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WHEN DISCOURSE STALLS: MOVING PAST FIVE SEMANTIC STOPSIGNS ABOUT GENERATIVE AI IN design RESEARCH

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arXiv 2025年

作者： van der Maden, Willem van der Burg, Vera Halperin, Brett A. Jääskeläinen, Petra Lindley, Joseph Lomas, Derek Merritt, Timothy HCI & Design Section ITU Copenhagen Copenhagen Denmark Designing Intelligence Lab Technical University Delft Delft Netherlands Human Centered Design & Engineering University of Washington SeattleWA United States EECS/HCT MID KTH Royal Institute of Technology Stockholm Sweden Lancaster University Lancaster United Kingdom TU Delft Delft Netherlands Dept. of Computer Science Human Centered Computing University Aalborg Denmark

This essay examines how Generative AI (GenAI) is rapidly transforming design practices and how discourse often falls into over-simplified narratives that impede meaningful research and practical progress. We identify and deconstruct five prevalent "semantic stopsigns"—reductive framings about GenAI in design that halt deeper inquiry and limit productive engagement. Reflecting upon two expert workshops at ACM conferences and semi-structured interviews with design practitioners, we analyze how these stopsigns manifest in research and practice. Our analysis develops mid-level knowledge that bridges theoretical discourse and practical implementation, helping designers and researchers interrogate common assumptions about GenAI in their own contexts. By recasting these stopsigns into more nuanced frameworks, we provide the design research community with practical approaches for thinking about and working with these emerging technologies. © 2025, CC BY.

关键词： Generative adversarial networks

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

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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

Hardware-Centric Exploration of the Discrete design Space in Transformer–LSTM Models for Wind Speed Prediction on Memory-Constrained Devices

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Energies 2025年第9期18卷 2153-2153页

作者： Aslam, Laeeq Zou, Runmin Awan, Ebrahim Shahzad Hussain, Sayyed Shahid Shakil, Kashish Ara Wani, Mudasir Ahmad Asim, Muhammad School of Automation Central South University Changsha410083 China School of Engineering Design and Built Environment Western Sydney University PenrithNSW2747 Australia Department of Computer Sciences College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia EIAS Data Science Lab College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia School of Electronic Information Central South University Changsha410083 China

Wind is one of the most important resources in the renewable energy basket. However, there are questions regarding wind as a sustainable solution, especially concerning its upfront costs, visual impact, noise pollution, and bird collisions. These challenges arise in commercial windmills, whereas for domestic small-scale windmills, these challenges are limited. On the other hand, accurate wind speed prediction (WSP) is crucial for optimizing power management in renewable energy systems. Existing research focuses on proposing model architectures and optimizing hyperparameters to improve model performance. This approach often results in larger models, which are hosted on cloud servers. Such models face challenges, including bandwidth utilization leading to data delays, increased costs, security risks, concerns about data privacy, and the necessity of continuous internet connectivity. Such resources are not available for domestic windmills. To overcome these obstacles, this work proposes a transformer model integrated with Long Short-Term Memory (LSTM) units, optimized for memory-constrained devices (MCDs). A contribution of this research is the development of a novel cost function that balances the reduction of mean squared error with the constraints of model size. This approach enables model deployment on low-power devices, avoiding the challenges of cloud-based deployment. The model, with its tuned hyperparameters, outperforms recent methodologies in terms of mean squared error, mean absolute error, model size, and R-squared scores across three different datasets. This advancement paves the way for more dynamic and secure on-device wind speed prediction (WSP) applications, representing a step forward in renewable energy management. © 2025 by the authors.

关键词： Windmill

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

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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

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

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

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

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

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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

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

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

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

Climate change resilience of freshwater supply on small islands: Research gaps and strategies for a case study in Grand Bahama

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Journal of Hydrology: Regional Studies 2025年 59卷

作者： Imig, Anne Welsh, Kristen Klausner, Sophia Hotta, Carolina Iwane McKenzie, Zeko Perosa, Francesca Stephens, Mark Turner, Amber Thomas, John Chaves, Henrique Marinho Leite Bowen-O′Connor, Clare Moxey, Andrew Dokou, Zoi Rein, Arno Chair of Hydrogeology School of Engineering and Design Technical University of Munich Germany State Key Laboratory of Soil Pollution Control and Safety School of Environmental Science and Engineering Southern University of Science and Technology Shenzhen 518055 China Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control School of Environmental Science and Engineering Southern University of Science and Technology Shenzhen 518055 China Small Island Sustainability Research Complex University of The Bahamas Nassau Bahamas Geosciences Department Oberlin College Oberlin OH United States Chair of Hydrology and River Basin Management School of Engineering and Design Technical University of Munich Germany Department of Earth and Biological Sciences Loma Linda University Loma Linda CA United States School of Chemistry Environmental and Life Sciences University of The Bahamas Nassau Bahamas Strata Sustainability Nassau Bahamas Watershed Management Lab EFL – School of Technology University of Brasilia DF Brasilia Brazil Centre for Education Programmes the University of Trinidad and Tobago Arima Trinidad and Tobago School of Chemistry Environmental and Life Sciences University of The Bahamas Freeport Bahamas Department of Civil Engineering College of Engineering and Computer Science California State University Sacramento CA United States

Study region: Grand Bahama island, the Commonwealth of The Bahamas. Study focus: In Grand Bahama, due to its low elevation, sea level rise and flooding during hurricane events can lead to the salinization of groundwater, which is the only source of potable water for the island. In this study, we assessed available literature of Grand Bahama's groundwater resources to identify key challenges and knowledge gaps for the protection of the potable water supply of the island. A literature review guided our assessment of various sustainable groundwater management approaches for the island. Understanding the hydrogeology and identifying strategies to maintain the freshwater lenses in Grand Bahama are key to ensure the island remains habitable. New hydrological insights for the region: Existing hydrogeological data highlight the urgent need to improve the island's water supply system and to identify safe groundwater yields to sustain its drinking water supply. Based on analysis of these data, we propose the implementation of nature-based solutions, such as managed aquifer recharge and reforestation measures, aimed at restoring the groundwater ecosystem service of freshwater supply. Moreover, we suggest wellfields should be identified in elevated areas on the island to mitigate over-wash induced saltwater intrusion in the aquifer. Furthermore, to ensure effective water supply management in Grand Bahama, it is recommended that legislation and regulations be adapted to actively involve both institutions and the public in the decision-making process. © 2025 The Authors

关键词： Climate change Freshwater lenses Groundwater resources Island sustainability Small island nations The Bahamian Archipelago

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

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Machine Learning with Applications

Machine Learning with Applications 2025年 20卷

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

关键词： Maize Disease classification Disease detection Deep learning models Explainable AI