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arXiv 2024年

作者： Niu, Zhenxing Sun, Yuyao Miao, Qiguang Jin, Rong Hua, Gang The Key Laboratory of Smart Human-Computer Interaction Wearable Technology of Shaanxi Province Xi-dian University China DAMO Academy Alibaba Group Beijing China Wormpex AI Research BellevueWA98004 United States

Deep Neural Networks (DNNs) are known to be vulnerable to both backdoor and adversarial attacks. In the literature, these two types of attacks are commonly treated as distinct robustness problems and solved separately, since they belong to training-time and inference-time attacks respectively. However, this paper revealed that there is an intriguing connection between them: (1) planting a backdoor into a model will significantly affect the model's adversarial examples;(2) for an infected model, its adversarial examples have similar features as the triggered images. Based on these observations, a novel Progressive Unified Defense (PUD) algorithm is proposed to defend against backdoor and adversarial attacks simultaneously. Specifically, our PUD has a progressive model purification scheme to jointly erase backdoors and enhance the model's adversarial robustness. At the early stage, the adversarial examples of infected models are utilized to erase backdoors. With the backdoor gradually erased, our model purification can naturally turn into a stage to boost the model's robustness against adversarial attacks. Besides, our PUD algorithm can effectively identify poisoned images, which allows the initial extra dataset not to be completely clean. Extensive experimental results show that, our discovered connection between backdoor and adversarial attacks is ubiquitous, no matter what type of backdoor attack. The proposed PUD outperforms the state-of-the-art backdoor defense, including the model repairing-based and data filtering-based methods. Besides, it also has the ability to compete with the most advanced adversarial defense methods. The code is available here. Copyright © 2024, The Authors. All rights reserved.

关键词： Purification

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Empowering Caregivers of People Living with Dementia to Use Music Therapeutically at Home: Design Opportunities 32

Empowering Caregivers of People Living with Dementia to Use ...

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32nd Australian computer-human interaction Conference, OzCHI 2020

作者： Carrasco, Romina Baker, Felicity A. Bukowska, Anna A. Clark, Imogen N. Flynn, Libby M. McMahon, Kate Odell-Miller, Helen Stensaeth, Karette Tamplin, Jeanette Sousa, Tanara Vieira Waycott, Jenny Wosch, Thomas Human-Computer Interaction Group The University of Melbourne Australia Music Therapy Faculty of Fine Arts and Music The University of Melbourne Australia Department of Occupational Therapy University of Physical Education Poland Cambridge Institute for Music Therapy Research Anglia Ruskin University United Kingdom Centre for Research in Music and Health Norwegian Academy of Music Norway Centre for Health Policy Health Economic Unit The University of Melbourne Australia Hochschule für Angewandte Wissenschaften Würzburg-Schweinfurt Germany

ISBN: (纸本)9781450389754

human-computer interaction researchers have explored how to design technologies to support people with dementia (PwD) and their caregivers, but limited attention has been given to how to facilitate music therapy in dementia care. The use of music to help manage the symptoms of dementia is often guided by a music therapist who adapts the intervention to respond to the changing needs of the person living with dementia. However, as the incidence of dementia increases worldwide, individualised therapy programs are less feasible, making it valuable to consider technology-based approaches. In this paper, we analyze data from case studies of home-based music therapy training interventions with two families. The findings show that embodied interactions supported the therapist in responding to the needs of the PwD and built an empathic environment that empowered the caregivers' learning. We discuss opportunities and challenges for designing technologies that support family caregivers' therapy-informed music use in dementia care. © 2020 ACM.

关键词： Neurodegenerative diseases

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visions and visioning in CHI CHI 2013 Special Interest group Meeting 31

Visions and Visioning in CHI CHI 2013 Special Interest Group...

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31st Annual CHI Conference on human Factors in Computing Systems:, CHI EA 2013

作者： Quigley, Aaron Dix, Alan Mackay, Wendy E. Ishii, Hiroshi Steimle, Jürgen St Andrews Computer Human Interaction Research Group University of St Andrews St AndrewsKY16 9SX United Kingdom University of Birmingham BirminghamB15 2TT United Kingdom INRIA Saclay Ile de France LRI Bâtiment 650 Université de Paris-Sud Orsay91405 France Tangible Media Group MIT Media Laboratory CambridgeMA02139 United States Fluid Interfaces Group MIT Media Laboratory CambridgeMA02139 United States

ISBN: (纸本)9781450318990

There are many visions that touch on the future of human computer interaction from a trans-human future to a post-technological UI. However visions related to the progress of technology are not new. Creative and insightful visionaries from Denis Diderot to Vannevar Bush have been postulating visions of possible futures or technology for centuries. Some idealised views end up discredited with advances in knowledge, while others now appear remarkably prescient. The question is, do visions and the process of creating them have a place in CHI, or are they simply flights of fancy? This SIG meeting provides a forum for visionaries;researchers and practitioners looking to consider the place and importance of visions within CHI. Can visions, the process of visioning and forming new visions help us refine, advance or develop new research or forms of interaction. And if visions are important to us, then are they part of the regular academic process? If so, should CHI provide venues for publishing new visions?.

关键词： human computer interaction

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The Future of HCI Publishing in Journals and Books 31

The Future of HCI Publishing in Journals and Books

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31st Annual CHI Conference on human Factors in Computing Systems:, CHI EA 2013

作者： Moderator, Joseph Kaye Ford, Beverley Murray, Dianne Sery, Doug Thomas, Peter Whittaker, Steve Zhai, Shumin Yahoo! Research HCI Research Group 701 First Ave SunnyvaleCA94089 United States Springer Computer Science 236 Graýs Inn Road LondonWC1X 8HB United Kingdom Interacting with Computers United States MIT Press 55 Hayward St CambridgeMA02142 United States Personal and Ubiquitous Computing Manifesto Group 99 Pellatt Street BeaumarisVIC3193 Australia HCI Human Computer Interaction Lab University of California at Santa Cruz High St Santa CruzCA95064 United States Google Inc. 1600 Amphitheatre Parkway Mountain ViewCA94043 United States

ISBN: (纸本)9781450318990

With the ongoing growth of digital media, academic presses and journals have had to answer some hard questions about the role of publishing in a world of blogs, social media, on-demand video and social networking. In this panel we bring together some of the top editors and publishers in HCI to explore and address these questions in a public forum.

关键词： Digital storage

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Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy

arXiv

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arXiv 2023年

作者： Marinov, Zdravko Jäger, Paul F. Egger, Jan Kleesiek, Jens Stiefelhagen, Rainer The Computer Vision for Human-Computer Interaction Lab Department of Informatics Karlsruhe Institute of Technology Adenauerring 10 Karlsruhe76131 Germany Girardetstraße 2 Essen45131 Germany Heidelberg Interactive Machine Learning Group Im Neuenheimer Feld 223 Heidelberg69120 Germany The Helmholtz Imaging DKFZ Im Neuenheimer Feld 223 Heidelberg69120 Germany

Interactive segmentation is a crucial research area in medical image analysis aiming to boost the efficiency of costly annotations by incorporating human feedback. This feedback takes the form of clicks, scribbles, or masks and allows for iterative refinement of the model output so as to efficiently guide the system towards the desired behavior. In recent years, deep learning-based approaches have propelled results to a new level causing a rapid growth in the field with 121 methods proposed in the medical imaging domain alone. In this review, we provide a structured overview of this emerging field featuring a comprehensive taxonomy, a systematic review of existing methods, and an in-depth analysis of current practices. Based on these contributions, we discuss the challenges and opportunities in the field. For instance, we find that there is a severe lack of comparison across methods which needs to be tackled by standardized baselines and benchmarks. Appendixes are here. © 2023, CC BY.

关键词： Medical imaging

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Context-Aware, Trust-Based Access Control for the *** Userware

Context-Aware, Trust-Based Access Control for the *** Userwa...

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New Technologies, Mobility and Security (NTMS)

作者： Marcel Heupel Lars Fischer Dogan Kesdogan Mohamed Bourimi Simon Scerri Fabian Hermann Rafael Gimenez IT Security and Privacy Group University of Siegen Germany Digital Enterprise Research Institute National University of Ireland Galway Ireland Competence-Center Human-Computer Interaction Fraunhofer IAO Stuttgart Germany Barcelona Digital Technology Centre Barcelona Spain

Trust calculation to inform privacy recommendations based on context information involvement (e.g. location information, nearby people) is an increasing need in pervasive environments. In this paper we present a multidimensional trust metric designed for access control decisions in scenarios of the EU funded *** project. Thereby each involved context information could represent a separate trust dimension. In the focus is the correctness of the suggested trust metric towards meaningful privacy recommendations and improvement of access control decisions for our scenarios. The suggested metric could help to avoid manipulations and attacks based on fundamental requirements for situational trust. Our proposed trust calculation considers a presence history of persons at locations, to increase the accuracy of our base trust value. We present our approach after explaining the fundamental requirements, the underlying ontology framework and architecture.

关键词： Context Privacy Measurement History Ontologies Access control Context modeling

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MIT educators share success

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computer Graphics (ACM) 2001年第1期35卷 20-23页

作者： Wolfe, Rosalee Belcher, John W. Mark Bessette, R. Massachusetts Institute of Technology Astrophysics Division MIT Physics Department Studio/TEAL Project MIT New England 3D Studio Max Users Group Physics Demonstration Group MIT Department of Physics Center for Space Research Massachusetts Institute of Technology Center for Educational Computing Initiatives Massachusetts Institute of Technology Division of Graphics and Human-Computer Interaction School of Computer Science Telecommunications and Information Systems DePaul University School of Computer Science DePaul University Chicago IL 60604

Professor John Belcher developed a series of short videos with animation and text of selected experiments to properly demonstrate the phenomena of electromagnetism. Such 3D animations are visually compelling, instilling in the student both a sense of wonder about the phenomena and a mental model of why and how it works. The products of these approach includes video clips of demonstrations, 3D animations of these demonstration which display time changing field-lines as a guide to understanding their dynamical effects and Java applets which allow the student to actively construct and animate 2D field lines for varying configurations of sources. These technologies are also implemented to develop animations for Faraday's insights, using video clips.

关键词： computer graphics

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FROM THE EDITORS OF THE SPECIAL ISSUE

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Applied Artificial Intelligence 2010年第6期24卷 489-493页

作者： Ruttkay, Zsófia Kipp, Michael Nijholt, Anton Vilhjálmsson, Hannes Högni Creative Technology Lab Moholy-Nagy University of Art and Design Budapest Hungary. DFKI Embodied Agents Research Group Saarbruecken Germany. Human-Media Interaction University of Twente Enschede The Netherlands. School of Computer Science Reykjavík University Reykjavík Iceland

The article discusses various reports published within the issue, including one on EMBR, a state-of-the-art character animation engine, another on the second dimension, identity and individuality and another on the ge... 详细信息

关键词： ENGINES MACHINERY

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"The support needs to be part of the system": designing inclusive eHealth applications for older adults with low eHealth literacy

"The support needs to be part of the system": designing incl...

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2022 Engaging Citizen Science Conference, CitSci 2022

作者： Prinzellner, Yvonne Simon, Ali Drachmann, Danielle Werner, Katharina Münter, Lars Bulsink, Vera Smits, Carolien Fitzpatrick, Geraldine Schwaninger, Isabel Johanniter Österreich Ausbildung und Forschung Gemeinnützige GmbH Ignaz-Köck-Straße 22 Vienna1210 Austria Danish Committee for Health Education Classensgade 71 5. Sal Copenhagen2100 Denmark TU Wien Institute of Visual Computing and Human-Centered Technology Human Computer Interaction Group Argentinierstraße 8 Vienna1040 Austria Roessingh Research and Development Roessinghsbleekweg 33b Enschede7522 Netherlands Arthur van Schendelstraat 600 Utrecht3511 MJ Netherlands

In the project "Got-IT", an online toolkit was developed to assist with the design of inclusive eHealth solutions targeting the promotion of healthy lifestyles among older adults (65+) with low eHealth literacy. The project consortium has used participatory citizen science as an approach to actively engage with the relevant stakeholders, including older adults with low eHealth literacy, eHealth developers, and secondary end users like eHealth professionals. We applied three different research designs, all centered on the active engagement of the different stakeholders, using the design of an eHealth app to ground the investigations. The outcomes of the Got-IT project align not only with the first objective of the AAL Programme ("better quality of life for older adults and their networks, by empowering the citizens to take care of their own health") but are also central components of citizen science as a whole, by actively engaging the different target groups at various levels of our research process. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)

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Progressive feature extraction by extended greedy information acquisition

Progressive feature extraction by extended greedy informatio...

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International Conference on Neural Information Processing

作者： R. Kamimura H. Takeuchi O. Uchida Information Science Laboratory and Future Science and Technology Joint Research Center Tokai University Hirakata Kanagawa Japan Human-Computer Interaction Group Institute for Human Science and Biomedical Engineering National Institute for Advanced Industrial Science and Technology Tsukuba Japan Department of Information Science and Future Science and Technology Joint Research Center Tokai University Hirakata Kanagawa Japan

We propose a new network growing method to detect salient features in input patterns. The new method is based upon a previous network growing model (R. Kamimura and T. Kamimura, 2002) and introduced to overcome some problems in the previous model. We have so far tried to build a model that can learn input patterns as efficiently as possible. To realize this efficiency, we impose upon networks a constraint that only connections into new competitive units must be updated to absorb as much information as possible from outside. However, one of the problems is that the previous improper feature extraction prevents networks from extracting appropriate features in the later learning stages. To overcome this problem, we relax the condition of the previous model, and we permit networks to update all connections for gradual feature extraction at the expense of computational efficiency. We applied the new method to a simple problem that the previous model cannot solve, and information education data analysis. In both problems, we found that the new method can appropriately extract features from input patterns.

关键词： Feature extraction Neurons Information science computer vision Data mining Computational efficiency humans Biomedical engineering Data analysis Information analysis

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