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arXiv 2024年

作者： Davari, Shakiba Stover, Daniel Giovannelli, Alexander Ilo, Cory Bowman, Doug A. Center for Human-Computer Interaction Department of Computer Science Virginia Tech BlacksburgVA United States

Recent advancements in Augmented Reality (AR) research have highlighted the critical role of context awareness in enhancing interface effectiveness and user experience. This underscores the need for intelligent AR (iAR) interfaces that dynamically adapt across various contexts to provide optimal experiences. In this paper, we (a) propose a comprehensive framework for contextaware inference and adaptation in iAR, (b) introduce a taxonomy that describes context through quantifiable input data, and (c) present an architecture that outlines the implementation of our proposed framework and taxonomy within iAR. Additionally, we present an empirical AR experiment to observe user behavior and record user performance, context, and user-specified adaptations to the AR interfaces within a context-switching scenario. We (d) explore the nuanced relationships between context and user adaptations in this scenario and discuss the significance of our framework in identifying these patterns. This experiment emphasizes the significance of context-awareness in iAR and provides a preliminary training dataset for this specific Scenario. © 2024, CC BY-NC-ND.

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Spaces to Think: A Comparison of Small, Large, and Immersive Displays for the Sensemaking Process

Spaces to Think: A Comparison of Small, Large, and Immersive...

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International Symposium on Mixed and Augmented Reality (ISMAR)

作者： Lee Lisle Kylie Davidson Leonardo Pavanatto Ibrahim A. Tahmid Chris North Doug A. Bowman Department of Computer Science Center for Human-Computer Interaction Virginia Tech Department of Computer Science Sanghani Center Virginia Tech

Analysts need to process large amounts of data in order to extract concepts, themes, and plans of action based upon their findings. Different display technologies offer varying levels of space and interaction methods that change the way users can process data using them. In a comparative study, we investigated how the use of single traditional monitor, a large, high-resolution two-dimensional monitor, and immersive three-dimensional space using the Immersive Space to Think approach impact the sensemaking process. We found that user satisfaction grows and frustration decreases as available space increases. We observed specific strategies users employ in the various conditions to assist with the processing of datasets. We also found an increased usage of spatial memory as space increased, which increases performance in artifact position recall tasks. In future systems supporting sensemaking, we recommend using display technologies that provide users with large amounts of space to organize information and analysis artifacts.

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Breath Tools: Exploring the Effects on Adherence and User Experience of 4 Sounds Assisting Runners with Coupling Breath to Steps

Breath Tools: Exploring the Effects on Adherence and User Ex...

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Extended Abstracts of the 2023 CHI Conference on human Factors in Computing Systems, CHI EA 2023

作者： Van Rheden, Vincent Harbour, Eric Finkenzeller, Thomas Meschtscherjakov, Alexander Human Computer Interaction Division Department of Artificial Intelligence and Human Interfaces University of Salzburg Salzburg Austria Department of Sport and Exercise Science University of Salzburg Rif Hallein Austria

ISBN: (纸本)9781450394222

Running is a popular sport worldwide. Locomotor-Respiratory Coupling (LRC), a breathing technique in which the breath is coupled to steps, can positively impact running experience and economy. LRC may be difficult to learn and maintain during a run. Sound cues are a promising method to guide LRC while running. We designed four guiding sounds and evaluated these in a preliminary study (N=9). All sounds supported participants with LRC (on average 74.9±16.2% of time attached to the LRC rhythm). Based on our findings we advocate sound guidance for LRC and present directions to improve adherence and run experience. © 2023 Owner/Author.

关键词： Sports

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Can I Trust You? Exploring the Impact of Misleading AI Suggestions on User's Trust 3

Can I Trust You? Exploring the Impact of Misleading AI Sugge...

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3rd IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2024

作者： Pretolesi, Daniele Zechner, Olivia Schrom-Feiertag, Helmut Tscheligi, Manfred Center for Technology Experience Austrian Institute of Technology Vienna Austria University of Salzburg Department of Artificial Intelligence and Human Interfaces Salzburg Austria University of Salzburg Human-Computer Interaction Division Department of Artificial Intelligence and Human Interfaces Salzburg Austria

ISBN: (纸本)9798350378009

Extended Reality (XR) offers immersive and interactive training experiences across fields like policing and medicine. However, tailoring training difficulty to individual needs remains a challenge. AI-driven training assistants could be a promising solution to personalize XR training by proposing adjustments based on user data. Yet, the effectiveness of such suggestions and the user's ability to discern accurate recommendations are not fully understood. This study investigates the effectiveness of suggestions within an AI-driven interface for proposing scenario changes in an XR training environment. We employ a 2× 2 experimental design, manipulating both the presence of stress during training (stressful versus neutral scenarios) and the biosignal information provided by the trainees during training (congruent versus incongruent). This work aims to determine whether trainers can reject misleading suggestions made by the system and how this affects their trust in the system. A sample of N=17 medical first responder trainers was recruited from three European countries. Our results show that the majority of the participants did not reject incorrect suggestions and the expected decrease in trust and perceived transparency towards the misleading interface was not observed. These results underscore the complex interplay between user perception, trust, and technological interface design, highlighting avenues for further research and refinement in the development of AI-driven suggestion systems. © 2024 IEEE.

关键词： Decision making

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Evaluating the Feasibility of Predicting Information Relevance During Sensemaking with Eye Gaze Data

Evaluating the Feasibility of Predicting Information Relevan...

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International Symposium on Mixed and Augmented Reality (ISMAR)

作者： Ibrahim A. Tahmid Lee Lisle Kylie Davidson Kirsten Whitley Chris North Doug A. Bowman Department of Computer Science Center for Human-Computer Interaction Virginia Tech US Department of Defense

Eye gaze patterns vary based on reading purpose and complexity, and can provide insights into a reader’s perception of the content. We hypothesize that during a complex sensemaking task with many text-based documents, we will be able to use eye-tracking data to predict the importance of documents and words, which could be the basis for intelligent suggestions made by the system to an analyst. We introduce a novel eye-gaze metric called ‘GazeScore’ that predicts an analyst’s perception of the relevance of each document and word when they perform a sensemaking task. We conducted a user study to assess the effectiveness of this metric and found strong evidence that documents and words with high GazeScores are perceived as more relevant, while those with low GazeScores were considered less relevant. We explore potential real-time applications of this metric to facilitate immersive sensemaking tasks by offering relevant suggestions.

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"I am a Technology Creator": Black Girls as Technosocial Change Agents in a Culturally-Responsive Robotics Camp 25

"I am a Technology Creator": Black Girls as Technosocial Cha...

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

作者： Li, Chun Solyst, Jaemarie Scott, Safiyyah Howse, Gabriella Nkrumah, Tara Walker, Erin Ogan, Amy Stewart, Angela E.B. Department of Informatics and Networked System University of Pittsburgh PittsburghPA United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States School of Computer Science Carnegie Mellon University PittsburghPA United States Center for Gender Equity in Science and Technology Arizona State University TempeAZ United States Department of Computer Science University of Pittsburgh PittsburghPA United States

ISBN: (纸本)9798400713941

Black girls and women have long been creators in computing spaces. However, much computing education positions Black girls as workers who execute tasks for others' purposes. Our work takes a different approach by positioning Black girls as technosocial change agents who challenge dominant narratives and construct more liberating identities and social relations as they create new technologies. We draw on data from seven Black girls, ages 9-12, who participated in a 20-hour culturally responsive computing (CRC) camp focused on robotics. Using a thematic analysis approach, we explore how these Black girls demonstrate and enhance their technosocial change agency (TSCA) throughout the camp. We identify themes related to how creating technology helps Black girls refine and fulfill their definitions of technical creators and develop agency through technology creation. We discuss computing education and technology design recommendations within the TSCA framework to support learners' emerging TSCA in future CRC programs. © 2025 Copyright held by the owner/author(s).

关键词： Educational robots

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Histo-Genomic Knowledge Association for Cancer Prognosis from Histopathology Whole Slide Images

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IEEE Transactions on Medical Imaging 2025年第5期44卷 2170-2181页

作者： Wang, Zhikang Zhang, Yumeng Xu, Yingxue Imoto, Seiya Chen, Hao Song, Jiangning Monash Biomedicine Discovery Institute Australia Monash University Department of Biochemistry and Molecular Biology Australia The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Hong Kong The University of Tokyo Human Genome Center Institute of Medical Science Japan

Histo-genomic multi-modal methods have emerged as a powerful paradigm, demonstrating significant potential for cancer prognosis. However, genome sequencing, unlike histopathology imaging, is still not widely accessible in underdeveloped regions, limiting the application of these multi-modal approaches in clinical settings. To address this, we propose a novel Genome-informed Hyper-Attention Network, termed G-HANet, which is capable of effectively learning the histo-genomic associations during training to elevate uni-modal whole slide image (WSI)-based inference for the first time. Compared with the potential knowledge distillation strategy for this setting (i.e., distilling a multi-modal network to a uni-modal network), our end-to-end model is superior in training efficiency and learning cross-modal interactions. Specifically, the network comprises cross-modal associating branch (CAB) and hyper-attention survival branch (HSB). Through the genomic data reconstruction from WSIs, CAB effectively distills the associations between functional geno-types and morphological phenotypes and offers insights into the gene expression profiles in the feature space. Subsequently, HSB leverages the distilled histo-genomic associations as well as the generated morphology-based weights to achieve the hyper-attention modeling of the patients from both histopathology and genomic perspectives to improve cancer prognosis. Extensive experiments are conducted on five TCGA benchmarking datasets and the results demonstrate that G-HANet significantly outperforms the state-of-the-art WSI-based methods and achieves competitive performance with genome-based and multi-modal methods. G-HANet is expected to be explored as a useful tool by the research community to address the current bottleneck of insufficient histo-genomic data pairing in the context of cancer prognosis and precision oncology. The code is available at https://***/ZacharyWang-007/G-HANet. © 1982-2012 IEEE.

关键词： Gene expression

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Exploring Multiscale Navigation of Homogeneous and Dense Objects with Progressive Refinement in Virtual Reality

Exploring Multiscale Navigation of Homogeneous and Dense Obj...

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IEEE Annual International Symposium Virtual Reality

作者： Leonardo Pavanatto Alexander Giovannelli Brian Giera Timo Bremer Haichao Miao Doug A. Bowman Department of Computer Science Center for Human-Computer Interaction Virginia Tech USA Center for Applied Scientific Computing Lawrence Livermore National Laboratory USA

ISBN: (数字)9798331536459

ISBN: (纸本)9798331536466

Locating small features in a large, dense object in virtual reality (VR) poses a significant interaction challenge. While existing multiscale techniques support transitions between various levels of scale, they are not focused on handling dense, homogeneous objects with hidden features. We propose a novel approach that applies the concept of progressive refinement to VR navigation, enabling focused inspections. We conducted a user study where we varied two independent variables in our design, navigation style (STRUCTURED vs. UNSTRUCTURED) and display mode (SELECTION vs. EVERYTHING), to better understand their effects on efficiency and awareness during multiscale navigation. Our results showed that unstructured navigation can be faster than structured and that displaying only the selection can be faster than displaying the entire object. However, using an everything display mode can support better location awareness and object understanding.

关键词： human computer interaction Location awareness Three-dimensional displays Navigation Virtual reality Inspection

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CLUE HOG: An Immersive Competitive Lock-Unlock Experience using Hook On Go-Go Technique for Authentication in the Metaverse

CLUE HOG: An Immersive Competitive Lock-Unlock Experience us...

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

作者： Alexander Giovannelli Francielly Rodrigues Shakiba Davari Ibrahim A. Tahmid Logan Lane Cherelle Connor Kylie Davidson Gabriella N. Ramirez Brendan David-John Doug A. Bowman Department of Computer Science Center for Human-Computer Interaction Virginia Tech Blacksburg VA USA

This paper presents our solution to the 2023 3DUI Contest challenge. Our goal was to provide an immersive VR experience to engage users in privately securing and accessing information in the Metaverse while improving authentication-related interactions inside our virtual environment. To achieve this goal, we developed an authentication method that uses a virtual environment's individual assets as security tokens. To improve the token selection process, we introduce the HOG interaction technique. HOG combines two classic interaction techniques, Hook and Go-Go, and improves approximate object targeting and further obfuscation of user password token selections. We created an engaging mystery-solving mini-game to demonstrate our authentication method and interaction technique.

关键词： Three-dimensional displays Metaverse Conferences Authentication Passwords User interfaces Observers

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Progress of research in the application of ultrasound technology for the treatment of Alzheimer’s disease

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Neural Regeneration Research 2025年第10期20卷 2823-2837页

作者： Qiuquan Cai Lianghui Meng Meina Quan Ling Wang Jing Ren Chenguang Zheng Jiajia Yang Dong Ming Academy of Medical Engineering and Translational Medicine Tianjin UniversityTianjinChina Innovation Center for Neurological Disorders Xuanwu HospitalCapital Medical UniversityBeijingChina Brain-Computer Interaction and Human-Machine Fusion Haihe Laboratory Tianjin UniversityTianjinChina Tianjin Key Laboratory of Brain Science and Neural Engineering Tianjin UniversityTianjinChina

Alzheimer’s disease is a common neurodegenerative disorder defined by decreased reasoning abilities,memory loss,and cognitive *** presence of the blood-brain barrier presents a major obstacle to the development of effective drug therapies for Alzheimer’s *** use of ultrasound as a novel physical modulation approach has garnered widespread attention in recent *** a safe and feasible therapeutic and drug-delivery method,ultrasound has shown promise in improving cognitive *** article provides a summary of the application of ultrasound technology for treating Alzheimer’s disease over the past 5 years,including standalone ultrasound treatment,ultrasound combined with microbubbles or drug therapy,and magnetic resonance imaging-guided focused ultrasound *** is placed on the benefits of introducing these treatment methods and their potential *** found that several ultrasound methods can open the blood-brain barrier and effectively alleviate amyloid-βplaque *** believe that ultrasound is an effective therapy for Alzheimer’s disease,and this review provides a theoretical basis for future ultrasound treatment methods.

关键词： Alzheimer’s disease blood-brain barrier drugs magnetic resonance imaging-guided focused ultrasound microbubbles scanning ultrasound ultrasound ultrasound stimulation

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