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Activity Recognition Based on RFID Object Usage for Smart Mobile Devices

Journal of computer science & Technology 2011年第2期26卷 239-246页

作者： Jaeyoung Yang Joonwhan Lee Joongmin Choi Human-Computer Interaction Institute Carnegie Mellon University Neowiz Internet Inc. Department of Computer Science and Engineering Hanyang University

Activity recognition is a core aspect of ubiquitous computing applications. In order to deploy activity recognition systems in the real world, we need simple sensing systems with lightweight computational modules to accurately analyze sensed data. In this paper, we propose a simple method to recognize human activities using simple object information involved in activities. We apply activity theory for representing complex human activities and propose a penalized naive Bayes classifier for performing activity recognition. Our results show that our method reduces computation up to an order of magnitude in both learning and inference without penalizing accuracy, when compared to hidden Markov models and conditional random fields.

关键词： activity recognition activity theory context-awareness RFID

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Perceiving and Countering Hate: The Role of Identity in Online Responses

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Proceedings of the ACM on human-computer interaction 2025年第2期9卷 1-28页

作者： Ping, Kaike Hawdon, James Rho, Eugenia H Department of Computer Science Virginia Tech Blacksburg United States College of Liberal Arts and Human Sciences Virginia Tech Blacksburg United States

This study investigates how online counterspeech, defined as direct responses to harmful online content with the intention of dissuading the perpetrator from further engaging in such behavior, is influenced by the match between a target of the hate speech and a counterspeech writer's identity. Using a sample of 458 English-speaking adults who responded to online hate speech posts covering race, gender, religion, sexual orientation, and disability status, our research reveals that the match between a hate post's topic and a counter-speaker's identity (topic-identity match, or TIM) shapes perceptions of hatefulness and experiences with counterspeech writing. Specifically, TIM significantly increases the perceived hatefulness of posts related to race and sexual orientation. TIM generally boosts counter-speakers' satisfaction and perceived effectiveness of their responses, and reduces the difficulty of crafting them, with an exception of gender-focused hate speech. In addition, counterspeech that displayed more empathy, was longer, had a more positive tone, and was associated with higher ratings of effectiveness and perceptions of hatefulness. Prior experience with, and openness to AI writing assistance tools like ChatGPT, correlate negatively with perceived difficulty in writing online counterspeech. Overall, this study contributes insights into linguistic and identity-related factors shaping counterspeech on social media. The findings inform the development of supportive technologies and moderation strategies for promoting effective responses to online hate. © 2025 Copyright held by the owner/author(s).

关键词： Tweets

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CompAct: Designing Interconnected Compliant Mechanisms with Targeted Actuation Transmissions 25

CompAct: Designing Interconnected Compliant Mechanisms with ...

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Proceedings of the 2025 CHI Conference on human Factors in Computing Systems

作者： Humphrey Yang I-Chao Shen Nikolas Martelaro Bo Zhu Haoran Xie Takeo Igarashi Lining Yao Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh Pennsylvania USA The University of Tokyo Graduate School of Information Science and Technology Tokyo Japan Georgia Institute of Technology Atlanta Georgia USA JAIST Ishikawa Japan The University of Tokyo Tokyo Japan Mechanical Engineering Department University of California Berkeley Berkeley California USA

ISBN: (纸本)9798400713941

Compliant mechanisms enable the creation of compact and easy-to-fabricate devices for tangible interaction. This work explores interconnected compliant mechanisms consisting of multiple joints and rigid bodies to transmit and process displacements as signals that result from physical interactions. As these devices are difficult to design due to their vast and complex design space, we developed a graph-based design algorithm and computational tool to help users program and customize such computational functions and procedurally model physical designs. When combined with active materials with actuation and sensing capabilities, these devices can also render and detect haptic interaction. Our design examples demonstrate the tool's capability to respond to relevant HCI concepts, including building modular physical interface toolkits, encrypting tangible interactions, and customizing user augmentation for accessibility. We believe the tool will facilitate the generation of new interfaces with enriched affordance.

关键词： Tangible interface

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Robust Phishing Detection in Consumer IoT Devices with ANOVA F-Test and Satin Bowerbird Optimization of Deep Learning Model

Robust Phishing Detection in Consumer IoT Devices with ANOVA...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Brij B. Gupta Akshat Gaurav Kwok Tai Chui Department of Computer Science and Information Engineering Asia University Taiwan University of Economics and Human Science Warsaw Poland Ronin Institute Montclair NJ USA Hong Kong Metropolitan University (HKMU) Hong Kong

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Combining ANOVA F-Test for feature selection with Satin Bowerbird Optimization (SBO) for hyperparameter tuning of a deep learning model, our proposed model delivers a strong phishing detection model for consumer IoT devices. In terms of accuracy and loss, the suggested CNN model, optimized using SBO, outfits GRU, LSTM, and RNN models. With a significant loss decrease, the model had a high accuracy of 92% and proved effective in spotting phishing attempts. Comprehensive assessments including feature selection, correlation analysis, and performance comparisons indicate the model's excellence in both training and testing stages, therefore providing an efficient means of improving security in IoT systems. This solution offers a scalable and effective means of phishing detection for smart home appliances.

关键词： Performance evaluation Accuracy Phishing Smart homes Feature extraction Internet of Things Security Optimization Analysis of variance Tuning

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A Graph-based Approach to Variant Extraction

arXiv

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arXiv 2025年

作者： Santcroos, Mark A. Kosters, Walter A. Lefter, Mihai Laros, Jeroen F.J. Vis, Jonathan K. Department of Human Genetics Leiden University Medical Center Netherlands Department of Clinical Genetics Leiden University Medical Center Netherlands Leiden Institute of Advanced Computer Science Leiden University Netherlands Netherlands

Motivation: Accurate variant descriptions are of paramount importance in the field of genetics. The domain is confronted with increasingly complex variants, making it more challenging to generate proper variant descriptions. Results: We present a graph based on all minimal alignments that is a complete representation of a variant and we provide three complementary extraction methods to derive variant descriptions from this graph. Our experiments show that our method in comparison with dbSNP results in identical HGVS descriptions for simple variants and more meaningful descriptions for complex variants. © 2025, CC BY.

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What do the Face and Voice Reveal? Investigating Trust Dynamics During human-Robot interaction 25

What do the Face and Voice Reveal? Investigating Trust Dynam...

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ACM/IEEE International Conference on human-Robot interaction (HRI)

作者： Abdullah Alzahrani Jauwairia Nasir Ahmad J. Tayeb Elisabeth André Muneeb I. Ahmad Department of Computer Science Swansea University Swansea United Kingdom Human-Centered Artificial Intelligence University of Augsburg Augsburg Germany Department of Information Technology King Abdulaziz University Jeddah Saudi Arabia

ISBN: (数字)9798350378931

ISBN: (纸本)9798350378948

Existing research has shown that vocal and non-vocal human cues correlate with human trust and distrust behaviours, suggesting their potential to measure human trust in robots in real-time. However, there is a lack of research in human-Robot interaction that integrates vocal and non-vocal cues into a comprehensive model to measure trust. This paper aims to estimate human trust in robots by examining vocal and non-vocal cues differences between trust and distrust states across multiple sessions of collaborative game-based HRI with 40 participants. Our analysis revealed that vocal and non-vocal human cues can indeed predict trust in HRI, with certain facial expressions, facial movements, and pitch being significant factors. Random Forest classifier achieved the highest accuracy (84 %) in classifying trust states, with key features such as facial expressions (fear, angry), facial blendshapes (cheekSquintRight, jawRight), and vocal characteristics (Duration, Harmonicity std) being the most predictive of trust. These findings demonstrate the importance of combining vocal and non-vocal cues for accurate trust measurement and highlight the potential for real-time trust assessment in robotic systems.

关键词： Accuracy Atmospheric measurements human-robot interaction Collaboration Particle measurements Real-time systems Robots Random forests Faces

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Synthesizing world models for bilevel planning

arXiv

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arXiv 2025年

作者： Ahmed, Zergham Tenenbaum, Joshua B. Bates, Christopher J. Gershman, Samuel J. Department of Computer Science Harvard University United States Department of Brain and Cognitive Sciences MIT United States Institute for Human and Machine Cognition Harvard University United States Department of Psychology Center for Brain Science Harvard University United States

Modern reinforcement learning (RL) systems have demonstrated remarkable capabilities in complex environments, such as video games. However, they still fall short of achieving human-like sample efficiency and adaptability when learning new domains. Theory-based reinforcement learning (TBRL) is an algorithmic framework specifically designed to address this gap. Modeled on cognitive theories, TBRL leverages structured, causal world models—"theories"—as forward simulators for use in planning, generalization and exploration. Although current TBRL systems provide compelling explanations of how humans learn to play video games, they face several technical limitations: their theory languages are restrictive, and their planning algorithms are not scalable. To address these challenges, we introduce TheoryCoder, an instantiation of TBRL that exploits hierarchical representations of theories and efficient program synthesis methods for more powerful learning and planning. TheoryCoder equips agents with general-purpose abstractions (e.g., "move to"), which are then grounded in a particular environment by learning a low-level transition model (a Python program synthesized from observations by a large language model). A bilevel planning algorithm can exploit this hierarchical structure to solve large domains. We demonstrate that this approach can be successfully applied to diverse and challenging grid-world games, where approaches based on directly synthesizing a policy perform poorly. Ablation studies demonstrate the benefits of using hierarchical abstractions. © 2025, CC BY.

关键词： Reinforcement learning

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Letters from Future Self: Augmenting the Letter-Exchange Exercise with LLM-based Future Self Agents to Enhance Young Adults’ Career Exploration

arXiv

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arXiv 2025年

作者： Jeon, Hayeon Yoon, Suhwoo Lee, Keyeun Kim, Seo Hyeong Kim, Esther Hehsun Cho, Seonghye Ko, Yena Yang, Soeun Dabbish, Laura Zimmerman, John Kim, Eun-Mee Lim, Hajin hci+d lab. Seoul National University Korea Republic of Department of Communication Seoul National University Korea Republic of Human-Computer Interaction Institute Carnegie Mellon University United States HCI Institute Carnegie Mellon University United States

Young adults often encounter challenges in career exploration. Self-guided interventions, such as the letter-exchange exercise, where participants envision and adopt the perspective of their future selves by exchanging letters with their envisioned future selves, can support career development. However, the broader adoption of such interventions may be limited without structured guidance. To address this, we integrated Large Language Model (LLM)-based agents that simulate participants’ future selves into the letter-exchange exercise and evaluated their effectiveness. A one-week experiment (N=36) compared three conditions: (1) participants manually writing replies to themselves from the perspective of their future selves (baseline), (2) future-self agents generating letters to participants, and (3) future-self agents engaging in chat conversations with participants. Results indicated that exchanging letters with future-self agents enhanced participants’ engagement during the exercise, while overall benefits of the intervention on future orientation, career self-concept, and psychological support remained comparable across conditions. We discuss design implications for AI-augmented interventions for supporting young adults’ career exploration. Copyright © 2025, The Authors. All rights reserved.

关键词： Chatbots

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Semantics-Controlled Gaussian Splatting for Outdoor Scene Reconstruction and Rendering in Virtual Reality

Semantics-Controlled Gaussian Splatting for Outdoor Scene Re...

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IEEE Annual International Symposium Virtual Reality

作者： Hannah Schieber Jacob Young Tobias Langlotz Stefanie Zollmann Daniel Roth Technical University of Munich Human-Centered Computing and Extended Reality Lab TUM University Hospital Clinic for Orthopedics and Sports Orthopedics Munich Germany Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany Department of Computer Science University of Otago Dunedin New Zealand Department of Computer Science Aarhus University Aarhus Denmark

ISBN: (数字)9798331536459

ISBN: (纸本)9798331536466

Advancements in 3D rendering like Gaussian Splatting (GS) allow novel view synthesis and real-time rendering in virtual reality (VR). However, GS-created 3D environments are often difficult to edit. For scene enhancement or to incorporate 3D assets, segmenting Gaussians by class is essential. Existing segmentation approaches are typically limited to certain types of scenes, e.g., "circular" scenes, to determine clear object boundaries. However, this method is ineffective when removing large objects in non-"circling" scenes such as large outdoor *** propose Semantics-Controlled GS (SCGS), a segmentation-driven GS approach, enabling the separation of large scene parts in uncontrolled, natural environments. SCGS allows scene editing and the extraction of scene parts for VR. Additionally, we introduce a challenging outdoor dataset, overcoming the "circling" setup. We outperform the state-of-the-art in visual quality on our dataset and in segmentation quality on the 3D-OVS dataset. We conducted an exploratory user study, comparing a 360-video, plain GS, and SCGS in VR with a fixed viewpoint. In our subsequent main study, users were allowed to move freely, evaluating plain GS and SCGS. Our main study results show that participants clearly prefer SCGS over plain GS. We overall present an innovative approach that surpasses the state-of-the-art both technically and in user experience.

关键词： Visualization Three-dimensional displays Semantics Virtual reality User interfaces Rendering (computer graphics) User experience Real-time systems

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Optimized Microfluidic Biosensor for Sensitive C-Reactive Protein Detection

Biosensors

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Biosensors 2025年第4期15卷 214-214页

作者： Tavakolidakhrabadi, Amirmahdi Stark, Matt Küenzi, Alexander Carrara, Sandro Bessire, Cédric Institute for Human Centered Engineering HuCE Department of Engineering and Computer Science Bern University of Applied Sciences Biel2501 Switzerland École Polytechnique Fédérale de Lausanne [EPFL Lausanne2002 Switzerland

Lateral flow immunoassays (LFIAs) were integrated into microfluidic chips and tested to enhance point-of-care testing (POCT), with the aim of improving sensitivity and expanding the range of CRP detection. The microfluidic approach improves upon traditional methods by precisely controlling fluid speed, thus enhancing sensitivity and accuracy in CRP measurements. The microfluidic approach also enables a one-step detection system, eliminating the need for buffer solution steps and reducing the nitrocellulose (NC) pad area to just the detection test line. This approach minimizes the non-specific binding of conjugated antibodies to unwanted areas of the NC pad, eliminating the need to block those areas, which enhances the sensitivity of detection. The gold nanoparticle method detects CRP in the high-sensitivity range of 1–10 (Formula presented.) g/mL, which is suitable for chronic disease monitoring. To broaden the CRP detection range, including infection levels beyond 10 (Formula presented.) g/mL, fluorescent labels were introduced, extending the measuring range from 1 to 70 (Formula presented.) g/mL. Experimental results demonstrate that integrating microfluidic technology significantly enhances operational efficiency by precisely controlling the flow rate and optimizing the mixing efficiency while reducing fabrication resources by eliminating the need for separate pads, making these methods suitable for resource-limited settings. Microfluidics also provides greater control over fluid dynamics compared to traditional LFIA methods, which contributes to enhanced detection sensitivity even with lower sample volumes and no buffer solution, helping to enhance the usability of POCT. These findings highlight the potential to develop accessible, accurate, and cost-effective diagnostic tools essential for timely medical interventions at the POC. © 2025 by the authors.

关键词： Microfluidics

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