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13th International Conference on Research in ComputIng at Feminine, RIF 2024

作者： AlFarra, Joud Alam, Leena Marir, Naila Sarirete, Akila Computer Science Department Effat College of Engineering Effat University Jeddah Saudi Arabia LIRE Laboratory Constantine 2-Abdelhamid Mehri University Constantine Algeria

The Internet of Vehicles (IoV), integrating vehicles with EV charging stations, faces significant vulnerabilities to cyber threats. Designing an efficient Intrusion Detection System (IDS) to detect sophisticated attacks within EV charging stations is crucial for safeguarding this interconnected network. However, creating IDS models using centralized machine learning approaches requires overcoming challenges such as scalability, time-consuming analysis, and real-time processing requirements. In this context, we propose a novel distributed system to IDS design for the IoV, leveraging distributed learning techniques to address these challenges and enhance EV charging station security. This study utilizes different machine learning models to improve the effectiveness of abnormal behavior detection. Based on the robustness and adaptability of machine learning models, the detection and classification of intrusion data are significantly enhanced. Our presented system is validated using the CICEV2023 dataset. Simulation results demonstrate that our algorithm achieves higher efficiency and accuracy compared to existing centralized schemes. © 2024 Copyright for this paper by its authors.

关键词： Network intrusion

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Compact Model Build Upon Dynamic Weighting Artificial Neural Network Approach for Complementary Field Effect Transistors

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IEEE Transactions on Electron Devices 2024年第1期71卷 246-253页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation The Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program The Institute of Communications Engineering the Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

In this work, a dynamic weighting-artificial neural network (DW-ANN) methodology is presented for quick and automated compact model (CM) generation. It takes advantage of both TCAD simulations for high accuracy and SPICE simulations for cost-effectiveness. This methodology is developed for gate-all-around (GAA) silicon (Si) nanosheet (NS) complementary field effect transistor (CFET), a potential candidate for future CMOS technology due to its innate properties. The critical process variation (PV) sources that severely degrade the CFET performance are estimated using DW-ANN. It reduces the computation cost and predicts the effects of PV sources with less than 1% error. Furthermore, a compact DW-ANN-based Verilog-A model has been developed that captures the dc as well as transient behavior accurately for circuit-level analysis. CFET-based circuits such as inverter, 6T-static random access memory (SRAM), and ring oscillator (RO) are characterized and implemented using DW-ANN-based CM. The overall average error of the model is reported as less than 2%. Therefore, the proposed device and circuit modeling approach provides a feasible solution for the rapid compact modeling of new emerging devices with good convergence and high accuracy. © 1963-2012 IEEE.

关键词： Neural networks

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Modif-SegUnet: Innovatively Advancing Liver Cancer Diagnosis and Treatment through Efficient and Meaningful Segmentation of 3D Medical Images

Modif-SegUnet: Innovatively Advancing Liver Cancer Diagnosis...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Chenhang Jin, Yujian Liang, Jiayi Yang, YuHan Nie, Shanling Hai, Yang East China University of Science and Technology Department of Computer Science and Engineering Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China The University of Sydney Faculty of Engineering Sydney Australia

ISBN: (纸本)9798350337488

Hepatocellular Carcinoma (HCC) holds a record of high incidence and severe global harm. In tasks of liver cancer segmentation based on 3D medical images, the majority of methods have endeavored to enhance the 3D U-net by integrating the latest modules from the field of computer Vision (such as the transformer), often overlooking the distinct characteristics of liver components. We introduced a novel deep learning architecture, Modif-SegUnet, to circumvent this limitation. This architecture extends the U-net model by incorporating a 3D-modifiable attention module, thereby fostering a heightened focus on distinguishing between normal liver sections and lesions. Furthermore, Modif-SegUnet ingeniously amalgamates the 3D-modifiable attention module with the Modif-transformer block, enabling efficient capture of relevant and valuable full-text information in CT images that contain liver or tumor regions. We subjected the proposed Modif-SegUnet to evaluation on the Liver Tumor Segmentation benchmark dataset. Experimental outcomes indicate that our methodology surpasses state-of-the-art approaches in liver tumor segmentation, suggesting potential pathways for advancing the diagnosis and treatment of liver cancer. © 2023 IEEE.

关键词： 3D medical image attention CT Liver cancer Segmentation transformer U-Net

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PGODE: Towards High-quality System Dynamics Modeling 41

PGODE: Towards High-quality System Dynamics Modeling

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41st International Conference on Machine Learning, ICML 2024

作者： Luo, Xiao Gu, Yiyang Jiang, Huiyu Zhou, Hang Huang, Jinsheng Ju, Wei Xiao, Zhiping Zhang, Ming Sun, Yizhou Department of Computer Science University of California Los Angeles United States National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China Department of Statistics and Applied Probability University of California Santa Barbara United States Department of Statistics University of California Davis United States

This paper studies the problem of modeling multi-agent dynamical systems, where agents could interact mutually to influence their behaviors. Recent research predominantly uses geometric graphs to depict these mutual interactions, which are then captured by powerful graph neural networks (GNNs). However, predicting interacting dynamics in challenging scenarios such as out-of-distribution shift and complicated underlying rules remains unsolved. In this paper, we propose a new approach named Prototypical Graph ODE (PGODE) to address the problem. The core of PGODE is to incorporate prototype decomposition from contextual knowledge into a continuous graph ODE framework. Specifically, PGODE employs representation disentanglement and system parameters to extract both object-level and system-level contexts from historical trajectories, which allows us to explicitly model their independent influence and thus enhances the generalization capability under system changes. Then, we integrate these disentangled latent representations into a graph ODE model, which determines a combination of various interacting prototypes for enhanced model expressivity. The entire model is optimized using an end-to-end variational inference framework to maximize the likelihood. Extensive experiments in both in-distribution and out-of-distribution settings validate the superiority of PGODE compared to various baselines. Copyright 2024 by the author(s)

关键词： Graph neural networks

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Learning on Graphs under Label Noise 48

Learning on Graphs under Label Noise

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Yuan, Jingyang Luo, Xiao Qin, Yifang Zhao, Yusheng Ju, Wei Zhang, Ming Peking University National Key Laboratory for Multimedia Information Processing Beijing China Peking University School of Computer Science Beijing China University of California Department of Computer Science Los Angeles United States

ISBN: (纸本)9781728163277

Node classification on graphs is a significant task with a wide range of applications, including social analysis and anomaly detection. Even though graph neural networks (GNNs) have produced promising results on this task, current techniques often presume that label information of nodes is accurate, which may not be the case in real-world applications. To tackle this issue, we investigate the problem of learning on graphs with label noise and develop a novel approach dubbed Consistent Graph Neural Network (CGNN) to solve it. Specifically, we employ graph contrastive learning as a regularization term, which promotes two views of augmented nodes to have consistent representations. Since this regularization term cannot utilize label information, it can enhance the robustness of node representations to label noise. Moreover, to detect noisy labels on the graph, we present a sample selection technique based on the homophily assumption, which identifies noisy nodes by measuring the consistency between the labels with their neighbors. Finally, we purify these confident noisy labels to permit efficient semantic graph learning. Extensive experiments on three well-known benchmark datasets demonstrate the superiority of our CGNN over competing approaches. © 2023 IEEE.

关键词： Contrastive Learning Graph Neural Networks Label Noise

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A scalable mobile context-aware recommender system for a smart city administration

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International Journal of Parallel, Emergent and Distributed Systems 2021年第2期36卷 97-116页

作者： Hoadjli, Abir Rezeg, Khaled Computer Science Department Smart Computer Sciences Laboratory Mohamed Khider Biskra University Biskra Algeria

With the innovation of new Information and Communication Technologies and the needs of information and knowledge sharing among the city, a smart city system aims to improve it's citizens life quality by offering a set of public services. Within this context, this research work proposes a smart city approach for using these technologies in public services delivery to allow real-time interactions with citizens;we propose a service providing framework based on context-aware recommendation approach to improve the city's digital services according to citizen’s context and the backend as a service (BAAS) approach on cloud to enhance the scalability of the system in large workloads. In this study, we evaluated the scalability and interactivity of the proposed approach by measuring response time and exchanged data metrics. To estimate the scalability, we performed series of load tests where response time values increased with the addition of requests in different cases but remained acceptable. In terms of interactivity, the exchanged data between the citizen application and the city’s backend was measured at maximum by 117 Kb/s. The experiments revealed that context-aware recommendation approach optimised interactions by reducing the amount of exchanged data, and the BAAS approach improved the scalability of the system and allowed handling concurrent requests. © 2019 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Scalability

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Guest Editorial: Special Issue on Human-Machine Fusion Decision-Making for Emergency Handling

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IEEE Transactions on Automation science and Engineering 2025年 22卷 4427-4433页

作者： Wu, Edmond Q. Li, Jianqiang Chen, Guimin Yuce, Mehmet R. Islam, Shafiqul Ser, Javier Del Yu, Hui Liu, Peter Xiaoping Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China College of Computer and Software Engineering Shenzhen University Shenzhen518060 China State Key Laboratory for Manufacturing Xi'an Jiaotong University Xi'an710049 China Department of Electrical and Computer Systems Engineering Monash University MelbourneVIC3800 Australia Xavier University of Louisiana New OrleansLA70125 United States Tecnalia Institute Bizkaia48160 Spain School of Creative Technologies University of Portsmouth PortsmouthPO1 2UP United Kingdom Department of Systems and Computer Engineering Carleton University OttawaONKIS 5B6 Canada

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On the Scalability of Computing Genomic Diversity Using SparkLeBLAST: A Feasibility Study

On the Scalability of Computing Genomic Diversity Using Spar...

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2024 IEEE High Performance Extreme Computing Conference, HPEC 2024

作者： Prabhu, Ritvik Moussad, Bernard Youssef, Karim Vatai, Emil Feng, Wu-Chun Virginia Tech Department of Computer Science United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory United States Riken Center for Computational Science Japan

ISBN: (纸本)9798350387131

Studying the genomic diversity of viruses can help us understand how viruses evolve and how that evolution can impact human health. Rather than use a laborious and tedious wet-lab approach to conduct a genomic diversity study, we take a computational approach, using the ubiquitous NCBI BLAST and our parallel and distributed SparkLeBLAST, across 53 patients (40,000,000 query sequences) on Fugaku, the world's fastest homogeneous supercomputer with 158,976 nodes, where each code contains a 48-core A64FX processor and 32 GB RAM. To project how long BLAST and SparkLeBLAST would take to complete a genomic diversity study of COVID-19, we first perform a feasibility study on a subset of 50 query sequences from a single COVID-19 patient to identify bottlenecks in sequence alignment processing. We then create a model using Amdahl's law to project the run times of NCBI BLAST and SparkLeBLAST on supercomputing systems like Fugaku. Based on the data from this 50-sequence feasibility study, our model predicts that NCBI BLAST, when running on all the cores of the Fugaku supercomputer, would take approximately 26.7 years to complete the full-scale study. In contrast, SparkLeBLAST, using both our query and database segmentation, would reduce the execution time to 0.026 years (i.e., 22.9 hours) - resulting in more than a 10,000× speedup over using the ubiquitous NCBI BLAST. © 2024 IEEE.

关键词： COVID-19

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Comparative Analysis of SVM and CNN Classifiers for EEG Signal Classification in Response to Different Auditory Stimuli

Comparative Analysis of SVM and CNN Classifiers for EEG Sign...

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2024 International Conference on Telecommunications and Intelligent Systems, ICTIS 2024

作者： Tibermacine, Ahmed Akrour, Djouher Khamar, Rofeida Tibermacine, Imad Eddine Rabehi, Abdelaziz University of Biskra Department of Computer Science Biskra Algeria Sapienza University of Rome Department of Computer Automation and Management Engineering Rome Italy Zian Achour University of Djelfa Laboratory of Telecommunications and Smart Systems Faculty of Sciences and Technology Djelfa17000 Algeria

ISBN: (纸本)9798331527396

This study aims to classify brainwave patterns using electroencephalogram (EEG) signals in response to various auditory stimuli, specifically Quran recitation, participants' favorite music, and Interstellar's main theme. By analyzing the unique EEG responses elicited by these stimuli, the study explores cognitive and emotional processing differences. Two prominent classifiers, Support Vector Machine (SVM) and Convolutional Neural Networks (CNN), were utilized. SVM, combined with Power Spectral Density (PSD) features, achieved the highest accuracy of 95% when distinguishing Quran recitation from rest. CNN, particularly with Kernel Density Estimation (KDE) features, exhibited strong performance in distinguishing between different auditory stimuli, with an average accuracy exceeding 90%. These results highlight the critical role of feature extraction techniques, such as PSD and KDE, in improving classification performance and deepening our understanding of EEG-based emotional and cognitive responses to auditory stimuli. © 2024 IEEE.

关键词： Convolutional neural networks

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Home healthcare: particle swarm optimization for human resource planning under uncertainty

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Multimedia Tools and Applications 2024年 1-34页

作者： Zarrouk, Rim Mahmoudi, Ramzi Bedoui, Mouhamed Hedi Hu, Yu-Chen Medical Imaging Technology Laboratory Faculty of Medicine LTIM-LR12ES06 University of Monastir Monastir5019 Tunisia Gaspard-Monge Computer-science Laboratory Paris-Est University Mixed Unit CNRS-UMLV-ESIEE UMR8049 ESIEE Noisy Le Grand Paris City Descartes93162 France Department of Computer Science Tunghai University Taichung City Taiwan

The home healthcare (HHC) staff scheduling problem involves allocating care tasks to healthcare staff at a minimal cost, subject to healthcare service requirements, labor law, organizational requirements, staff preferences, and other restrictions. Healthcare service providers strive to meet the time window restrictions specified by patients to improve their service quality. Nevertheless, many of those models are deterministic and neglect to take into account the system’s dynamic and uncertain characteristics, as well as the unpredictability of particular patient data. This study proposes a Two-Level particle swarm optimization variant (2LPSO) for solving the HHC staff’s scheduling problem with Staff Workload Equalization and Uncertainty management while avoiding overtime work. This paper provides a solution for good planning within reasonable computation times with the treatment of two cases: patient cancellation and new patient requests. The computational results obtained in this study demonstrate the efficiency and effectiveness of our approach. Overall, the proposed optimization structure achieves robust scheduling performance so that staff work schedules are balanced to meet the time window restrictions specified by patients to improve their quality of service by taking into account a new patient request or an incoming cancellation request. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Particle swarm optimization (PSO)

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