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9th International Conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Tian, Pingfang Liu, Deting Department of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Institute of Scientific and Technical Information of China The Key Laboratory of the Rich Media Knowledge Organisation and Service of Digital Publishing Content Beijing100038 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan430065 China

ISBN: (纸本)9798350376548

Topic shift detection aims to identify whether there is a change in the current topic of conversation or if a change is needed. The study found previous work did not evaluate the performance of large language models like ChatGPT on the task of topic shift. Therefore, this paper's main task and innovation lie in analyzing ChatGPT's performance on topic shift detection. To provide a more comprehensive evaluation, we conducted topic shift detection tasks on ChatGPT from three aspects: single utterances, adjacent utterances, and contextual levels. Additionally, to gauge the performance of large language models, we conducted experiments on multiple small-scale models and compared the results of the two models. Experimental results on the publicly available English TIAGE dataset showed that small-scale models exhibited lower recall in all three aspects, while ChatGPT performed better in the recall. This suggests that compared to small-scale models, large models are more capable of accurately detecting topic shifts. However, large models also exhibited lower precision, indicating that while ChatGPT can recognize content differences in utterances, its judgment on whether these different contents belong to the same topic is poor. © 2024 IEEE.

关键词： adjacent utterances level ChatGPT contextual level single utterances level topic shift detection

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Towards Ordinal data Science

arXiv

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arXiv 2023年

作者： Stumme, Gerd Dürrschnabel, Dominik Hanika, Tom Knowledge & Data Engineering Group Research Center for Information System Design Department of Electrical Engineering and Computer Science University of Kassel Germany Institute of Computer Science University of Hildesheim Germany Berlin School of Library and Information Science Humboldt-Universität zu Berlin Germany

Order is one of the main instruments to measure data. In this paper, we will therefore discuss difthe relationship between objects in (empirical) data. ferent means for measuring and ‘calculating’ with However, compared to methods that use numerical ordinal structures — a specific class of directed properties of objects, the amount of ordinal meth- graphs — and show how to infer knowledge from ods developed is rather small. One reason for this is them. Our aim is to establish Ordinal data Science the limited availability of computational resources as a fundamentally new research agenda. Besides in the last century that would have been required cross-fertilization with other cornerstone machine for ordinal computations. Another reason — partic- learning and knowledge representation methods, a ularly important for this line of research — is that broad range of disciplines will benefit from this en-order-based methods are often seen as too mathe- deavor, including, psychology, sociology, economics, matically rigorous for applying them to real-world web science, knowledge engineering, *** Codes 03G10, 06A15, 68T27, 68T30 Copyright © 2023, The Authors. All rights reserved.

关键词： data Science

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A Framework on Semantic Thing Retrieval Method in IoT and IoE Environment

A Framework on Semantic Thing Retrieval Method in IoT and Io...

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International Conference on Platform Technology and Service (PlatCon)

作者： Jaehak Yu SoonHyun Kwon Hyunjoong Kang Sun-Jin Kim Ji-Hoon Bae Cheol-Sig Pyo Self-Machine Learning Data Intelligence Research Section Electronics and Telecommunications Research Institute (ETRI) Daejeon Republic of Korea KSB Convergence System Research Section Electronics and Telecommunications Research Institute (ETRI) Daejeon Republic of Korea Department of KSB(Knowledge-converged Super Brain) Convergence Research Electronics and Telecommunications Research Institute (ETRI) Daejeon Republic of Korea

The concept of Internet of Things (IoT) and Internet of Everything (IoE) was proposed to realize intelligent to thing and smart device communications using Web standard technology. To many, the large scale data generated by IoT and IoE environment are considered having highly valuable and useful information. Increasing requirements to generate new values through data analysis and autonomous mash-up have resulted in active studies on integration with semantic web technologies in the IoT and IoE. Particularly, in order to utilize IoE data in real environment, connectivity and mutual cooperation technology suitable for various services are required. In this paper, we proposed a semantic thing retrieval system in IoT and IoE that can perform efficient thing retrieval operation with various thing metadata and things with states information in IoT and IoE.

关键词： Semantics Ontologies Internet of Things Semantic Web Context Telecommunications Standards

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Efficient mining of class association rules with the itemset constraint

Efficient mining of class association rules with the itemset...

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作者： Nguyen, Dang Nguyen, Loan T.T. Vo, Bay Pedrycz, Witold Division of Data Science Ton Duc Thang University Ho Chi Minh City Viet Nam Division of Knowledge and System Engineering for ICT Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Information Technology Ho Chi Minh City University of Technology Viet Nam College of Electronics and Information Engineering Sejong University Seoul Korea Republic of Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2V4 Canada Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Systems Research Institute Polish Academy of Sciences Warsaw Poland

Mining class association rules (CARs) with the itemset constraint is concerned with the discovery of rules, which contain a set of specific items in the rule antecedent and a class label in the rule consequent. This task is commonly encountered in mining medical data. For example, when classifying which section of the population is at high risk for the HIV infection, epidemiologists often concentrate on rules which include demographic information such as gender, age, and marital status in the rule antecedent, and HIV-Positive in the rule consequent. There are two naive strategies to solve this problem, namely pre-processing and post-processing. The post-processing methods have to generate and consider a huge number of candidate CARs while the performance of the pre-processing methods depend on the number of records filtered out. Therefore, such approaches are time consuming. This study proposes an efficient method for mining CARs with the itemset constraint based on a lattice structure and the difference between two sets of object identifiers (diffset). Firstly, a lattice structure is built to store all frequent itemsets in the dataset. To reduce memory usage, instead of the entire set of object identifiers, the diffset is used. Secondly, the lattice is traversed to generate only rules which satisfy the itemset constraint. The experimental results show that the proposed algorithm outperforms existing methods in terms of both the mining time and memory usage. © 2016 Elsevier B.V. All rights reserved.

关键词： data mining

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Evaluating the Performance to Detecting Topic Shift of Large Language Models: A Study

Evaluating the Performance to Detecting Topic Shift of Large...

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6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Pingfang Tian Deting Liu Department of Computer Science and Technology Wuhan University of Science and Technology Wuhan China The Key Laboratory of the Rich Media Knowledge Organisation and Service of Digital Publishing Content Institute of Scientific and Technical Information of China Beijing China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

关键词： Technological innovation Large language models Oral communication Signal processing Chatbots Context modeling

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Unlocking the Secrets Behind Advanced Artificial Intelligence Language Models in Deidentifying Chinese-English Mixed Clinical Text: Development and Validation Study

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Journal of Medical Internet Research 2024年第1期26卷 e48443页

作者： Lee, You-Qian Chen, Ching-Tai Chen, Chien-Chang Lee, Chung-Hong Chen, Peitsz Wu, Chi-Shin Dai, Hong-Jie Dialogue System Technical Department Asustek Computer Inc Taipei Taiwan Intelligent System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Bioinformatics and Medical Engineering Asia University Taichung Taiwan Center for Precision Health Research Asia University Taichung Taiwan Electromagnetic Sensing Control and AI Computing System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Knowledge Discovery and Data Mining Lab Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Chemical Engineering Feng Chia University Taichung Taiwan National Center for Geriatrics and Welfare Research National Health Research Institutes Zhunan Taiwan National Institute of Cancer Research National Health Research Institutes Tainan Taiwan School of Post-Baccalaureate Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Center for Big Data Research Kaohsiung Medical University Kaohsiung Taiwan

Background: The widespread use of electronic health records in the clinical and biomedical fields makes the removal of protected health information (PHI) essential to maintain privacy. However, a significant portion of information is recorded in unstructured textual forms, posing a challenge for deidentification. In multilingual countries, medical records could be written in a mixture of more than one language, referred to as code mixing. Most current clinical natural language processing techniques are designed for monolingual text, and there is a need to address the deidentification of code-mixed text. Objective: The aim of this study was to investigate the effectiveness and underlying mechanism of fine-tuned pretrained language models (PLMs) in identifying PHI in the code-mixed context. Additionally, we aimed to evaluate the potential of prompting large language models (LLMs) for recognizing PHI in a zero-shot manner. Methods: We compiled the first clinical code-mixed deidentification data set consisting of text written in Chinese and English. We explored the effectiveness of fine-tuned PLMs for recognizing PHI in code-mixed content, with a focus on whether PLMs exploit naming regularity and mention coverage to achieve superior performance, by probing the developed models’ outputs to examine their decision-making process. Furthermore, we investigated the potential of prompt-based in-context learning of LLMs for recognizing PHI in code-mixed text. Results: The developed methods were evaluated on a code-mixed deidentification corpus of 1700 discharge summaries. We observed that different PHI types had preferences in their occurrences within the different types of language-mixed sentences, and PLMs could effectively recognize PHI by exploiting the learned name regularity. However, the models may exhibit suboptimal results when regularity is weak or mentions contain unknown words that the representations cannot generate well. We also found that the availability of cod

关键词： ChatGPT code mixing deidentification electronic health record large language model pretrained language model

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Biogeography-based optimization for cluster analysis

Biogeography-based optimization for cluster analysis

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International Conference on Computer, Communication and Computational Sciences, ICCCCS 2016

作者： Wu, Xueyan Wang, Hainan Chen, Zhimin Lu, Zhihai Phillips, Preetha Wang, Shuihua Zhang, Yudong School of Computer Science and Technology Nanjing Normal University NanjingJiangsu210023 China Key Laboratory of Statistical Information Technology & Data Mining State Statistics Bureau ChengduSichuan610225 China School of Computer and Information Engineering Henan Normal University XinxiangHenan453000 China School of Electronic Information Shanghai Dianji University Shanghai200240 China Key Laboratory of Symbolic Computation & Knowledge Engineering of Ministry of Education Jilin University ChangchunJilin130012 China Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology GuilinGuangxi541004 China School of Natural Sciences and Mathematics Shepherd University ShepherdstownWV25443 United States West Virginia School of Osteopathic Medicine 400 N Lee St LewisburgWV24901 United States Department of Electrical Engineering The City College of New York CUNY New YorkNY10031 United States State Key Lab of CAD & CG Zhejiang University HangzhouZhejiang310027 China Manchester Metropolitan University ManchesterM156BH United Kingdom

ISBN: (纸本)9789811037696

With the aim of resolving the issue of cluster analysis more precisely and validly, a new approach was proposed based on biogeography-based optimization (abbreviated as BBO) algorithm. (Method) First, we reformulated the problem with an optimization model based on the variance ratio criterion (VARAC). Then, BBO was presented to search the optimal solution of the VARAC. There are 400 data of four groups in the experimental dataset, which have the degrees of overlapping of three distinct scales. The first one is nonoverlapping, the second one is partial overlapping, and the last is severely overlapping. BBO algorithm was compared with three different state-of-the-art approaches. We ran every algorithm 20 times. In this experiment, our results demonstrate the maximum VARAC values that can be found by BBO. The conclusion is that BBO is predominant which is extremely quick for the issue of clustering analysis. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Cluster analysis

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CLEER - AN AI-system DEVELOPED TO ASSIST EQUIPMENT ARRANGEMENTS ON WARSHIPS

引用

NAVAL ENGINEERS JOURNAL 1989年第3期101卷 127-137页

作者： ZHOU, HH SILVERMAN, BG SIMKOL, J Dr. H. Harry Zhou:is a research professor at the Institute for Artificial Intelligence of The George Washington University. Dr. Zhou received his master's degree and Ph.D. in computer science from Vanderbilt University in 1984 and 1987 respectively. He did his dissertation in the fields of artificial intelligence analogical reasoning and machine learning. His research interests include: classifier systems genetic algorithms learning by analogy inductive learning adaptive expert systems automated knowledge acquisition and adaption. He is also interested in data base design programming languages mental modeling and software engineering. Dr. Barry G. Silverman:is director of the Institute for Artificial Intelligence and a professor at the Engineering Administration Department of The George Washington University. He is also president of IntelliTek Inc. an AI consulting firm. Dr. Silverman received the B.S.E. M.S.E. and Ph.D. degrees from the University of Pennsylvania. He has been a principal developer of four generic AI products as well as eight AI applications. Since 1979 he has written over 100 papers and reports on these AI efforts. Joel Simkol:is a research scientist currently engaged in designing expert system architectures to support electronic warfare vulnerability analyses threat assessments shipboard topside antenna arrangements and C3countermeasures employment. Mr. Simkol's work in applying expert systems technology to electromagnetic interference and to spectrum management has generated increased interest and participation from all branches of government agencies.

This paper describes a modularized AI system being built to help improve electromagnetic compatibility (EMC) among shipboard topside equipment and their associated systems. CLEER is intended to act as an easy to use integrator of existing expert knowledge and pre-existing data bases and large scale analytical models. Due to these interfaces; to the need for portability of the software; and to artificial intelligence related design requirements (such as the need for spatial reasoning, expert data base management, model base management, track-based reasoning, and analogical (similar ship) reasoning) it was realized that traditional expert system shells would be inappropriate, although relatively off-the-shelf AI technology could be incorporated. In the same vein, the rapid prototyping approach to expert system design and knowledge engineering was not pursued in favor of a rigorous systems engineering methodology. The critical design decisions affecting CLEER's development are summarized in this paper along with lessons learned to date all in terms of “how,” “why,” and “when” specific features are being developed.

关键词： Artificial Intelligence

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Correction to: Non-extensitivity and criticality of atomic hydropathicity around a voltage-gated sodium channel's pore: a modeling study

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Journal of biological physics 2022年第4期48卷 477-478页

作者： Markos N Xenakis Dimos Kapetis Yang Yang Jordi Heijman Stephen G Waxman Giuseppe Lauria Catharina G Faber Hubert J Smeets Patrick J Lindsey Ronald L Westra Department of Toxicogenomics Section Clinical Genomics Maastricht University PO Box 616 6200 MD Maastricht The Netherlands. mrksxenakis@***. Research School for Mental Health and Neuroscience (MHeNS) Maastricht University PO Box 616 6200 MD Maastricht The Netherlands. mrksxenakis@***. Neuroalgology Unit Fondazione IRCCS Istituto Neurologico "Carlo Besta" Via Celoria 11 20133 Milan Italy. Department of Medicinal Chemistry and Molecular Pharmacology Purdue University College of Pharmacy IN 47907 West Lafayette USA. Purdue Institute for Integrative Neuroscience IN 47907 West Lafayette USA. Department of Cardiology CARIM School for Cardiovascular Diseases Maastricht University PO Box 616 6200 MD Maastricht The Netherlands. Department of Neurology and Center for Neuroscience and Regeneration Research Yale University School of Medicine New Haven CT 06510 USA. Rehabilitation Research Center Veterans Affairs Connecticut Healthcare System West Haven CT 06516 USA. Department of Biomedical and Clinical Sciences "Luigi Sacco" University of Milan Via G.B. Grassi 74 20157 Milan Italy. Department of Neurology Maastricht University Medical Center PO Box 5800 6202 AZ Maastricht The Netherlands. Department of Toxicogenomics Section Clinical Genomics Maastricht University PO Box 616 6200 MD Maastricht The Netherlands. Research School for Mental Health and Neuroscience (MHeNS) Maastricht University PO Box 616 6200 MD Maastricht The Netherlands. Research School for Oncology and Developmental Biology (GROW) Maastricht University PO Box 616 6200 MD Maastricht The Netherlands. Department of Data Science and Knowledge Engineering Maastricht University PO Box 616 6200 MD Maastricht The Netherlands.

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Correction to: Making sense of diabetes medication decisions: a mixed methods cluster randomized trial using a conversation aid intervention

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Endocrine 2023年第1期79卷 221-222页

作者： Marleen Kunneman Megan E Branda Jennifer L Ridgeway Kristina Tiedje Carl R May Mark Linzer Jonathan Inselman Angela L H Buffington Jordan Coffey Deborah Boehm James Deming Sara Dick Holly van Houten Annie LeBlanc Juliette Liesinger Janet Lima Joanne Nordeen Laurie Pencille Sara Poplau Steven Reed Anna Vannelli Kathleen J Yost Jeanette Y Ziegenfuss Steven A Smith Victor M Montori Nilay D Shah Knowledge and Evaluation Research Unit Division of Endocrinology Diabetes Metabolism and Nutrition Mayo Clinic Rochester MN USA. Medical Decision Making Department of Biomedical Data Sciences Leiden University Medical Center Leiden The Netherlands. Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado-Denver Anschutz Medical Campus Aurora CO USA. Division of Biomedical Statistics and Informatics Department of Health Sciences Research Mayo Clinic Rochester MN USA. Division of Health Care Policy and Research Department of Health Sciences Research Mayo Clinic Rochester MN USA. Laboratoire d'anthropologie des enjeux contemporains Lyon France. Faculty of Public Health and Policy London School of Hygiene and Tropical Medicine London UK. Department of Medicine Hennepin Healthcare and University of Minnesota Minneapolis MN USA. Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery Rochester MN USA. Department of Psychiatry and Psychology Mayo Clinic Health System Mankato MN USA. Department of Family Medicine and Community Health University of Minnesota Medical School Minneapolis MN USA. Practice-Based Research Network Mayo Clinic Rochester MN USA. Center for Translational Science Activities Mayo Clinic Rochester MN USA. Center for Patient and Provider Experience Hennepin County Medical Center Minneapolis MN USA. School of Nursing University of Minnesota Minneapolis MN USA. Decision Partners for Health Richfield MN USA. Mayo Clinic Health System Northwest Wisconsin (dept) Home Health and Hospice Eau Claire WI USA. Department of Family and Emergency Medicine Faculty of Medicine Laval University Quebec QC Canada. Park Nicollet International Diabetes Center St. Louis Park MN USA. Mayo Clinic Health System La Crosse WI USA. Kern Center for the Science of Health Care Deliver Mayo Clinic Rochester MN USA. Office of Professional Worklife Hennepin Healthcare Minne

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