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A Learning Model to Improve Learning Outcome on Experiential Learning in a Multi-Phase Internship: a Case Study of the Internship Program of a Thai University

A Learning Model to Improve Learning Outcome on Experiential...

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IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Chanakarn Kingkaew Thepchai Supnithi Thanaruk Theeramunkong Kai Morita Koji Tanaka Mitsuru Ikeda School of Knowledge Science Japan Advanced Institute of Science and Technology Japan School of Information Computer and Communication Technology Sirindhorn International Institute of Technology Thammasat University Thailand Language and Semantic Technology Laboratory National Electronics and Computer Technology Center Thailand School of Information Computer and Communication Technology Sirindhorn International Institute of Technology Thailand Department of Psychological Science College of Informatics and Human Communication Kanazawa Institute of Technology Nonoichi Japan

The internship is one of the crucial learning processes that provide a unique experience to combine what they learned in university courses and how to use it in the real society during their university lives as it provides the opportunity to learn through work experience offered by the external organization. Although learner can learn career skills through the internship, most learner cannot make good reflection and cannot learn how to learn from experience. We think the cause of this problem is that most learner cannot reflect their experience and cannot clear the abstract concept and apply it to the next trial. This implies that those learners can improve their learning through internship by acquiring experiential learning skills. In the internship in general, the mentor has the important education roles. One is to foster career skills learning, and the other is to promote experiential learning. However, in most case, the mentor is very good at career skill teaching, but most of the mentor is not well prepared to support experiential learning. In response to the above problems, to support learners to realized good learning, we provide a learning environment for the learner to support experiential learning. A guideline for the learner to let them aware of the critical concept of internship and experiential learning. Also, we provide a web system to practice reflective writing to understand and able to apply experiential learning concepts. In this paper, as the preliminary research to develop learning environment, the analysis of the learner learning activity of the current internship program and show the prototype of the conceptualization of the educational concepts to support the experiential learning.

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A study of a Thai-English translation comparing on applying phrase-based and hierarchical phrase-based translation 11th

A study of a Thai-English translation comparing on applying ...

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11th International Symposium on Natural language Processing, SNLP-2016 and 1st Workshop in Intelligent Informatics and Smart technology, 2016

作者： Luekhong, Prasert Ruangrajitpakorn, Taneth Sukhahuta, Rattasit Supnithi, Thepchai College of Integrated Science and Technology Rajamangala University of Technology Lanna Chiang Mai Thailand Computer Science Department Faculty of Science Chiang Mai University Chiang Mai Thailand Department of Computer Science Faculty of Science and Technology Thammasat University Pathumthanee Thailand Language and Semantic Technology Laboratory National Electronics and Computer Technology Center Khlong Luang Thailand

ISBN: (纸本)9783319700151

This work presents the comparative study between phrase-based translation (PBT) and hierarchical phrase-based translation (HPBT) to find the potential and suitability for Thai-English translation. By the experiment result, we found that HPBT slightly outpaced PBT by measuring with BLEU point. The best language model was 4-gram for Thai to English translation while 5-gram gave the best result for English to Thai translation. © Springer International Publishing AG 2018.

关键词： Computer aided language translation

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A study of lexical ambiguity in large forum discussions for multidisciplinary knowledge engineering 13

A study of lexical ambiguity in large forum discussions for ...

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13th International Conference on Knowledge, Information and Creativity Support Systems, KICSS 2018

作者： Takhom, Akkharawoot Boonkwan, Prachya Ulrich Hoppe, H. Ikeda, Mitsuru Usanavasin, Sasiporn Supnithi, Thepchai Japan Advanced Institute of Science and Technology School of Knowledge Science Nomi Japan School of Information Computer and Communication Technology Sirindhorn International Institute of Technology Thammasat University Pathum Thani Thailand Language and Semantic Technology Laboratory National Electronics and Computer Technology Center Pathum Thani Thailand Department of Computer Science and Applied Cognitive Science Faculty of Engineering University of Duisburg-Essen Duisburg Germany

ISBN: (纸本)9781728101613

Lexical ambiguity is a challenging issue in multidisciplinary knowledge engineering due to the tendency that lexical terms can be used among different domains with different specific meanings. Particularly in large forum discussions, such ambiguous cross-disciplinary terms hard to be identified and detected by the discussion participants because domain expertise from several relevant fields is required to detect those terms and discover the actual divergence of interpretation. Having many ambiguous terms in the discussion context will result in gradual misunderstanding and delayed knowledge construction. We studied the effects of data sizes and morphological analysis in discovering ambiguous cross-disciplinary terms in large forum discussions. Our findings are twofold. First, it is more likely to discover cross-disciplinary terms as forum discussions deepen. This correlates with domain experts' tendency to use general terms in metaphorically describing domain-specific concepts, therefore causing lexical ambiguity. Second, we found that lemmatization outperforms stemming in forming more understandable key terms. This is because lemmatization eliminates only inflectional affixes and keeps derivational affixes. On the other hand, stemming eliminates both types of affixes, causing semantic bleaching. © 2018 IEEE.

关键词： Knowledge engineering

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Quality Classification of ASEAN Wikipedia Articles using Statistical Features

Quality Classification of ASEAN Wikipedia Articles using Sta...

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International Joint Symposium on Artificial Intelligence and Natural language Processing (iSAI-NLP)

作者： Kanchana Saengthongpattana Thepchai Supnithi Nuanwan Soonthornphisaj Language and Semantic Technology Laboratory National Electronics and Computer Technology Center (NECTEC)) Pathumthani Thailand Department of Computer Science Kasetsart University Bangkok Thailand

ISBN: (纸本)9781728101651;9781728101644

The quality of Wikipedia articles is still the main concerned in all languages. Wikipedia relies mostly on human editors and administrators to provide the quality of content. But the magnitude of Wikipedia content makes locating all instances of article very time consuming. Therefore, we need the automatic quality detection that can help users to evaluate the quality of articles. In this paper, we propose the feature set to applied for the ASEAN language Wikipedia articles. We investigate the statistical features such as # of link, # of infobox, length of article, # of headings, # of files, # of contributors, # of viewer, # of written articles found in other languages, and # of templates applied in the article. The experiments are perform using Naïve Bayes and Decision tree algorithm. We found that the accuracy of Decision tree (96.34%) outperform Naïve Bayes (86.47%). Moreover, we found that the statistical features play an important role in quality classification of Vietnamese, Indonesian, Malaysian, Thai, and Tagalog/Philippines Wikipedia articles.

关键词： Encyclopedias Electronic publishing Internet Image color analysis Decision trees Metadata

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Local studies provide a global perspective of the impacts of climate change on Indigenous Peoples and local communities

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Sustainable Earth Reviews 2024年第1期7卷 1-11页

作者： Reyes-García, Victoria García-Del-Amo, David Porcuna-Ferrer, Anna Schlingmann, Anna Abazeri, Mariam Attoh, Emmanuel M. N. A. N. Vieira da Cunha Ávila, Julia Ayanlade, Ayansina Babai, Daniel Benyei, Petra Calvet-Mir, Laura Carmona, Rosario Caviedes, Julián Chah, Jane Chakauya, Rumbidzayi Cuní-Sanchez, Aida Fernández-Llamazares, Álvaro Galappaththi, Eranga K. Gerkey, Drew Graham, Sonia Guillerminet, Théo Huanca, Tomás Ibarra, José Tomás Junqueira, André B. Li, Xiaoyue López-Maldonado, Yolanda Mattalia, Giulia Samakov, Aibek Schunko, Christoph Seidler, Reinmar Sharakhmatova, Victoria Singh, Priyatma Tofighi-Niaki, Adrien Torrents-Ticó, Miquel Institució Catalana de Recerca I Estudis Avançats Barcelona Spain Institut de Ciència i Tecnologia Ambientals (ICTA-UAB) Universitat Autònoma de Barcelona Bellaterra Spain Departament d’Antropologia Social i Cultural Universitat Autònoma de Barcelona Bellaterra Spain CIRAD UMR SENS Montpellier France Rosenstiel School of Marine Atmospheric and Earth Science University of Miami Miami USA International Water Management Institute Battaramulla Sri Lanka Mamirauá Sustainable Development Institute Tefé Brazil National Tropical Botanical Garden Kalaheo USA Department of Geography Obafemi Awolowo University Ile-Ife Nigeria Department of Geography and Regional Research University of Vienna Vienna Austria HUN-REN Research Centre for the Humanities Institute of Ethnology Budapest Hungary Institut Metròpoli Universitat Autònoma de Barcelona Bellaterra Spain Center for Intercultural and Indigenous Research (CIIR) Pontificia Universidad Católica de Chile Santiago Chile Center for Local Development (CEDEL) Center for Intercultural and Indigenous Research (CIIR) & Center of Applied Ecology and Sustainability (CAPES) ECOS (Ecosystem-Complexity-Society) Co-Laboratory Pontificia Universidad Católica de Chile Villarrica Chile Cape Horn International Center (CHIC) Universidad de Magallanes Puerto Williams Chile Department of Agricultural Extension University of Nigeria Nsukka Nsukka Nigeria College of Agriculture and Environmental Sciences (CAES) University of South Africa Johannesburg South Africa Department of Environment and Geography York Institute for Tropical Ecosystems University of York York UK Department of International Environmental and Development Studies (NORAGRIC) Norwegian University of Life Sciences Ås Norway Faculty of Biological and Environmental Sciences Helsinki Institute of Sustainability Science (HELSUS) University of Helsinki Helsinki Finland Department of Geography Virginia Polytechnic Institute and State University (Virginia Tech) Blacksburg USA

Indigenous Peoples and local communities with nature-dependent livelihoods are disproportionately affected by climate change impacts, but their experience, knowledge and needs receive inadequate attention in climate research and policy. Here, we discuss three key findings of a collaborative research consortium arising from the Local Indicators of Climate Change Impacts project. First, reports of environmental change by Indigenous Peoples and local communities provide holistic, relational, placed-based, culturally-grounded and multi-causal understandings of change, largely focused on processes and elements that are relevant to local livelihoods and cultures. These reports demonstrate that the impacts of climate change intersect with and exacerbate historical effects of socioeconomic and political marginalization. Second, drawing on rich bodies of inter-generational knowledge, Indigenous Peoples and local communities have developed context-specific responses to environmental change grounded in local resources and strategies that often absorb the impacts of multiple drivers of change. Indigenous Peoples and local communities adjust in diverse ways to impacts on their livelihoods, but the adoption of responses often comes at a significant cost due to economic, political, and socio-cultural barriers operating at societal, community, household, and individual levels. Finally, divergent understandings of change challenge generalizations in research examining the human dimensions of climate change. Evidence from Indigenous and local knowledge systems is context-dependent and not always aligned with scientific evidence. Exploring divergent understandings of the concept of change derived from different knowledge systems can yield new insights which may help prioritize research and policy actions to address local needs and priorities. →Place-based communities provide holistic, culturally-grounded, and multi-causal reports of change. →Place-based communities rely

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A framework to generate carrier path using semantic similarity of competencies in job position 14th

A framework to generate carrier path using semantic similari...

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14th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2016

作者： Na Chai, Wasan Ruangrajitpakorn, Taneth Buranarach, Marut Supnithi, Thepchai Language and Semantic Technology Laboratory National Electronics and Computer Technology Center Pathumthanee Thailand Department of Computer Science Faculty of Science and Technology Thammasat University Pathumthanee Thailand

ISBN: (纸本)9783319606743

A career path is necessary for students and workers to keep themselves in track for their career goal. However, a career path following a job standard in general is very rare. This paper presents a method to find a semantic similarity within competencies of Job positions for realising a path to relate career. By a development of Thai WordNet containing terms used in competency description, a distance of classes of WordNet structure is used to determine a semantic similarity of competencies. Paths to relate job positions are assumed for the job positions sharing similar competencies, and the more they share, the more transferrable job is viable. From the usage scenario, the proposed framework proved that semantic of words is more useful than using character based similarity in competency comparison. © Springer International Publishing AG 2017.

关键词： semantics

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Preface

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Advances in Intelligent Systems and Computing 2019年 807卷 v-vi页

作者： Theeramunkong, Thanaruk Supnithi, Thepchai Kongkachandra, Rachada Ketcham, Mahasak Hnoohom, Narit Hashimoto, Kiyota Songmuang, Pokpong Thammasat University Bangkadi Muang Pathumthani Thailand Faculty of Science and Technology Department of Computer Science Thammasat University Pathumthani Thailand Bangkok Thailand Department of Computer Engineering Faculty of Engineering Mahidol University Nakorn Pathom Thailand Prince of Songkla University Phuket Thailand National Science and Technology Development Agency Language and Semantic Technology Laboratory National Electronics and Computer Technology Center Pathum Thani Thailand Faculty of Science and Technology Department of Computer Science Thammasat University Pathum Thani Thailand

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A Study of Lexical Ambiguity in Large Forum Discussions for Multidisciplinary Knowledge Engineering

A Study of Lexical Ambiguity in Large Forum Discussions for ...

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International Conference on Knowledge, Information and Creativity Support Systems (KICSS)

作者： Akkharawoot Takhom Prachya Boonkwan H. Ulrich Hoppe Mitsuru Ikeda Sasiporn Usanavasin Thepchai Supnithi School of Knowledge Science Japan Advanced Institute of Science and Technology Nomi Japan Language and Semantic Technology Laboratory National Electronics and Computer Technology Center Pathum Thani Thailand Department of Computer Science and Applied Cognitive Science University of Duisburg-Essen Duisburg Germany School of Information Computer and Communication Technology Sirindhorn International Institute of Technology Thammasat University Pathum Thani Thailand

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Music, Computing, and Health: A Roadmap for the Current and Future Roles of Music technology for Health Care and Well-Being

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Music and science 2021年 4卷

作者： Agres, Kat R. Schaefer, Rebecca S. Volk, Anja van Hooren, Susan Holzapfel, Andre Dalla Bella, Simone Müller, Meinard de Witte, Martina Herremans, Dorien Ramirez Melendez, Rafael Neerincx, Mark Ruiz, Sebastian Meredith, David Dimitriadis, Theo Magee, Wendy L. Yong Siew Toh Conservatory of Music National University of Singapore Singapore Social and Cognitive Computing Department Institute of High Performance Computing A*STAR Singapore Institute for Psychology Health Medical Neuropsychology Unit Leiden University Leiden Netherlands Leiden Institute for Brain and Cognition Leiden University Leiden Netherlands Academy of Creative and Performing Arts Leiden University Leiden Netherlands Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Faculty of Health care Department of Arts Therapies Zuyd University of Applied Sciences Heerlen Netherlands KenVaK Research Centre for the Arts Therapies and Psychomotricity Heerlen Netherlands Faculty of Psychology Open University of The Netherlands Heerlen Netherlands Division of Media Technology and Interaction Design KTH Royal Institute of Technology Stockholm Sweden International Laboratory for Brain Music and Sound Research (BRAMS) Outremont QC Canada Department of Psychology University of Montreal Montreal QC Canada Centre for Research on Brain Language and Music (CRBLM) Montreal QC Canada University of Economics and Human Sciences in Warsaw Warsaw Poland International Audio Laboratories Erlangen Friedrich-Alexander Universität Erlangen-Nürnberg Erlangen Germany HAN University of Applied Sciences Department of Arts Therapies and Psychological Studies Nijmegen Netherlands University of Amsterdam Research Institute of Child Development and Education Amsterdam Netherlands Treatment Centre for People with Mild Intellectual Disabilities and Psychiatric and Behavioral Disorders Gennep Netherlands Information Systems Technology and Design Singapore University of Technology and Design Singapore Music and Machine Learning Lab Music Technology Group Universitat Pompeu Fabra Barcelona Spain Faculty of EEMCS Interactive Intelligence Group Delft University of Technology Delft Netherlands Centre for Digital Music School of Electronic Engineerin

The fields of music, health, and technology have seen significant interactions in recent years in developing music technology for health care and well-being. In an effort to strengthen the collaboration between the involved disciplines, the workshop “Music, Computing, and Health” was held to discuss best practices and state-of-the-art at the intersection of these areas with researchers from music psychology and neuroscience, music therapy, music information retrieval, music technology, medical technology (medtech), and robotics. Following the discussions at the workshop, this article provides an overview of the different methods of the involved disciplines and their potential contributions to developing music technology for health and well-being. Furthermore, the article summarizes the state of the art in music technology that can be applied in various health scenarios and provides a perspective on challenges and opportunities for developing music technology that (1) supports person-centered care and evidence-based treatments, and (2) contributes to developing standardized, large-scale research on music-based interventions in an interdisciplinary manner. The article provides a resource for those seeking to engage in interdisciplinary research using music-based computational methods to develop technology for health care, and aims to inspire future research directions by evaluating the state of the art with respect to the challenges facing each field. © The Author(s) 2021.

关键词： health care interdisciplinarity MedTech music information retrieval (MIR) music neuroscience Music psychology music technology music therapy well-being

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Automatic Extraction of Medication Mentions from Tweets-Overview of the BioCreative VII Shared Task 3 Competition

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Database : the journal of biological databases and curation 2023年第2023期2023卷 baac108页

作者： Davy Weissenbacher Karen O'Connor Siddharth Rawal Yu Zhang Richard Tzong-Han Tsai Timothy Miller Dongfang Xu Carol Anderson Bo Liu Qing Han Jinfeng Zhang Igor Kulev Berkay Köprü Raul Rodriguez-Esteban Elif Ozkirimli Ammer Ayach Roland Roller Stephen Piccolo Peijin Han V G Vinod Vydiswaran Ramya Tekumalla Juan M Banda Parsa Bagherzadeh Sabine Bergler João F Silva Tiago Almeida Paloma Martinez Renzo Rivera-Zavala Chen-Kai Wang Hong-Jie Dai Luis Alberto Robles Hernandez Graciela Gonzalez-Hernandez Department of Computational Biomedicine Cedars-Sinai Medical Center Los Angeles CA USA. DBEI The Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Computer Science and Information Engineering National Central University No. 300 Zhongda Rd Zhongli District Taoyuan 320 Taiwan. IoX Center National Taiwan University Da'an District Section 4 Roosevelt Rd No. 1 Barry Lam Hall Taipei 106 Taiwan. Research Center for Humanities and Social Sciences Academia Sinica No. 128 Section 2 Academia Rd Nangang District Taipei 115 Taiwan. Computational Health Informatics Program Boston Children's Hospital Boston MA USA. Department of Pediatrics Harvard Medical School Boston MA USA. NVIDIA Santa Clara CA USA. Department of Statistics Florida State University Tallahassee FL USA. Data and Analytics Chapter F. Hoffmann-La Roche Ltd Switzerland. Pharmaceutical Research and Early Development Roche Innovation Center Basel Switzerland. Speech and Language Technology Lab DFKI Berlin Germany. Department of Biology Brigham Young University Provo UT USA. Department of Computational Medicine and Bioinformatics Medical School University of Michigan Ann Arbor MI USA. Department of Learning Health Sciences Medical School University of Michigan Ann Arbor MI USA. School of Information University of Michigan Ann Arbor MI USA. Department of Computer Science Georgia State University Atlanta GA USA. CLaC Labs Concordia University Montreal Canada. DETI Institute of Electronics and Informatics Engineering of Aveiro University of Aveiro Portugal. Department of Computation University of A Coruña Spain. Computer Science and Engineering Department Universidad Carlos III de Madrid Madrid Spain. Big Data Laboratory Chunghwa Telecom Laboratories Taoyuan Taiwan. Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan. Department of Electrical Engineering College of Electrical Engineering and Computer Sci

This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user's publicly available tweets (the user's 'timeline'). In general, detecting health-related tweets is notoriously challenging for natural language processing tools. The main challenge, aside from the informality of the language used, is that people tweet about any and all topics, and most of their tweets are not related to health. Thus, finding those tweets in a user's timeline that mention specific health-related concepts such as medications requires addressing extreme imbalance. Task 3 called for detecting tweets in a user's timeline that mentions a medication name and, for each detected mention, extracting its span. The organizers made available a corpus consisting of 182 049 tweets publicly posted by 212 Twitter users with all medication mentions manually annotated. The corpus exhibits the natural distribution of positive tweets, with only 442 tweets (0.2%) mentioning a medication. This task was an opportunity for participants to evaluate methods that are robust to class imbalance beyond the simple lexical match. A total of 65 teams registered, and 16 teams submitted a system run. This study summarizes the corpus created by the organizers and the approaches taken by the participating teams for this challenge. The corpus is freely available at https://***/tasks/biocreative-vii/track-3/. The methods and the results of the competing systems are analyzed with a focus on the approaches taken for learning from class-imbalanced data.

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