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Reinforcement learning based edge computing in B5G

Digital Communications and Networks 2024年第1期10卷 1-6页

作者： Jiachen Yang Yiwen Sun Yutian Lei Zhuo Zhang Yang Li Yongjun Bao Zhihan Lv School of Electrical and Information Engineering Tianjin UniversityTianjin300072China Technology and Data Center ***Beijing100176China School of Data Science and Software Engineering Qingdao UniversityQingdao266101China

The development of communication technology will promote the application of Internet of Things,and Beyond 5G will become a new technology *** the same time,Beyond 5G will become one of the important supports for the development of edge computing *** paper proposes a communication task allocation algorithm based on deep reinforcement learning for vehicle-to-pedestrian communication scenarios in edge *** trial and error learning of agent,the optimal spectrum and power can be determined for transmission without global information,so as to balance the communication between vehicle-to-pedestrian and *** results show that the agent can effectively improve vehicle-to-infrastructure communication rate as well as meeting the delay constraints on the vehicle-to-pedestrian link.

关键词： Reinforcement learning Edge computing Beyond 5G Vehicle-to-pedestrian

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Face Anti-Spoofing with Unknown Attacks:A Comprehensive Feature Extraction and Representation Perspective

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Journal of Computer science & Technology 2024年第4期39卷 827-840页

作者： Li-Min Li Bin-Wu Wang Xu Wang Peng-Kun Wang Yu-Dong Zhang Yang Wang School of Software Engineering University of Science and Technology of ChinaHefei 230000China School of Data Science University of Science and Technology of ChinaHefei 23000China Suzhou Institute for Advanced Research University of Science and Technology of ChinaSuzhou 215000China

Face anti-spoofing aims at detecting whether the input is a real photo of a user(living)or a fake(spoofing)*** new types of attacks keep emerging,the detection of unknown attacks,known as Zero-Shot Face Anti-Spoofing(ZSFA),has become increasingly important in both academia and *** ZSFA methods mainly focus on extracting discriminative features between spoofing and living ***,the nature of the spoofing faces is to trick anti-spoofing systems by mimicking the livings,therefore the deceptive features between the known attacks and the livings,which have been ignored by existing ZSFA methods,are essential to comprehensively represent the ***,existing ZSFA models are incapable of learning the complete representations of living faces and thus fall short of effectively detecting newly emerged *** tackle this problem,we propose an innovative method that effectively captures both the deceptive and discriminative features distinguishing between genuine and spoofing *** method consists of two main components:a two-against-all training strategy and a semantic *** two-against-all training strategy is employed to separate deceptive and discriminative *** address the subsequent invalidation issue of categorical functions and the dominance disequilibrium issue among different dimensions of features after importing deceptive features,we introduce a modified semantic *** autoencoder is designed to map all extracted features to a semantic space,thereby achieving a balance in the dominance of each feature *** combine our method with the feature extraction model ResNet50,and experimental results show that the trained ResNet50 model simultaneously achieves a feasible detection of unknown attacks and comparably accurate detection of known *** results confirm the superiority and effectiveness of our proposed method in identifying the living with the interference of both known

关键词： face anti-spoofing spoof detection zero-shot learning convolutional neural network deep learning

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Query Authentication Using Intel SGX for Blockchain Light Clients

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计算机科学技术学报（英文版） 2023年第3期38卷 714-734页

作者：邵奇峰张召金澈清周傲英 School of Data Science and Engineering East China Normal University Shanghai 200062 China School of Software Zhongyuan University of Technology Zhengzhou 450007 China

Due to limited computing and storage resources,light clients and full nodes coexist in a typical blockchain *** query from light clients must be forwarded to full nodes for execution,and light clients verify the integrity of query results *** existing verifiable queries based on an authenticated data structure(ADS)suffer from signifi-cant network,storage and computing overheads by virtue of verification objects(VOs),an alternative way turns to the trusted execution environment(TEE),with which light clients do not need to receive or verify any ***,state-of-the-art TEEs cannot deal with large-scale applications conveniently due to the limited secure memory space(e.g.,the size of the enclave in Intel SGX(software guard extensions),a typical TEE product,is only 128 MB).Hence,we organize data hierarchically in trusted(enclave)and untrusted memory,along with hot data buffered in the enclave to reduce page swapping overhead between two kinds of *** cost analysis and empirical study validate the effectiveness of our proposed *** VO size of our scheme is reduced by one to two orders of magnitude compared with that of the tra-ditional scheme.

关键词： blockchain query authentication Merkle B-tree (MB-tree) Intel software guard extensions (SGX)

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Multivariate Time Series Forecasting with Transfer Entropy Graph

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Tsinghua science and Technology 2023年第1期28卷 141-149页

作者： Ziheng Duan Haoyan Xu Yida Huang Jie Feng Yueyang Wang School of Big Data and Software Engineering Chongqing UniversityChongqing 401331China College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China

Multivariate Time Series(MTS)forecasting is an essential problem in many *** forecasting results can effectively help in making *** date,many MTS forecasting methods have been proposed and widely ***,these methods assume that the predicted value of a single variable is affected by all other variables,ignoring the causal relationship among *** address the above issue,we propose a novel end-to-end deep learning model,termed graph neural network with neural Granger causality,namely CauGNN,in this *** characterize the causal information among variables,we introduce the neural Granger causality graph in our *** variable is regarded as a graph node,and each edge represents the casual relationship between *** addition,convolutional neural network filters with different perception scales are used for time series feature extraction,to generate the feature of each ***,the graph neural network is adopted to tackle the forecasting problem of the graph structure generated by the *** benchmark datasets from the real world are used to evaluate the proposed CauGNN,and comprehensive experiments show that the proposed method achieves state-of-the-art results in the MTS forecasting task.

关键词： Multivariate Time Series(MTS)forecasting neural Granger causality graph Transfer Entropy(TE)

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Causal Label Enhancement

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IEEE Transactions on Knowledge and data engineering 2024年第7期37卷 3933-3946页

作者： Liu, Xinyuan Zhu, Jihua Yue, Kun Zheng, Qinghai Li, Zhongyu Tian, Zhiqiang Zhu, Mingchen Xi'an Jiaotong University School of Software Engineering Xi'an China Yunnan University School of Information Science and Engineering Kunming China Fuzhou University College of Computer and Data Science Fuzhou China

Label enhancement (LE) is still a challenging task to mitigate the dilemma of the lack of label distribution. Existing LE work typically focuses on primarily formulating a projection between feature space and label distribution space from discriminative model perspective, which preserves the relevance consistency that the sign of recovered label distribution should be consistent with the logical label. Different from previous algorithms, we formulate this problem from a causal perspective and present a novel LE method via the structured causal model (LESCM). Specifically, the proposed LESCM deliberates establishing the causal graph with assuming that label distribution is a cause of feature and logical label, which naturally satisfies the definition of label distribution learning (LDL). With capturing the underlying causal relationships, we can significantly boost the interpretability and identifiability of label enhancement. Meanwhile, except for the relevance consistency, LESCM are encouraged to sustain the order consistency that assigns higher description degree of the recovered label distribution to the positive labels, as compared with the negative labels. Empirically, sufficient experiments on several label distribution learning data sets validate the effectiveness of LESCM. © 1989-2012 IEEE.

关键词： Contrastive Learning

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A survey on cross-user federated recommendation

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science China(Information sciences) 2025年第4期68卷 7-32页

作者： Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING College of Computer Science and Software Engineering Shenzhen University School of Electrical Engineering and Computer Science The University of Queensland WeBank AI Lab WeBank Department of Computer Science and Engineering Hong Kong University of Science and Technology College of Big Data and Internet Shenzhen Technology University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.

关键词： cross-user federated recommendation federated recommendation federated learning recommender systems user privacy

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An Efficient and Fast Area Optimization Approach for Mixed Polarity Reed-Muller Logic Circuits

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Chinese Journal of Electronics 2024年第5期33卷 1165-1180页

作者： Yuhao ZHOU Zhenxue HE Jianhui JIANG Xiaojun ZHAO Fan ZHANG Limin XIAO Xiang WANG School of Software Engineering Tongji University Key Laboratory of Agricultural Big Data of Hebei Province Hebei Agricultural University School of Computer Science and Engineering Beihang University School of Electronic and Information Engineering Beihang University

Area has become one of the main bottlenecks restricting the development of integrated circuits. The area optimization approaches of existing XNOR/OR-based mixed polarity Reed-Muller(MPRM) circuits have poor optimization effect and efficiency. Given that the area optimization of MPRM logic circuits is a combinatorial optimization problem, we propose a whole annealing adaptive bacterial foraging algorithm(WAA-BFA), which includes individual evolution based on Markov chain and Metropolis acceptance criteria, and individual mutation based on adaptive probability. To address the issue of low conversion efficiency in existing polarity conversion approaches, we introduce a fast polarity conversion algorithm(FPCA). Moreover, we present an MPRM circuits area optimization approach that uses the FPCA and WAA-BFA to search for the best polarity corresponding to the minimum circuits area. Experimental results demonstrate that the proposed MPRM circuits area optimization approach is effective and can be used as a promising EDA tool.

关键词： Integrated circuits Microorganisms Annealing Logic circuits Circuits Benchmark testing Delays

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Collaborative graph contrastive learning for recommendation

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Journal of Intelligent and Fuzzy Systems 2025年第1-2期48卷 33-46页

作者： Wei, Tao Yang, Changchun Zheng, Yanqi Zhang, Jingxue School of Computer Science and Artificial Intelligence Aliyun School of Big Data School of Software Changzhou University Jiangsu Changzhou China School of Microelectronics and Control Engineering Changzhou University Jiangsu Changzhou China

Recently, Graph Neural Networks (GNNs) using aggregating neighborhood collaborative information have shown effectiveness in recommendation. However, GNNs-based models suffer from over-smoothing and data sparsity problems. Due to its self-supervised nature, contrastive learning has gained considerable attention in the field of recommendation, aiming at alleviating highly sparse data. Graph contrastive learning models are widely used to learn the consistency of representations by constructing different graph augmentation views. Most current graph augmentation with random perturbation destroy the original graph structure information, which mislead embeddings learning. In this paper, an effective graph contrastive learning paradigm CollaGCL is proposed, which constructs graph augmentation by using singular value decomposition to preserve crucial structure information. CollaGCL enables perturbed views to effectively capture global collaborative information, mitigating the negative impact of graph structural perturbations. To optimize the contrastive learning task, the extracted meta-knowledge was propagate throughout the original graph to learn reliable embedding representations. The self-information learning between views enhances the semantic information of nodes, thus alleviating the problem of over-smoothing. Experimental results on three real-world datasets demonstrate the significant improvement of CollaGCL over state-of-the-art methods. © 2025 – IOS Press. All rights reserved.

关键词： Self-supervised learning

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Energy-efficient task allocation for reliable parallel computation of cluster-based wireless sensor network in edge computing

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Digital Communications and Networks 2023年第2期9卷 473-482页

作者： Jiabao Wen Jiachen Yang Tianying Wang Yang Li Zhihan Lv School of Electrical and Information Engineering Tianjin UniversityTianjin300072China College of Mechanical and Electrical Engineering Shihezi UniversityXinjiang832003China School of Data Science and Software Engineering Qingdao UniversityQingdao266000China

To efficiently complete a complex computation task,the complex task should be decomposed into subcomputation tasks that run parallel in edge *** Sensor Network(WSN)is a typical application of parallel *** achieve highly reliable parallel computation for wireless sensor network,the network's lifetime needs to be ***,a proper task allocation strategy is needed to reduce the energy consumption and balance the load of the *** paper proposes a task model and a cluster-based WSN model in edge *** our model,different tasks require different types of resources and different sensors provide different types of resources,so our model is heterogeneous,which makes the model more *** we propose a task allocation algorithm that combines the Genetic Algorithm(GA)and the Ant Colony Optimization(ACO)*** algorithm concentrates on energy conservation and load balancing so that the lifetime of the network can be *** experimental result shows the algorithm's effectiveness and advantages in energy conservation and load balancing.

关键词： Wireless sensor network Parallel computation Task allocation Genetic algorithm Ant colony optimization algorithm Energy-efficient Load balancing

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A Multimodal Multiobjective Evolutionary Algorithm for Filter Feature Selection in Multilabel Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4428-4442页

作者： Hancer, Emrah Xue, Bing Zhang, Mengjie Victoria University of Wellington Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Mehmet Akif Ersoy University Department of Software Engineering Burdur15039 Turkey

Multilabel learning is an emergent topic that addresses the challenge of associating multiple labels with a single instance simultaneously. Multilabel datasets often exhibit high dimensionality with noisy, irrelevant, and redundant features. In recent years, multilabel feature selection (MLFS) has gained prominence as a crucial and emerging machine learning task due to its ability to handle such data effectively. However, existing approaches for MLFS often prioritize top-ranked features based on intrinsic data criteria, disregarding relationships within the feature subset. Additionally, compared with conventional feature selection, multiobjective evolutionary algorithms (MOEAs) have not been widely explored in the context of MLFS. This study aims to address these gaps by proposing a multimodal multiobjective evolutionary algorithm (MMOEA) called MMDE_SICD which incorporates a preelimination scheme, an improved initialization scheme, an exploration scheme inspired by genetic operations and a statistically inspired crowding distance scheme. The results show that the proposed MMDE_SICD algorithm can outperform a variety of MOEAs and MMOEAs as well as conventional MLFS algorithms. Notably, this study is the first of its kind to consider MLFS as a multimodal multiobjective problem. © 2020 IEEE.

关键词： Multiobjective optimization

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