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41st International Conference on Machine Learning, ICML 2024

作者： Fu, Qiang Wilson, Ashia Department of Computer Science Yale University New HavenCT United States Department of Electrical Engineering and Computer Science MIT CambridgeMA United States

We propose a new method called the N-particle underdamped Langevin algorithm for optimizing a special class of non-linear functionals defined over the space of probability measures. Examples of problems with this formulation include training mean-field neural networks, maximum mean discrepancy minimization and kernel Stein discrepancy minimization. Our algorithm is based on a novel spacetime discretization of the mean-field underdamped Langevin dynamics, for which we provide a new, fast mixing guarantee. In addition, we demonstrate that our algorithm converges globally in total variation distance, bridging the theoretical gap between the dynamics and its practical implementation. Copyright 2024 by the author(s)

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A Multilevel Learning Model for Predicting CPU Utilization in Cloud Data Centers

A Multilevel Learning Model for Predicting CPU Utilization i...

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2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Daraghmeh, Mustafa Agarwal, Anjali Jararweh, Yaser Concordia University Electrical and Computer Engineering Montreal Canada Jordan University of Science and Technology Computer Science Irbid Jordan

ISBN: (纸本)9798350304602

In the contemporary era of cloud computing, efficient and precise prediction of CPU utilization ensures optimal performance and energy efficiency in data centers. Traditional predictive models often need to be improved as these data centers grow in complexity and scale, necessitating more nuanced and integrative solutions. This paper introduces an advanced multi-layered learning framework meticulously designed to meet the demands of modern cloud data centers. Our innovative approach synergistically combines anomaly detection, data clustering, and ensemble-based regression prediction. The Isolation Forest algorithm is used during the preliminary stage to identify and address anomalies within the data. Subsequent phases harness the K-Means clustering algorithm, refine data categorization based on recurrent CPU usage patterns, and employ multilevel ensemble-based prediction models for accurate forecasting rooted in historical and real-time data trends. Through comprehensive evaluations, our model demonstrates significant improvements in prediction accuracy and robustness against the dynamism inherent in cloud environments. Our research paves the way for a more resilient, proactive, and efficient approach to CPU utilization prediction, laying the foundational stone for future innovations in cloud computing resource management. © 2023 IEEE.

关键词： Forecasting

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Enhancing surrogate-assisted evolutionary optimization for medium-scale expensive problems: a two-stage approach with unsupervised feature learning and Q-learning

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Neural Computing and Applications 2024年第25期36卷 15545-15565页

作者： Gong, Yiyun Yu, Haibo Kang, Li Sun, Chaoli Zeng, Jianchao School of Electrical and Control Engineering North University of China Taiyuan030051 China School of Computer Science and Technology North University of China Taiyuan030051 China School of Environment and Safety Engineering North University of China Taiyuan030051 China School of Computer Science and Technology Taiyuan University of Science and Technology Taiyuan030024 China

This paper presents a novel two-stage progressive search approach with unsupervised feature learning and Q-learning (TSLL) to enhance surrogate-assisted evolutionary optimization for medium-scale expensive problems. The method attempts to address the challenges posed by multi-polar and multi-variable coupling properties in such problems. During the iteration, TSLL splits the optimization process into two distinct stages. First, two unsupervised feature learning techniques: principal component analysis (PCA) and Autoencoder, are utilized to improve the representation of potential optimal samples in the solution space. PCA is used for feature reduction, extracting the most important features. On the other hand, Autoencoder focuses on reconstructing features within the medium-scale solution space. To ensure comprehensive exploration of the entire solution space, TSLL employs two distinct surrogate modeling approaches along with Q-learning in the second stage to dynamically select the mutation strategy for the differential evolution operator. Numerous experiments demonstrate the superiority of TSLL over five state-of-the-art surrogate-assisted approaches and two sophisticated evolutionary algorithms including the winner of CEC 2017 on medium-scale benchmark problems and a wind farm layout problem. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Reinforcement learning

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Differential Coaxial Coils for Frequency-Independent Deep Magnetic Focusing on Implantable Devices

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IEEE Transactions on Antennas and Propagation 2025年第6期73卷 3792-3801页

作者： Shah, Syed Ahson Ali Kim, Ye R. Kim, Yun-Su Jeong, Seog Y. Rim, Chun T. Department of Electrical Engineering and Computer Science Gwangju61005 Korea Republic of Samsung Electronics Company Ltd. Research and Development Campus Seoul06765 Korea Republic of

Magnetic field focusing in longitudinal direction has been a missing link for 3-D synthesized magnetic focusing (3D SMF). Deep magnetic focusing (DMF) by multiple coaxial coils, a sort of 3D SMF, is first proposed in this article, where magnetic field becomes maximum at the focal range in longitudinal direction. DMF is requisite for transcranial magnetic stimulation (TMS) and wireless power transfer (WPT). As the simplest DMF, a set of differential coils with opposite current direction is implemented. Optimum coil currents, coil radii, and coil distance are designed to provide with zero magnetic field at zero range and maximum magnetic field at a focal range. This DMF performance is found to be achieved with a penalty of peak magnetic field reduction. Experiments for the differential coils of diameters 10 and 5 cm with 3 cm gap showed 4 cm depth DMF within 6 cm diameter. Specific numbers of turns make the DMF be driven by only a single-voltage source, which is first adopted in SMF. The frequency versatile DMF characteristic is experimentally verified for 60 Hz and 40 kHz, where 27 mW is delivered to a focal point that is a demonstration for safer WPT of implantable devices. © 1963-2012 IEEE.

关键词： Inductive power transmission

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Group Based Privacy Preserving Incentive Mechanism for Federated Learning 3

Group Based Privacy Preserving Incentive Mechanism for Feder...

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3rd IEEE International Conference on Control, Instrumentation, Energy and Communication, CIEC 2024

作者： Kanchan, Sneha Raj, Shivam Ahmed, Abrar Choi, Bong Jun Universiti Tunku Abdul Rahman Internet Engineering and Computer Science Kajang Malaysia Electrical Engineering Iit Kharagpur West Bengal India Soongsil University Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9798350313703

Federated Learning (FL) ensures the privacy of clients' data by allowing them to train their data by themselves and sharing only updated gradients to the server instead of the raw data. Various incentive mechanisms are proposed to boost clients' participation in the FL process, which awards the clients based on their contribution. However, clients may selfishly minimize their computation resources to maximize profit and incentives. Some malicious clients may abort their local training process in the middle or reduce the size of training data and declare that they have trained their data completely so that they may claim maximum incentives. As a result, the quality of the trained model is reduced. Therefore, we propose a group signature-based privacy-preserving incentive mechanism in which the admin in charge of the group signing process can efficiently identify any fraudulent activity the client does. Moreover, our scheme ensures that the server can easily detect the clients that abort their process. The performance analysis shows how our algorithm is more efficient than most existing incentive algorithms in FL. Security analysis discusses that our algorithm is resistant to most of the attacks in FL and more secure than existing methods. © 2024 IEEE.

关键词： Privacy-preserving techniques

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HASHL: Dynamic Hash Verification for Detecting and Preventing Eclipse Attacks

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IEEE Internet of Things Journal 2025年第13期12卷 23524-23535页

作者： He, Daojing Gong, Wei Tu, Chen Chan, Sammy Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Jiangxi University of Science and Technology School of Information Engineering Jiangxi Ganzhou China City University of Hong Kong Department of Electrical Engineering Hong Kong

With the rapid development of blockchain technology, P2P networks are facing increasing security threats, among which Eclipse attacks, as a type of network isolation attack, have seriously affected the normal operation of the network and the integrity of data. To address this challenge, this study implements node authentication and dynamic reputation evaluation by leveraging a dynamic hash computation mechanism that integrates challenge strings, node identifiers, and the latest active time, ensuring the uniqueness of node identities and the authenticity of operations. Based on a dynamic hash chain behavior evaluation mechanism, node behaviors are quantified across three dimensions: integrity, consistency, and temporal consistency, enabling precise identification of anomalous nodes. Furthermore, a network prevention repository framework is proposed, which dynamically adjusts the trust index of nodes by combining historical behavior with real-time data, effectively detecting and defending against stealthy Eclipse attacks. In addition, extensive testing on both Bitcoin and Ethereum platforms has shown that the method proposed in this study not only can effectively coexist on these two platforms, but also significantly improves the security and stability of the network, effectively reducing the occurrence of Eclipse attacks. © 2014 IEEE.

关键词： Blockchain

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Edge Computing Based Early Yellow Rust Disease Detection in Wheat Plants

Edge Computing Based Early Yellow Rust Disease Detection in ...

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2024 International Conference on Microelectronics, ICM 2024

作者： Ahsan, Ali Iqbal, Muhammad Sajid Ahmar, Muzammil Adnan, Muhammad Akbar, Muhammad Ali Bermak, Amine National University of Computer and Emerging Sciences Electrical Engineering Department Chiniot Pakistan Hammad Bin Khalifa University Computer Science and Engineering Department Doha Qatar

ISBN: (纸本)9798350379396

The agriculture industry contributes most to expanding economies and populations and is essential to the production of high-quality food. Plant diseases are dependent on a variety of environmental variables that can significantly impact the production gains. Manual approaches for identification are laborious and prone-to-error. The advent of computer vision along with the growing trend of cloud computing, have opened the door for deep learning-based plant disease diagnostics. However, a reliable and fast internet connection is required to use cloud computing which is not feasible in most developing countries. In this work, we have suggested a method for detecting wheat crop yellow rust disease using edge computing. Jetson Nano is used to create typical benchmark deep learning models, including ResNet-18, ResNet-50, RegNet_x_3f_2gf, EfficientNet-B2, MobileNet V3, and DenseNet-101. The yellow rust dataset is used to track the training times and detection accuracies of these models. Based on the testing results, the top-performing networks are ResNet-18 and DenseNet-101. Out of the six models under evaluation, they had the least loss of less than 0.30 with the accuracy of 87%. In comparison to other deep learning models, Resnet-18 needed the least amount of training and testing time, making it the best model for edge computing. © 2024 IEEE.

关键词： Mobile edge computing

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Intelligent ADHD Detection Using Explainable Multimodal Fusion Model 4

Intelligent ADHD Detection Using Explainable Multimodal Fusi...

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4th IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Tsai, Ho-Jung Wu, Hsiao-Kuang Chen, Chun-Chuan Yeh, Chen-Hsin Chu, Tzu-Yueh Yeh, Shih-Ching National Central University Department of Computer Science and Information Engineering Taiwan National Central University Department of Biomedical Sciences and Engineering Taiwan Hunan University College of Computer Science and Electrical Engineering China

ISBN: (纸本)9798350363586

Deep learning has been proved to diagnose Attention Deficit/Hyperactivity Disorder (ADHD) accurately, but it has raised concerns about trustworthiness because of the lack of explainability. Fortunately, the development of explainable artificial intelligence (XAI) offers a solution to this problem. In this study, we employed a VR-based GO/NOGO task with distractions, capturing participants' eye movement, head movement, and electroencephalography (EEG) data. We used the collected data to train an explainable multimodal fusion model. Besides classifying between ADHD and normal children, the proposed model also generates explanation heatmaps. The heatmaps provide the importance of specific variables and timestamps in the EEG data to help us analyze the patterns captured by the model. According to our observations, the model identified specific time intervals that related to specific event-related potentials (ERPs) components. The heatmaps also demonstrate that the impacts of distractions vary between not only the GO and NOGO events but also ADHD and normal children. © 2024 IEEE.

关键词： Eye movements

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Ensemble Learning for Predicting Task Connectivity over Time in Cloud Data Centers

Ensemble Learning for Predicting Task Connectivity over Time...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Daraghmeh, Mustafa Agarwal, Anjali Jararweh, Yaser Electrical and Computer Engineering Concordia University Montreal Canada Jordan University of Science and Technology Computer Science Irbid Jordan

ISBN: (纸本)9781665494274

The rapid growth and interdependencies of cloud-hosted services and applications have made it imperative to optimize data center resource management while maintaining low operating costs. Inefficient resource use, rising energy consumption, and operating costs impact cloud provider's ability to provide high-quality services on an elastic basis. However, changing the selection and decision-making processes based on how the tasks are set up can improve scheduling and resource management in cloud data centers. In this paper, we develop a multivariate time series prediction model for task connectivity based on windowing characterization and ensemble learning methods. The high cardinality features are handled using a counter encoder, and the task trace data is transformed using a sliding window, from which features are extracted and used in conjunction with the task profile data to train and tune the candidate estimators. The best model outcomes are then used to construct an ensembled estimator. As part of the evaluation, a baseline comparison is performed in order to determine how well ensemble learning predicts task connectivity over time. The model outcomes are assisted using standard classification metrics such as accuracy, precision, and recall, including the F1 score, Kappa, and Matthews correlation coefficient. The results show that the proposed model outperformed the traditional models in most performance metrics, indicating the successful implementation of an ensemble learning approach for task connectivity predictions in large-scale cloud data centers. © 2023 IEEE.

关键词： Learning systems

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FOD-YOLO NET: Fasteners fault and object detection in railway tracks using deep yolo network1

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8123-8137页

作者： Brintha, K. Jawhar, S. Joseph Department of Computer Science and Engineering Arunachala College of Engineering for Women Manavilai Tamil Nadu Nagercoil India Department of Electrical and Electronics Engineering Arunachala College of Engineering for Women Manavilai Tamil Nadu Nagercoil India

Automated railway security systems prevent train collisions with trackside obstructions that cause accidents in high-speed railways. Rail safety is being improved and accident rates reduced through continuous research. A rapid advancement in deep learning has promoted new possibilities for research in this field. In this work, a novel deep learning-based FOD-YOLO net is proposed for detecting the fasteners faults and objects in the railway tracks. There are two basic components in the deep learning-based YOLOv8: the backbone and the head. YOLOv8 utilizes an improved version of the CSPDarknet53 network for detecting objects on the railway track. The head of YOLOv8 consists of EfficientNet with various convolutional layers with squeeze and excitation blocks for detecting any defect in the track fasteners. These layers are liable for detecting the objectness scores, bounding boxes and class probabilities structured with fully connected layers for the objects and faults in tracks. Based on the results from the Yolo network, the alert message is sent to the loco pilot to avoid accidents using fuzzy logic. The experimental fallouts of proposed FOD-YOLO net achieve higher accuracy and yields better evaluation results with 98.14% accuracy, 98.84% precision and 95.94% recall. From the experimental results, the FOD-YOLO net improves the overall accuracy range by 5.44%, 4.72%, 0.73%, and 13.18% better than Fast RCNN, YOLOv5s-VF, YOLO-GD, and 2D-SSA + Deep network respectively. © 2024 – IOS Press. All rights reserved.

关键词： Object detection

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