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arXiv 2024年

作者： Pavanatto, Leonardo Biener, Verena Chandran, Jennifer Kalamkar, Snehanjali Lu, Feiyu Dudley, John J. Hu, Jinghui Nikki Ramirez-Saffy, G. Kristensson, Per Ola Giovannelli, Alexander Schlueter, Luke Müller, Jörg Grubert, Jens Bowman, Doug A. The Center for Human-Computer Interaction Department of Computer Science Virginia Tech BlacksburgVA United States The Visualization Research Center University of Stuttgart Germany Coburg University of Applied Sciences and Arts Germany The University of Bayreuth Germany The Department of Engineering University of Cambridge United Kingdom

Although access to sufficient screen space is crucial to knowledge work, workers often find themselves with limited access to display infrastructure in remote or public settings. While virtual displays can be used to extend the available screen space through extended reality (XR) head-worn displays (HWD), we must better understand the implications of working with them in public settings from both users’ and bystanders’ viewpoints. To this end, we conducted two user studies. We first explored the usage of a hybrid AR display across real-world settings and tasks. We focused on how users take advantage of virtual displays and what social and environmental factors impact their usage of the system. A second study investigated the differences between working with a laptop, an AR system, or a VR system in public. We focused on a single location and participants performed a predefined task to enable direct comparisons between the conditions while also gathering data from bystanders. The combined results suggest a positive acceptance of XR technology in public settings and show that virtual displays can be used to accompany existing devices. We highlighted some environmental and social factors. We saw that previous XR experience and personality can influence how people perceive the use of XR in public. In addition, we confirmed that using XR in public still makes users stand out and that bystanders are curious about the devices, yet have no clear understanding of how they can be used. Copyright © 2024, The Authors. All rights reserved.

关键词： Virtual environments

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AI-AR for Remote Visual Bridge Inspection by Drone 11

AI-AR for Remote Visual Bridge Inspection by Drone

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11th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2022

作者： Lapointe, Jean-Francois Sekkati, Hicham Allili, Mohand Said Hebbache, Loucif Amirkhani, Dariush Hammouche, Nadir Human-Computer Interaction Team Digital Technologies Research Centre National Research Council Canada Department of Computer Science and Engineering University of Quebec in Outaouais GatineauQCJ8X 3X7 Canada

Regular inspections of bridges and overpasses are required to ensure the safety of those infrastructures. This paper presents a system developed to do the remote visual inspection of concrete bridges and overpasses by combining drone, artificial intelligence and augmented reality technologies with the goal to assist bridge inspectors in conducting a remote visual inspection. It also reports on the results of a first field trial of this complex human-machine system. Deep learning networks can be trained to detect defects on independent images but the user interface must be adapted to track defects in a stream of images at a speed consistent with human vision to allow inspection. This first field trial allows for an initial estimate of a speed up factor of ten (10x) to conduct visual inspections. The developed system could lead to significant cost reduction to inspect the bridges and overpasses as well as significant reduction of downtimes for these transportation infrastructures. © 2022 International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.

关键词： Deep learning

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A NOVEL MODEL FOR THE MATERIAL SELECTION OF SMART OBJECTS FOR HAND-REHABILITATION: A CASE STUDY 24

A NOVEL MODEL FOR THE MATERIAL SELECTION OF SMART OBJECTS FO...

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24th International Conference on Engineering Design, ICED 2023

作者： Bos, Mikaël Ivar Karahanoǧlu, Armaǧan Haarman, Juliet Nizamis, Kostas Chair of Systems Engineering and Multidisciplinary Design Department of Design Production and Management Faculty of Engineering Technology University of Twente Enschede Netherlands Interaction Design Research Group Department of Design Production and Management Faculty of Engineering Technology University of Twente Enschede Netherlands Human Media Interaction Group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands

Home hand rehabilitation for stroke is becoming increasingly important due to logistic and financial challenges. Developing Daily-life Integrated Hand-rehabilitation Products (DIHP) aims to enable the application of at-home rehabilitation. The materials of these products are essential for their success, however, selecting materials for DIHP has not been investigated yet. Previous research on material selection showed that it is done strictly on material properties or based on a human-centered approach. Hence, in this study, we propose a hybrid model for choosing materials for DIHP. To achieve this, we first combined the findings of previous material selection processes into a comprehensive material selection model. We applied this model in a case study, in which we first selected three materials based on their properties. Following, we 3d printed a DIHP out of the chosen materials and tested the feeling of the materials with multiple expert groups. Our findings suggest that the proposed material selection method is promising and highlights that our comprehensive model provides more insights when compared to a strict material property-based selection. © The Author(s), 2023. Published by Cambridge University Press.

关键词： 3D printing

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ArsenicSkinNet: A Deep Learning Approach for Arsenicosis Skin Lesion Classification 17th

ArsenicSkinNet: A Deep Learning Approach for Arsenicosis Sk...

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17th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2024

作者： Aakash, Abhinav O’Halloran, Tony Obaido, George Mienye, Ibomoiye Domor Mienye, Ebikella Ogunruku, Oyindamola Omolara Aliyu, Mikail Department of Computer Science KIIT University Bhubaneswar India Department of Computer Science National University of Ireland Galway Ireland Center for Human-Compatible Artificial Intelligence Berkeley Institute for Data Science University of California BerkeleyCA United States Institute of Intelligent Systems University of Johannesburg Johannesburg South Africa Department of Decision Sciences Western Illinois University MacombIL61455 United States School of Public Health University of California BerkeleyCA United States

ISBN: (纸本)9789819606917

In many low-resource settings, the debilitating effects of arsenicosis remain a critical public health concern. This condition leads to serious health problems, including skin lesions, skin cancer, cardiovascular disease, and neurological impairment. Visual inspection is the standard practice for diagnosing arsenicosis;however, it is often subjective and can lead to variability in diagnosis. To address the urgent need for efficient arsenicosis skin detection, we propose ArsenicSkinNet, adapted from the EfficientNet architecture for the automatic classification of arsenicosis skin disease. ArsenicSkinNet is a state-of-the-art technique based on the EfficientNet neural network architecture and transfer learning, known for achieving high performance with fewer computational resources compared to traditional models. Our results show that ArsenicSkinNet achieved a classification accuracy of 99.61% on the arsenicosis skin lesion dataset. Our study demonstrates that deep learning could significantly improve the capacity for early detection and management of arsenicosis, thereby mitigating its impact on affected populations in low-resource settings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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

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

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： culturally responsive computing

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Opportunities for Serious Game Technologies to Engage Children with Autism in a Pakistani Sociocultural and Institutional Context: An investigation of the design space for serious game technologies to enhance engagement of children with autism and to facilitate external support provided 22

Opportunities for Serious Game Technologies to Engage Childr...

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Proceedings of the 34th Australian Conference on human-computer interaction

作者： Sara Muneeb Laurianne Sitbon Faizan Ahmad Research Center for Human Computer Interaction COMSATS University Islamabad Lahore Campus Pakistan and School of Computer Science Queensland University of Technology (QUT) Australia School of Computer Science Queensland University of Technology (QUT) Australia Department of Computer Science School of Technologies Cardiff Metropolitan University United Kingdom

ISBN: (纸本)9798400700248

Usability and design of serious game technologies have been widely investigated as academic interventions tools for children with autism. However, besides the technology, the sociocultural and institutional context also impacts the gameplay activities of children with autism as well as the external support provided by instructors. Therefore, this paper investigates i) the impact of sociocultural and institutional context over the engagement of children with autism with serious game technologies, ii) the types of external support children with autism are offered or require during gameplay session, and iii) the type of rewards and encouragers that can act as unbiased support to increase the engagement of the children with autism during *** paper investigates these factors with an inductive analysis of educational gameplay activities of 23 children with autism in Lahore, Pakistan. Our study found that sociocultural and institutional context has substantial impact over the gameplay activities of children with autism. Children required both physical and verbal support during the gameplay activities, and institutional context influences the behavior of instructors as well. Finally, dynamic animations, rewards and encouragers can keep the children engaged for longer in the gameplay.

关键词： serious game technologies children with autism Game-based learning support context and encouragers rewards

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Technology, Movement, and Play Is Hampering and Boosting Interactive Play 23

Technology, Movement, and Play Is Hampering and Boosting Int...

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10th ACM SIGCHI Annual Symposium on computer-human interaction in Play, CHI PLAY 2023

作者： Van Delden, Robby Reidsma, Dennis Postma, Dees Weijdom, Joris Márquez Segura, Elena Turmo Vidal, Laia Vega-Cebrián, José Manuel Tajadura-Jiménez, Ana Waern, Annika Park, Solip Hämäläinen, Perttu Font, José Maria Johnsson, Mats Rasmussen, Lærke Schjødt Elbæk, Lars Human Media Interaction University of Twente Enschede Netherlands Hku Theatre Hku University of the Arts Netherlands Department of Computer Science and Engineering Universidad Carlos Iii de Madrid Madrid Spain Ucl Interaction Centre University College London London United Kingdom Department of Informatics and Media Uppsala University Uppsala Sweden Aalto University Finland Department of Computer Science and Media Technology Faculty of Technology and Society Malmö University Sweden Department of Sport Sciences Faculty of Education and Society Malmö University Sweden Department of Sports Science and Clinical Biomechanics University of Southern Denmark Odense Denmark

ISBN: (纸本)9798400700293

In this paper, we highlight how including technology, movement or play can boost a design process but with unbalanced amounts can also hamper the process. We provide a set of examples where we miscalculated the amount of technology, movement, or play that was needed in a design activity in such a way that it became counterproductive and for each example mention possible adaptations. Finally, we highlight three existing approaches that can balance the overabundance of technology, movement, and play in design processes: activity-centered design, somaesthetic design, and perspective-changing movement-based design. © 2023 Owner/Author.

关键词： and process balancing movement play product technology

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Task-Agnostic Learning to Accomplish New Tasks

arXiv

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arXiv 2022年

作者： Zhang, Xianqi Wang, Xingtao Liu, Xu Wang, Wenrui Fan, Xiaopeng Zhao, Debin Research Center of Intelligent Interface and Human Computer Interaction the Department of Computer Science and Technology Harbin Institute of Technology Harbin150001 China

Reinforcement Learning (RL) and Imitation Learning (IL) have made great progress in robotic control in recent years. However, these methods show obvious deterioration for new tasks that need to be completed through new combinations of actions. RL methods heavily rely on reward functions that cannot generalize well for new tasks, while IL methods are limited by expert demonstrations which do not cover new tasks. In contrast, humans can easily complete these tasks with the fragmented knowledge learned from task-agnostic experience. Inspired by this observation, this paper proposes a task-agnostic learning method (TAL for short) that can learn fragmented knowledge from task-agnostic data to accomplish new tasks. TAL consists of four stages. First, the task-agnostic exploration is performed to collect data from interactions with the environment. The collected data is organized via a knowledge graph. Compared with the previous sequential structure, the knowledge graph representation is more compact and fits better for environment exploration. Second, an action feature extractor is proposed and trained using the collected knowledge graph data for task-agnostic fragmented knowledge learning. Third, a candidate action generator is designed, which applies the action feature extractor on a new task to generate multiple candidate action sets. Finally, an action proposal is designed to produce the probabilities for actions in a new task according to the environmental information. The probabilities are then used to select actions to be executed from multiple candidate action sets to form the plan. Experiments on a virtual indoor scene show that the proposed method outperforms the state-of-the-art offline RL method: CQL by 35.28% and the IL method: BC by 22.22%. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Ruffle&Riley: Towards the Automated Induction of Conversational Tutoring Systems

arXiv

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arXiv 2023年

作者： Schmucker, Robin Xia, Meng Azaria, Amos Mitchell, Tom Machine Learning Department Carnegie Mellon University United States Human-Computer Interaction Institute Carnegie Mellon University United States Department of Computer Science Ariel University

Conversational tutoring systems (CTSs) offer learning experiences driven by natural language interaction. They are known to promote high levels of cognitive engagement and benefit learning outcomes, particularly in reasoning tasks. Nonetheless, the time and cost required to author CTS content is a major obstacle to widespread adoption. In this paper, we introduce a novel type of CTS that leverages the recent advances in large language models (LLMs) in two ways: First, the system induces a tutoring script automatically from a lesson text. Second, the system automates the script orchestration via two LLM-based agents (Ruffle&Riley) with the roles of a student and a professor in a learning-by-teaching format. The system allows a free-form conversation that follows the ITS-typical inner and outer loop structure. In an initial between-subject online user study (N = 100) comparing Ruffle&Riley to simpler QA chatbots and reading activity, we found no significant differences in post-test scores. Nonetheless, in the learning experience survey, Ruffle&Riley users expressed higher ratings of understanding and remembering and further perceived the offered support as more helpful and the conversation as coherent. Our study provides insights for a new generation of scalable CTS technologies. Copyright © 2023, The Authors. All rights reserved.

关键词： Natural language processing systems

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What does ChatGPT know about natural science and engineering?

arXiv

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arXiv 2023年

作者： Balhorn, Lukas Schulze Weber, Jana M. Buijsman, Stefan Hildebrandt, Julian R. Ziefle, Martina Schweidtmann, Artur M. Delft University of Technology Delft Netherlands Human-Computer Interaction Center Chair of Communication Science RWTH Aachen University Aachen Germany

ChatGPT is a powerful language model from OpenAI that is arguably able to comprehend and generate text. ChatGPT is expected to have a large impact on society, research, and education. An essential step to understand ChatGPT’s expected impact is to study its domain-specific answering capabilities. Here, we perform a systematic empirical assessment of its abilities to answer questions across the natural science and engineering domains. We collected 594 questions from 198 faculty members across 5 faculties at Delft University of Technology. After collecting the answers from ChatGPT, the participants assessed the quality of the answers using a systematic scheme. Our results show that the answers from ChatGPT are on average perceived as "mostly correct". Two major trends are that the rating of the ChatGPT answers significantly decreases (i) as the complexity level of the question increases and (ii) as we evaluate skills beyond scientific knowledge, e.g., critical attitude. Copyright © 2023, The Authors. All rights reserved.

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