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16th IEEE International Conference on Computer Science and Education, ICCSE 2021

作者： Li, Wei Hei, Xinhong Wang, Lei Wang, Xiaofan Xi'An University of Technology School of Computer Science and Engineering Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China Shaanxi University of Technology School of Mathematics and Computer Science Hanzhong China

ISBN: (纸本)9781665414685

This paper analyzes the current situation of the construction of the first-class computer major, and puts forward the construction scheme of the first-class computer major in local colleges based on the concept of outcome based education (OBE). The reform scheme of the construction of the first-class computer major is elaborated from four aspects: training objectives, curriculum system, teaching mode, and evaluation mechanism. © 2021 IEEE.

关键词： Training Computer science Knowledge engineering Problem-solving System analysis and design

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Neural network-Based Safety Optimization Control for Constrained Discrete-Time Systems

Neural Network-Based Safety Optimization Control for Constra...

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Annual Conference of Industrial Electronics Society

作者： Xueli Wang Shangwei Zhao Ming Yang Xiaoming Wu Xin Wang Shuo Xu Xu Hu Key Laboratory of Computing Power Network and Information Security Shandong Computer Science Center Qilu University of Technology Jinan China Department of Automation Shanghai Jiao Tong University Shanghai China Public Security Bureau of Jinan Jinan China

This paper proposes a constraint-aware safety control approach via adaptive dynamic programming (ADP) to address the control optimization issues for discrete-time systems subjected to state constraints. First, the constrained control framework is established via the primal-dual approach with the modified Lagrangian based on the relaxed barrier function. Herein, the Lagrangian multiplier is designed to achieve the trade-off between optimization performance and state constraints. In addition, the sub optimality of the dual method is built by proving that the dual gap can be arbitrarily small. And the value iteration algorithm is utilized to implement the constraint-aware ADP controller. Furthermore, the weight estimation error is proved to be bounded when the learning rate satisfies a given sufficient condition. Finally, numerical simulation proves the effectiveness and superiority of the proposed method.

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A Nature-Inspired Energy-Efficient Clustering Protocol Based on Improved Hybrid Grey Wolf Optimization for FANETs 7

A Nature-Inspired Energy-Efficient Clustering Protocol Based...

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7th IEEE International Conference on Electronics and Communication Engineering, ICECE 2024

作者： Xu, Mengying Zu, Yunxiao Zhou, Jie Dai, Jiapeng Chen, Jing Liu, Meiru School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China Xinjiang Production and Construction Corps Key Laboratory of Computing Intelligence and Network Information Security Shihezi China Shihezi University Department of Information Science and Technology Shihezi China

ISBN: (纸本)9798331517755

Flying ad hoc networks (FANETs) are composed of multiple UA V nodes that are distributed across a wide area and linked via wireless communication to gather and transmit environmental data. However, the battery-powered UAVs in FANETs imposes significant energy limitations, thereby shortening the network's operational lifespan. This paper proposes an energy-efficient clustering protocol based on improved hybrid grey wolf optimization (EEC-IHGWO), which can optimally select cluster heads (CHs), significantly extend network lifetime and maximize energy efficiency. The selection criteria include factors like remaining energy, the distance from cluster members to CHs, and node density. The performance of the EEC-IHGWO method, measured by network lifetime, the number of living nodes, and remaining energy, is assessed against traditional algorithms such as LEACH, as well as the more recent PSO and FCM. The simulation results demonstrate that EEC-IHGWO sustains a longer network lifetime and higher energy efficiency. © 2024 IEEE.

关键词： Ad hoc networks

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Recommendation Model Based on Social Homogeneity Factor and Social Influence Factor 17th

Recommendation Model Based on Social Homogeneity Factor and ...

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17th EAI International Conference on Collaborative computing: networking, Applications, and Worksharing, CollaborateCom 2021

作者： Ying, Weizhi Yu, Qing Wang, Zuohua Tianjin Key Laboratory of Intelligence Computing and Network Security Tianjin University of Technology Tianjin300384 China China Everbright Bank CO. LTD. Beijing100035 China

ISBN: (纸本)9783030926342

In recent years, more and more recommendation algorithms incorporate social information. However, most social recommendation algorithms often only consider the social homogeneity factor between users and do not consider the social influence factor. To make the recommendation model more in line with the real-life situation, this paper proposes a novel graph attention network to model the homogeneity effect and the influence effect in the user domain. Besides, we also extended this idea to the item domain, using information from similar items to alleviate the problem of data sparsity. Also, considering that there will be interactions between the user domain and the item domain, which together affect the user’s preference for the item, we use a contextual multi-armed bandit to weigh the interaction between the two domains. We have conducted extensive comparative experiments and ablation experiments on two real public datasets. The experimental results show that the performance of our proposed model in the rating prediction task is better than other social recommendation model. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Recommender systems

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RobustPFL: Robust Personalized Federated Learning

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Chen, Guorong Wang, Wei Wu, Yufang Li, Chao Xu, Guangquan Ji, Shouling Li, Tao Shen, Meng Han, Yufei Xi'an Jiaotong University Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an China Beijing Jiaotong University 3 Shangyuancun Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing100044 China Tianjin University Tianjin Key Laboratory of Advanced Networking College of Intelligence and Computing Tianjin300350 China Zhejiang University College of Computer Science and Technology Hangzhou310027 China ITAI Haihe Lab Tianjin China Nankai University College of Computer Science Tianjin300350 China Beijing Institute of Technology School of Cyberspace Security Beijing100081 China Peng Cheng Laboratory Cyberspace Security Research Center Shenzhen518066 China INRIA Bretagne Rennes35042 France

Conventional federated learning (FL) coordinated by a central server focuses on training a global model and protecting the privacy of clients' training data by storing it locally. However, the statistical heterogeneity hinders the global model from adapting to the non-IID distributions among clients. Moreover, untrusted and unreliable central servers and malicious clients may compromise model integrity and availability, thus degrading the robustness of FL. To address these challenges, we present RobustPFL, a decentralized personalized federated learning (PFL) approach that combines α-based Layer-position Normalized Similarity (α-LNS) and local collaborative training to improve personalized performance while utilizing a blockchain-based committee mechanism to coordinate the aggregation process, thereby achieving high personalized accuracy and robustness. Extensive experiments show that our RobustPFL approach outperforms multiple algorithms, including Local training, FedAvg, FedReptile, Per-FedAvg, FedBN, and SPFL, on MNIST, CIFAR10, EMNIST, and N-BaIoT datasets in four non-IID settings. We also evaluate RobustPFL's effectiveness against attacks - poisoning attacks and free-riding attacks. Particularly, for three prevalent poisoning attacks (backdoor, label flipping, and model poisoning attacks), we compare non-defensive (FedAvg) and defensive (Krum, trimmed mean, Bulyan, FedBN, FLAME, and FangTrmean) methods with our proposed RobustPFL. The results show that our approach achieves significant defensive effects. © 2004-2012 IEEE.

关键词： Federated learning

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SPM-GCN: An adaptive reordering algorithm for sparse LU factorization via GCN

SPM-GCN: An adaptive reordering algorithm for sparse LU fact...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Min Tian Huazeng Liu Junjie Wang Qi Liu Wei Du Zanjun Zhang Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Key Laboratory of Computing Power Network and Information Security Qilu University of Technology (Shandong Academy of Sciences) Jinan Key Laboratory of High Performance Computing Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shaanxi Key Laboratory of Large Scale Electromagnetic Computing Xidian University Xi’an China

Sparse LU factorization is a critical kernel in scientific computing and engineering applications. A better nonzero pattern of sparse matrixes can accelerate LU factorization by reordering. Traditionally it’s difficult to predict which non-zero pattern is optimal for a sparse matrix. In this paper, we proposed a graph convolutional neural network (GCN) for adaptively selecting the optimal reordering algorithm from five candidate reordering algorithms in the Matrix preprocessing step, referred to as SPM-GCN. After using our SPM-GCN, the average numerical factorization time outperformed the other five algorithms, and the average numerical factorization time was reduced by 17.3% compared with the default method of swSuperLU.

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A Nature-Inspired Energy-Efficient Clustering Protocol Based on Improved Hybrid Grey Wolf Optimization for FANETs

A Nature-Inspired Energy-Efficient Clustering Protocol Based...

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IEEE International Conference on Electronics and Communication Engineering (ICECE)

作者： Mengying Xu Yunxiao Zu Jie Zhou Jiapeng Dai Jing Chen Meiru Liu School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China Xinjiang Production and Construction Corps Key Laboratory of Computing Intelligence and Network Information Security Shihezi China Department of Information Science and Technology Shihezi University Shihezi China

ISBN: (数字)9798331517755

ISBN: (纸本)9798331517762

关键词： Wireless communication Protocols Machine learning algorithms Simulation Clustering algorithms Energy efficiency Ad hoc networks Magnetic heads Particle swarm optimization Optimization

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Multi-Channel Hypergraph Convolution Group Recommendation with Member Information Enhancement 25

Multi-Channel Hypergraph Convolution Group Recommendation wi...

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25th IEEE International Conferences on High Performance computing and Communications, 9th International Conference on Data Science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Chen, Tianhao Gao, Qian Fan, Jun Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan250014 China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd Shandong Jinan250101 China

ISBN: (纸本)9798350330014

Group recommendation involves comprehensively considering various aspects, including members and items, to predict the overall interests of a group and recommend suitable items through a recommendation system. With the rapid evolution of the Internet, online group activities have become increasingly prevalent, making group recommendation a highly discussed topic within the realm of recommendation systems. However, current research in group recommendation still confronts the following challenges. Firstly, a predominant portion of group recommendation research focuses solely on aggregating group-level information, neglecting the valuable contribution of higher-order member-level insights to group recommendation. Moreover, some aggregation strategies overly prioritize fairness, thereby disregarding common real-world patterns. Secondly, previous studies have primarily relied on aggregating individual member interests to establish group preferences, lacking a holistic consideration of group dynamics. To tackle these challenges, this study introduces an innovative approach by implementing multi-channel hypergraph convolution within the member perspective. This approach aims to effectively extract higher-order insights from members by utilizing a member information enhancement module. By establishing group interconnections based on similarity and employing an adaptive fusion network to amalgamate multiple viewpoints, a final representation is derived. Experimental results demonstrate that our proposed model surpasses baseline models by 3% to 4% in terms of hit rate and accuracy, validating the efficacy of our approach. © 2023 IEEE.

关键词： Recommender systems

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An Early Detection Model of Prostate Cancer Based on MRI 9

An Early Detection Model of Prostate Cancer Based on MRI

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Liu, Yuankun Xing, Lumin Wang, Di Nie, Xin Zhang, Xueyan Wu, Di Liu, Wenjian The First Affiliated Hospital of Shandong First Medical University Shandong Provincial Qianfoshan Hospital Jinan China City University of Macau Faculty of Data Science China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Chongqing Jianzhu College Chongqing China Shandong Youth University of Political Science School of Information Work Office Jinan China University of Southern Queensland School of Mathematics Physics and Computing QLD Australia

ISBN: (纸本)9798350391367

Prostate cancer is a prevalent form of cancer among men, with a high rate of occurrence but relatively lower mortality compared to other types of cancer. Early detection is crucial because it allows for earlier intervention, potentially preventing progression to more severe stages, and improving patient outcomes. However, human diagnosis is time-consuming and inefficient, an autonomous early detection system is essential for saving lives. To address this challenge, we propose a novel MoE-MRIViT framework that uses advanced machine learning and computer vision techniques to analyze MRI images and identify features indicative of prostate cancer. The system processes the data using a vision transformer to extract precise prostate cancer features from MRI scans, and uses attention mechanisms to focus on significant areas. Through the MoE-MRIViT intervention in two stages, we have achieved superior results compared to the state-of-the-art (SOTA) methods. Experimental results demonstrate that our model achieves an impressive accuracy of 94.84%, outperforming the ResNet-101 and the basic ViT models, which achieve accuracies of 89.4% and 92.31%, respectively. © 2024 IEEE.

关键词： Urology

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RKSeg+: make full use of Runge–Kutta methods in medical image segmentation

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Multimedia Systems 2024年第2期30卷 65-65页

作者： Zhu, Mai Fu, Chong Wang, Xingwei School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang110819 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China

The dynamical system perspective has been used to build efficient image classification networks and semantic segmentation networks. Furthermore, the Runge–Kutta (RK) methods are powerful tools for building networks from the dynamical systems perspective. Hence, the Runge–Kutta segmentation network (RKSeg) for medical image segmentation was born. Skip connections and multiple scaling are often used in common models but lack mathematical explanations. RKSeg interprets and uses skip connections based on the RK methods. Therefore, RKSeg greatly improves segmentation efficiency. However, it does not explain and use multiple scales from a dynamical system perspective but only inherits the multi-scale scheme of existing models. We compensate for this shortcoming by interpreting and using multiple scales based on the RK methods. In addition, the network structure also limits the excellent image classification networks as the backbones of RKSegs. Therefore, we modify the network structure to support more image classification networks as backbones. As a result, we propose a novel network structure RKSeg+. Our proposed RKSeg+ achieves better segmentation results with fewer parameters than RKSeg. Furthermore, RKSeg+, well configured with few parameters, outperforms state-of-the-art models on six of the ten organ datasets in the Medical Segmentation Decathlon. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Semantic Segmentation

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