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Do boxes affect exploration behavior and performance in group-in-a-box layouts?

JOURNAL OF VISUALIZATION 2025年第2期28卷 449-462页

作者： Ueno, Yuki Natsukawa, Hiroaki Koyamada, Koji Arizona State Univ Ira A Fulton Sch Engn Tempe AZ 85281 USA Osaka Seikei Univ Fac Data Sci Osaka Japan

The group-in-a-box (GIB) layout is an efficient graph-drawing method designed to visualize the group structure of graphs. The layout communicates group sizes and both within-group and between-group network structures simultaneously. The layout is characterized by its composition of multiple elements, including nodes, edges, and boxes. However, there is limited empirical guidance on how these elements should be combined. In this paper, we measured participants' task performance and eye movements while identifying the group with the largest number of internal edges. We investigated the effect of visualization elements on task performance while controlling the density of internal edges and the box size. The results revealed that the box size in a GIB layout significantly affects the task accuracy either positively or negatively while eye-tracking data suggest that participants focused on internal edges, not the box size. These findings contribute empirical guidance for GIB layout design and lay the groundwork for future research as GIB layout becomes more widely used.

关键词： human-centered computing Visualization Empirical studies in visualization Network visualization

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The tension of usable safety, security and privacy

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i-com 2025年第1期24卷 1-7页

作者： Kaufhold, Marc-André Mentler, Tilo Nestler, Simon Reuter, Christian Technical University of Darmstadt Darmstadt Germany Human Computer Interaction und User Experience Trier University of Applied Sciences Trier Germany Faculty of Computer Science Technische Hochschule Ingolstadt Ingolstadt Germany Technical University of Darmstadt Darmstadt Germany

Local disasters such as the Ahr Valley flood in Germany, the international backdrop of the Russo-Ukrainian War, or the global impact of the COVID-19 pandemic place high demands on the people and organisations that are involved in these situations and contexts to save lives, mitigate damage, provide comfort, or organise reconstruction. Novel technologies are constantly making their way into everyday life, such as artificial intelligence, big data, decentralised networks, internet of things, or virtual reality. Their adaptation, acceptance, usability, usefulness, and legal framework conditions for safety-critical systems must be researched and tested thoroughly. In this special issue, we investigate the use of computer-based solutions in areas and situations of direct relevance to people's lives and well-being (Usable Safety), as well as contributions to user-oriented resilience concepts of sociotechnical systems concerning potential attacks (Usable Security) and data protection mechanisms (Usable Privacy). © 2025 the author(s), published by De Gruyter, Berlin/Boston.

关键词： usable safety usable security safety-critical systems human-centered computing

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A Cooperative Game Theory-Based Feature Selection for Efficient Hand Grasp Classification Using Minimal Number of sEMG Signals

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ACM TRANSACTIONS ON computing FOR HEALTHCARE 2025年第1期6卷 1-22页

作者： Singh, Shashank kumar Chaturvedi, Amrita Indian Inst Technol BHU Dept Comp Sci & Engn Varanasi India

Deploying bio-electrical signals and image processing (visual) techniques are the two popular means to provide input to generate grasp control for robotic and prosthetic devices. Visual perception-based techniques rely on computationally expensive image processing algorithms and are affected by lighting conditions. In contrast, grasp control based on bio-electric signals such as surface electromyography (sEMG) is invariant to lighting conditions. It can reflect human intent to hand motion or grasp with lesser computational costs. In this article, we propose an efficient machine learning pipeline to classify hand grasp using a minimal number of sEMG sensors. A cooperative game theory-based feature selection technique is applied to find the representative feature subset. The feature selection method uses a modified marginal contribution based on the class distribution coefficient to generate feature ranking. This feature ranking is further used to find the most representative feature subset from the extracted feature set. Our proposed pipeline has been evaluated on a benchmark dataset and has achieved a classification accuracy of 98.20%, using single-channel EMG when coupled with the XGBoost classifier. Thorough assessments were conducted to confirm the reliability of the results obtained. Our proposed pipeline holds the potential to facilitate the development of cost-effective sEMG prosthetics.

关键词： human-centered computing computing methodologies Neural Networks

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Advancing Wearable BCI: Headphone EEG for Cognitive Load Detection in Lab and Field

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PROCEEDINGS OF THE ACM ON INTERACTIVE MOBILE WEARABLE AND UBIQUITOUS TECHNOLOGIES-IMWUT 2025年第1期9卷 1-26页

作者： Knierim, Michael t. Zimny, Christian Ivucic, Gabriel Roeddiger, Tobias Karlsruhe Inst Technol Karlsruhe Germany Univ Munster Munster Germany Univ Bremen Bremen Germany

By tracking changes in brain activity, researchers are constantly working to revolutionise human-technology interaction. Unfortunately, such brain-computer interfaces still face limitations in terms of wearability, making large-scale data collection difficult. While gel-based wearable solutions exist, validated dry electrode systems are needed for practical everyday use. In this article, two experiments with 50 participants and 146 recordings were conducted in laboratory and field settings to compare the wearability and performance of two dry-electrode EEG systems: the Open ExG headphones and the OpenBCI Ultracortex full-head EEG. Our results show that the headphone EEG is perceived as more wearable, has equal signal quality and recording reliability when set up by a trained experimenter in the lab, and shows reliable performance in a relevant application scenario: classification of cognitive load levels across four tasks. Field evaluations further validate these results through reliable load monitoring across recording sessions, after self-setup by study participants at home. While some limitations remain for wider field use of dry-electrode headphone EEG, we highlight necessary and achievable improvements for future system and study designs for real-world use.

关键词： human-centered computing Empirical studies in HCI Hardware Neural systems

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Exploring Cultural and Intergenerational Dynamics in Voice Assistant Design for Chinese Older Adults

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PROCEEDINGS OF THE ACM ON INTERACTIVE MOBILE WEARABLE AND UBIQUITOUS TECHNOLOGIES-IMWUT 2025年第1期9卷 1-18页

作者： Qian, Zhigu Fu, Jiaojiao Zhou, Yangfan Fudan Univ Sch Comp Sci Shanghai Peoples R China Shanghai Key Lab Intelligent Informat Proc Shanghai Peoples R China East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai Peoples R China

In this study, we examined voice assistant (VA) use among Chinese older adults through interviews with 12 older adults and 6 of their adult children, as well as observations of VA use in solo and family settings. Our findings reveal that older adults are motivated to use VA for social interaction but encounter barriers due to limited cultural and linguistic customization, such as difficulty understanding regional dialects. Additionally, adult children play a dual role, providing necessary support while sometimes limiting independent use through overprotective tendencies. These results highlight the importance of designing VA with culturally responsive features and adaptable language models that consider the unique linguistic and social needs of older Chinese adults. This study contributes to the development of VA that balances autonomy and family support, enriching the technology's effectiveness for older adults in China and potentially in other similar cultural contexts.

关键词： human-centered computing Empirical studies in HCI

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Transient Authentication from First-Person-View Video

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PROCEEDINGS OF THE ACM ON INTERACTIVE MOBILE WEARABLE AND UBIQUITOUS TECHNOLOGIES-IMWUT 2025年第1期9卷 1-31页

作者： Nguyen, Le ngu Findling, Rainhard dieter Poikela, Maija Zuo, Si Sigg, Stephan Univ Oulu Oulu Finland Aalto Univ Aalto Finland Charite Univ Med Berlin Berlin Inst Hlth Berlin Germany

We propose PassFrame, a system which utilizes first-person-view videos to generate personalized authentication challenges based on human episodic memory of event sequences. From the recorded videos, relevant (memorable) scenes are selected to form image-based authentication challenges. These authentication challenges are compatible with a variety of screen sizes and input modalities. As the popularity of using wearable cameras in daily life is increasing, PassFrame may serve as a convenient personalized authentication mechanism to screen-based appliances and services of a camera wearer. We evaluated the system in various settings including a spatially constrained scenario with 12 participants and a deployment on smartphones with 16 participants and more than 9 hours continuous video per participant. The authentication challenge completion time ranged from 2.1 to 9.7 seconds (average: 6 sec), which could facilitate a secure yet usable configuration of three consecutive challenges for each login. We investigated different versions of the challenges to obfuscate potential privacy leakage or ethical concerns with 27 participants. We also assessed the authentication schemes in the presence of informed adversaries, such as friends, colleagues or spouses and were able to detect attacks from diverging login behaviour.

关键词： Hardware Emerging technologies Security and privacy human-centered computing Ubiquitous and mobile computing systems and tools human and societal aspects of security and privacy

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LGL-BCI: A Motor-Imagery-Based Brain-Computer Interface with Geometric Learning

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PROCEEDINGS OF THE ACM ON INTERACTIVE MOBILE WEARABLE AND UBIQUITOUS TECHNOLOGIES-IMWUT 2025年第1期9卷 1-28页

作者： Lu, Jianchao Tian, Yuzhe Zhang, Yang Sheng, Quan Z. Zheng, Xi Macquarie Univ Sch Comp Macquarie Pk NSW 2109 Australia Univ North Texas Dept Informat Sci Denton TX 76207 USA

Brain-computer interfaces are groundbreaking technology whereby brain signals are used to control external devices. Despite some advances in recent years, electroencephalogram (EEG)-based motor-imagery tasks face challenges, such as amplitude and phase variability and complex spatial correlations, with a need for smaller models and faster inference. In this study, we develop a prototype, called the Lightweight Geometric Learning Brain-Computer Interface (LGL-BCI), which uses our customized geometric deep learning architecture for swift model inference without sacrificing accuracy. LGL-BCI contains an EEG channel selection module via a feature decomposition algorithm to reduce the dimensionality of a symmetric positive definite matrix, providing adaptiveness among the continuously changing EEG signal. Meanwhile, a built-in lossless transformation helps boost the inference speed. The performance of our solution was evaluated using two real-world EEG devices and two public EEG datasets. LGL-BCI demonstrated significant improvements, achieving an accuracy of 82.54% compared to 62.22% for the state-of-the-art approach. Furthermore, LGL-BCI uses fewer parameters (64.9Kvs. 183.7K), highlighting its computational efficiency. These findings underscore both the superior accuracy and computational efficiency of LGL-BCI, demonstrating the feasibility and robustness of geometric deep learning in motor-imagery brain-computer interface applications.

关键词： human-centered computing Ubiquitous and mobile computing

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What is human-centered AI and Why Does It Matter? 2025

What is Human-Centered AI and Why Does It Matter?

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56th Technical Symposium on Computer Science Education

作者： Aragon, Cecilia Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9798400705328

There have been extraordinary advances in our ability to collect, analyze, and interpret vast amounts of data which have transformed the fundamental nature of artificial intelligence (AI). The human aspects of AI, including how to support creativity and human insight without violating individual rights, how to address ethical concerns, and the consideration of societal impacts, have received less attention. Yet these human issues are becoming increasingly vital to the future of AI. Dr. Aragon will reflect on a 30-year career in data science and AI in industry, government, and academia, discuss what it means for AI to be both rigorous and human-centered, and speculate upon future directions for data science and AI.

关键词： human-centered AI human-centered computing

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Analyzing the Shifts in Users Data Focus in Exploratory Visual Analysis 25

Analyzing the Shifts in Users Data Focus in Exploratory Visu...

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30th International Conference on Intelligent User Interfaces, IUI 2025

作者： Saha, Sanad Aryal, Nischal Battle, Leilani Termehchy, Arash Computer Science Oregon State University CorvallisOR United States Paul G. Allen School of Computer Science and Engineering University of Washington SeattleWA United States

ISBN: (纸本)9798400713064

Users often begin exploratory visual analysis (EVA) without clear analysis goals but iteratively refine them as they learn more about their data. As an essential step in data science, researchers want to aid EVA by developing responsive and personalized visualization tools. For this, accurate models of users' exploration behavior are becoming increasingly vital. However, many computational models assume that the human exploration behavior isstatic, which goes against thedynamic nature of EVA. In this benchmark study, we investigate how users dynamically shift their data focus in EVA and seek to find the best online learning methods for modeling users' data focus shifts. Through empirical analyses, we find reinforcement learning algorithms are better in this regard than existing approaches from visualization research. Furthermore, we discuss our findings and their impact on the future of user modeling for visualization system design. © 2025 Copyright held by the owner/author(s).

关键词： human-centered computing

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H₂E: Hand, Head, Eye a Multimodal Cascade of Natural Inputs

H<sub>2</sub>E: Hand, Head, Eye a Multimodal Cascade of Natu...

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2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025

作者： Patel, Khushman Phadnis, Vrushank Gonzalez, Eric J. Gellersen, Hans Pfeuffer, Ken Gonzalez-Franco, Mar Google United States Lancaster and Aarhus University Aarhus University Denmark

Eye-based interaction techniques for extended reality, such as gaze and pinch, are simple to use however suffer from input precision issues. We present H2E, an integrated fine and coarse-grained pointing framework tha... 详细信息

ISBN: (纸本)9798331514846

Eye-based interaction techniques for extended reality, such as gaze and pinch, are simple to use however suffer from input precision issues. We present H₂E, an integrated fine and coarse-grained pointing framework that cascades Hand, Head, and Eye inputs. We further introduce the MagicPinch gesture as an example of the framework, which allows for smooth transitioning between fine and coarse-grained pointing. When combined together, after users initiate a pinch gesture, a cursor appears midway during the pinch at the position of the gaze, which can be dragged by head pointing if needed before pinch confirmation. This has the advantage that it can add a precision component without changing the semantics of the technique. In this paper, we describe the design of the H₂E framework and implementation of the MagicPinch technique. Furthermore, we present an evaluation of our method in a Fitts-based user study, exploring the speed-accuracy trade-offs against a gaze and pinch interaction baseline. © 2025 IEEE.

关键词： human-centered computing human-centered computing human-centered computing Mixed/augmented reality User interface toolkits Virtual reality

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