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Advancement in Electronics & Communication Engineering (AECE), International Conference on

作者： Anurag Tiwari School of Artificial Intelligence & Data Science Indian Institute of Technology Jodhpur India Department of Computer Science & Engineering Babu Banarasi Das Institute of Technology& Management Lucknow India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

Network security is crucial to ensure the protection of information systems in an increasingly connected world, and network traffic attack detection is an important aspect of maintaining system integrity. This study addresses this by investigating the use of a powerful and flexible machine learning algorithm, namely Support Vector Machines (SVM), to detect network traffic attacks. As a result, SVM has been successfully applied in different domains like network security, etc. In this study, SVM is applied for normal and malicious network traffic classification, including several attack types like Denial-of-Service (DoS), User-to-Root (U2R), and Remote-to-Local (R2L) attacks. To this end, the KDD Cup 99 dataset is employed for training and testing the model. These metrics are then used to evaluate the performance of the SVM-based intrusion detection system. The results demonstrate that SVM is able to recognize a high level of accurateness on network data traffic attacks as well as it has performed well in a difficult attack trends. In this case, the F1-scores for the Linear and RBF did result in 96.65/99.24, respectively, indicating that SVM also proved to be an effective algorithm choice in this example. Finally, the study either propose new algorithms or improve existing algorithms for SVM and demonstrate their effectiveness in the area of network security. In conclusion, this study reinforces the importance of SVM as an effective instrument in the detection of attacks on network traffic and strengthening cyber defense capabilities.

关键词： Support vector machines Training Accuracy Scalability Intrusion detection Telecommunication traffic Network security Servers Kernel Testing

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Model Parallel Training and Transfer Learning for Convolutional Neural Networks by Domain Decomposition

arXiv

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arXiv 2024年

作者： Klawonn, Axel Lanser, Martin Weber, Janine Department of Mathematics and Computer Science University of Cologne Weyertal 86-90 Köln50931 Germany Center for Data and Simulation Science University of Cologne Köln50923 Germany

Deep convolutional neural networks (CNNs) have been shown to be very successful in a wide range of image processing applications. However, due to their increasing number of model parameters and an increasing availability of large amounts of training data, parallelization strategies to efficiently train complex CNNs are necessary. In previous work by the authors, a novel model parallel CNN architecture was proposed which is loosely inspired by domain decomposition. In particular, the novel network architecture is based on a decomposition of the input data into smaller subimages. For each of these subimages, local CNNs with a proportionally smaller number of parameters are trained in parallel and the resulting local classifications are then aggregated in a second step by a dense feedforward neural network (DNN). In the present work, we compare the resulting CNN-DNN architecture to less costly alternatives to combine the local classifications into a final, global decision. Additionally, we investigate the performance of the CNN-DNN trained as one coherent model as well as using a transfer learning strategy, where the parameters of the pre-trained local CNNs are used as initial values for a subsequently trained global coherent CNN-DNN *** Codes 68T07, 68W10, 68W15, 65N55 Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Exploring Sampler Strategies in Unsupervised Person Re-identification Training: Insights and Performance Analysis

Exploring Sampler Strategies in Unsupervised Person Re-ident...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Ge Cao Qing Tang Tran Tien Dat Ashraf Uddin Russo Adri Priadana Kanghyun Jo Department of Electrical Electronic and Computer Engineering University of Ulsan Ulsan Republic of Korea Data Science Group INTERX Ulsan Republic of Korea

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

Person re-identification holds significant research value within supervised systems characterized by non-overlapping multiple cameras. In recent years, unsupervised learning has made notable strides and has gradually approached the training efficacy of supervised learning. This paper focuses on exploring the influence and analysis of various sampling strategies on overall unsupervised training. We initially delineate a proxylevel memory bank scheme based on camera labels and employ a hard sample mining strategy for selecting negative pairs in a contrastive learning loss. Various sampling strategies, Random sampling, triplet sampling with dissimilar labels, and group sampling yield markedly distinct outcomes across three large-scale datasets, i.e. Market-1501, DukeMTMC-reID, and MSMT17. Detailed analysis and discussion of these results are provided in this study.

关键词： Training Industrial electronics Supervised learning Buildings Contrastive learning Cameras Performance analysis

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3DMambaIPF: A State Space Model for Iterative Point Cloud Filtering via Differentiable Rendering

arXiv

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arXiv 2024年

作者： Zhou, Qingyuan Yang, Weidong Fei, Ben Xu, Jingyi Zhang, Rui Liu, Keyi Luo, Yeqi He, Ying School of Computer Science Fudan University China Department of Information Engineering The Chinese University of Hong Kong Hong Kong College of Computing and Data Science Nanyang Technological University Singapore

Noise is an inevitable aspect of point cloud acquisition, necessitating filtering as a fundamental task within the realm of 3D vision. Existing learning-based filtering methods have shown promising capabilities on commonly used datasets. Nonetheless, the effectiveness of these methods is constrained when dealing with a substantial quantity of point clouds. This limitation primarily stems from their limited denoising capabilities for dense and large-scale point clouds and their inclination to generate noisy outliers after denoising. To deal with this challenge, we introduce 3DMambaIPF, for the first time, exploiting Selective State Space Models (SSMs) architecture to handle highly-dense and large-scale point clouds, capitalizing on its strengths in selective input processing and large context modeling capabilities. Additionally, we present a robust and fast differentiable rendering loss to constrain the noisy points around the surface. In contrast to previous methodologies, this differentiable rendering loss enhances the visual realism of denoised geometric structures and aligns point cloud boundaries more closely with those observed in real-world objects. Extensive evaluations on commonly used datasets (typically with up to 50K points) demonstrate that 3DMambaIPF achieves state-of-the-art results. Moreover, we showcase the superior scalability and efficiency of 3DMam-baIPF on highly dense and large-scale point clouds with up to 500K points compared to off-the-shelf methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Wiener filtering

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Domain-decomposed image classification algorithms using linear discriminant analysis and convolutional neural networks

arXiv

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arXiv 2024年

作者： Klawonn, Axel Lanser, Martin Weber, Janine Department of Mathematics and Computer Science University of Cologne Weyertal 86-90 50931 Köln Germany Center for Data and Simulation Science University of Cologne 50923 Köln Germany

MSC Codes 68T07, 68W10, 68W15, 65N55In many modern computer application problems, the classification of image data plays an important role. Among many different supervised machine learning models, convolutional neural networks (CNNs) and linear discriminant analysis (LDA) as well as sophisticated variants thereof are popular techniques. In this work, two different domain decomposed CNN models are experimentally compared for different image classification problems. Both models are loosely inspired by domain decomposition methods and in addition, combined with a transfer learning strategy. The resulting models show improved classification accuracies compared to the corresponding, composed global CNN model without transfer learning and besides, also help to speed up the training process. Moreover, a novel decomposed LDA strategy is proposed which also relies on a localization approach and which is combined with a small neural network model. In comparison with a global LDA applied to the entire input data, the presented decomposed LDA approach shows increased classification accuracies for the considered test problems. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Sex Differences in Coupled Dynamic Functional Connectivity and Structural Brain Morphology: Insights from the ABCD Study

Sex Differences in Coupled Dynamic Functional Connectivity a...

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IEEE International Symposium on Biomedical Imaging

作者： Aline Kotoski Sir-Lord Wiafe Vince Calhoun Tri-Institutional Center for Translational Research in Neuroimaging and Data Science Atlanta USA Neuroscience Institute Georgia State University Atlanta USA Department of Computer Science Georgia State University Atlanta USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

This study investigates sex-based differences in brain structure-function coupling using a novel dynamic intermodality source coupling (dIMSC) method, which we use to integrate dynamic functional network connectivity (dFNC) and structural brain morphometry (SBM) data from the ABCD study. This study aims to examine the coupling between dFNC and sMRI gray matter volume, comparing the coupling in relation to sex differences across brain regions. By examining the coupling between dFNC and SBM matrices, we identified distinct functional-structural coupling patterns across sexes in various brain regions. Females showed stronger correlations in areas previously associated with social and language processing, such as the inferior frontal gyrus and superior parietal lobule, while males exhibited stronger correlations in regions that have previously been linked to spatial processing, including the superior temporal gyrus, lingual gyrus, and cerebellum. These findings underscore nuanced sex-specific variations in brain structure-function relationships, offering insights into distinct patterns of the brain.

关键词： Couplings Cerebellum Correlation Limiting Head Magnetic resonance imaging Morphology Grey matter Magnetic heads Hippocampus

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Population-Based Incremental Learning for Effective IoT Service Composition with Replication

Population-Based Incremental Learning for Effective IoT Serv...

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Software Services Engineering (SSE) IEEE International Conference on

作者： Fengyang Sun Gang Chen Hui Ma Sven Hartmann Centre for Data Science and Artificial Intelligence & School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand Department of Informatics Clausthal University of Technology Clausthal-zellerfeld Germany

ISBN: (数字)9798350368512

ISBN: (纸本)9798350368529

Internet of Things Service Composition (SCIoT) aims to find the best composite IoT service to fulfil users' requirements. Given the NP-hard complexity of SCIoT, Evolutionary Computation methods, especially Estimation of Distribution Algorithms (EDAs), have received increasing attention to solve SCIoT problems. As one of the most popular EDA methods, Population-Based Incremental Learning (PBIL) has demonstrated its strong competency in optimising composed services in SCIoT. However, conventional PBIL does not explicitly utilize problem knowledge such as QoS and service replication, limiting its effectiveness for IoT service composition. In this paper, we propose a new PBIL based approach, named Population-Based Incremental Learning to Improve Service Composition (PBILISC), to solve the SCIoT problem. Different from traditional PBIL, PBILISC seamlessly integrates PBIL with QoS-aware local search (QLS) to effectively handle replicated services in the SCIoT problem. Specifically, PBILISC evolves a series of populations of solutions jointly through PBIL and QLS. PBIL leverages a probability distribution for population updates, while QLS focuses on improving the best evolved solution by searching promising neighboring solutions under the guidance of QoS. Experimental results show that PBILISC can outperform PBIL and several state-of-the-art methods on multiple benchmark SCIoT problems.

关键词： Incremental learning Limiting Software algorithms Quality of service Evolutionary computation Benchmark testing Search problems

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database Design for Pre-Eclampsia Case Administration

Database Design for Pre-Eclampsia Case Administration

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Information Technology and Digital Applications (ICITDA), IEEE International Conference on

作者： Faisal Asadi Gregorius Natanael Elwirehardja Joko Pebrianto Trinugroho Reza Rahutomo Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

The Ministry of Health of Indonesia has referred to pre-eclampsia as one of the most severe diseases affecting women. As an urgency, it is crucial to administrate pre-eclampsia cases for disease prevention as a long-term national healthcare strategy. Regarding health science, case data was significant in developing research and innovation. However, the main problem regarding pre-eclampsia case administration is data handling, recording, and management incompetence. Hence, this research proposed a conceptual design of a database for pre-eclampsia case administration. The proposed design covered conceptual, logical, and physical design. We elaborate the concept into three concepts of pre-eclampsia disease: pre-treatment, treatment, and post-treatment. This study proposed a solution to gain more data and study pre-eclampsia disease in Indonesia.

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Enhancing Kidney Stone Detection in CT scans with Image Processing and the DCNN-RELM Model

Enhancing Kidney Stone Detection in CT scans with Image Proc...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Manjula Prabakaran Jesmin Zakaria S Kannan Santhiya P Rajasekar P M V Rathnamma Department of CSE- (Data Science) Madanapalle Institute of Technology & Science Andhra Pradesh India Department of Electronics & Communication Engineering (ECE) NSHM Knowledge Campus Durgapur West Bengal India Department of Computer Science and Engineering School of Engineering and Technology Dhanalakshmi Srinivasan University Trichy Tamilnadu India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamilnadu India Department of CSE KLM College of Engineering for Women Kadapa Andhra Pradesh India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

CT of the urinary system, abdomen, and kidneys appears to improve kidney stone screening and identification. Segmentation and categorization are essential for kidney stone identification and management. This work developed a semi-automatic kidney stone detection system using geometric concepts and image processing. The main steps include model training, feature extraction, segmentation, embossing, and preprocessing. Preprocessing involves trimming the input image to remove unnecessary kidney regions to focus on critical CT scan locations. Relief embossing enhanced image details. Segmentation accurately classified veins and blood arteries to define kidney stone boundaries. CNN-RELM was our training model. Compared to CNN and ELM models, the recommended system performed better. Its 93.87% kidney stone detection and localization accuracy prove its reliability. The semi-automatic system's reliable kidney stone detection and segmentation method may improve clinical decision-making. Combining CNN-RELM modeling and enhanced image processing could lead to medical imaging applications.

关键词： Training Image segmentation Accuracy Three-dimensional displays Computed tomography Embossing data preprocessing Reliability Kidney Biomedical imaging

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Let AI Read First: Enhancing Reading Abilities for Individuals with Dyslexia through Artificial Intelligence

Let AI Read First: Enhancing Reading Abilities for Individua...

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Zhao, Sihang Xiong, Shoucong Carol Pang, Bo Tang, Xiaoying He, Pinjia School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Department of Marketing School of Management Zhejiang University Zhejiang Hangzhou China Computer Network Information Center Chinese Academy of Sciences Beijing China School of Science and Engineering Chinese University of Hong Kong Guangdong Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9798400713958

Dyslexia, a neurological condition affecting approximately 12% of the global population, presents significant challenges to reading ability and quality of life. Existing assistive technologies are limited by factors such as unsuitability for quiet environments, high costs, and the risk of distorting meaning or failing to provide real-time support. To address these issues, we introduce LARF (Let AI Read First), the first strategy that employs large language models to annotate text and enhance readability while preserving the original content. We evaluated LARF in a large-scale between-subjects experiment, involving 150 participants with dyslexia. The results show that LARF significantly improves reading performance and experience for individuals with dyslexia. Results also prove that LARF is particularly helpful for participants with more severe reading difficulties. Furthermore, this work discusses potential research directions opened up by LARF for the HCI community. © 2025 Copyright held by the owner/author(s).

关键词： accessibility Dyslexia

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