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FedIERF: Federated Incremental Extremely Random Forest for Wearable Health Monitoring

Journal of computer Science & Technology 2023年第5期38卷 970-984页

作者：胡春雨忽丽莎袁林陆佃杰吕蕾陈益强 Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250000China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan 25000China Institute of Information Technology Hebei University of Economics and BusinessShijiazhuang 050061China School of Information Science and Engineering Shandong Normal UniversityJinan 250358China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Wearable health monitoring is a crucial technical tool that offers early warning for chronic diseases due to its superior portability and low power ***,most wearable health data is distributed across dfferent organizations,such as hospitals,research institutes,and companies,and can only be accessed by the owners of the data in compliance with data privacy *** first challenge addressed in this paper is communicating in a privacy-preserving manner among different *** second technical challenge is handling the dynamic expansion of the federation without model *** address the first challenge,we propose a horizontal federated learning method called Federated Extremely Random Forest(FedERF).Its contribution-based splitting score computing mechanism significantly mitigates the impact of privacy protection constraints on model *** on FedERF,we present a federated incremental learning method called Federated Incremental Extremely Random Forest(FedIERF)to address the second technical *** introduces a hardness-driven weighting mechanism and an importance-based updating scheme to update the existing federated model *** experiments show that FedERF achieves comparable performance with non-federated methods,and FedIERF effectively addresses the dynamic expansion of the *** opens up opportunities for cooperation between different organizations in wearable health monitoring.

关键词： federated learning incremental learning random forest wearable health monitoring

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EVADE: Targeted Adversarial False Data Injection Attacks for State Estimation in Smart Grid

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第3期10卷 534-546页

作者： Tian, Jiwei Shen, Chao Wang, Buhong Ren, Chao Xia, Xiaofang Dong, Runze Cheng, Tianhao Ministry of Education Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi'an Jiaotong University Xi'an China Air Traffic Control and Navigation College Air Force Engineering University Xi'an China Information and Navigation College Air Force Engineering University Xi'an China School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Computer Science and Technology Xidian University Xi'an China

Although conventional false data injection attacks can circumvent the detection of bad data detection (BDD) in sustainable power grid cyber physical systems, they are easily detected by well-trained deep learning-based detectors. Still, state estimation models with deep leaning-based detectors are not secure due to the vulnerabilities and fragility of deep learning models. Using the related laws of conventional false data injection attacks and adversarial sample attacks, this paper proposes the targEted adVersarial fAlse Data injEction (EVADE) strategy to explore targeted adversarial false data injection attacks for state estimation in Smart Grid. The proposed EVADE attack strategy selects key state variables based on adversarial saliency maps to improve the attack efficiency and perturbs as few state variables as possible to reduce the attack cost. In this way, the EVADE attack strategy can bypass the detection of BDD and neural attack detection (NAD) methods (that is, maintaining deep stealthy) with a high success rate and achieve the attack target simultaneously. Experimental results demonstrate the effectiveness of the proposed strategy, posing serious and pressing concerns for sustainable cyber physical power system security. © 2016 IEEE.

关键词： Cyber attacks

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Diverse Co-saliency Feature Learning for Text-Based Person Retrieval

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5465-5477页

作者： You, Shuai Chen, Cuiqun Feng, Yujian Liu, Hai Ji, Yimu Ye, Mang Nanjing University of Posts and Telecommunications (NJUPT) School of Internet of Things Nanjing China Anhui University School of Computer Science and Technology Hefei China South China Normal University school of computer Guangdong China NJUPT School of Computer Science Nanjing China Wuhan University National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Institute of Artificial Intelligence School of Computer Science Wuhan 430072 China

Text-based Person Retrieval (TPR) plays a pivotal role in video surveillance systems for safeguarding public safety. As a fine-grained retrieval task, TPR faces the significant challenge of precisely capturing highly discriminative features across image and text modalities. Existing methods primarily focus on establishing modality-shared feature spaces to bridge cross-modal discrepancies. However, these methods are prone to disturbances from irrelevant information, such as background noises in the visual modality, and often over-emphasize specific local regions while neglecting the capture of diverse discriminative modal features, thereby limiting the robustness of cross-modal matching. In this paper, we introduce a novel framework, termed the Diverse Co-saliency Feature Learning network (DCFL), which mines the co-saliency information between image and text modalities and enhances the diversity of cross-modal discriminative features while mitigating the interference of noise. Specifically, to construct cross-modal co-saliency features, we devise the Intra-modal Saliency Feature Learning (ISFL) and Cross-modal Saliency Feature Matching (CSFM) modules. ISFL employs a weighted mask mechanism to guide the model in reducing the impact of noise information in both modalities. Complementing ISFL, CSFM establishes consistent relationships between saliency features across modalities, leveraging text descriptions to align pedestrian-relevant visual regions. Furthermore, we propose the Diverse Co-saliency Feature Mining (DCFM) to bolster the diversity of discriminative co-saliency features across both image and text modalities. This module integrates a diversity regularization term, enabling the extraction of varied visual cues and capturing comprehensive features of the target individual. Extensive benchmark experiments demonstrate a substantial superiority of our approach over the state-of-the-art methods. The code will be released publicly. © 2005-2012 IEEE.

关键词： Co-saliency features modeling Diverse Co-saliency features Mining Text-based person retrieval

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Universal and General Quantum Simultaneous Secret Distribution with Dense Coding by Using One-Dimensional High-Level Cluster States

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Journal of computer Science & Technology 2021年第1期36卷 221-230页

作者： Zhi-Hao Liu Han-Wu Chen School of Computer Science and Engineering Southeast UniversityNanjing 211189China Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of Education Nanjing 211189China

A universal and general quantum simultaneous secret distribution(QSSD)protocol is put forward based on the properties of the one-dimensional high-level cluster states,in which one sender dispatches different high-level classical secret messages to many users at the same *** to the idea of quantum dense coding,the sender can send different two-dit classical messages(two d-level classical numbers)to different receivers simultaneously by using a one-dimensional d-level cluster state,which means that the information capacity is up to the *** estimate the security of quantum channels,a new eavesdropping check strategy is put ***,a new attack model,the general individual attack is proposed and *** is shown that the new eavesdropping check strategy can effectively prevent the traditional attacks including the general individual *** addition,multiparty quantum secret report(MQSR,the same as quantum simultaneous secret submission(QSSS))in which different users submit their different messages to one user simultaneously can be gotten if the QSSD protocol is changed a little.

关键词： quantum simultaneous secret distribution(QSSD) multiparty quantum secret report(MQSR) quantum simultaneous secret submission(QSSS) quantum secret sharing quantum broadcast communication

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Smoothing Neural network for Non-Lipschitz Optimization with Linear Inequality Constraints

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Chinese Journal of Electronics 2021年第4期30卷 634-643页

作者： YU Xin WU Lingzhen XIE Mian WANG Yanlin XU Liuming LU Huixia XU Chenhua Department of Computer and Electronic Information Guangxi University The Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Science and Engineering Guilin University of Aerospace Technology Department of Electrical Engineering Guangxi University

This paper presents a smoothing neural network to solve a class of non-Lipschitz optimization problem with linear inequality constraints. The proposed neural network is modelled with a differential inclusion equation, which introduces the smoothing approximate techniques. Under certain conditions, we prove that the trajectory of neural network reaches the feasible region in finite time and stays there thereafter, and that any accumulation point of the solution is a stationary point of the original optimization problem. Furthermore, if all stationary points of the optimization problem are isolated,then the trajectory converges to a stationary point of the optimization problem. Two typical numerical examples are given to verify the effectiveness of the proposed neural network.

关键词： Non-Lipschitz optimization Neural networks Stationary points Convergence

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Verifiable Secure Aggregation Protocol Under Federated Learning 1st

Verifiable Secure Aggregation Protocol Under Federated Learn...

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1st International Conference on Artificial Intelligence Security and Privacy, AIS and P 2023

作者： Xu, Peiming Zheng, Meiling Xu, Lingling Guangdong Provincial Key Laboratory of Power System Network Security Electric Power Research Institute CSG Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9789819997848

Federated learning is a new machine learning paradigm used for collaborative training models among multiple devices. In federated learning, multiple clients participate in model training locally and use decentralized learning methods to ensure the privacy of client data. However, although federated learning protects the privacy of client data, the update gradients uploaded by clients may still contain sensitive information. To solve this problem, this paper proposes a secure aggregation protocol which can verify the aggregation results under federated learning and protect gradient privacy. The core idea of this aggregation protocol is to use encryption technology to achieve secure computation between clients, ensuring the privacy of gradients during the aggregation process. At the same time, bilinear pairing technology is used to achieve the verifiability of aggregation results, ensuring the correctness and usability of the model after aggregation. In order to evaluate the security of the protocol, this paper conducts a detailed security analysis. The results show that this protocol has higher security properties compared to the existing related protocols. In addition, the computation and communication costs of the protocol are analyzed, which show that the protocol has good credibility and applicability in practical federated learning scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Sensitive data

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An Improved YOLOv5s-Based Smoke Detection System for Outdoor Parking Lots

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computers, Materials & Continua 2024年第8期80卷 3333-3349页

作者： Ruobing Zuo Xiaohan Huang Xuguo Jiao Zhenyong Zhang State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou310027China Key Laboratory of Computing Power Network and Information Security Ministry of EducationQilu University of Technology(Shandong Academy of Sciences)Jinan250353China Text Computing&Cognitive Intelligence Engineering Research Center of National Education Ministry Guizhou UniversityGuiyang550025China

In the rapidly evolving urban landscape,outdoor parking lots have become an indispensable part of the city’s transportation *** growth of parking lots has raised the likelihood of spontaneous vehicle combus-tion,a significant safety hazard,making smoke detection an essential preventative ***,the complex environment of outdoor parking lots presents additional challenges for smoke detection,which necessitates the development of more advanced and reliable smoke detection *** paper addresses this concern and presents a novel smoke detection technique designed for the demanding environment of outdoor parking ***,we develop a novel dataset to fill the gap,as there is a lack of publicly available *** dataset encompasses a wide range of smoke and fire scenarios,enhanced with data augmentation to ensure robustness against diverse outdoor ***,we utilize an optimized YOLOv5s model,integrated with the Squeeze-and-Excitation network(SENet)attention mechanism,to significantly improve detection accuracy while maintaining real-time processing ***,this paper implements an outdoor smoke detection system that is capable of accurately localizing and alerting in real time,enhancing the effectiveness and reliability of emergency *** show that the system has a high accuracy in terms of detecting smoke incidents in outdoor scenarios.

关键词： YOLOv5s smoke detection deep learning SENet

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LGDB-Net: Dual-Branch Path for Building Extraction from Remote Sensing Image 30

LGDB-Net: Dual-Branch Path for Building Extraction from Remo...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Ronghuan Zhao, Jing Li, Ming Zou, Qingzhi Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong University of Traditional Chinese Medicine School of Intelligence and Information Engineering Jinan China

ISBN: (纸本)9798331515966

Extracting buildings from remote sensing images using deep learning techniques is a widely applied and crucial task. Convolutional Neural networks (CNNs) adopt hierarchical feature representation, showcasing powerful capabilities in extracting local information but facing challenges in capturing global features. Transformers can address this limitation, but they perform poorly in extracting local features and significantly increase memory requirements and computational complexity. To overcome these challenges, we propose a method for building extraction from remote sensing images called LGDB-Net (Local-Global Dual-Branch network), employing a dual-branch approach. Firstly, inspired by Swin Transformer, we designed GB-Former(Global Branch-Former) as the backbone network to model global information. We use a linear multi-head self-attention mechanism to reduce time and memory complexity while maintaining a large global receptive field. Additionally, we replace the traditional multi-layer perceptron with a convolution-enhanced multi-layer perceptron to improve channel feature representation, reduce model parameters, and enhance segmentation performance. Secondly, we use multiple Depth-wise Conv3×3 + LN (Layer Normalization) + GeLU (Gaussian Error Linear Unit) modules as the auxiliary branch for local detailed feature extraction. Finally, we adopt a multi-scale feature fusion strategy to integrate feature information from both branches. We conduct a series of experiments on three datasets: WHU, Massachusetts, and Inria. The experimental results demonstrate that the proposed method not only effectively improves segmentation accuracy with lower building omission and commission rates but also significantly reduces model parameters and computational complexity. © 2024 IEEE.

关键词： Convolutional neural networks

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Intermittent Fault Diagnosis of Split-Star networks and its Applications

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IEEE Transactions on Parallel and Distributed Systems 2023年第4期34卷 1253-1264页

作者： Song, Jiankang Lin, Limei Huang, Yanze Hsieh, Sun-Yuan Fujian Normal University College of Computer and Cyber Security Key Laboratory of Network Security and Cryptology Fuzhou Fujian350117 China Fujian University of Technology School of Computer Science and Mathematics Fuzhou Fujian350118 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan

With the rapid increase of the number of processors in multiprocessor systems and the fast expansion of interconnection networks, the reliability of interconnection network is facing severe challenges, where the fast recognition of fault processors is crucial. In practice, most of the processor failures are intermittent faults. In this article, we first determine the intermittent fault diagnosability t I PMC(S n 2) tIPMC(Sn2) of n-dimensional split-star network Sn2 under the PMC model. In addition, we propose a fast intermittent fault probabilistic diagnosis algorithm FIFPDPMC to identify the nodes with intermittent fault in the n-dimensional split-star network Sn2 under the PMC model, and we calculated the time complexity of the algorithm FIFPDPMC. Then we implement the algorithm FIFPDPMC in the IoT-based wireless sensor network (IoTWSN) and a randomly generated network (RGN) under different number of nodes with intermittent fault, and we evaluate the performance and efficiency of the algorithm FIFPDPMC in terms of accuracy, precision, recall (TPR), F1, G-mean, FPR, TNR and FNR. Experimental results show that, as the number of stages of executing the algorithm FIFPDPMC increases, the number of nodes with intermittent fault being diagnosed by the algorithm FIFPDPMC increases, which implies that the algorithm FIFPDPMC has good performance and efficiency in both IoTWSN and RGN. © 1990-2012 IEEE.

关键词： Fault detection

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Design and implementation of handlebar defect detection method based on machine recognition

Design and implementation of handlebar defect detection meth...

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2024 International Conference on computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2024

作者： Zhang, Tong Ren, Sunan Zhang, Wenjing Liu, Yajun School of Computer Science and Engineering Xi’an University of Technology Shaanxi Xi’an 710048 China Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi’an University of Technology Shaanxi Xi’an 710048 China

ISBN: (纸本)9781510689275

As an important part of electric vehicles, handlebar installation defects are an important factor in the failure of electric vehicles. In the production process, many large-scale manufacturers still assemble the handlebar manually by workers, and the workload is large, and there may always be errors such as tilting, concave and other assembly defects, and due to the large number of assemblies, the identification of the handlebar assembly defects through manual work will have a significant impact on work efficiency. Therefore, the use of machine recognition instead of traditional manual recognition has become the trend of the development of the industry. In this study, we designed a defect detection method based on machine recognition. First, we collect the image data of the electric vehicle and extract the region of interest group of the electric vehicle through image preprocessing. Then, according to the area of interest information, the electric vehicle baseline group is determined, and the electric vehicle baseline group is used to determine the basis for the handlebar assembly defects. Finally, the angle and distance information between the reference lines are calculated to determine the defects of the handlebar installation. Experiments show that our method can not only reduce the consumption of human resources in the defect detection of electric vehicle handlebar installation by electric vehicle manufacturers, but also use strict filtering conditions to obtain reliable reference lines and detection points in the region of interest, so that the subsequent detection results are more reliable and credible, and the detection error is reduced. © 2025 SPIE.

关键词： datum line defect detection Handlebar Installation Defects machine recognition

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