarized. Finally, the current research bottlenecks, major open problems, and future challenges are discussed.

Abstract:Smart contracts, as automatically executed computer transaction protocols, are widely applied in blockchain networks to implement various types of business logic. However, the strict immutability of blockchain poses significant challenges for smart contract maintenance, making upgradeability a prominent research topic. This study focuses on upgradeable smart contracts, systematically reviewing their development status both domestically and internationally, and introducing seven mainstream upgradeable contract models. The research is summarized from four key perspectives: upgradeable smart contracts, application requirements, upgrade frameworks, and security oversight. It covers multiple stages, including design, implementation, testing, deployment, and maintenance. The goal is to provide insights and references for the further development of blockchain applications.

Abstract:With the rapid development of technologies such as deep learning and significant breakthroughs in areas including computer hardware and cloud computing, increasingly mature artificial intelligence (AI) technologies are being applied to software systems across various fields. Software systems that incorporate AI models as core components are collectively referred to as intelligence software systems. Based on the application fields of AI technologies, these systems are categorized into image processing, natural language processing, speech processing, and other applications. Unlike traditional software systems, AI models adopt a data-driven programming paradigm in which all decision logic is learned from large-scale datasets. This paradigm shift renders traditional code-based test case generation methods ineffective for evaluating the quality of intelligence software systems. As a result, numerous testing methods tailored for intelligence software systems have been proposed in recent years, including novel approaches for test case generation and evaluation that address the unique characteristics of such systems. This study reviews 80 relevant publications, classifies existing methods according to the types of systems they target, and systematically summarizes test case generation methods for image processing, natural language processing, speech processing, point cloud processing, multimodal data processing, and deep learning models. Potential future directions for test case generation in intelligence software systems are also discussed to provide a reference for researchers in this field.

Abstract:Blockchain, as a distributed ledger technology, ensures data security, transparency, and immutability through encryption and consensus mechanisms, offering transformative solutions across various industries. In China, blockchain-based software has attracted widespread attention and application, demonstrating considerable potential in fields such as cross-border payments, supply chain finance, and government services. These applications not only enhance the efficiency and transparency of business processes but also reduce trust costs and offer new approaches for the digital transformation of traditional industries. This study investigates the development trends and core technologies of Chinese blockchain software, focusing on key technological breakthroughs, promoting integration and innovation, and providing a foundation for the formulation of technical standards. The aim is to enhance the competitiveness of Chinese blockchain technologies, broaden application scenarios, and support the standardized development of the industry. Three core research questions are addressed: (1) What are the development trends of Chinese blockchain software? (2) What are the core technologies involved? (3) What are the differences in core technologies between Chinese and foreign blockchain software? To address these questions, 1268 blockchain software entries have been collected through three channels. Based on information regarding affiliated companies and chief technology officers (CTOs), 103 Chinese blockchain software entries are identified. A statistical analysis of basic software attributes is conducted, examining development trends from three perspectives: software development history, distribution, and interrelationships. Given the importance of technical and development documentation, 39 high-quality blockchain software entries containing detailed technical information are further selected. Subsequently, a statistical and analytical evaluation of the core technologies of these 39 software systems is conducted across six technical layers of blockchain architecture. Based on this analysis, differences in core technologies between Chinese and foreign blockchain software are compared. In total, 28 phenomena and 13 insights are identified. These findings provide researchers, developers, and practitioners with a comprehensive understanding of the current state of Chinese blockchain development and offer valuable references for future adoption and improvement of Chinese blockchain software.

Abstract:Blockchain ensures data security and consistency without relying on trusted third-party institutions through full replication mechanisms and consensus protocols. However, this design significantly limits system scalability. Sharding addresses this limitation by partitioning the blockchain into multiple parallel working shards, with state sharding further distributing state data across these shards, significantly improving system throughput and storage efficiency. Currently, state sharding faces several challenges, including the following three problems: (1) Cross-shard transactions require collaborative processing across multiple shards, introducing additional communication and consensus overhead, necessitating improvements in processing efficiency; (2) State partitioning optimization is needed to reduce the proportion of cross-shard transactions and balance shard workloads; (3) Efficient state data migration between shards is required during dynamic state partitioning and adjustment. This study systematically reviews the key challenges and mainstream implementations of state sharding, focusing on cross-shard transaction processing, state allocation, and state migration. Finally, the study identifies current limitations of state sharding and suggests possible directions for future research.

Abstract:Autonomous driving systems (ADSs) have gained significant attention from both industry and academia due to their substantial economic, safety, and societal benefits, leading to in-depth research and the gradual popularization of applications. However, the introduction of such complex ecosystems can give rise to new safety issues that threaten the lives of pedestrians and impact the existing legal system. Therefore, it is imperative to validate ADSs through various methods such as simulation testing, access reviews, and pilot operations before the implementation and commercialization of ADSs. While the research on module safety has matured, there is still a lack of comprehensive research and organization regarding the safety of complete vehicle systems. Therefore, this study systematically analyzes vehicle system safety testing for ADSs and comprehensively reviews the current mainstream work. First, the architecture of ADSs and the basic procedure of simulation testing are outlined. The literature on vehicle system safety testing over the past six years is reviewed. Based on a universal testing framework, an autonomous driving safety testing framework tailored for vehicle systems is developed. Second, five core research issues are identified based on the aforementioned framework, namely critical scenario generation, test adequacy, adversarial sample generation, test optimization, and test oracle. A detailed analysis and organization of the key technologies, research status, and development context for each issue are provided. The commonly used evaluation metrics and comparative methods in current research are also summarized. Finally, the severe challenges faced by various research directions are summarized, and future research opportunities are anticipated, along with potential solutions.

Abstract:Contrastive learning is a self-supervised learning technique widely used in various fields such as computer vision and natural language processing. Graph contrastive learning (GCL) refers to methods that apply contrastive learning techniques to graph data. A review is presented on the basic concepts, methods, and applications of graph contrastive learning. First, the background and significance of GCL, as well as its basic concepts on graph data, are introduced. Then, the mainstream GCL methods are elaborated in detail, including methods with different graph data augmentation strategies, methods with different graph neural network (GNN) encoder structures, and methods with different contrastive loss objectives. Finally, three research ideas for GCL are proposed. Research findings demonstrate that graph contrastive learning is an effective approach for addressing various downstream tasks, including node classification and graph classification.

Abstract:In the era of artificial intelligence, efficiently completing the pre-training of large language models to meet requirements for scalability, performance, and stability presents a critical challenge. These systems leverage accelerators and high-speed network interfaces to execute parallel tensor computations and communications, significantly enhancing training efficiency. However, these advancements bring a series of unresolved system design challenges. Based on an analysis of the pre-training process, this study first outlines the training procedures and workload characteristics of large language models. It then reviews system technologies from the perspectives of scalability, performance, and reliability, covering their classifications, underlying principles, current research progress, and key challenges. Finally, this study provides an in-depth analysis of the broader challenges facing large language model pre-training systems and discusses potential directions for future development.

Abstract:Geo-distributed consortium blockchains leverage the characteristics of decentralization, immutability, and traceability to support large-scale applications such as e-commerce, supply chain management, and finance by distributing nodes across multiple data centers. However, traditional consortium blockchains face challenges in performance, scalability, and elasticity in large-scale deployment. Existing blockchains have proposed various approaches in consensus algorithms, concurrency control, and ledger sharding to address the above challenges. First, consensus algorithms are categorized based on network topology, the number of primary nodes, and network models, and different communication optimization strategies during consensus are explored. Second, the advantages and disadvantages of optimistic concurrency control, dependency graph, deterministic concurrency control, and coordination-free consistency in geo-distributed scenarios are discussed. Next, cross-shard commit protocols for blockchain are categorized, and their cross-region coordination overheads are analyzed. Finally, the technical challenges of existing geo-distributed consortium blockchains are highlighted, and future research directions are provided.

Abstract:In distributed system environments, ensuring high availability of databases poses multiple challenges, including network latency, node failures, and the maintenance of data consistency. Addressing these challenges requires not only advanced technical solutions but also flexible architectural design and refined management strategies. High availability plays a crucial role in maintaining data integrity and consistency, as well as in improving system performance and enhancing fault tolerance. This study provides a comprehensive review of the current challenges and issues associated with high availability in distributed databases. Important concepts, theoretical foundations, and technical approaches are examined, and the current state of research is analyzed across three levels: system and network, data and computing, and application and service. The study aims to deepen the understanding of the difficulties to be addressed and the existing solutions while offering recommendations for future research and technological advancements in the field.

Abstract:Query optimization is a critical component in database systems, where execution costs are minimized by identifying the most efficient query execution plan. Traditional query optimizers typically rely on fixed rules or simple heuristic algorithms to refine or select candidate plans. However, with the growing complexity of relational schemas and queries in real-world applications, such optimizers struggle to meet the demands of modern applications. Learned query optimization algorithms integrate machine learning techniques into the optimization process. They capture features of query plans and complex schemas to assist traditional optimizers. These algorithms offer innovative and effective solutions in areas such as cost modeling, join optimization, plan generation, and query rewriting. This study reviews recent achievements and developments in four main categories of learned query optimization algorithms. Future research directions are also discussed, aiming to provide a comprehensive understanding of the current state of research and to support further investigation in this field.

Abstract:In recent years, a series of data-driven AIGC applications, represented by large language models, have profoundly changed people’s lifestyles, drawing significant attention from nations to issues such as data circulation and data privacy. Establishing robust data market regulations and improving data circulation mechanisms have become important research topics. However, existing research on data privacy generally focuses on individual aspects of data circulation and does not provide a comprehensive view of the entire process. Technical research is often fragmented, leading to issues such as incompatibility. As a result, data service providers often need to invest additional manpower in practical production activities to implement comprehensive data privacy protection. This study focuses on data circulation, dividing the entire circulation process into three stages, and establishes a systematic classification framework for privacy technologies at each stage. This study uses data circulation as a carrier and privacy technology as the subject, comprehensively covering the entire process of data circulation. It also provides an in-depth analysis of the latest developments and future challenges in various fields. It enables researchers to quickly develop a systematic understanding of privacy technologies across the data circulation process and lays a foundation for establishing a comprehensive full-process data circulation protection paradigm for future research.

Abstract:In recent years, the increasing complexity of space missions has led to an exponential growth in space-generated data. However, limited satellites-to-ground bandwidth and scarce frequency resources pose significant challenges to traditional bent-pipe architecture, which faces severe transmission bottlenecks. In addition, onboard data must wait for satellites to pass over ground stations before transmission. The large-scale construction of ground stations is not only cost-prohibitive but also carries geopolitical and economic risks. Satellite edge computing has emerged as a promising solution to these bottlenecks by integrating mobile edge computing technology into satellite edges. This approach significantly enhances user experience and reduces redundant network traffic. By enabling onboard data processing, satellite edge computing shortens data acquisition times and reduces reliance on extensive ground station infrastructure. Furthermore, the integration of artificial intelligence (AI) and edge computing technologies offers an efficient and forward-looking path to address existing challenges. This study reviews the latest progress in intelligent satellite edge computing. First, the demands and applications of satellite edge computing in various typical scenarios are discussed. Next, key challenges and recent research advancements in this field are analyzed. Finally, several open research topics are highlighted, and new ideas are proposed to guide future studies. This discussion aims to provide valuable insights to promote technological innovation and the practical implementation of satellite edge computing.

Abstract:As core programmable components of blockchain, smart contracts are responsible for asset management and the execution of complex business logic, forming the foundation of decentralized finance (DeFi) protocols. However, with the rapid advancement of blockchain technology, security issues related to smart contracts and DeFi protocols have become increasingly prominent, attracting numerous attackers seeking to exploit vulnerabilities for illicit gains. In recent years, several major security incidents involving smart contracts and DeFi protocols have highlighted the importance of vulnerability detection research, making it a critical area for security defense. This study systematically reviews existing literature and proposes a comprehensive framework for research on vulnerability detection in smart contracts and DeFi protocols. Specifically, vulnerabilities and detection techniques are categorized and analyzed for both domains. For smart contracts, the study focuses on the application of large language models (LLM) as primary detection engines and their integration with traditional methods. For DeFi protocols, it categorizes and details various protocol-level vulnerabilities and their detection methods, analyzing the strengths and limitations of detection strategies before and after attacks, addressing gaps in existing reviews on DeFi vulnerability detection. Finally, this study summarizes the challenges faced by current detection approaches and outlines future research directions, aiming to provide new insights and theoretical support for the security detection of smart contracts and DeFi protocols.

Abstract:Segment routing over IPv6 (SRv6), as a key enabling technology for the next-generation network architecture, introduces a flexible segment routing forwarding plane, offering revolutionary opportunities to enhance network intelligence and expand service capabilities. This study aims to provide a comprehensive review of the evolution and research status of SRv6 in recent years. First, the study systematically summarizes the applications of SRv6 in network architecture and performance, network management and operation, and emerging service support, highlighting the unique advantages of SRv6 in fine-grained scheduling, flexible programming, and service convergence. Meanwhile, the study deeply analyzes the key challenges SRv6 faces in performance and efficiency, reliability and security, and deployment and evolution strategies, and focuses on discussing the current mainstream solutions and development trends. Finally, from the perspectives of industrial ecosystem construction, artificial intelligence integration, and industry convergence innovation, the study provides forward-looking thoughts and prospects on the future development directions and challenges of SRv6. The research findings of this study will provide theoretical references and practical guidance for operators in building open, intelligent, and secure next-generation networks.

Abstract:With the rapid development of Lattice-based post-quantum cryptography, algorithms for hard problems in Lattices have become an essential tool for evaluating the security of post-quantum cryptographic schemes. Algorithms such as enumeration, sieve, and Lattice basis reduction have been developed under the classical computing model, while quantum algorithms for solving hard problems in Lattices, such as quantum sieve and quantum enumeration, are gradually attracting attention. Although Lattice problems possess post-quantum properties, techniques such as quantum search can accelerate a range of Lattice algorithms. Given the challenges involved in solving hard problems in Lattices, this study first summarizes and analyzes the research status of quantum algorithms for such problems and organizes their design principles. Then, the quantum computing techniques applied in these algorithms are introduced, followed by an analysis and comparison of their computational complexities. Finally, potential future developments and research directions for quantum algorithms addressing Lattice-based hard problems are discussed.

Abstract:The evolution of RFID-based passive Internet of Things (IoT) systems comprises three stages: traditional UHF RFID (also referred to as standalone or Passive 1.0), local area network-based coverage (networked or Passive 2.0), and wide-area cellular coverage (cellular or Passive 3.0). Wireless sensing in passive IoT is characterized by zero power consumption, low cost, and ease of deployment, enabling object tagging and close-proximity sensing. With the emergence of cellular passive IoT, passive IoT wireless sensing is playing an increasingly important role in enabling ubiquitous sensing within IoT systems. This study first introduces the concept and development path of passive IoT. Based on fundamental sensing principles, recent research advancements are reviewed across four representative objectives: localization and tracking, object status detection, human behavior recognition, and vital sign monitoring. Given that most existing research relies on commercial UHF RFID devices to extract signal features for data processing, the development direction of passive IoT wireless sensing technology is further examined from the perspectives of new architecture, new air interface, and new capabilities. Moreover, this study offers reflections on the integration of communication and sensing in the design of next-generation air interfaces from a sensing-oriented perspective, aiming to provide new insights into the advancements in passive IoT wireless sensing technologies.

Abstract:Side-channel analysis (SCA) is a technique that extracts leaked information generated during hardware or software execution to compromise cryptographic keys. Among various approaches, profiling side-channel analysis has been proven to be a powerful method for attacking cryptographic systems. In recent years, the integration of artificial intelligence technology into profiling side-channel analysis has significantly enriched attack strategies and improved efficiency. During the profiling phase, leakage information related to the target device is typically collected by accessing a cloned device. However, practical scenarios often involve discrepancies between the cloned and target devices. Most existing studies rely on a single device for training and validation, resulting in methods that are highly environment-dependent, with limited applicability and poor portability. This study focuses on the portability challenges encountered in complex application scenarios. Challenges arising from variations in parameter settings, algorithm implementations, and hardware differences are analyzed in detail. Solutions and analysis results proposed in recent years are systematically reviewed. Based on this survey, current limitations in portability research on side-channel analysis are summarized, and potential future directions are discussed.

Abstract:Resource public key infrastructure (RPKI) is a key technology for enhancing border gateway protocol (BGP) security, using cryptographic verification to prevent attacks such as prefix hijacking. Since its formal deployment in 2012, RPKI has grown to cover over half of Internet prefixes. Ongoing research on RPKI deployment helps to provide insights into current trends and identify security issues. This study reviews existing works on RPKI measurement from three perspectives: RPKI data object measurement, ROV measurement, and RPKI infrastructure measurement. It analyzes RPKI data object and ROV coverage metrics, deployment trends, and the effectiveness of different measurement approaches. Moreover, key security vulnerabilities and data quality issues are identified, and recommendations to promote large-scale RPKI deployment are proposed.