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SSRN 2023年

作者： Huang, Qihan Zhang, Jing Zeng, Zuanyang He, Ding Ye, Xiucai Chen, Yi School of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Department of Computer Science University of Tsukuba Tsukuba Japan Department of Computer and Information Security Management Fujian Police College Fuzhou China

Mobile Crowd-Sensing (MCS) is a distributed data fusion system that leverages multiple data sources to improve the performance. By incorporating data from numerous individuals, MCS can provide more accurate, comprehensive, and valuable information than any single source could offer alone. As an essential application in MCS, intelligent transportation system deploys a large number of Transport Mode Inference (TMI) models. However, tens of thousands of trajectories are utilized for TMI model training, which will bring the risk of privacy disclosure. Existing solutions cannot address the privacy concerns of trajectories in MCS. In order to achieve the secure distributed architecture for eliminating such concerns in collaboration, a Federated Learning-based Transport Mode Inference Model with Privacy-Preserving data Fusion (PPDF-FedTMI) is proposed in this paper. Firstly, federated learning is utilized in the TMI model to solve the isolated data islands problem of MCS systems. Subsequently, a fuzzy fusion algorithm based on the fuzzy clustering of label information is proposed to train models stably and effectively under non-IID distributed datasets. Furthermore, a privacy-preserving joint training method applying local differential privacy is designed to enhance the gradient privacy, which can prevent attackers from deriving the original through the gradient. Finally, the experimental results demonstrate that PPDF-FedTMI can provide higher accuracy of mode inference and effective privacy-preserving compared to other models. © 2023, The Authors. All rights reserved.

关键词： data fusion

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Out-of-Distribution Fault Diagnosis of Industrial Cyber-Physical Systems Based on Orthogonal Anchor Clustering With Adaptive Balance

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 48-60页

作者： Liu, Ruonan Hu, Puyuan Zhao, Siheng Sun, Zhijian Han, Te Pang, Zhibo Zhang, Weidong Shanghai Jiao Tong University Department of Automation Shanghai200240 China National Key Laboratory of Marine Engine Science and Technology Shanghai201108 China Crossocean of Suzhou Technology Suzhou215121 China Beijing Institute of Technology Center for Energy and Environmental Policy Research School of Management Beijing100081 China ABB Corporate Research Sweden Department of Automation Technology Vasteras722 26 Sweden KTH Royal Institute of Technology Department of Intelligent Systems Stockholm114 28 Sweden Henan University Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Kaifeng475001 China Shanghai Jiaotong University Department of Automation Shanghai200240 China

Given the critical role of rotating machinery in industrial cyber-physical systems (ICPS), ensuring their reliable operation is essential for the stability and safety of ICPS. Deep neural networks have demonstrated competitive results for intelligent fault diagnosis, which are usually trained via the historical data of all fault modes. However, in real engineering, it is usually difficult to collect samples and exhaust all failures during the training stage. As a result, out-of-distribution fault diagnosis (OOD-FD) becomes a more realistic problem that requires the methods to not only accurately diagnose the known faults, but also effectively recognize unknown ones. Therefore, a novel orthogonal anchor clustering with focal attention (FA-OAC) is proposed in this paper for OOD-FD. Firstly, an orthogonal anchor clustering (OAC) algorithm is proposed to fix the class center of each fault mode orthogonally and distinguish the known and unknown faults at the class level. Then, because the identifiability of different fault modes changes a lot in OOD-FD, the focal attention mechanism is applied to dynamically adjust the attention to different fault modes according to the distance loss of OAC, thus addressing the identifiability imbalance problem. To verify the effectiveness of the proposed method, different OOD-FD tasks are designed based on two rotating machinery datasets. The experimental results and comparison with state-of-the-art fault diagnosis methods have demonstrated that the proposed method has improved the OOD-FD performance greatly and therefore provides an effective tool in real engineering. © 2023 IEEE.

关键词： Rotating machinery

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A New Partial Task Offloading Method in a Cooperation Mode under Multi-Constraints for Multi-UE

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computers, Materials & Continua 2023年第9期76卷 2879-2900页

作者： Shengyao Sun Ying Du Jiajun Chen Xuan Zhang Jiwei Zhang Yiyi Xu School of Information Science and Technology Zhengzhou Normal UniversityZhengzhou450044China Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifeng475004China School of Geography and Tourism Zhengzhou Normal UniversityZhengzhou450044China Science and Engineering College South China University of TechnologyGuangzhou510641China Department of Electrical and Electronic Engineering Luohe Vocational Technology CollegeLuohe462002China School of Computer Science Beijing University of Posts and TelecommunicationsBeijing100876China Cardiff School of Engineering Cardiff UniversityCardiffCF103XQ 15UK

In Multi-access Edge Computing(MEC),to deal with multiple user equipment(UE)’s task offloading problem of parallel relationships under the multi-constraints,this paper proposes a cooperation partial task offloading method(named CPMM),aiming to reduce UE’s energy and computation consumption,while meeting the task completion delay as much as *** first studies the task offloading of single-UE and then considers the task offloading ofmulti-UE based on single-UE task *** the critical path algorithmto divide the modules into key and non-key *** to some constraints of UE-self when offloading tasks,it gives priority to non-key modules for offloading and uses the evaluation decision method to select some appropriate key modules for *** on fully considering the competition between multiple UEs for communication resources and MEC service resources,CPMM uses the weighted queuing method to alleviate the competition for communication resources and uses the branch decision algorithm to determine the location of module offloading by BS according to the MEC servers’*** achieves its goal by selecting reasonable modules to offload and using the cooperation ofUE,MEC,andCloudCenter to determine the execution location of *** experiments demonstrate that CPMM obtains superior performances in task computation consumption reducing around 6%on average,task completion delay reducing around 5%on average,and better task execution success rate than other similar methods.

关键词： MEC partial task offloading parallel dependencies completion delay

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Incomplete multi-view clustering via local and global co-regularization

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Science China(Information Sciences) 2022年第5期65卷 96-111页

作者： Jiye LIANG Xiaolin LIU Liang BAI Fuyuan CAO Dianhui WANG Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information Technology Shanxi University Artificial Intelligence Research Institute China University of Mining and Technology Department of Computer Science and Computer Engineering La Trobe University

The incompleteness of multi-view data is a phenomenon associated with real-world data mining applications, which brings a huge challenge for multi-view clustering. Although various types of clustering methods, which try to obtain a complete and consensus clustering result from a latent subspace, have been developed to overcome this problem, most methods excessively rely on views-public instances to bridge the connection with view-private instances. When lacking sufficient views-public instances, existing methods fail to transmit the information among incomplete views effectively. To overcome this limitation, we propose an incomplete multi-view clustering algorithm via local and global co-regularization(IMVC-LG). In this algorithm, we define a new objective function that is composed of two terms: local clustering from each view and global clustering from multiple views, which constrain each other to exploit the local clustering information from different incomplete views and determine a global consensus clustering result, ***, an iterative optimization method is proposed to minimize the objective function. Finally, we compare the proposed algorithm with other state-of-the-art incomplete multi-view clustering methods on several benchmark datasets to illustrate its effectiveness.

关键词： incompleteness multi-view clustering local clustering global clustering co-regularization

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A Synchronous Parallel Method with Parameters Communication Prediction for Distributed Machine Learning 19th

A Synchronous Parallel Method with Parameters Communication...

引用

19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Zeng, Yanguo Xue, Meiting Xu, Peiran Shi, Yukun Zeng, Kaisheng Zhang, Jilin Yue, Lupeng School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Key Laboratory for Modeling and Simulation of Complex Systems Ministry of Education Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China National University of Defense Technology Changsha China School of Cyberspace HangZhou Dianzi University Hangzhou China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9783031545306

With the development of machine learning technology in various fields, such as medical care, smart manufacturing, etc., the data has exploded. It is a challenge to train a deep learning model for different application domains with large-scale data and limited resources of a single device. The distributed machine-learning technology, which uses a parameter server and multiple clients to train a model collaboratively, is an excellent method to solve this problem. However, it needs much communication between different devices with limited communication resources. The stale synchronous parallel method is a mainstream communication method to solve this problem, but it always leads to high synchronization delay and low computing efficiency as the inappropriate delay threshold value set by the user based on experience. This paper proposes a synchronous parallel method with parameters communication prediction for distributed machine learning. It predicts the optimal timing for synchronization, which can solve the problem of long synchronization waiting time caused by the inappropriate threshold settings in the stale synchronous parallel method. Moreover, it allows fast nodes to continue local training while performing global synchronization, which can improve the resource utilization of work nodes. Experimental results show that compared with the delayed synchronous parallel method, the training time and quality, and resource usage of our method are both significantly improved. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Forecasting

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Nailfold Capillary Segmentation Based on U-Net and Attention Mechanism 23

Nailfold Capillary Segmentation Based on U-Net and Attention...

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3rd International Conference on Bioinformatics and Intelligent Computing, BIC 2023

作者： Chen, Shupin Wei, Dan Gu, Shenming Yang, Zhangru School of Information Engineering Zhejiang Ocean University Zhoushan316022 China Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhejiang Ocean University Zhoushan316022 China Department of Radiation Oncology Shanghai Chest Hospital Shanghai Jiao Tong University No. 241 West Huaihai Road Shanghai200030 China

ISBN: (纸本)9798400700200

Microcirculation refers to the blood circulation composed of arterioles and venules, which can be used as a reference indicator for doctors to diagnose diseases. The traditional manual reading method is relatively time-consuming and labor-consuming and is not conducive to the standardization and digitization of diagnosis. A feasible way to solve this problem is to use machine learning to assist in image processing and diagnosis. In this study, we proposed a method to segment the nailfold vascular images based on the U-Net backbone network combined with the residual mechanism and the attention mechanism. The results show that: 1. Adding the CBAM attention mechanism can improve the anti-noise ability of the model. 2. Using residual blocks to replace the convolution blocks of the backbone network can improve the accuracy of models. 3. Contrast enhancement can improve the accuracy of blood vessel segmentation. The optimal method in this study is Res_CBAM_Unet and the contrast enhancement uses CLAHE (clipLimit = 2). The overall accuracy is 94.31%, the Dice score is 0.8297, the IoU score is 0.7090, the VOE score is 0.2910. The generalization ability of the proposed method is verified on the public dataset DRIVE. The results show that the accuracy of the proposed method is equivalent to the state-of-the-art methods, and it has an advantage in the sensitivity. This paper provides an effective method for nailfold capillary segmentation, which lays a foundation for the automation of diagnosis based on microcirculation images. © 2023 ACM.

关键词： Microcirculation

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Uncertainty-Aware Adaptation of Large Language Models for Protein-Protein Interaction Analysis

arXiv

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arXiv 2025年

作者： Jantre, Sanket Wang, Tianle Park, Gilchan Chopra, Kriti Jeon, Nicholas Qian, Xiaoning Urban, Nathan M. Yoon, Byung-Jun Computing and Data Sciences Directorate Brookhaven National Laboratory UptonNY United States Department of Electrical and Computer Engineering Texas A&M University College StationTX United States

Identification of protein-protein interactions (PPIs) helps derive cellular mechanistic understanding, particularly in the context of complex conditions such as neurodegenerative disorders, metabolic syndromes, and cancer. Large Language Models (LLMs) have demonstrated remarkable potential in predicting protein structures and interactions via automated mining of vast biomedical literature;yet their inherent uncertainty remains a key challenge for deriving reproducible findings, critical for biomedical applications. In this study, we present an uncertainty-aware adaptation of LLMs for PPI analysis, leveraging fine-tuned LLaMA-3 and BioMedGPT models. To enhance prediction reliability, we integrate LoRA ensembles and Bayesian LoRA models for uncertainty quantification (UQ), ensuring confidence-calibrated insights into protein behavior. Our approach achieves competitive performance in PPI identification across diverse disease contexts while addressing model uncertainty, thereby enhancing trustworthiness and reproducibility in computational biology. These findings underscore the potential of uncertainty-aware LLM adaptation for advancing precision medicine and biomedical research. © 2025, CC BY.

关键词： Neurodegenerative diseases

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Reinforcement Learning-Based Sensitive Semantic Location Privacy Protection for VANETs

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China Communications 2021年第6期18卷 244-260页

作者： Minghui Min Weihang Wang Liang Xiao Yilin Xiao Zhu Han Department of Information and Communication Engineering Xiamen UniversityXiamen 361005China School of Information and Control Engineering ChinaUniversity of Mining and TechnologyXuzhou 221116China The Affiliated Hospital of China University of Mining and Technology Xuzhou 221116China Department of Electrical and Computer Engineering University of HoustonHouston TX 77004USA Beijing Key Laboratory of Mobile Computing and Pervasive Device No.6 Kexueyuan South RoadBeijing 100190China

Location-based services(LBS)in vehicular ad hoc networks(VANETs)must protect users’privacy and address the threat of the exposure of sensitive locations during LBS *** release not only their geographical but also semantic information of the visited places(e.g.,hospital).This sensitive information enables the inference attacker to exploit the users’preferences and life *** this paper we propose a reinforcement learning(RL)based sensitive semantic location privacy protection *** scheme uses the idea of differential privacy to randomize the released vehicle locations and adaptively selects the perturbation policy based on the sensitivity of the semantic location and the attack *** scheme enables a vehicle to optimize the perturbation policy in terms of the privacy and the quality of service(QoS)loss without being aware of the current inference attack model in a dynamic privacy protection *** solve the location protection problem with high-dimensional and continuous-valued perturbation policy variables,a deep deterministic policy gradientbased semantic location perturbation scheme(DSLP)is *** actor part is used to generate continuous privacy budget and perturbation angle,and the critic part is used to estimate the performance of the *** demonstrate the DSLP-based scheme outperforms the benchmark schemes,which increases the privacy,reduces the QoS loss,and increases the utility of the vehicle.

关键词： semantic location sensitivity locationbased services VANET differential privacy reinforcement learning

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A blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social internet of vehicles

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Digital Communications and Networks 2022年第6期8卷 976-983页

作者： Ziming Liu Yang Xu Cheng Zhang Haroon Elahi Xiaokang Zhou College of Information Science and Engineering Northeastern UniversityShenyang110819China College of Computer Science and Electronic Engineering Hunan UniversityChangsha410082China Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province Zhejiang UniversityHangzhou310058China Department of Computing Science Umea UniversityUmea90187Sweden Faculty of Data Science Shiga UniversityHikone522-8522Japan RIKEN Center for Advanced Intelligence Project RIKENTokyo103-0027Japan

Social Internet of Vehicles(SIoV)falls under the umbrella of social Internet of Things(IoT),where vehicles are socially connected to other vehicles and roadside units that can reliably share information and services with other social entities by leveraging the capabilities of 5G technology,which brings new opportunities and challenges,e.g.,collaborative power trading can address the mileage anxiety of electric ***,it relies on a trusted central party for scheduling,which introduces performance bottlenecks and cannot be set up in a distributed network,in addition,the lack of transparency in state-of-the-art Vehicle-to-Vehicle(V2V)power trading schemes can introduce further trust *** this paper,we propose a blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social vehicular networks that uses a distributed market mechanism to introduce trusted power trading and avoids the dependence on a centralized dispatch *** on the game theory,we design the pricing and trading matching mechanism for V2V power trading to obtain maximum social *** use blockchain to record power trading data for trusted pricing and use smart contracts for transaction *** simulation results verify the effectiveness of the proposed scheme in improving social welfare and reducing the load on the grid.

关键词： Social internet of vehicles Bl ockchain Collaborative power trading Vehicle-to-vehicle charging 5G

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Innovative Artificial Intelligence-Based Internet of Things for Smart Cities and Smart Homes

引用

Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Sung, Tien-Wen Lee, Chao-Yang Gaber, Tarek Nassar, Hamed Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan School of Science Engineering & Environment University of Salford Manchester United Kingdom Department of Computer Science Suez Canal University Ismailia Egypt

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