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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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Distributed user pairing and effective computation offloading in aerial edge networks

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Chinese Journal of Aeronautics 2024年第4期37卷 378-390页

作者： Wei LIANG Shuhui WEN Lixin LI Jingjing CUI School of Electronics and Information Northwestern Polytechnical UniversityXi’an CN710072China The State Key Laboratory of Integrated Services Networks Xidian UniversityXi’an CN710126China School of Electronics and Computer Science University of SouthamptonSouthampton SO167NSUK

Future Sixth-Generation (6G) mobile communication networks extremely require the global connectivity and the ground Base Stations (BSs) are difficult to develop in some specific areas,such as mountainous ***,the aerial networks are the key framework for the future communications,in which the aerial vehicle could act as the access ***,Mobile Edge Computing (MEC) is also essential to enhance the data processing capabilities of aerial *** this paper,we study a comprehensive communication-computation resource management problem for jointly optimizing user pairing,power and time allocation in the MEC aided Cognitive Radio (CR) aerial networks,namely CR-MEC aerial ***,this multilevel optimization problem could be decomposed into the user pairing and power allocation as well as time allocation *** the conceived CR-MEC aerial networks,we propose a User Pairing and Computation Offloading Management Algorithm (UPCOMA) based on three-sided matching theory,aiming to minimize the system’s energy *** the first step of UPCOMA,multiple Primary Users (PUs) and Cognitive Users (CUs) on the ground negotiate to each other with the suitable power allocation coefficients and construct the stable user ***,the stable user pairs would match to a high altitude platform who act as the base station,which is for appropriately allocating Transmission Time Slots (TSs) at the second step of ***,a hybrid offloading mode is proposed in our conceived networks in order to support ground users to offload their tasks adaptively according to their individual ***,the simulation results reveal that the relative performance of UPCOMA is close to that of the Brute-Force Search Algorithm (BFSA) with low ***,the hybrid offloading mode with less energy consumption supports much more ground user pairs to offload tasks compared to the binary mode.

关键词： Aerial edge computing Binary offloading Cognitive radio Hybrid offloading Resource management

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An Accountable Anonymous Voting Scheme Based on One-Time Ring Signature 3

An Accountable Anonymous Voting Scheme Based on One-Time Rin...

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3rd International Conference on Frontiers of Electronics, information and Computation Technologies, ICFEICT 2023

作者： Gu, Yunqi Shen, Zhuowei Southeast University School of Cyber Science and Engineering China Ministry of Education Key Laboratory of Computer Networks and Information Nanjing China

ISBN: (纸本)9798350302356

Electronic voting plays an increasingly important role in both economic life and social activities. The electronic voting scheme based on one-Time ring signature and blockchain solves the problems of voter anonymity, double voting, and public verification, while achieving the openness and transparency of voting. However, it is not applicable to the multi-round voting scenario where the voting group is relatively fixed, nor does it consider the need for anonymous accountability and differential privacy protection. In this paper, based on one-Time ring signature, stealth address, blacklist and whitelist, an improved accountability anonymous voting scheme is proposed to enhance the practicability of the scheme. The security analysis shows that the scheme confirms to the basic security standards of an electronic voting system. © 2023 IEEE.

关键词： Electronic voting

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MemADet: A Representative Memory Bank Approach for Industrial Image Anomaly Detection

MemADet: A Representative Memory Bank Approach for Industria...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Li, Min He, Jinghui Ying, Zuobin Li, Gang Zhou, Mingle Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China City University of Macau Faculty of Data Science China

ISBN: (纸本)9781665410205

In the field of industrial production, anomaly detection is crucial for ensuring product quality and maintaining production efficiency. With the continuous advancement of computer vision technology, it has shown tremendous potential in industrial applications. However, the scarcity of labeled anomaly samples in real-world operating environments poses significant challenges for traditional anomaly detection techniques. To address this, we propose MemADet, a novel anomaly detection model that employs an unsupervised approach and leverages a representative memory bank. It employs a dynamic decision mechanism to control the representativeness of the features stored in the memory bank, and employs a weighted anomaly score calculation mechanism to further enhance the performance of image anomaly detection. Our evaluations indicate that MemADet performs robustly in industrial image anomaly detection across three datasets, with particularly no-table detection accuracy on the MVTec AD dataset. Its efficacy is further validated by competitive results on two additional datasets, highlighting its consistent and effective performance in various settings. © 2024 IEEE.

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Anomaly-Free Prior Guided Knowledge Distillation for Industrial Anomaly Detection

Anomaly-Free Prior Guided Knowledge Distillation for Industr...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Li, Gang Chen, Tianjiao Li, Min Han, Delong Zhou, MingLe Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China City University of Macau Faculty of Data Science China

ISBN: (纸本)9781665410205

In industrial manufacturing, visual anomaly detection is critical for maintaining product quality by detecting and preventing production anomalies. Anomaly detection methods based on knowledge distillation demonstrate promising performance in addressing the unpredictability and diversity of anomalies. However, they suffer from a lack of effective guidance from anomaly-free priors when handling anomalous features and underutilize multi-scale features during the segmentation scoring stage, yielding suboptimal detection results. To alleviate these issues, we propose an Anomaly-free Prior Guided knowledge distillation (APG) for industrial anomaly detection. Firstly, it filters the abnormal features by training the de-noising target network with knowledge distillation structure. Concurrently, we propose the Prior Perception Propagation Module (P3M), which extracts more efficient anomaly-free features by imposing constraints on anomalous features. Secondly, we propose the Multi-scale Prior Guided Fusion Module (MPGFM) to improve anomaly detection accuracy by utilizing anomaly-free features from the target network as priors to guide the generation and fusion of cross-scale differential features. Finally, the Global Perception Enhancement Module (GPEM) is proposed to construct an anomaly scoring network, leveraging comprehensive scene features to enhance the detection and localization performance of numerous small-target anomalies in industrial manufacturing. Extensive experiments on the MVTecAD and BTAD datasets show that the proposed method demonstrates a consistent and significant outperformance against competing methods. © 2024 IEEE.

关键词： Anomaly detection

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Fundamental Limits of Multiple-Access Integrated Sensing and Communication Systems

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IEEE Transactions on information Theory 2025年第6期71卷 4317-4341页

作者： Liu, Yao Li, Min Liu, An Ong, Lawrence Yener, Aylin Zhejiang University College of Information Science and Electronic Engineering Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China University of Newcastle School of Engineering CallaghanNSW2308 Australia The Ohio State University Department of Electrical and Computer Engineering ColumbusOH43210 United States

A state-dependent discrete memoryless multiple access channel is considered to model an integrated sensing and communication system, where two transmitters wish to convey messages to a receiver while simultaneously estimating the state parameter sequences through echo signals. In particular, the sensing state parameters are assumed to be correlated with the channel state. In this setup, improved inner and outer bounds for capacity-distortion region are derived. The inner bound is based on an achievable scheme that combines message cooperation and joint compression of past transmitted codewords and echo signals at each transmitter, resulting in unified cooperative communication and sensing. The outer bound is based on the ideas of dependence balance for communication rate, genie-aided state estimator and rate-limited constraints on sensing distortion. The proposed inner and outer bounds are proved to improve the state-of-the-art bounds. Finally, numerical examples are provided to demonstrate that our new inner and outer bounds strictly improve the existing results. © 1963-2012 IEEE.

关键词： Signal distortion

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An overview on IRS-enabled sensing and communications for 6G: architectures, fundamental limits, and joint beamforming designs

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Science China(information Sciences) 2025年第5期68卷 170-193页

作者： Xianxin SONG Yuan FANG Feng WANG Zixiang REN Xianghao YU Ye ZHANG Fan LIU Jie XU Derrick Wing Kwan NG Rui ZHANG Shuguang CUI School of Science and Engineering (SSE) Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Department of Electrical Engineering City University of Hong Kong School of Information Engineering Guangdong University of Technology Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Computer School Beijing Information Science and Technology University National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University School of Electrical Engineering and Telecommunications University of New South Wales School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.

关键词： integrated sensing and communication (ISAC) intelligent reflecting surface (IRS) non-line-of-sight (NLoS) sensing sensing and communication tradeoff

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Robust Voice Activity Detection using Locality-Sensitive Hashing and Residual Frequency-Temporal Attention 25

Robust Voice Activity Detection using Locality-Sensitive Has...

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25th Interspeech Conferece 2024

作者： Li, Shu Zhang, Peng Li, Ye Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science China

For voice activity detection (VAD), recent works focus on learning the attention distribution on contextual information of speech to reduce the impact of irrelevant noise. However, contextual frames selected with specific steps may not be relevant, and these attention mechanisms can not fully discover the structure and characteristics of speech. In this paper, we explore a self-attention-inspired locality-sensitive hashing algorithm (SALSH) for dynamic and efficient contextual frame selection to enrich the frame-level features into a 2D partial spectrogram. Then, we propose a residual frequency-temporal attention model (FTAM) for VAD, consisting of an interval branch, an analogous hourglass structure with channel attention, and an attention learning mechanism for speech based on frequency-temporal attention. On the LibriSpeech and TIMIT datasets, the proposed method outperforms the others in terms of area under the curve (AUC), even under extremely low signal-to-noise ratio of -15dB. © 2024 International Speech Communication Association. All rights reserved.

关键词： Speech recognition

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A Novel 3D Medical Image Segmentation Model Using Improved SAM

A Novel 3D Medical Image Segmentation Model Using Improved S...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Kuang, Yuansen Ma, Xitong Zhao, Jing Wang, Guangchen Zeng, Yijie Liu, Song Key Laboratory of Computing Power Network and Information Security 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

ISBN: (纸本)9781665410205

3D medical image segmentation is an essential task in the medical image field, which aims to segment organs or tumours into different labels. A number of issues exist with the current 3D medical image segmentation task: existing models cannot simultaneously obtain the space correlation and depth correlation of 3D slices;previous models suffer from local detail loss of positional embedding in 3D images;previous approaches often have blurring of boundaries in segmenting 3D images. To solve these shortcomings, we propose a 3D medical image segmentation model named TPM-SAM. In our model, we design a twinchannel image encoder to simultaneously capture the space correlation and depth correlation of 3D slices through a multi-head attention mechanism and improved adapters. Furthermore, we design a prompt encoding generator, which divides the volumetric image into small blocks and better captures the local detail information. In addition, we introduce a multi-layer aggregation decoder by employing U-Net with multi-level skip connection to solve the blurring of boundaries in processing 3D images. Finally, we experimented and evaluated our model on KiTS21 and LiTS17 datasets to compare with other baseline models. © 2024 IEEE.

关键词： Image segmentation

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Overview of Task Offloading of Wireless Sensor Network in Edge Computing Environment 6

Overview of Task Offloading of Wireless Sensor Network in Ed...

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6th IEEE International Conference on Electronic information and Communication Technology, ICEICT 2023

作者： Hou, XiQiang Hu, YiFan Wang, Fuqiang School of Information and Control Engineering Qingdao University of Technology Qingdao China Shandong Computer Science Center Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350399059

This review article introduces the concepts, server architecture and application scenarios of Mobile Edge Computing (MEC) and Wireless Sensor Network (WSN). By differentiating between rechargeable and non-rechargeable systems, it presents the research status of task offloading in WSN in an edge computing environment. Various strategies are analyzed and compared in terms of their characteristics and application domains. The existing methods can reduce offloading latency and energy consumption, but still face some issues. Based on this, the research hotspots of edge computing task offloading for WSN are summarized, and its research and development trends are pointed out. © 2023 IEEE.

关键词： Energy harvesting

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