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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Huang, Yuyang Dong, Shiwen Wang, Dandan Wan, Chunling Yang, Yang Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai China Bio-X Institutes Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders Ministry of Education Shanghai Jiao Tong University Shanghai200030 China Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai200240 China

ISBN: (纸本)9781665468190

Niacin skin-flushing test is a promising method for fast and objective diagnosis of schizophrenia and affective disorder. Compared to healthy controls, the patients have attenuated flushing extent and reduced flushing rate. The traditional analysis of niacin test relies on manually evaluated flushing extent, while flushing patterns sh own in the sk in test images have not yet been considered. To exploit the potential of raw images for diagnosis and avoid subjective and time-consuming estimation of the flushing levels, we propose a CNN-LSTM hybrid model to learn the time-series of images captured in the niacin skin-flushing test. Moreover, an attention layer is introduced to enhance the model performance and interpretability. We collect a data set of 796 participants, and the experimental results show that the new model outperforms the traditional 4-point scaled measurement and conventional deep learning methods by large margins. Besides, the attention scores yielded by the model reveals key time points during the flushing reaction. © 2022 IEEE.

关键词： Time series

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A hybrid deep learning architecture using 3D CNNs and GRUs for human action recognition

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International Journal of Engineering, Transactions B: Applications 2020年第5期33卷 959-965页

作者： Savadi Hosseini, M. Ghaderi, F. Human-Computer Interaction lab Department of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

Video contents have variations in temporal and spatial dimensions, and recognizing human actions requires considering the changes in both directions. To this end, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) and their combinations have been used to tackle the video dynamics. However, a hybrid architecture usually results in a more complex model and hence a greater number of parameters to be optimized. In this study, we propose to use a stack of gated recurrent unit (GRU) layers on top of a two-stream inflated convolutional neural network. Raw frames and optical flow of the video are processed in the first and second streams, respectively. We first segment the video frames in order to be able to track the video contents in more details and by using 3D CNNs extract spatial-temporal features, called local features. We then import the sequence of local features to the GRU network, and use a weighted averaging operator to aggregate the outcome of the two processing flows, called global features. The evaluations confirm acceptable results for the two HMDB51 and UCF101 datasets. The proposed method resulted in a 1.6% improvement in the classification accuracy of the HMDB51 challenging dataset compared to the best reported results. © 2020 Materials and Energy Research Center. All rights reserved.

关键词： Convolutional neural networks

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Cloudless Skies? Decentralizing Mobile interaction 20

Cloudless Skies? Decentralizing Mobile Interaction

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22nd International Conference on human-computer interaction with Mobile Devices and Services

作者： Britta Schulte Sujay Shalawadi Max Van Kleek Florian Echtler Human-Computer Interaction Bauhaus-University Weimar Germany Bauhaus Universität Germany Department of Computer Science University of Oxford United Kingdom HCC Group Aalborg University Denmark

ISBN: (纸本)9781450380522

Mobile interaction is now almost exclusively dependent on an intransparent cloud infrastructure beyond the users’ control, resulting in multiple issues related to security, privacy, and availability. While alternative approaches based on decentralized network architectures exist, these have their own set of issues such as lack of usability or content moderation. In this workshop, we bring together researchers from related, but rarely interconnected fields to discuss issues and opportunities related to decentralization. We will develop and discuss current and future scenarios for decentralized mobile interaction, both utopian and dystopian. We invite researchers interested in implementation and networking aspects of mobile decentralization as well as those focusing on ethical, legal and social implications (ELSI) of these alternative approaches to the mobile ecosystem.

关键词： social aspects of computing peer-to-peer networks mobile computing decentralization cloud services ELSI

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Bidirectional human-AI Alignment: Emerging Challenges and Opportunities

Bidirectional Human-AI Alignment: Emerging Challenges and Op...

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2025 CHI Conference on human Factors in Computing Systems, CHI EA 2025

作者： Shen, Hua Knearem, Tiffany Ghosh, Reshmi Liu, Michael Xieyang Monroy-Hernández, Andrés Wu, Tongshuang Yang, Diyi Huang, Yun Mitra, Tanushree Li, Yang Hearst, Marti The Information School University of Washington Seattle WA United States Google San Francisco CA United States Microsoft Corp Microsoft AI Development Acceleration Program Cambridge MA United States Google DeepMind Pittsburgh PA United States Princeton University Princeton NJ United States Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh PA United States Computer Science Department Stanford University Stanford CA United States School of Information Sciences University of Illinois at Urbana-Champaign Champaign IL United States University of Washington Seattle WA United States Google Research Mountain View CA United States UC Berkeley Berkeley CA United States

ISBN: (纸本)9798400713958

Recent advancements in general-purpose AI have highlighted the urgent need to align AI systems with the goals, ethical principles, and values of individuals and society. Existing alignment research has been primarily approached as an AI-centered, static, and unidirectional process. However, this unidirectional perspective falls short of taking into account the dynamic and evolving interaction between humans and AI, necessitating a shift toward a bidirectional, interconnected mode of human-AI alignment. This SIG aims to outline the emerging areas of bidirectinoal human-AI alignment research, propose a blueprint of future goals and challenges for fundamental alignment research, and establish a shared platform to bring together experts from HCI, AI, social sciences, and more to advance interdisciplinary research and collaboration on human-AI alignment. © 2025 Copyright held by the owner/author(s).

关键词： bidirectional human-AI alignment human-AI interactive alignment social impact of AI alignment value-centered design

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Textessence: A tool for interactive analysis of semantic shifts between corpora

arXiv

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arXiv 2021年

作者： Newman-Griffis, Denis Sivaraman, Venkatesh Perer, Adam Fosler-Lussier, Eric Hochheiser, Harry Department of Biomedical Informatics University of Pittsburgh United States Human-Computer Interaction Institute Carnegie Mellon University United States Department of Computer Science and Engineering Ohio State University United States

Embeddings of words and concepts capture syntactic and semantic regularities of language;however, they have seen limited use as tools to study characteristics of different corpora and how they relate to one another. We introduce TextEssence, an interactive system designed to enable comparative analysis of corpora using embeddings. TextEssence includes visual, neighbor-based, and similarity-based modes of embedding analysis in a lightweight, web-based interface. We further propose a new measure of embedding confidence based on nearest neighborhood overlap, to assist in identifying high-quality embeddings for corpus analysis. A case study on COVID-19 scientific literature illustrates the utility of the system. TextEssence is available from https://***/drgriffis/text-essence. Copyright © 2021, The Authors. All rights reserved.

关键词： Embeddings

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MSA-GCN:Multiscale Adaptive Graph Convolution Network for Gait Emotion Recognition

arXiv

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arXiv 2022年

作者： Yin, Yunfei Jing, Li Huang, Faliang Yang, Guangchao Wang, Zhuowei The School of of Computer Science and Technology Chongqing University Chongqing China Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision Nanning Normal University Nanning530001 China Faculty of Engineering and Information Technology University of Technology Sydney Australia

Gait emotion recognition plays a crucial role in the intelligent system. Most of the existing methods recognize emotions by focusing on local actions over time. However, they ignore that the effective distances of different emotions in the time domain are different, and the local actions during walking are quite similar. Thus, emotions should be represented by global states instead of indirect local actions. To address these issues, a novel MultiScale Adaptive Graph Convolution Network (MSA-GCN) is presented in this work through constructing dynamic temporal receptive fields and designing multiscale information aggregation to recognize emotions. In our model, a adaptive selective spatial-temporal graph convolution is designed to select the convolution kernel dynamically to obtain the soft spatiotemporal features of different emotions. Moreover, a Cross-Scale mapping Fusion Mechanism (CSFM) is designed to construct an adaptive adjacency matrix to enhance information interaction and reduce redundancy. Compared with previous state-of-the-art methods, the proposed method achieves the best performance on two public datasets, improving the mAP by 2%. We also conduct extensive ablations studies to show the effectiveness of different components in our methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Emotion Recognition

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Theory of Mind Abilities Predict Robot's Gaze Effects on Object Preference

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IEEE Transactions on Affective Computing 2025年

作者： Manzi, Federico Ishikawa, Mitsuhiko Dio, Cinzia Di Itakura, Shoji Kanda, Takayuki Ishiguro, Hiroshi Massaro, Davide Marchetti, Antonella Università Cattolica del Sacro Cuore Research Unit on Theory of Mind Department of Psychology Milan Italy Università Cattolica del Sacro Cuore Research Unit in Psychology and Robotics in the Lifespan Department of Psychology Milan Italy IRCCS Fondazione Don Carlo Gnocchi Milan Italy Hitotsubashi University Hitotsubashi Institute for Advanced Study Japan Ritsumeikan University Kyoto Japan Kyoto University Department of Computer Science Human-Robot Interaction Laboratory Kyoto Japan IRC'HIL Keihana Science City Advanced Telecommunications Research Institute International Kyoto Japan Osaka University Department of Intelligent Systems Toyonaka Japan

This study investigated the differences between human and robot gaze in influencing preference formation, and examined the role of Theory of Mind (ToM) abilities in this process. human eye gaze is one of the most important sources of information for social interaction and research has demonstrated its effectiveness in influencing people's preference. With increasing technological development, we will interact with robots that can exhibit gaze behavior and influence people's preference. It is unclear whether there are any differences between humans and robots in this process. The present study aimed to analyze the role of the gaze of a robot and a human in influencing the ascription of a preference to the gazer and the participants' preference. Furthermore, we examined the role of ToM abilities in preference formation. The results showed that the gaze has a greater effect on the gazer preference compared to participants' preference regardless of the agent (human or robot). In addition, ToM abilities predict both gazer and individual preferences in the robot's condition only even though different socio-cognitive mechanisms are involved. The study suggests that adults are cognitively able to process the gaze of a robot similar to a human, recognizing the underlying mental state. However, only for the robot, different cognitive mechanisms are involved in the gazer (i.e., perspective taking) and participants' preference formation (i.e., advanced ToM). © 2010-2012 IEEE.

关键词： Social robots

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Structural and functional neural correlates of sensorimotor deficits in progression of hepatic encephalopathy

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Magnetic Resonance Letters 2025年第2期5卷

作者： Sha, Guanchen Liu, Yuefan Cao, Yutong Zhang, Qi Zhang, Yining Chen, Yuanyuan Fan, Qiuyun Cheng, Yue Department of Biomedical Engineering College of Precision Instruments and Optoelectronics Engineering Tianjin University Tianjin300072 China Tianjin Key Laboratory of Brain Science and Neuroengineering Tianjin300072 China Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin300072 China Department of Medical Imaging Tianjin First Central Hospital Tianjin300192 China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin300392 China State Key Laboratory of Advanced Medical Materials and Devices Tianjin300072 China

Hepatic encephalopathy (HE) is a neurological condition that occurs as a complication of liver dysfunction that involves sensorimotor symptoms in addition to cognitive and behavioral changes, particularly in cases of severe liver disease or cirrhosis. Previous studies have reported spatially distributed structural and functional abnormalities related to HE, but the exact relationship between the structural and functional alterations with respect to disease progression remains unclear. In this study, we performed surface-based cortical thickness comparisons and functional connectivity (FC) analyses between three cross-sectional groups: healthy controls (HC, N = 51), patients with minimal hepatic encephalopathy (MHE, N = 50), patients with overt hepatic encephalopathy (OHE, N = 51). In addition to the distributed cortical thinning that is extensively thought to be associated with cognitive decline in HE, we found significant cortical thickening in the left parahippocampal gyrus cortex in the OHE group (p © 2025 The Authors

关键词： Diseases

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GustosonicSense: Towards understanding the design of playful gustosonic eating experiences

arXiv

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arXiv 2024年

作者： Wang, Yan Obie, Humphrey O. Li, Zhuying Salim, Flora D. Grundy, John Mueller, Florian Floyd Exertion Game Lab Department of Human-Centred Computing Monash University MelbourneVIC Australia Department of Computing Technologies Swinburne University of Technology HumaniSE Lab Department of Software Systems and Cybersecurity Monash University MelbourneVIC Australia School of Computer Science and Engineering Southeast University Jiangsu Nanjing China Sydney SydneyNSW Australia HumaniSE Lab Department of Software Systems and Cybersecurity Monash University MelbourneVIC Australia Exertion Games Lab Department of Human-Centred Computing Monash University MelbourneVIC Australia

The pleasure that often comes with eating can be further enhanced with intelligent technology, as the field of human-food interaction suggests. However, knowledge on how to design such pleasure-supporting eating systems is limited. To begin filling this knowledge gap, we designed "GustosonicSense", a novel gustosonic eating system that utilizes wireless earbuds for sensing different eating and drinking actions with a machine learning algorithm and trigger playful sounds as a way to facilitate pleasurable eating experiences. We present the findings from our design and a study that revealed how we can support the "stimulation", "hedonism", and "reflexivity" for playful human-food interactions. Ultimately, with our work, we aim to support interaction designers in facilitating playful experiences with food. © 2024, CC BY.

关键词： Learning algorithms

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Position: social choice should guide AI alignment in dealing with diverse human feedback 24

Position: social choice should guide AI alignment in dealing...

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Proceedings of the 41st International Conference on Machine Learning

作者： Vincent Conitzer Rachel Freedman Jobst Heitzig Wesley H. Holliday Bob M. Jacobs Nathan Lambert Milan Mossé Eric Pacuit Stuart Russell Hailey Schoelkopf Emanuel Tewolde William S. Zwicker Foundations of Cooperative AI Lab Computer Science Department Carnegie Mellon University Pittsburgh and Institute for Ethics in AI University of Oxford Oxford UK Center for Human-Compatible AI Department of Electrical Engineering and Computer Sciences University of California Berkeley Potsdam Institute for Climate Impact Research Potsdam Brandenburg Germany Department of Philosophy University of California Berkeley Department of Philosophy and Moral Sciences Ghent University Ghent Begium Allen Institute for AI Berkeley California Department of Philosophy University of Maryland College Park EleutherAI Foundations of Cooperative AI Lab Computer Science Department Carnegie Mellon University Pittsburgh Department of Mathematics Union College Schenectady and Murat Sertel Center for Advanced Economic Studies Istanbul Bilgi University Istanbul Turkey

Foundation models such as GPT-4 are fine-tuned to avoid unsafe or otherwise problematic behavior, such as helping to commit crimes or producing racist text. One approach to fine-tuning, called reinforcement learning from human feedback, learns from humans' expressed preferences over multiple outputs. Another approach is constitutional AI, in which the input from humans is a list of high-level principles. But how do we deal with potentially diverging input from humans? How can we aggregate the input into consistent data about "collective" preferences or otherwise use it to make collective choices about model behavior? In this paper, we argue that the field of social choice is well positioned to address these questions, and we discuss ways forward for this agenda, drawing on discussions in a recent workshop on Social Choice for AI Ethics and Safety held in Berkeley, CA, USA in December 2023.

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