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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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MOCAS: A Multimodal Dataset for Objective Cognitive Workload Assessment on Simultaneous Tasks

arXiv

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arXiv 2022年

作者： Jo, Wonse Wang, Ruiqi Cha, Go-Eum Sun, Su Senthilkumaran, Revanth Krishna Foti, Daniel Min, Byung-Cheol SMART Lab. Department of Computer and Information Technology Purdue University West LafayetteIN47907 United States Department of Psychological Sciences Purdue University West LafayetteIN United States

This paper presents MOCAS, a multimodal dataset dedicated for human cognitive workload (CWL) assessment. In contrast to existing datasets based on virtual game stimuli, the data in MOCAS was collected from realistic closed-circuit television (CCTV) monitoring tasks, increasing its applicability for real-world scenarios. To build MOCAS, two off-the-shelf wearable sensors and one webcam were utilized to collect physiological signals and behavioral features from 21 human subjects. After each task, participants reported their CWL by completing the NASA-Task Load Index (NASA-TLX) and Instantaneous Self-Assessment (ISA). Personal background (e.g., personality and prior experience) was surveyed using demographic and Big Five Factor personality questionnaires, and two domains of subjective emotion information (i.e., arousal and valence) were obtained from the Self-Assessment Manikin (SAM), which could serve as potential indicators for improving CWL recognition performance. Technical validation was conducted to demonstrate that target CWL levels were elicited during simultaneous CCTV monitoring tasks;its results support the high quality of the collected multimodal signals. © 2022, CC BY.

关键词： NASA

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Visual Localization for Autonomous Driving: Mapping the Accurate Location in the City Maze

Visual Localization for Autonomous Driving: Mapping the Accu...

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International Conference on Pattern Recognition

作者： Dongfang Liu Yiming Cui Xiaolei Guo Wei Ding Baijian Yang Yingjie Chen Computer Graphics Technology Purdue University Electrical & Computer Engineering University of Florida Computer Information Technology Purdue University Ray W. Herrick Laboratories Mechanical Engineering Purdue University

Accurate localization is a foundational capacity, required for autonomous vehicles to accomplish other tasks such as navigation or path planning. It is a common practice for vehicles to use GPS to acquire location information. However, the application of GPS can result in severe challenges when vehicles run within the inner city where different kinds of structures may shadow the GPS signal and lead to inaccurate location results. To address the localization challenges of urban settings, we propose a novel feature voting technique for visual localization. Different from the conventional front-view-based method, our approach employs views from three directions (front, left, and right) and thus significantly improves the robustness of location prediction. In our work, we craft the proposed feature voting method into three state-of-the-art visual localization networks and modify their architectures properly so that they can be applied for vehicular operation. Extensive field test results indicate that our approach can predict location robustly even in challenging inner-city settings. Our research sheds light on using the visual localization approach to help autonomous vehicles to find accurate location information in a city maze, within a desirable time constraint. The source code is available at ***/HappyDonkey13/Visual- Localization-for- Autonomous-Driving.

关键词： Location awareness Visualization Urban areas Pipelines Robustness Pattern recognition Time factors

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CMOS photonic integrated source of broadband polarization-entangled photons

Optica Quantum

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Optica Quantum 2024年第4期2卷 254-259页

作者： Alexander Miloshevsky Lucas M. Cohen Karthik V. Myilswamy Muneer Alshowkan Saleha Fatema Hsuan-Hao Lu Andrew M. Weiner Joseph M. Lukens Quantum Information Science Section Computational Sciences and Engineering Division Oak Ridge National Laboratoryhttps://***/01qz5mb56 Oak Ridge Tennessee 37831 USA School of Electrical and Computer Engineering and Purdue Quantum Science and Engineering Institute Purdue University West Lafayette Indiana 47907 USA Research Technology Office and Quantum Collaborative Arizona State University Tempe Arizona 85287 USA

We showcase a fully on-chip CMOS-fabricated silicon photonic integrated circuit employing a bidirectionally pumped microring and polarization splitter-rotators tailored for the generation of broadband (>9 THz), high-fidelity (90–98%) polarization-entangled photons. Spanning the optical C+L-band and producing over 116 frequency-bin pairs on a 38.4-GHz-spaced grid, this source is ideal for flex-grid wavelength-multiplexed entanglement distribution in multiuser networks.

关键词： Beam splitters Entanglement distribution Optical directional couplers Parametric down conversion Silicon photonics Superconducting nanowire single photon detectors

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Joint User Association and Resource Pricing for Metaverse: Distributed and Centralized Approaches

arXiv

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arXiv 2022年

作者： Huang, Xumin Zhong, Weifeng Nie, Jiangtian Hu, Qin Xiong, Zehui Kang, Jiawen Quek, Tony Q.S. School of Automation Guangdong University of Technology Guangzhou China School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computer and Information Science Indiana University Purdue University Indianapolis United States Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore

Metaverse as the next-generation Internet provides users with physical-virtual world interactions. To improve the quality of immersive experience, users access to Metaverse service providers (MSPs) and purchase bandwidth resource to reduce the communication latency of the Metaverse services. The MSPs decide selling price of the bandwidth resource to maximize the revenue. This leads to a joint user association and resource pricing problem between all users and MSPs. To tackle the problem, we formulate a Stackelberg game where the MSPs are game leaders and users are game followers. We resolve the Stackelberg equilibrium via the distributed and centralized approaches, according to different privacy requirements. In the distributed approach, the MSPs compete against each other to maximize the individual revenue, and a user selects an MSP in a probabilistic manner. The Stackelberg equilibrium is achieved in a privacy-friendly way. In the centralized approach, all MSPs and users accept the unified management and their strategies are instructed. The centralized approach acquires the superior decision-making performance but sacrifices the privacy of the game players. Finally, we provide numerical results to demonstrate the effectiveness and efficiency of our schemes. Copyright © 2022, The Authors. All rights reserved.

关键词： Decision making

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Ground Based-cloud Classification Based on Comparing Different Classification Models

Ground Based-cloud Classification Based on Comparing Differe...

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作者： Tao Zhang Yantian Ding Wangjiang Gong Zeyu Hou Department of Computer Science Purdue University School of Information Science and Engineering Lanzhou University Reading Academy Nanjing University of Information Science & Technology Beijing No.80 High School

Clouds are one of the most common things in our daily life,and different types of clouds will foreshadow different weather ***,accurately identifying and classifying them are important for human forecasting the local ***,artificially classifying clouds could possibly cause a minor *** the helping of machine learning classification model could avoid this situation as much as *** this study,our goal is to get sufficiently high accuracy of based-cloud *** used LBP as a preprocessing approach,and used KSVM,MLP,Custom Vision,and Resnet to compare each *** dataset contains around 15 thousand cloud images in 5 types in the same format and *** the implementation of four classification models,experiments showed that ResNet34 came out a great result with a test accuracy 94%.Therefore,this study demonstrates ResNet34 is a good image classification model in based-cloud classification field.

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+TrajVis: a visual clinical decision support system to translate artificial intelligence trajectory models in the precision management of chronic kidney disease

arXiv

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arXiv 2024年

作者： Li, Zuotian Liu, Xiang Tang, Ziyang Zhang, Pengyue Jin, Nanxin Eadon, Michael T. Song, Qianqian Chen, Yingjie Su, Jing Department of Biostatistics and Health Data Science Indiana University School of Medicine Indiana United States Department of Computer Graphics Technology Purdue University Indiana United States Department of Computer and Information Technology Purdue University Indiana United States Department of Medicine Indiana University School of Medicine Indiana United States Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida Florida United States Gerontology and Geriatric Medicine Wake Forest School of Medicine North Carolina United States

Objective: Our objective is to develop and validate TrajVis, an interactive tool that assists clinicians in using artificial intelligence (AI) models to leverage patients' longitudinal electronic medical records (EMR) for personalized precision management of chronic disease progression. Methods: We first perform requirement analysis with clinicians and data scientists to determine the visual analytics tasks of the TrajVis system as well as its design and functionalities. A graph AI model for chronic kidney disease (CKD) trajectory inference named DEPOT is used for system development and demonstration. TrajVis is implemented as a full-stack web application with synthetic EMR data derived from the Atrium Health Wake Forest Baptist Translational Data Warehouse and the Indiana Network for Patient Care research database. A case study with a nephrologist and a user experience survey of clinicians and data scientists are conducted to evaluate the TrajVis system. Results: The TrajVis clinical information system is composed of four panels: the Patient View for demographic and clinical information, the Trajectory View to visualize the DEPOT-derived CKD trajectories in latent space, the Clinical Indicator View to elucidate longitudinal patterns of clinical features and interpret DEPOT predictions, and the Analysis View to demonstrate personal CKD progression trajectories. System evaluations suggest that TrajVis supports clinicians in summarizing clinical data, identifying individualized risk predictors, and visualizing patient disease progression trajectories, overcoming the barriers of AI implementation in healthcare. Conclusion: TrajVis bridges the gap between the fast-growing AI/ML modeling and the clinical use of such models for personalized and precision management of chronic diseases. © 2024, CC BY-NC-ND.

关键词： Diseases

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Husformer: A Multi-Modal Transformer for Multi-Modal Human State Recognition

arXiv

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arXiv 2022年

作者： Wang, Ruiqi Jo, Wonse Zhao, Dezhong Wang, Weizheng Yang, Baijian Chen, Guohua Min, Byung-Cheol Department of Computer and Information Technology Purdue University West LafayetteIN47907 United States College of Mechanical and Electrical Engineering Beijing University of Chemical Technology Beijing China

Human state recognition is a critical topic with pervasive and important applications in human-machine systems. Multi-modal fusion, which involves combining metrics from multiple data sources, has been shown to be an effective method for improving the recognition performance. While promising results have been reported by recent multi-modal-based models, they generally fail to leverage the sophisticated fusion strategies that would model sufficient cross-modal dependencies when producing the fusion representation;instead, they rely on costly and inconsistent feature crafting and alignment. To address this limitation, we propose an end-to-end multi-modal transformer framework for multi-modal human state recognition called Husformer. Specifically, we propose using cross-modal transformers, which inspire one modality to reinforce itself through directly attending to latent relevance revealed in other modalities, to fuse different modalities while ensuring sufficient awareness of the cross-modal interactions introduced. Subsequently, we utilize a self-attention transformer to further prioritize contextual information in the fusion representation. Extensive experiments on two human emotion corpora (DEAP and WESAD) and two cognitive load datasets (MOCAS and CogLoad) demonstrate that in the recognition of human state, our Husformer outperforms both state-of-the-art multi-modal baselines and the use of a single modality by a large margin, especially when dealing with raw multi-modal features. We also conducted an ablation study to show the benefits of each component in Husformer1 Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Detecting Multimedia Generated by Large AI Models: A Survey

arXiv

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arXiv 2024年

作者： Lin, Li Gupta, Neeraj Zhang, Yue Ren, Hainan Liu, Chun-Hao Ding, Feng Wang, Xin Li, Xin Verdoliva, Luisa Hu, Shu Department of Computer and Information Technology Purdue University IN46202 United States School of Software Nanchang University Jiangxi Nanchang330031 China Amazon Prime Video SunnyvaleCA94089 United States Department of Epidemiology and Biostatistics School of Public Health Department of Computer Science College of Nanotechnology Science and Engineering both at University at Albany SUNY AlbanyNY12222 United States Department of Electrical Engineering and Information Technology University Federico II of Naples Naples80125 Italy

The rapid advancement of Large AI Models (LAIMs), particularly diffusion models and large language models, has marked a new era where AI-generated multimedia is increasingly integrated into various aspects of daily life. Although beneficial in numerous fields, this content presents significant risks, including potential misuse, societal disruptions, and ethical concerns. Consequently, detecting multimedia generated by LAIMs has become crucial, with a marked rise in related research. Despite this, there remains a notable gap in systematic surveys that focus specifically on detecting LAIM-generated multimedia. Addressing this, we provide the first survey to comprehensively cover existing research on detecting multimedia (such as text, images, videos, audio, and multimodal content) created by LAIMs. Specifically, we introduce a novel taxonomy for detection methods, categorized by media modality, and aligned with two perspectives: pure detection (aiming to enhance detection performance) and beyond detection (adding attributes like generalizability, robustness, and interpretability to detectors). Additionally, we have presented a brief overview of generation mechanisms, public datasets, and online detection tools to provide a valuable resource for researchers and practitioners in this field. Furthermore, we identify current challenges in detection and propose directions for future research that address unexplored, ongoing, and emerging issues in detecting multimedia generated by LAIMs. Our aim for this survey is to fill an academic gap and contribute to global AI security efforts, helping to ensure the integrity of information in the digital realm. The project link is https://***/purdue-M2/Detect-LAIM-generated-Multimedia-Survey. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational linguistics

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An improved machine learning model for data analysis in Big-data environment

An improved machine learning model for data analysis in Big-...

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International Conference on Computing and Networking technology (ICCNT)

作者： Sheetal Temara Sagar Varma Samanthapudi Piyush Rohella Ketan Gupta T. Kiruthiga Dept of Computer & Information Sciences University of the Cumberlands Williamsburg KY USA Applied Data Science The Polis Center at Indiana University Purdue University Indianapolis Indianapolis Indiana USA Payments &Commerce GoDaddy Edmonds Washington USA Dept. of Information Technology University of The Cumberlands Williamsburg KY USA Department of ECE Vetri Vinayaha College of Engineering and Technology Trichy Tamil Nadu INDIA

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This technical abstract offers an outline of the ability of artificial Intelligence (AI) and device studying (ML) answers for managing leverage threats in banking. Leverage is a key element in banking danger management, as it could expose the organization to disproportionate liabilities if left unchecked. AI and ML may be used to automate the tracking system of leverage hazards, allowing extra correct and welltimed identification and management of exposures. The abstract examines how AI/ML-driven solutions can leverage to be had facts to detect subtle modifications in leverage through the years, combining techniques from supervised and unsupervised ML to generate actionable insights. It then opinions viable implementation fashions and capacity challenges and gives first-rate practices for a hit implementation. The summary concludes that AI/ML-driven hazard management structures offer a promising new approach to leverage threat management inside the banking sector. In addition, research is warranted on this location.

关键词： Analytical models Data analysis Banking Organizations Machine learning Hazards Data models Pattern recognition Artificial intelligence

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