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A Novel Semi-supervised IoT Time Series Anomaly Detection Model Using Graph Structure Learning 19th

A Novel Semi-supervised IoT Time Series Anomaly Detection Mo...

19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Song, Weijian Chen, Peng Chen, Juan Xia, Yunni Li, Xi Xi, Qinghui He, Hongxia School of Computer and Software Engineering XiHua University Chengdu China School of Computer Science Chongqing University Chongqing China

ISBN: (纸本)9783031545276

Internet of Things (IoT) is an evolving paradigm for building smart cross-industry. The data gathered from IoT devices may have anomalies or other errors for various reasons, such as malicious activities or sensor failures. Anomaly detection is thus in high need for guaranteeing trustworthy execution of IoT applications. Existing IoT anomaly detection methods are usually built upon unsupervised methods and thus can be inadequate when facing complex IoT data regularity. In this article, we propose a semi-supervised approach for detecting IoT time series anomalies based on Graph Structure Learning (GSL) using multi-layer perceptron Graph Convolutional Networks (GCN) and the Mean Teachers (MT) mechanism. The proposed model is capable of leveraging a small amount of labeled data (1% to 10%) to achieve high detection accuracy. We adopt Mean Teachers to utilize unlabeled data for enhancing the model’s detection performance. Moreover, we design a novel graph structure learning layer to adaptively capture the IoT data features among different nodes. Experimental results clearly suggest that the proposed model outperforms its competitors on two public IoT datasets, achieving 82.85% in terms of F1 score and 22.8% increase. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Time series

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GOAL: Generalized Jointly Sparse Linear Discriminant Regression for Feature Extraction

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 4959-4971页

作者： Lu, Haoquan Lai, Zhihui Zhang, Junhong Yu, Zhuozhen Wen, Jiajun Shenzhen University Computer Vision Institute College of Computer Science and Software Engineering Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen518060 China Peking University Shenzhen Graduate School School of Electronic and Computer Engineering Shenzhen518055 China

Ridge regression (RR)-based methods aim to obtain a low-dimensional subspace for feature extraction. However, the subspace's dimensionality does not exceed the number of data categories, hence compromising its capability of feature representation. Moreover, these methods with L2-norm metric and regularization cannot extract highly robust features from data with corruption. To address these problems, in this article, we propose generalized jointly sparse linear discriminant regression (GOAL), a novel regression method based on joint L2,1-norm and capped-L2-norm, which can integrate sparsity, locality, and discriminability into one model to learn a full-rank robust feature extractor. The sparsely selected discriminative features are robust enough to characterize the decision boundary between classes. Locality is related to manifold structure and Laplacian smoothing, which can enhance the robustness of the model. By using the multinorm metric and regularization regression framework, the proposed method obtains the projection with joint sparsity and guarantees that the rank of the projection matrix will not be limited by the number of classes. An iterative algorithm is proposed to compute the optimal solution. Complexity analysis and proofs of convergence are also given in the article. Experiments on well-known datasets demonstrate our model's superiority and generalization ability. © 2020 IEEE.

关键词： Data mining

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Camouflaged Variational Graph AutoEncoder against Attribute Inference Attacks for Cross-Domain Recommendation

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IEEE Transactions on Knowledge and Data engineering 2025年第7期37卷 3916-3932页

作者： Xiong, Yudi Guo, Yongxin Pan, Weike Yang, Qiang Ming, Zhong Zhang, Xiaojin Yu, Han Lin, Tao Tang, Xiaoying Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China School of Science and Engineering Shenzhen518172 China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong WeBank Shenzhen China Huazhong University of Science and Technology School of Computer Science and Technology Wuhan430074 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore Westlake University School of Engineering Hangzhou China

Cross-domain recommendation (CDR) aims to alleviate the data sparsity problem by leveraging the benefits of modeling two domains. However, existing research often focuses on the recommendation performance while ignores the privacy leakage issue. We find that an attacker can infer user attribute information from the knowledge (e.g., user preferences) transferred between the source and target domains. For example, in our experiments, the average inference accuracies of attack models on gender and age attributes are 0.8323 and 0.3897. The best-performing attack model achieves accuracies of 0.8847 and 0.4634, exceeding a random inference by 25.10% and 64.04%. We can see that the leakage of user attribute information may significantly exceed what would be expected from random inference. In this paper, we propose a novel recommendation framework named CVGAE (short for camouflaged variational graph autoencoder), which effectively models user behaviors and mitigates the risk of user attribute information leakage at the same time. Specifically, our CVGAE combines the strengths of VAEs in capturing latent features and variability with the ability of GCNs in exploiting high-order relational information. Moreover, to ensure against attribute inference attacks without sacrificing the recommendation performance, we design a user attribute protection module that fuses user attribute-camouflaged information with knowledge transfer during cross-domain processes. We then conduct extensive experiments on three real-world datasets, and find our CVGAE is able to achieve strong privacy protection while making little sacrifices in recommendation accuracy. © 1989-2012 IEEE.

关键词： Recommender systems

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MILG:Realistic lip-sync video generation with audio-modulated image inpainting

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Visual Informatics 2024年第3期8卷 71-81页

作者： Han Bao Xuhong Zhang Qinying Wang Kangming Liang Zonghui Wang Shouling Ji Wenzhi Chen School of Software Technology Zhejiang UniversityHangzhouChina College of Computer Science Zhejiang UniversityHangzhouChina College of Engineering Zhejiang UniversityHangzhouChina

Existing lip synchronization(lip-sync)methods generate accurately synchronized mouths and faces in a generated ***,they still confront the problem of artifacts in regions of non-interest(RONI),e.g.,background and other parts of a face,which decreases the overall visual *** solve these problems,we innovatively introduce diverse image inpainting to lip-sync *** propose Modulated Inpainting Lip-sync GAN(MILG),an audio-constraint inpainting network to predict synchronous *** utilizes prior knowledge of RONI and audio sequences to predict lip shape instead of image generation,which can keep the RONI ***,we integrate modulated spatially probabilistic diversity normalization(MSPD Norm)in our inpainting network,which helps the network generate fine-grained diverse mouth movements guided by the continuous audio ***,to lower the training overhead,we modify the contrastive loss in lipsync to support small-batch-size and few-sample *** experiments demonstrate that our approach outperforms the existing state-of-the-art of image quality and authenticity while keeping lip-sync.

关键词： Lip-sync Image inpainting Face generation Modulated SPD normalization

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Wireless Sensing for Material Identification: A Survey

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IEEE Communications Surveys and Tutorials 2024年第3期27卷 1598-1617页

作者： Chen, Yande Xu, Chongzhi Li, Kexin Zhang, Jia Guo, Xiuzhen Jin, Meng Zheng, Xiaolong He, Yuan Tsinghua University School of Software Beijing China Beijing University of Posts and Telecommunications School of Computer Science Beijing China Shanghai Jiao Tong University John Hopcroft Center for Computer Science Shanghai China Zhejiang University College of Control Science and Engineering Hangzhou China

As an application of fine-grained wireless sensing, RF-based material identification follows the paradigm of RF computing that fetches the information during RF signal propagation. Specifically, the RF signal accesses the objects' material-related information and carries the information with its electromagnetic properties. With a variety of important applications, research on RF-based material identification has gained significant progress in recent years. However, several fundamental problems remain insufficiently studied, such as the sensing models, signal processing approaches, performance and future extensions. This paper presents the first comprehensive survey of RF-based material identification. According to the basic sensing model used for sensing, we propose a taxonomy to classify the existing works into two categories: reflection-based and penetration-based. The works in each category are further grouped by the type of RF signals used, with elaborated discussion of the detailed approaches and the common challenges. We provide a framework that benchmarks the performance of the existing works, followed by a thorough discussion of future extensions. © 1998-2012 IEEE.

关键词： II-VI semiconductors

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Optimization-Inspired Graph Neural Network for Cellular Network Optimization

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IEEE Transactions on Mobile Computing 2025年第07期24卷 6446-6459页

作者： He, Pengcheng Tang, Yijia Xu, Fan Shi, Qingjiang Tongji University School of Computer Science and Technology Shanghai China Shenzhen Research Institute of Big Data Shenzhen China Tongji University School of Software Engineering Shanghai China Tongji University School of Electronic and Information Engineering Shanghai China

The rapid development of wireless communications have driven the need for careful optimization of network parameters to improve network performance and reduce operational cost. Traditional methods, however, struggle with the vast number of tunable parameters and lack scalability in diverse network scenarios. To address these challenges, this paper introduces an optimization-inspired bipartite graph neural network (Bi-GNN) approach for scalable network optimization. Our approach leverages the bipartite structure of network topologies, and incorporates a message-passing mechanism by unfolding the Zeroth-Order Block Coordinate Projected Gradient Descent (ZO-BCPGD) algorithm, which ensures not only high-performance optimization but also manageable computational demand. We demonstrate the permutation and dimensionality equivariance property of the Bi-GNN, which significantly enhances the model's generalizability across various network structures and sizes. Furthermore, we theoretically analyze the expressive power and generalization ability of the Bi-GNN, demonstrating its adeptness at complex network optimization tasks. The training process, parallel execution, and practical implementation techniques are also discussed to ensure the model's applicability in real-world scenarios. Numerical results verify that the Bi-GNN outperforms existing methods in both coverage ratios and computational cost. Furthermore, our approach exhibits robust scalability across various network scenarios, making it a versatile tool for optimizing a wide range of wireless networks. © 2002-2012 IEEE.

关键词： Graph neural networks

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E±cient Construction of Practical Python Call Graphs with Entity Knowledge Base

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International Journal of software engineering and Knowledge engineering 2024年第7期34卷 999-1024页

作者： Cao, Yulu Chen, Lin Chen, Zhifei Zhong, Jiacheng Zhang, Xiaowei Wang, Linzhang State Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing China

Call graphs facilitate various tasks in software engineering. However, for the dynamic language Python, the complex language features and external library dependencies pose enormous challenges for building the call graphs of real projects. Some program analysis techniques used for call graph construction in other languages are impractical for Python. In this paper, we present STAR, a practical technique for the construction of Python static call graphs. We reformulate call graph construction as an entity identification task. STAR leverages inter-module summary and cross-project dependencies to construct a fine-grained entity knowledge base to identify the possible nodes and edges of the call graph in the code, and then construct the call graph. Our evaluation of three benchmarks shows that (1) STAR improves recall in three benchmarks compared to three baseline tools. Especially, STAR improves the recall of reachable nodes and reachable edges compared with the state-of-the-art tool by 11.3% and 9.8%, respectively;(2) STAR achieves comparable performance as three baseline tools in execution time and memory usage and is more efficient in large projects;(3) STAR can be effectively used for the task of detecting vulnerability propagation with real-world cases. We expect our results will attract more exploration of practical methods and improve the application of Python call graphs. © World Scientific Publishing Company.

关键词： Python

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ACbot:an IIoT platform for industrial robots

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Frontiers of computer science 2025年第4期19卷 13-27页

作者： Rui WANG Xudong MOU Tianyu WO Mingyang ZHANG Yuxin LIU Tiejun WANG Pin LIU Jihong YAN Xudong LIU School of Computer Science and Engineering Beihang UniversityBeijing 100191China Zhongguancun Laboratory Beijing 100194China School of Software Beihang UniversityBeijing 100191China School of Mechatronics Engineering Harbin Institute of TechnologyHarbin 150001China Hangzhou Innovation Institute Beihang UniversityHangzhou 310051China School of Information Engineering China University of GeosciencesBeijing 100083China

As the application of Industrial Robots(IRs)scales and related participants increase,the demands for intelligent Operation and Maintenance(O&M)and multi-tenant collaboration *** methods could no longer cover the requirements,while the Industrial Internet of Things(IIoT)has been considered a promising ***,there’s a lack of IIoT platforms dedicated to IR O&M,including IR maintenance,process optimization,and knowledge *** this context,this paper puts forward the multi-tenant-oriented ACbot platform,which attempts to provide the first holistic IIoT-based solution for O&M of *** on an information model designed for the IR field,ACbot has implemented an application architecture with resource and microservice management across the cloud and multiple *** this basis,we develop four vital applications including real-time monitoring,health management,process optimization,and knowledge *** have deployed the ACbot platform in real-world scenarios that contain various participants,types of IRs,and *** date,ACbot has been accessed by 10 organizations and managed 60 industrial robots,demonstrating that the platform fulfills our ***,the application results also showcase its robustness,versatility,and adaptability for developing and hosting intelligent robot applications.

关键词： IIoT platform industrial robots cloud-edge collaboration intelligent applications

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Scale adaptive fitness evaluation‐based particle swarm optimisation for hyperparameter and architecture optimisation in neural networks and deep learning

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 849-862页

作者： Ye‐Qun Wang Jian‐Yu Li Chun‐Hua Chen Jun Zhang Zhi‐Hui Zhan School of Computer Science and Engineering South China University of TechnologyGuangzhouChina School of Software Engineering South China University of TechnologyGuangzhouChina Hanyang University AnsanSouth Korea

Research into automatically searching for an optimal neural network(NN)by optimi-sation algorithms is a significant research topic in deep learning and artificial ***,this is still challenging due to two issues:Both the hyperparameter and ar-chitecture should be optimised and the optimisation process is computationally *** tackle these two issues,this paper focusses on solving the hyperparameter and architecture optimization problem for the NN and proposes a novel light‐weight scale‐adaptive fitness evaluation‐based particle swarm optimisation(SAFE‐PSO)***,the SAFE‐PSO algorithm considers the hyperparameters and architectures together in the optimisation problem and therefore can find their optimal combination for the globally best ***,the computational cost can be reduced by using multi‐scale accuracy evaluation methods to evaluate ***,a stagnation‐based switch strategy is proposed to adaptively switch different evaluation methods to better balance the search performance and computational *** SAFE‐PSO algorithm is tested on two widely used datasets:The 10‐category(i.e.,CIFAR10)and the 100−cate-gory(i.e.,CIFAR100).The experimental results show that SAFE‐PSO is very effective and efficient,which can not only find a promising NN automatically but also find a better NN than compared algorithms at the same computational cost.

关键词： deep learning evolutionary computation hyperparameter and architecture optimisation neural networks particle swarm optimisation scale‐adaptive fitness evaluation

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A New End-to-End Image Contrastive Clustering Based on Mining Latent Positive Samples 7

A New End-to-End Image Contrastive Clustering Based on Minin...

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7th International Conference on Machine Learning and Natural Language Processing, MLNLP 2024

作者： Feng, Bo Yan, Xi Yan, Li Luo, Bing Pei, Zheng School of Science Xihua University Chengdu China School of Computer Science and Software Engineering Xihua University Chengdu China

ISBN: (纸本)9798350354973

The integration of the contrastive learning paradigm into deep clustering has led to enhanced performance in image clustering. However, in existing researches, the samples in the class of the target may be still treated as negative samples, this means that more semantic sample pairs cannot be constructed and the intra-class compactness may be compromised. In this paper, we propose a new contrastive clustering method based on mining latent positive samples (CCLPS), which utilizes the nearest neighbor relationship between samples to mine semantic positive samples in the same class. In more detail, by mining latent positive samples in both the feature and cluster spaces, CCLPS can obtain more cluster-friendly feature representation of samples to enhance clustering performance. Samples mined from the feature space fuel the cluster-level contrastive learning task, while samples mined from the cluster space fuel the instance-level task. Three image datasets CIFAR-10, CIFAR-100 and Tiny-ImageNet are employed to experimentally demonstrate usefulness and effectiveness of the proposed method in image clustering, and transfer learning based on mining latent positive samples is also discussed and comparisons are made with 19 representative clustering methods on the datasets. Experimental results indicate that the proposed method can serve as a pre-training model for transfer learning and exhibits commendable feature modeling capabilities. © 2024 IEEE.

关键词： Contrastive Learning

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