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science Bulletin 2025年第6期 934-942页

作者：李灏萱江泽铧管洲榆包玉倩刘月星胡婷婷李佳佳刘茹涵吴量程棣纪宏伟王勇王亚星 Carol Y.Cheung 郑颖丰王继红李震吴卫兵 Cynthia Ciwei Lim Yong Mong Bee Hong Chang Tan Elif I.Ekinci David C.Klonoff Justin B.Echouffo-Tcheugui Nestoras Mathioudakis Leonor Corsino Rafael Simó Charumathi Sabanayagam Gavin Siew Wei Tan 程景煜黄天荫李华婷蔡淳毛丽娟 Lee-Ling Lim 覃宇宗盛斌贾伟平 Shanghai University of Sport School of Clinical Medicine Beijing Tsinghua Changgung Hospital Tsinghua Medicine Tsinghua University Beijing Visual Science and Translational Eye Research Institute(BERI) Tsinghua Medicine Tsinghua University Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes MoE Key Lab of Artificial Intelligence Artificial Intelligence Institute Shanghai Jiao Tong University Department of Cardiology the Affiliated Hospital of Qingdao University CEMS NCMIS HCMS MADIS Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematics University of Chinese Academy of Sciences Chinese Academy of Sciences Key Laboratory of Systems Health Science of Zhejiang Province School of Life Science Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Beijing Institute of Ophthalmology Beijing Tongren Hospital Capital Medical University Beijing Ophthalmology and Visual Sciences Key Laboratory Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangdong Provincial Clinical Research Center for Ocular Diseases Department of Renal Medicine Singapore General Hospital Sing Health-Duke Academic Medical Centre Department of Endocrinology Singapore General Hospital Department of Endocrinology Austin Health Department of Medicine The University of Melbourne(Austin Health) Australian Centre for Accelerating Diabetes Innovations(ACADI) The Univ

Diabetes poses a considerable global health challenge, with varying levels of diabetes knowledge among healthcare professionals, highlighting the importance of diabetes training. Large Language Models(LLMs)provide new insights into diabetes training, but their performance in diabetes-related queries remains uncertain, especially outside the English language like Chinese. We first evaluated the performance of ten LLMs: Chat GPT-3.5, Chat GPT-4.0, Google Bard, Lla MA-7B, Lla MA2-7B, Baidu ERNIE Bot, Ali Tongyi Qianwen, Med GPT, Huatuo GPT, and Chinese Lla MA2-7B on diabetes-related queries, based on the Chinese National Certificate Examination for Primary Diabetes Care in China(NCE-CPDC) and the English Specialty Certificate Examination in Endocrinology and Diabetes of Membership of the Royal College of Physicians of the United Kingdom. Second, we assessed the training of primary care physicians(PCPs) without and with the assistance of Chat GPT-4.0 in the NCE-CPDC examination to ascertain the reliability of LLMs as medical assistants. We found that Chat GPT-4.0 outperformed other LLMs in the English examination, achieving a passing accuracy of 62.50%, which was significantly higher than that of Google Bard, Lla MA-7B, and Lla MA2-7B. For the NCE-CPFC examination, Chat GPT-4.0, Ali Tongyi Qianwen, Baidu ERNIE Bot, Google Bard, Med GPT, and Chat GPT-3.5 successfully passed, whereas Lla MA2-7B, Huatuo GPT,Chinese LLa MA2-7B, and Lla MA-7B failed. Chat GPT-4.0(84.82%) surpassed all PCPs and assisted most PCPs in the NCE-CPDC examination(improving by 1%–6.13%). In summary, LLMs demonstrated outstanding competence for diabetes-related questions in both the Chinese and English language, and hold great potential to assist future diabetes training for physicians globally.

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Using Language-Aligned Gesture Embeddings for Understanding Gestures Accompanying Math Terms

Using Language-Aligned Gesture Embeddings for Understanding ...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Tristan Maidment Purav J Patel Erin Walker Adriana Kovashka Intelligent Systems Program University of Pittsburgh Pittsburgh PA USA University of Maryland College Park MD USA Computer Science University of Pittsburgh Pittsburgh PA USA Learning Research and Development Center University of Pittsburgh Pittsburgh PA USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper, we introduce an approach for recognizing and classifying gestures that accompany mathematical terms, in a new collection we name the "GAMT" dataset. Our method uses language as a means of providing context to classify gestures. Specifically, we use a CLIP-style framework to construct a shared embedding space for gestures and language, experimenting with various methods for encoding gestures within this space. We evaluate our method on our new dataset containing a wide array of gestures associated with mathematical terms. The shared embedding space leads to a substantial improvement in gesture classification. Furthermore, we identify an efficient model that excelled at classifying gestures from our unique dataset, thus contributing to the further development of gesture recognition in diverse interaction scenarios.

关键词： computer vision Conferences Computational modeling Gesture recognition Mathematical models Encoding

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Can GPT-4 Support Analysis of Textual Data in Tasks Requiring Highly Specialized Domain Expertise?

arXiv

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arXiv 2023年

作者： Savelka, Jaromir Ashley, Kevin D. Gray, Morgan A. Westermann, Hannes Xu, Huihui Computer Science Department Carnegie Mellon University PittsburghPA United States Intelligent Systems Program University of Pittsburgh PA United States Cyberjustice Laboratory Faculté de droit Université de Montréal Montréal Canada

We evaluated the capability of generative pre-trained transformers (GPT-4) in analysis of textual data in tasks that require highly specialized domain expertise. Specifically, we focused on the task of analyzing court opinions to interpret legal concepts. We found that GPT-4, prompted with annotation guidelines, performs on par with well-trained law student annotators. We observed that, with a relatively minor decrease in performance, GPT-4 can perform batch predictions leading to significant cost reductions. However, employing chain-of-thought prompting did not lead to noticeably improved performance on this task. Further, we demonstrated how to analyze GPT-4’s predictions to identify and mitigate deficiencies in annotation guidelines, and subsequently improve the performance of the model. Finally, we observed that the model is quite brittle, as small formatting related changes in the prompt had a high impact on the predictions. These findings can be leveraged by researchers and practitioners who engage in semantic/pragmatic annotations of texts in the context of the tasks requiring highly specialized domain expertise. © 2023, CC BY.

关键词： Forecasting

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A scheme for integrating e-services in establishing virtual enterprises

A scheme for integrating e-services in establishing virtual ...

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International Workshop on Research Issues in Data Engineering

作者： A. Berfield P.K. Chrysanthis I. Tsamardinos M.E. Pollack S. Banerjee Department of Computer Science University of Pittsburgh Pittsburgh PA USA Intelligent Systems Program University of Pittsburgh Pittsburgh PA USA Department of EE andC omputer Science University of Michigan Ann Arbor MI USA Info. Science & Telecom. Department University of Pittsburgh Pittsburgh PA USA

ISBN: (纸本)0769514804

An important aspect of business to business e-commerce is the agile virtual enterprise (VE). VEs are established when existing enterprises dynamically form temporary alliances, joining their business in order to share their costs, skills and resources in supporting certain activities. Currently, existing enterprises use workflows to automate their operation and integrate their information systems and human resources. Thus, the establishment of a VE has been viewed as a problem of dynamically expanding and integrating workflows. We present an approach to combining workflows from different enterprises, using techniques developed in the artificial intelligence literature on planning. Our method takes two workflow views, one representing a service request and the other a service provision (advertisement), with a mix of vital and nonvital steps and a rich set of constraints, and returns a list of possible legal combinations, if any exist. It then uses plan-merging techniques to find potential conflicts between the two workflows, and to suggest additional constraints that can resolve the conflicts. The returned solutions represent terms for the establishment of a new VE, and can be evaluated by each side to determine which is most desirable.

关键词： Virtual enterprises Artificial intelligence Electronic commerce Costs Data engineering Merging computer science intelligent systems Telecommunications Information systems

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Learning Adaptation and Generalization from Human-Inspired Meta-Reinforcement Learning Using Bayesian Knowledge and Analysis

Learning Adaptation and Generalization from Human-Inspired M...

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Artificial Intelligence and Knowledge Engineering (AIKE)

作者： Joshua Ho Chien-Min Wang Chung-Ta King Yi-Hsin You Chi-Wei Feng Yen-Min Chen Bo-Yi Kuo Academia Sinica Institute of Information Science Taipei Taiwan Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Center of Intelligent Healthcare National Taiwan University Hospital Taipei Taiwan Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Computer Science National Taiwan University Taipei Taiwan

Over the last decades, there has been growing interest in research in multiple and interdisciplinary fields of human-AI computing. In particular, approaches integrating the intersecting design with reinforcement learning (RL) have received more attention. However, the current research on RL may need to consider its enhancement from a human-inspired approach further. In the present work, we focus on enabling a meta-reinforcement learning (meta-RL) agent to achieve adaptation and generalization according to modeling Markov decision processes using Bayesian knowledge and analysis. By introducing a novel framework called human-inspired meta-RL (HMRL), we incorporate the agent performing resilient actions to leverage the dynamic dense reward based on the knowledge and prediction of a Bayesian analysis. The proposed framework can make the agent learn generalization and prevent the agent from failing catastrophically. The experimental results show that our approach helps the agent reduce computational costs with learning adaptation. Finally, we conclude and anticipate that integrating human-inspired meta-RL can enable learning more formulations relating to robustness and scalability, leading to promising directions and more complex AI goals in the future.

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A spectral learning approach to knowledge tracing 6

A spectral learning approach to knowledge tracing

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6th International Conference on Educational Data Mining, EDM 2013

作者： Falakmasir, Mohammad H. Pardos, Zachary A. Gordon, Geoffrey J. Brusilovsky, Peter Intelligent Systems Program University of Pittsburgh 210 South Bouquet Street PittsburghPA United States Computer Science AI Lab Massachusetts Institute of Technology 77 Massachusetts Ave CambridgeMA02139 United States Machine Learning Department Carnegie Mellon University 5000 Forbes Avenue PittsburghPA15213 United States School of Information Sciences University of Pittsburgh 135 North Bellefield Ave. PittsburghPA15260 United States

ISBN: (纸本)9780983952527

Bayesian Knowledge Tracing (BKT) is a common way of determining student knowledge of skills in adaptive educational systems and cognitive tutors. The basic BKT is a Hidden Markov Model (HMM) that models student knowledge based on five parameters: prior, learn rate, forget, guess, and slip. Expectation Maximization (EM) is often used to learn these parameters from training data. However, EM is a time-consuming process, and is prone to converging to erroneous, implausible local optima depending on the initial values of the BKT parameters. In this paper we address these two problems by using spectral learning to learn a Predictive State Representation (PSR) that represents the BKT HMM. We then use a heuristic to extract the BKT parameters from the learned PSR using basic matrix operations. The spectral learning method is based on an approximate factorization of the estimated covariance of windows from students’ sequences of correct and incorrect responses;it is fast, local-optimum-free, and statistically consistent. In the past few years, spectral techniques have been used on real-world problems involving latent variables in dynamical systems, computer vision, and natural language processing. Our results suggest that the parameters learned by the spectral algorithm can replace the parameters learned by EM;the results of our study show that the spectral algorithm can improve knowledge tracing parameter-fitting time significantly while maintaining the same prediction accuracy, or help to improve accuracy while still keeping parameter-fitting time equivalent to EM. © 2013 International Educational Data Mining Society. All rights reserved.

关键词： Students

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The Impact of Social Performance Visualization on Students

The Impact of Social Performance Visualization on Students

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International Conference on Advanced Learning Technologies (ICALT)

作者： Mohammad Hassan Falakmasir I-Han Hsiao Luca Mazzola Nancy Grant Peter Brusilovsky Intelligent Systems Program University of Pittsburgh Pittsburgh PA USA School of Information Sciences University of Pittsburgh Pittsburgh PA USA Faculty of Communication Sciences University of Lugano Lugano Switzerland Department of Computer Information Science Community College of Allegheny West Mifflin PA USA

Over the last 10 years two major research directions explored the benefits of visualizing student learning progress. One stream of research on learning performance visualization attempts to build a visual presentation of students' learning progress, targeting the needs of instructors and academic advisors. The other stream of research on Open Student Modeling (OSM) attempts to visualize the state of individual student's knowledge and present the visualization directly to the student. The results of the studies in that area show that, presenting students with basic representation of their knowledge will result in facilitating their metacognitive activities and promoting self-reflection and awareness. This paper tries to study the impact of a more sophisticated form of performance visualization on students. We believe that our visualization tool can positively influence students by granting them the opportunity to get a view of their performance in the content of the class progress. Moreover, we tried to boost their motivation by building a positive sense of competition using a representation of average class performance. In this paper we present study comparing two groups of students, one using the visualization and another without visualization. The results of the study shows that: 1) the students are likely to use the social visualization tool during the whole semester to monitor their progress in comparison with their peers; 2) the visualization tool encourages students to use the learning materials in a more continuous manner during the whole semester and 3) students will achieve a higher success rate in answering self-assessment quizzes.

关键词： Data visualization Adaptation models Educational institutions Visualization Artificial intelligence Communities

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Drum-aware ensemble architecture for improved joint musical beat and downbeat tracking

arXiv

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arXiv 2021年

作者： Chiu, Ching-Yu Su, Alvin Wen-Yu Yang, Yi-Hsuan The Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University Academia Sinica Taiwan The Department of Computer Science and Information Engineering National Cheng Kung University Taiwan Yating Music Team Taiwan AI Labs Taiwan Research Center for IT Innovation Academia Sinica Taiwan

This paper presents a novel system architecture that integrates blind source separation with joint beat and downbeat tracking in musical audio signals. The source separation module segregates the percussive and non-percussive components of the input signal, over which beat and downbeat tracking are performed separately and then the results are aggregated with a learnable fusion mechanism. This way, the system can adaptively determine how much the tracking result for an input signal should depend on the input's percussive or non-percussive components. Evaluation on four testing sets that feature different levels of presence of drum sounds shows that the new architecture consistently outperforms the widely-adopted baseline architecture that does not employ source separation. Copyright © 2021, The Authors. All rights reserved.

关键词： Blind source separation

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ISHM implementation for Constellation systems

ISHM implementation for Constellation Systems

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AIAA/ASME/SAE/ASEE 42nd Joint Propulsion Conference

作者： Figueroa, Fernando Holland, Randy Schmalzel, John Duncavage, Dan Crocker, Alan Alena, Rick NASA Stennis Space Center Stennis Space Center MS 39529 Rowan University Dept. of Electrical and Computer Engineering Glassboro NJ 08028 NASA Johnson Space Center Houston TX 77058 NASA Ames Research Center Moffett Field CA 94035 Science and Technology Division EA41 Stennis Space Center MS 39529 AIAA Department of Electrical and Computer Engineering Rowan University Glassboro NJ 08028 International Space Station Program OD NASA Johnson Space Center Houston TX 77058 Systems Division Mission Operations Directorate DF NASA Johnson Space Center Houston TX 77058 Intelligent Systems Division MS 269-4 Moffett Field CA 94035

ISBN: (纸本)1563478188

This paper addresses methodic implementation of a credible ISHM capability for elements of Constellation systems. ISHM capability must be implemented by a combination of on-board and ground elements, each contributing to determining the health of systems in the most effective manner. Implementation of ISHM capability must evolve continuously to reach higher and higher levels of functional capability. The implication is that a sustainable approach must be defined in order to accomplish a believable capability. This paper describes the methodology, the elements, and the technology to implement ISHM capability throughout the life cycle of elements of Constellation systems, reaching beyond individual elements that might have a limited life while the constellation moves on.

关键词： Spacecraft propulsion

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How to interpret the results of medical time series data analysis: Classical statistical approaches versus dynamic Bayesian network modeling

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Journal of Pathology Informatics 2016年第1期7卷 50-50页

作者： Onisko, Agnieszka Druzdzel, Marek J. Austin, R. Marshall Department of Pathology University of Pittsburgh Medical Center Magee-Womens Hospital Pittsburgh United States Faculty of Computer Science Bialystok University of Technology Bialystok 15-351 Poland Decision Systems Laboratory School of Information Sciences and Intelligent Systems Program University of Pittsburgh Pittsburgh PA United States

Background: Classical statistics is a well-established approach in the analysis of medical data. While the medical community seems to be familiar with the concept of a statistical analysis and its interpretation, the Bayesian approach, argued by many of its proponents to be superior to the classical frequentist approach, is still not well-recognized in the analysis of medical data. Aim: The goal of this study is to encourage data analysts to use the Bayesian approach, such as modeling with graphical probabilistic networks, as an insightful alternative to classical statistical analysis of medical data. Materials and Methods: This paper offers a comparison of two approaches to analysis of medical time series data: (1) classical statistical approach, such as the Kaplan-Meier estimator and the Cox proportional hazards regression model, and (2) dynamic Bayesian network modeling. Our comparison is based on time series cervical cancer screening data collected at Magee-Womens Hospital, University of Pittsburgh Medical Center over 10 years. Results: The main outcomes of our comparison are cervical cancer risk assessments produced by the three approaches. However, our analysis discusses also several aspects of the comparison, such as modeling assumptions, model building, dealing with incomplete data, individualized risk assessment, results interpretation, and model validation. Conclusion : Our study shows that the Bayesian approach is (1) much more flexible in terms of modeling effort, and (2) it offers an individualized risk assessment, which is more cumbersome for classical statistical approaches. © 2016 Journal of Pathology Informatics | Published by Wolters Kluwer -Medknow.

关键词： Cervical cancer screening Cox proportional hazards regression model dynamic Bayesian networks Kaplan-Meier estimator time series data

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