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Joint Scheduling, Computing, and Load Balancing for Time Sensitive Traffic in SDN-Enabled Space-Air-Ground Integrated 6 G Networks: A Federated Reinforcement Learning Approach

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IEEE Transactions on Mobile Computing 2025年

作者： Sun, Haitong Zhang, Haijun Ma, Hui Leung, Victor C.M Hebei Key Laboratory of Space-Air-Ground Intelligent Communication Beijing100083 China University of Science and Technology Beijing Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Beijing100083 China Shenzhen University Department of Computer Science and Software Engineering Shenzhen518061 China University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Low Earth Orbit (LEO) constellations and Unmanned Aerial Vehicle (UAV) networks enable wide coverage for the sixth generation (6G) mobile communication. However, it is a challenge to achieve high scheduling success rate, ultra-low latency, and efficient load balance in the Space-Air-Ground Integrated 6G Network (SAGGIN). This paper addresses the following issue: How to effectively and orderly transmit time-sensitive traffic in SAGGIN under strict deadlines, limited computational ability, and restrained link capacity? Specifically, this paper uses software-Defined Networking (SDN) and designs a joint optimization method to enhance the traffic transmission ability of SAGGIN. Considering response time, computing cost, and link capacity in SAGGIN, the scheduling, computing, and load balance issues are modeled as a multi-objective optimization problem that minimizes the worst-case response time and computing cost of data frames while maximizing the network flow. Then, this paper leverages a Federated Reinforcement Learning (FRL) scheme to solve the problem. Results show that the FRL could achieve great scheduling, computing, and load balance performance. Specifically, our method can successfully schedule 80% of the traffic at most when the current network load is around 90%. Furthermore, the computational delay could reduce around 50%. © 2002-2012 IEEE.

关键词： Reinforcement learning

Hybrid RAG-empowered Multi-modal LLM for Secure Data Management in Internet of Medical Things: A Diffusion-based Contract Approach

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arXiv 2024年

作者： Su, Cheng Wen, Jinbo Kang, Jiawen Wang, Yonghua Su, Yuanjia Pan, Hudan Zhong, Zishao Hossain, M. Shamim School of Automation The GuangdongHongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangdong University of Technology Guangzhou510006 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Automation The Guangdong Basic Research Center of Excellence for Ecological Security and Green Development Key Laboratory for City Cluster Environmental Safety Green Development The Ministry of Education Guangdong University of Technology Guangzhou510006 China School of Automation The Key Laboratory of Intelligent Detection and IoT in Manufacturing Ministry of Education Guangdong University of Technology Guangzhou510006 China School of Automation Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangdong University of Technology Guangzhou510006 China Guangzhou University of Traditional Chinese Medicine State Key Laboratory of Traditional Chinese Medicine Syndrome The Second Affiliated Hospital of Guangzhou University of Chinese Medicine Chinese Medicine Guangdong Laboratory Hengqin 519000 China Second Affiliated Hospital of Guangzhou University of Chinese Medicine Chinese Medicine Guangdong Laboratory Hengqin 519000 China Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh12372 Saudi Arabia

Secure data management and effective data sharing have become paramount in the rapidly evolving healthcare landscape, especially with the growing integration of the Internet of Medical Things (IoMT). The rise of generative artificial intelligence has further elevated Multi-modal Large Language Models (MLLMs) as essential tools for managing and optimizing healthcare data in IoMT. MLLMs can support multi-modal inputs and generate diverse types of content by leveraging large-scale training on vast amounts of multi-modal data. However, critical challenges persist in developing medical MLLMs, including security and freshness issues of healthcare data, affecting the output quality of MLLMs. To this end, in this paper, we propose a hybrid Retrieval-Augmented Generation (RAG)-empowered medical MLLM framework for healthcare data management. This framework leverages a hierarchical cross-chain architecture to facilitate secure data training. Moreover, it enhances the output quality of MLLMs through hybrid RAG, which employs multi-modal metrics to filter various unimodal RAG results and incorporates these retrieval results as additional inputs to MLLMs. Additionally, we employ age of information to indirectly evaluate the data freshness impact of MLLMs and utilize contract theory to incentivize healthcare data holders to share their fresh data, mitigating information asymmetry during data sharing. Finally, we utilize a generative diffusion model-based deep reinforcement learning algorithm to identify the optimal contract for efficient data sharing. Numerical results demonstrate the effectiveness of the proposed schemes, which achieve secure and efficient healthcare data management. Copyright © 2024, The Authors. All rights reserved.

关键词： Information management

ReactFace: Online Multiple Appropriate Facial Reaction Generation in Dyadic Interactions

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arXiv 2023年

作者： Luo, Cheng Song, Siyang Xie, Weicheng Spitale, Micol Ge, Zongyuan Shen, Linlin Gunes, Hatice Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China Department of Computer Science University of Nottingham Ningbo China Ningbo315100 China Computer Sciences University of Exeter ExeterEX4 4PY United Kingdom Department of Computer Science and Technology University of Cambridge CambridgeCB3 0FT United Kingdom Airdoc-Monash Research Centre Monash University Faculty of IT Monash University Melbourne Australia

In dyadic interaction, predicting the listener’s facial reactions is challenging as different reactions could be appropriate in response to the same speaker’s behaviour. Previous approaches predominantly treated this task as an interpolation or fitting problem, emphasizing deterministic outcomes but ignoring the diversity and uncertainty of human facial reactions. Furthermore, these methods often failed to model short-range and long-range dependencies within the interaction context, leading to issues in the synchrony and appropriateness of the generated facial reactions. To address these limitations, this paper reformulates the task as an extrapolation or prediction problem, and proposes an novel framework (called ReactFace) to generate multiple different but appropriate facial reactions from a speaker behaviour rather than merely replicating the corresponding listener facial behaviours. Our ReactFace generates multiple different but appropriate photo-realistic human facial reactions by: (i) learning an appropriate facial reaction distribution representing multiple different but appropriate facial reactions;and (ii) synchronizing the generated facial reactions with the speaker verbal and non-verbal behaviours at each time stamp, resulting in realistic 2D facial reaction sequences. Experimental results demonstrate the effectiveness of our approach in generating multiple diverse, synchronized, and appropriate facial reactions from each speaker’s behaviour. The quality of the generated facial reactions is intimately tied to the speaker’s speech and facial expressions, achieved through our novel speaker-listener interaction modules. Our code is made publicly available at https://***/lingjivoo/ReactFace. Copyright © 2023, The Authors. All rights reserved.

关键词： Extrapolation

RETRACTED ARTICLE: Automatically human action recognition (HAR) with view variation from skeleton means of adaptive transformer network

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Soft Computing 2023年第2期28卷 485-485页

作者： Mehmood, Faisal Chen, Enqing Abbas, Touqeer Akbar, Muhammad Azeem Khan, Arif Ali School of Electrical and Information Engineering Zhengzhou University Zhengzhou China Henan Xintong Intelligent IOT Co. Ltd. No.1-303 Intersection of Ruyun Road and Meihe Road Zhengzhou China Department of Computer Science and Technology Beijing University of Chemical Technology Beijing China Department of Software Engineering LUT University Lahti Finland M3S Empirical Software Engineering Research Unit University of Oulu Oulu Finland

A TPAA-SBFE Based Partitioning Algorithm for Structural Viscoelastic Dynamic Analysis With Cyclic Symmetry

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International Journal for Numerical Methods in engineering 2025年第11期126卷

作者： Yang Yu Chongshuai Wang Ean Tat Ooi Sundararajan Natarajan Haitian Yang State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment Department of Engineering Mechanics International Research Centre for Computational Mechanics Dalian University of Technology Dalian China College of Civil Engineering Jilin Jianzhu University Changchun China State Key Laboratory of Intelligent Power Distribution Equipment and System School of Electrical Engineering Hebei University of Technology Tianjin China Institute of Innovation Science and Sustainability Federation University Australia Ballarat Australia Integrated Modelling and Simulation Lab Department of Mechanical Engineering Indian Institute of Technology Chennai India

An innovative and efficient reduced order algorithm is proposed for the dynamic analysis of 2-D and 3-D viscoelastic structures featuring cyclic symmetry, which is applicable to complete cyclically symmetric structures with or without common nodes, as well as incomplete cyclically symmetric structures. The proposed algorithm is developed on a recursive platform based on TPAA-SBFEM and inherits all the advantages of TPAA-SBFEM. For complete cyclically symmetric structures with or without common nodes, the global stiffness and mass matrices are proved to be block-circulant and can be generated via a cyclically symmetric part instead of the whole domain. And then a recursive partitioning algorithm is rendered, such that the solution scale and computational cost can be efficiently reduced via solving a series of independent problems with small scales. For incomplete cyclically symmetric structures, the complete cyclic symmetry and Woodbury formula are utilized to reduce computational expense. Four numerical examples are provided to elucidate the effectiveness and efficiency of the proposed approach with satisfactory results.

关键词： cyclically symmetric dynamic analysis partitioning algorithm scaled boundary finite element method (SBFEM) viscoelasticity Woodbury formula

Towards explainable multi-party learning: A contrastive knowledge sharing framework

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arXiv 2021年

作者： Gao, Yuan Li, Jiawei Gong, Maoguo Xie, Yu Qin, A.K. The School of Electronic Engineering Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education Xidian University Shaanxi Province Xi'an710071 China The Key Laboratory of Computational Intelligence Chinese Information Processing Ministry of Education Shanxi University Taiyuan030006 China The department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia

Multi-party learning provides solutions for training joint models with decentralized data under legal and practical constraints. However, traditional multi-party learning approaches are confronted with obstacles such as system heterogeneity, statistical heterogeneity, and incentive design. How to deal with these challenges and further improve the efficiency and performance of multi-party learning has become an urgent problem to be solved. In this paper, we propose a novel contrastive multi-party learning framework for knowledge refinement and sharing with an accountable incentive mechanism. Since the existing parameter averaging method is contradictory to the learning paradigm of neural networks, we simulate the process of human cognition and communication, and analogize multiparty learning as a many-to-one knowledge sharing problem. The approach is capable of integrating the acquired explicit knowledge of each client in a transparent manner without privacy disclosure, and it reduces the dependence on data distribution and communication environments. The proposed scheme achieves significant improvement in model performance in a variety of scenarios, as we demonstrated through experiments on several real-world datasets. Copyright © 2021, The Authors. All rights reserved.

关键词： Knowledge management

Enhancing Device-Free Gesture Recognition Capability of Mobile Communication Signals

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IEEE Transactions on Communications 2025年

作者： Wu, Jingmiao He, Weijie Sun, Kai Huang, Wei Zhang, Haijun Leung, Victor C. M. Inner Mongolia University School of Electronic Information Engineering Inner Mongolia Key Laboratory of Intelligent Communication and Sensing and Signal Processing Hohhot010021 China University of Science and Technology at Beijing Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Beijing100083 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Device-free gesture recognition using mobile communication signals is a convenient and efficient technology with broad application prospects in smart homes and human-computer interaction. It utilizes the effect of gestures on surrounding signals to achieve gesture recognition. The cell-specific reference signals (CRS) information can be used to achieve the task in close-range training scenarios. However, when gestures are performed at long-range or in non-training scenarios, the recognition performance will significantly degrade. To enhance device-free gesture recognition capability in arbitrary scenarios, we propose the signal quality enhancement algorithm and the gesture spectrogram construction method to solve this problem. Specifically, we superimpose the CRS information from multiple carriers to improve the gesture signal-to-noise ratio and increase the gesture sensing range. Then, we extract the gesture dynamic components from the CRS information and construct gesture spectrograms to represent scenario-independent gesture motion patterns. Using the gesture spectrogram features, we design a deep network to accomplish the gesture recognition task. We built a prototype system on a software-defined radio platform. Experimental results show that our proposed method can effectively increase the gesture sensing range from 30m² to 228m² and achieve an average recognition accuracy of 82.5% for five types of gestures in arbitrary scenarios. © 1972-2012 IEEE.

关键词： Gesture recognition

Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges

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arXiv 2023年

作者： Jha, Debesh Sharma, Vanshali Banik, Debapriya Bhattacharya, Debayan Roy, Kaushiki Hicks, Steven A. Tomar, Nikhil Kumar Thambawita, Vajira Krenzer, Adrian Ji, Ge-Peng Poudel, Sahadev Batchkala, George Alam, Saruar Ahmed, Awadelrahman M.A. Trinh, Quoc-Huy Khan, Zeshan Nguyen, Tien-Phat Shrestha, Shruti Nathan, Sabari Gwak, Jeonghwan Jha, Ritika K. Zhang, Zheyuan Schlaefer, Alexander Bhattacharjee, Debotosh Bhuyan, M.K. Das, Pradip K. Fan, Deng-Ping Parasa, Sravanthi Ali, Sharib Riegler, Michael A. Halvorsen, Pål de Lange, Thomas Bagci, Ulas Machine & Hybrid Intelligence Lab Department of Radiology Northwestern University Chicago United States SimulaMet Oslo Norway Oslo Metropolitan University Oslo Norway Jadavpur University Kolkata India Indian Institute of Technology Guwahati India Institute of Medical Technology and Intelligent Systems Technische Universität Hamburg Germany University of Oslo Norway Julius-Maximilian University of Würzburg Germany Faculty of Information Technology University of Science VNU-HCM Viet Nam Department of IT Convergence Engineering Gachon University Seongnam13120 Korea Republic of School of Computing University of Leeds LeedsLS2 9JT United Kingdom College of Engineering Australian National University Canberra Australia Department of Engineering Science University of Oxford Oxford United Kingdom National University of Computer and Emerging Sciences Karachi Campus Pakistan Swedish Medical Center Seattle United States Kathmandu Nepal Couger Inc Tokyo Japan Department of Software Korea National University of Transportation Chungju Korea Republic of University of Bergen Bergen Norway Department of Medicine and Emergencies Mölndal Sahlgrenska University Hospital Region Västra Götaland Sweden Department of Molecular and Clinical Medicin Sahlgrenska Academy University of Gothenburg Sweden ETH Zurich Zurich Switzerland

Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challenging due to various factors such as variation of skills and experience among the endoscopists, lack of attentiveness, and fatigue leading to a high polyp miss-rate. Therefore, there is a need for an automated system that can flag missed polyps during the examination and improve patient care. Deep learning has emerged as a promising solution to this challenge as it can assist endoscopists in detecting and classifying overlooked polyps and abnormalities in real time, improving the accuracy of diagnosis and enhancing treatment. In addition to the algorithm's accuracy, transparency and interpretability are crucial to explaining the whys and hows of the algorithm's prediction. Further, conclusions based on incorrect decisions may be fatal, especially in medicine. Despite these pitfalls, most algorithms are developed in private data, closed source, or proprietary software, and methods lack reproducibility. Therefore, to promote the development of efficient and transparent methods, we have organized the "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image Segmentation (MedAI 2021)" competitions. The Medico 2020 challenge received submissions from 17 teams, while the MedAI 2021 challenge also gathered submissions from another 17 distinct teams in the following year. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic. Our analysis revealed that the participants improved dice coefficient metrics from 0.8607 in 2020 to 0.8993 in 2021 despite adding diverse and challenging frames (containing irregular, smaller, sessile, or flat polyps), which are frequently missed during a

关键词： Computer aided diagnosis

Corrigendum to “LightSOD: Towards lightweight and efficient network for salient object detection” [J. Comput. Vis. Imag. Underst. 249 (2024) 104148]

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Computer Vision and Image Understanding 2025年 252卷

作者： Thien-Thu Ngo Hoang Ngoc Tran Md. Delowar Hossain Eui-Nam Huh School of Information Technology Center for Applied Intelligent Systems Research Halmstad University Kristian IV:s väg 3 Halmstad 30118 Halland Sweden Department Computer Science and Engineering KyungHee University Global Campus Deogyeong-daero Yongin-si 17104 Gyeonggi-do Republic of Korea Department of Software Engineering FPT University 600 Nguyen Van Cu Street Ninh Kieu 94000 Can Tho Viet Nam Department of Computer Science and Engineering Hajee Mohammad Danesh Science & Technology University Basherhat Dinajpur 5200 Rangpur Division Bangladesh