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Industrial Automation, Robotics and Control Engineering (IARCE), International Conference on

作者： Qian Si Yang Li School of Cyber Science and Technology Beihang University Beijing China Department of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Virtual Reality Technology and Systems Advanced Institute of Information Technology Peking University Beihang University Beijing China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

In multi-site brain disease diagnosis studies, traditional centralized training methods necessitate sharing medical data, posing significant privacy risks. Federated learning (FL) offers a privacy-preserving solution by enabling global model training through aggregating locally trained models from multiple data centers without sharing raw data. However, current FL approaches rely on a server-based network topology, where central server failure disrupts training. Additionally, data heterogeneity across sites often slows convergence and reduces accuracy. To overcome these issues, we introduce a decentralized personalized federated learning collaborative aggregation network (pFedCAN). This framework has two core components: (1) separating local models into shared and personalized layers, and (2) forming a collaborative aggregation network via similarity detection in the shared layers. Specifically, each center trains its local model, then separates it into shared and personalized layers. The shared layer is exchanged with other centers, while the personalized layer remains local. Data centers analyze similarities in received shared layers to build a collaborative network, where shared layers from similar centers are aggregated to refine the model. This approach flexibly adapts to varying levels of data heterogeneity, enhancing model training efficiency. Validation on public datasets, ABIDE I and ADHD, shows that the proposed method outperforms current leading techniques.

关键词： Training Adaptation models Data centers Federated learning Service robots Collaboration Brain modeling Data models Servers Medical diagnostic imaging

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Online Contentâ€™s Popularity Metrics: RDF-Based Normalization 20th

Online Contentâ€™s Popularity Metrics: RDF-Based Normaliz...

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20th International Conference on Enterprise information systems, ICEIS 2018

作者： Sebei, Hiba Hadj Taib, Mohamed Ali Ben Aouicha, Mohamed Multimedia InfoRmation Systems and Advanced Computing Laboratory Sfax Tunisia

ISBN: (纸本)9783030261689

Social network websites are mainly constructed around the notion of user identities, as set up on the bases of their profiles, and online generated contents such as texts, videos, photos. Still, while some profiles gain an important position in the network, others do not. Similarly, some online generated contents appear to gain a great deal of attention from the part of users, whereas others are completely ignored. In this context, the notion of profile and online content related popularity has come to the forefront. Additionally, several studies turn out to be focused on the area of popularity associated analysis and prediction. Noteworthy, however, is that the popularity evaluative metrics prove to vary from a social network to another. In this respect, the present work is conceived to deal with such challenges through an advanced proposal whereby a unified presentation of popularity metrics related to each social entity, across several social networks, is put forward. Accordingly, a hierarchical structure of popularity metrics, as enhanced with a particular RDF presentation, is suggested, along with a brief summary of wide range of methods used to analyze such entities related popularities. © 2019, Springer Nature Switzerland AG.

关键词： Social networking (online)

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GUEST EDITORIAL

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China Communications 2023年第2期 3-6页

作者： Yuan Wu Xu Chen Feng Lyu Xianfu Chen Xumin Huang Jie Gao Yueyue Dai Tony Q.S.Quek Yan Zhang the State Key Laboratory of Internet of Things for Smart City University of Macau the Department of Computer and Information Science University of Macau Sun Yat-sen University Sun Yat-sen University Institute of Advanced Networking and Computing Systems Sun Yat-sen University National and Local Joint Engineering Laboratory the School of Computer Science and Engineering Central South University the VTT Technical Research Centre of Finland the School of Automation Guangdong University of Technology the School of Information Technology Carleton University Huazhong University of Science and Technology Singapore University of Technology and Design(SUTD) the IEEE Communications Society Technical Committee on Green Communications and Computing(TCGCC)

With the growing maturity of the advanced edge-cloud collaboration and integrated sensing-communication-computing systems, edge intelligence has been envisioned as one of the enabling technologies for ubiquitous and l...

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Green Floating Blockchain-Empowered Co-Trust Security Mechanism with Energy Efficiency Against Attack Threat for 6G-IoV

Green Floating Blockchain-Empowered Co-Trust Security Mechan...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Yibing Liu Lijun Huo Hansong Xu Qianqian Pan Jun Wu State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha China School of Engineering The University of Tokyo Tokyo Japan Graduate School of Information Production and Systems Waseda University Fukuoka Japan

The Internet of Vehicles (IoV) based on the 6-th Generation (6G) communication brings convenience, but also raises anxiety about information security. Researchers have developed static security schemes based on the blockchain, but it results in excessive resource occupation. And it is difficult to resist some attacks such as desynchronization, Denial of Service (DoS), and covert intrusion. In response to the above problems, this paper proposes the Floating Blockchain Consensus Security (FBCS) scheme. It models the attack risk of Road Side Unit (RSU) through the traffic flow prediction to present the credibility evaluation and constructs the floating blockchain based on the results. And the security capability is adjusted through the dynamic joining and exit mode of trusted nodes and untrusted nodes. FBCS also establishes a relationship between blockchain size and energy consumption. Once the system resources are found to be insufficient, it applies the cloud center-based supplementary certification mechanism to provide authentication to untrusted nodes to enhance the stability of the *** analysis and simulation experiments prove that the FBCS can afford data privacy, maintain moderate security capabilities, and adapt defense capabilities according to the attack environment, reduce resource occupation to save cost.

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Hand pose estimation system based on combined features for mobile devices

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International Journal of Intelligent information and Database systems 2020年第2-4期13卷 436-453页

作者： Lahiani, Houssem Neji, Mahmoud National School of Electronics and Telecommunications University of Sfax Tunisia Faculty of Economics and Management University of Sfax Tunisia Multimedia Information Systems and Advanced Computing Laboratory Technopark of Sfax Tunis Road 10 km P.O. Box 242 SFAX 3021 Tunisia

Today's mobile devices or smartphones have a revolutionary impact on how we communicate, especially after the advent of devices like smart watches and Google glasses that require new ways to interact with them. To optimise the use of mobile devices, special input and output peripherals have been designed over the years to facilitate communication with them. The well known peripherals are the multi-touch screens. Smartphones are too small to work freely using their input screens. To solve this problem, recent research has focused on contactless and natural gestural interfaces. In this context, we propose a hand gesture recognition system for mobile devices as a simple way of communication with smartphones. In this work, we describe a hand gesture recognition system for Android devices based on a combination of HOG and LBP features and the SVM classifier. An accuracy rate of about 95% is obtained on the improved 'NUS database I'. In addition, we conduct experiments on different Android devices to know the impact of using such a recognition task on embedded systems like smartphones. © 2020 Inderscience Enterprises Ltd.

关键词： Gesture recognition

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A Novel Remaining Useful Life Prediction Model Based on Adaptive Feature Fusion and its Application to Rolling Bearings

SSRN

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SSRN 2024年

作者： Guo, Kai Ma, Jun Wu, Jiande Xiong, Xin Faculty of Information Engineering & Automation Kunming University of Science and Technology Kunming650500 China Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming650500 China

As a key component in the transmission system of high-speed trains, once a failure occurs, it will directly affect the safety of train operation. Therefore, it is of great significance to construct a degradation index that can comprehensively characterize the running state of bearings and establish a reliable remaining useful life model to ensure the safety of train operation. Aiming at the problems that the redundant features in the existing multi-feature fusion process affect the diagnosis performance and the spurious fluctuations phenomenon of the fusion features causes the inaccurate determination of the start fault time and leads to the low prediction accuracy of remaining useful life (RUL), A method based on adaptive feature fusion and autoregressive integrated moving average model is proposed for rolling bearings prediction. The research in this study demonstrates that by comparing and verifying with two sets of public data, the proposed method is effective in accurately identifying the features that are most sensitive to degradation trends, accurately determine the initial fault moment of bearings, achieve effective prediction of the RUL of bearings, and the related research results can provide reference basis for predictive maintenance of train bearings. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Efficient Meta Neural Heuristic for Multi-Objective Combinatorial Optimization

arXiv

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arXiv 2023年

作者： Chen, Jinbiao Wang, Jiahai Zhang, Zizhen Cao, Zhiguang Ye, Te Chen, Siyuan School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Computing and Information Systems Singapore Management University Singapore

Recently, neural heuristics based on deep reinforcement learning have exhibited promise in solving multi-objective combinatorial optimization problems (MOCOPs). However, they are still struggling to achieve high learning efficiency and solution quality. To tackle this issue, we propose an efficient meta neural heuristic (EMNH), in which a meta-model is first trained and then fine-tuned with a few steps to solve corresponding single-objective subproblems. Specifically, for the training process, a (partial) architecture-shared multi-task model is leveraged to achieve parallel learning for the meta-model, so as to speed up the training;meanwhile, a scaled symmetric sampling method with respect to the weight vectors is designed to stabilize the training. For the fine-tuning process, an efficient hierarchical method is proposed to systematically tackle all the subproblems. Experimental results on the multi-objective traveling salesman problem (MOTSP), multi-objective capacitated vehicle routing problem (MOCVRP), and multi-objective knapsack problem (MOKP) show that, EMNH is able to outperform the state-of-the-art neural heuristics in terms of solution quality and learning efficiency, and yield competitive solutions to the strong traditional heuristics while consuming much shorter time. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Modeling and Clustering of Parabolic Granular Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3728-3742页

作者： Tang, Yiming Gao, Jianwei Pedrycz, Witold Hu, Xianghui Xi, Lei Ren, Fuji Hu, Min Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Hefei University of Technology School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Southeast University School of Computer Science and Engineering Nanjing211189 China University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China

At present, there exist some problems in granular clustering methods, such as lack of nonlinear membership description and global optimization of granular data boundaries. To address these issues, in this study, revolving around the parabolic granular data, we propose an overall architecture for parabolic granular modeling and clustering. To begin with, novel coverage and specificity functions are established, and then a parabolic granular data structure is proposed. The fuzzy c-means (FCM) algorithm is used to obtain the numeric prototypes, and then particle swarm optimization (PSO) is introduced to construct the parabolic granular data from the global perspective under the guidance of principle of justifiable granularity (PJG). Combining the advantages of FCM and PSO, we propose the parabolic granular modeling and optimization (PGMO) method. Moreover, we put forward attribute weights and sample weights as well as a distance measure induced by the Gaussian kernel similarity, and then come up with the algorithm of weighted kernel fuzzy clustering for parabolic granularity (WKFC-PG). In addition, the assessment mechanism of parabolic granular clustering is discussed. In summary, we set up an overall architecture including parabolic granular modeling, clustering, and assessment. Finally, comparative experiments on artificial, UCI, and high-dimensional datasets validate that our overall architecture delivers a good improvement over previous strategies. The parameter analysis and time complexity are also given for WKFC-PG. In contrast with related granular clustering algorithms, it is observed that WKFC-PG performs better than other granular clustering algorithms and has superior stability in handling outliers, especially on high-dimensional datasets. © 2020 IEEE.

关键词： Fuzzy clustering

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Procedia Computer Science 2020年 176卷 763-770页

作者： Randa Ben Ammar Yassine Ben Ayed Multimedia Information System and Advanced Computing Laboratory Sfax University Tunisia

Alzheimer’s disease (AD) is the most leading symptom of neurodegenerative dementia; AD is defined now as one of the most costly chronic diseases. For that automatic diagnosis and control of Alzheimer’s disease may have a significant effect on society along with patient well-being. Language disorder is regarded to be among the most common symptoms of AD, as a direct and natural result of cognitive impairment. Hence, the diagnosis of Alzheimer’s disease using speech-based features is gaining growing attention. The aim of this study is to extract linguistic features following a proposed taxonomy of language impairment of AD patients. Obtained results indicate that the proposed taxonomy of the linguistic features extracted from the speech samples can be used to differentiate between Alzheimer’s disease patients and the healthy control group. Support Vector Machine (SVM) classifier obtained classification accuracy over 90 percent.

关键词： Alzheimer’s disease Speech analysis Machine learning

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Lightweight Task-Oriented Semantic Communication Empowered by Large-Scale AI Models

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IEEE Transactions on Vehicular Technology 2025年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Chen, Mingzhe Quek, Tony Q. S. Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL United States Singapore University of Technology and Design Dept. of Information Systems Technology and Design 487372 Singapore

Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models pose significant challenges for real-time communication scenarios. To address this, this paper proposes utilizing knowledge distillation (KD) techniques to extract and condense knowledge from LAI models, effectively reducing model complexity and computation latency. Nevertheless, the inherent complexity of LAI models leads to prolonged inference times during distillation, while their lack of channel awareness compromises the distillation performance. These limitations make standard KD methods unsuitable for task-oriented semantic communication scenarios. To address these issues, we propose a fast distillation method featuring a pre-stored compression mechanism that eliminates the need for repetitive inference, significantly improving efficiency. Furthermore, a channel adaptive module is incorporated to dynamically adjust the transmitted semantic information based on varying channel conditions, enhancing communication reliability and adaptability. In addition, an information bottleneck-based loss function is derived to guide the fast distillation process. Simulation results verify that the proposed scheme outperform baselines in term of task accuracy, model size, computation latency, and training data requirements. © 1967-2012 IEEE.

关键词： NP-hard

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