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arXiv 2018年

作者： Hu, BingZhang Gao, Yan Guan, Yu Long, Yang Lane, Nicholas Ploetz, Thomas Open Lab School of Computing Newcastle University Newcastle Upon Tyne United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Interactive Computing Georgia Institute of Technology Atlanta United States School of Computing Sciences University of East Anglia Norwich United Kingdom

Gait is an important biometric trait for surveillance and forensic applications, which can be used to identify individuals at a large distance through CCTV cameras. However, it is very difficult to develop robust automated gait recognition systems, since gait may be affected by many covariate factors such as clothing, walking surface, walking speed, camera view angle, etc. Out of them, large view angle was deemed as the most challenging factor since it may alter the overall gait appearance substantially. Recently, some deep learning approaches (such as CNNs) have been employed to extract view-invariant features, and achieved encouraging results on small data sets. However, they do not scale well to large dataset, and the performance decreases significantly w.r.t. number of subjects, which is impractical to large-scale surveillance applications. To address this issue, in this work we propose a Discriminant Gait Generative Adversarial Network (DiGGAN) framework, which not only can learn view-invariant gait features for cross-view gait recognition tasks, but also can be used to reconstruct the gait templates in all views — serving as important evidences for forensic applications. We evaluated our DiGGAN framework on the world’s largest multi-view OU-MVLP dataset (which includes more than 10000 subjects), and our method outperforms state-of-the-art algorithms significantly on various cross-view gait identification scenarios (e.g., cooperative/uncooperative mode). Our DiGGAN framework also has the best results on the popular CASIA-B dataset, and it shows great generalisation capability across different datasets. Copyright © 2018, The Authors. All rights reserved.

关键词： Gait analysis

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Quantum Games and interactive Tools for Quantum Technologies Outreach and Education

arXiv

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arXiv 2022年

作者： Seskir, Zeki C. Migdal, Piotr Weidner, Carrie Anupam, Aditya Case, Nicky Davis, Noah Decaroli, Chiara Ercan, İlke Foti, Caterina Gora, Pawel Jankiewicz, Klementyna La Cour, Brian R. Malo, Jorge Yago Maniscalco, Sabrina Naeemi, Azad Nita, Laurentiu Parvin, Nassim Scafirimuto, Fabio Sherson, Jacob F. Surer, Elif Wootton, James Yeh, Lia Zabello, Olga Chiofalo, Marilù Karlsruhe Institute of Technology ITAS Karlstraße 11 Karlsruhe76133 Germany Quantum Flytrap ul. Ceglowska 29 Warsaw01-809 Poland Quantum Engineering Technology Laboratories H.H. Wills Physics Laboratory Department of Electrical and Electronic Engineering University of Bristol BristolBS8 1FD United Kingdom Georgia Institute of Technology School of Literature Media and Communication 686 Cherry St NW AtlantaGA30313 United States The University of Texas at Austin Center for Quantum Research Applied Research Laboratories 10000 Burnet Rd AustinTX78758 United States National Quantum Computing Center Oxfordshire DidcotOX11 0GD United Kingdom TU Delft Department of Microelectronics Mekelweg 4 Delft2628 CD Netherlands QPlayLearn Aalto University InstituteQ - The Finnish Quantum Institute Department of Applied Physics AaltoFI-00076 11000 Finland Quantum AI Foundation Sanocka 9/103 Warsaw02-110 Poland University of Pisa Department of Physics Largo Bruno Pontecorvo 3 PisaI-56126 Italy University of Helsinki InstituteQ - The Finnish Quantum Institute Department of Physics Helsinki Finland Georgia Institute of Technology School of Electrical and Computer Engineering 777 Atlantic Drive NW AtlantaGA30332 United States Quarks Interactive Strada Bailor 93a Miercurea-Ciuc Harghita4500 Romania IBM Quantum IBM Research - Zurich Säumerstrasse 4 Rüschlikon8803 Switzerland ScienceAtHome Fuglesangs Allé 4 Aarhus8210 Denmark Aarhus University Institute for Physics and Astronomy Ny Munkegade 120 Aarhus8000 Denmark Middle East Technical University Graduate School of Informatics Department of Modeling and Simulation Üniversiteler Mah. Dumlupınar Blv. No:1 Ankara06800 Turkey University of Oxford Department of Computer Science Wolfson Building Parks Road OxfordOX1 3QD United Kingdom IBM Quantum IBM T. J. Watson Research Center Yorktown HeightsNY10598 United States Offenburg University of Applied Sciences Department of Business and Industrial Eng

In this article, we provide an extensive overview of a wide range of quantum games and interactive tools that have been employed by the community in recent years. The paper presents selected tools, as described by their developers. The list includes Hello Quantum, Hello Qiskit, Particle in a Box, Psi and Delta, QPlayLearn, Virtual Lab by Quantum Flytrap, Quantum Odyssey, scienceAtHome, and The Virtual Quantum Optics Laboratory. Additionally, we present events for quantum game development: hackathons, game jams, and semester projects. Furthermore, we discuss the Quantum Technologies Education for Everyone (QUTE4E) pilot project, which illustrates an effective integration of these interactive tools with quantum outreach and education activities. Finally, we aim at providing guidelines for incorporating quantum games and interactive tools in pedagogic materials to make quantum technologies more accessible for a wider population. Copyright © 2022, The Authors. All rights reserved.

关键词： Quantum optics

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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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Supporting team-first visual analytics through group activity representations 43

Supporting team-first visual analytics through group activit...

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43rd Graphics Interface, GI 2017

作者： Badam, Sriram Karthik Zeng, Zehua Wall, Emily Endert, Alex Elmqvist, Niklas Department of Computer Science University of Maryland College ParkMD United States College of Information Studies University of Maryland College ParkMD United States School of Computer Science Georgia Institute of Technology AtlantaGA United States School of Interactive Computing Georgia Institute of Technology AtlantaGA United States

ISBN: (纸本)9780994786821

Collaborative visual analytics (CVA) involves sensemaking activities within teams of analysts based on coordination of work across team members, awareness of team activity, and communication of hypotheses, observations, and insights. We introduce a new type of CVA tools based on the notion of "team-first" visual analytics, where supporting the analytical process and needs of the entire team is the primary focus of the graphical user interface before that of the individual analysts. To this end, we present the design space and guidelines for team-first tools in terms of conveying analyst presence, focus, and activity within the interface. We then introduce InsightsDrive, a CVA tool for multidimensional data, that contains team-first features into the interface through group activity visualizations. This includes (1) in-situ representations that show the focus regions of all users integrated in the data visualizations themselves using color-coded selection shadows, as well as (2) ex-situ representations showing the data coverage of each analyst using multidimensional visual representations. We conducted two user studies, one with individual analysts to identify the affordances of different visual representations to inform data coverage, and the other to evaluate the performance of our team-first design with exsitu and in-situ awareness for visual analytic tasks. Our results give an understanding of the performance of our team-first features and unravel their advantages for team coordination. © 2017 Canadian Information Processing Society. All rights reserved.

关键词： Graphical user interfaces

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Teaching computational creativity 8

Teaching computational creativity

引用

8th International Conference on Computational Creativity, ICCC 2017

作者： Ackerman, Margareta Goel, Ashok Johnson, Colin G. Jordanous, Anna León, Carlos Pérez y Pérez, Rafael Toivonen, Hannu Ventura, Dan Computer Engineering Department Santa Clara University United States School of Interactive Computing Georgia Tech United States School of Computing University of Kent United Kingdom Department of Software Engineering and Artificial Intelligence Complutense University of Madrid Spain Depto. de Tecnologías de la Información Universidad Autónoma Metropolitana Mexico Department of Computer Science University of Helsinki Finland Computer Science Department Brigham Young University United States

ISBN: (纸本)9780692895641

The increasing popularity of computational creativity (CC) in recent years gives rise to the need for educational resources. This paper presents several modules that together act as a guide for developing new CC courses as well as improving existing curricula. As well as introducing core CC concepts, we address pedagogical approaches to this interdisciplinary subject. An accessible overview of the field lets this paper double as an introductory tutorial to computational creativity. © ICCC 2017.

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Enter the matrix: Safely interruptible autonomous systems via virtualization

arXiv

引用

arXiv 2017年

作者： Riedl, Mark O. Harrison, Brent School of Interactive Computing Georgia Institute of Technology United States Department of Computer Science University of Kentucky United States

Autonomous systems that operate around humans will likely always rely on kill switches that stop their execution and allow them to be remote-controlled for the safety of humans or to prevent damage to the system. It is theoretically possible for an autonomous system with sufficient sensor and effector capability that learn online using reinforcement learning to discover that the kill switch deprives it of long-term reward and thus learn to disable the switch or otherwise prevent a human operator from using the switch. This is referred to as the big red button problem. We present a technique that prevents a reinforcement learning agent from learning to disable the kill switch. We introduce an interruption process in which the agent's sensors and effectors are redirected to a virtual simulation where it continues to believe it is receiving reward. We illustrate our technique in a simple grid world environment. Copyright © 2017, The Authors. All rights reserved.

关键词： Remote control

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Atention transfer from web images for video recognition 25

Atention transfer from web images for video recognition

引用

25th ACM International Conference on Multimedia, MM 2017

作者： Li, Junnan Wong, Yongkang Zhao, Qi Kankanhalli, Mohan S. NUS Graduate School for Integrative Sciences and Engineering National University of Singapore Singapore Interactive and Digital Media Institute National University of Singapore Singapore Department of Computer Science and Engineering University of Minnesota United States School of Computing National University of Singapore Singapore

ISBN: (纸本)9781450349062

Training deep learning based video classifiers for action recognition requires a large amount of labeled videos. The labeling process is labor-intensive and time-consuming. On the other hand, large amount of weakly-labeled images are uploaded to the Internet by users everyday. To harness the rich and highly diverse set of Web images, a scalable approach is to crawl these images to train deep learning based classifier, such as Convolutional Neural Networks (CNN). However, due to the domain shift problem, the performance of Web images trained deep classifiers tend to degrade when directly deployed to videos. One way to address this problem is to fine-tune the trained models on videos, but sufficient amount of annotated videos are still required. In this work, we propose a novel approach to transfer knowledge from image domain to video domain. The proposed method can adapt to the target domain (i.e. video data) with limited amount of training data. Our method maps the video frames into a low-dimensional feature space using the class-discriminative spatial attention map for CNNs. We design a novel Siamese EnergyNet structure to learn energy functions on the attention maps by jointly optimizing two loss functions, such that the attention map corresponding to a ground truth concept would have higher energy. We conduct extensive experiments on two challenging video recognition datasets (i.e. TVHI and UCF101), and demonstrate the efficacy of our proposed method. © 2017 Association for computing Machinery.

关键词： Neural networks

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Data-Driven Traditional Chinese Medicine Clinical Herb Modeling and Herb Pair Recommendation

Data-Driven Traditional Chinese Medicine Clinical Herb Model...

引用

International Conference on Digital Home (ICDH)

作者： Gansen Zhao Xutian Zhuang Xinming Wang Weimin Ning Zijing Li Jianfei Wang Qiang Chen Zefeng Mo Bingchuan Chen Huiyan Chen School of Computer Science Guangzhou Key Laboratory of Cloud Computing Security and Assessment Technology Dongguan Hospital of Traditional Chinese Medicine Guangdong University of Education School of Mathematical Sciences South China Normal University School of Statistics and Mathematics Guangdong University of Finance and Economics Guangzhou Flaming Interactive Information Technology Co. LTD Guangdong China

As an important branch of medical field, Traditional Chinese Medicine(TCM) continues to be explored in data mining research. Taking advantage of machine learning models and deep learning methods, researchers dive into symptom analysis, disease prediction and medicine law. The combination of TCM herbs is the essential basis for compatibility of clinical prescriptions and its research has attracted plenty of attention. However, literature on herb recommendation for clinical diagnosis, to our best knowledge, is slightly lacking. The clinical herbs collocation will be chosen by doctors in consideration of not only the characteristics and pharmacodynamics of the herbs, but also the mutual effects formed with other herbs. Based on the real clinical prescription data, this paper constructs an analytical model to represent the relationship between prescription herbs and syndromes, and develops herb recommendation model. Firstly, by constructing a modeling process based on the LDA topic model, this paper shows the analysis model and presentation method for prescription herbs. Then, based on the mentioned modeling, we propose a double-end fusion recommendation framework, including methods of adjusting weight proportion and similarity remapping. This research conducts experiments on relevant outpatient medical record data, which confirm that the proposed model can reflect the basic principles of herb combination in clinical diagnosis and the proposed fusion recommendation model has good performance in evaluation metrics.

关键词： Data models Medical diagnostic imaging Analytical models Hidden Markov models Diseases Data mining

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Guiding reinforcement learning exploration using natural language

arXiv

引用

arXiv 2017年

作者： Harrison, Brent Ehsan, Upol Riedl, Mark O. Department of Computer Science University of Kentucky Lexington KY United States School of Interactive Computing Georgia Institute of Technology AtlantaGA United States

In this work we present a technique to use natural language to help reinforcement learning generalize to unseen environments. This technique uses neural machine translation, specifically the use of encoder-decoder networks, to learn associations between natural language behavior descriptions and state-action information. We then use this learned model to guide agent exploration using a modified version of policy shaping to make it more effective at learning in unseen environments. We evaluate this technique using the popular arcade game, Frogger, under ideal and non-ideal conditions. This evaluation shows that our modified policy shaping algorithm improves over a Q-learning agent as well as a baseline version of policy shaping. Copyright © 2017, The Authors. All rights reserved.

关键词： Reinforcement learning

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Confronting autism in urban bangladesh: Unpacking infrastructural and cultural challenges

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EAI Endorsed Transactions on Pervasive Health and Technology 2018年第14期4卷

作者： Ehsan, Upol Sakib, Nazmus Haque, Md Munirul Soron, Tanjir Saxena, Devansh Ahamed, Sheikh Iqbal Schwichtenberg, Amy Rabbani, Golam Akter, Shaheen Alam, Faruq Begum, Azima Ahmed, Syed Ishtiaque School of Interactive Computing Georgia Institute of Technology AtlantaGA30332 United States Department of MSCS Marquette University MilwaukeeWI53233 United States Regenstrief Center for Healthcare Engineering Purdue University West LafayetteIN United States Neurodevelopmental Disability Protection Trust Bangladesh Institute of Pediatric Neurodisorder and Autism Bangladesh National Institute of Mental Health Dhaka Bangladesh Dhaka Medical College and Hospital Dhaka Bangladesh Department of Computer Science University of Toronto Toronto Canada

Autism Spectrum Disorder (ASD) is a critical problem worldwide;however, low and middle-income countries (LMICs) often suffer more from it due to the lack of contextual research and effective care infrastructure. Moreover, ASD in LMICs offers unique challenges as cultural misperceptions and social practices often impede effective care there. However, the recent growth in mobile phone adoption in many LMICs has created a timely opportunity for improving ASD care practices through digital means. We present qualitative findings on the challenges of designing mobile assistive technologies for ASD in Bangladesh. Our data reveals a set of technical, social, and cultural challenges with caregivers and care professionals for designing a meaningful technology for them. We show how those challenges are deeply rooted in the broader infrastructural and cultural tensions of the country. We also present embedded design implications to overcome these challenges. © 2018 Upol Ehsan et al., licensed to EAI.

关键词： mHealth

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