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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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A Survey of Recommender systems Based on Hypergraph Neural Networks 1

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7th International Conference on Smart computing and Communication, SmartCom 2022

作者： Liu, Canwei He, Tingqin Zhu, Hangyu Li, Yanlu Xie, Songyou Hosam, Osama School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Guangdong Financial High-tech Zone "Blockchain +" Fintech Research Institute Guangzhou510623 China Hunan Key Laboratory for Service computing and Novel Software Technology Xiangtan411201 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Higher Colleges of Technology Abu Dhabi United Arab Emirates

ISBN: (数字)9783031281242

ISBN: (纸本)9783031281235

Unlike highly purposeful search, a recommender system tends to uncover the user’s potential interests and is a personalized information filtering system. Recently, the performance of hypergraph neural networks in classification tasks has attracted much attention. Compared with traditional recommender systems, hypergraph neural network-based recommender systems have better mining higher-order associations, accurate modeling of multivariate relationships, handling of multimodal and heterogeneous data, and clustering advantages. This fact drives the development of recommendation algorithms based on hypergraph neural networks. To this end, we 1) define generic links of recommender systems, and systematically analyze the challenges of hypergraph neural network-based recommender systems in different research directions. 2) present some new perspectives on existing weaknesses and future developments. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Recommender systems

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Variational Bayes for Federated Continual Learning

arXiv

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arXiv 2024年

作者： Yao, Dezhong Li, Sanmu Dai, Yutong Xu, Zhiqiang Hu, Shengshan Zhao, Peilin Sun, Lichao The National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab. Cluster and Grid Computing Lab. School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Lehigh University PA18015 United States Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Tencent AI Lab. Shenzhen China

Federated continual learning (FCL) has received increasing attention due to its potential in handling real-world streaming data, characterized by evolving data distributions and varying client classes over time. The constraints of storage limitations and privacy concerns confine local models to exclusively access the present data within each learning cycle. Consequently, this restriction induces performance degradation in model training on previous data, termed "catastrophic forgetting". However, existing FCL approaches need to identify or know changes in data distribution, which is difficult in the real world. To release these limitations, this paper directs attention to a broader continuous framework. Within this framework, we introduce Federated Bayesian Neural Network (FedBNN), a versatile and efficacious framework employing a variational Bayesian neural network across all clients. Our method continually integrates knowledge from local and historical data distributions into a single model, adeptly learning from new data distributions while retaining performance on historical distributions. We rigorously evaluate FedBNN’s performance against prevalent methods in federated learning and continual learning using various metrics. Experimental analyses across diverse datasets demonstrate that FedBNN achieves state-of-the-art results in mitigating forgetting. Copyright © 2024, The Authors. All rights reserved.

关键词： Digital storage

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Optimizing Model-Driven Federated Learning for Rational and Data-Efficient in Social Mobile Network

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IEEE Network 2024年

作者： Lu, Jianfeng Zhang, Ying Cao, Shuqin Wang, Wei Tang, Changbing Wuhan University of Science and Technology School of Computer Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology China Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology Ministry of Education China Zhejiang Normal University College of Physics and Electronic Information Engineering Jinhua321004 China

With the rise of data-intensive services in mobile networks, the demand for intelligent and efficient learning frameworks has grown exponentially. Federated Learning (FL), as an emerging paradigm, enables decentralized edge devices to collaboratively train large models without sharing raw data, ensuring privacy preservation. However, deploying FL in resource-constrained mobile environments faces significant challenges, such as free-riding and malicious behavior. These challenges hinder model performance and raise concerns about data integrity and inter-user cooperation. To address these issues, we propose a layered dynamic framework for social mobile networks, called FedSMN, which aims to enhance the generalization ability of collaboratively trained large models in FL environments. FedSMN leverages Bayesian inference to evaluate user behavior and employs evolutionary game theory to simulate dynamic interactions, thereby optimizing cooperation strategies and reducing communication overhead. Unlike traditional approaches that assume fully rational actors, FedSMN accounts for irrational behavior and fluctuating resources, designing an efficient incentive mechanism to improve data utilization. Extensive experiments on large-scale datasets demonstrate that FedSMN increases model accuracy by 21% and reduces communication overhead by 49%, paving the way for scalable, intelligent mobile networks powered by large models. © 1986-2012 IEEE.

关键词： Collaborative learning

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Deep Learning-Based Dimension Measurement system for Industrial Part Inspection

Deep Learning-Based Dimension Measurement System for Industr...

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2024 China Automation Congress, CAC 2024

作者： Li, Xiao-Di Fan, Xing-Rong Zhang, Xian-Ming Zhang, Han-Zhi Huang, Chun-Jie Xu, Cheng Chongqing Engineering Laboratory for Detection Control and Integrated System School of Artificial Intelligence Chongqing Technology and Business University Chongqing China Engineering Research Centre for Waste Oil Recovery Technology and Equipment Ministry of Education Chongqing Key Laboratory Chongqing Technology and Business University Chongqing China School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing China Chongqing Intelligent Supply Chain Engineering & Technology Research Center Chongqing Jiaotong University Chongqing China Chongqing Aerospace Bashan Motorcycle Manufacturing Co. Ltd. Chongqing China

ISBN: (纸本)9798350368604

This paper develops a deep learning-based system for the dimension measurement of industrial parts. The system employs a general self-supervised image segmentation network to perform dichotomous image segmentation (DIS) on the parts, effectively extracting binary images of the target components from complex backgrounds without requiring preprocessing steps such as filtering or denoising. Following segmentation, contour detection, and dimension measurement are executed. The system utilizes a CMOS area scan camera and was tested on a computer equipped with a GTX1050 graphics card. The tests, conducted on two typical thrust ball bearing components (i.e., the ball retainer and the shaft ring) ü yielded ten measurements per part. Comparison with a vernier caliper as the reference standard reveals that under normal operating conditions, the maximum standard deviation is 0.007 mm and the maximum absolute error is 0.017 mm. © 2024 IEEE.

关键词： Ball bearings

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A-VMD: Adaptive Variational Mode Decomposition Scheme for Noise Reduction in Sensor-Cloud 2nd

A-VMD: Adaptive Variational Mode Decomposition Scheme for No...

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2nd International Conference on Ubiquitous Security, UbiSec 2022

作者： Huo, Zhenru Jia, Guoqing Fang, Weidong Chen, Wei Zhang, Wuxiong School of Physics and Electronic Information Engineering Qinghai Minzu University Xining810007 China Science and Technology On Micro-System Laboratory Shanghai Institute of Micro-System and Information Technology Chinese Academy of Sciences Shanghai201899 China University of Chinese Academy of Sciences Beijing100049 China Shanghai Research and Development Center for Micro-Nano Electronics Shanghai201210 China School of Mechanical Electronic and Information Engineering China University of Mining and Technology-Beijing Beijing100083 China School of Computer Science and Technology China University of Mining and Technology Xuzhou221116 China

ISBN: (纸本)9789819902712

Digital signal processing is critical during the use of sensor-clouds, and the data information acquired by sensors is inevitably noisy. The Variational Mode Decomposition (VMD) algorithm can be used to reduce noise on the signal. The selection of the modal decomposition number of the VMD algorithm and the selection of the modal components of the reconstructed signal affect the effect of signal noise reduction. In this paper, an adaptive variational mode decomposition scheme is proposed. It adaptively selects the modal decomposition number through the established modal decomposition number and the input signal sample entropy model, selects the optimal modal component according to the sample entropy threshold, and reconstructs the signal after the selected modal component is processed by data smoothing. The results show that compared with traditional variational mode decomposition, empirical mode decomposition algorithms, and other algorithms, the effect of adaptive variational mode decomposition in filtering out signal noise is analyzed. The proposed adaptive VMD (A-VMD) algorithm can effectively filter out the noise of the signal obtained by the sensor. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Variational mode decomposition

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Write-Optimized B^(+) Tree Index technology for Persistent Memory

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Journal of computer science & technology 2021年第5期36卷 1037-1050页

作者： Rui-Xiang Ma Fei Wu Bu-Rong Dong Meng Zhang Wei-Jun Li Chang-Sheng Xie Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430070China Key Laboratory of Information Storage System Ministry of Education of ChinaWuhan 430070China Engineering Research Center of Data Storage Systems and Technology Huazhong University of Science and Technology Wuhan 430070China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430070China Shenzhen DAPU Microelectronics Co. LtdShenzhen 518116China

Due to its low latency,byte-addressable,non-volatile,and high density,persistent memory(PM)is expected to be used to design a high-performance storage ***,PM also has disadvantages such as limited endurance,thereby proposing challenges to traditional index technologies such as B(+)tree.B(+)tree is originally designed for dynamic random access memory(DRAM)-based or disk-based systems and has a large write amplification *** high write amplification is detrimental to a PM-based *** paper proposes WO-tree,a write-optimized B(+)tree for ***-tree adopts an unordered write mechanism for the leaf nodes,and the unordered write mechanism can reduce a large number of write operations caused by maintaining the entry order in the leaf *** the leaf node is split,WO-tree performs the cache line flushing operation after all write operations are completed,which can reduce frequent data flushing ***-tree adopts a partial logging mechanism and it only writes the log for the leaf *** inner node recognizes the data inconsistency by the read operation and the data can be recovered using the leaf node information,thereby significantly reducing the logging ***,WO-tree adopts a lock-free search for inner nodes,which reduces the locking overhead for concurrency *** evaluate WO-tree using the Yahoo!Cloud Serving Benchmark(YCSB)*** with traditional B(+)tree,wB-tree,and Fast-Fair,the number of cache line flushes caused by WO-tree insertion operations is reduced by 84.7%,22.2%,and 30.8%,respectively,and the execution time is reduced by 84.3%,27.3%,and 44.7%,respectively.

关键词： persist memory B^(+)tree write amplication consistency YCSB(Yahoo!Cloud Serving Benchmark)

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Mixed Color Channels (MCC): A Universal Module for Mixed Sample Data Augmentation Methods

Mixed Color Channels (MCC): A Universal Module for Mixed Sam...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Wei, Yang Ma, Jianfeng Jiang, Zhongyuan Xiao, Bin School of Cyber Engineering Xidian University China Shaanxi Key Laboratory of Network and System Security China School of Computer Science and Technology Chongqing University of Posts and Telecommunication China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Color invariance is critical for computer vision systems since it significantly increases the robustness and effectiveness of the system. MSDA approaches (e.g., FMix and CutMix) have attracted considerable attention in recent years since they are simple, effective, and do not require extra computation con-sumption. By mixing samples, these approaches extend the distribution of training samples. However, the color information of these mixed samples is not changed, which makes it still difficult for trained models to achieve color invariance. To address this issue, we propose a universal module called Mixed Color Channels (MCC) that implements color changes by mixing the sample and its color variants, which enables trained models to achieve color invariance. In the experimen-tal section, we insert MCC into four state-of-the-art MSDA approaches, evaluate its effectiveness, and embed MCC into a non-MSDA method to demonstrate its extensibility. © 2022 IEEE.

关键词： Color

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Design of Airborne Intelligent computing system Oriented to Intelligent Task Processing 26

Design of Airborne Intelligent Computing System Oriented to ...

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26th ACIS International Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed computing, SNPD-Winter 2023

作者： Bai, Linting Wen, Pengcheng Xie, Jianchun Li, Yunxi Lin, Rongchao School of Computer Science Northwestern Polytechnical University Xi'an Aeronautics Computing Technique Research Institute Avic Xi'an China Xi'an Aeronautics Computing Technique Research Institute Avic Aviation Key Laboratory of Science and Technology on Airborne and Missile-borne Computer Xi'an China Xi'an Aeronautics Computing Technique Research Institute Avic Aeronautics Data and Sensor Business Unit Avic Xi'an China

ISBN: (纸本)9798350345865

The rapid development of artificial intelligence technology promotes the gradual evolution of the 'three integration' of mechanization, informatization and intelligence. The mass data characteristics of airborne mission informatization put forward higher demands on the storage, transmission and management capabilities of airborne data. The characteristics of high complexity, strong confrontation, strong real-time, and uncertainty of airborne intelligent tasks pose more challenges to the intelligent processing of airborne data. This paper analyzes the data characteristics and mission characteristics in the process of airborne intelligence missions, and proposes an airborne intelligent computing architecture for airborne data processing of intelligent missions. The composition and function of each technical layer are expounded, and several problems of airborne intelligent computing technology are analyzed. Finally, the development trend of airborne intelligent technology development is prospected. © 2023 IEEE.

关键词： Digital storage

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Optimizations and Deoptimizations for Escape Analysis in Open World

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Chinese Journal of Electronics 2023年第2期19卷 211-216页

作者： Xiaohua Shi Gansha Wu Jin Maozhong Guei-Yuan Lueh School of Computer Science Beihang University Beijing China Programming System Laboratory Microprocessor Technology Labs Intel Corporation Beijing China

This paper introduced the optimization and deoptimization technologies for Escape analysis in open world. These technologies are used in a novel Escape analysis framework that has been implemented in Open runtime platform, Intel's open-source Java virtual machine. We introduced the optimization technologies for synchronization removal and object stack allocation, as well as the runtime deoptimization and compensation work. The deoptimization and compensation technologies are crucial for a practical Escape analysis in open world. We evaluated the runtime efficiency of the deoptimization and compensation work on benchmarks like SPECjbb2000 and SPECjvm98.

关键词： Java Runtime Education Medical services Benchmark testing Virtual machining Synchronization

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