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检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

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Counterfactual Brain Graph Augmentation Guided Bi-Level Contrastive learning for Disorder Analysis 24

Counterfactual Brain Graph Augmentation Guided Bi-Level Cont...

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24th IEEE international conference on data Mining, ICDM 2024

作者： Dong, Guangwei Luo, Xuexiong Du, Jing Wu, Jia Xue, Shan Yang, Jian Beheshti, Amin School of Computing Macquarie University Australia

ISBN: (纸本)9798331506681

Deep learning is increasingly crucial in scientific discovery, accelerating research in various fields. Exploring brain science using deep learning has garnered significant interest, particularly in the recognition of brain disorders. However, existing methods face limitations in the discriminability of learned brain graph representations and the identification of neurological biomarkers associated with brain disorders. Moreover, the lack of model explainability leads to suboptimal brain graph analysis. To address these challenges, we propose a bi-level brain graph contrastive learning framework with an interpretable learning kernel for brain disorder analysis. Our framework diverges from traditional graph contrastive learning methods by augmenting meaningful brain graphs using the counterfactual thinking-based mechanism, ensuring reliable graph generation that preserves discriminative information. Secondly, we introduce bi-level contrastive loss with intra-and inter-class contrasts to enhance the brain graph representation learning. Most importantly, we design an interpretable brain graph learning kernel to highlight critical regions and connections, thereby facilitating the discovery of potential neurological biomarkers associated with brain disorders in subsequent analysis. The effectiveness of our method11https://***/JustinGie/Cf-BCL in brain graph representation learning and discriminative substructure detection is demonstrated through the evaluation of disorder prediction and pathogenic analysis on three real-world brain disorder datasets. Moreover, our framework may provide novel insights into brain science based on the results of comparison with medical research. © 2024 IEEE.

关键词： Contrastive learning

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machine learning-Powered Intrusion Detection for Network Security 12th

Machine Learning-Powered Intrusion Detection for Network Sec...

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12th international conference on Recent Trends in computing, ICRTC 2024

作者： Nandish, H.D. Rahul, R. Dasari, Hari Charan Kumaran, U. Umah, B. Nagarajan, Senthil Murugan Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Mathematics Faculty of Science Technology and Medicine University of Luxembourg Esch-sur-AlzetteL-4365 Luxembourg

ISBN: (纸本)9789819788354

In today's digital age, network security is paramount. This paper introduces a robust web application designed to swiftly identify network attacks and real-time anomalies, enabling users to protect their networks effectively. By leveraging cutting-edge technologies and machine learning, our system provides insights into network accuracy, F1-score, and precision, allowing users to gauge their network's safety. Our intrusion detection system classifies attacks into four categories: Denial of Service (DoS), Probe, Remote-to-User (R2L), and User-to-Root (U2R). Positioned strategically within the network, it monitors data traffic from all connected devices and promptly responds to suspicious activity, ensuring network security. Users receive instant alerts to take immediate action. Key technologies include data preprocessing (LabelEncoder and MinMaxScaler), user-friendly web interface (MERN stack), and enhanced security with Google Earth 2.0 for authentication and MongoDB for secure data storage. The Python-based backend operates on the NSL-KDD dataset, facilitating effective intrusion detection evaluation. To enhance accuracy, our system employs four machine learning algorithms: K-Nearest Neighbors (KNN), Convolutional Neural Networks (CNN), Random Forest, and Long Short-Term Memory (LSTM), offering comprehensive protection against network threats. In summary, this paper presents a potent network intrusion detection system for real-time threat identification and security assessment. By combining advanced technologies and machine learning, it ensures accuracy and reliability, serving as a valuable tool for network administrators and security professionals in safeguarding digital networks from evolving threats. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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Federated learning for Enhanced ECG Signal Classification with Privacy Awareness 46

Federated Learning for Enhanced ECG Signal Classification wi...

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46th Annual international conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Phan, Quoc Bao Nguyen, Linh Bui, Ngoc Thang Nguyen, Dinh C. Zhang, Lan Nguyen, Tuy Tan Northern Arizona University School of Informatics Computing and Cyber Systems FlagstaffAZ86011 United States Mayo Clinic Department of Radiology RochesterMN55905 United States The University of Alabama in Huntsville Department of Electrical and Computer Engineering HuntsvilleAL35899 United States

ISBN: (纸本)9798350371499

This paper presents a novel approach for classifying electrocardiogram (ECG) signals in healthcare applications using federated learning and stacked convolutional neural networks (CNNs). Our innovative technique leverages the distributed nature of federated learning to collaboratively train a high-performance model while preserving data privacy on local devices. We propose a stacked CNN architecture tailored for ECG data, effectively extracting discriminative features across different temporal scales. The evaluation confirms the strength of our approach, culminating in a final model accuracy of 98.6% after 100 communication rounds, significantly exceeding baseline performance. This promising result paves the way for accurate and privacy-preserving ECG classification in diverse healthcare settings, potentially leading to improved diagnosis and patient monitoring. © 2024 IEEE.

关键词： Federated learning

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MultiVFL: A Training-Efficient Framework for Multiple Vertical Federated learning 10

MultiVFL: A Training-Efficient Framework for Multiple Vertic...

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10th international conference on Big data computing and Communications, BIGCOM 2024

作者： Huang, Ju Zhang, Lan Ding, Cheng Huang, Dongbo Xu, Lan School of Computer Science and Technology University of Science and Technology of China Hefei China Tencent Shanghai China

ISBN: (纸本)9798331509538

Vertical Federated learning (VFL) is an effective paradigm which matches the enterprises' demands of leveraging more valuable features to achieve better model performance while keeping their data decentralized. Training costs, especially communication costs among parties have always been one of the major bottlenecks in VFL. Almost all existing approaches is aimed at optimizing the overhead of a single VFL task. However, there are situations where a passive party cooperates with multiple active parties to jointly train VFL models task by task, which causing the passive party incurring significant duplication of training costs. In this scenario, because these methods tailored for single VFL task cannot leverage the inter-task correlations, they might be suboptimal in terms of optimization and the model's performance may also be less satisfactory. In this article, we focus on reducing the training overhead of the passive party in multiple VFL collaborations. We utilize all local data of the passive party, including data discarded in the VFL task, for unsupervised training to accelerate the convergence speed of VFL training. Some new problems arise due to the data heterogeneity between tasks and the issue of catastrophic forgetting in models. In order to solve these issues, we propose a training-efficient algorithm named MultiVFL for multiple VFL collaborations. MultiVFL adopt parameter-level knowledge retention and fine-tuning algorithm, motivated by the sparsity of the bottom model in VFL. Through this approach, MultiVFL is able to maintain efficient training across various scenarios. We conducted experiments on two datasets containing five tasks each, and the results indicate that MultiVFL can continuously and efficiently learn tasks in multiple rounds of VFL collaboration. Compared to traditional VFL methods, MultiVFL saves about 70% of training overhead on tabular datasets and 21% on image datasets with excellent model accuracy. © 2024 IEEE.

关键词： Federated learning

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Next-Generation Stock Market Prediction: Integrating CNN and LSTM Model 2

Next-Generation Stock Market Prediction: Integrating CNN and...

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2nd IEEE international conference on data science and Network Security, ICDSNS 2024

作者： Agnihotri, Kuldeep Yadav, Sameer Dahiya, Suman Subramanyam, M. Patil, Harshal Kalra, Gourav Isba Group of Institutes Madhya Pradesh Indore India University of Allahabad Department of Commerce and Business Administration Uttar Pradesh Prayagraj India Universal Ai University Department of Management Maharashtra India Koneru Lakshmaiah Education Foundation Department of Commerce AP Vaddeswaram India Iilm University Department of Computer Science and Engineering Uttar Pradesh Greater Noida India Department of Mechanical Engineering Ambala Mullana India

ISBN: (纸本)9798350373110

Stock market forecasting is one of the most challenging tasks in the financial industry due to the non-parametric nature of the stock market's time series, which is also complex, dynamic, chaotic, volatile, noisy, and otherwise non-linear. Fortunately, with the help of modern computing, intelligent models can assist expert analysts and investors in reducing investment risk. Preprocessing, feature selection, and training the model are the three stages that make up this suggested approach. When preprocessing data, the empirical wavelet transform decomposition algorithm is employed. When it comes to feature selection, one unsupervised and competitive learning algorithm that has proven effective is the self-organizing feature map. The model was trained using a CNN-LSTM. The proposed technique outperforms CNN and LSTM with an average accuracy of 90.56 percent. © 2024 IEEE.

关键词： Marketplaces

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Cao Robot for Taiwanese/English Knowledge Graph Application 27

Cao Robot for Taiwanese/English Knowledge Graph Application

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27th conference on the Oriental COCOSDA international Committee for the Co-Ordination and Standardisation of Speech databases and Assessment Techniques, O-COCOSDA 2024

作者： Lee, Chang-Shing Wang, Mei-Hui Tseng, Guan-Ying Yue, Chao-Cyuan Hsieh, Hao-Chun Reformat, Marek National University of Tainan Dept. of Computer Science and Information Engineering Tainan Taiwan University of Alberta Dept. of Electrical and Computer Engineering EdmontonAB Canada

ISBN: (纸本)9798331506032

This paper proposes a Content Attention Ontology (CAO) robot for constructing Taiwanese/English Knowledge Graphs (KGs) by prompting audio or texts to Large Language Models (LLMs), including TAIDE, Zephyr, and Llama 3.1. The collected data includes lecture videos from the IEEE WCCI 2024 in Japan and the 2024 National Language Development Forum in Taiwan, along with students' learning data from the 2024 Summer School on Taiwanese/English Human and Robot Co-learning at Rende Elementary School (RDES). In addition, the fundamental concepts of Computational Intelligence (CI) and Quantum CI (QCI) learning were incorporated into the study. The generative KGs highlight important concepts, relations, and communities within the collected teaching and learning data. Additionally, we utilized data from subjects wearing braincomputer interface (BCI) devices while speaking Taiwanese/English to generate KGs. We also compared the differences in these KGs and analyzed the similarities between the transcribed texts of lectures and learners. In the future, we plan to expand the CAO robot to more validation fields across Taiwan, aiming to engage young students in speaking Taiwanese while concurrently enhancing their English language skills through interaction with the robot. © 2024 IEEE.

关键词： Knowledge graph

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SoCFlow: Efficient and Scalable DNN Training on SoC-Clustered Edge Servers 24

SoCFlow: Efficient and Scalable DNN Training on SoC-Clustere...

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29th ACM international conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)

作者： Xu, Daliang Xu, Mengwei Lou, Chiheng Zhang, Li Huang, Gang Jin, Xin Liu, Xuanzhe Peking Univ Minist Educ Key Lab High Confidence Software Technol Beijing Peoples R China Peking Univ Sch Comp Sci Beijing Peoples R China State Key Lab Networking & Switching Technol Beijing Peoples R China Natl Key Lab Data Space Technol & Syst Beijing Peoples R China

ISBN: (纸本)9798400703720

SoC-Cluster, a novel server architecture composed of massive mobile system-on-chips (SoCs), is gaining popularity in industrial edge computing due to its energy efficiency and compatibility with existing mobile applications. However, we observe that the deployed SoC-Cluster servers are not fully utilized, because the hosted workloads are mostly usertriggered and have significant tidal phenomena. To harvest the free cycles, we propose to co-locate deep learning tasks on them. We present SoCFlow, the first framework that can efficiently train deep learning models on SoC-Cluster. To deal with the intrinsic inadequacy of commercial SoC-Cluster servers, SoCFlow incorporates two novel techniques: (1) the group-wise parallelism with delayed aggregation that can train deep learning models fast and scalably without being influenced by the network bottleneck;(2) the data-parallel mixed-precision training algorithm that can fully unleash the heterogeneous processors' capability of mobile SoCs. We have fully implemented SoCFlow and demonstrated its effectiveness through extensive experiments. The experiments show that SoCFlow significantly and consistently outperforms all baselines regarding the training speed while preserving the convergence accuracy, e.g., 1.6x-740x convergence speedup with 32 SoCs. Compared to commodity GPU (NVIDIA V100) under the same power budget, SoCFlow achieves comparable training speed but reduces energy consumption by 2.31x-10.23x with the same convergence accuracy.

关键词： SoC-Cluster distributed machine learning mixed precision training

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Hyperbolic Geometric Latent Diffusion Model for Graph Generation 41

Hyperbolic Geometric Latent Diffusion Model for Graph Genera...

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41st international conference on machine learning, ICML 2024

作者： Fu, Xingcheng Gao, Yisen Wei, Yuecen Sun, Qingyun Peng, Hao Li, Jianxin Li, Xianxian Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Institute of Artificial Intelligence Beihang University Beijing China School of Software Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China

Diffusion models have made significant contributions to computer vision, sparking a growing interest in the community recently regarding the application of them to graph generation. Existing discrete graph diffusion models exhibit heightened computational complexity and diminished training efficiency. A preferable and natural way is to directly diffuse the graph within the latent space. However, due to the non-Euclidean structure of graphs is not isotropic in the latent space, the existing latent diffusion models effectively make it difficult to capture and preserve the topological information of graphs. To address the above challenges, we propose a novel geometrically latent diffusion framework HypDiff. Specifically, we first establish a geometrically latent space with interpretability measures based on hyperbolic geometry, to define anisotropic latent diffusion processes for graphs. Then, we propose a geometrically latent diffusion process that is constrained by both radial and angular geometric properties, thereby ensuring the preservation of the original topological properties in the generative graphs. Extensive experimental results demonstrate the superior effectiveness of HypDiff for graph generation with various topologies. Copyright 2024 by the author(s)

关键词： Graph theory

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Integrating Artificial Intelligence for Sustainable and Adaptable Architecture 2

Integrating Artificial Intelligence for Sustainable and Adap...

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2nd international conference Computational and Characterization Techniques in Engineering and sciences, IC3TES 2024

作者： Lalitha, Valapula Suneetha, Regidi Basi Reddy, A. Mate, Nilesh R. Mujeebunnisa, Syeeda India Sanketika Vidya Parishad Engineering College Department of Ece Visakhapatnam India Mohan Babu University School of Computing Department of Computer Science and Engineering Tirupati India Centre for Distance and Online Education Pune India Cmr University School of Science and Computer Studies Bengaluru India

ISBN: (纸本)9798350364699

In nature, flexibility is essential, and AI may enhance it in architecture. The article integrates AI to develop self-learning buildings that can adapt to future obstacles. This research aims to create structures that use sensors to gather environmental data and respond. Phases comprise this study's strategy. In Phase 1, AI in architecture is extensively researched. That study in Phase 1 guided Phase 2's building design. The parametric building design in phase 2 uses machine learning and computational design techniques. Grasshopper scripting and Rhino modelling were used to create an algorithm that would create shapes that would both biomimetic the process of coral growth and be tailored to the project's chosen location. The selection procedure for the produced experimental investigations is shown in Phase 3. To determine which type used the least amount of energy, many evaluations were conducted, including measurements of sunshine, radiation, and shadows. Phase 4 involves developing the shape to improve the building's efficiency. Phase 5 involves conducting performance assessments to demonstrate that the final form is highly adaptable and climatic or ecologically sensitive. This building's radiation exposure, according to the research, is between 200 and 300 kWh/m2. According to the shadow study, the building shape casts a shadow for eight hours. According to the analysis, the building's shape makes it more adaptive by reducing its energy use. An AI engine technology is used in the last stage to forecast the building's future growth. By incorporating technology into building design, we may create flexible structures that reduce building energy use and contribute to the creation of a sustainable world. © 2024 IEEE.

关键词： Green buildings

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Artificial Intelligence aware Knowledge Graphs and Deep learning-Based Diagnostic Prediction Model in Healthcare 7

Artificial Intelligence aware Knowledge Graphs and Deep Lear...

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7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Babu, Ramenani Hari Keshta, Ismail Soni, Mukesh Chafik, Berdjouh Byeon, Haewon Rai, Vertika Teerthanker Mahaveer University Teerthanker Mahaveer College of Pharmacy Department of Pharmacy Practice Uttar Pradesh Moradabad India College of Applied Sciences AlMaarefa University Computer Science and Information Systems Department Riyadh Saudi Arabia Dr. D. Y. Patil School of Science & Technology Dr. D. Y. Patil Vidyapeeth Pune India University of El Oued Liap Laboratory PO Box 789 El Oued39000 Algeria Inje University Department of Ai and Software Gimhae50834 Korea Republic of Galgotias University School of Allied Health Sciences Department of Medical Laboratory Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350350067

Diagnostic prediction experts use a patient's medical history to anticipate their future diagnosis. Attention mechanisms and recurrent neural networks are typically utilized for this purpose;however, data shortages often cause complications. Additionally, healthcare information can help diagnostic prediction algorithms. This study links healthcare ideas using AI-aware, intelligent healthcare ontologies and patient visit records as an intelligent graph. Next, utilize a graph convolution module to collect geographical attributes that show patient visit healthcare code linkages. Using a multihead attention technique, visit information and healthcare knowledge are compared to predict the patient's future health. On two open-source healthcare datasets, the suggested solution outperforms state-of-the-art diagnostic prediction algorithms and better uses IMG data. © 2024 IEEE.

关键词： Deep learning Diagnostic Prediction Healthcare Healthcare Knowledge Historical Health Records

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