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arXiv 2025年

作者： Santamaria-Pang, Alberto Tuan, Frank Campbell, Ross Zhang, Cindy Jindal, Ankush Surapur, Roopa Holloman, Brad Hanisch, Deanna Buckley, Rae Cooney, Carisa Tarapov, Ivan Peairs, Kimberly S. Hasselfeld, Brian Greene, Peter Microsoft Health AI RedmondWA United States Adjunct Faculty Department of Biomedical Informatics & Data Science Johns Hopkins University BaltimoreMD United States Johns Hopkins Medicine BaltimoreMD United States University of Washington SeattleWA United States Department of Biomedical Informatics & Data Science Johns Hopkins University BaltimoreMD United States Microsoft BaltimoreMD United States Department of Plastic and Reconstructive Surgery Johns Hopkins Medicine BaltimoreMD United States Department of Plastic and Reconstructive Surgery Johns Hopkins University BaltimoreMD United States Department of Medicine Johns Hopkins University BaltimoreMD United States

Background: The COVID-19 pandemic has led to significant increases in the use of telemedicine as well as patient messaging via electronic medical portals (patient medical advice requests, or PMARs). While these systems offer benefits such as increased access to healthcare for patients, they have also created a substantial burden for healthcare providers due to surges in PMARs. This study addresses the need for an efficient tool for message triaging to reduce physician burden and improve patient-provider communication. Methods: We developed OPTIC (Optimizing Patient-Provider Triaging & Improving Communications in Clinical Operations), a robust tool for message triaging using GPT-4 for data labeling and BERT for model distillation. The study utilized a dataset of 405,487 patient messaging encounters at Johns Hopkins Medicine between January to June 2020. We employed prompt engineering strategies with GPT-4 to create a high-quality labelled dataset, which was then used to train a BERT model for classifying messages as "Admin" or "Clinical". Results: The BERT model achieved an accuracy of 88.85% on the test set derived from validated GPT-4 labeling, with a 88.29% sensitivity, 89.38% specificity, and an F1 score of 0.8842. BERTopic analysis identified 81 distinct topics within the test data, with the model achieving over 80% accuracy in classification for 58 topics. The system was successfully deployed through Epic's Nebula Cloud Platform, demonstrating its applicability in healthcare settings. Conclusion: OPTIC offers a scalable and efficient tool for triaging PMARs in health systems. Leveraging advanced natural language processing techniques can reduce the administrative burden on healthcare providers and streamline patient care coordination. © 2025, CC BY.

关键词： Labeled data

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Educational data mining: a 10-year review

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Discover Computing 2025年第1期28卷 1-25页

作者： Kalita, Emi Oyelere, Solomon Sunday Gaftandzhieva, Silvia Rajesh, Kandala N. V. P. S. Jagatheesaperumal, Senthil Kumar Mohamed, Asmaa Elbarawy, Yomna M. Desuky, Abeer S. Hussain, Sadiq Cifci, Mehmet Akif Theodorou, Paraskevi Hilčenko, Slavoljub Hazarika, Jiten Ali, Tazid Centre for Computer Science and Applications Dibrugarh University Dibrugarh India Department of Computer Science Electrical and Space Engineering Luleå University of Technology Skellefteå Sweden Department of Computer Science Faculty of Environment Science and Economy University of Exeter Exeter England Research Group On Data Artificial Intelligence and Innovations for Digital Transformation Johannesburg Business School (JBS) University of Johannesburg Johannesburg South Africa Faculty of Mathematics and Informatics University of Plovdiv “Paisii Hilendarski” Plovdiv Bulgaria School of Electronics Engineering VIT-AP University Vijayawada India Department of Electronics & Communication Engineering Mepco Schlenk Engineering College Sivakasi India Department of Mathematics Faculty of Science Al-Azhar University (Girls Branch) Cairo Egypt Department of Computer Engineering Bandirma Onyedi Eylul University Balikesir Türkiye Engineering and Informatics Department Klaipėdos Valstybinė Kolegija/Higher Education Institution Klaipeda Lithuania Department of Digital Systems University of Piraeus Piraeus Greece College for Vocational Education of Preschool Teachers and Coaches Subotica Serbia Department of Statistics Dibrugarh University Dibrugarh India

This systematic review comprehensively examines the application and impacts of Educational data Mining (EDM) over the past decade. It explores the use of various data mining tools and techniques, statistics, and machine learning algorithms in education. The review discusses how EDM helps understand and improve the learning experience, educational strategies, and institutional efficiency. It highlights the iterative process of EDM, its applications, and the benefits it offers to different stakeholders, including students, teachers, and educational institutions. The paper also discusses the challenges related to data ethics, privacy, and security in EDM. Key sections include a methodology for conducting the systematic review, exploring different data mining techniques and learning styles, and using Artificial Intelligence in EDM. The review concludes with a discussion of findings, future research directions, and a summary of the study’s contributions and limitations.

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On One-Parameter Families of Hermiticity-Preserving Superoperators which Are Not Positive

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Open Systems & Information Dynamics 2025年第01期32卷

作者： Grzegorz Pastuszak Alicja Jaworska-Pastuszak Takeo Kamizawa Faculty of Mathematics and Computer Science Nicolaus Copernicus University Toruń Poland Department of Informatics and Data Science Sanyo-Onoda City University Sanyo-Onoda Japan

A one-parameter family of hermiticity-preserving superoperators is a time-dependent family { Φ t : 𝕄 n ( ℂ ) → 𝕄 n ( ℂ ) } t ∈ ℝ of hermiticity-preserving superoperators determined, in a certain sense, by real and complex polynomial functions in the variable t ∈ ℝ . The paper studies sufficient computable criteria for nonpositivity of superoperators in one-parameter families. More precisely, we give sufficient conditions for the following assertions to hold: ( 1 ) every Φ t is not positive, ( 2 ) Φ t is not positive for t in some ( u , v ) ⊆ ℝ and ( 3 ) there is some Φ t which is not positive. We show that ( 3 ) implies ( 2 ) . Our approach to the problem is based on the Descartes rule of signs and the Sturm-Tarski theorem . In order to apply these facts, we introduce the sign variation formulas . These formulas are first order logical formulas in one variable t , generalising sign sequences of polynomials used in Descartes rule of signs.

关键词： Hermitian superoperators one-parameter families of superoperators nonpositive maps Sturm-Tarski theorem Descartes rule of signs Choi-Jamiołkowski isomorphism effective methods in quantum information theory

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Mobility-aware Seamless Service Migration and Resource Allocation in Multi-edge IoV Systems

arXiv

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arXiv 2025年

作者： Chen, Zheyi Huang, Sijin Min, Geyong Ning, Zhaolong Li, Jie Zhang, Yan College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Ministry of Education Fujian Key Laboratory of Network Fuzhou350002 China Computing and Intelligent Information Processing [Fuzhou University Fuzhou350116 China Department of Computer Science Faculty of Environment Science and Economy University of Exeter ExeterEX4 4QF United Kingdom School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Informatics University of Oslo Oslo0316 Norway

Mobile Edge Computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard to maintain uninterrupted and high-quality services without proper service migration among MEC servers. Existing solutions commonly rely on prior knowledge and rarely consider efficient resource allocation during the service migration process, making it hard to reach optimal performance in dynamic IoV environments. To address these important challenges, we propose SR-CL, a novel mobility-aware seamless Service migration and Resource allocation framework via Convex-optimization-enabled deep reinforcement Learning in multi-edge IoV systems. First, we decouple the Mixed Integer Nonlinear Programming (MINLP) problem of service migration and resource allocation into two sub-problems. Next, we design a new actor-critic-based asynchronous-update deep reinforcement learning method to handle service migration, where the delayed-update actor makes migration decisions and the one-step-update critic evaluates the decisions to guide the policy update. Notably, we theoretically derive the optimal resource allocation with convex optimization for each MEC server, thereby further improving system performance. Using the real-world datasets of vehicle trajectories and testbed, extensive experiments are conducted to verify the effectiveness of the proposed SR-CL. Compared to benchmark methods, the SR-CL achieves superior convergence and delay performance under various scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

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A Literature Review of Machine Learning Techniques for Dance Recognition and Robotic Vision

A Literature Review of Machine Learning Techniques for Dance...

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Robotics and Technologies for Industrial Automation (ROBOTHIA), IEEE International Conference on

作者： Lee Wei San Samuel-Soma M. Ajibade Muhammed Basheer Jasser Adefemi Ayodele Babatunde Adedotun Ajayi Mbiatke Anthony Bassey Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Darul Ehsan Malaysia Research Centre for Nanomaterials and Energy Technology (RCNMET) Faculty of Engineering and Technology Sunway University Selangor Malaysia Research Centre for Human-Machine Collaboration (HUMAC) Faculty of Engineering and Technology Sunway University Petaling Jaya Selangor Malaysia University of East London London UK Department of Cyber security Faculty of Computing & Informatics Ladoke Akintola University of Technology Ogbomoso Nigeria Department of Business Administration Universiti Tun Hussein Onn Malaysia Batu Pahat Malaysia

ISBN: (数字)9798350356755

ISBN: (纸本)9798350356762

Image recognition, powered by machine learning (ML), has significantly advanced applications in both dance movement recognition and robotic vision. This review examines key ML techniques, including Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Self-Organizing Maps (SOMs), and Long Short-Term Memory (LSTM) networks, alongside pose estimation methods like OpenPose and Part Affinity Fields (PAFs). These techniques enhance dance classification, real-time feedback, and motion analysis, with OpenPose + LSTMs and PAFs + LSTMs demonstrating the highest accuracy. Notwithstanding progress, obstacles such as high computational costs, data dependency, and real-time implementation challenges persist. Beyond dance, these methods are critical in robotic vision, intelligent automation, and industrial image processing, enabling autonomous robotic navigation, defect detection in manufacturing, and AI-driven motion tracking. By leveraging human movement analysis for robotics, ML improves human-robot interaction, robotic-assisted rehabilitation, and industrial automation. Despite progress, challenges such as high computational demands, data dependency, and real-time constraints remain. This review explores future directions, including multimodal data fusion, hybrid AI models, and real-time optimization, bridging the gap between AI-driven motion systems and intelligent automation to enhance adaptability and efficiency across domains.

关键词： Intelligent automation Humanities Solid modeling Image recognition Accuracy Service robots Robot sensing systems Real-time systems Robots Long short term memory

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A Near-Optimal Category Information Sampling in RFID Systems

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IEEE Transactions on Mobile Computing 2025年

作者： Wang, Xiujun Liu, Zhi Zhou, Xiaokang Liao, Yong Hu, Han Zheng, Xiao Li, Jie Anhui University of Technology School of Computer Science Ma'anshan243032 China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Ma'anshan243032 China University of Electro-Communications Graduate School of Informatics and Engineering Tokyo182- 8585 Japan Shiga University Faculty of Data Science Hikone522-8522 Japan RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan University of Science and Technology of China School of Cyber Science and Technology Hefei China Beijing Institute of Technology School of Information and Electronics Beijing China School of Electronic Information and Electrical Engineering Shanghai200240 China

In many RFID-enabled applications, objects are classified into different categories, and the information associated with each object's category (called category information) is written into the attached tag, allowing the reader to access it later. The category information sampling in such RFID systems, which is to randomly choose (sample) a few tags from each category and collect their category information, is fundamental for providing real-time monitoring and analysis in RFID. However, to the best of our knowledge, two technical challenges, i.e., how to guarantee a minimized execution time and reduce collection failure caused by missing tags, remain unsolved for this problem. In this paper, we address these two limitations by considering how to use the shortest possible time to sample a different number of random tags from each category and collect their category information sequentially in small batches. In particular, we first obtain a lower bound on the execution time of any protocol that can solve this problem. Subsequently, we present a near-OPTimal Category information sampling protocol (OPT-C) that solves the problem with an execution time close to the lower bound. Finally, extensive simulation results demonstrate the superiority of OPT-C over existing protocols, while real-world experiments further validate its practicality. © 2002-2012 IEEE.

关键词： category information sampling execution time lower bound near-optimal protocol RFID systems

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Expert of Experts Verification and Alignment (EVAL) Framework for Large Language Models Safety in Gastroenterology

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npj Digital Medicine 2025年第1期8卷 1-14页

作者： Giuffrè, Mauro You, Kisung Pang, Ziteng Kresevic, Simone Chung, Sunny Chen, Ryan Ko, Youngmin Chan, Colleen Saarinen, Theo Ajcevic, Milos Crocè, Lory S. Garcia-Tsao, Guadalupe Gralnek, Ian Sung, Joseph J. Y. Barkun, Alan Laine, Loren Sekhon, Jasjeet Stadie, Bradly Shung, Dennis L. Section of Digestive Diseases Department of Medicine Yale School of Medicine New Haven United States Department of Mathematics Baruch College The City University of New York New York United States Department of Statistics and Data Science Northwestern University Chicago United States Department of Engineering and Architecture University of Trieste Trieste Italy Department of Statistics and Data Science Yale University New Haven United States Department of Statistics University of California Berkley Berkley United States Department of Medical Surgical and Health Sciences University of Trieste Trieste Italy Rappaport Faculty of Medicine Technion Israel Institute of Technology Haifa Israel Lee Kong Chian School of Medicine Nanyang Technological University Singapore Singapore Division of Gastroenterology McGill University Montreal Canada Department of Biomedical Informatics and Data Science Yale School of Medicine New Haven United States

Large language models generate plausible text responses to medical questions, but inaccurate responses pose significant risks in medical decision-making. Grading LLM outputs to determine the best model or answer is time-consuming and impractical in clinical settings;therefore, we introduce EVAL (Expert-of-Experts Verification and Alignment) to streamline this process and enhance LLM safety for upper gastrointestinal bleeding (UGIB). We evaluated OpenAI’s GPT-3.5/4/4o/o1-preview, Anthropic’s Claude-3-Opus, Meta’s LLaMA-2 (7B/13B/70B), and Mistral AI’s Mixtral (7B) across 27 configurations, including zero-shot baseline, retrieval-augmented generation, and supervised fine-tuning. EVAL uses similarity-based ranking and a reward model trained on human-graded responses for rejection sampling. Among the employed similarity metrics, Fine-Tuned ColBERT achieved the highest alignment with human performance across three separate datasets (ρ = 0.81–0.91). The reward model replicated human grading with 87.9% of cases across temperature settings and significantly improved accuracy through rejection sampling by 8.36% overall. EVAL offers scalable potential to assess accuracy for high-stakes medical decision-making. © The Author(s) 2025.

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Enhanced Global Optimization Using a Novel Hybrid Sine Cosine-Gazelle Algorithm with Brownian Motion and Lévy Flight Mechanisms

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International Journal of Computational Intelligence Systems 2025年第1期18卷 1-55页

作者： Singh, Gyan Biswas, Saptadeep Ezugwu, Absalom El-Shamir Simic, Vladimir Bera, Uttam Kumar Saleem, Kashif Abualigah, Laith Department of Mathematics National Institute of Technology Agartala Tripura India Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 South Africa Faculty of Transport and Traffic Engineering University of Belgrade Vojvode Stepe 305 Belgrade11010 Serbia Department of Industrial Engineering and Management College of Engineering Yuan Ze University Taoyuan City320315 Taiwan Department of Computer Science and Engineering College of Informatics Korea University Seoul02841 Korea Republic of Department of Computer Science and Engineering College of Applied Studies and Community Service King Saud University Riyadh11362 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq25113 Jordan

Metaheuristic algorithms are crucial for solving intricate optimization problems in diverse fields. The Sine Cosine Algorithm (SCA), known for its efficiency and simplicity in global search, sometimes struggles with premature convergence and inadequate exploitation. To address these challenges, this study introduces a novel hybrid SCA-Gazelle Optimisation Algorithm (HSCAGOA) by integrating the Gazelle Optimisation Algorithm’s (GOA) exploitation strategy. Inspired by gazelle behaviour, GOA enhances local search capabilities, improving the balance between the exploration and exploitation phases. Additionally, HSCAGOA incorporates Brownian motion and Lévy flight mechanisms to further enhance exploration capabilities. This research rigorously evaluates HSCAGOA through extensive computational experiments on 33 benchmark test problems and six engineering design challenges. Comparisons with classical SCA and various state-of-the-art optimisation algorithms show that HSCAGOA consistently achieves faster convergence and higher solution quality across diverse optimisation landscapes. To validate these results, ranking analysis is conducted using the Wilcoxon rank-sum test and the Wilcoxon signed-rank test, confirming the efficacy of HSCAGOA. Furthermore, employing the Combined Compromise Solution (CoCoSo) method for multi-criteria decision-making enables systematic ranking and comparison of HSCAGOA’s performance against other algorithms. Additionally, a comparative analysis was conducted against renowned CEC competition winners, including LSHADEcnEpSin, LSHADESPACMA, and CMA-ES. HSCAGOA is evaluated through extensive computational experiments involving CEC 2017 benchmark test functions. Moreover, sensitivity analysis is performed to assess the robustness of HSCAGOA under varying configurations, including different population sizes and maximum iteration counts on CEC 2022 benchmark test functions. The findings highlight the algorithm’s adaptability and reliability in addressin

关键词： Design for testability

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GAIA: A Global, Multi-modal, Multi-scale Vision-Language dataset for Remote Sensing Image Analysis

arXiv

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arXiv 2025年

作者： Zavras, Angelos Michail, Dimitrios Zhu, Xiao Xiang Demir, Begüm Papoutsis, Ioannis Orion Lab School of Rural Surveying and Geoinformatics Engineering National Technical University of Athens Athens15772 Greece Institute of Astronomy Astrophysics Space Applications and Remote Sensing National Observatory of Athens Athens11810 Greece Department of Informatics and Telematics Harokopio University of Athens Athens17676 Greece Chair of Data Science in Earth Observation Technical University of Munich Munich80333 Germany Munich Center for Machine Learning Munich80333 Germany Faculty of Electrical Engineering and Computer Science Technische Universität Berlin Berlin10623 Germany BIFOLD - Berlin Institute for the Foundations of Learning and Data Berlin10623 Germany

The continuous operation of Earth-orbiting satellites generates vast and ever-growing archives of Remote Sensing (RS) images. Natural language presents an intuitive interface for accessing, querying, and interpreting the data from such archives. However, existing Vision-Language Models (VLMs) are predominantly trained on web-scraped, noisy image-text data, exhibiting limited exposure to the specialized domain of RS. This deficiency results in poor performance on RS-specific tasks, as commonly used datasets often lack detailed, scientifically accurate textual descriptions and instead emphasize solely on attributes like date and location. To bridge this critical gap, we introduce GAIA, a novel dataset designed for multi-scale, multi-sensor, and multi-modal RS image analysis. GAIA comprises of 205,150 meticulously curated RS image-text pairs, representing a diverse range of RS modalities associated to different spatial resolutions. Unlike existing vision-language datasets in RS, GAIA specifically focuses on capturing a diverse range of RS applications, providing unique information about environmental changes, natural disasters, and various other dynamic phenomena. The dataset provides a spatially and temporally balanced distribution, spanning across the globe, covering the last 25 years with a balanced temporal distribution of observations. GAIA’s construction involved a two-stage process: (1) targeted web-scraping of images and accompanying text from reputable RS-related sources, and (2) generation of five high-quality, scientifically grounded synthetic captions for each image using carefully crafted prompts that leverage the advanced vision-language capabilities of GPT-4o. We also release an automated processing framework developed for this purpose, enabling the broader research community to generate captions for RS images using the web-crawled image-text data. Our extensive experiments, including fine-tuning of CLIP and BLIP2 models, demonstrate that GAIA significan

关键词： Visual languages

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Developing diverse ensemble architectures for automatic brain tumor classification

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Multimedia Tools and Applications 2024年 1-44页

作者： Bogacsovics, Gergo Harangi, Balazs Hajdu, Andras Department of Data Science and Visualization Faculty of Informatics University of Debrecen PO Box 400 Debrecen4002 Hungary

Brain tumors pose a serious threat in our modern society, with a clear increase in global cases each year. Therefore, developing robust solutions that could automatically and reliably detect brain tumors in their early stages is of utmost importance. In our paper, we revisit the problem of building performant ensembles for clinical usage by maximizing the diversity of the member models during the training procedure. We present an improved, more robust, extended version of our framework and propose solutions that could be integrated into a Computer-Aided Diagnosis system to accurately classify some of the most common types of brain tumors: meningioma, glioma, and pituitary tumors. We show that the new framework based on the histogram loss can be seen as a natural extension of the former approach, as it also calculates the inner products of the latent vectors produced by each member to measure similarity, but at the same time, it also makes it possible to capture more complex patterns. We also present several variants of our framework to incorporate member models with varying dimensional feature vectors and to cope with imbalanced datasets. We evaluate our solutions on a clinically tested dataset of 3,064 T1-weighted contrast-enhanced magnetic resonance images and show that they greatly outperform other state-of-the-art approaches and the base architectures as well, achieving over 92% accuracy, 92% macro and weighted precision, 91% macro and 92% weighted F1 score, and over 90% macro and 92% weighted sensitivity. © The Author(s) 2024.

关键词： Magnetic resonance

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