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2020 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2020

作者： Nakamura, Shoichi Kumada, Taiga Onuma, Ryo Nakayama, Hiroki Kaminaga, Hiroaki Miyadera, Youzou Dept. Computer Science and Mathematics Fukushima University Fukushima-shi Fukushima960-1296 Japan Graduate School of Comprehensive Human Sciences University of Tsukuba Tsukuba-shi Ibaraki305-8550 Japan Global Education Center Waseda University Shinjuku-ku Tokyo169-8050 Japan Division of Natural Science Tokyo Gakugei University Koganei-shi Tokyo184-8501 Japan

ISBN: (纸本)9781728192420

Along with the rise of e-learning, the importance of enriching teaching materials has increased. Although there are standards such as SCORM (Sharable Content Object Reference Model), teaching materials has not been distributed or shared as widely as expected. The main factors that hinder the enrichment of teaching material include the large burden put on creators of teaching materials that conform to the standards, and the difficulty for users to find suitable teaching materials. In short, standards for promoting the use of teaching materials have hindered both authors and users of teaching materials. In this research, we aim to develop methods to automatically generate a repository of latent teaching materials on the Web. This paper mainly describes the outline of our methods along with the tidy of problems involved in enriching e-learning contents. This paper also describes the experiment of identifying the teaching materials and the effectiveness of our methods are discussed. © 2020 IEEE.

关键词： Regulatory compliance

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Digital Innovation in Faculty Development: Crafting Academic Portfolios for the Next Era

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Procedia computer science 2024年 246卷 4075-4083页

作者： Hideyuki Kanematsu Akiko Ogawa Takeshi Kogo Risa Kawai Dana M. Barry Nobuyuki Ogawa Shin-nosuke Suzuki Goichi Hagiwara Kayoko Kurita National Institute of Technology (KOSEN) Suzuka College Shiroko-cho Suzuka Mie 510-0294 Dept. Materials & Manufacturing Science Graduate School of Engineering Osaka University 2-1 Yamada-Oka Suita Osaka 565-0871 Japan Faculty of Science Nara Women’s University Uoya Nishimachi Nara 630-8506 Nara Pref. Japan Department of Electrical & Computer Engineering Clarkson University Potsdam/SUNY Canton Canton NY USA National Institute of Technology (KOSEN) Gifu College Kami-makuwa Motosu Gifu 510-0495 Japan National Institute of Technology (KOSEN) 771 Nakakuki Oyama Tochigi 323-0806 Japan Faculty of Human Sciences Kyushu Sangyo University Matsukadai 2-chome Higashi-ku Fukuoka-shi Fukuoka 813-8503 Japan Center for Research and Development in Higher Education the University of Tokyo 7-3-1 Hongo Bunkyo-ku 113-0033m Tokyo Japan

This paper presents one project on integrating digital innovation in faculty development by creating academic portfolios. Academic portfolios document the multifaceted achievements of faculty members and help them in their professional progress and self-evaluation. This initiative adopted a systematic approach following the TU guidelines, incorporating lectures, mentoring, and collaborative sessions, and guiding educators to create portfolios that integrate teaching, research, and service activities. The project’s use of digital tools enabled time efficiency, accessibility, and iterative improvement and improved the transparency and scope of faculty performance. The digital platform also facilitated real-time feedback and integration of the portfolio with a wider digital repository, which aligns with environmentally sustainable practices. Participant feedback highlighted increased motivation and a deeper understanding of the interaction between teaching and research. The project demonstrated that the digital portfolio powerfully aligned teachers’ activities with their professional ethos and promoted continuous development and personal growth goals. This approach suggests the potential for transforming faculty development in the academic community.

关键词： Academic Portfolio faculty development SAP sheet

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[email protected]: Theoretical and Methodological Challenges in Intelligent Conversational User Interface interactions

[email protected]: Theoretical and Methodological Challenges...

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26th International Conference on Intelligent User Interfaces - Companion

作者： Philip R Doyle Daniel John Rough Justin Edwards Benjamin R. Cowan Leigh Clark Martin Porcheron Stephan Schlögl María Inés Torres Cosmin Munteanu Christine Murad Jaisie Sin Minha Lee Matthew Peter Aylett Heloisa Candello School of Information & Communication Studies University College Dublin Ireland School of Science and Engineering University of Dundee United Kingdom Information and Communication Studies University College Dublin Ireland Computer Science Swansea University United Kingdom Dept. Management Communication & IT MCI Management Center Innsbruck Austria Speech Interactive Research Group Universidad del Pais Vasco UPV/EHU Spain Institute for Communication Culture Information and Technology University of Toronto Mississauga Canada University of Toronto Canada Human Technology Interaction Technical University of Eindhoven Netherlands CereProc Ltd. United Kingdom IBM Research Brazil

ISBN: (纸本)9781450380188

This workshop aims to bring together the Intelligent User Interface (IUI) and Conversational User Interface (CUI) communities to understand the theoretical and methodological challenges in designing, deploying and evaluating CUIs. CUIs have continued to prosper with the increased use and technological developments in both text-based chatbots and speech-based systems. However, challenges remain in creating established theoretical and methodological approaches for CUIs, and how these can be used with recent engineering advances. These include assessing the impact of interface design on user behaviours and perceptions, developing design guidelines, understanding the role of personalisation and issues of ethics and privacy. Our half-day multidisciplinary workshop brings together researchers and practitioners from the IUI and CUI communities in academia and industry. We aim to (1) identify and map out key focus areas and research challenges to address these critical theoretical and methodological gaps and (2) foster strong relationships between disciplines within and related to Artificial Intelligence (AI) and human-computer interaction (HCI).

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Evaluating deep transfer learning for whole-brain cognitive decoding

arXiv

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arXiv 2021年

作者： Thomas, Armin W. Lindenberger, Ulman Samek, Wojciech Müller, Klaus-Robert Machine Learning Group Dept. of Computer Science and Electrical Engineering Technische Universität Berlin Berlin Germany Center for Lifespan Psychology Max Planck Institute for Human Development Berlin Germany Stanford Data Science Stanford University StanfordCA United States Dept. of Psychology Stanford University StanfordCA United States Max Planck UCL Centre for Computational Psychiatry and Ageing Research Berlin Germany Dept. of Artificial Intelligence Fraunhofer Heinrich Hertz Institute Berlin Germany BIFOLD – Berlin Institute for the Foundations of Learning and Data Berlin Germany Dept. of Artificial Intelligence Korea University Seoul Korea Republic of Max Planck Institute for Informatics Saarbrücken Germany

Research in many fields has shown that transfer learning (TL) is well-suited to improve the performance of deep learning (DL) models in datasets with small numbers of samples. This empirical success has triggered interest in the application of TL to cognitive decoding analyses with functional neuroimaging data. Here, we systematically evaluate TL for the application of DL models to the decoding of cognitive states (e.g., viewing images of faces or houses) from whole-brain functional Magnetic Resonance Imaging (fMRI) data. We first pre-train two DL architectures on a large, public fMRI dataset and subsequently evaluate their performance in an independent experimental task and a fully independent dataset. The pre-trained models consistently achieve higher decoding accuracies and generally require less training time and data than model variants that were not pre-trained, clearly underlining the benefits of pre-training. We demonstrate that these benefits arise from the ability of the pre-trained models to reuse many of their learned features when training with new data, providing deeper insights into the mechanisms giving rise to the benefits of pre-training. Yet, we also surface nuanced challenges for whole-brain cognitive decoding with DL models when interpreting the decoding decisions of the pre-trained models, as these have learned to utilize the fMRI data in unforeseen and counterintuitive ways to identify individual cognitive states. © 2021, CC BY.

关键词： Decoding

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Supporting Image Geolocation with Diagramming and Crowdsourcing 5

Supporting Image Geolocation with Diagramming and Crowdsourc...

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5th AAAI Conference on human Computation and Crowdsourcing, HCOMP 2017

作者： Kohler, Rachel Purviance, John Luther, Kurt Dept. of Computer Science Center for Human-Computer Interaction Virginia Tech BlacksburgVA United States

ISBN: (纸本)9781577357933

Geolocation, the process of identifying the precise location in the world where a photo or video was taken, is central to many types of investigative work, from debunking fake news posted on social media to locating terrorist training camps. Professional geolocation is often a manual, timeconsuming process that involves searching large areas of satellite imagery for potential matches. In this paper, we explore how crowdsourcing can be used to support expert image geolocation. We adapt an expert diagramming technique to overcome spatial reasoning limitations of novice crowds, allowing them to support an expert's search. In two experiments (n=1080), we found that diagrams work significantly better than ground-level photos and allow crowds to reduce a search area by half before any expert intervention. We also discuss hybrid approaches to complex image analysis combining crowds, experts, and computer vision. Copyright © 2017, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Crowdsourcing

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Second workshop on trust in the age of automated driving 18

Second workshop on trust in the age of automated driving

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10th ACM International Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2018

作者： Wintersberger, Philipp Noah, Brittany E. Kraus, Johannes McCall, Roderick Mirnig, Alexander G. Kunze, Alexander Thakkar, Shailie Walker, Bruce N. Ingolstadt Germany Georgia Institute of Technology Atlanta United States Ulm University Ulm United States Environmental Research and Innovation Dept. Luxembourg Institute of Science and Technology Luxembourg Center for Human-Computer Interaction University of Salzburg Austria Loughborough Design School Loughborough University United Kingdom Mapbox San Francisco United States

ISBN: (纸本)9781450359474

This workshop addresses key trust-related issues in the context of automated driving and aims at establishing a common ground for future research. Building on the outcome of the previous workshop at AutoUI 2017, three main aspects are targeted within interactive sessions: (1) Formulation of a comprehensive set of definitions for trust in automated systems;(2) Development of interface approaches for mitigating overtrust and undertrust issues;(3) Identification of an appropriate timing of trust-related cues. Thereby, the current research efforts of both workshop organizers and participants are used as a starting point for several breakout sessions, each addressing one of the three main workshop goals. The outcome of this workshop will provide a benchmark for future work in the field and is also intended to inspire joint publications among the participants. © 2018 ACM.

关键词： Automation

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Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 3 — Ex vivo imaging: data processing, comparisons with microscopy, and tractography

arXiv

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arXiv 2024年

作者： Schilling, Kurt G. Howard, Amy F.D. Grussu, Francesco Ianus, Andrada Hansen, Brian Barrett, Rachel L.C. Aggarwal, Manisha Michielse, Stijn Nasrallah, Fatima Syeda, Warda Wang, Nian Veraart, Jelle Roebroeck, Alard Bagdasarian, Andrew F. Eichner, Cornelius Sepehrband, Farshid Zimmermann, Jan Soustelle, Lucas Bowman, Christien Tendler, Benjamin C. Hertanu, Andreea Jeurissen, Ben Verhoye, Marleen Frydman, Lucio van de Looij, Yohan Hike, David Dunn, Jeff F. Miller, Karla Landman, Bennett A. Shemesh, Noam Anderson, Adam McKinnon, Emilie Farquharson, Shawna Dell' Acqua, Flavio Pierpaoli, Carlo Drobnjak, Ivana Leemans, Alexander Harkins, Kevin D. Descoteaux, Maxime Xu, Duan Huang, Hao Santin, Mathieu D. Grant, Samuel C. Obenaus, Andre Kim, Gene S. Wu, Dan Le Bihan, Denis Blackband, Stephen J. Ciobanu, Luisa Fieremans, Els Bai, Ruiliang Leergaard, Trygve B. Zhang, Jiangyang Dyrby, Tim B. Johnson, G. Allan Cohen-Adad, Julien Budde, Matthew D. Jelescu, Ileana O. Radiology and Radiological Sciences Vanderbilt University Medical Center NashvilleTN United States Vanderbilt University Institute of Imaging Science Vanderbilt University NashvilleTN United States Department of Bioengineering Imperial College London London United Kingdom FMRIB Centre Wellcome Centre for Integrative Neuroimaging Nuffield Department of Clinical Neurosciences University of Oxford Oxford United Kingdom Radiomics Group Vall d’Hebron Institute of Oncology Vall d’Hebron Barcelona Hospital Campus Barcelona Spain Queen Square MS Centre Queen Square Institute of Neurology Faculty of Brain Sciences University College London London United Kingdom Champalimaud Research Champalimaud Foundation Lisbon Portugal Center of Functionally Integrative Neuroscience Aarhus University Aarhus Denmark Department of Neuroimaging Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom NatBrainLab Department of Forensics and Neurodevelopmental Sciences Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins University School of Medicine BaltimoreMD United States Maastricht University Medical Center Maastricht Netherlands The Queensland Brain Institute The University of Queensland Queensland Australia Melbourne Neuropsychiatry Centre The University of Melbourne ParkvilleVIC Australia Department of Radiology and Imaging Sciences Indiana University IN United States Stark Neurosciences Research Institute Indiana University School of Medicine IN United States Center for Biomedical Imaging NYU Grossman School of Medicine New YorkNY United States Faculty of psychology and Neuroscience Maastricht University Maastricht Netherlands Department of Chemical & Biomedical Engineering FAMU-FSU College of Engineering Florida State University TallahasseeFL United States Center for Interdisciplinary Magnetic Res

Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high signal-to-noise ratio (SNR) images, cutting-edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a 3-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing and model fitting, and tractography. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing, and point towards open-source software and databases specific to small animal and ex vivo imaging. © 2024, CC BY.

关键词： Data Sharing

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OmniTrack: A flexible self-tracking approach leveraging semi-automated tracking

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Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2017年第3期1卷 1-28页

作者： Kim, Young-Ho Jeon, Jae Ho Lee, Bongshin Choe, Eun Kyoung Seo, Jinwook Human-Computer Interaction Lab Dept. of Computer Science and Engineering Seoul National University Seoul Korea Republic of Kakao Corporation Seongnam Gyeonggi-do Korea Republic of Microsoft Research RedmondWA United States Human-Computer Interaction Lab College of Information Studies University of Maryland College ParkMD United States

We now see an increasing number of self-tracking apps and wearable devices. Despite the vast number of available tools, however, it is still challenging for self-trackers to find apps that suit their unique tracking needs, preferences, and commitments. Furthermore, people are bounded by the tracking tools' initial design because it is difficult to modify, extend, or mash up existing tools. In this paper, we present OmniTrack, a mobile self-tracking system, which enables self-trackers to construct their own trackers and customize tracking items to meet their individual tracking needs. To inform the OmniTrack design, we first conducted semi-structured interviews (N = 12) and analyzed existing mobile tracking apps (N = 62). We then designed and developed OmniTrack as an Android mobile app, leveraging a semi-automated tracking approach that combines manual and automated tracking methods. We evaluated OmniTrack through a usability study (N = 10) and improved its interfaces based on the feedback. Finally, we conducted a 3-week deployment study (N = 21) to assess if people can capitalize on OmniTrack's flexible and customizable design to meet their tracking needs. From the study, we showed how participants used OmniTrack to create, revise, and appropriate trackers-ranging from a simple mood tracker to a sophisticated daily activity tracker. We discuss how OmniTrack positively influences and supports self-trackers' tracking practices over time, and how to further improve OmniTrack by providing more appropriate visualizations and sharable templates, incorporating external contexts, and supporting researchers' unique data collection needs. © 2017 Copyright held by the owner/author(s). Publication rights licensed to Association for Computing Machinery.

关键词： Automation

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Spatial referring expression generation for HRI: Algorithms and evaluation framework

Spatial referring expression generation for HRI: Algorithms ...

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2017 AAAI Fall Symposium

作者： Kunze, Lars Hawes, Nick Williams, Tom Scheutz, Matthias Oxford Robotics Institute Dept. of Engineering Science University of Oxford United Kingdom Intelligent Robotics Lab School of Computer Science University of Birmingham United Kingdom Human-Robot Interaction Lab Dept. of Computer Science Tufts University United States

ISBN: (纸本)9781577357940

The ability to refer to entities such as objects, locations, and people is an important capability for robots designed to interact with humans. For example, a referring expression (RE) such as "Do you mean the box on the left?" might be used by a robot seeking to disambiguate between objects. In this paper, we present and evaluate algorithms for Referring Expression Generation (REG) in small-scale situated contexts. We first present data regarding how humans generate smallscale spatial referring expressions (REs). We then use this data to define five categories of observed small-scale spatial REs, and use these categories to create an ensemble of REG algorithms. Next, we evaluate REs generated by those algorithms and by humans both subjectively (by having participants rank REs), and objectively, (by assessing task performance when participants use REs) through a set of interrelated crowdsourced experiments. While our machine generated REs were subjectively rated lower than those generated by humans, they objectively significantly outperformed human REs. Finally, we discuss the main contributions of this work: (1) a dataset of images and REs, (2) a categorization of observed small-scale spatial REs, (3) an ensemble of REG algorithms, and (4) a crowdsourcing-based framework for subjectively and objectively evaluating REG. Copyright © 2017, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： human robot interaction

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Considerations and Recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 1 — In vivo small-animal imaging

arXiv

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arXiv 2022年

作者： Jelescu, Ileana O. Grussu, Francesco Ianus, Andrada Hansen, Brian Barrett, Rachel L.C. Aggarwal, Manisha Michielse, Stijn Nasrallah, Fatima Syeda, Warda Wang, Nian Veraart, Jelle Roebroeck, Alard Bagdasarian, Andrew F. Eichner, Cornelius Sepehrband, Farshid Zimmermann, Jan Soustelle, Lucas Bowman, Christien Tendler, Benjamin C. Hertanu, Andreea Jeurissen, Ben Verhoye, Marleen Frydman, Lucio de Looij, Yohan van Hike, David Dunn, Jeff F. Miller, Karla Landman, Bennett A. Shemesh, Noam Anderson, Adam McKinnon, Emilie Farquharson, Shawna Acqua, Flavio Dell Pierpaoli, Carlo Drobnjak, Ivana Leemans, Alexander Harkins, Kevin D. Descoteaux, Maxime Xu, Duan Huang, Hao Santin, Mathieu D. Grant, Samuel C. Obenaus, Andre Kim, Gene S. Wu, Dan Le Bihan, Denis Blackband, Stephen J. Ciobanu, Luisa Fieremans, Els Bai, Ruiliang Leergaard, Trygve B. Zhang, Jiangyang Dyrby, Tim B. Johnson, G. Allan Cohen-Adad, Julien Budde, Matthew D. Schilling, Kurt G. Department of Radiology Lausanne University Hospital University of Lausanne Lausanne Switzerland CIBM Center for Biomedical Imaging Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland Radiomics Group Vall d’Hebron Institute of Oncology Vall d’Hebron Barcelona Hospital Campus Barcelona Spain Queen Square MS Centre Queen Square Institute of Neurology Faculty of Brain Sciences University College London London United Kingdom Champalimaud Research Champalimaud Foundation Lisbon Portugal Center of Functionally Integrative Neuroscience Aarhus University Aarhus Denmark Department of Neuroimaging Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom NatBrainLab Department of Forensics and Neurodevelopmental Sciences Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins University School of Medicine BaltimoreMD United States Maastricht University Medical Center Maastricht Netherlands The Queensland Brain Institute The University of Queensland Queensland Australia Melbourne Neuropsychiatry Centre The University of Melbourne ParkvilleVIC Australia Department of Radiology and Imaging Sciences Indiana University IN United States Stark Neurosciences Research Institute Indiana University School of Medicine IN United States Center for Biomedical Imaging NYU Grossman School of Medicine New YorkNY United States Faculty of psychology and Neuroscience Maastricht University Maastricht Netherlands Department of Chemical & Biomedical Engineering FAMU-FSU College of Engineering Florida State University TallahasseeFL United States Center for Interdisciplinary Magnetic Resonance National HIgh Magnetic Field Laboratory TallahasseeFL United States Department of Neuropsychology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany USC Stevens Neuroimaging and Informatics Instit

Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected recommendations and guidelines from the diffusion community, on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We then give guidelines for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including pre-processing, model-fitting, and tractography. Finally, we provide an online resource which lists publicly available preclinical dMRI datasets and software packages, to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. While we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal herein is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge. © 2022, CC BY-NC-ND.

关键词： Invertebrates

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