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International Conference on Parallel and Distributed systems (ICPADS)

作者： Yan Zeng ChengChuang Huang YiJie Ni ChunBao Zhou JiLin Zhang Jue Wang MingYao Zhou MeiTing Xue YunQuan Zhang School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Key Laboratory for Modeling and Simulation of Complex Systems Ministry of Education Hangzhou China Data Security Governance Zhejiang Engineering Research Center Hangzhou China School of ITMO Joint Institute Hangzhou Dianzi University Hangzhou China Institute of Computer Network Information Center of the Chinese Academy of Sciences Beijing China HuaWei State Key Laboratory of Computer Architecture Institute of Computing Technology of the Chinese Academy of Sciences Beijing China

As the size of datasets and neural network models increases, automatic parallelization methods for models have become a research hotspot in recent years. The existing auto-parallel methods based on machine learning or graph algorithms still have issues with search efficiency and applicability. This paper proposes an automatic parallel method based on a dual-population genetic algorithm, TGA, which transforms model partitioning and placement into an integer linear programming problem and constructs a cost model to evaluate the solution. The solution space is built using the neural network’s dataflow graph and device cluster’s topology, and the dual-population genetic algorithm is used to search for the optimal model parallel strategy. Experiments with various models show that the proposed method can improve single-step execution time by up to 42% compared to the Baechi method and up to 37.7% compared to the Hierarchical method.

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Preserving the Privacy of Healthcare Data over Social Networks Using Machine Learning

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Computational Intelligence and Neuroscience 2022年 2022卷 4690936页

作者： Veeramakali, T. Shobanadevi, A. Nayak, Nihar Ranjan Kumar, Sumit Singhal, Sunita Subramanian, Manoharan Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Sri Venkateswara College of Engineering Technology Chittoor India Indian Institute of Management Kozhikode India Department of Computer Science and Engineering Manipal University Jaipur Jaipur303007 India Department of Computer Science School of Informatics and Electrical Engineering Hachalu Hundesa Campus Ambo University Ambo Ethiopia

A key challenge in clinical recommendation systems is the problem of aberrant patient profiles in social networks. As a result of a person's abnormal profile, numerous vests might be used to make fake remarks about them, cyber bullying, or cyber-attacks. Many clinical researchers have done extensive study on this topic. The most recent studies on this topic are summarized, and an overarching framework is provided. When it comes to the methods and datasets that make up the data collection, the feature presentation and algorithm selection layers provide an overview of the various types of algorithm selections available. The categorization and evaluation of diseases and disorders has been one of the major advantages of machine learning in medical. Because it was harder to predict, it rendered it more controllable. It might range from difficult-to-find cancers in the early stages to certain other illnesses spread through the bloodstream. In healthcare, we may pick methods in machine learning depending on reliable outcomes. To do so, we must run the findings through each method. The major issue arises during information training and validation. Because the dataset is so large, eliminating mistakes might be difficult. The providers, other characteristics, various algorithms, data labelling techniques, and assessment criteria are all presented and contrasted in depth. Detecting anomalous users in medical social networks, on the other hand, is a work in progress. The result evaluation layer provides an explanation of how to evaluate and mark up the results of the various algorithm selection layers. Finally, it looks forward to more study in this area. © 2022 T. Veeramakali et al.

关键词： Diseases

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Residual2Vec: debiasing graph embedding with random graphs 21

Residual2Vec: debiasing graph embedding with random graphs

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Proceedings of the 35th International Conference on Neural Information Processing systems

作者： Sadamori Kojaku Jisung Yoon Isabel Constantino Yong-Yeol Ahn Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University Department of Industrial Management and Engineering Pohang University of Science and Technology South Korea Indiana University Network Science Institute Indiana University Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University and Indiana University Network Science Institute Indiana University and Connection Science Massachusetts Institute of Technology

ISBN: (纸本)9781713845393

Graph embedding maps a graph into a convenient vector-space representation for graph analysis and machine learning applications. Many graph embedding methods hinge on a sampling of context nodes based on random walks. However, random walks can be a biased sampler due to the structural properties of graphs. Most notably, random walks are biased by the degree of each node, where a node is sampled proportionally to its degree. The implication of such biases has not been clear, particularly in the context of graph representation learning. Here, we investigate the impact of the random walks' bias on graph embedding and propose residual2vec, a general graph embedding method that can debias various structural biases in graphs by using random graphs. We demonstrate that this debiasing not only improves link prediction and clustering performance but also allows us to explicitly model salient structural properties in graph embedding.

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Cooperation and interdependence in global science funding

arXiv

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arXiv 2023年

作者： Miao, Lili Larivière, Vincent Wang, Feifei Ahn, Yong-Yeol Sugimoto, Cassidy R. Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University Bloomington India École de Bibliothéconomie et des Sciences de l’Information Université de Montréal Canada Observatoire des Sciences et des Technologies Université du Québec à Montréal Canada School of Economics and Management Beijing University of Technology China School of Public Policy Georgia Institute of Technology AtlantaGA United States

Investments in research and development are key to scientific and economic growth and to the well-being of society1–3. Scientific research demands significant resources making national scientific investment a crucial driver of scientific production4. As scientific production becomes increasingly multinational, it is critical to study how nations’ scientific activities are funded both domestically and internationally5. By tracing research grants acknowledged in scholarly publications, our study reveals a shifting duopoly of China and the United States in the global funding landscape, with a contrasting funding pattern;while China has surpassed the United States in publications with acknowledged domestic and international funding, the United States largely maintains its role as the most important global research partner. Our results also highlight the precarity of low- and middle-income countries to global funding disruptions. By revealing the complex interdependence and collaboration between countries in the global scientific enterprise, this work informs future studies investigating the national and global scientific enterprise and how funding leads to both productive cooperation and vulnerable dependencies. © 2023, CC BY.

关键词： Finance

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HCI Across Borders: Building a Collective Vision for the Future

HCI Across Borders: Building a Collective Vision for the Fut...

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2025 CHI Conference on Human Factors in computing systems, CHI EA 2025

作者： Moin, Sara Cannanure, Vikram Kamath Mthoko, Hafeni Rivera-Loaiza, Cuauhtémoc Kano, Tsuyoshi Varghese, Delvin Till, Sarina C. Gautam, Aakash Wong-Villacres, Marisol Gaytán-Lugo, Laura Sanely Dray, Susan Sturm, Christian Mélange Lab. IIITD Delhi India Saarland University Saarland Informatics Campus Saarbrücken Germany The Independent Institute of Education?s Varsity College Sandton South Africa Universidad Michoacana Morelia Mexico Department of Management Systems Kanazawa Institute of Technology Ishikawa Kanazawa Japan Action Lab. Monash University MelbourneVIC Australia University of Cape Town Western Cape Cape Town South Africa School of Computing and Information University of Pittsburgh PittsburghPA United States Facultad de Ingeniería en Electricidad y Computación Escuela Superior Politécnica del Litoral Guayaquil Ecuador School of Mechanical and Electrical Engineering Universidad de Colima Coquimatlan Mexico Dray & Associate MinneapolisMN United States Hamm-Lippstadt University of Applied Sciences Hamm Germany

ISBN: (纸本)9798400713958

The HCI Across Borders (HCIxB) workshop at CHI 2025 will focus on "Building a Collective Vision for the Future." HCIxB has gathered a diverse audience annually by conducting workshops and symposia since CHI 2016. This year, we hope to regroup as a community to reflect on the growth of HCIxB over the past ten years, engage in mentoring sessions for present early career researchers, and collectively form a vision for the future. This full-day hybrid workshop aims to gather researchers and practitioners to explore how we can collectively shape the future of HCI across diverse cultures and geographies. We will have a panel discussion featuring invited speakers sharing insights from a recent HCIxB Book Chapter summarizing our future. Attendees will showcase their research through posters, highlighting HCIxB’s trends worldwide, particularly in the Global South. Lastly, we will collaborate to create a vision for our community’s future and provide initial resources to future HCIxB organizers. © 2025 Copyright held by the owner/author(s).

关键词： Engineering education

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Towards Safe AI Clinicians: A Comprehensive Study on Large Language Model Jailbreaking in Healthcare

arXiv

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arXiv 2025年

作者： Zhang, Hang Lou, Qian Wang, Yanshan Intelligent Systems Program School of Computing and Information University of Pittsburgh PA United States Department of Computer Science University of Central Florida OrlandoFL United States Department of Health Information Management University of Pittsburgh PA United States Department of Biomedical Informatics University of Pittsburgh PA United States Clinical and Translational Science Institute University of Pittsburgh PittsburghPA United States University of Pittsburgh Medical Center PittsburghPA United States

Large language models (LLMs) are increasingly utilized in healthcare applications. However, their deployment in clinical practice raises significant safety concerns, including the potential spread of harmful information. This study systematically assesses the vulnerabilities of seven LLMs to three advanced black-box jailbreaking techniques within medical contexts. To quantify the effectiveness of these techniques, we propose an automated and domain-adapted agentic evaluation pipeline. Experiment results indicate that leading commercial and open-source LLMs are highly vulnerable to medical jailbreaking attacks. To bolster model safety and reliability, we further investigate the effectiveness of Continual Fine-Tuning (CFT) in defending against medical adversarial attacks. Our findings underscore the necessity for evolving attack methods evaluation, domain-specific safety alignment, and LLM safety-utility balancing. This research offers actionable insights for advancing the safety and reliability of AI clinicians, contributing to ethical and effective AI deployment in healthcare. © 2025, CC BY-NC-SA.

关键词： Patient treatment

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Systematic comparison of graph embedding methods in practical tasks

arXiv

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arXiv 2021年

作者： Zhang, Yi-Jiao Yang, Kai-Cheng Radicchi, Filippo Institute of Computational Physics and Complex Systems Lanzhou University Gansu Lanzhou730000 China Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States

Network embedding techniques aim at representing structural properties of graphs in geometric space. Those representations are considered useful in downstream tasks such as link prediction and clustering. However, the number of graph embedding methods available on the market is large, and practitioners face the non-trivial choice of selecting the proper approach for a given application. The present work attempts to close this gap of knowledge through a systematic comparison of eleven different methods for graph embedding. We consider methods for embedding networks in the hyperbolic and Euclidean metric spaces, as well as non-metric community-based embedding methods. We apply these methods to embed more than one hundred real-world and synthetic networks. Three common downstream tasks - mapping accuracy, greedy routing, and link prediction - are considered to evaluate the quality of the various embedding methods. Our results show that some Euclidean embedding methods excel in greedy routing. As for link prediction, community-based and hyperbolic embedding methods yield overall performance superior than that of Euclidean-space-based approaches. We compare the running time for different methods and further analyze the impact of different network characteristics such as degree distribution, modularity, and clustering coefficients on the quality of the different embedding methods. We release our evaluation framework to provide a standardized benchmark for arbitrary embedding methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Forecasting

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Primitive Simultaneous Optimization of Similarity Metrics for Image Registration 9th

Primitive Simultaneous Optimization of Similarity Metrics f...

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9th International Workshop on Brain Lesion workshop, BrainLes 2023 and 3rd Stroke Workshop on Imaging and Treatment CHallenges, SWITCH 2023 Held in Conjunction with 26th Medical Image computing and Computer Assisted Intervention, MICCAI 2023

作者： Waldmannstetter, Diana Wiestler, Benedikt Schwarting, Julian Ezhov, Ivan Metz, Marie Bakas, Spyridon Baheti, Bhakti Chakrabarty, Satrajit Rueckert, Daniel Kirschke, Jan S. Heckemann, Rolf A. Piraud, Marie Menze, Bjoern H. Kofler, Florian Department of Informatics Technical University of Munich Munich Germany Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Munich Germany University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Electrical and Systems Engineering Washington University in St. Louis St. LouisMO United States Artificial Intelligence in Healthcare and Medicine Technical University of Munich Munich Germany Department of Computing Imperial College London London United Kingdom Department of Medical Radiation Sciences University of Gothenburg Gothenburg Sweden Helmholtz AI Helmholtz Zentrum München Munich Germany

ISBN: (纸本)9783031761591

Even though simultaneous optimization of similarity metrics is a standard procedure in the field of semantic segmentation, surprisingly, this is much less established for image registration. To help closing this gap in the literature, we investigate in a complex multi-modal 3D setting whether simultaneous optimization of registration metrics, here implemented by means of primitive summation, can benefit image registration. We evaluate two challenging datasets containing collections of pre- to post-operative and pre- to intra-operative Magnetic Resonance (MR) images of glioma. Employing the proposed optimization, we demonstrate improved registration accuracy in terms of Target Registration Error (TRE) o on expert neuroradiologists’ landmark annotations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Semantic Segmentation

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The stratified K-folds cross-validation and class-balancing methods with high-performance ensemble classifiers for breast cancer classification

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Healthcare Analytics 2023年 4卷 100247-100247页

作者： T R, Mahesh V, Vinoth Kumar V, Dhilip Kumar Geman, Oana Margala, Martin Guduri, Manisha Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore 562112 India School of Computer Science Engineering & Information Systems (SCORE) Vellore Institute of Technology (VIT) 632014 India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai 600062 India Department of Computers Electronics and Automation Faculty of Electrical Engineering and Computer Science Stefan cel Mare University of Suceava Suceava 720229 Romania School of Computing and Informatics University of Louisiana at Lafayette United States

Breast cancer is one of the most common causes of death among women, and early diagnosis is vital for reducing the fatality rate. This study evaluates the most widely used machine-learning breast cancer prediction and diagnosis methods. We use synthetic minority over-sampling to handle imbalanced data in the breast cancer diagnosis dataset obtained from the Wisconsin Machine Learning Repository. We use a variety of machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Classification and Regression Tree (CART), Naive Bayes (NB), and well-known ensembles methods like Majority-Voting, eXtreme Gradient Boosting algorithm (XGBoost), and Random Forest (RF) for the breast cancer classification. The findings show that the Majority-Voting ensemble method, built on the top three classifiers (LR, SVM, and CART), outperforms all other individual classifiers and offers the highest accuracy of 99.3%. © 2023 The Author(s)

关键词： Breast cancer Class-balancing Cross-validation Diagnostic analytics Ensemble method Machine learning

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Spatiotemporal forecasting for dengue, chikungunya fever and Zika using machine learning and artificial expert committees based on meta-heuristics

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Research on Biomedical Engineering 2022年第2期38卷 499-537页

作者： da Silva, Cecilia Cordeiro de Lima, Clarisse Lins da Silva, Ana Clara Gomes Moreno, Giselle Machado Magalhães Musah, Anwar Aldosery, Aisha Dutra, Livia Ambrizzi, Tercio Borges, Iuri V. G. Tunali, Merve Basibuyuk, Selma Yenigün, Orhan Massoni, Tiago Lima Jones, Kate Campos, Luiza Kostkova, Patty da Silva Filho, Abel Guilhermino dos Santos, Wellington Pinheiro Center for Informatics CIn-UFPE Federal University of Pernambuco Recife Brazil Polytechnique School of the University of Pernambuco Poli-UPE Recife Brazil Department of Biomedical Engineering Federal University of Pernambuco Recife Brazil Department of Atmospheric Sciences IAG-USP University of São Paulo São Paulo Brazil Centre for Digital Public Health and Emergencies Institute for Risk and Disaster Reduction University College London London United Kingdom Institute of Environmental Sciences Boaziçi University Istanbul Turkey Department Systems & Computing Federal University of Campina Grande Campina Grande Brazil Centre for Biodiversity and Environment Research Department of Genetics Evolution and Environment University College London London United Kingdom Department of Civil Environmental & Geomatic Engineering University College London London United Kingdom

Purpose: Dengue is considered one of the biggest public health problems in recent decades. Climate and demographic changes, the disorderly growth of cities and international trade have brought new arboviruses such as chikungunya and Zika. Control of arboviruses depends on control of the vector: the Aedes aegypti mosquito. Objective: In this work, we propose a methodology for building disease predictors capable of predicting infected cases and locations based on machine learning. We also propose an artificial experts committee based on meta-heuristic methods to detect the most relevant risk factors. Method As a case study, we applied the methodology to forecast dengue, chikungunya and Zika, with data from the City of Recife, Brazil, from 2013 to 2016. We used arboviruses cases data and climatic and environmental information: wind speeds, temperatures and precipitation. Results The best prediction results were obtained with 10-tree Random Forest regression, with Pearson’s correlation above 0.99 and RMSE (%) below 6%. Additionally, the artificial experts committee was able to present the most relevant factors for predicting cases in each two-month period. Conclusion: The spatiotemporal prediction results showed the evolution of arboviruses, pointing out as major focuses on both regions richer in urban green areas and low-income neighborhood with irregular water supply. Determining the most relevant factors for prediction, as well as the spatial distribution of cases, can be useful for the planning and execution of public policies aimed at improving the health infrastructure and planning and controlling the vector. © 2022, Sociedade Brasileira de Engenharia Biomedica.

关键词： Forecasting

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