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International Students’ Tutoring Experiences and Cultural Awareness in the Digital learning Companion Project

Journal of Educational Media and Library Sciences 2019年第3期56卷 373-399页

作者： Otanga, Sarah Weng, Cathy Weng, Apollo Chu, Regina JuChun Graduate Institute of Digital Learning and Education National Taiwan University of Science and Technology Taiwan Graduate Institute of Digital Learning and Education National Taiwan University of Science and Technology Taiwan Department of Digital Multimedia Design China University of Technology Taiwan Graduate Institute of Learning Sciences and Technology National Tsing Hua University Taiwan

This study explored international students’ experiences in online tutoring of remote learners from four elementary and junior high schools in Taiwan. A second objective of the study was to investigate whether online tutoring cultural exchange between the international students and the remote learners could promote cultural awareness. 39 international students from a Taiwanese university were paired with 39 Taiwanese remote school learners. The international students reflected on their tutoring experiences and cultural awareness using both videos and essays after participating in the online tutoring program for a period of ten weeks. The results indicated that by participating in the program, the international students gained some skills in teaching, learned a new language, received encouragement from their peers, and helped the tutees to gain confidence to communicate using English more. The international students also appreciated the opportunity that the project gave them to exchange their culture with the students of Taiwan. Although some of them faced a language barrier challenge, they were able to learn about some aspects of the culture of Taiwan from the tutees. Thus, being a more cost effective option for learning a culture than for instance visiting a country to learn a culture, learning institutions could provide opportunities and encourage students to participate in such cultural exchange programs. © 2016. All rights reserved.

关键词： Cultural awareness Cultural exchange Online tutoring Online tutoring experience

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RFID Characteristics Test as Business Needs at Logistic Companies in Indonesia 3

RFID Characteristics Test as Business Needs at Logistic Comp...

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3rd International Conference on Computing and Applied Informatics 2018, ICCAI 2018

作者： Irsan, Muhamad Fitria Murad, Dina Fernando, Erick Touriano, Derist Corradini, Andrea Tangerang15118 Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information System Bina Nusantara University Jakarta11480 Indonesia Information System Faculty of Information Technology University of Adi Wangsa Jambi Indonesia Copenhagen School of Design and Technology Kea Digital Copenhagen Denmark

In embedded RFID chips for storing records or data, such as Electronic Product Code one of them using barcode, in tag. Tags have functions to be able to transmit data for readable readers or stationary readers. using radio transmission technology to read data in tags. Each computer in the same system network can share and track data, regardless of where the item is searched for within the scope of the marked or restricted area. Available information may include item code, item date, delivery date, and item price. This study presents a framework based on RFID Proxied technology Characteristics Real Time Data Processing, Continuous Data Tracking and Discrete Data, and Reuse and explores its relationship with Business Needs. The observed populations are supervisors and managers working in logistic companies taken cross section obtained from a number of logistics companies incorporated in the Indonesian Logistics Association (ALI) in 2015. Sampling is done using convenience sampling method (convenience sampling method) because the population of respondents working in logistics companies in Indonesia is not known with certainty. This research can contribute to how users can select and measure RFID technology from their characteristics so that the application of RFID systems and technology becomes an added value for the company not just as a complementary technology only. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Radio frequency identification (RFID)

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Hybrid neuro-evolutionary method for predicting wind turbine power output

arXiv

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arXiv 2020年

作者： Neshat, Mehdi Abbasnejad, Ehsan Heydari, Azim Garcia, Davide Astiaso Nezhad, Meysam Majidi Groppi, Daniele Tjernberg, Lina Bertling Alexander, Bradley Wagner, Markus Optimization and Logistics Group School of Computer Science University of Adelaide Australia Australian Institute for Machine Learning University of Adelaide Australia Sapienza University of Rome Italy Department of Planning Design and Technology of Architecture Sapienza University of Rome Italy School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

Reliable wind turbine power prediction is imperative to the planning, scheduling and control of wind energy farms for stable power production. In recent years Machine learning (ML) methods have been successfully applied in a wide range of domains, including renewable energy. However, due to the challenging nature of power prediction in wind farms, current models are far short of the accuracy required by industry. In this paper, we deploy a composite ML approach-namely a hybrid neuro-evolutionary algorithm-for accurate forecasting of the power output in wind-turbine farms. We use historical data in the supervisory control and data acquisition (SCADA) systems as input to estimate the power output from an onshore wind farm in Sweden. At the beginning stage, the k-means clustering method and an Autoencoder are employed, respectively, to detect and filter noise in the SCADA measurements. Next, with the prior knowledge that the underlying wind patterns are highly non-linear and diverse, we combine a self-adaptive differential evolution (SaDE) algorithm as a hyper-parameter optimizer, and a recurrent neural network (RNN) called Long Short-term memory (LSTM) to model the power curve of a wind turbine in a farm. Two short time forecasting horizons, including ten-minutes ahead and one-hour ahead, are considered in our experiments. We show that our approach outperforms its counterparts. Copyright © 2020, The Authors. All rights reserved.

关键词： Long short-term memory

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An evolutionary deep learning method for short-term wind speed prediction: A case study of the lillgrund offshore wind farm

arXiv

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arXiv 2020年

作者： Neshat, Mehdi Nezhad, Meysam Majidi Abbasnejad, Ehsan Tjernberg, Lina Bertling Garcia, Davide Astiaso Alexander, Bradley Wagner, Markus Optimization and Logistics Group School of Computer Science University of Adelaide Australia Sapienza University of Rome Italy Australian Institute for Machine Learning The University of Adelaide Australia School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Planning Design and Technology of Architecture Sapienza University of Rome Italy

Accurate short-term wind speed forecasting is essential for large-scale integration of wind power generation. However, the seasonal and stochastic characteristics of wind speed make forecasting a challenging task. This study uses a new hybrid evolutionary approach that uses a popular evolutionary search algorithm, CMA-ES, to tune the hyper-parameters of two long-term-short-term (LSTM) ANN models for wind prediction. The proposed hybrid approach is trained on data gathered from an offshore wind turbine installed in a Swedish wind farm located in the Baltic Sea. Two forecasting horizons including ten-minutes ahead (absolute short term) and one-hour ahead (short term) are considered in our experiments. Our experimental results indicate that the new approach is superior to five other applied machine learning models, i.e., polynomial neural network (PNN), feed-forward neural network (FNN), nonlinear autoregressive neural network (NAR) and adaptive neuro-fuzzy inference system (ANFIS), as measured by five performance criteria. Copyright © 2020, The Authors. All rights reserved.

关键词： Stochastic systems

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Eye tracking: empirical foundations for a minimal reporting guideline

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Behavior research methods 2023年第1期55卷 364-416页

作者： Kenneth Holmqvist Saga Lee Örbom Ignace T C Hooge Diederick C Niehorster Robert G Alexander Richard Andersson Jeroen S Benjamins Pieter Blignaut Anne-Marie Brouwer Lewis L Chuang Kirsten A Dalrymple Denis Drieghe Matt J Dunn Ulrich Ettinger Susann Fiedler Tom Foulsham Jos N van der Geest Dan Witzner Hansen Samuel B Hutton Enkelejda Kasneci Alan Kingstone Paul C Knox Ellen M Kok Helena Lee Joy Yeonjoo Lee Jukka M Leppänen Stephen Macknik Päivi Majaranta Susana Martinez-Conde Antje Nuthmann Marcus Nyström Jacob L Orquin Jorge Otero-Millan Soon Young Park Stanislav Popelka Frank Proudlock Frank Renkewitz Austin Roorda Michael Schulte-Mecklenbeck Bonita Sharif Frederick Shic Mark Shovman Mervyn G Thomas Ward Venrooij Raimondas Zemblys Roy S Hessels Department of Psychology Nicolaus Copernicus University Torun Poland. kenneth.holmqvist@ur.de. Department of Computer Science and Informatics University of the Free State Bloemfontein South Africa. kenneth.holmqvist@ur.de. Department of Psychology Regensburg University Regensburg Germany. kenneth.holmqvist@ur.de. Department of Psychology Regensburg University Regensburg Germany. Experimental Psychology Helmholtz Institute Utrecht University Utrecht The Netherlands. Lund University Humanities Lab and Department of Psychology Lund University Lund Sweden. Department of Ophthalmology SUNY Downstate Health Sciences University Brooklyn NY USA. Tobii Pro AB Danderyd Sweden. Social Health and Organizational Psychology Utrecht University Utrecht The Netherlands. Department of Computer Science and Informatics University of the Free State Bloemfontein South Africa. TNO Soesterberg The Netherlands. Department of Ergonomics Leibniz Institute for Working Environments and Human Factors Dortmund Germany. Institute of Informatics LMU Munich Munich Germany. Institute of Child Development University of Minnesota Minneapolis USA. School of Psychology University of Southampton Southampton UK. School of Optometry and Vision Sciences Cardiff University Cardiff UK. Department of Psychology University of Bonn Bonn Germany. Vienna University of Economics and Business Vienna Austria. Department of Psychology University of Essex Essex UK. Department of Neuroscience Erasmus MC Rotterdam The Netherlands. Machine Learning Group Department of Computer Science IT University of Copenhagen Copenhagen Denmark. SR Research Ltd Ottawa Canada. Human-Computer Interaction University of Tübingen Tübingen Germany. University of British Columbia Columbia Canada. Department of Eye and Vision Science Institute of Life Course and Medical Sciences University of Liverpool Liverpool UK. Department of Education and Pedagogy Division Education Faculty of Social and Behavioral Sciences Ut

In this paper, we present a review of how the various aspects of any study using an eye tracker (such as the instrument, methodology, environment, participant, etc.) affect the quality of the recorded eye-tracking data and the obtained eye-movement and gaze measures. We take this review to represent the empirical foundation for reporting guidelines of any study involving an eye tracker. We compare this empirical foundation to five existing reporting guidelines and to a database of 207 published eye-tracking studies. We find that reporting guidelines vary substantially and do not match with actual reporting practices. We end by deriving a minimal, flexible reporting guideline based on empirical research (Section "An empirically based minimal reporting guideline").

关键词： Data quality Eye movements Eye tracking Replicability Reporting guidelines Reporting practices Reporting standards Reproducibility

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On the design space between molecular mechanics and machine learning force fields

arXiv

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arXiv 2024年

作者： Wang, Yuanqing Takaba, Kenichiro Chen, Michael S. Wieder, Marcus Xu, Yuzhi Zhu, Tong Zhang, John Z.H. Nagle, Arnav Yu, Kuang Wang, Xinyan Cole, Daniel J. Rackers, Joshua A. Cho, Kyunghyun Greener, Joe G. Eastman, Peter Martiniani, Stefano Tuckerman, Mark E. Simons Center for Computational Physical Chemistry Department of Chemistry New York University New YorkNY10003 United States Center for Data Science New York University New YorkNY10004 United States Courant Institute of Mathematical Sciences New York University New YorkNY10003 United States Asahi Kasei Pharma Corporation 632-1 Mifuku Shizuoka Izunokuni410-2321 Japan Open Molecular Software Foundation DavisCA95616 United States NYU-ECNU Center for Computational Chemistry Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning NYU Shanghai Shanghai200062 China Faculty of Synthetic Biology Shenzhen University of Advanced Technology Shenzhen518055 China University of Washington SeattleWA98195 United States Institute of Materials Research Tsinghua Shenzhen International Graduate School Tsinghua Univ. Shenzhen518055 China DP Technology Beijing100089 China School of Natural and Environmental Sciences Newcastle University Newcastle upon TyneNE1 7RU United Kingdom Prescient Design Genentech New YorkNY10004 United States Medical Research Council Laboratory of Molecular Biology CambridgeCB2 0QH United Kingdom Department of Chemistry Stanford University StanfordCA94305 United States Center for Soft Matter Research Department of Physics New York University New YorkNY10003 United States

A force field as accurate as quantum mechanics (QM) and as fast as molecular mechanics (MM), with which one can simulate a biomolecular system efficiently enough and meaningfully enough to get quantitative insights, is among the most ardent dreams of biophysicists—a dream, nevertheless, not to be fulfilled any time soon. Machine learning force fields (MLFFs) represent a meaningful endeavor towards this direction, where differentiable neural functions are parametrized to fit ab initio energies, and furthermore forces through automatic differentiation. We argue that, as of now, the utility of the MLFF models is no longer bottlenecked by accuracy but primarily by their speed (as well as stability and generalizability), as many recent variants, on limited chemical spaces, have long surpassed the chemical accuracy of 1 kcal/mol—the empirical threshold beyond which realistic chemical predictions are possible—though still magnitudes slower than MM. Hoping to kindle explorations and designs of faster, albeit perhaps slightly less accurate MLFFs, in this review, we focus our attention on the design space (the speed-accuracy tradeoff) between MM and ML force fields. After a brief review of the building blocks of force fields of either kind, we discuss the desired properties and challenges now faced by the force field development community, survey the efforts to make MM force fields more accurate and ML force fields faster, and envision what the next generation of MLFF might look like. © 2024, CC BY.

关键词： Molecular mechanics

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Full-spin-wave-scaled Finite element stochastic micromagnetism: Mesh-independent FUSSS LLG simulations of ferromagnetic resonance and reversal

arXiv

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arXiv 2021年

作者： Oezelt, Harald Qu, Luman Kovacs, Alexander Fischbacher, Johann Gusenbauer, Markus Beigelbeck, Roman Praetorius, Dirk Yano, Masao Shoji, Tetsuya Kato, Akira Chantrell, Roy Winklhofer, Michael Zimanyi, Gergely T. Schrefl, Thomas Department for Integrated Sensor Systems Danube University Krems Wiener Neustadt2700 Austria Department of Physics University of California DavisCA95616 United States Christian Doppler Laboratory for Magnet Design Through Physics Informed Machine Learning Wiener Neustadt2700 Austria Institute for Analysis and Scientific Computing Vienna University of Technology Wien1040 Austria Toyota Motor Corp. Toyota City471-8572 Japan Department of Physics University of York YorkYO10 5DD United Kingdom Institute for Biology and Environmental Sciences IBU Carl von Ossietzky University of Oldenburg Oldenburg26129 Germany

In this paper, we address the problem that standard stochastic Landau-Lifshitz-Gilbert (sLLG) simulations typically produce results that show unphysical mesh-size dependence. The root cause of this problem is that the effects of spin wave fluctuations are ignored in sLLG. We propose to represent the effect of these fluctuations by a"FUll-Spinwave-Scaled Stochastic LLG", or FUSSS LLG method. In FUSSS LLG, the intrinsic parameters of the sLLG simulations are first scaled by scaling factors that integrate out the spin wave fluctuations up to the mesh size, and the sLLG simulation is then performed with these scaled parameters. We developed FUSSS LLG by studying the Ferromagnetic Resonance (FMR) in Nd2Fe14B cubes. The nominal scaling greatly reduced the mesh size dependence relative to sLLG. We further discovered that adjusting one scaling exponent by less than 10% delivered fully mesh-size-independent results for the FMR peak. We then performed three tests and validations of our FUSSS LLG with this modified scaling. 1) We studied the same FMR but with magnetostatic fields included. 2) We simulated the total magnetization of the Nd2Fe14B cube. 3) We studied the effective, temperature- and sweeping rate-dependent coercive field of the cubes. In all three cases we found that FUSSS LLG delivered essentially mesh-size-independent results, which tracked the theoretical expectations better than unscaled sLLG. Motivated by these successful validations, we propose that FUSSS LLG provides marked, qualitative progress towards accurate, high precision modeling of micromagnetics in hard, permanent magnets. © 2021, CC BY.

关键词： Stochastic systems

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Preface

Lecture Notes of the Institute for Computer Sciences, Social...

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Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST 2020年 328 LNICST卷 v-viii页

作者： Brooks, Anthony Brooks, Eva Irene Department of Architecture Design and Media Technology Aalborg University Aalborg Denmark Department of Culture and Learning Aalborg University Aalborg Denmark

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Using Open Data and Open-Source Software to Develop Spatial Indicators of Urban design and Transport Features for Achieving Healthy and Sustainable Cities

arXiv

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arXiv 2022年

作者： Boeing, Geoff Higgs, Carl Liu, Shiqin Giles-Corti, Billie Sallis, James F. Cerin, Ester Lowe, Melanie Adlakha, Deepti Hinckson, Erica Vernez Moudon, Anne Salvo, Deborah Adams, Marc A. Vizeu Barrozo, Ligia Bozovic, Tamara Delclòs-Alió, Xavier Dygrýn, Jan Ferguson, Sara Gebel, Klaus Phuong Ho, Thanh Lai, Poh-Chin Carles Martori, Joan Nitvimol, Kornsupha Queralt, Ana Roberts, Jennifer D. Sambo, Garba H. Schipperijn, Jasper Vale, David Van de Weghe, Nico Vich, Guillem Arundel, Jonathan Department of Urban Planning and Spatial Analysis Sol Price School of Public Policy University of Southern California Los AngelesCA United States Healthy Liveable Cities Lab RMIT University MelbourneVIC Australia School of Public Policy and Urban Affairs Northeastern University BostonMA United States School of Population Health The University of Western Australia PerthWA Australia Mary MacKillop Institute for Health Research Australian Catholic University MelbourneVIC Australia Herbert Wertheim School of Public Health and Human Longevity Science University of California San DiegoCA United States School of Public Health The University of Hong Kong Hong Kong Department of Geography The University of Hong Kong Hong Kong Melbourne Centre for Cities University of Melbourne MelbourneVIC Australia Transport Health and Urban Design Research Lab Melbourne School of Design University of Melbourne MelbourneVIC Australia Department of Landscape Architecture and Environmental Planning Natural Learning Initiative College of Design North Carolina State University RaleighNC United States Human Potential Centre School of Sport and Recreation Auckland University of Technology Auckland New Zealand Department of Urban Design and Planning Urban Form Lab University of Washington SeattleWA United States Prevention Research Center Brown School Washington University in St Louis St LouisMO United States College of Health Solutions Julie Ann Wrigley Global Futures Laboratory Arizona State University PhoenixAZ United States Department of Geography School of Philosophy Literature and Human Sciences University of São Paulo São Paulo Brazil Institute of Advanced Studies University of São Paulo São Paulo Brazil Barcelona Institute for Global Health Barcelona Spain Department of Geography Rovira i Virgili University Vilaseca Spain Faculty of Physical Culture Palacký University Olomouc Olomouc Czech Republic School of Natural and Built Environment Queen's University

Benchmarking and monitoring urban design and transport features is critical to achieving local and international health and sustainability goals. However, most urban indicator frameworks use coarse spatial scales that only allow between-city comparisons or require expensive, technical, local spatial analyses for within-city comparisons. This study developed a reusable open-source urban indicator computational framework using open data to enable consistent local and global comparative analyses. We demonstrate this framework by calculating spatial indicators-for 25 diverse cities in 19 countries-of urban design and transport features that support health and sustainability. We link these indicators to cities' policy contexts and identify populations living above and below critical thresholds for physical activity through walking. Efforts to broaden participation in crowdsourcing data and to calculate globally consistent indicators are essential for planning evidence-informed urban interventions, monitoring policy impacts, and learning lessons from peer cities to achieve health, equity, and sustainability goals. Copyright © 2022, The Authors. All rights reserved.

关键词： Open Data

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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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