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14th International Conference on Semantic Web Applications and Tools for Health Care and Life sciences, SWAT4HCLS 2023

作者： Sun, Chang van Soest, Johan Dumontier, Michel Institute of Data Science Faculty of Science and Engineering Maastricht University Maastricht Netherlands Department of Advanced Computing Sciences Faculty of Science and Engineering Maastricht University Maastricht Netherlands Brightlands Institute of Smart Society Faculty of Science and Engineering Maastricht University Maastricht Netherlands GROW School for Oncology and Reproduction Maastricht University Medical Centre Maastricht Netherlands

TIDAL is a Solid (SOcial Linked data)-based, citizen-centric data platform that facilitates interactions by citizens and researchers for health research. In this demonstration, we will show how TIDAL 1) can store personal data in Solid pods as RDF with well-known health-related vocabularies (e.g., SNOMED CT), 2) controls access to query fine-grained subsets of personal data, 3) enables researchers to post a human- and machine-readable digital consent using data Privacy Vocabulary, and 4) uses federated learning to analyze personal health data from multiple individuals using a Personal Health Train framework. TIDAL offers a new data paradigm of sharing and using personal data for research and ultimately increase the availability of personal data for societal relevant uses. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： data privacy

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Unleashing the Potential of Decentralized Federated Learning in Healthcare: A Comprehensive Survey on Insights and Challenges 9

Unleashing the Potential of Decentralized Federated Learning...

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9th IEEE International Conference on data science in Cyberspace, DSC 2024

作者： Cheng, Haibo Liu, Wenjian Qu, Youyang Fu, Kexue Wang, Qinyuan City University of Macau Faculty of Data Science China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350391367

Federated learning (FL) is a distributed learning method that reduces data transmission and privacy risks. However, the existence of a central server may become a target of attackers, resulting in an increased risk of user privacy leakage. The emergence of Decentralized Federated Learning (DFL) is posited to enhance data privacy and system reliability by reducing reliance on central servers and enhancing local data control, especially in the medical field, where personal health data is protected. This research investigates the application of (DFL) in the medical domain, focusing on its limitations. This study employs a systematic literature review, adhering to PRISMA guidelines, to comprehensively understand DFL's current landscape and challenges in healthcare. The key findings highlight four principal obstacles faced by DFL: security and privacy, communication efficiency, data and model heterogeneity, and incentive mechanisms. It also explores the potential for DFL to facilitate personalized healthcare treatments by large datasets distributed across various medical institutions. This research fills a significant void by systematically reviewing DFL technologies in healthcare. It provides insights into applications, challenges, and future directions for participants seeking secure and efficient healthcare data handling. © 2024 IEEE.

关键词： Federated learning

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Evolution of GPU virtualization to resource pooling 2

Evolution of GPU virtualization to resource pooling

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2nd International Conference on Electronic Information Technology, EIT 2023

作者： Liang, Guicai Daud, Siti Norbaya Ismail, Nor Alina Binti Center for Outcome-Based Education Research Faculty of Engineering Built Environment & Information Technology SEGi University Kota Damansara Malaysia Faculty of Data Science and Computing University Malaysia Kelantan Kelantan Kota Bharu Malaysia

ISBN: (数字)9781510666627

ISBN: (纸本)9781510666610

With the rapid development of artificial intelligence, the demand for Graphics Processing Unit (GPU) resources is also increasing rapidly. To improve the efficiency and utilization of GPU resources, virtualization technology has been widely used in the field of GPUs. This article reviews the evolution from GPU virtualization to resource pooling, including device simulation, GPU pass-through, hardware-assisted virtualization, GPU full virtualization, GPU remote-sharing, and GPU resource pools. The advantages and disadvantages of each stage and the challenges faced during the evolution process are also analyzed. This article discusses the difficulties and solutions of GPU resource pool construction, as well as the construction steps and framework, providing a reference for the research and application of GPU resource pooling. © 2023 SPIE.

关键词： Graphics processing unit

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Peer Tutoring and its Impact on Academic Achievements

Peer Tutoring and its Impact on Academic Achievements

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IT and Industrial Technologies (ICIT), International Conference on

作者： Muhammad Aqeel Manahil Hassan Affaf Ghani Manahil Zaheer Bilal Anjum Faisal Bukhari Department of Data Science Faculty of Computing and Information Technologys University of the Punjab Lahore Pakistan

ISBN: (数字)9798331504465

ISBN: (纸本)9798331504472

Peer tutoring is gaining attention rapidly nowadays, providing a learning environment where students have the opportunity to learn and grow through collaboration and effective communication. This study focuses on the impact of peer tutoring by collecting data from different students as well as surveys and then analyzing the pre and post-tutoring assessments to determine the impact of peer tutoring. It employs statistical techniques to study the impact of peer tutoring on both tutee and tutor, their self-confidence, and the overall academic achievement gained through tutoring.

关键词： Training Surveys Job shop scheduling Statistical analysis Collaboration Cultural differences Socioeconomics

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Analysis of Factors Influencing Early Detection of Breast Cancer

Analysis of Factors Influencing Early Detection of Breast Ca...

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Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE), International Conference on

作者： Hassal Malik Sonia Umaira Ahmad Sial Faisal Bukhari Department of Data Science Faculty of Computing and Information Technology University of the Punjab Lahore Pakistan

ISBN: (数字)9798331504861

ISBN: (纸本)9798331504878

Carcinoma, particularly breast cancer, poses a formidable challenge, especially in Pakistan where it stands as the most prevalent cancer among women. This research investigates the complex landscape of breast cancer in Pakistan, utilizing a dataset from local hospitals. Through comprehensive statistical analyses and advanced machine learning techniques, including XGBoost and RandomForest, the study uncovers significant associations between socio-economic and psychological factors and the timing of breast cancer diagnosis among Pakistani women. Factors such as embarrassment, fear of treatment, and lack of awareness emerge as crucial determinants. The findings provide valuable insights for tailored interventions and contribute to the global fight against breast cancer.

关键词： Hospitals Statistical analysis Psychology Machine learning Forestry Market research Breast cancer Timing Decision trees Socioeconomics

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Digital Twin System for Smart Campuses

Digital Twin System for Smart Campuses

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2024 IEEE International Conference on Control science and Systems Engineering, ICCSSE 2024

作者： Wang, Huijie Cai, Yuanyuan Yue, Fabu Luo, Hao Abdipoor, Sina Goh, Say Leng School of Big Data BaoShan University Yunnan BaoShan678000 China Universiti Putra Malaysia Department of Computer Science 43400 Malaysia Universiti Malaysia Sabah Faculty of Computing and Informatics Labuan87000 Malaysia

ISBN: (纸本)9798331517199

In an era where educational institutions are increasingly embracing technology to improve efficiency and enhance learning environments, the concept of smart campuses has emerged as a crucial development. The traditional approach of monitoring campus environments through video footage and manual inspections is becoming outdated as technology advances. To empower smarter campus management and enhance the educational experience, this study presents a systematic approach for smart campuses built on the foundation of digital twins. This technology creates a virtual replica of the physical campus, enabling real-time monitoring of various aspects like security, teaching, maintenance, environment, assets, and energy usage. We simulate real-world campus behavior by constructing this digital counterpart. The resulting digital twin image can be visualized through various user-friendly interfaces, including virtual reality and 3D models, empowering managers to make informed decisions for a more efficient and secure campus environment. Implementing a digital twin system in a smart campus setting demonstrates significant improvements in operational efficiency, security management, and environmental sustainability. By leveraging the power of digital twins, educational institutions can transition towards smarter, more responsive, and sustainable campus environments, ultimately enriching the educational experience for students and staff. © 2024 IEEE.

关键词： 3d modeling campus management digital twin real-time monitoring

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Generative Adversarial Network with Soft-Dynamic Time Warping and Parallel Reconstruction for Energy Time Series Anomaly Detection

arXiv

引用

arXiv 2024年

作者： Prabhu, Hardik Valadi, Jayaraman Arjunan, Pandarasamy Robert Bosch Centre for Cyber Physical Systems Indian Institute of Science Bengaluru India School of Computing and Data Sciences FLAME University Pune India

In this paper, we employ a 1D deep convolutional generative adversarial network (DCGAN) for sequential anomaly detection in energy time series data. Anomaly detection involves gradient descent to reconstruct energy sub-sequences, identifying the noise vector that closely generates them through the generator network. Soft-DTW is used as a differentiable alternative for the reconstruction loss and is found to be superior to Euclidean distance. Combining reconstruction loss and the latent space’s prior probability distribution serves as the anomaly score. Our novel method accelerates detection by parallel computation of reconstruction of multiple points and shows promise in identifying anomalous energy consumption in buildings, as evidenced by performing experiments on hourly energy time series from 15 buildings. © 2024, CC BY.

关键词： Anomaly detection

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Feature Selection in Socio-Economic Analysis: A Multi-Method Approach for Accurate Predictive Outcomes

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International Journal of Crowd science 2025年第1期9卷 64-78页

作者： Al-Qerem, Ahmad Ali, Ali Mohd Jebreen, Issam Nabot, Ahmad Rajab, Mohammed Alauthman, Mohammad Aldweesh, Amjad Aburub, Faisal Alangari, Someah Alzgol, Musab Computer Science Department Faculty of Information Technology Zarqa University Zarqa13110 Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman19328 Jordan University Headquarter University of Anbar Ramadi31001 Iraq Department of Information Security University of Petra Amman11196 Jordan College of Computing and Information Technology Shaqra University Shaqra11911 Saudi Arabia Department of Business Intelligence and Data Analytics University of Petra Amman11196 Jordan Computer Information Systems Department Faculty of Information Technology Isra University Amman11622 Jordan

Feature selection is a cornerstone in advancing the accuracy and efficiency of predictive models, particularly in nuanced domains like socio-economic analysis. This study explores nine distinct feature selection methods, utilizing a heart disease dataset as a representative model for complex socio-economic systems. Our findings identified four universally recognized features as critical across all selection methods. However, the divergence in significance attributed to other features by different methods underscores the inherent variability in selection techniques. When the top four features were incorporated into twelve classification models, a noticeable surge in predictive accuracy was observed, emphasizing their foundational role in enhancing model outcomes. The variations among methods stress the need for a methodical and discerning approach to feature selection, especially in data-rich socio-economic landscapes. As we venture further into an era defined by data-driven decision-making, rigour and precision in feature selection become indispensable. Future research should extend this approach to broader datasets, ensuring the robustness and adaptability of our findings. © The author(s) 2025.

关键词： Prediction models

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Evaluating the Cultural Sensitivity of Large Language Models in Mental Health Support: A Framework Inspired by Ubuntu Values 12th

Evaluating the Cultural Sensitivity of Large Language Models...

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12th International Conference on Big data and Artificial Intelligence, BDA 2024

作者： Forane, Sontaga G. Ezugwu, Absalom E. Igwe, Kevin Unit for Data Science and Computing North-West University 11 Hoffman Street North-West Potchefstroom2520 South Africa Department of Psychology Faculty of Humanities University of Johannesburg Auckland Park Bunting Road Johannesburg2092 South Africa

ISBN: (纸本)9783031818202

Africa’s growing mental health crisis underscores a significant lack of accessible and culturally relevant mental health services, with conditions such as depression and anxiety increasingly prevalent. While Cognitive Behavioral Therapy (CBT) has proven effective, it often lacks cultural resonance in African contexts, highlighting the need for innovative approaches. This study proposes a framework for enhancing AI-driven mental health applications by integrating CBT principles with Ubuntu and fine-tuning large language models (LLMs) to embed cultural context. The goal is to improve user engagement and the effectiveness of mental health interventions in South Africa. This framework emphasizes diversity, equity, and inclusion (DEI), with a particular focus on cultural adaptation. Through theoretical exploration, the study demonstrates the potential of Africa-centric LLM applications to offer culturally sensitive support, helping to bridge the mental health service gap across the continent. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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Beyond Normal: Learning Spatial Density Models of Node Mobility

arXiv

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arXiv 2024年

作者： Gao, Wanxin Nikolaidis, Ioanis Harms, Janelle Faculty of Data Science City University of Macau China Department of Computing Science University of Alberta Edmonton Canada

Learning models of complex spatial density functions, representing the steady-state density of mobile nodes moving on a two-dimensional terrain, can assist in network design and optimization problems, e.g., by accelerating the computation of the density function during a parameter sweep. We address the question of applicability for off-the-shelf mixture density network models for the description of mobile node density over a disk. We propose the use of Möbius distributions to retain symmetric spatial relations, yet be flexible enough to capture changes as one radially traverses the disk. The mixture models for Möbius versus Gaussian distributions are compared and the benefits of choosing Möbius distributions become evident, yet we also observe that learning mixtures of Möbius distributions is a fragile process, when using current tools, compared to learning mixtures of Gaussians. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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