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34th International Conference on Software engineering and Knowledge engineering, SEKE 2022

作者： Tian, Zhenzhou Huang, Yaqian Tian, Jie Wang, Zhongmin Chen, Yanping Chen, Lingwei School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an710121 China Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an710121 China Department of Computer Science and Engineering Wright State University DaytonOH United States

ISBN: (纸本)1891706543

Smart contracts are programs that run on a blockchain, where Ethereum is one of the most popular ones supporting them. Due to the fact that they are immutable, it is essential to design smart contracts bug-free before they are deployed. However, various defects have been found in the deployed smart contracts, causing huge economic losses and lowing people's trust. Writing secure smart contracts is far from trivial, where developers tend to engage in reliable resources or social coding platforms to reuse code. This leads to a large number of similar contracts with potential security risks. Therefore, detecting similarity of smart contracts helps to avoid vulnerabilities, identify threats, and improve the security of Ethereum. In this paper, we design a learning-effective and cost-efficient model, called SmartSD, for Ethereum smart contract similarity detection. Different from the current research efforts, SmartSD is performed on a bytecode level and leverages deep neural networks to learn the latent representations from the opcode sequences for smart contract bytecodes, where the representation learning and similarity measurement are supervised via siamese neural networks. The experimental evaluations demonstrate that SmartSD outperforms EClone's 93.27% accuracy, achieving 98.37% high detection accuracy and 0.9850 F1-score, which is computationally tractable and effectively mitigates the interference caused by compilers. © 2022 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Smart contract

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Task Offloading and Primary Node Selection in Blockchain, Digital Twin, and MEC Enabled Internet of Vehicles 100

Task Offloading and Primary Node Selection in Blockchain, Di...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Du, Jianbo Fang, Huifang Li, Shulei Du, Pengfei Kong, Ziwen Kang, Jiawen Niyato, Dusit Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security School of Communications and Information Engineering Xi'an China Xidian University School of Communications Engineering Xi'an710071 China Tianyuan Ruixin Communication Technology Co. LTD. Xi'an710071 China Xihua University Engineering Research Center of Intelligent Airground Integrated Vehicle and Traffic Control Ministry of Education Chengdu610039 China Guangdong University of Technology School of Automation China Nanyang Technological University School of Computer Science and Engineering Singapore

ISBN: (纸本)9798331517786

In this paper, we investigate the safe task offloading and primary node selection in blockchain, digital twin (DT) and Multi-access Edge Computing (MEC) enabled Internet of Vehicles (IoV). Edge servers centers provide computing power for task processing for Mobile Vehicles (MVs), while blockchain can provide security guarantees for MVs during task offloading. Based on the above system, we propose a joint optimization scheme for vehicle task offloading decision and the Practical Byzantine Fault Tolerance (PBFT) consensus process. Due to the large number of optimization variables and constraints, the problem becomes more complex. Traditional convex optimization and dynamic programming methods are difficult to effectively solve this problem. To address this issue, we propose a deep reinforcement learning based algorithm that utilizes Proximal Policy Optimization (PPO). The experimental results show that the algorithm proposed in this paper outperforms the benchmark algorithm in terms of convergence and other aspects. © 2024 IEEE.

关键词： Dynamic programming

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A Wideband Circularly Polarized Folded Transmitarray Antenna With Linearly Polarized Feed 7

A Wideband Circularly Polarized Folded Transmitarray Antenna...

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7th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2024

作者： Sun, Xiangdong Guo, Xulong Li, Yin Zhang, Yongliang Li, Yingsong Inner Mongolia University School of Electronic Information Engineering Hohhot010021 China Inner Mongolia University Inner Mongolia Branch of National Computer Network Emergency Response Technical Team Coordination Center of China Hohhot010020 China Peng Cheng Laboratory Shenzhen518000 China Inner Mongolia University School of Transportation Institute Hohhot010021 China Ministry of Education Anhui University Key Laboratory of Intelligent Computing and Signal Processing China

ISBN: (纸本)9798350384437

A low-profile, high-gain, wideband circularly polarized folded transmitarray antenna (CPFTA) is proposed, and a polarization-selective linear-to-circular polarization (LP-CP) conversion element is introduced. The CPFTA consists of a linearly polarized (LP) magnetoelectric dipole antenna and a circularly polarized (CP) helical patch antenna. The CPFTA demonstrates excellent performance with a 3dB gain bandwidth of 29.5% and a 3dB axial ratio (AR) bandwidth of 40.9%. © 2024 IEEE.

关键词： Circular polarization

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Coronavirus Pandemic Analysis Through Tripartite Graph Clustering in Online Social networks

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Big Data Mining and Analytics 2021年第4期4卷 242-251页

作者： Xueting Liao Danyang Zheng Xiaojun Cao Department of Computer Science Georgia State UniversityAtlantaGA 30302USA Suzhou Key Laboratory of Advanced Optical Communication Network Technology School of Electronic and Information EngineeringSoochow UniversitySuzhou 215006China

The COVID-19 pandemic has hit the world *** reaction to the pandemic related issues has been pouring into social platforms,such as *** public officials and governments use Twitter to make policy *** keep close track of the related information and express their concerns about the policies on *** is beneficial yet challenging to derive important information or knowledge out of such Twitter *** this paper,we propose a Tripartite Graph Clustering for Pandemic Data Analysis(TGC-PDA)framework that builds on the proposed models and analysis:(1)tripartite graph representation,(2)non-negative matrix factorization with regularization,and(3)sentiment *** collect the tweets containing a set of keywords related to coronavirus pandemic as the ground truth *** framework can detect the communities of Twitter users and analyze the topics that are discussed in the *** extensive experiments show that our TGC-PDA framework can effectively and efficiently identify the topics and correlations within the Twitter data for monitoring and understanding public opinions,which would provide policy makers useful information and statistics for decision making.

关键词： COVID-19 clustering online social network Twitter

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Incentive Sharing: A Consumer-Data Privacy Trusted Sharing Paradigm Via Proximal Policy Optimization in Industry 5.0

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IEEE Transactions on Consumer Electronics 2024年

作者： Cai, Ting Hu, Ying Li, Xiang Chen, Wuhui Wu, Yuxin Ye, Zhiwei Zheng, Zibin Hubei University of Technology China China Hubei University of Technology School of Computer Science The Provincial Key Laboratory of Green Intelligent Computing Network Wuhan430068 China Sun Yat-sen University China Pengcheng Laboratory China Hubei University of Arts and Science School of Computer Engineering Xiangyang441053 China Sun Yat-sen University School of Software Engineering Zhuhai519000 China

Industry 5.0 emphasizes consumer data sharing to drive data-driven innovation and enhance user experiences in consumer electronics. Blockchain-integrated federated learning (blockchained FL) has recently emerged as a privacy-Trusted paradigm to facilitate sharing. However, most blockchained FL sharing paradigms cannot adapt to the complexity or variability of consumer data and network conditions such as: 1) inadequate balancing of the trade-off between data quality and training costs during device selection;2) incentives are designed to maximize instantaneous profit without considering the long-Term dynamics of electronic devices. These limitations lead to low-quality model training and security degradation. This paper proposes a novel blockchained FL incentive-sharing paradigm that dynamically selects consumer devices from a long-Term perspective while balancing data quality and training costs. Specifically, we first present a reputation strategy to enhance security by applying a predefined threshold to prevent malicious and low-quality consumers from participating in FL. Then, to optimize device selection under complex and dynamic consumer environments with high-dimensional system states, a Proximal Policy Optimization (PPO)-based incentive mechanism is proposed, which includes: 1) building an 'intelligent engine' to select the optimal consumer devices for each round of FL based on data quality and costs, and 2) implementing incentives to achieve long-Term maximization of the system's social welfare. Extensive simulations show that our sharing paradigm has, on average, 46.97% higher social welfare and 77.27% lower social costs than baselines and can guarantee high data quality in a trustless environment. © 1975-2011 IEEE.

关键词： Differential privacy

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Task Offloading and Resource Allocation with Privacy Constraints in End-Edge-Cloud Environment 17th

Task Offloading and Resource Allocation with Privacy Constra...

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17th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2022

作者： Zhu, Xia Sun, Wei Li, Xiaoping School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University Nanjing China School of Cyber Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9789819923847

The end-edge-cloud architecture brings an efficient solution to the big data processing problem caused by massive IoT devices. The characteristics of this architecture, such as massive devices, heterogeneous resources, and complex layers, bring new challenges to privacy protection issues. This paper considers the cooperation among terminal devices, edge nodes, and remote clouds to solve the task scheduling problem with privacy constraints by optimizing the offloading decision and resource allocation. A heuristic privacy-aware task offloading and resource allocation algorithm is proposed to maximize the number of successful tasks, which offloads low-privacy and non-privacy tasks to find sub-optimal offloading decisions by offloading sequence generation rules and decision adjustment. Task scheduling algorithms are presented by communication and computing resource allocation strategies for low-privacy and non-privacy tasks. ANOVA technique is used to verify the performance of the proposed algorithm. The experimental results show that the proposed algorithm is superior to others in terms of the number of devices, the amount of different task data, and the proportion of privacy tasks at different levels. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cloud computing architecture

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On the Impact of Spatial Mode Dispersion for Strongly-Coupled Multicore Fiber Submarine Transmission

On the Impact of Spatial Mode Dispersion for Strongly-Couple...

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2023 Optical Fiber Communications Conference and Exhibition, OFC 2023

作者： Sun, Lin Chen, Bin Liu, Gordon Ning Cai, Yi Li, Zhaohui Lu, Chao Shen, Gangxiang Soochow University Suzhou Key Laboratory of Advanced Optical Communication Network Technology School of Electronic and Information Engineering Suzhou215006 China Hefei University of Technology School of Computer Science and Information Engineering Hefei China Sun Yat-Sen University Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips School of Electronics and Information Technology Guangzhou511400 China The Hong Kong Polytechnic University Photonics Research Center Department of Electrical and Information Engineering Kowloon Hung Hom Hong Kong

Strongly-coupled multicore fibers exhibit the improved tolerance to fiber non-linearity. Their potentials in optical submarine communications are investigated with considering the coupling length and spatial mode disp... 详细信息

ISBN: (纸本)9781957171180

关键词： Submarines

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Local Fault Diagnosis Analysis Based on Block Pattern of Regular Diagnosable networks

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IEEE/ACM Transactions on networking 2024年

作者： Lin, Limei Guan, Kaineng Huang, Yanze Hsieh, Sun-Yuan Chen, Gaolin Fujian Normal University College of Computer and Cyber Security Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Fuzhou350117 China Fujian University of Technology School of Computer Science and Mathematics Fujian Fuzhou350118 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan

Fault diagnosability can reflect the actual self diagnosing capability of a multiprocessor system better. However, people usually focus on the overall information and neglect the important local information. In order to reflect the locality of a system at a node better, this paper proposes a novel fault diagnosis strategy, called x-block local fault diagnosability (x-BLFD), where the x-block condition requires more than x connected fault-free nodes. Then, we characterize some important properties about the x-BLFD of multiprocessors interconnected networks under the Preparata/Metze/Chien model (P/M/C), and further propose the x-BLFD in an f(x)-extended block network with the minimum (x+1)-subnetwork degree at some node. We also establish an approximate algorithm to calculate the x-BLFD of a large-scale diagnosable network at some node, and analyze the experimental performance of large-scale networks. Furthermore, we apply our proposed conclusion to obtain the x-BLFD of 16 well-known networks at some node directly under P/M/C, including dual cubes, hierarchical cubic networks, DQcubes, twisted hypercubes, Bicube networks, crossed cubes, folded hypercubes, k-ary n-cubes, balanced hypercubes, BC graphs, (n,k)-star graphs, Cayley graphs generated by transposition trees, bubble-sort star graphs, split-star networks, data center networks, and (n,k)-arrangement graphs. Finally, we compare the x-BLFD with the diagnosability, conditional diagnosability, pessimistic diagnosability, and t/k-diagnosability by a large number of detailed numerical analysis. It can be seen that the x-BLFD is greater than all the other types of fault diagnosabilities. © 1993-2012 IEEE.

关键词： Hypercube networks

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A trajectory association strategy for multi-object tracking 6

A trajectory association strategy for multi-object tracking

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6th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2023

作者： Ma, Sugang Duan, Shuaipeng Pu, Lei Hou, Zhiqiang School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Shaanxi Xi'an China School of Operational Support Rocket Force Engineering University Shaanxi Xi'an710025 China

ISBN: (纸本)9798400707674

When the multi-object tracking (MOT) algorithm confronts complex scenarios such as target occlusion and blurring, the trajectory missing and identity switching problems frequently occur. To address this issue, a trajectory association strategy for MOT is presented. First, using the similarity relationship between two frames, the similarity matrix is introduced to predict a more trustworthy offset vector and lower the number of Identity Switch. Then, a trajectory reconnection strategy is provided in order to retain a portion of the low-score detection frames due to occlusion and to perform secondary matching on the low-score detection frames in order to reduce missing trajectories. On a typical multi-object tracking dataset MOT17, experimental comparison study is performed with several known advanced algorithms, and MOTA and IDF1 achieve 67.9% and 67.8%, respectively. The experimental results show that the proposed algorithm better solves the trajectory missing and identity switching problems, and achieves excellent tracking performance in complex scenarios. © 2023 ACM.

关键词： Trajectories

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A Multi-Channel Composite Recommendation Method in Federated Learning

A Multi-Channel Composite Recommendation Method in Federated...

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2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Chen, Shi Li, Qianmu Wang, Qi Zhao, Xinjian Ge, Hao Li, Nianzhe State Grid Jiangsu Information & Telecommunication Company Nanjing China School of Computer Science Ana Engineering Nanjing University of Science and Technology Nanjing China State Grid Smart Grid Research Institute Co. Ltd. State Grid Key Laboratory of Information & Network Security Nanjing China

ISBN: (纸本)9798350304602

There are various data security risks in power grid systems, and recommendation systems are a good solution to the selection of security strategies. Federated learning is a machine learning framework that can protect the data privacy of each participant and can effectively solve the data security external collaborative training modeling challenges. This paper proposed a flexible implantable recommendation algorithm based on knowledge graph and multi-channel composite to enhance federated learning on demand. The algorithm designs three different feature extraction modules to learn to construct multi-dimensional features on multi-attribute collaborative knowledge graphs. Among them, the distance translation module utilizes the Trans R recommendation algorithm to learn the internal semantic structure information in knowledge graph triples and terminal-security policy selection history;the random walk module uses the Node2Vec algorithm to learn the homogeneous and Structured information in the multi-attribute collaborative knowledge graph;the graph convolutional network module learns the high-order neighborhood multi-attribute collaborative information of terminal/security policy nodes through multi-layer feature propagation and aggregation. The composite network module adopts a convolutional neural network-like operation to first locally optimize the three modules, and then extracts the highly informative elements to obtain the final feature representation of the terminal/security policy. Finally, the algorithm merges the vectors of terminal-security policy pairs into the multi-layer perceptron to learn and calculate the prediction score for recommendation. © 2023 IEEE.

关键词： Recommender systems

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