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A DDPG Hybrid with Graph Scheme for Resource Management in Digital Twin-assisted Biomedical Cyber-Physical Systems

IEEE Transactions on Network and Service Management 2025年

作者： Xu, Yi-Han Ding, Rong-Xing Xu, Xiao-Ren Zhou, Ding Zhou, Wen Qinghai University School of Computer Technology and Application Xining810016 China Nanjing Forestry University College of Information Science and Technology and Artificial Intelligence Nanjing210037 China The University of New South Wales School of Electrical Engineering and Telecommunications Sydney2052 Australia Universiti Malaya Faculty of Engineering Kuala Lumpur50603 Malaysia Guangzhou Maritime University College of Low Altitude Equipment and Intelligent Control Guangzhou510725 China

In this paper, we focus on the resource optimization issue in Wireless Body Area Networks (WBANs) section of a novel Biomedical Cyber Physical System (BM-CPS) in which the Digital Twin (DT) technique is adopted concurrently. We propose a scenario where multiple physiological sensor nodes continuously monitor the electrophysiology signals from patients and connect to a Cyber Managing Center (Cyb-MC) in a wireless way to transmit the physiological indices to the paramedic reliably. Specifically, to optimize the energy efficiency of physiological sensors while ensuring the reliability of emergency-critical electrophysiology signals transmissions and modeling the uncertain stochastic environments, the optimization issue is transformed into a Markov Decision Process (MDP) in terms of joint transmission mode, relay assignment, transmission power and time slot scheduling consideration. Subsequently, we propose a Random Graph-enabled Deep Deterministic Policy Gradient (RG-DDPG) scheme to tackle the challenge while releasing computing complexity. In addition, as an innovative paradigm for reshaping cyberspace applications, the DT of WBAN is created to capture the time-varying resource status, where virtualized and intelligent resource management can be performed uniformly. Finally, extensive simulation studies verified the advancement of the proposed scheme and demonstrated that the DT can mirror the topology, predict the behavior, and administrate the resources of the WBANs. © 2004-2012 IEEE.

关键词： Resource allocation

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NTIRE 2023 Challenge on Light Field Image Super-Resolution: dataset, Methods and Results

NTIRE 2023 Challenge on Light Field Image Super-Resolution: ...

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Wang, Yingqian Wang, Longguang Liang, Zhengyu Yang, Jungang Timofte, Radu Guo, Yulan Jin, Kai Wei, Zeqiang Yang, Angulia Guo, Sha Gao, Mingzhi Zhou, Xiuzhuang Van Duong, Vinh Huu, Thuc Nguyen Yim, Jonghoon Jeon, Byeungwoo Liu, Yutong Cheng, Zhen Xiao, Zeyu Xu, Ruikang Xiong, Zhiwei Liu, Gaosheng Jin, Manchang Yue, Huanjing Yang, Jingyu Gao, Chen Zhang, Shuo Chang, Song Lin, Youfang Chao, Wentao Wang, Xuechun Wang, Guanghui Duan, Fuqing Xia, Wang Wang, Yan Xia, Peiqi Wang, Shunzhou Lu, Yao Cong, Ruixuan Sheng, Hao Yang, Da Chen, Rongshan Wang, Sizhe Cui, Zhenglong Chen, Yilei Lu, Yongjie Cai, Dongjun An, Ping Salem, Ahmed Ibrahem, Hatem Yagoub, Bilel Kang, Hyun-Soo Zeng, Zekai Wu, Heng National University of Defense Technology China Aviation University of Air Force China University of Würzburg Germany Eth Zürich Switzerland Sun Yat-sen University The Shenzhen Campus of Sun Yat-sen University China Bigo Technology Pte. Ltd. Singapore Smart Medical Innovation Lab Beijing University of Posts and Telecommunications China Global Explorer Ltd. Suzhou China National Engineering Research Center of Visual Technology School of Computer Science Peking University China School of Artificial Intelligence Beijing University of Posts and Telecommunications China Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of University of Science and Technology of China China School of Electrical and Information Engineering Tianjin University China Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University China Beijing Normal University China Toronto Metropolitan University Canada Beijing Institute of Technology China Shenzhen MSU-BIT University China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beihang Hangzhou Innovation Institute Yuhang China Faculty of Applied Sciences Macao Polytechnic University China School of Communication and Information Engineering Shanghai University China School of Information and Communication Engineering Chungbuk National University Korea Republic of Guangdong University of Technology China

ISBN: (纸本)9798350302493

In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4. This challenge develops a new LF dataset called NTIRE-2023 for validation and test, and provides a toolbox called BasicLFSR to facilitate model development. Compared with single image SR, the major challenge of LF image SR lies in how to exploit complementary angular information from plenty of views with varying disparities. In total, 148 participants have registered the challenge, and 11 teams have successfully submitted results with PSNR scores higher than the baseline method LF-InterNet [1]. These newly developed methods have set new state-of-the-art in LF image SR, e.g., the winning method achieves around 1 dB PSNR improvement over the existing state-of-the-art method DistgSSR [2]. We report the solutions proposed by the participants, and summarize their common trends and useful tricks. We hope this challenge can stimulate future research and inspire new ideas in LF image SR. © 2023 IEEE.

关键词： Image enhancement

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AI-Enabled Muscle Oxygen Sensors for Personalized Exercise Intensity Monitoring and Recovery Analysis 5

AI-Enabled Muscle Oxygen Sensors for Personalized Exercise I...

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5th IEEE International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Balachander, K. Balasubadra, K. Tidke, Bharat Sree Southry, S. Meenakshi, B. Murugan, S. R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Puduvoyal Tamil Nadu Chennai India R.M.D. Engineering College Department of Information Technology Kavaraipettai Tamil Nadu Chennai India University Nagpur Campus Pune India Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem India Sri Sairam Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350356236

Through the integration of artificial intelligence (AI) with muscle oxygen sensors, this work presents a unique way to exercise intensity monitoring and recovery analysis. Subjective assessments or heavy equipment are the backbone of the traditional approaches to evaluating exercise intensity and recovery. Using the latest developments in AI and wearable sensor technology, our system offers real-time personalized feedback to athletes and fitness enthusiasts. Our Muscle oxygen sensors were vigorously tested to ensure their precision and performance. Wearing the sensors, participants varied their workout regimen while the sensors sent data on the participants' muscle oxygen levels to an AI program. According to the results, the algorithm could correctly determine workout intensity levels and establish individual recuperation timeframes. Moreover, the AI system learns the user's physiological traits and can personalize recommendations and training programs for everyone. Our approach may help improve athletic performance, avoid overtraining injuries, and maximize fitness results by giving practical advice on recovering from exercises at the right intensity. © 2025 IEEE.

关键词： Sensory perception

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Accelerating Wireless Federated Learning via Nesterov's Momentum and Distributed Principle Component Analysis

arXiv

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arXiv 2023年

作者： Dong, Yanjie Wang, Luya Chi, Yuanfang Wang, Jia Zhang, Haijun Yu, Fei Richard Leung, Victor C.M. Hu, Xiping The Faculty of Engineering Shenzhen MSU-BIT University Shenzhen China The College of Computer Science and Software Engineering Shenzhen University Shenzhen China Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Institute of Artificial Intelligence University of Science and Technology Beijing Beijing China The Department of Systems and Computer Engineering Carleton University Ottawa Canada The Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

A wireless federated learning system is investigated by allowing a server and workers to exchange uncoded information via orthogonal wireless channels. Since the workers frequently upload local gradients to the server via bandwidth-limited channels, the uplink transmission from the workers to the server becomes a communication bottleneck. Therefore, a one-shot distributed principle component analysis (PCA) is leveraged to reduce the dimension of uploaded gradients such that the communication bottleneck is relieved. A PCA-based wireless federated learning (PCA-WFL) algorithm and its accelerated version (i.e., PCA-AWFL) are proposed based on the low-dimensional gradients and the Nesterov's momentum. For the non-convex loss functions, a finite-time analysis is performed to quantify the impacts of system hyper-parameters on the convergence of the PCA-WFL and PCA-AWFL algorithms. The PCA-AWFL algorithm is theoretically certified to converge faster than the PCA-WFL algorithm. Besides, the convergence rates of PCA-WFL and PCA-AWFL algorithms quantitatively reveal the linear speedup with respect to the number of workers over the vanilla gradient descent algorithm. Numerical results are used to demonstrate the improved convergence rates of the proposed PCA-WFL and PCA-AWFL algorithms over the benchmarks. © 2023, CC BY.

关键词： Momentum

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A memetic algorithm based on edge-state learning for max-cut

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Expert Systems with Applications 2022年 209卷

作者： Zeng, Zhi-zhong lü, Zhi-peng Yu, Xin-guo Wu, Qing-hua Wang, Yang Zhou, Zhou National Engineering Research Center for E-Learning Faculty of Artificial Intelligence in Education Central China Normal University Wuhan430079 China School of Computer Science and Technology Huazhong University of Science and Technology 430074 China School of Management Huazhong University of Science and Technology 430074 China School of Management Northwestern Polytechnical University Xi'an710072 China

Max-cut is one of the most classic NP-hard combinatorial optimization problems. The symmetry nature of it leads to special difficulty in extracting meaningful configuration information for learning;none of the state-of-the-art algorithms has employed any learning operators. This paper proposes an original learning method for max-cut, namely post-flip edge-state learning (PF-ESL). Different from previous algorithms, PF-ESL regards edge-states (cut or not cut) rather than vertex-positions as the critical information of a configuration, and extracts their statistics over a population for learning. It is based on following observations. 1) Edges are the only factors considered by the objective function. 2) Edge-states keep invariant when rotating a local configuration to its symmetry position, but vertex-positions do not. These suggest that edge-states contain more meaningful information about a configuration than vertex-positions do. It is impossible to set the state of an edge without influencing some other edges’ states due to their dependencies. Therefore, instead of setting edge-states directly, PF-ESL samples the flips on vertices. Flips on vertices are sampled according to their capacities in increasing the similarity on edge-states between the given solution and a population. PF-ESL is employed in an EDA (Estimation of Distribution Algorithm) perturbation operator and a path-relinking operator. Experimental results show that our algorithm is competitive, and show that edge-state learning is value-added for both the two operators. The main contributions of this paper are as follows. Firstly, previous state-of-the-art evolutionary algorithms for max-cut focus on vertex positions in their evolutionary operation, this paper proposes a new and more reasonable perspective suggesting that edge-states are the critical information of divided graphs rather than vertex positions, and introduces a novel method to measure and utilize their similarities based on it. Such a pe

关键词： Population statistics

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TEVA: Training-Efficient and Verifiable Aggregation for Federated Learning for Consumer Electronics in Industry 5.0

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IEEE Transactions on Consumer Electronics 2024年

作者： Xia, Yuanjun Liu, Yining Chen, Jingxue Liang, Yangfan Khan, Fazlullah Alturki, Ryan Wang, Xiaopei Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software School of Computer and Information Security Guilin541004 China Wenzhou University of Technology School of Data Science and Artificial Intelligence Wenzhou325027 China University of Technology School of Mathematical and Physical Sciences SydneyNSW2007 Australia Jiaxing University Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems The Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province The Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing314001 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Zhejiang Ningbo315104 China Umm AI-Qura University Makkah Department of Software Engineering College of Computing Saudi Arabia Riverside Department of Computer Science and Engineering University of California CA92521 United States

Federated learning (FL) has been widely used for privacy-preserving model updates in Industry 5.0, facilitated by 6G networks. Despite FL's privacy-preserving advantages, it remains vulnerable to attacks where adversaries can infer private data from local models or manipulate the central server (CS) to deliver falsified global models. Current privacy-preserving approaches, primarily based on the FedAvg algorithm, fail to optimize training efficiency for non-independent and identically distributed (non-IID) data. This article proposes training-efficient and verifiable aggregation (TEVA) for FL to resolve these issues. This scheme combines threshold Paillier homomorphic encryption (TPHE), verifiable aggregation, and an optimized double momentum update mechanism (OdMum). TEVA not only leverages TPHE to protect the privacy of local models but also ensures the integrity of the global model through a verifiable aggregation mechanism. Additionally, TEVA integrates the OdMum algorithm to effectively address the challenges posed by non-IID data, promoting rapid model convergence and significantly enhancing overall training efficiency. Security analysis indicates that TEVA meets the requirements for privacy protection. Extensive experimental results demonstrate that TEVA can accelerate model convergence while incurring lower computational and communication overheads. © 1975-2011 IEEE.

关键词： Differential privacy

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Bipartite Flocking Control for Multi-Agent Systems with Switching Topologies and Time Delays Under Coopetition Interactions

Bipartite Flocking Control for Multi-Agent Systems with Swit...

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IEEE Conference on Decision and Control

作者： Zhuangzhuang Ma Bowen Li Jinliang Shao Yuhua Cheng Wei Xing Zheng School of Automation Engineering University of Electronic Science and Technology of China Chengdu China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing China School of Computer Data and Mathematical Sciences Western Sydney University Sydney NSW Australia

This paper investigates the bipartite flocking behavior of multi-agent systems with coopetition interactions, where communications between agents are described by signed digraphs. The scenario with switching topologies due to the movement of agents, and time delays caused by the limited data transmission capability, is considered comprehensively. Nonlinear weight functions are designed to describe the relationship between the communication distance of agents and the coopetition degree in real biological networks. A distributed update rule based on the neighbors' information and the designed weight functions is proposed. By the aid of the graph theory and sub-stochastic matrix properties, the effectiveness of the proposed update rule is proved theoretically, and the algebraic conditions for achieving the bipartite flocking behavior are obtained. Finally, the theoretical results are verified by numerical simulations.

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Method for creating privacy-preserving information using Probabilistic Latent Semantic Analysis

Method for creating privacy-preserving information using Pro...

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IEEE International Conference on Big data

作者： Yuki Sugawara Eiichi Sakurai Yoichi Motomura Yukihiko Okada Kai Tanabe Akiko Tsukao Shinya Kuno National Institute of Advanced Industrial Science and Technology Koto Japan University of Tsukuba Tsukuba Japan Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology Koto Japan Institute of Systems and Information Engineering Center for Artificial Intelligence Research University of Tsukuba Tsukuba Japan Faculty of Health and Sport Sciences R&D Center for Smart Wellness City Policies University of Tsukuba Tsukuba Japan Tsukuba Wellness Research Co. Ltd Kashiwa Japan

In recent years, rapid digitization and technological advances have enabled governments and companies to utilize various types of personal information, including environment, medical, and lifestyle. Privacy protection is an important issue in the utilization of personal information, and anonymization methods such as k-anonymization and 1-diversity have been proposed in the past. However, to utilize data effectively, it is also important not to lose the usefulness of the information. Privacy protection and preserving the usefulness of information are in a trade-off relationship, and balancing these two aspects has been a difficult problem. Recently, microaggregation using clustering algorithms has been attracting attention as an anonymization method that ensures the usefulness of the data while protecting privacy. In this study, we proposed a new microaggregation method based on iterative processing using probabilistic latent semantic analysis and evaluated the proposed method based on the use case. The use case is a classification of social isolation and an analysis of the relationship with physical frailty risk using the classified groups. Compared to microaggregation using quasi-identifiers and microaggregation based on a conventional clustering algorithm, the proposed method was able to find social isolation characteristics obtained in the raw data with higher accuracy. In addition, in a more detailed analysis focusing on a population with certain characteristics, the proposed method was able to replicate the analysis results obtained using the raw data and to make appropriate assessments of the health risks.

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Machine Learning as the Shield: Mitigating ARP Poisoning Attacks in Software Defined Networks

Machine Learning as the Shield: Mitigating ARP Poisoning Att...

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Information technology (ACIT)

作者： Rabee Alquran Mahmoud Al Jamal Mohammad Aljaidi Mohammad AL-Jamal Bashar Khassawneh Ayoub Alsarhan Omar Alidmat Ghassan Samara Sattam Almatarneh Department of Information Technology Faculty of Prince Al-Hussien bin Abdullah for IT The Hashemite University Zarqa Jordan Department of Cybersecurity Irbid National University Irbid Jordan Department of Computer Science Zarqa University Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Statistics Yarmouk University Irbid Jordan Department of Computer Science Faculty of Computer Science and Informatics Amman Arab University Amman Jordan Department of Computer Science Zarqa University Zarqa Jordan Department of Artificial Intelligence Zarqa University Zarqa Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Software-defined networking (SDN) revolutionizes network management by centralizing control, but this centralization also introduces notable vulnerabilities, especially Address Resolution Protocol (ARP) poisoning attacks. These attacks exploit the ARP protocol, redirecting network traffic to malicious destinations. This research provides a deep dive into the nuances of these threats within the SDN context, outlining their mechanisms and potential countermeasures. A significant highlight of this paper is exploring Machine Learning (ML) as a pivotal mitigation approach. By analyzing and distinguishing between benign and malicious traffic patterns in real-time, ML models present a robust solution to enhance SDN security. The findings of our paper underscore the criticality of integrating these advanced predictive techniques in SDNs, paving the way for a safer and more resilient networking future.

关键词： Protocols Prevention and mitigation Machine learning Telecommunication traffic Traffic control Real-time systems Threat assessment Robustness Security Software defined networking

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A Successful Spam Detection Technique for Industrial IoT Devices based on Machine Learning Techniques

A Successful Spam Detection Technique for Industrial IoT Dev...

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Applied artificial intelligence and Computing (ICAAIC), International Conference on

作者： B N Manjunatha Madhavarapu Chandan Sreekanth Kottu Sherin Rappai Hema Priya K Kishan Singh Rawat Swagata Sarkar Department of Computer Science and Engineering R L Jalappa Institute of Technology Bangalore Karnataka India Department of Computer science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Information Technology St Peter's Engineering College Medchal-Malkajgiri Telangana India Department of Computer Science Kristu Jayanti College Bengaluru Karnataka India Department of Computer Science and Engineering Panimalar Institute of Technology Chennai Tamil Nadu India Department of Civil Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Artificial Intelligence and Data Science Sri Sairam Engineering College Chennai Tamil Nadu India

This research study discusses about the detection of botnets that use the Internet Relay Chat protocol. This application protocol, popularly used by programs in large chat rooms is the predominant protocol among botnet Command and Control Servers, although its use has declined in recent years. This study analyzed the use of Machine Learning techniques for the detection of Botnets based on the IRC protocol in a network, through the analysis of bidirectional flows. Similar research studies are becoming increasingly relevant to the use of networked devices that can be integrated into botnets by further increasing the power of their attacks. As a result, it is possible to conclude that this research study accomplished its objectives by investigating the application and efficiency of different algorithms, as well as the usage of characteristics employed in previous studies and explores new possibilities. According to the results, the tested classifiers were effective in completing their tasks and achieving high levels of accuracy. The best results were obtained by the Tree-based approaches., followed by the Support Vector Machine methods, and finally by the Naive Bayes method.

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