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arXiv 2021年

作者： Maher, Kevin Huang, Zeyuan Song, Jiancheng Deng, Xiaoming Lai, Yu-Kun Ma, Cuixia Wang, Hao Liu, Yong-Jin Wang, Hongan Institute of Software Chinese Academy of Sciences Tsinghua University China State Key Laboratory of Computer Science Beijing Key Lab of Human-Computer Interaction Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences China Cardiff University United Kingdom Alibaba Group China Tsinghua University China

What makes speeches effective has long been a subject for debate, and until today there is broad controversy among public speaking experts about what factors make a speech effective as well as the roles of these factors in speeches. Moreover, there is a lack of quantitative analysis methods to help understand effective speaking strategies. In this paper, we propose E-ffective, a visual analytic system allowing speaking experts and novices to analyze both the role of speech factors and their contribution in effective speeches. From interviews with domain experts and investigating existing literature, we identified important factors to consider in inspirational speeches. We obtained the generated factors from multi-modal data that were then related to effectiveness data. Our system supports rapid understanding of critical factors in inspirational speeches, including the influence of emotions by means of novel visualization methods and interaction. Two novel visualizations include E-spiral (that shows the emotional shifts in speeches in a visually compact way) and E-script (that connects speech content with key speech delivery information). In our evaluation we studied the influence of our system on experts' domain knowledge about speech factors. We further studied the usability of the system by speaking novices and experts on assisting analysis of inspirational speech effectiveness. Copyright © 2021, The Authors. All rights reserved.

关键词： Visualization

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A Research Framework for human Aspects in the Internet of Production – An Intra-company Perspective 1

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AHFE Virtual Conference on Production Management and Process Control, human Aspects of Advanced Manufacturing and the Additive Manufacturing, Modeling Systems and 3D Prototyping, 2020

作者： Brauner, Philipp Brillowski, Florian Dammers, Hannah Königs, Peter Kordtomeikel, Frauke Petruck, Henning Schaar, Anne Kathrin Schmitz, Seth Steuer-Dankert, Linda Mertens, Alexander Gries, Thomas Leicht-Scholten, Carmen Nagel, Saskia Nitsch, Verena Schuh, Günther Ziefle, Martina Human-Computer Interaction Center RWTH Aachen University Aachen Germany Institut für Textiltechnik of RWTH Aachen University Aachen Germany Human Technology Center RWTH Aachen University Aachen Germany Research Group Gender and Diversity in Engineering RWTH Aachen University Aachen Germany Institute of Industrial Engineering and Ergonomics RWTH Aachen University Aachen Germany Laboratory for Machine Tools and Production Engineering RWTH Aachen University Aachen Germany

ISBN: (数字)9783030519810

ISBN: (纸本)9783030519803

Digitalization in the production sector aims at transferring concepts and methods from the Internet of Things (IoT) to the industry and is, as a result, currently reshaping the production area. Besides technological progress, changes in work processes and organization are relevant for a successful implementation of the "Internet of Production" (IoP). Focusing on the labor organization and organizational procedures emphasizes to consider intra-company factors such as (user) acceptance, ethical issues, and ergonomics in the context of IoP approaches. In the scope of this paper, a research approach is presented that considers these aspects from an intra-company perspective by conducting studies on the shop floor, control level and management level of companies in the production area. Focused on four central dimensions—governance, organization, capabilities, and interfaces—this contribution presents a research framework that is focused on a systematic integration and consideration of human aspects in the realization of the IoP. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Internet of things

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A SURVEY ON BRAIN-INSPIRED DEEP LEARNING VIA PREDICTIVE CODING

arXiv

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arXiv 2023年

作者： Salvatori, Tommaso Mali, Ankur Buckley, Christopher L. Lukasiewicz, Thomas Rao, Rajesh P.N. Friston, Karl Ororbia, Alexander VERSES AI Research Lab Los AngelesCA United States Institute of Logic and Computation Vienna University of Technology Austria University of South Florida TampaFL33620 United States Sussex AI Group Department of Informatics University of Sussex Brighton United Kingdom Department of Computer Science University of Oxford United Kingdom Paul G. Allen School of Computer Science and Engineering University of Washington SeattleWA United States The Wellcome Centre for Human Neuroimaging Queen Square Institute of Neurology University College London United Kingdom The Neural Adaptive Computing Laboratory Rochester Institute of Technology RochesterNY14623 United States

Artificial intelligence (AI) is rapidly becoming one of the key technologies of this century. The majority of results in AI thus far have been achieved using deep neural networks trained with the error backpropagation learning algorithm. However, such an algorithm has always been considered biologically implausible. To this end, recent works have studied learning algorithms for deep neural networks inspired by the neurosciences. One such theory, called predictive coding (PC), has shown promising properties that make it potentially valuable for the machine learning community: it can model information processing in different areas of the brain, can be used in cognitive control and robotics, has a solid mathematical foundation in variational inference, and performs its computations asynchronously. Inspired by such properties, works that propose novel PC-like algorithms are starting to be present in multiple sub-fields of machine learning and artificial intelligence at large. Here, we survey such efforts by first providing a broad overview of the history of PC to provide a common ground for the understanding of the recent developments, then by surveying current efforts and results, and concluding with a large discussion of possible implications and ways forward. Copyright © 2023, The Authors. All rights reserved.

关键词： Adversarial machine learning

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AlloHaptic: Robot-Mediated Haptic Collaboration for Learning Linear Functions*

AlloHaptic: Robot-Mediated Haptic Collaboration for Learning...

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IEEE International Workshop on Robot and human Communication (ROMAN)

作者： Hala Khodr Soheil Kianzad Wafa Johal Aditi Kothiyal Barbara Bruno Pierre Dillenbourg Computer Human Interaction in Learning and Instruction Laboratory (CHILI) Swiss Federal Institute of Technology (EPFL) Lausanne Switzerland Department of Computer Science University of British Columbia University of New South Wales Sydney Australia MOBOTS Group in BIOROB EPFL Lausanne

ISBN: (数字)9781728160757

ISBN: (纸本)9781728160764

Collaborative learning appears in a joint intellectual efforts of individuals to understand an object of knowledge collectively. In their search for understanding the problems, meanings, and solutions, learners employ different multi-modal strategies. In this work, we explore the role of force feedback in learners interaction with tangible hand-held robots. We designed a collaborative learning environment to provide embodied intuitions on linear mathematical functions combined with graphical representations and ran a first study involving 24 participants. Our analysis shows a positive learning gain for our learning activity. Moreover, to explore the link between different types of force feedback and learners' collaboration, we designed a focus group study with 12 participants. Our results suggest that the haptic communication channel affects the collaboration dynamic differently according to the nature of the learning task. We finish by proposing design insights for future exploration of haptic in collaborative learning.

关键词：

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When Positive Perception of the Robot Has No Effect on Learning

When Positive Perception of the Robot Has No Effect on Learn...

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IEEE International Workshop on Robot and human Communication (ROMAN)

作者： Jauwairia Nasir Utku Norman Barbara Bruno Pierre Dillenbourg Computer-Human Interaction in Learning and Instruction (CHILI) Lab Swiss Federal Institute of Technology in Lausanne (EPFL) Switzerland MOBOTS group within the Biorobotics Laboratory (BioRob) Swiss Federal Institute of Technology in Lausanne (EPFL) Switzerland

ISBN: (数字)9781728160757

ISBN: (纸本)9781728160764

humanoid robots, with a focus on personalised social behaviours, are increasingly being deployed in educational settings to support learning. However, crafting pedagogical HRI designs and robot interventions that have a real, positive impact on participants' learning, as well as effectively measuring such impact, is still an open challenge. As a first effort in tackling the issue, in this paper we propose a novel robot-mediated, collaborative problem solving activity for school children, called JUSThink, aiming at improving their computational thinking skills. JUSThink will serve as a baseline and reference for investigating how the robot's behaviour can influence the engagement of the children with the activity, as well as their collaboration and mutual understanding while working on it. To this end, this first iteration aims at investigating (i) participants' engagement with the activity (Intrinsic Motivation Inventory-IMI), their mutual understanding (IMIlike) and perception of the robot (Godspeed Questionnaire); (ii) participants' performance during the activity, using several performance and learning metrics. We carried out an extensive user-study in two international schools in Switzerland, in which around 100 children participated in pairs in one-hour long interactions with the activity. Surprisingly, we observe that while a teams' performance significantly affects how team members evaluate their competence, mutual understanding and task engagement, it does not affect their perception of the robot and its helpfulness, a fact which highlights the need for baseline studies and multi-dimensional evaluation metrics when assessing the impact of robots in educational activities.

关键词： Conferences humanoid robots Collaboration Particle measurements Collaborative work Task analysis Robots

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A Multi-Task Mean Teacher for Semi-Supervised Shadow Detection

A Multi-Task Mean Teacher for Semi-Supervised Shadow Detecti...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zhihao Chen Lei Zhu Liang Wan Song Wang Wei Feng Pheng-Ann Heng College of Intelligence and Computing Tianjin University Department of Computer Science and Engineering The Chinese University of Hong Kong Department of Computer Science and Engineering University of South Carolina Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Existing shadow detection methods suffer from an intrinsic limitation in relying on limited labeled datasets, and they may produce poor results in some complicated situations. To boost the shadow detection performance, this paper presents a multi-task mean teacher model for semi-supervised shadow detection by leveraging unlabeled data and exploring the learning of multiple information of shadows simultaneously. To be specific, we first build a multi-task baseline model to simultaneously detect shadow regions, shadow edges, and shadow count by leveraging their complementary information and assign this baseline model to the student and teacher network. After that, we encourage the predictions of the three tasks from the student and teacher networks to be consistent for computing a consistency loss on unlabeled data, which is then added to the supervised loss on the labeled data from the predictions of the multi-task baseline model. Experimental results on three widely-used benchmark datasets show that our method consistently outperforms all the compared state-of- the-art methods, which verifies that the proposed network can effectively leverage additional unlabeled data to boost the shadow detection performance.

关键词： Task analysis Image edge detection Feature extraction Training Radio frequency Fuses Solid modeling

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Publisher Correction: Propagation of errors in citation networks: a study involving the entire citation network of a widely cited paper published in, and later retracted from, the journal Nature

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Research integrity and peer review 2024年第1期9卷 14页

作者： Paul E van der Vet Harm Nijveen Human Media Interaction Group Department of Computer Science University of Twente Drienerlolaan 5 7522 NB Enschede the Netherlands. ZGT Academy Ziekenhuisgroep Twente Zilvermeeuw 1 7609 PP Almelo the Netherlands. Bioinformatics Laboratory Wageningen University Droevendaalsesteeg 1 6708 PB Wageningen the Netherlands. harm.nijveen@wur.nl. Wageningen Seed Lab Laboratory of Plant Physiology Wageningen University Droevendaalsesteeg 1 6708 PB Wageningen the Netherlands. harm.nijveen@wur.nl.

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The lone Wolf dies, the pack survives? 18

The lone Wolf dies, the pack survives?

引用

18th Koli Calling Conference on computing Education Research, Koli Calling 2018

作者： Ehlenz, Matthias Leonhardt, Thiemo Cherek, Christian Wilkowska, Wiktoria Schroeder, Ulrik Learning Technologies RWTH Aachen University Aachen Germany Inside Unternehmensgruppe Aachen Germany Media Computing Group RWTH Aachen University Aachen Germany Human-Computer Interaction Center RWTH Aachen University Aachen Germany

ISBN: (纸本)9781450365352

Learning Regular Expressions has been an unavoidable, mostly repetitive and unpleasant experience for many students of theoretical computer science. In this paper, we present the results of our research on learning regular expression in an innovative environment. Using large multitouch tabletop displays provides us the opportunity to design a serious game to convey this topic as a collaborative learning experience for four players in extracurricular setting. In this paper, we introduce our platform, the developed game and the underlying didactical approach as well as our study design. The results of the study are presented and discussed, focussing on interaction events and timing and latencies affected by interaction outcome. Lastly, the outlook provides our ideas of benefits for collaborative serious game design and further approaches of designing and research CS learning applications. © 2018 Copyright held by the owner/author(s).

关键词： Serious games

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Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2021年 12856 LNCS卷 v页

作者： Hernández-Leo, Davinia Hishiyama, Reiko Zurita, Gustavo Weyers, Benjamin Nolte, Alexander Ogata, Hiroaki Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain Department of Management Control and Information Systems University of Chile Santiago Chile Institute of Computer Science University of Tartu Tartu Estonia School of Creative Science Waseda University Tokyo Japan Human-Computer Interaction Group University of Trier Trier Germany Academic Center for Computing and Media Studies Kyoto University Kyoto Japan

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Challenges in Cybersecurity: Lessons from Biological Defense Systems

SSRN

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SSRN 2023年

作者： Schrom, Edward Kinzig, Ann P. Forrest, Stephanie Graham, Andrea L. Levin, Simon A. Bergstrom, Carl T. Castillo-Chavez, Carlos Collins, James P. de Boer, Rob J. Doupé, Adam Ensafi, Roya Feldman, Stuart Grenfell, Bryan T. Halderman, J. Alex Huijben, Silvie Maley, Carlo Moses, Melanie Perelson, Alan S. Perrings, Charles Plotkin, Joshua Rexford, Jennifer Tiwari, Mohit Department of Ecology and Evolutionary Biology Princeton University PrincetonNJ08544 United States School of Life Sciences Arizona State University TempeAZ85287 United States Biodesign Center for Biocomputation Security and Society Arizona State University TempeAZ85287 United States School of Computing and Augmented Intelligence Arizona State University TempeAZ85287 United States Santa Fe Institute Santa FeNM87501 United States Department of Biology University of Washington SeattleWA98195 United States School of Human Evolution and Social Change Arizona State University TempeAZ85287 United States Theoretical Biology and Bioinformatics Utrecht University Utrecht3584 CH Netherlands Center for Cybersecurity and Trusted Foundations Global Security Initiative Arizona State University TempeAZ85287 United States Department of Electrical Engineering and Computer Science Computer Science and Engineering Division University of Michigan Ann ArborMI48109 United States Schmidt Futures New YorkNY10011 United States Princeton School of Public and International Affairs Princeton University PrincetonNJ08544 United States Center for Computer Security and Society University of Michigan Ann ArborMI48109 United States Arizona Cancer Evolution Center Arizona State University TempeAZ85287 United States Department of Computer Science University of New Mexico AlbuquerqueNM87131 United States Department of Biology University of New Mexico AlbuquerqueNM87131 United States Theoretical Biology and Biophysics Group Los Alamos National Laboratory Los AlamosNM87545 United States Department of Biology University of Pennsylvania PhiladelphiaPA19104 United States Department of Computer Science Princeton University PrincetonNJ08540 United States Department of Electrical and Computer Engineering University of Texas AustinTX78712 United States

Defending against novel, repeated, or unpredictable attacks, while avoiding attacks on the ‘self’, are the central problems of both mammalian immune systems and computer systems. Both systems have been studied in great detail, but with little exchange of information across the different disciplines. Here, we present a conceptual framework for structured comparisons across the fields of biological immunity and cybersecurity, by framing the context of defense, considering different (combinations of) defensive strategies, and evaluating defensive performance. Throughout this paper, we pose open questions for further exploration. We hope to spark the interdisciplinary discovery of general principles of optimal defense, which can be understood and applied in biological immunity, cybersecurity, and other defensive realms. © 2023, The Authors. All rights reserved.

关键词： Cybersecurity

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