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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Xu, Fan Wang, Nan Zhao, Xibin School of Computer Science and Technology University of Science and Technology of China Hefei230026 China School of Software Beijing Jiaotong University Beijing100044 China Key Laboratory for Information System Security Beijing100084 China

ISBN: (纸本)9798350337020

Anomaly detection aims to detect data that do not conform to regular patterns, and such data is also called outliers. The anomalies to be detected are often tiny in proportion, containing crucial information, and are suitable for application scenes like intrusion detection, fraud detection, fault diagnosis, e-commerce platforms, et al. However, in many realistic scenarios, only the samples following normal behavior are observed, while we can hardly obtain any anomaly information. To address such problem, we propose an anomaly detection method GALDetector which is combined of global and local information based on observed normal samples. The proposed method can be divided into a three-stage method. Firstly, the global similar normal scores and the local sparsity scores of unlabeled samples are computed separately. Secondly, potential anomaly samples are separated from the unlabeled samples corresponding to these two scores and corresponding weights are assigned to the selected samples. Finally, a weighted anomaly detector is trained by loads of samples, then the detector is utilized to identify else anomalies. To evaluate the effectiveness of the proposed method, we conducted experiments on three categories of real-world datasets from diverse domains, and experimental results show that our method achieves better performance when compared with other state-of-the-art methods. © 2023 IEEE.

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Temporal Refinement Graph Convolutional Network for Skeleton-Based Action Recognition

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1586-1598页

作者： Zhuang, Tianming Qin, Zhen Ding, Yi Deng, Fuhu Chen, Leduo Qin, Zhiguang Raymond Choo, Kim-Kwang University of Electronic Science and Technology of China Network and Data Security Key Laboratory of Sichuan Province Chengdu Sichuan610054 China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu Sichuan610054 China School of Advanced Technology Suzhou Jiangsu215123 China University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX78249 United States

Human skeleton data, which has served in the aspect of human activity recognition, ought to be the most representative biometric characteristics due to its intuitivity and visuality. The state-of-the-art approaches mainly focus on improving modeling spatial correlations within graph topologies. However, the interframes motional representations are also of vital importance, and we argue that they are worth paying attention to and exploring. Therefore, a temporal refinement module with contrastive learning mechanism is proposed, fuzing as a complementary to the conventional spatial graph convolution layer. In addition, in order to further exploiting the interframe variances and generalizing graph convolutional network (GCN) operation to temporal dimension, a temporal-correlation matrix is introduced to effectively capture dynamic dependencies within frame-pairs, enhancing semantic feature representation. Moreover, since GCN is a typical local operator which lacks of capability to fully model the long-term relations along spatial and temporal variation, to move beyond the limitation, a spatial-temporal cascaded aggregation (STCA) module is designed to enlarge the receptive filter scale. The overall recognition framework consists of three above novelties, which is capable of achieving remarkable performance by evaluating on benchmark datasets (i.e., NTU RGB+D 60, NTU RGB+D 120, PKU-MMD, and Kinetics Skeleton 400). Extensive experiments demonstrate the effectiveness of the proposed framework, e.g., performing recognition accuracy rate of 90.9% and 96.8% on NTU RGB+D 60, 87.9% and 88.9% on NTU RGB+D 120. © 2020 IEEE.

关键词： Convolution

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HRNet-U: Combining the Strengths of U-Net and HRNet for Efficient Pose Estimation 2

HRNet-U: Combining the Strengths of U-Net and HRNet for Effi...

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2nd International Conference on Algorithm, Image Processing and Machine Vision, AIPMV 2024

作者： Guo, Biao Guo, Fangmin He, Qian Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software Guilin China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798350390254

High-resolution network (HRNet) shows excellent performance in 2D multi-person pose estimation tasks. It can maintain high-resolution representation in the network and exchange information between multi-scale features, significantly improving the ability of key point prediction. However, this multiscale fusion strategy leads to computational redundancy and increases processing delays, limiting its application to resource-constrained edge devices. To solve this problem, this paper proposes a new lightweight network structure (HRNet-U), which combines the high resolution parallel advantage of HRNet and the computational efficiency of U-Net, and adopts the U-Net structure for multiscale feature fusion at each stage. Through this design, HRNet-U still maintains high resolution branch and multi-scale information, which is more suitable for the application scenarios of edge devices. This model achieves comparable or even better performance on the COCO and MPII validation sets. © 2024 IEEE.

关键词： Feature extraction

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DMCPose: A Binocular 3D Pose Estimation Network Integrating Disparity and Multi-View Consistency via Attention

DMCPose: A Binocular 3D Pose Estimation Network Integrating ...

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2024 International Symposium on Digital Home, ISDH 2024

作者： Zhang, Jialun Guo, Biao School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software Guilin China

ISBN: (纸本)9798331509873

In 3D human pose estimation, binocular vision typically relies on stereo matching to obtain depth information and calculates 3D keypoints using the disparity principle. However, the high computational cost of stereo matching limits its application in real-time scenarios. Alternatively, multi-view consistency can estimate 3D keypoints across multiple views, but with only two views, the constraints are insufficient, often resulting in suboptimal keypoint accuracy. To address these challenges, this paper proposes a binocular 3D pose estimation network based on attention mechanisms, which effectively integrates the advantages of multi-view consistency and disparity methods. Unlike traditional binocular stereo vision systems, our approach leverages a high-precision 2D pose estimation network and dynamically fuses multi-source information via attention mechanisms, thus avoiding the complexity of stereo matching and significantly reducing computational overhead. Additionally, by utilizing multi-view disparity information, we further optimize the estimation of 3D keypoints, improving the accuracy of pose reconstruction. Experiments on the MADS dataset show that DMCPose achieves an average 3D MPJPE of 102.4 mm, outperforming several existing methods, validating its advantages in both accuracy and efficiency. © 2024 IEEE.

关键词： Binoculars

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A Review on Binary Code Analysis Datasets 18th

A Review on Binary Code Analysis Datasets

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18th International Conference on Wireless Artificial Intelligent Computing systems and Applications, WASA 2024

作者： Huang, Zhijian Song, Shuguang Liu, Han Kuang, Hongyu Zhang, Jingjing Hu, Pengfei National Key Laboratory of Science and Technology on Information System Security Systems Engineering Institute Academy of Military Sciences Beijing China School of Computer Science and Technology Shandong University Qingdao China

ISBN: (纸本)9783031714696

Binary code analysis serves as the foundation for research in vulnerability discovery, software protection, and malicious code analysis. However, analyzing binary files is challenging due to the lack of high-level semantic information, leading to heavy dependence on analysts’ expertise and significantly impacting the efficiency of binary code analysis. Recent years has witnessed the blossom of machine learning models for binary analysis, but few researches address the problem of binary code datasets. In this paper, we review all the existing and available datasets, and make classification according to their application. We set up experiments to illustrate how dataset quality could affect the performance of machine learning models in binary function recognition. Based on the experimental evaluation, we present a discussion on the ground truth as well as quality evaluation problems for binary code datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Differentially Private and Heterogeneity-Robust Federated Learning With Theoretical Guarantee

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6369-6384页

作者： Wang, Xiuhua Wang, Shuai Li, Yiwei Fan, Fengrui Li, Shikang Lin, Xiaodong Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen361024 China University of Guelph School of Computer Science GuelphONN1G 2W1 Canada

Federated learning (FL) is a popular distributed paradigm where enormous clients collaboratively train a machine learning (ML) model under the orchestration of a central server without knowing the clients' private raw data. The development of effective FL algorithms faces multiple practical challenges including data heterogeneity and clients' privacy protection. Despite that numerous attempts have been made to deal with data heterogeneity or rigorous privacy protection, none have effectively tackled both issues simultaneously. In this article, we propose a differentially private and heterogeneity-robust FL algorithm, named DP-FedCVR to mitigate the data heterogeneity by following the client-variance-reduction strategy. Besides, it adopts a sophisticated differential privacy (DP) mechanism where the privacy-amplified strategy is applied, to achieve a rigorous privacy protection guarantee. We show that the proposed DP-FedCVR algorithm maintains its heterogeneity-robustness though DP noises are incorporated, while achieving a sublinear convergence rate for a nonconvex FL problem. Numerical experiments based on image classification tasks are presented to demonstrate that DP-FedCVR provides superior performance over the benchmark algorithms in the presence of data heterogeneity and various DP privacy budgets. © 2020 IEEE.

关键词： Federated learning

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A generalized reliability analysis of 3-ary n-cubes 16

A generalized reliability analysis of 3-ary n-cubes

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16th International Conference on Communication software and Networks, ICCSN 2024

作者： Bai, Zefang Wang, Guijuan Ma, Meijie School of Mathematics and Statistics Jinan250353 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China

ISBN: (纸本)9798331506322

Evaluating the reliability of interconnection networks is crucial. The edge-connectivity is a parameter used to assess reliability. In this paper, we discuss a conditional edge-connectivity called the h-extra g-component edge-connectivity. It combines the extra edge-connectivity and the component edge-connectivity. First, this paper proved that c λ3h(Qn3)=4nh-4eh(Qn3)-h for 1 ≤ h ≤ 3(1/2), n ≥ 3. Then, the paper given the upper bound of h-extra g-component edge-connectivity of the 3-ary n-cubes for g ∞ {4,3c3+1}, 1 ≤ c≤ [n/2] -1, n ≥ 3. © 2024 IEEE.

关键词： Interconnection networks (circuit switching)

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Deployment optimization for target perpetual coverage in energy harvesting wireless sensor network

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Digital Communications and Networks 2024年第2期10卷 498-508页

作者： Zhenkun Jin Yixuan Geng Chenlu Zhu Yunzhi Xia Xianjun Deng Lingzhi Yi Xianlan Wang Wuhan Business University 430056China Wuhan Research Institute of Post and Telecommunication Wuhan430074China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data SecuritySchool of Cyber Science and EngineeringHuazhong University of Science and TechnologyWuhan430074China Hubei Chutian Expressway Digital Technology Co.Ltd Wuhan430074China School of Information and Safety Engineering Zhongnan University of Economics and LawWuhan430073China

Energy limitation of traditional Wireless Sensor Networks(WSNs)greatly confines the network lifetime due to generating and processing massive sensing data with a limited *** energy harvesting WSN is a novel network architecture to address the limitation of traditional ***,existing coverage and deployment schemes neglect the environmental correlation of sensor nodes and external energy with respect to physical *** considering the spatial correlation of the environment and the uneven distribution of energy in energy harvesting WSN,we investigate how to deploy a collection of sensor nodes to save the deployment cost while ensuring the target perpetual *** Confident information Coverage(CIC)model is adopted to formulate the CIC Minimum Deployment Cost Target Perpetual Coverage(CICMTP)problem to minimize the deployed sensor *** the CICMTP is NP-hard,we devise two approximation algorithms named Local Greedy Threshold Algorithm based on CIC(LGTA-CIC)and Overall Greedy Search Algorithm based on CIC(OGSA-CIC).The LGTA-CIC has a low time complexity and the OGSA-CIC has a better approximation *** simulation results demonstrate that the OGSA-CIC is able to achieve lower deployment cost and the performance of the proposed algorithms outperforms GRNP,TPNP and EENP algorithms.

关键词： Energy harvesting WSN Deployment optimization Confident information coverage(CIC) Target perpetual coverage

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Quantum Fuzzy Support Vector Machine for Binary Classification

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Computer systems Science & Engineering 2023年第6期45卷 2783-2794页

作者： Xi Huang Shibin Zhang Chen Lin Jinyue Xia School of Cybersecurity Chengdu University of Information TechnologyChengdu610225China Sichuan Key Laboratory of Advanced Cryptography and System Security Chengdu610225China International Business Machines Corporation(IBM) New York14201USA

In the objective world,how to deal with the complexity and uncertainty of big data efficiently and accurately has become the premise and key to machine *** support vector machine(FSVM)not only deals with the classifi-cation problems for training samples with fuzzy information,but also assigns a fuzzy membership degree to each training sample,allowing different training samples to contribute differently in predicting an optimal hyperplane to separate two classes with maximum margin,reducing the effect of outliers and noise,Quantum computing has super parallel computing capabilities and holds the pro-mise of faster algorithmic processing of ***,FSVM and quantum com-puting are incapable of dealing with the complexity and uncertainty of big data in an efficient and accurate *** paper research and propose an efficient and accurate quantum fuzzy support vector machine(QFSVM)algorithm based on the fact that quantum computing can efficiently process large amounts of data and FSVM is easy to deal with the complexity and uncertainty *** central idea of the proposed algorithm is to use the quantum algorithm for solving linear systems of equations(HHL algorithm)and the least-squares method to solve the quadratic programming problem in the *** proposed algorithm can deter-mine whether a sample belongs to the positive or negative class while also achiev-ing a good generalization ***,this paper applies QFSVM to handwritten character recognition and demonstrates that QFSVM can be run on quantum computers,and achieve accurate classification of handwritten *** compared to FSVM,QFSVM’s computational complexity decreases expo-nentially with the number of training samples.

关键词： Quantum fuzzy support vector machine(QFSVM) fuzzy support vector machine(FSVM) quantum computing

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On-chain is not enough:Ensuring pre-data on the chain credibility for blockchain-based source-tracing systems

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Digital Communications and Networks 2023年第5期9卷 1053-1060页

作者： Yilei Wang Zhaojie Wang Guoyu Yang Shan Ai Xiaoyu Xiang Chang Chen Minghao Zhao School of Computer Science Qufu Normal UniversityRizhao276826China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic TechnologyGuilin541004China Institute of Artificial Intelligence and Blockchain Guangzhou UniversityGuangzhou510006China School of Software Tsinghua UniversityTsinghua UniversityBeijing100191China

The blockchain provides a reliable and scalable method for enabling source-tracing functionality in large-scale Internet of Things(IoT)*** blockchain-based source tracing applications are generally based on the hypothesis that the raw data collected by each IoT node are credible and consistent,which however may not always be the *** no mechanism ensures the reliability of the original data collected from the IoT devices,these data may be accidently screwed up or maliciously tampered with before they are uploaded *** address this issue,we propose the Multi-dimensional Certificates of Origin(MCO)method to filter out the potentially incredible data-till all the data uploaded to the chain are *** achieve this,we devise the Multidimensional information Cross-Verification(MICV)and Multi-source Data Matching Calculation(MDMC)*** verifies whether a to-be-uploaded datum is consistent or credible,and MDMC determines which data should be discarded and which data should be kept to retain the most likely credible/untampered ones in the circumstance when data inconsistency ***-scale experiments show that our scheme ensures on the credibility of data and off the chain with an affordable overhead.

关键词： Blockchain Traceability Blockchain security

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