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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Yuan Li Center for Human-Computer Interaction and Dept. of Computer Science Virginia Tech

ISBN: (数字)9781728165325

ISBN: (纸本)9781728165332

Augmented Reality (AR) systems can effectively enhance collaboration in the real world by providing useful spatial cues through virtual overlays. In particular, AR can be used to facilitate referencing a location in space as required in many collaborative activities. However, the goal of supporting spatial referencing anywhere at any time is hindered by issues with current AR technology, such as the lack of a precise environment model, unreliable tracking, and difficulty in synchronizing among different devices. This paper proposes a research agenda that innovates in the domain of interaction techniques for spatial referencing for everywhere AR.

关键词： Biological system modeling Three-dimensional displays Collaboration Augmented reality Visualization Reliability Computational modeling

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Cross-Subject Decision Confidence Estimation from EEG Signals Using Spectral-Spatial-Temporal Adaptive GCN with Domain Adaptation

Cross-Subject Decision Confidence Estimation from EEG Signal...

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International Joint Conference on Neural Networks (IJCNN)

作者： Rong-Fei Gu Rui Li Wei-Long Zheng Bao-Liang Lu Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai China RuiJin-Mihoyo Laboratory Clinical Neuroscience Center RuiJin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China

The study of the human decision-making process has long been a valuable field for both scientific research and practical application. Towards knowing and taking control of the decision-making process, evaluating the reliability of human decisions objectively plays an important role. Various studies have demonstrated that the confidence level of humans during the decision-making process is an important factor that reflects the correctness of decisions. In literature, several deep learning based methods have been developed to estimate decision confidence using Electroencephalography (EEG). Among these approaches, the spectral-spatial-temporal adaptive graph convolutional neural network (SST-AGCN) stands out. However, SST-AGCN focuses on specific subjects, and may lead to less efficiency in cross-subject situations, which are more common in application scenarios. In this paper, we propose a deep learning model called SST-AGCN with Domain Adaptation (SST-AGCN-DA) for cross-subject decision confidence estimation. To examine the effectiveness of our proposed model, we compare our SST-AGCN-DA with the original SST-AGCN, three typical domain adaption algorithms in the field, and the SST-AGCN with Domain Generalization (SST-AGCN-DG), which is another transfer learning model we developed in this paper. We conduct cross-subject confidence estimation experiments on an EEG dataset collected under a text-based decision-making task. The averaged results of leave-one-out cross-validation come out that the F1-scores of our proposed SST-AGCN-DA and SST-AGCN-DG are 79.45% and 77.04%, respectively, while the original SST-AGCN and the best of the existing domain adaptation algorithms are 74.15% and 74.25%, respectively.

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FAIR Data Point:A FAIR-Oriented Approach for Metadata Publication

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Data Intelligence 2023年第1期5卷 163-183页

作者： Luiz Olavo Bonino da Silva Santos Kees Burger Rajaram Kaliyaperumal Mark D.Wilkinson Services and Cybersecurity group Faculty of Electrical EngineeringMathematics and Computer ScienceUniversity of Twente-Enschede7513GBthe Netherlands Biosemantics group Department of Human GeneticsLeiden University Medical Center-LeidenNL 2333 ZCthe Netherlands Escuela Técnica Superior de Ingenieria Agronomica Alimentaria y de BiosistemasCentro de Biotecnologia y Genomica de Plantas UPM-INIAUniversidad Politecnica de Madrid(UPM)-Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria(INIA)MadridES 28223Spain

Metadata,data about other digital objects,play an important role in FAIR with a direct relation to all FAIR *** this paper we present and discuss the FAIR Data Point(FDP),a software architecture aiming to define a common approach to publish semantically-rich and machine-actionable metadata according to the FAIR *** present the core components and features of the FDP,its approach to metadata provision,the criteria to evaluate whether an application adheres to the FDP specifications and the service to register,index and allow users to search for metadata content of available FDPs.

关键词： FAIR FAIR data point FAIR principles Metadata Interoperability Linked data Semantic interoperability

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Deep Learning Networks for View-Independent Gait Events Detection

SSRN

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SSRN 2022年

作者： Jamsrandorj, Ankhzaya Jung, Dawoon Kumar, Konki Sravan Arshad, Muhammad Zeeshan Mun, Kyung-Ryoul Kim, Jinwook Center for Artificial Intelligence Korea Institute of Science and Technology Seoul Korea Republic of Department of Human Computer Interface & Robotics Engineering University of Science & Technology Daejeon Korea Republic of

Accurate gait detection is crucial in utilizing the ample health information embedded in it. As an alternative of the laborious and demanding sensor-based detection, vision-based approaches ***, its complicate feature engineering process and heavy reliance on the lateral views serve as challenges. This study aimed to propose a view-independent vision-based gait event detection using deep learning networks that requires no pre-processing. A total of 22 participants performed seven different walking and running-related actions and the sequential video frames acquired from their actions were used as the input of the deep learning networks to produce the probability of gait events as outputs. The Transformer network and ResNet18 trained with sequential video frames achieved an F1-score of 0.956 or higher for walking straight and walking around. The detection performance on the frontal, lateral, and backside views did not differ much. The findings enhance applicability of vision-based approach and contribute to increasing its utility in health monitoring. © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Wear leveling revisited 31

Wear leveling revisited

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31st International Symposium on Algorithms and Computation, ISAAC 2020

作者： Onodera, Taku Shibuya, Tetsuo Department of Computer Science University of Helsinki Finland Human Genome Center Institute of Medical Science The University of Tokyo Japan

ISBN: (纸本)9783959771733

Wear leveling – a technology designed to balance the write counts among memory cells regardless of the requested accesses – is vital in prolonging the lifetime of certain computer memory devices, especially the type of next-generation non-volatile memory, known as phase change memory (PCM). Although researchers have been working extensively on wear leveling, almost all existing studies mainly focus on the practical aspects and lack rigorous mathematical analyses. The lack of theory is particularly problematic for security-critical applications. We address this issue by revisiting wear leveling from a theoretical perspective. First, we completely determine the problem parameter regime for which Security Refresh – one of the most well-known existing wear leveling schemes for PCM – works effectively by providing a positive result and a matching negative result. In particular, Security Refresh is not competitive for the practically relevant regime of large-scale memory. Then, we propose a novel scheme that achieves better lifetime, time/space overhead, and wear-free space for the relevant regime not covered by Security Refresh. Unlike existing studies, we give rigorous theoretical lifetime analyses, which is necessary to assess and control the security risk. © Taku Onodera and Tetsuo Shibuya.

关键词： Phase change memory

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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 SeattleWA United States Google San FranciscoCA United States Microsoft Corp Microsoft AI Development Acceleration Program CambridgeMA United States Google DeepMind PittsburghPA United States Princeton University PrincetonNJ United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States Computer Science Department Stanford University StanfordCA United States School of Information Sciences University of Illinois at Urbana-Champaign ChampaignIL United States University of Washington SeattleWA United States Google Research Mountain ViewCA United States UC Berkeley BerkeleyCA 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： Sociology

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How Deep is Love in LLMs’ Hearts? Exploring Semantic Size in human-like Cognition

arXiv

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arXiv 2025年

作者： Yao, Yao Yang, Yifei Ma, Xinbei Yang, Dongjie Zhang, Zhuosheng Li, Zuchao Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 Shanghai China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan University Wuhan430072 China

How human cognitive abilities are formed has long captivated researchers. However, a significant challenge lies in developing meaningful methods to measure these complex processes. With the advent of large language models (LLMs), which now rival human capabilities in various domains, we are presented with a unique testbed to investigate human cognition through a new lens. Among the many facets of cognition, one particularly crucial aspect is the concept of semantic size—the perceived magnitude of both abstract and concrete words or concepts. This study seeks to investigate whether LLMs exhibit similar tendencies in understanding semantic size, thereby providing insights into the underlying mechanisms of human cognition. We begin by exploring metaphorical reasoning, comparing how LLMs and humans associate abstract words with concrete objects of varying sizes. Next, we examine LLMs’ internal representations to evaluate their alignment with human cognitive processes. Our findings reveal that multi-modal training is crucial for LLMs to achieve more human-like understanding, suggesting that real-world, multi-modal experiences are similarly vital for human cognitive development. Lastly, we examine whether LLMs are influenced by attention-grabbing headlines with larger semantic sizes in a real-world web shopping scenario. The results show that multi-modal LLMs are more emotionally engaged in decision-making, but this also introduces potential biases, such as the risk of manipulation through clickbait headlines. Ultimately, this study offers a novel perspective on how LLMs interpret and internalize language, from the smallest concrete objects to the most profound abstract concepts like ‘love’. The insights gained not only improve our understanding of LLMs but also provide new avenues for exploring the cognitive abilities that define human intelligence. Our code and dataset is publicly available at https://***/Zoeyyao27/LLM-Semantic-Size-Understanding. © 2025, CC BY.

关键词： Semantics

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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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Learning what to do by simulating the past

arXiv

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arXiv 2021年

作者： Lindner, David Shah, Rohin Abbeel, Pieter Dragan, Anca Department of Computer Science ETH Zurich Center for Human-Compatible AI UC Berkeley

Since reward functions are hard to specify, recent work has focused on learning policies from human feedback. However, such approaches are impeded by the expense of acquiring such feedback. Recent work proposed that agents have access to a source of information that is effectively free: in any environment that humans have acted in, the state will already be optimized for human preferences, and thus an agent can extract information about what humans want from the state (Shah et al., 2019). Such learning is possible in principle, but requires simulating all possible past trajectories that could have led to the observed state. This is feasible in gridworlds, but how do we scale it to complex tasks? In this work, we show that by combining a learned feature encoder with learned inverse models, we can enable agents to simulate human actions backwards in time to infer what they must have done. The resulting algorithm is able to reproduce a specific skill in MuJoCo environments given a single state sampled from the optimal policy for that skill. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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