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Logistic Regression Model for Measuring Perception on Open and Distance Learning (ODL) during COVID-19 Pandemic based on Impeding Factors among Students

Logistic Regression Model for Measuring Perception on Open a...

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computing (ICOCO), IEEE International Conference on

作者： Alia Najwa Abdul Mizan Farah Aliah Nur Mansor Nor Azura Md Ghani Adina Najwa Kamarudin Mahayaudin M. Mansor Siti Afiqah Muhamad Jamil Nurain Ibrahim College of Computing Informatics and Media Universiti Teknologi MARA Shah Alam Selangor Malaysia Department of Mathematical Sciences Faculty of Science Universiti Teknologi Malaysia Johor Bahru Malaysia Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Kompleks Al-Khawarizmi Universiti Teknologi MARA Shah Alam Selangor Malaysia

ISBN: (纸本)9781665489973

Authorities have suggested emergency remote instruction to guarantee that students are not left idle during the pandemic due to the sudden closing of educational facilities. Then for the time being, traditional methods (face-to-face) have been replaced by Open and Distance Learning (ODL). Face-to-face learning was preferred by the majority of students over online learning since students were not able transit to online learning and lacked inspiration. Hence, this study focuses on perception towards ODL during COVID-19 among statistics’ students at FSKM UiTM Shah Alam based on some impeding factors such as social issue, lecturer issue, accessibility issue, academic issue, generic skills and learner intentions. The aim of this study is to investigate the perception of statistics’ students on ODL based on impeding factors and to identify the significant impeding factors effect on statistics students’ perception on ODL. There are 160 observations that are used in this study. The methods that are being used in this study are descriptive analysis and logistic regression. Overall, from the result obtained, students’ perception on ODL are approximately to agree for social issue, academic issue and learner intentions variables. Meanwhile, the significance impeding factors in this study are social issue and learner intentions. This study may help higher education institution to improve and make a better strategy to improve the existing teaching method that have been applied by all lecturers.

关键词： COVID-19 Computer aided instruction Pandemics Computational modeling Education Logistics

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Beware of "Explanations" of AI

arXiv

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arXiv 2025年

作者： Martens, David Shmueli, Galit Evgeniou, Theodoros Bauer, Kevin Janiesch, Christian Feuerriegel, Stefan Gabel, Sebastian Goethals, Sofie Greene, Travis Klein, Nadja Kraus, Mathias Kühl, Niklas Perlich, Claudia Verbeke, Wouter Zharova, Alona Zschech, Patrick Provost, Foster University of Antwerp Department of Engineering Management Antwerp2000 Belgium National Tsing Hua University Institute of Service Science Hsinchu30013 Taiwan INSEAD Technology and Business Fontainebleau77300 France Goethe University Frankfurt Department of Information Systems Frankfurt60629 Germany TU Dortmund University Department of Computer Science Dortmund44227 Germany LMU Munich Munich Center for Machine Learning Munich80539 Germany Erasmus University Rotterdam School of Management Rotterdam3062 Netherlands Copenhagen Business School Department of Digitalization Copenhagen2000 Denmark Karlsruhe Institute of Technology Scientific Computing Center Karlsruhe76131 Germany University of Regensburg Faculty of Informatics and Data Science Regensburg93053 Germany University of Bayreuth Faculty of Law Business and Economics Bayreuth95440 Germany New York University Department of Technology Operations and Statistics New YorkNY10012 United States KU Leuven Faculty of Economics and Business Leuven3000 Belgium Humboldt-Universität zu Berlin School of Business and Economics Berlin10099 Germany Leipzig University Faculty of Economics and Management Science Leipzig04109 Germany

Understanding the decisions made and actions taken by increasingly complex AI system remains a key challenge. This has led to an expanding field of research in explainable artificial intelligence (XAI), highlighting the potential of explanations to enhance trust, support adoption, and meet regulatory standards. However, the question of what constitutes a "good" explanation is dependent on the goals, stakeholders, and context. At a high level, psychological insights such as the concept of mental model alignment can offer guidance, but success in practice is challenging due to social and technical factors. As a result of this ill-defined nature of the problem, explanations can be of poor quality (e.g. unfaithful, irrelevant, or incoherent), potentially leading to substantial risks. Instead of fostering trust and safety, poorly designed explanations can actually cause harm, including wrong decisions, privacy violations, manipulation, and even reduced AI adoption. Therefore, we caution stakeholders to beware of explanations of AI: while they can be vital, they are not automatically a remedy for transparency or responsible AI adoption, and their misuse or limitations can exacerbate harm. Attention to these caveats can help guide future research to improve the quality and impact of AI explanations. © 2025, CC BY.

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DieT Transformer model with PCA-ADE integration for advanced multi-class brain tumor classification

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Intelligence-Based Medicine 2025年 11卷 100192-100192页

作者： Amin, Mohammad Nahar, Khalid M.O. Gharaibeh, Hasan Nasayreh, Ahmad Alsalmanc, Neda'a Alomar, Alaa Malkawi, Majd Alqasem, Noor Smerat, Aseel Zitar, Raed Abu Nusier, Shawd Ezugwu, Absalom E. Abualigah, Laith Department of Information Technology and Computer Sciences Yarmouk University Irbid 211633 Jordan Faculty of Computer Studies Arab Open University P.O.Box 84901 Riyadh 11681 Saudi Arabia Department of Computer Science Faculty of Information Technology Jordan University of Science and Technology Irbid 22110 Jordan Department of Medicine Faculty of Medicine Jordan University of Science and Technology Irbid 22110 Jordan Department of Biomedical Systems and Informatics Engineering Hijjawi Faculty for Engineering Technology Yarmouk University Irbid 21163 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates Department of Health and Clinical Outcomes Research Saint Louis University St. Louis MO United States Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan

Early and accurate diagnosis of brain tumors is crucial to improving patient outcomes and optimizing treatment strategies. Long-term brain injury results from aberrant proliferation of either malignant or nonmalignant tissues in the brain. MRIs, or magnetic resonance imaging, are one of the most used approaches for detecting brain tumors. Professionals physically evaluate people after they have had MRI filtering, the process of enhancing MRI scans for radiologist interpretation, to establish if they have a brain tumor. Because different specialists use different frames to make judgments on the same MRI image, their analyses may yield contradictory results. Furthermore, simply detecting a tumor is insufficient. Inconsistent diagnoses can lead to delays in treatment, impacting survival rates and quality of care. It is also crucial to diagnose the patient's tumor so that treatment can begin as soon as possible. In this research, we investigate the multi-class classification of brain tumors utilizing a cutting-edge methodology that includes feature extraction from pictures using the DieT Transformer model, dimensionality reduction with PCA, and feature selection using the ADE algorithm. The proposed model, known in the publication as ADE_DieT, obtained an accuracy of 96.09 %. In addition, this article analyzes the performance of various pre-trained models, including MobileNetV3, NasNet, ResNet50, VGG16, VGG19, and DeiT. The proposed approach shortens the time required for manual diagnosis by clinicians by assisting in the rapid and accurate identification of brain tumors using MRI data. In oncology, this is important since it allows for early treatment. Integrating ADE_DieT into clinical workflows can support radiologists by reducing diagnosis time and enhancing diagnostic consistency. © 2024 The Authors

关键词： Brain tumor Medical image analysis MRI Vision Transformer

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Deep learning for embodied visual navigation research: A survey

arXiv

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arXiv 2021年

作者： Zhu, Fengda Zhu, Yi Lee, Vincent C.S. Liang, Xiaodan Chang, Xiaojun Department of Data Science and AI Faculty of Information Technology Monash University University of Chinese Academy of Sciences School of Intelligent Systems Engineering Sun Yat-sen University School of Computing Technologies RMIT University

"Embodied visual navigation" problem requires an agent to navigate in a 3D environment mainly rely on its first-person observation. This problem has attracted rising attention in recent years due to its wide application in vacuum cleaner, and rescue robot, etc. A navigation agent is supposed to have various intelligent skills, such as visual perceiving, mapping, planning, exploring and reasoning, etc. Building such an agent that observes, thinks, and acts is a key to real intelligence. The remarkable learning ability of deep learning methods empowered the agents to accomplish embodied visual navigation tasks. Despite this, embodied visual navigation is still in its infancy since a lot of advanced skills are required, including perceiving partially observed visual input, exploring unseen areas, memorizing and modeling seen scenarios, understanding cross-modal instructions, and adapting to a new environment, etc. Recently, embodied visual navigation has attracted rising attention of the community, and numerous works has been proposed to learn these skills. This paper attempts to establish an outline of the current works in the field of embodied visual navigation by providing a comprehensive literature survey. We summarize the benchmarks and metrics, review different methods, analysis the challenges, and highlight the state-of-the-art methods. Finally, we discuss unresolved challenges in the field of embodied visual navigation and give promising directions in pursuing future research. © 2021, CC BY.

关键词： Navigation

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Attention Is Not the Only Choice: Counterfactual Reasoning for Path-Based Explainable Recommendation

arXiv

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arXiv 2024年

作者： Li, Yicong Sun, Xiangguo Chen, Hongxu Zhang, Sixiao Yang, Yu Xu, Guandong Data Science and Machine Intelligence Lab Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW2007 Australia Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong999077 Hong Kong School of Information Technology and Electrical Engineering The University of Queensland BrisbaneQLD4072 Australia Department of Systems Engineering and Engineering Management Nanyang Technological University Singapore999077 Singapore Department of Computing The Hong Kong Polytechnic University Hong Kong999077 Hong Kong

Compared with only pursuing recommendation accuracy, the explainability of a recommendation model has drawn more attention in recent years. Many graph-based recommendations resort to informative paths with the attention mechanism for the explanation. Unfortunately, these attention weights are intentionally designed for model accuracy but not explainability. Recently, some researchers have started to question attention-based explainability because the attention weights are unstable for different reproductions, and they may not always align with human intuition. Inspired by the counterfactual reasoning from causality learning theory, we propose a novel explainable framework targeting path-based recommendations, wherein the explainable weights of paths are learned to replace attention weights. Specifically, we design two counterfactual reasoning algorithms from both path representation and path topological structure perspectives. Moreover, unlike traditional case studies, we also propose a package of explainability evaluation solutions with both qualitative and quantitative methods. We conduct extensive experiments on four real-world datasets, the results of which further demonstrate the effectiveness and reliability of our method. Copyright © 2024, The Authors. All rights reserved.

关键词： Graphic methods

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Fast Coding Mode Prediction for Intra Prediction in VVC SCC

Fast Coding Mode Prediction for Intra Prediction in VVC SCC

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IEEE International Conference on Image Processing

作者： Dayong Wang Junyi Yu Xin Lu Frederic Dufaux Hongwei Guo Hui Guo Ce Zhu Chongqing Key Laboratory on Big Data for Bio Intelligence Chongqing University of Posts and Telecommunications Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Faculty of Computing Engineering and Media (CEM) De Montfort University Université Paris-Saclay CNRS CentraleSupélec Laboratoire des signaux et systèmes School of Engineering Honghe University Mengzi Yunnan China University of Electronic Science and Technology of China

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Currently, screen content video applications are increasingly widespread in our daily lives. The latest Screen Content Coding (SCC) standard, known as Versatile Video Coding (VVC) SCC, employs screen content Coding Modes (CMs) selection. While VVC SCC achieves high coding efficiency, its coding complexity poses a significant obstacle to the further widespread adoption of screen content video. Hence, it is crucial to enhance the coding speed of VVC SCC. In this paper, we propose a fast mode and splitting decision for Intra prediction in VVC SCC. Specifically, we initially exploit deep learning techniques to predict content types for all CUs. Subsequently, we examine CM distributions of different content types to predict candidate CMs for CUs. We then introduce early skip and early terminate CM decisions for different content types of CUs to further eliminate unlikely CMs. Finally, we develop Block-based Differential Pulse-Code Modulation (BDPCM) early termination to improve coding speed. Experimental results demonstrate that the proposed algorithm can improve coding speed by $34.95 \%$ on average while maintaining almost the same coding efficiency.

关键词： Video coding Deep learning Image coding Modulation Prediction algorithms Complexity theory Copper

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Z-Inspection®: A Process to Assess Trustworthy AI

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2021年第2期2卷 83-97页

作者： Zicari, Roberto V. Brodersen, John Brusseau, James Dudder, Boris Eichhorn, Timo Ivanov, Todor Kararigas, Georgios Kringen, Pedro McCullough, Melissa Moslein, Florian Mushtaq, Naveed Roig, Gemma Sturtz, Norman Tolle, Karsten Tithi, Jesmin Jahan Van Halem, Irmhild Westerlund, Magnus Frankfurt Big Data Lab Goethe University Frankfurt Frankfurt Germany Department of Public Health Section of General Practice and Research Unit for General Practice Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark Philosophy Department Pace University New YorkNY United States Department of Computer Science University of Copenhagen Copenhagen Denmark Department of Physiology Faculty of Medicine University of Iceland Iceland Institute of the Law and Regulation of Digitalization Philipps-University Marburg Marburg Germany Parallel Computing Labs Intel Labs Santa ClaraCA United States Department of Computer Science Arcada University of Applied Sciences Helsinki Finland Primary Health Care Research Unit Copenhagen1014 Denmark

The ethical and societal implications of artificial intelligence systems raise concerns. In this article, we outline a novel process based on applied ethics, namely, Z-Inspection®, to assess if an AI system is trustworthy. We use the definition of trustworthy AI given by the high-level European Commission's expert group on AI. Z-Inspection® is a general inspection process that can be applied to a variety of domains where AI systems are used, such as business, healthcare, and public sector, among many others. To the best of our knowledge, Z-Inspection® is the first process to assess trustworthy AI in practice. © 2020 IEEE.

关键词： Deep learning

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Towards Lightweight and Efficient Distributed Intrusion Detection Framework

Towards Lightweight and Efficient Distributed Intrusion Dete...

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2021 IEEE Global Communications Conference (GLOBECOM)

作者： Shuai Yuan Hongwei Li Rui Zhang Meng Hao Yiran Li Rongxing Lu School of Computer Science and Engineering University of Electronic Science and Technology of China China Chinese Academy of Sciences Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chongqing China Faculty of Computer Science University of New Brunswick Canada

ISBN: (纸本)9781728181059

Federated learning (FL), as a promising distributed learning paradigm, has put many efforts into distributed intrusion detection systems (IDS), for defending against various malicious attacks, such as SQL injection and DDoS attacks. Compared with traditional IDS based on centralized deep learning (DL), FL-based solutions require not to share users' raw data while yielding better detection performance. However, state-of-the-art FL-based methods still suffer from two key limitations: 1) insufficient detection performance on non-independent and identically distributed (non-IID) data, and 2) high communication and computational overheads due to the utilization of large-scale neural network models. In this paper, we propose a lightweight collaborative intrusion detection framework, called CoLGBM, the first of its kind in the regime of decentralized IDS, where decision tree and light gradient boosting machine (LGBM) are combined for constructing the detection scheme. The main insight is that through combining user-trained decision trees (each user's decision tree is derived from its own data with unique distribution), our framework can perform effectively on non-IID data while working efficiently for handling enormous samples. Compared with the current FL-based methods, our CoLGBM achieves higher accuracy and lower overhead on both IID and non-IID data. Extensive experiment results demonstrate our scheme with high-level performance.

关键词： Deep learning Distance learning Neural networks Intrusion detection Distributed databases SQL injection Boosting

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Mathematical capabilities of ChatGPT 23

Mathematical capabilities of ChatGPT

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Simon Frieder Luca Pinchetti Alexis Chevalier Ryan-Rhys Griffiths Tommaso Salvatori Thomas Lukasiewicz Philipp Petersen Julius Berner Department of Computer Science University of Oxford Oxford UK School of Mathematics Institute for Advanced Study Princeton Department of Physics University of Cambridge Cambridge UK VERSES AI Research Lab Los Angeles and Institute of Logic and Computation Vienna University of Technology Vienna Austria Institute of Logic and Computation Vienna University of Technology Vienna Austria and Department of Computer Science University of Oxford Oxford UK Faculty of Mathematics University of Vienna Vienna Austria and Research Network Data Science University of Vienna Vienna Austria Department of Computing and Mathematical Sciences Caltech Pasadena

We investigate the mathematical capabilities of two versions of ChatGPT (released 9-January-2023 and 30-January-2023) and of GPT-4 by testing them on publicly available datasets, as well as hand-crafted ones, using a novel evaluation scheme. In contrast to formal mathematics, where large databases of formal proofs are available (e.g., mathlib, the Lean Mathematical Library), current datasets of natural-language mathematics used to benchmark language models either cover only elementary mathematics or are very small. We address this by publicly releasing two new datasets: GHOSTS and miniGHOSTS. These are the first natural-language datasets curated by working researchers in mathematics that (1) aim to cover graduate-level mathematics, (2) provide a holistic overview of the mathematical capabilities of language models, and (3) distinguish multiple dimensions of mathematical reasoning. These datasets test, by using 1636 human expert evaluations, whether ChatGPT and GPT-4 can be helpful assistants to professional mathematicians by emulating use cases that arise in the daily professional activities of mathematicians. We benchmark the models on a range of fine-grained performance metrics. For advanced mathematics, this is the most detailed evaluation effort to date. We find that ChatGPT and GPT-4 can be used most successfully as mathematical assistants for querying facts, acting as mathematical search engines and knowledge base interfaces. GPT-4 can additionally be used for undergraduate-level mathematics but fails on graduate-level difficulty. Contrary to many positive reports in the media about GPT-4 and ChatGPT's exam-solving abilities (a potential case of selection bias), their overall mathematical performance is well below the level of a graduate student. Hence, if you aim to use ChatGPT to pass a graduate-level math exam, you would be better off copying from your average peer!

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Current progress in corrosion of multi principal element alloys

arXiv

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arXiv 2024年

作者： Ghorbani, M. Li, Z. Qiu, Y. Marcus, P. Scully, J.R. Gharbi, O. Luo, H. Gupta, R.K. Zeng, Z.R. Fraser, H.L. Taheri, M.L. Birbilis, N. Faculty of Science Engineering and the Built Environment Deakin University VIC Australia College of Engineering Computing and Cybernetics Australian National University ACT Australia Faculty of Materials Wuhan University of Science and Technology Wuhan China PSL University CNRS Chimie ParisTech Institut de Recherche de Chimie Paris Physical Chemistry of Surfaces Group Paris France Department of Materials Science and Engineering University of Virginia VA United States CNRS Sorbonne Université Laboratoire de Réactivité de Surface LRS Paris France National Materials Corrosion and Protection Data Center Institute for Advanced Materials and Technology University of Science and Technology Beijing Beijing China Department of Materials Science and Engineering North Carolina State University RaleighNC United States State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Hunan University Changsha China Department of Materials Science and Engineering The Ohio State University ColumbusOH United States Department of Materials Science and Engineering Johns Hopkins University BaltimoreMD United States

Whilst multi-principal element alloys (MPEAs) remain a promising class of materials owing to several attractive mechanical properties, their corrosion performance is also unique. In this concise review, we present an emerging overview of some of the general features related to MPEA corrosion, following a decade of work in the field. This includes highlighting some of the key aspects related to the electrochemical phenomena in MPEA corrosion, and the relevant future works required for a holistic mechanistic understanding. In addition, a comprehensive database of the reported corrosion performance of MPEAs is presented – based on works reported to date. The database is assembled to also allow users to undertake machine learning or their own data analysis, with a parsed representation of alloy composition, test electrolyte, and corrosion related parameters. © 2024, CC BY-NC-ND.

关键词： database systems

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