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arXiv 2020年

作者： Khan, Wasiq Hussain, Abir Khan, Sohail Ahmed Al-Jumailey, Mohammed Nawaz, Raheel Liatsis, Panos Department of Computing and Mathematics Liverpool John Moores University LiverpoolL33AF United Kingdom Department of Computer Science DeepCamera Research Lab Interactive Media Smart System Emerging Technologies Center Nicosia Cyprus The Regenerative Clinic Queen Anne Medical Centre Harley Street Medical Area London United Kingdom Department of Computing and Mathematics Manchester Metropolitan University ManchesterM156BH United Kingdom Department of Electrical Engineering and Computer Science Khalifa University PO Box 127788 Abu Dhabi United Arab Emirates

Since the coronavirus disease (COVID-19) outbreak in December 2019, studies have been addressing diverse aspects in relation to COVID-19 and Variant of Concern 202012/01 (VOC 202012/01) such as potential symptoms and predictive tools. However, limited work has been performed towards the modelling of complex associations between the combined demographic attributes and varying nature of the COVID-19 infections across the globe. This study presents an intelligent approach to investigate the multi-dimensional associations between demographic attributes and COVID-19 global variations. We gather multiple demographic attributes and COVID-19 infection data (by 8 January 2021) from reliable sources, which are then processed by intelligent algorithms to identify the significant associations and patterns within the data. Statistical results and experts’ reports indicate strong associations between COVID-19 severity levels across the globe and certain demographic attributes, e.g. female smokers, when combined together with other attributes. The outcomes will aid the understanding of the dynamics of disease spread and its progression, which in turn may support policy makers, medical specialists and society, in better understanding and effective management of the disease. © 2020, CC BY.

关键词： Population statistics

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An Online Resource Scheduling for Maximizing Quality-of-Experience in Meta Computing

arXiv

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arXiv 2023年

作者： Li, Yandi Guo, Jianxiong Li, Yupeng Wang, Tian Jia, Weijia The Guangdong Key Lab of AI and Multi-Modal Data Processing Department of Computer Science BNU-HKBU United International College Zhuhai519087 China The Advanced Institute of Natural Sciences Beijing Normal University Zhuhai519087 China The Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai519087 China The Department of Interactive Media Hong Kong Baptist University Hong Kong

Meta Computing is a new computing paradigm, which aims to solve the problem of computing islands in current edge computing paradigms and integrate all the resources on a network by incorporating cloud, edge, and particularly terminal-end devices. It throws light on solving the problem of lacking computing power. However, at this stage, due to technical limitations, it is impossible to integrate the resources of the whole network. Thus, we create a new meta computing architecture composed of multiple meta computers, each of which integrates the resources in a small-scale network. To make meta computing widely applied in society, the service quality and user experience of meta computing cannot be ignored. Consider a meta computing system providing services for users by scheduling meta computers, how to choose from multiple meta computers to achieve maximum Quality-of-Experience (QoE) with limited budgets especially when the true expected QoE of each meta computer is not known as a priori? The existing studies, however, usually ignore the costs and budgets and barely consider the ubiquitous law of diminishing marginal utility. In this paper, we formulate a resource scheduling problem from the perspective of the multi-armed bandit (MAB). To determine a scheduling strategy that can maximize the total QoE utility under a limited budget, we propose an upper confidence bound (UCB) based algorithm and model the utility of service by using a concave function of total QoE to characterize the marginal utility in the real world. We theoretically upper bound the regret of our proposed algorithm with sublinear growth to the budget. Finally, extensive experiments are conducted, and the results indicate the correctness and effectiveness of our algorithm. Copyright © 2023, The Authors. All rights reserved.

关键词： Budget control

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Adversarial Bandits with Multi-User Delayed Feedback: Theory and Application

arXiv

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arXiv 2023年

作者： Li, Yandi Guo, Jianxiong Li, Yupeng Wang, Tian Jia, Weijia Hong Kong Baptist University Hong Kong Guangdong Key Lab of AI and Multi-Modal Data Processing Department of Computer Science BNU-HKBU United International College Zhuhai519087 China Advanced Institute of Natural Sciences Beijing Normal University Zhuhai519087 China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai519087 China Department of Interactive Media Hong Kong Baptist University Hong Kong

The multi-armed bandit (MAB) models have attracted significant research attention due to their applicability and effectiveness in various real-world scenarios such as resource allocation, online advertising, and dynamic pricing. As an important branch, the adversarial MAB problems with delayed feedback have been proposed and studied by many researchers recently where a conceptual adversary strategically selects the reward distributions associated with each arm to challenge the learning algorithm and the agent experiences a delay between taking an action and receiving the corresponding reward feedback. However, the existing models restrict the feedback to be generated from only one user, which makes models inapplicable to the prevailing scenarios of multiple users (e.g. ad recommendation for a group of users). In this paper, we consider that the delayed feedback results are from multiple users and are unrestricted on internal distribution. In contrast, the feedback delay is arbitrary and unknown to the player in advance. Also, for different users in a round, the delays in feedback have no assumption of latent correlation. Thus, we formulate an adversarial MAB problem with multi-user delayed feedback and design a modified EXP3 algorithm MUD-EXP3, which makes a decision at each round by considering the importance-weighted estimator of the received feedback from different users. On the premise of known terminal round index T, the number of users M, the number of arms N, and upper bound of delay dmax, we prove a regret of O(pTM2 ln N(Ne + 4dmax)). Furthermore, for the more common case of unknown T, an adaptive algorithm AMUD-EXP3 is proposed with a sublinear regret with respect to T. Finally, extensive experiments are conducted to indicate the correctness and effectiveness of our algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Adaptive algorithms

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Proposal and evaluation of clustering method for loci of HRTFs in an orthogonal basis space

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Kyokai Joho Imeji Zasshi/Journal of the Institute of Image Information and Television Engineers 2002年第11期56卷 1759-1768页

作者： Yamamoto, Ken-Ichiro Mochizuki, Kenji Naemura, Takeshi Saito, Takahiro Harashima, Hiroshi Department of Info. and Commun. Eng. University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-8656 Japan Multi-media Ambiance Commun. Project TAO 1-33-16 Hakusan Bunkyo-ku Tokyo 113-0001 Japan Graphics Lab. Stanford University Gates Computer Science Build. 3B-390 Stanford CA 94305-9035 United States Faculty of Engineering Kanagawa University 3-27-1 Rokkakubashi Kanagawa-ku Yokohama 221-8686 Japan

The head related transfer function (HRTF) method of providing spatial sound images has not been capable of dealing with individual differences between human listeners yet. The HRTF varies from person to person and with the direction of the sound source. We focus on this directional variation of the individual HRTF. Principal component analysis is applied to draw the loci of the variation in an orthogonal basis space. Each locus corresponds to a single individual HRTF, and the loci are utilized for clustering the individual HRTF. Some typical ones can be extracted from clusters;we show that they are useful in compensating for the individual differences found in HRTFs.

关键词： Acoustics

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Next Steps for Human-computer Integration 20

Next Steps for Human-Computer Integration

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2020 ACM CHI Conference on Human Factors in Computing Systems, CHI 2020

作者： Mueller, Florian Floyd Lopes, Pedro Strohmeier, Paul Ju, Wendy Seim, Caitlyn Weigel, Martin Nanayakkara, Suranga Obrist, Marianna Li, Zhuying Delfa, Joseph Nishida, Jun Gerber, Elizabeth M. Svanaes, Dag Grudin, Jonathan Greuter, Stefan Kunze, Kai Erickson, Thomas Greenspan, Steven Inami, Masahiko Marshall, Joe Reiterer, Harald Wolf, Katrin Meyer, Jochen Schiphorst, Thecla Wang, Dakuo Maes, Pattie Exertion Games Lab Monash University Melbourne Australia University of Chicago Chicago United States University of Copenhagen Copenhagen Denmark and Saarland University Saarbrucken Germany Cornell Tech New York United States Stanford University Stanford United States Honda Research Institute Europe Offenbach Germany Augmented Human Lab University of Auckland Auckland New Zealand Schi Lab University of Sussex Brighton United Kingdom Northwestern University EvanstonIL United States Department of Computer Science Ntnu Trondheim Norway and It University of Copenhagen Denmark Microsoft RedmondWA United States Deakin University Melbourne VIC Australia KMD Keio University Tokyo Japan Independent Researcher MinneapolisMN United States Strategic Research Ca Technologies PittsburghPA United States University of Tokyo Tokyo Japan Mixed Reality Lab University of Nottingham Nottingham United Kingdom University of Konstanz Konstanz Germany Beuth University of Applied Sciences Berlin Berlin Germany OFFIS-Institute for Information Technology Oldenburg Germany School of Interactive Arts Simon Fraser University Vancouver Canada Ibm Research Cambridge United States Mit Media Lab CambridgeMA United States

ISBN: (纸本)9781450367080

Human-computer Integration (HInt) is an emerging paradigm in which computational and human systems are closely interwoven. Integrating computers with the human body is not new. however, we believe that with rapid technological advancements, increasing real-world deployments, and growing ethical and societal implications, it is critical to identify an agenda for future research. We present a set of challenges for HInt research, formulated over the course of a five-day workshop consisting of 29 experts who have designed, deployed and studied HInt systems. This agenda aims to guide researchers in a structured way towards a more coordinated and conscientious future of human-computer integration. © 2020 ACM.

关键词： Integration

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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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