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Virtual Reality in Manufacturing Education: A Scoping Review Indicating State-of-the-Art, Benefits, and Challenges Across Domains, Levels, and Entities

arXiv

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arXiv 2025年

作者： Ipsita, Ananya Kaki, Ramesh Liu, Ziyi Patel, Mayank Duan, Runlin Deshpande, Lakshmi Yuan, Lin-Ping Lowell, Victoria Maharaj, Ashok Peppler, Kylie Feiner, Steven Ramani, Karthik School of Mechanical Engineering Purdue University West LafayetteIN47907 United States Department of Mechanical Engineering Birla Institute of Technology and Science Pilani Hyderabad Campus Telangana Hyderabad500078 India School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Learning Design and Technology Purdue University West LafayetteIN47907 United States Department of Computer Science Columbia University New YorkNY10027 United States School of Information and Computer Sciences University of California IrvineCA92697 United States School of Education University of California IrvineCA92697 United States Hong Kong University of Science and Technology Hong Kong TCS Avapresence- Spatial Intelligence Lab

To address the shortage of a skilled workforce in the U.S. manufacturing industry, immersive Virtual Reality (VR)-based training solutions hold promising potential. To effectively utilize VR to meet workforce demands, it is important to understand the role of VR in manufacturing education. Therefore, we conduct a scoping review in the field. As a first step, we used a 5W1H (What, Where, Who, When, Why, How) formula as a problem-solving approach to define a comprehensive taxonomy that can consider the role of VR from all relevant possibilities. Our taxonomy categorizes VR applications across three key aspects: (1) Domains, (2) Levels, and (3) Entities. Using a systematic literature search and analysis, we reviewed 108 research articles to find the current state, benefits, challenges, and future opportunities of VR in the field. It was found that VR has been explored in a variety of areas and provides numerous benefits to learners. Despite these benefits, its adoption in manufacturing education is limited. This review discusses the identified barriers and provides actionable insights to address them. These insights can enable the widespread usage of immersive technology to nurture and develop a workforce equipped with the skills required to excel in the evolving landscape of manufacturing. © 2025, CC BY.

关键词： Virtual environments

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AiGet: Transforming Everyday Moments into Hidden Knowledge Discovery with AI Assistance on Smart Glasses

arXiv

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arXiv 2025年

作者： Cai, Runze Janaka, Nuwan Kim, Hyeongcheol Chen, Yang Zhao, Shengdong Huang, Yun Hsu, David Synteraction Lab School of Computing National University of Singapore Singapore Synteraction Lab Smart Systems Institute National University of Singapore Singapore Synteraction Lab National University of Singapore Singapore College of Design and Engineering National University of Singapore Singapore Synteraction Lab School of Creative Media Department of Computer Science City University of Hong Kong Hong Kong China School of Information Sciences University of Illinois at Urbana-Champaign Champaign IL United States School of Computing Smart Systems Institute National University of Singapore Singapore

Unlike the free exploration of childhood, the demands of daily life reduce our motivation to explore our surroundings, leading to missed opportunities for informal learning. Traditional tools for knowledge acquisition are reactive, relying on user initiative and limiting their ability to uncover hidden interests. Through formative studies, we introduce AiGet, a proactive AI assistant integrated with AR smart glasses, designed to seamlessly embed informal learning into low-demand daily activities (e.g., casual walking and shopping). AiGet analyzes real-time user gaze patterns, environmental context, and user profiles, leveraging large language models to deliver personalized, context-aware knowledge with low disruption to primary tasks. In-lab evaluations and real-world testing, including continued use over multiple days, demonstrate AiGet’s effectiveness in uncovering overlooked yet surprising interests, enhancing primary task enjoyment, reviving curiosity, and deepening connections with the environment. We further propose design guidelines for AI-assisted informal learning, focused on transforming everyday moments into enriching learning experiences. © 2025, CC BY.

关键词： Contrastive Learning

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AiGet: Transforming Everyday Moments into Hidden Knowledge Discovery with AI Assistance on Smart Glasses 25

AiGet: Transforming Everyday Moments into Hidden Knowledge D...

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

作者： Runze Cai Nuwan Janaka Hyeongcheol Kim Yang Chen Shengdong Zhao Yun Huang David Hsu Synteraction Lab School of Computing National University of Singapore Singapore Singapore Smart Systems Institute Synteraction lab National University of Singapore Singapore Singapore Synteraction Lab National University of Singapore Singapore Singapore College of Design and Engineering National University of Singapore Singapore Singapore School of Creative Media & Department of Computer Science City University of Hong Kong Hong Kong China School of Information Sciences University of Illinois at Urbana-Champaign Champaign Illinois USA School of Computing Smart Systems Institute National University of Singapore Singapore Singapore

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Communication Strategy on Macro-and-Micro Traffic State in Cooperative Deep Reinforcement Learning for Regional Traffic Signal Control

arXiv

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arXiv 2025年

作者： Gu, Hankang Wang, Shangbo Jia, Dongyao Zhang, Yuli Luo, Yanrong Mao, Guoqiang Wang, Jianping Lim, Eng Gee School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou215123 China Department of Computer Science University of Liverpool LiverpoolL69 3GJ United Kingdom Department of Engineering and Design University of Sussex BrightonBN1 9RH United Kingdom School of Advanced Technology China International Business School Suzhou Xi’an Jiaotong-Liverpool University Suzhou215123 China ISN State Key Lab Xidian University Xi’an710126 China Department of Computer Science City University of Hong Kong Kowloon Hong Kong City University of Hong Kong Shenzhen Research Institute China

Adaptive Traffic Signal Control (ATSC) has become a popular research topic in intelligent transportation systems. Regional Traffic Signal Control (RTSC) using the Multi-agent Deep Reinforcement Learning (MADRL) technique has become a promising approach for ATSC due to its ability to achieve the optimum trade-off between scalability and optimality. Most existing RTSC approaches partition a traffic network into several disjoint regions, followed by applying centralized reinforcement learning techniques to each region. However, the pursuit of cooperation among RTSC agents still remains an open issue and no communication strategy for RTSC agents has been investigated. In this paper, we propose communication strategies to capture the correlation of micro-traffic states among lanes and the correlation of macro-traffic states among intersections. We first justify the evolution equation of the RTSC process is Markovian via a system of store-and-forward queues. Next, based on the evolution equation, we propose two GAT-Aggregated (GA2) communication modules—GA2-Naive and GA2-Aug to extract both intra-region and inter-region correlations between macro and micro traffic states. While GA2-Naive only considers the movements at each intersection, GA2-Aug also considers the lane-changing behavior of vehicles. Two proposed communication modules are then aggregated into two existing novel RTSC frameworks—RegionLight and Regional-DRL. Experimental results demonstrate that both GA2-Naive and GA2-Aug effectively improve the performance of existing RTSC frameworks under both real and synthetic scenarios. Hyperparameter testing also reveals the robustness and potential of our communication modules in large-scale traffic networks. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Pixel Memories: Do Lifelog Summaries Fail to Enhance Memory but Offer Privacy-Aware Memory Assessments? 25

Pixel Memories: Do Lifelog Summaries Fail to Enhance Memory ...

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

作者： Passant Elagroudy Rufat Rzayev Tonja-Katrin Machulla Huy Viet Le Tilman Dingler Lars Lischke Sarah Clinch Geoffrey Ward Albrecht Schmidt German Research Centre for Artificial Intelligence (DFKI) Kaiserslautern Germany and RPTU Kaiserslautern Kaiserslautern Germany Interactive Media Lab Dresden TUD Dresden University of Technology Dresden Germany Institute for Media Research TU Chemnitz Chemnitz Germany University of Stuttgart Stuttgart Germany Industrial Design Engineering Delft University of Technology Delft Netherlands User Centric Data Science Vrije Universiteit Amsterdam Amsterdam Netherlands Department of Computer Science The University of Manchester Manchester United Kingdom Department of Psychology University of Essex Essex United Kingdom LMU Munich Munich Germany

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Deep learning models for enhanced in-field maize leaf disease diagnosis

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Machine Learning with Applications 2025年 20卷

作者： Nakatumba-Nabende, Joyce Murindanyi, Sudi Department of Computer Science College of Computing and Information Sciences Makerere University Uganda Marconi Machine Learning Lab College of Engineering Design Art and Technology Makerere University Uganda Makerere Center for Artificial Intelligence and Data Science Makerere University Uganda

Maize leaf diseases significantly threaten crop yields, and there is need for accurate, and accessible diagnostic tools. This research addresses this need by developing and evaluating deep learning (DL) and machine learning (ML) models for in-field classification and detection of four critical maize diseases: Maize Leaf Blight, Maize Lethal Necrosis, Maize Streak Virus, and Fall Armyworm damage. Utilizing field imagery captured via digital cameras and smartphones across Uganda, Tanzania, Ghana, and Namibia, we developed and compared custom Convolutional Neural Networks (CNNs), transfer learning (MobileNetV2, InceptionResNetV2), Vision Transformers (ViT), and classical ML models. For detection, a transformer-enhanced YOLOv10 architecture was implemented. Explainable AI (XAI) techniques (Grad-CAM, LIME) were incorporated to ensure model transparency. MobileNetV2 achieved the highest classification accuracy (97%), while the enhanced YOLOv10 reached a mean Average Precision (mAP) of 0.995 for object detection. The best models were integrated into a mobile application deployed on edge devices for real-time diagnosis by smallholder farmers in Uganda, with performance validated through field tests. This study demonstrates a powerful, interpretable, and field-deployable solution combining advanced DL, transformers, and XAI for effective maize health management. © 2025

关键词： Convolutional neural networks

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Exploring the Effects of Social VR Coupling Modes on Engagement and Task Performance for Older Adults 25

Exploring the Effects of Social VR Coupling Modes on Engagem...

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

作者： Thuan T Vo Satabdi Das Shamim Noroozi Lakshay Dang Jaisie Sin Jennifer N Boger Jennifer Jakobi Khalad Hasan University of British Columbia Kelowna British Columbia Canada Computer Science The University of British Columbia Kelowna British Columbia Canada School of Engineering University of British Columbia-Okanagan Kelowna British Columbia Canada Okanagan Visualization and Interaction Lab The University of British Columbia Okanagan Kelowna British Columbia Canada Carleton University Ottawa Ontario Canada Systems Design Engineering University of Waterloo Waterloo Ontario Canada and The University of British Columbia British Columbia Canada Aging in Place Research Cluster The University of British Columbia Okanagan British Columbia Canada The University of British Columbia Okanagan Kelowna British Columbia Canada

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Recent progress and trends in predictive visual analytics

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Frontiers of computer science 2017年第2期11卷 192-207页

作者： Junhua LU Wei CHEN Yuxin MA Junming KE Zongzhuang LI Fan ZHANG Ross MACIEJEWSKI State Key Lab of Computer Aided Design and Computer Graphics Zhejiang University Hangzhou 310058 China College of Science Zhejiang University of Technology Hangzhou 310023 China College of Computer Science and Technology Zhejiang University of Technology Hangzhou 310023 China School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ 85287-8809 USA

A wide variety of predictive analytics techniques have been developed in statistics, machine learning and data mining; however, many of these algorithms take a black-box approach in which data is input and future predictions are output with no insight into what goes on during the process. Unfortunately, such a closed system approach often leaves little room for injecting domain expertise and can result in frustration from analysts when results seem snurious or confusing. In order to allow for more human-centric approaches, the visualization community has begun developing methods to enable users to incorporate expert knowledge into the pre- diction process at all stages, including data cleaning, feature selection, model building and model validation. This paper surveys current progress and trends in predictive visual ana- lytics, identifies the common framework in which predictive visual analytics systems operate, and develops a summariza- tion of the predictive analytics workfiow.

关键词： predictive visual analytics visualization visual analytics data mining predictive analysis

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A work domain analysis for network management

A work domain analysis for network management

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Proceedings of the XIVth Triennial Congress of the International Ergonomics Association and 44th Annual Meeting of the Human Factors and Ergonomics Association, 'Ergonomics for the New Millennnium'

作者： Burns, Catherine M. Barsalou, Ed Handler, Cynthia Kuo, Johnson Harrigan, Kevin Advanced Interface Design Lab Dept. of Systems Design Engineering University of Waterloo Canada Department of Computer Science University of Waterloo Canada

computer networks are pervasive and critical to the operation of today's businesses. While most of us take computer networks for granted, network managers who must configure, operate, and fix faults in computer networks face a daunting task that becomes more complex everyday. Ecological interface design has been shown to be a promising approach for complex systems such as power plants or petrochemical systems. computer networks are a very different work domain characterized by rapid technological change, extreme decentralization, and the existence of "soft" components. This paper presents the first work domain analysis of this new domain creating an abstraction hierarchy for general signal transfer systems and general computing networks. It shows how the abstraction hierarchy can be applied to this new domain and presents the initial work on an ecological interface design for network management.

关键词： computer networks

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Annotating traditional Chinese paintings for immersive virtual exhibition

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Journal on Computing and Cultural Heritage 2012年第2期5卷 1–12页

作者： Ma, Wei Wang, Yizhou Xu, Ying-Qing Li, Qiong Ma, Xin Gao, Wen National Engineering Lab for Video Technology School of Electrical Engineering and Computer Science Peking University China School of Art and Design Tsinghua University Microsoft Research Asia China Palace Museum Beijing China Microsoft Research Asia China College of Computer Science and Technology Beijing University of Technology China

We propose a new method of annotating a masterpiece of traditional Chinese painting with voice dubbings and environmental sounds. The painting was created with moving focus drawing technique without rigorous perspective. A novel algorithm is proposed to infer the 3D space of the painting according to its layout and embed the audio annotations. For exhibition, the masterpiece is scanned into a high-resolution gigapixel image for presenting the drawing details, and we develop an interactive multimedia system with a panning and zooming interface to enable smooth navigation on the giant painting and exploring the historical culture. During the navigation, the system estimates the 3D position of the user's viewpoint from his/her actions, and subsequently synthesizes a realistic stereo audio field according to the viewer's orientation and distance from the annotations in the 3D space. The proposed system provides an immersive user experience by rendering a visual-audio consistent perception. © 2012 ACM 1556-4673/2012/07-ART6 $15.00.

关键词： Interactive computer systems

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