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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Bonda Kiran Kumar Anurima Majumdar Saidatul Akmar Ismail Rohit R Dixit Hairani Wahab Md Hafizi Ahsan KL School of Architecture KL Deemed to be University Koneru Lakshmaiah Educational Foundation vaddeswaram Andhra Pradesh Department of Electronics and Communication Engineering Guru Nanak Institute of Technology School of Information Science College of Computing Informatics & Mathematics Universiti Teknologi MARA Shah Alam Malaysia Siemens Healthineers School of Information Science College of Computing Informatics and Mathematics Universiti Teknologi MARA Labuan Faculty of International Finance Universiti Malaysia Sabah

The recent severe outbreak of the disease has been linked to the COVID-19 virus. It is essential for healthcare professionals and individuals with the capacity to provide intensive care to offer clear explanations of this situation. To address this issue, the World Health Organization (WHO) has implemented several measures. The National Emergency Telecritical Care Network (NETCCN) has the capability to assess these actions using digital technologies. This section provides a comprehensive explanation of these developments, and NETCCN has put forth a recommendation. Version 2.0 of this digital health data will be made available; it will significantly enhance potential crises in the future. Artificial Intelligence (AI) is used in the suggested digital prediction approach to gather past data on the infection. Telemetry, sophisticated maintenance robotics, and monitoring intelligence may all be found with this technology. These may be created to incorporate data relevant to various infections. Health experts will coordinate all of these. To maintain sustained preparedness for emergency catastrophe protection, frequent testing should be conducted.

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A Study of Weisfeiler–Leman Colorings on Planar Graphs

arXiv

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arXiv 2022年

作者： Kiefer, Sandra Neuen, Daniel Max Planck Institute for Software Systems Saarland Informatics Campus Saarbrücken Germany School of Computing Science Simon Fraser University Burnaby Canada

The Weisfeiler–Leman (WL) algorithm is a combinatorial procedure that computes colorings on graphs, which can often be used to detect their (non-)isomorphism. Particularly the 1- and 2-dimensional versions 1-WL and 2-WL have received much attention, due to their numerous links to other areas of computer science. Knowing the expressive power of a certain dimension of the algorithm usually amounts to understanding the computed colorings. An increase in the dimension leads to finer computed colorings and, thus, more graphs can be distinguished. For example, on the class of planar graphs, 3-WL solves the isomorphism problem. However, the expressive power of 2-WL on the class is poorly understood (and, in particular, it may even well be that it decides isomorphism). In this paper, we investigate the colorings computed by 2-WL on planar graphs. Towards this end, we analyze the graphs induced by edge color classes in the graph. Based on the obtained classification, we show that for every 3-connected planar graph, it holds that: a) after coloring all pairs with their 2-WL color, the graph has fixing number 1 with respect to 1-WL, or b) there is a 2-WL-definable matching that can be used to transform the graph into a smaller one, or c) 2-WL detects a connected subgraph that is essentially the graph of a Platonic or Archimedean solid, a prism, a cycle, or a bipartite graph K2,. In particular, the graphs from case (a) are identified by 2-WL. Copyright © 2022, The Authors. All rights reserved.

关键词： Graph theory

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ECA-RetinaNet: A Novel Self-Attention RetinaNet for Environmental Microorganism Image Object Detection

ECA-RetinaNet: A Novel Self-Attention RetinaNet for Environm...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Yang, Hechen Yang, Jinzhu Jiang, Tao Zhao, Xin Zhang, Jinghua Zhao, Peng Chen, Ao Nie, Qianqing Grzegorzek, Marcin Li, Chen Northeastern University Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Shenyang China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Liaoning Shenyang China Chengdu University of Traditional Chinese Medicine School of Intelligent Medicine Chengdu China Chengdu University of Information Technology International Joint Institute of Robotics and Intelligent Systems Chengdu China Institute of Medical Informatics University of Luebeck Luebeck Germany

ISBN: (纸本)9798350324457

The detection of environmental microorganisms is always a difficult task, e specially when the multi-scale environment is complex. For tiny objects in microscopic images, current detection methods face the challenge of accurate identification and localization. In contrast, we propose a convolutional neural network (ECA-RetinaNet) for microscopic object detection of which underlying dataset is a high-quality EMDS-7 dataset. The accuracy of ECA-RetinaNet is high, with a high mean Average Precision (mAP) value of 81.42% in the Environmental Microorganisms (EMS) detection task. Its accuracy has been higher than that of the two-stage object detection network. © 2023 IEEE.

关键词： Attention Convolutional Neural Network Environmental Miroorganisms Feature Fusion Object Detection

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AI driven B-cell Immunotherapy Design

arXiv

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arXiv 2023年

作者： da Silva, Bruna Moreira Ascher, David B. Geard, Nicholas Pires, Douglas E.V. Systems and Computational Biology Bio21 Institute University of Melbourne MelbourneVIC Australia Computational Biology and Clinical Informatics Baker Heart and Diabetes Institute MelbourneVIC Australia School of Computing and Information Systems University of Melbourne MelbourneVIC Australia The School of Chemistry and Molecular Biosciences The University of Queensland BrisbaneQLD Australia

Antibodies, a prominent class of approved biologics, play a crucial role in detecting foreign antigens. The effectiveness of antigen neutralisation and elimination hinges upon the strength, sensitivity, and specificity of the paratope-epitope interaction, which demands resource-intensive experimental techniques for characterisation. In recent years, artificial intelligence and machine learning methods have made significant strides, revolutionising the prediction of protein structures and their complexes. The past decade has also witnessed the evolution of computational approaches aiming to support immunotherapy design. This review focuses on the progress of machine learning-based tools and their frameworks in the domain of B-cell immunotherapy design, encompassing linear and conformational epitope prediction, paratope prediction, and antibody design. We mapped the most commonly used data sources, evaluation metrics, and method availability and thoroughly assessed their significance and limitations, discussing the main challenges ahead. © 2023, CC BY-NC-ND.

关键词： Forecasting

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Comparative Study of Machine Learning Algorithms and Text Vectorization Methods for Fake News Detection

Comparative Study of Machine Learning Algorithms and Text Ve...

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International Conference on Information, Intelligence, systems and Applications (IISA)

作者： Andreas Kanavos Ioannis Karamitsos Alaa Mohasseb Vassilis C. Gerogiannis Department of Informatics Ionian University Corfu Greece Department of Graduate and Research Rochester Institute of Technology Dubai UAE School of Computing University of Portsmouth Portsmouth UK Department of Digital Systems University of Thessaly Larissa Greece

The detection of fake news is a crucial task in today's society, given the widespread use of social media and online platforms. In this study, we investigate the application of Machine Learning (ML) algorithms for the detection of fake news. We consider two different datasets of categorized news articles of various sizes and apply various ML algorithms, along with two methods of text vectorization. Specifically, we examine Bag of Words and Tf-Idf, with the use of stemming and with different n-gram values. The resulting vectors are processed by Naive Bayes algorithms, Linear algorithms, Support Vector Machines, and Random Forest classifiers. F1-score and computational time for each algorithm-vectorization combination were recorded. Our results have shown that Linear Algorithms and Support Vector Machines combined with Tf-Idf vectors and n-gram value of (1,2) produced the highest accuracies, with an F1-score up to 96.8%.

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Comparison of Claude (Sonnet and Opus) and ChatGPT (GPT-4, GPT-4o, GPT-o1) in Analyzing Educational Image-Based Questions from Block-Based Programming Assessments

Comparison of Claude (Sonnet and Opus) and ChatGPT (GPT-4, G...

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Information and Education Technology (ICIET), International Conference on

作者： Wan Chong Choi Iek Chong Choi Chi In Chang Lai Chu Lam Department of Computer Science Illinois Institute of Technology US Faculty of Applied Sciences Macao Polytechnic University Macao SAR China School of Education City University of Macau Macao SAR China Department of Psychology Golden Gate University US School of Informatics Computing and Cyber Systems Northern Arizona University US

ISBN: (数字)9798331537845

ISBN: (纸本)9798331537852

This study presented a novel application of OpenAI's GPT-o1 model for analyzing educational image-based questions derived from block-based programming assessments, contributing to the first systematic exploration of this advanced AI model in this field. We evaluated GPT-o1 alongside other state-of-the-art models, including GPT-4, GPT-4o, GPT-4o mini, Claude3.5 Sonnet, and Claude-3 Opus, to assess their accuracy in solving multiple-choice and fill-in-the-blank questions from a standardized block-based programming test. Our findings demonstrated that GPT-o1 significantly outperformed other models, achieving an accuracy of 80 %, which we attributed to its enhanced image analysis capabilities. Comparative analyses revealed GPT-01's superior performance in interpreting and responding to visual prompts, particularly in tasks requiring logical reasoning and computational thinking. Furthermore, the Pearson correlation test highlighted significant relationships between GPT-o1 and GPT-4o. Our contributions highlighted the potential applications of advanced chatbots in block-based programming education. These technologies can serve as personalized learning tutors to guide students in understanding and solving block-based programming tasks independently. Additionally, they can assist educators in automating the grading process and generating tailored programming exercises, thus improving teaching efficiency.

关键词： Analytical models Visualization Accuracy Correlation Systematics Large language models Education Training data Chatbots Programming profession

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Edge computing-Based Joint Client Selection and Networking Scheme for Federated Learning in Vehicular IoT

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China Communications 2021年第6期18卷 39-52页

作者： Wugedele Bao Celimuge Wu Siri Guleng Jiefang Zhang Kok-Lim Alvin Yau Yusheng Ji School of computer science and information engineering Hohhot Minzu CollegeHohhot 010051China Graduate School of Informatics and Engineering The University of Electro-Communications1-5-1ChofugaokaChofu-shiTokyo182-8585 Japan Institute of Intelligent Media Technology Communication University of ZhejiangZhejiang 310018China School of Science and Technology Sunway University5Jalan UniversitiBandar Sunway47500 Petaling JayaSelangorMalaysia Information Systems Architecture Research Division National Institute of Informatics2-1-2HitotsubashiChiyoda-kuTokyo 101-8430 Japan

In order to support advanced vehicular Internet-of-Things(IoT)applications,information exchanges among different vehicles are required to find efficient solutions for catering to different application requirements in complex and dynamic vehicular *** learning(FL),which is a type of distributed learning technology,has been attracting great interest in recent years as it performs knowledge exchange among different network entities without a violation of user ***,client selection and networking scheme for enabling FL in dynamic vehicular environments,which determines the communication delay between FL clients and the central server that aggregates the models received from the clients,is still *** this paper,we propose an edge computing-based joint client selection and networking scheme for vehicular *** proposed scheme assigns some vehicles as edge vehicles by employing a distributed approach,and uses the edge vehicles as FL clients to conduct the training of local models,which learns optimal behaviors based on the interaction with *** clients also work as forwarder nodes in information sharing among network *** client selection takes into account the vehicle velocity,vehicle distribution,and the wireless link connectivity between vehicles using a fuzzy logic algorithm,resulting in an efficient learning and networking *** use computer simulations to evaluate the proposed scheme in terms of the communication overhead and the information covered in learning.

关键词： vehicular IoT federated learning client selection networking scheme

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An Auto-Parallel Method for Deep Learning Models Based on Genetic Algorithm 29

An Auto-Parallel Method for Deep Learning Models Based on Ge...

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29th IEEE International Conference on Parallel and Distributed systems, ICPADS 2023

作者： Zeng, Yan Huang, Chengchuang Ni, Yijie Zhou, Chunbao Zhang, Jilin Wang, Jue Zhou, Mingyao Xue, Meiting Zhang, Yunquan Hangzhou Dianzi University School of Computer Science and Technology Hangzhou310018 China Ministry of Education Key Laboratory for Modeling and Simulation of Complex Systems Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China Hangzhou Dianzi University School of ITMO Joint Institute Hangzhou310018 China Institute of Computer Network Information Center of the Chinese Academy of Sciences Beijing100086 China HuaWei China Institute of Computing Technology of the Chinese Academy of Sciences State Key Laboratory of Computer Architecture Beijing100086 China

ISBN: (纸本)9798350330717

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. © 2023 IEEE.

关键词： Auto-Parallel Distributed machine learning Genetic algorithm Integer linear programming Model parallelism

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RMM: reinforced memory management for class-incremental learning 21

RMM: reinforced memory management for class-incremental lear...

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

作者： Yaoyao Liu Bernt Schiele Qianru Sun Max Planck Institute for Informatics Saarland Informatics Campus School of Computing and Information Systems Singapore Management University

ISBN: (纸本)9781713845393

Class-Incremental Learning (CIL) [40] trains classifiers under a strict memory budget: in each incremental phase, learning is done for new data, most of which is abandoned to free space for the next phase. The preserved data are exemplars used for replaying. However, existing methods use a static and ad hoc strategy for memory allocation, which is often sub-optimal. In this work, we propose a dynamic memory management strategy that is optimized for the incremental phases and different object classes. We call our method reinforced memory management (RMM), leveraging reinforcement learning. RMM training is not naturally compatible with CIL as the past, and future data are strictly non-accessible during the incremental phases. We solve this by training the policy function of RMM on pseudo CIL tasks, e.g., the tasks built on the data of the 0-th phase, and then applying it to target tasks. RMM propagates two levels of actions: Level-1 determines how to split the memory between old and new classes, and Level-2 allocates memory for each specific class. In essence, it is an optimizable and general method for memory management that can be used in any replaying-based CIL method. For evaluation, we plug RMM into two top-performing baselines (LUCIR+AANets and POD+AANets [30]) and conduct experiments on three benchmarks (CIFAR-100, ImageNet-Subset, and ImageNet-Full). Our results show clear improvements, e.g., boosting POD+AANets by 3.6%, 4.4%, and 1.9% in the 25-Phase settings of the above benchmarks, respectively.

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A Toolbox of Feminist Wonder: Theories and methods that can make a difference 26

A Toolbox of Feminist Wonder: Theories and methods that can ...

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26th ACM Conference on Computer-Supported Cooperative Work and Social computing, CSCW 2023

作者： Hansson, Karin Bardzell, Shaowen Bhandari, Aparajita Boulicault, Marion Doyle, Dylan Thomas Erete, Sheena Cerratto Pargman, Teresa Lazem, Shaimaa Muller, Michael Normark, Maria Petterson, Adrian Rosén, Anton Poikolainen Taylor, Alex S. Thomas, Jakita O. Watson, Julia Södertörn University The School of Natural Sciences Technology and Environmental Studies Sweden Georgia Institute of Technology The School of Interactive Computing United States Cornell University Department of Communication United States University of Edinburgh Department of Philosophy United States University of Colorado Boulder Department of Information Science United States University of Maryland College of Information United States Stockholm University The Department of Computer and Systems Sciences Sweden City for Scientific Research and Technological Applications The Informatics Research Institute Egypt Ibm Research Human Centered Ai United States Uppsala University Department of Informatics and Media Sweden University of Toronto Faculty of Information Canada Aalto University The School of Arts Design and Architecture Finland City University of London The Centre for Human Computer Interaction Design United Kingdom Auburn University Department of Computer Science and Software Engineering United States University of Toronto Department of Computer Science Canada

ISBN: (纸本)9798400701290

This one-day hybrid workshop builds on previous feminist CSCW workshops to explore feminist theoretical and methodological approaches that have provided us with useful tools to see things differently and make space for change. Since its inception over a decade ago, feminist HCI has progressed from the margins to mainstream HCI, with numerous references in the literature. Feminist HCI has also evolved to incorporate other critical HCI practices such as Queer HCI, participatory design, and speculative design. While feminist approaches have grown in popularity and become mainstream, it is getting more difficult to distinguish the feminist emancipatory core from other attempts of developing and improving society in various ways. In this workshop, we therefore want to revisit our feminist roots, where theory is a liberatory and creative practice, motivated by affect, curiosity, and wonder. From this standpoint, we consider which of our feminist tools can make a significant difference today, in a highly datafied world. The goal of this workshop is to;1) create an inventory of feminist theories and concepts that have had an impact on our work as designers, educators, researchers, and activists;2) develop a feminist toolbox for the CSCW community to strengthen our feminist literacy. © 2023 Owner/Author.

关键词： Computation theory

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