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arXiv 2023年

作者： Yang, Yunchao Zhou, Yipeng Hu, Miao Wu, Di Sheng, Quan Z. School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Computing Faculty of Science and Engineering Macquarie University Sydney Australia

Federated learning (FL) is a prospective distributed machine learning framework that can preserve data privacy. In particular, cross-silo FL can complete model training by making isolated data islands of different organizations collaborate with a parameter server (PS) via exchanging model parameters for multiple communication rounds. In cross-silo FL, an incentive mechanism is indispensable for motivating data owners to contribute their models to FL training. However, how to allocate the reward budget among different rounds is an essential but complicated problem largely overlooked by existing works. The challenge of this problem lies in the opaque feedback between reward budget allocation and model utility improvement of FL, making the optimal reward budget allocation complicated. To address this problem, we design an online reward budget allocation algorithm using Bayesian optimization named BARA (Budget Allocation for Reverse Auction). Specifically, BARA can model the complicated relationship between reward budget allocation and final model accuracy in FL based on historical training records so that the reward budget allocated to each communication round is dynamically optimized so as to maximize the final model utility. We further incorporate the BARA algorithm into reverse auction-based incentive mechanisms to illustrate its effectiveness. Extensive experiments are conducted on real datasets to demonstrate that BARA significantly outperforms competitive baselines by improving model utility with the same amount of reward budget. Copyright © 2023, The Authors. All rights reserved.

关键词： data privacy

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SFDA-rPPG: Source-Free Domain Adaptive Remote Physiological Measurement with Spatio-Temporal Consistency

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arXiv 2024年

作者： Xie, Yiping Yu, Zitong Wu, Bingjie Xie, Weicheng Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Singapore

Remote Photoplethysmography (rPPG) is a non-contact method that uses facial video to predict changes in blood volume, enabling physiological metrics measurement. Traditional rPPG models often struggle with poor generalization capacity in unseen domains. Current solutions to this problem is to improve its generalization in the target domain through Domain Generalization (DG) or Domain Adaptation (DA). However, both traditional methods require access to both source domain data and target domain data, which cannot be implemented in scenarios with limited access to source data, and another issue is the privacy of accessing source domain data. In this paper, we propose the first Source-free Domain Adaptation benchmark for rPPG measurement (SFDA-rPPG), which overcomes these limitations by enabling effective domain adaptation without access to source domain data. Our framework incorporates a Three-Branch Spatio-Temporal Consistency Network (TSTC-Net) to enhance feature consistency across domains. Furthermore, we propose a new rPPG distribution alignment loss based on the Frequency-domain Wasserstein Distance (FWD), which leverages optimal transport to align power spectrum distributions across domains effectively and further enforces the alignment of the three branches. Extensive cross-domain experiments and ablation studies demonstrate the effectiveness of our proposed method in source-free domain adaptation settings. Our findings highlight the significant contribution of the proposed FWD loss for distributional alignment, providing a valuable reference for future research and applications. The source code is available at https://***/XieYiping66/SFDA-rPPG. Copyright © 2024, The Authors. All rights reserved.

关键词： Photoplethysmography

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Improved U-Net model for remote sensing image classification method based on distributed storage

Improved U-Net model for remote sensing image classification...

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作者： Jing, Weipeng Zhang, Mingwei Tian, Dongxue College of Information and Computer Engineering Northeast Forestry University HarbinHeilongjiang China Key Laboratory of Forestry Data Science and Cloud Computing of State Forestry Administration HarbinHeilongjiang China

Aiming at the low efficiency of traditional methods for the management and classification of massive remote sensing image data, a mass remote sensing image classification method based on distributed storage is proposed. The aim is to obtain near real-time image classification in mobile devices or internet applications. In this paper, we designed two levels of an image processing structure. A distributed file system is taken as the underlying storage architecture to efficiently manage and query massive remote sensing images. The upper layer uses a GPU server to train the remote sensing image classification model to improve the classification accuracy. To improve the classification accuracy, we add two parameters to adjust the data of the current layer in U-Net. The experimental results show that the proposed method based on distributed storage has a high degree of scalability, and it has a short processing time while maintaining a high classification accuracy for remote sensing images. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Image classification

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Multi-agent Collaboration for Vehicular Task Offloading Using Federated Deep Reinforcement Learning

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IEEE Transactions on Mobile computing 2025年

作者： Chen, Xing Xiao, Bohuai Lin, Xinyu Chen, Zheyi Min, Geyong Fuzhou University College of Computer and Data Science Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China University of Exeter Department of Computer Science ExeterEX4 4QF United Kingdom

Mobile Edge computing (MEC) distributes resources such as computing, storage, and bandwidth to the side close to users, which can provide low-latency services to in-vehicle users, thus promising a more efficient and safer driving environment. However, due to the dynamic scale of vehicle and the variability of resource requirements, it is a significant challenge to quickly obtain effective task offloading in large-scale vehicle scenarios. The existing studies generally adopt the centralized decision-making method, with long decision-making time and high computational overhead, which cannot effectively achieve good offloading decisions in large-scale scenarios. To address these problems, we propose a Multi-agent Collaborative Method for vehicular task offloading using Federated Deep Reinforcement Learning called MCM-FDRL. First, each vehicle as an agent, independently makes offloading decisions based on local information. Next, the offloading decision model of each vehicle is obtained through federated reinforcement learning training. At runtime, an effective vehicle offloading plan can be gradually developed through multi-agent collaboration. Using two real-world datasets, experiments show that the MCM-FDRL has good adaptability and scalability. Moreover, compared to the state-of-the-art methods, the task's average response time of the MCM-FDRL is reduced by 9.75%-64.90%, respectively. © 2002-2012 IEEE.

关键词： Federated learning

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DG-CNN:Introducing Margin Information into Convolutional Neural Networks for Breast Cancer Diagnosis in Ultrasound Images

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Journal of Computer Science & Technology 2022年第2期37卷 277-294页

作者： Xiao-Zheng Xie Jian-Wei Niu Senior Member Xue-Feng Liu Qing-Feng Li Yong Wang Jie Han Shaojie Tang State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityHangzhou 310051China Department of Diagnostic Ultrasound National Cancer CenterChinese Academy of Medical SciencesPeking Union Medical CollegeBeijing 100021China Naveen Jindal School of Management The University of Texas at DallasRichardsonTX 75080-3021U.S.A.

Although using convolutional neural networks(CNNs)for computer-aided diagnosis(CAD)has made tremendous progress in the last few years,the small medical datasets remain to be the major bottleneck in this *** address this problem,researchers start looking for information out of the medical *** efforts mainly leverage information from natural images via transfer *** recent research work focuses on integrating knowledge from medical practitioners,either letting networks resemble how practitioners are trained,how they view images,or using extra *** this paper,we propose a scheme named Domain Guided-CNN(DG-CNN)to incorporate the margin information,a feature described in the consensus for radiologists to diagnose cancer in breast ultrasound(BUS)*** DG-CNN,attention maps that highlight margin areas of tumors are first generated,and then incorporated via different approaches into the *** have tested the performance of DG-CNN on our own dataset(including 1485 ultrasound images)and on a public *** results show that DG-CNN can be applied to different network structures like VGG and ResNet to improve their *** example,experimental results on our dataset show that with a certain integrating mode,the improvement of using DG-CNN over a baseline network structure ResNet 18 is 2.17%in accuracy,1.69%in sensitivity,2.64%in specificity and 2.57%in AUC(Area Under Curve).To the best of our knowledge,this is the first time that the margin information is utilized to improve the performance of deep neural networks in diagnosing breast cancer in BUS images.

关键词： medical consensus domain knowledge breast cancer diagnosis margin map deep neural network

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An overview on the designs of distributed observers in LTI multi-agent systems

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Science China(Technological Sciences) 2021年第11期64卷 2337-2346页

作者： PEI YingQing GU HaiBo LIU KeXin LU JinHu Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain ComputingBeihang UniversityBeijing 100191China

With the rapid development of information technologies and cloud computing, sensor networks play an increasingly important role in our society. Over the past few decades, distributed observer theory has attracted unprecedented attention due to its wide potential applications in different areas. Meanwhile, various approaches and algorithms have been proposed and investigated. The design of distributed observers is one of the frontier topics of system and control research, which has the significant theoretical values and broad application prospects. This paper attempts to review the representative models and the corresponding approaches for distributed observer design in linear time-invariant(LTI) systems. Firstly, the research backgrounds and main advances of distributed observer designs are briefly reviewed. Then, recent results of distributed observer designs for discrete-time and continuous-time LTI multi-agent systems(MASs) are introduced in detail, respectively. Finally, the prospects and the future work directions of the design of distributed observers are put forward. The main purpose of this paper is to promote the emerging topic on the designs of distributed observers, with focuses on the interdisciplinary interest from technological sciences.

关键词： multi-agent system linear time-invarient system distributed observer consensus

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Seeing Your Stories:visualization for Narrative Medicine

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Health data Science 2024年第1期4卷 390-405页

作者： Hua Ma Xiaoru Yuan Xu Sun Glyn Lawson Qingfeng Wang Faculty of Science and Engineering University of NottinghamNingbo 315100China Digital Art Department Art&Design Technology InstituteSuzhou 215104China National Key Laboratory of General Artiffcial Intelligence and School of Intelligence Science and Technology Peking UniversityBeijing 100871China Health Data Visualization and Visual Analytics Research Center National Institute of Health Data Science at PKUBeijing 100191China Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute University of Nottingham Ningbo ChinaNingbo 315100China Human Factors Research Group Faculty of EngineeringUniversity of NottinghamNottingham NG72RDUK Nottingham University Business School China University of NottinghamNingbo 315100China

Importance:Narrative medicine(NM),in which patient stories play a crucial role in their diagnosis and treatment,can potentially support a more holistic approach to patient care than traditional scientiffc ***,there are some challenges in the implementation of narrative medicine,for example,differences in understanding illnesses between physicians and patients and physicians’increased workloads and overloaded *** paper ffrst presents a review to explore previous visualization research for narrative medicine to bridge the gap between visualization researchers and narrative medicine experts and explore further visualization ***:The review is conducted from 2 perspectives:(a)the contexts and domains in which visualization has been explored for narrative medicine and(b)the forms and solutions applied in these *** applied domains are deffned,including understanding patients from narrative records,medical communication,medical conversation training in education,and psychotherapy and emotional wellness ***:A future work framework illustrates some opportunities for future research,including groups of speciffc directions and future points for the 4 domains and 3 technological exploration opportunities(combination of narrative and medical data visualization,taskaudience-based visual storytelling,and user-centered interactive visualization).Speciffcally,3 directions of future work in medical communication(asynchronous online physician-patient communication,synchronous face-to-face medical conversation,and medical knowledge dissemination)were concluded.

关键词： directions education visualization

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Resource Allocation and User Pairing for Rate Splitting Multiple Access Based Wireless Networked Control Systems

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IEEE Transactions on Communications 2024年

作者： Wang, Xudong Du, Hongyang Feng, Lei Niyato, Dusit Zhou, Fanqin Yang, Zhixiang Li, Wenjing Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore

Wireless networked control systems (WNCSs) have emerged as a new paradigm in industrial Internet of Things (IIoT), where base station (BS) transmits control commands generated by the remote controller to actuators of multiple control subsystems through shared wireless channels. This paper investigates a novel rate splitting multiple access (RSMA) enabled ultra-reliable and low-latency (URLLC) transmission design for industrial control applications in WNCSs, where control commands are splitted and transmitted with finite blocklength regime. This design aims to maximize the system sum rate (SR) by optimizing beamforming at BS, rate control for each control subsystem, and user pairing between control subsystems and subcarriers, while ensuring the control stability requirements for all control subsystems. We first derive the control convergence constraint into a communication reliability constraint expressed in terms of outage probability. Then we propose a nested iterative algorithm adopting alternating optimization (AO). During the inner iteration, we propose a resource allocation method leveraging successive convex approximation (SCA) to jointly optimize beamforming and rate control, while during the outer iteration, a hypergraph game-theoretic based matching method is provided to obtain the optimal pairing result between control subsystems and subcarriers. Simulation results demonstrate that the proposed transmission design outperforms existing schemes in terms of communication rate and control cost. © 1972-2012 IEEE.

关键词： Resource allocation

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Multistatic Cooperative Sensing Assisted Secure Transmission via IRS

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IEEE Transactions on Wireless Communications 2025年

作者： Yu, Xianglin Xu, Jinlei Qin, Xiaoqi Tang, Jie Zhao, Nan Niyato, Dusit Dalian University of Technology School of Information and Communication Engineering Dalian116024 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China South China University of Technology School of Electronic and Information Engineering Guangzhou510641 China Nanyang Technological University College of Computing and Data Science 639798 Singapore

Benefiting from the performance enhancement brought by multistatic cooperative sensing, integrated sensing and communication (ISAC) can capture more environmental information to address the security risk of information leakage caused by the openness of wireless channels. In this paper, we study the multistatic cooperative sensing assisted secure transmission via intelligent reflecting surface (IRS). In particular, we propose a multistatic cooperative sensing scheme to obtain the angle of arrival and the localization of the eavesdropping target to achieve the beam alignment accurately. The goal is to maximize the sum secrecy rate by jointly optimizing the association variables of base station (BS) and users, the BS beamforming and the IRS phase shifts, subject to the requirement of target sensing. Due to the coupling of variables and non-convex objective function, the formulated problem is intractable to solve directly. As such, we decompose it into three subproblems and develop an alternative optimization algorithm to solve them iteratively. The association variables are first optimized by successive convex approximation. Then, the BS transmit beamforming can be derived via the semidefinite relaxation. Finally, we adopt an alternating direction method of multiplier for the IRS phase-shift design. Simulation results indicate the feasibility of the proposed scheme, and the multistatic cooperative sensing via IRS can enhance the sensing performance and guarantee the secure transmission. © 2002-2012 IEEE.

关键词： Beamforming

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ABXI: Invariant Interest Adaptation for Task-Guided Cross-Domain Sequential Recommendation 25

ABXI: Invariant Interest Adaptation for Task-Guided Cross-Do...

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34th ACM Web Conference, WWW 2025

作者： Bian, Qingtian de Carvalho, Marcus Li, Tieying Xu, Jiaxing Fang, Hui Ke, Yiping College of Computing and Data Science Nanyang Technological University Singapore School of Computer Science and Engineering Northeastern University Shenyang China Research Institute for Interdisciplinary Sciences Key Laboratory of Interdisciplinary Research of Computation and Economics Shanghai University of Finance and Economics Shanghai China

ISBN: (纸本)9798400712746

Cross-Domain Sequential Recommendation (CDSR) has recently gained attention for countering data sparsity by transferring knowledge across domains. A common approach merges domain-specific sequences into cross-domain sequences, serving as bridges to connect domains. One key challenge is to correctly extract the shared knowledge among these sequences and appropriately transfer it. Most existing works directly transfer unfiltered cross-domain knowledge rather than extracting domain-invariant components and adaptively integrating them into domain-specific modelings. Another challenge lies in aligning the domain-specific and cross-domain sequences. Existing methods align these sequences based on timestamps, but this approach can cause prediction mismatches when the current tokens and their targets belong to different domains. In such cases, the domain-specific knowledge carried by the current tokens may degrade performance. To address these challenges, we propose the A-B-Cross-to-Invariant Learning Recommender (ABXI). Specifically, leveraging LoRA’s effectiveness for efficient adaptation, ABXI incorporates two types of LoRAs to facilitate knowledge adaptation. First, all sequences are processed through a shared encoder that employs a domain LoRA for each sequence, thereby preserving unique domain characteristics. Next, we introduce an invariant projector that extracts domain-invariant interests from cross-domain representations, utilizing an invariant LoRA to adapt these interests into modeling each specific domain. Besides, to avoid prediction mismatches, all domain-specific sequences are aligned to match the domains of the cross-domain ground truths. Experimental results on three datasets demonstrate that our approach outperforms other CDSR counterparts by a large margin. © 2025 Copyright held by the owner/author(s).

关键词： cross-domain sequential recommendation

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