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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6369-6384页

作者： Wang, Xiuhua Wang, Shuai Li, Yiwei Fan, Fengrui Li, Shikang Lin, Xiaodong Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen361024 China University of Guelph School of Computer Science GuelphONN1G 2W1 Canada

Federated learning (FL) is a popular distributed paradigm where enormous clients collaboratively train a machine learning (ML) model under the orchestration of a central server without knowing the clients' private raw data. The development of effective FL algorithms faces multiple practical challenges including data heterogeneity and clients' privacy protection. Despite that numerous attempts have been made to deal with data heterogeneity or rigorous privacy protection, none have effectively tackled both issues simultaneously. In this article, we propose a differentially private and heterogeneity-robust FL algorithm, named DP-FedCVR to mitigate the data heterogeneity by following the client-variance-reduction strategy. Besides, it adopts a sophisticated differential privacy (DP) mechanism where the privacy-amplified strategy is applied, to achieve a rigorous privacy protection guarantee. We show that the proposed DP-FedCVR algorithm maintains its heterogeneity-robustness though DP noises are incorporated, while achieving a sublinear convergence rate for a nonconvex FL problem. Numerical experiments based on image classification tasks are presented to demonstrate that DP-FedCVR provides superior performance over the benchmark algorithms in the presence of data heterogeneity and various DP privacy budgets. © 2020 IEEE.

关键词： Federated learning

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Online Learning Behavior Analysis and Prediction Based on Spiking Neural Networks

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Journal of Social Computing 2024年第2期5卷 180-193页

作者： Yanjing Li Xiaowei Wang Fukun Chen Bingxu Zhao Qiang Fu the Institute of Education Science Research Heilongjiang UniversityHarbin 150080China the School of Cyberspace Security Shandong University of Political Science and LawJinan 250014China the School of Computer Science and Technology Harbin Engineering UniversityHarbin 150001China the Faculty of Electrical and Computer Engineering University of VictoriaVictoriaV8P 5C2Canada. the Guangxi Key Lab of Brain-Inspired Computing and Intelligent Chips School of Electronic and Information EngineeringGuangxi Normal UniversityGuilin 541004China

The vast amount of data generated by large-scale open online course platforms provide a solid foundation for the analysis of learning behavior in the field of *** study utilizes the historical and final learning behavior data of over 300000 learners from 17 courses offered on the edX platform by Harvard University and the Massachusetts Institute of technology during the 2012-2013 academic *** have developed a spike neural network to predict learning outcomes,and analyzed the correlation between learning behavior and outcomes,aiming to identify key learning behaviors that significantly impact these *** goal is to monitor learning progress,provide targeted references for evaluating and improving learning effectiveness,and implement intervention measures *** results demonstrate that the prediction model based on online learning behavior using spiking neural network achieves an impressive accuracy of 99.80%.The learning behaviors that predominantly affect learning effectiveness are found to be students’academic performance and level of participation.

关键词： online learning learning outcomes prediction learning behavior analysis spiking neural network

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Advanced BERT and CNN-Based Computational Model for Phishing Detection in Enterprise Systems

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computer Modeling in engineering & sciences 2024年第12期141卷 2165-2183页

作者： Brij B.Gupta Akshat Gaurav Varsha Arya Razaz Waheeb Attar Shavi Bansal Ahmed Alhomoud Kwok Tai Chui Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India University Centre for Research and Development(UCRD) Chandigarh UniversityChandigarh140413India Computer Engineering Ronin InstituteMontclairNJ 07043USA Department of Business Administration Asia UniversityTaichung413Taiwan Department of Electrical and Computer Engineering Lebanese American UniversityBeirut1102Lebanon College of Business Administration Management DepartmentPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Research and Innovation Insights2TechinfoJaipur302001India Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Electronic Engineering and Computer Science Hong Kong Metropolitan University(HKMU)Hong Kong518031China

Phishing attacks present a serious threat to enterprise systems,requiring advanced detection techniques to protect sensitive *** study introduces a phishing email detection framework that combines Bidirectional Encoder Representations from Transformers(BERT)for feature extraction and CNN for classification,specifically designed for enterprise information ***’s linguistic capabilities are used to extract key features from email content,which are then processed by a convolutional neural network(CNN)model optimized for phishing *** an accuracy of 97.5%,our proposed model demonstrates strong proficiency in identifying phishing *** approach represents a significant advancement in applying deep learning to cybersecurity,setting a new benchmark for email security by effectively addressing the increasing complexity of phishing attacks.

关键词： Phishing BERT convolutional neural networks email security deep learning

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FOGO - an Optimized Fog and Edge Computing Method for VANETs 46

FOGO - an Optimized Fog and Edge Computing Method for VANETs

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46th ICT and Electronics Convention, MIPRO 2023

作者： Corluka, Renato Matos, Ivona Damjanovic, Davor Balen, Josip Josip Juraj Strossmayer University of Osijek Faculty of Electrical Engineering Computer Science and Information Technology Osijek Kneza Trpimira 2b Osijek31000 Croatia

ISBN: (纸本)9789532331042

Vehicular Ad-hoc Networks (VANETs) have many specific issues and challenges due to a heterogeneous and highly dynamic environment, and therefore traditional solutions need to be improved and adopted in order to satisfy the networking and processing requirements. Fog and edge computing principles enable VANETs to achieve a more realistic and dependable architecture by using several layers for information processing. In this paper, the developed FOGO method is improved and customized for VANETs. The proposed solution is using an enhanced infrastructure in which additional mobile fog nodes are added to the network together with the existing stationary nodes. Small and medium sized messages are processed by mobile fog nodes, which then disseminate the results across the network. Roadside units (RSUs) are fixed, highly-capable devices positioned throughout the network to assist the processing of increasing volumes of data. Additionally, the cloud is taken into account, but only when processing large amounts of data. Along with the presented method flowcharts, operating algorithms, and message structure, the proposed system architecture is described. Furthermore, potential use cases are suggested together with the metrics for the system performance evaluation. The LuST scenario was used to evaluate the proposed architecture and the results showed improved message processing efficiency. © 2023 MIPRO Croatian Society.

关键词： Fog

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Wireless Federated Learning With Hybrid Local and Centralized Training: A Latency Minimization Design

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IEEE Journal on Selected Topics in Signal Processing 2023年第1期17卷 248-263页

作者： Huang, Ning Dai, Minghui Wu, Yuan Quek, Tony Q. S. Shen, Xuemin State Key Lab of Internet of Things for Smart City China University of Macau Department of Computer and Information Science China University of Macau State Key Laboratory of Internet of Things for Smart City The Department of Computer and Information Science China Zhuhai Um Science and Technology Research Institute Zhuhai519031 China Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore National Cheng Kung University Taiwan University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Wireless federated learning (FL) is a collaborative machine learning (ML) framework in which wireless client-devices independently train their ML models and send the locally trained models to the FL server for aggregation. In this paper, we consider the coexistence of privacy-sensitive client-devices and privacy-insensitive yet computing-resource constrained client-devices, and propose an FL framework with a hybrid centralized training and local training. Specifically, the privacy-sensitive client-devices perform local ML model training and send their local models to the FL server. Each privacy-insensitive client-device can have two options, i.e., (i) conducting a local training and then sending its local model to the FL server, and (ii) directly sending its local data to the FL server for the centralized training. The FL server, after collecting the data from the privacy-insensitive client-devices (which choose to upload the local data), conducts a centralized training with the received datasets. The global model is then generated by aggregating (i) the local models uploaded by the client-devices and (ii) the model trained by the FL server centrally. Focusing on this hybrid FL framework, we firstly analyze its convergence feature with respect to the client-devices' selections of local training or centralized training. We then formulate a joint optimization of client-devices' selections of the local training or centralized training, the FL training configuration (i.e., the number of the local iterations and the number of the global iterations), and the bandwidth allocations to the client-devices, with the objective of minimizing the overall latency for reaching the FL convergence. Despite the non-convexity of the joint optimization problem, we identify its layered structure and propose an efficient algorithm to solve it. Numerical results demonstrate the advantage of our proposed FL framework with the hybrid local and centralized training as well as our proposed alg

关键词： information management

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Implementation and Evaluation of a Deep Neural Network for Spam Detection: An Empirical Study of Accuracy and Efficiency

Implementation and Evaluation of a Deep Neural Network fo...

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International Symposium on Innovative and Interdisciplinary Applications of Advanced Technologies, IAT 2023

作者： Varga, Luka Livada, Časlav Baumgartner, Alfonzo Šojo, Robert Faculty of Electrical Engineering Computer Science and Information Technology Osijek Josip Juraj Strossmayer University in Osijek Kneza Trpimira 2B Osijek31000 Croatia

ISBN: (纸本)9783031430558

The problem of spam emails is a widespread issue that creates a lot of inconvenience for individuals and organizations. According to statistics, approximately 84% of emails received on a daily basis are recognized as spam. This paper aims to present a solution to this problem by proposing the use of a neural network capable of identifying and classifying potential spam emails. The neural network was developed using Python, TensorFlow, Keras, Google Colaboratory, and Jupyter. These tools were chosen because they are widely used and well-suited for the task of creating a deep learning model. The results of the network were found to be satisfactory, with an accuracy rate of approximately 99%. This is comparable to the results achieved by large companies such as Google and Yahoo! who are known to use similar methods to combat spam. Overall, this paper demonstrates that neural networks can be a powerful tool for addressing the problem of spam emails and that the proposed solution has the potential to improve the efficiency and effectiveness of spam filtering for individuals and organizations. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Electronic mail

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Differential evolution using multi-strategy for the improvement of optimization performance

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Neural Computing and Applications 2024年 1-28页

作者： Liu, Nengxian Luo, Jianbin Chang, Jie Pan, Jeng-Shyang College of Computer and Data Science Fuzhou University No. 2 Wulongjiang North Avenue Fuzhou University Town Fujian Fuzhou350108 China College of Civil Engineering Fuzhou University No. 2 Wulongjiang North Avenue Fuzhou University Town Fujian Fuzhou350108 China Department of Medical Information Wannan Medical College Higher Education Park No. 22 Wenchang West Road Anhui Wuhu240001 China College of Computer Science and Engineering Shandong University of Science and Technology No. 579 Qianwangang Road Huangdao District Shandong Qingdao266590 China

Differential evolution (DE) is an effective population-based optimization approach that has been widely used to deal with scientific and engineering problems. However, the performance of DE method is largely dependent on its trial vector produce strategy, namely, mutation strategy, crossover operation and its corresponding control parameters. As claimed by the ‘No free Lunch theorem’, each mutation or crossover strategy has its fatal flaws;hence, the DE method having a single operation strategy cannot solve all types of optimization problems. Therefore, we propose a novel multi-strategy DE (MS-DE) in this study. First, the proposed algorithm uses combined mutation strategies including two powerful mutation strategies and selects them in a probabilistic way. Second, an improved crossover operation is introduced to tackle the stagnation problem. When a stagnation occurs, DE employs the top p-best vector to conduct crossover operation. Third, the control parameters are tuned in novel adaptation schemes. Finally, a local search is utilized in the proposed method to accelerate the convergence. The proposed MS-DE method is examined on CEC2017 test suite, and experiment results confirm its outperformance over several state-of-the-art DE methods. Furthermore, the proposed MS-DE is applied to two constrained engineering problems. The comparison results on these two problems also demonstrate the efficiency of our proposed MS-DE. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Optimization algorithms

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Vision Transformers with Hierarchical Attention

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Machine Intelligence Research 2024年第4期21卷 670-683页

作者： Yun Liu Yu-Huan Wu Guolei Sun Le Zhang Ajad Chhatkuli Luc Van Gool Institute for Infocomm Research(I2R) A*STARSingapore 138632Singapore Institute of High Performance Computing(IHPC) A*STARSingapore 138632Singapore Computer Vision Lab ETH ZürichZürich 8092Switzerland School of Information and Communication Engineering University of Electronic Science and Technology of China(UESTC)Chengdu 611731China

This paper tackles the high computational/space complexity associated with multi-head self-attention(MHSA)in vanilla vision *** this end,we propose hierarchical MHSA(H-MHSA),a novel approach that computes self-attention in a hierarchical ***,we first divide the input image into patches as commonly done,and each patch is viewed as a ***,the proposed H-MHSA learns token relationships within local patches,serving as local relationship ***,the small patches are merged into larger ones,and H-MHSA models the global dependencies for the small number of the merged *** last,the local and global attentive features are aggregated to obtain features with powerful representation *** we only calculate attention for a limited number of tokens at each step,the computational load is reduced ***,H-MHSA can efficiently model global relationships among tokens without sacrificing fine-grained *** the H-MHSA module incorporated,we build a family of hierarchical-attention-based transformer networks,namely *** demonstrate the superiority of HAT-Net in scene understanding,we conduct extensive experiments on fundamental vision tasks,including image classification,semantic segmentation,object detection and instance ***,HAT-Net provides a new perspective for vision *** and pretrained models are available at https://***/yun-liu/HAT-Net.

关键词： Vision transformer hierarchical attention global attention local attention scene understanding.

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Cuckoo Search-Optimized Deep CNN for Enhanced Cyber Security in IoT Networks

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computers, Materials & Continua 2024年第12期81卷 4109-4124页

作者： Brij B.Gupta Akshat Gaurav Varsha Arya Razaz Waheeb Attar Shavi Bansal Ahmed Alhomoud Kwok Tai Chui Department of Computer Science and Information Engineering Asia UniversityTaichung413TaiwanChina Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057MaharashtraIndia Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India University Centre for Research and Development(UCRD) Chandigarh UniversityChandigarh140413India Computer Engineering Ronin InstituteMontclairNJ 07043USA Department of Business Administration Asia UniversityTaichung413TaiwanChina Department of Electrical and Computer Engineering Lebanese American UniversityBeirut1102Lebanon Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Research and Innovation Insights2TechinfoJaipur302001India Department of Computer Science Faculty of ScienceNorthern Border UniversityArar91431Saudi Arabia Department of Electronic Engineering and Computer Science Hong Kong Metropolitan University(HKMU)Hong Kong518031China

Phishing attacks seriously threaten information privacy and security within the Internet of Things(IoT)*** phishing attack detection solutions have been developed for IoT;however,many of these are either not optimally efficient or lack the lightweight characteristics needed for practical *** paper proposes and optimizes a lightweight deep-learning model for phishing attack *** model employs a two-fold optimization approach:first,it utilizes the analysis of the variance(ANOVA)F-test to select the optimal features for phishing detection,and second,it applies the Cuckoo Search algorithm to tune the hyperparameters(learning rate and dropout rate)of the deep learning ***,our model is trained in only five epochs,making it more lightweight than other deep learning(DL)and machine learning(ML)*** proposed model achieved a phishing detection accuracy of 91%,with a precision of 92%for the’normal’class and 91%for the‘attack’***,the model’s recall and F1-score are 91%for both *** also compared our approach with traditional DL/ML models and past literature,demonstrating that our model is more *** study enhances the security of sensitive information and IoT devices by offering a novel and effective approach to phishing detection.

关键词： Deep learning phishing Cuckoo Search cable news network(CNN) IoT ANOVA F-test

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High-Voltage SF6 Circuit Breaker Maintenance 33rd

High-Voltage SF6 Circuit Breaker Maintenance

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33rd International Conference on Organization and technology of Maintenance, OTO 2024

作者： Soldo, Luka Knežević, Goran Zovko, Mario Croatian Transmission System Operator Transmission Area Osijek Vukovarska Cesta 217 Osijek31000 Croatia Faculty of Electrical Engineering Computer Science and Information Technology Osijek Kneza Trpmira 2b Osijek31000 Croatia

ISBN: (纸本)9783031805967

Nowadays, the need for uninterrupted power supply is greater than ever. One of the most effective ways to ensure an uninterrupted supply of electricity is the correct selection of the method and interval of maintenance of elements in a high-voltage substation. In this way, not only is a high-quality delivery of electricity ensured, but also the possibility of equipment failure that can be dangerous for people in the substation is reduced. A high-voltage circuit breaker is a device that performs several different functions. It is used for network configuration changes, and also provides protection for equipment and personnel. High voltages and thermal (current) stresses cause aging and wear of switch parts, and through various forms of maintenance and testing, damage parts are determined and potential hazards are safely removed. This paper explains the maintenance procedures of the high-voltage SF6 circuit breaker. Using the example of a high-voltage line-bay circuit breaker, the entire 6Y circuit breaker revision procedure (revision that is carried out in six-year cycles) is presented. In addition to visual inspection, checking, cleaning and replacement of worn parts, the switch on and off times were recorded. The quality of SF6 gas was also tested by measuring the concentration of SF6 gas decomposition products and the concentration of mineral oil aerosols in SF6 gas, by measuring the dew point of the gas and controlling the volume percentage of SF6 gas. The voltage drops at the primary connections of the switches were measured, and the operation of the pressure relays was also checked. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mineral oils

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