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Minimal Context-Switching Data Race Detection with Dataflow Tracking

Journal of Computer Science & Technology 2024年第1期39卷 211-226页

作者：郑龙李洋辛杰刘海峰郑然廖小飞金海 National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Services computing Technology and System Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Cluster and Grid computing Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Data race is one of the most important concurrent anomalies in multi-threaded *** con-straint-based techniques are leveraged into race detection,which is able to find all the races that can be found by any oth-er sound race ***,this constraint-based approach has serious limitations on helping programmers analyze and understand data ***,it may report a large number of false positives due to the unrecognized dataflow propa-gation of the ***,it recommends a wide range of thread context switches to schedule the reported race(in-cluding the false one)whenever this race is exposed during the constraint-solving *** ad hoc recommendation imposes too many context switches,which complicates the data race *** address these two limitations in the state-of-the-art constraint-based race detection,this paper proposes DFTracker,an improved constraint-based race detec-tor to recommend each data race with minimal thread context ***,we reduce the false positives by ana-lyzing and tracking the dataflow in the *** this means,DFTracker thus reduces the unnecessary analysis of false race *** further propose a novel algorithm to recommend an effective race schedule with minimal thread con-text switches for each data *** experimental results on the real applications demonstrate that 1)without removing any true data race,DFTracker effectively prunes false positives by 68%in comparison with the state-of-the-art constraint-based race detector;2)DFTracker recommends as low as 2.6-8.3(4.7 on average)thread context switches per data race in the real world,which is 81.6%fewer context switches per data race than the state-of-the-art constraint based race ***,DFTracker can be used as an effective tool to understand the data race for programmers.

关键词： data race satisfiability modulo theory multi-threaded program dynamic detection

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Research and application of key technologies for data delivery in railway engineering design based on metadata

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High-Speed Railway 2024年第1期2卷 51-56页

作者： Xiangru Lyu Xunxiao Yin Kun Wang Yongwen Wei institute of computing Technologies China Academy of Railway Sciences Corporation LimitedBeijing 100081China

in view of the problems of inconsistent data semantics,inconsistent data formats,and difficult data quality assurance between the railway engineering design phase and the construction and operation phase,as well as the difficulty in fully realizing the value of design results,this paper proposes a design and implementation scheme for a railway engineering collaborative design *** railway engineering collaborative design platform mainly includes functional modules such as metadata management,design collaboration,design delivery management,model component library,model rendering services,and Building information Modeling(BiM)application *** on this,research is conducted on multi-disciplinary parameterized collaborative design technology for railway engineering,infrastructure data management and delivery technology,and design multi-source data fusion and application *** railway engineering collaborative design platform is compared with other railway design software to further validate its advantages and advanced *** platform has been widely applied in multiple railway construction projects,greatly improving the design and project management efficiency.

关键词： Railway engineering Metadata Data delivery Collaborative designplatform Design results BiM services

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Stochastic sampled-data multi-objective control of active suspension systems for in-wheel motor driven electric vehicles

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Journal of Automation and intelligence 2024年第1期3卷 2-18页

作者： iftikhar Ahmad Xiaohua Ge Qing-Long Han School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia

This paper addresses the sampled-data multi-objective active suspension control problem for an in-wheel motor driven electric vehicle subject to stochastic sampling periods and asynchronous premise *** focus is placed on the scenario that the dynamical state of the half-vehicle active suspension system is transmitted over an in-vehicle controller area network that only permits the transmission of sampled data *** this purpose,a stochastic sampling mechanism is developed such that the sampling periods can randomly switch among different values with certain mathematical ***,an asynchronous fuzzy sampled-data controller,featuring distinct premise variables from the active suspension system,is constructed to eliminate the stringent requirement that the sampled-data controller has to share the same grades of ***,novel criteria for both stability analysis and controller design are derived in order to guarantee that the resultant closed-loop active suspension system is stochastically stable with simultaneous𝐻2 and𝐻∞performance ***,the effectiveness of the proposed stochastic sampled-data multi-objective control method is verified via several numerical cases studies in both time domain and frequency domain under various road disturbance profiles.

关键词： Active suspension system Electric vehicles in-wheel motor Stochastic sampling Dynamic dampers Sampled-data control Multi-objective control

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ioT Task Offloading in Edge computing Using Non-Cooperative Game Theory for Healthcare Systems

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Computer Modeling in Engineering & Sciences 2024年第5期139卷 1487-1503页

作者： Dinesh Mavaluru Chettupally Anil Carie Ahmed i.Alutaibi Satish Anamalamudi Bayapa Reddy Narapureddy Murali Krishna Enduri Md Ezaz Ahmed School of computing and informatics Saudi Electronic UniversityRiyadhSaudi Arabia Department of Computer Engineering SRM University APGunturIndia College of Computer and information Sciences Majmaah UniversityAl Majma’ahSaudi Arabia Department of Public Health College of Applied Medical SciencesKing Khalid UniversityAbhaSaudi Arabia

in this paper,we present a comprehensive system model for industrial internet of Things(iioT)networks empowered by Non-Orthogonal Multiple Access(NOMA)and Mobile Edge computing(MEC)*** network comprises essential components such as base stations,edge servers,and numerous iioT devices characterized by limited energy and computing *** central challenge addressed is the optimization of resource allocation and task distribution while adhering to stringent queueing delay constraints and minimizing overall energy *** system operates in discrete time slots and employs a quasi-static approach,with a specific focus on the complexities of task partitioning and the management of constrained resources within the iioT *** study makes valuable contributions to the field by enhancing the understanding of resourceefficient management and task allocation,particularly relevant in real-time industrial *** results indicate that our proposed algorithmsignificantly outperforms existing approaches,reducing queue backlog by 45.32% and 17.25% compared to SMRA and ACRA while achieving a 27.31% and 74.12% improvement in Qn ***,the algorithmeffectively balances complexity and network performance,as demonstratedwhen reducing the number of devices in each group(Ng)from 200 to 50,resulting in a 97.21% reduction in complexity with only a 7.35% increase in energy *** research offers a practical solution for optimizing iioT networks in real-time industrial settings.

关键词： internet of Things edge computing offloading NOMA

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integrating Neighborhood Geographic Distribution and Social Structure influence for Social Media User Geolocation

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Computer Modeling in Engineering & Sciences 2024年第9期140卷 2513-2532页

作者： Meng Zhang Xiangyang Luo Ningbo Huang Key Laboratory of Cyberspace Situation Awareness of Henan Province Zhengzhou450001China State Key Laboratory of Mathematical Engineering and Advanced computing Zhengzhou450001China

Geolocating social media users aims to discover the real geographical locations of users from their publicly available data,which can support online location-based applications such as disaster alerts and local content *** relationship-based methods represent a classical approach for geolocating social ***,geographically proximate relationships are sparse and challenging to discern within social networks,thereby affecting the accuracy of user *** address this challenge,we propose user geolocation methods that integrate neighborhood geographical distribution and social structure influence(NGSi)to improve geolocation ***,we propose a method for evaluating the homophily of locations based on the k-order neighbor-hood geographic distribution(k-NGD)similarity among *** are notable differences in the distribution of k-NGD similarity between location-proximate and non-location-proximate *** this distinction,we filter out non-location-proximate social relationships to enhance location homophily in the social *** better utilize the location-proximate relationships in social networks,we propose a graph neural network algorithm based on the social structure *** algorithm enables us to perform a weighted aggregation of the information of users’multi-hop neighborhood,thereby mitigating the over-smoothing problem of user features and improving user geolocation *** results on real social media dataset demonstrate that the neighborhood geographical distribution similarity metric can effectively filter out non-location-proximate social ***,compared with 7 existing social relationship-based user positioning methods,our proposed method can achieve multi-granularity user geolocation and improve the accuracy by 4.84%to 13.28%.

关键词： User geolocation social media neighborhood geographic distribution structure influence

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HEN:a novel hybrid explainable neural network based framework for robust network intrusion detection

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Science China(information Sciences) 2024年第7期67卷 72-90页

作者： Wei WEi Sijin CHEN Cen CHEN Heshi WANG Jing LiU Zhongyao CHENG Xiaofeng ZOU School of Computer Science and Engineering Xi'an University of TechnologyShaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Technology Wuhan University of Science and Technology School of Future Technology South China University of Technology Shenzhen Research institute of Hunan University School of Computer Science Hunan University of Technology and Business institute for infocomm Research (i2R) Agency for Science Technology and Reseach (A*STAR)

With the rapid development of network technology and the automation process for 5G, cyberattacks have become increasingly complex and threatening. in response to these threats, researchers have developed various network intrusion detection systems(NiDS) to monitor network traffic. However, the incessant emergence of new attack techniques and the lack of system interpretability pose challenges to improving the detection performance of NiDS. To address these issues, this paper proposes a hybrid explainable neural network-based framework that improves both the interpretability of our model and the performance in detecting new attacks through the innovative application of the explainable artificial intelligence(XAi)method. We effectively introduce the Shapley additive explanations(SHAP) method to explain a light gradient boosting machine(Light GBM) model. Additionally, we propose an autoencoder long-term short-term memory(AE-LSTM) network to reconstruct SHAP values previously generated. Furthermore, we define a threshold based on reconstruction errors observed during the training phase. Any network flow that surpasses the specified threshold is classified as an attack flow. This approach enhances the framework's ability to accurately identify attacks. We achieve an accuracy of 92.65%, a recall of 95.26%, a precision of 92.57%,and an F1-score of 93.90% on the dataset NSL-KDD. Experimental results demonstrate that our approach generates detection performance on par with state-of-the-art methods.

关键词： explainable artificial intelligence light gradient boosting machine machine learning network intrusion detection Shapley additive explanation hybrid explainable neural network (HEN)

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The quantum ising model for perfect matching and solving it with variational quantum eigensolver

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Science China(information Sciences) 2024年第9期67卷 306-320页

作者： Qilin ZHENG Pingyu ZHU Chao WU Miaomiao YU Weihong LUO Ping XU institute for Quantum information and State Key Laboratory of High Performance computing College of Computer Science and Technology National University of Defense Technology Hefei National Laboratory

Obtaining all perfect matchings of a graph is a tough problem in graph theory, and its complexity belongs to the #P-Complete class. The problem is closely related to combinatorics, marriage matching problems, dense subgraphs, the Gaussian boson sampling, chemical molecular structures, and dimer *** this paper, we propose a quadratic unconstrained binary optimization formula of the perfect matching problem and translate it into the quantum ising model. We can obtain all perfect matchings by mapping them to the ground state of the quantum ising Hamiltonian and solving it with the variational quantum eigensolver. Adjusting the model's parameters can also achieve the maximum or minimum weighted perfect matching. The experimental results on a superconducting quantum computer of the Origin Quantum computing Technology Company show that our model can encode 2~n dimensional optimization space with only O（n） qubits consumption and achieve a high success probability of the ground state corresponding to all perfect matchings. in addition, the further simulation results show that the model can support a scale of more than 14 qubits, effectively resist the adverse effects of noise, and obtain a high success probability at a shallow variational depth. This method can be extended to other combinatorial optimization problems.

关键词： perfect matching ising model quantum Hamiltonian variational quantum eigensolver quadratic unconstrained binary optimization

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Multi-party privacy-preserving decision tree training with a privileged party

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Science China(information Sciences) 2024年第8期67卷 253-268页

作者： Yiwen TONG Qi FENG Min LUO Debiao HE Key Laboratory of Aerospace information Security and Trusted computing Ministry of Education School of Cyber Science and Engineering Wuhan University Shanghai Technology innovation Centre of Distributed Privacy-Preserving Artificial intelligence Matrix Elements Technologies Key Laboratory of computing Power Network and information Security Ministry of EducationShandong Computer Science Center Qilu University of Technology(Shandong Academy of Sciences)

Currently, a decision tree is the most commonly used data mining algorithm for classification tasks. While a significant number of studies have investigated privacy-preserving decision trees, the methods proposed in these studies often have shortcomings in terms of data privacy breach or efficiency. Additionally,these methods typically only apply to symmetric frameworks, which consist of two or more parties with equal privilege, and are not suitable for asymmetric scenarios where parties have unequal privilege. in this paper,we propose SecureCART, a three-party privacy-preserving decision tree training scheme with a privileged party. We adopt the existing pMPL framework and design novel secure interactive protocols for division,comparison, and asymmetric multiplication. Compared to similar schemes, our division protocol is 93.5–560.4× faster, with the communication overhead reduced by over 90%; further, our multiplication protocol is approximately 1.5× faster, with the communication overhead reduced by around 20%. Our comparison protocol based on function secret sharing maintains good performance when adapted to pMPL. Based on the proposed secure protocols, we implement SecureCART in C++ and analyze its performance using three real-world datasets in both LAN and WAN environments. he experimental results indicate that SecureCART is significantly faster than similar schemes proposed in past studies, and that the loss of accuracy while using SecureCART remains within an acceptable range.

关键词： decision trees privacy protection privileged party secret sharing secure multi-party computation

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Blockchain-Enabled Cybersecurity Provision for Scalable Heterogeneous Network:A Comprehensive Survey

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工程与科学中的计算机建模(英文) 2024年第1期138卷 43-123页

作者： Md.Shohidul islam Md.Arafatur Rahman Mohamed Ariff Bin Ameedeen Husnul Ajra Zahian Binti ismail Jasni Mohamad Zain Faculty of computing Universiti Malaysia PahangKuantan26600Malaysia School of Engineering Computing&Mathematical SciencesUniversity ofWolverhamptonWolverhamptonUK institute for Big Data Analytics and Artificial intelligence(iBDAAi) Komplek Al-KhawarizmiUniversiti Teknologi MARAShah AlamSelangor40450Malaysia

Blockchain-enabled cybersecurity system to ensure and strengthen decentralized digital transaction is gradually gaining popularity in the digital era for various areas like finance,transportation,healthcare,education,and supply chain *** interactions in the heterogeneous network have fascinated more attention due to the authentication of their digital application ***,the exponential development of storage space capabilities across the blockchain-based heterogeneous network has become an important issue in preventing blockchain distribution and the extension of blockchain *** is the biggest challenge of data integrity and scalability,including significant computing complexity and inapplicable latency on regional network diversity,operating system diversity,bandwidth diversity,node diversity,etc.,for decision-making of data transactions across blockchain-based heterogeneous *** security and privacy have also become the main concerns across the heterogeneous network to build smart ioT *** address these issues,today’s researchers have explored the potential solutions of the capability of heterogeneous network devices to perform data transactions where the system stimulates their integration reliably and securely with *** key goal of this paper is to conduct a state-of-the-art and comprehensive survey on cybersecurity enhancement using blockchain in the heterogeneous *** paper proposes a full-fledged taxonomy to identify the main obstacles,research gaps,future research directions,effective solutions,andmost relevant blockchain-enabled cybersecurity *** addition,Blockchain based heterogeneous network framework with cybersecurity is proposed in this paper tomeet the goal of maintaining optimal performance data transactions among ***,this paper provides an in-depth description based on the critical analysis to overcome the existing work gaps for future research where it

关键词： Blockchain cybersecurity data transaction diversity heterogeneous

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Multi-user reinforcement learning based task migration in mobile edge computing

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Frontiers of Computer Science 2024年第4期18卷 161-173页

作者： Yuya CUi Degan ZHANG Jie ZHANG Ting ZHANG Lixiang CAO Lu CHEN Tianjin Key Lab of intelligent computing and Novel Software Technology Tianjin University of TechnologyTianjin 300384China School of internet of Things Engineering Jiangsu Vocational College of Information TechnologyWuxi 214153China School of Electronic and information Engineering Beijing Jiaotong UniversityBeijing 100044China School of Sports Economics and Management Tianjin University of SportTianjin 301617China

Mobile Edge computing(MEC)is a promising *** service migration is a key technology in *** order to maintain the continuity of services in a dynamic environment,mobile users need to migrate tasks between multiple servers in real *** to the uncertainty of movement,frequent migration will increase delays and costs and non-migration will lead to service ***,it is very challenging to design an optimal migration *** this paper,we investigate the multi-user task migration problem in a dynamic environment and minimizes the average service delay while meeting the migration *** order to optimize the service delay and migration cost,we propose an adaptive weight deep deterministic policy gradient(AWDDPG)*** distributed execution and centralized training are adopted to solve the high-dimensional *** show that the proposed algorithm can greatly reduce the migration cost and service delay compared with the other related algorithms.

关键词： mobile edge computing mobility service migration deep reinforcement learning deep deterministic policy gradient

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