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computers in human Behavior: Artificial humans 2025年 4卷

作者： Christina Pasalidou Chris Lytridis Avgoustos Tsinakos Nikolaos Fachantidis Laboratory of Informatics & Robotic Applications in Education and Society Department of Educational and Social Policy University of Macedonia Thessaloniki Greece Human-Machines Interaction (HUMAIN) Lab Department of Computer Science Democritus University of Thrace Kavala Greece Advanced Educational Technologies and Mobile Applications (AETMA) Lab Department of Computer Science Democritus University of Thrace Kavala Greece

Integrating cutting-edge technologies into education has been a continuous goal to enhance teaching and learning experiences. Augmented Reality (AR) and robotics are two emerging technologies that have shown promise in transforming educational environments. This paper presents a systematic review of the literature on the combination of AR and robotics for educational purposes, identifying key applications, benefits, and trends. Using the PRISMA methodology, 69 relevant studies from five major databases were analysed and categorised into three themes: (a) AR and Socially Assistive Robots (SAR), (b) AR-assisted educational robotics, and (c) AR in robotics/engineering education. The review provides insights into how different AR-enhanced robotics applications across primary, secondary, and higher education, provide visualizations, multimodal feedback, and immersive experiences. Key findings suggest that while interactive features of AR and the embodiment of robots show promising results for learning, fostering motivation, excitement, positive attitudes, and enriched educational experiences, challenges such as technological complexity and cost remain barriers to widespread adoption. Future research should focus on pedagogical frameworks and large-scale implementations to optimize AR-robotics integration in diverse educational settings.

关键词： Robotics Augmented reality HRI Education

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E-ffective: A visual analytic system for exploring the emotion and effectiveness of inspirational speeches

arXiv

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arXiv 2021年

作者： Maher, Kevin Huang, Zeyuan Song, Jiancheng Deng, Xiaoming Lai, Yu-Kun Ma, Cuixia Wang, Hao Liu, Yong-Jin Wang, Hongan Institute of Software Chinese Academy of Sciences Tsinghua University China State Key Laboratory of Computer Science Beijing Key Lab of Human-Computer Interaction Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences China Cardiff University United Kingdom Alibaba Group China Tsinghua University China

What makes speeches effective has long been a subject for debate, and until today there is broad controversy among public speaking experts about what factors make a speech effective as well as the roles of these factors in speeches. Moreover, there is a lack of quantitative analysis methods to help understand effective speaking strategies. In this paper, we propose E-ffective, a visual analytic system allowing speaking experts and novices to analyze both the role of speech factors and their contribution in effective speeches. From interviews with domain experts and investigating existing literature, we identified important factors to consider in inspirational speeches. We obtained the generated factors from multi-modal data that were then related to effectiveness data. Our system supports rapid understanding of critical factors in inspirational speeches, including the influence of emotions by means of novel visualization methods and interaction. Two novel visualizations include E-spiral (that shows the emotional shifts in speeches in a visually compact way) and E-script (that connects speech content with key speech delivery information). In our evaluation we studied the influence of our system on experts' domain knowledge about speech factors. We further studied the usability of the system by speaking novices and experts on assisting analysis of inspirational speech effectiveness. Copyright © 2021, The Authors. All rights reserved.

关键词： Visualization

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AnyTSR: Any-Scale Thermal Super-Resolution for UAV

arXiv

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arXiv 2025年

作者： Li, Mengyuan Fu, Changhong Lu, Ziyu Zhang, Zijie Zuo, Haobo Yao, Liangliang School of Mechanical Engineering Tongji University Shanghai201804 China Shanghai Key Laboratory of Wearable Robotics and Human-Machine Interaction Tongji University Shanghai201804 China Department of Computer Science University of Hong Kong Hong Kong999077 Hong Kong

Thermal imaging can greatly enhance the application of intelligent unmanned aerial vehicles (UAV) in challenging environments. However, the inherent low resolution of thermal sensors leads to insufficient details and blurred boundaries. Super-resolution (SR) offers a promising solution to address this issue, while most existing SR methods are designed for fixed-scale SR. They are computationally expensive and inflexible in practical applications. To address above issues, this work proposes a novel any-scale thermal SR method (AnyTSR) for UAV within a single model. Specifically, a new image encoder is proposed to explicitly assign specific feature code to enable more accurate and flexible representation. Additionally, by effectively embedding coordinate offset information into the local feature ensemble, an innovative any-scale upsampler is proposed to better understand spatial relationships and reduce artifacts. Moreover, a novel dataset (UAV-TSR), covering both land and water scenes, is constructed for thermal SR tasks. Experimental results demonstrate that the proposed method consistently outperforms state-of-the-art methods across all scaling factors as well as generates more accurate and detailed high-resolution images. The code is located at https:// ***/vision4robotics/AnyTSR. Copyright © 2025, The Authors. All rights reserved.

关键词： Drones

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QL-PGD: An efficient defense against membership inference attack

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Journal of Information Security and Applications 2025年 92卷

作者： Pham, Tuan Dung Nguyen, Bao Dung Mai, Son T. Ta, Viet Cuong Human Machine Interaction Laboratory VNU University of Engineering and Technology 144 Xuan Thuy Street Cau Giay District Hanoi100000 Viet Nam School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom

In membership inference attacks (MIAs), attackers exploit the overfitting phenomenon when training deep learning models to identify whether a specific data sample was used to train a victim model. Consequently, many defenses have been introduced to mitigate the risk of being attacked by MIAs. However, state-of-the-art defenses against MIAs often suffer from poor privacy-utility balance and high training or inference computational time. To overcome these limitations, we propose Quantized Layer-wise Perturbed Gradient Descent (QL-PGD), a novel, lightweight and effective generalization method to protect machine learning models from Membership Inference Attacks. The key idea of QL-PGD is to regularize the model to prevent overfitting by adjusting the injected noise added to the gradient at each layer which explicitly regularizes the gradient passing through it to achieve a stronger privacy defense while maintaining similar levels of accuracy. Moreover, quantization is further performed on both model weight and gradient to reduce computational overhead. Extensive experiments are conducted to evaluate the performance of our method compared to other state-of-the-art generalization defenses against multiple attacks. The results show that QL-PGD can withstand both black-box and white-box attacks and preserve the target model's utility with efficiency in terms of speed, memory and energy. © 2025

关键词： Deep learning

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Harnessing the power of AI in qualitative research: Exploring, using and redesigning ChatGPT

Computers in Human Behavior: Artificial Humans

引用

computers in human Behavior: Artificial humans 2025年 4卷

作者： He Zhang Chuhao Wu Jingyi Xie Yao Lyu Jie Cai John M. Carroll College of Information Sciences and Technology The Pennsylvania State University Westgate Building University Park PA 16803 USA The School of Information University of Michigan MI 48109 USA The Pervasive Human Computer Interaction Laboratory Department of Computer Science and Technology Tsinghua University Future Internet Technology Research Center (FIT) Haidian District Beijing 100084 China

AI tools, particularly large-scale language model (LLM) based applications such as ChatGPT, have the potential to mitigate qualitative research workload. In this study, we conducted semi-structured interviews with 17 participants and held a co-design session with 13 qualitative researchers to develop a framework for designing prompts specifically crafted to support junior researchers and stakeholders interested in leveraging AI for qualitative research. Our findings indicate that improving transparency, providing guidance on prompts, and strengthening users' understanding of LLMs' capabilities significantly enhance their ability to interact with ChatGPT. By comparing researchers' attitudes toward LLM-supported qualitative analysis before and after the co-design process, we reveal that the shift from an initially negative to a positive perception is driven by increased familiarity with the LLM's capabilities and the implementation of prompt engineering techniques that enhance response transparency and, in turn, foster greater trust. This research not only highlights the importance of well-designed prompts in LLM applications but also offers reflections for qualitative researchers on the perception of AI's role. Finally, we emphasize the potential ethical risks and the impact of constructing AI ethical expectations by researchers, particularly those who are novices, on future research and AI development.

关键词： ChatGPT Qualitative analysis Prompt design Large language models AI-Assisted research Ethical considerations

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Sustainability in the Internet of Production: Interdisciplinary Opportunities and Challenges

Sustainability in the Internet of Production: Interdisciplin...

引用

International Symposium on Technology and Society (ISTAS)

作者： Sebastian Bernhard Sebastian Pütz Calvin Röhl Ralph Baier Philipp Brauner Ester Christou Hannah Dammers Roman Flaig Leon M. Gorißen Jan-Christoph Heilinger Christian Hinke István Koren Dirk Lüttgens Michael Millan Kai Müller Alexander Schollemann Luisa Vervier Thomas Gries Alexander Mertens Saskia K. Nagel Frank T. Piller Günther Schuh Martina Ziefle Verena Nitsch Carmen Leicht-Scholten Gender and Diversity in Engineering RWTH Aachen University Aachen Germany Institute of Industrial Engineering and Ergonomics RWTH Aachen University Aachen Germany Human-Computer Interaction Center RWTH Aachen University Aachen Germany Institute for Technology and Innovation Management RWTH Aachen University Aachen Germany Institut für Textiltechnik RWTH Aachen University Aachen Germany Chair for Laser Technology RWTH Aachen University Aachen Germany Human Technology Center/Applied Ethics RWTH Aachen University Aachen Germany Chair of Process and Data Science RWTH Aachen University Aachen Germany Laboratory for Machine Tools and Production Engineering RWTH Aachen University Aachen Germany

The vision of the Internet of Production (loP) is focused on optimizing manufacturing processes, with the help of Industry 4.0 technologies (14Ts). However, considering global megatrends such as climate change and the need to achieve the Sustainable Development Goals (SDGs), it is a growing imperative for the manufacturing industry to become more sustainable. This opens the door to transforming the IoP into an Internet of Sustainable Production (loSP). Accordingly, this paper proposes a novel four-step loSP-framework that establishes an Information System (IS) allowing researchers and practitioners to acknowledge and adapt to the interconnected nature of sustainability. Further, to test its applicability, an inter- and cross-disciplinary perspective is adopted to illustrate case-related challenges, opportunities, and pathways - revealed by the proposed framework - in the example of a digital economy for sustainability data, strategic design of global production networks, human-robot collaboration, digital photonic production, and the textile industry. Together, the framework demonstrates the usefulness of generating holistic information on the interconnected nature of sustainability derived from process-specific and contextualized data, while simultaneously assessing the framework's utilization for sustainability, as well as the sustainability of its use.

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Difficulty-aware meta-learning for rare disease diagnosis 23rd

Difficulty-aware meta-learning for rare disease diagnosis

引用

23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Li, Xiaomeng Yu, Lequan Jin, Yueming Fu, Chi-Wing Xing, Lei Heng, Pheng-Ann Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin Hong Kong Department of Radiation Oncology Stanford University Stanford United States Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9783030597092

Rare diseases have extremely low-data regimes, unlike common diseases with large amount of available labeled data. Hence, to train a neural network to classify rare diseases with a few per-class data samples is very challenging, and so far, catches very little attention. In this paper, we present a difficulty-aware meta-learning method to address rare disease classifications and demonstrate its capability to classify dermoscopy images. Our key approach is to first train and construct a meta-learning model from data of common diseases, then adapt the model to perform rare disease classification. To achieve this, we develop the difficulty-aware meta-learning method that dynamically monitors the importance of learning tasks during the meta-optimization stage. To evaluate our method, we use the recent ISIC 2018 skin lesion classification dataset, and show that with only five samples per class, our model can quickly adapt to classify unseen classes by a high AUC of 83.3%. Also, we evaluated several rare disease classification results in the public Dermofit Image Library to demonstrate the potential of our method for real clinical practice. © Springer Nature Switzerland AG 2020.

关键词： Learning systems

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Image-guided Breast Biopsy of MRI-visible Lesions with a Hand-mounted Motorised Needle Steering Tool

Image-guided Breast Biopsy of MRI-visible Lesions with a Han...

引用

International Symposium on Medical Robotics (ISMR)

作者： Marta Lagomarsino Vincent Groenhuis Maura Casadio Marcel K. Welleweerd Françoise J. Siepel Stefano Stramigioli Robotics and Mechatronics (RAM) Laboratory Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede The Netherlands University of Genoa Genoa Italy Human-Robot Interfaces and Physical Interaction (HRII) Laboratory Istituto Italiano di Tecnologia Genoa Italy Politecnico di Milano Milan Italy

ISBN: (纸本)9781665406239

A biopsy is the only diagnostic procedure for accurate histological confirmation of breast cancer. When sonographic placement is not feasible, a Magnetic Resonance Imaging(MRI)-guided biopsy is often preferred. The lack of real-time imaging information and the deformations of the breast make it challenging to bring the needle precisely towards the tumour detected in pre-interventional Magnetic Resonance (MR) images. The current manual MRI-guided biopsy workflow is inaccurate and would benefit from a technique that allows real-time tracking and localisation of the tumour lesion during needle insertion. This paper proposes a robotic setup and software architecture to assist the radiologist in targeting MR-detected suspicious tumours. The approach benefits from image fusion of preoperative images with intraoperative optical tracking of markers attached to the patient’s skin. A hand-mounted biopsy device has been constructed with an actuated needle base to drive the tip toward the desired direction. The steering commands may be provided both by user input and by computer guidance. The workflow is validated through phantom experiments. On average, the suspicious breast lesion is targeted with a radius down to 2.3 mm. The results suggest that robotic systems taking into account breast deformations have the potentials to tackle this clinical challenge.

关键词： Target tracking Biopsy Phantoms Breast Needles Real-time systems Skin

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AI-Assisted Bayesian Structural Time Series Modeling for Assessing PM 2.5 Air Quality Improvements During the Beijing 2022 Winter Olympics

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Atmospheric Environment 2025年

作者： Weizhao Liang Yuan Li Xuan Liu Qili Dai Yinchang Feng Key Laboratory of Urban Air Particulate Pollution Prevention and Control of Ministry of Ecology and Environment College of Environmental Science and Engineering Nankai University Tianjin 300350 China China Meteorological Administration-Nankai University Cooperative Laboratory for Atmospheric Environment-Health Research Tianjin 300350 China Tianjin Key Laboratory of Software Experience and Human Computer Interaction Tianjin China 300457

To prevent poor air quality during the Beijing Winter Olympics (BWO), substantial air pollution control measures were implemented in Beijing and surrounding cities. However, the actual causal effects of these measures remain uncertain, primarily due to the difficulty of establishing an appropriate control group under complex meteorological conditions and high anthropogenic emissions in cold season. This study addresses that challenge by employing an AI-assisted Bayesian Structural Time Series (BSTS) model to estimate the causal effects of intervention measures. Utilizing time variables, meteorological data, and PM 2.5 concentration records from 2015 to 2021, counterfactual PM 2.5 concentrations were first predicted using a Random Forest-Long Short-Term Memory (RF-LSTM) hybrid model, which generated covariates for the BSTS model to enable robust causal inference with minimal parametric assumptions. The results indicate that, in the absence of interventions, Beijing’s PM 2.5 during the Winter Olympics would have been 20 μg/m 3 (34 %) higher. The implemented measures significantly reduced peak PM 2.5 levels during the Winter Olympics and Winter Paralympics. Rigorous validation confirmed the accuracy of the predicted counterfactual PM 2.5 concentration, thereby strengthening the reliability of the causal inference results. overall, the AI-Assisted BSTS approach effectively quantified the PM 2.5 reductions attributable to the air quality assurance measures during the BWO, providing strong evidence for the effectiveness of pollution control efforts.

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Influence of multi-modality on moving target selection in virtual reality

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Virtual Reality & Intelligent Hardware 2019年第3期1卷 303-315页

作者： Yang LI Dong WU Jin HUANG Feng TIAN Hong'an WANG Guozhong DAI Beijing Key Laboratory of Human-Computer Interaction Institute of SoftwareChinese Academy of SciencesBeijing 100190China State Key Laboratory of Computer Science Chinese Academy of SciencesBeijing 100190China Institute of Remote Sensing and Digital Earth Chinese Academy of SciencesBeijing 100190China University Chinese Academy Sciences Beijing 100190China

Background Owing to recent advances in virtual reality(VR)technologies,effective user interaction with dynamic content in 3D scenes has become a research *** target selection is a basic interactive task in which the user performance research in tasks is significant to user interface design in *** from the existing static target selection studies,the moving target selection in VR is affected by the change in target speed,angle and size,and lack of research on some key *** This study designs an experimental scenario in which the users play badminton under the condition of *** adding seven kinds of modal clues such as vision,audio,haptics,and their combinations,five kinds of moving speed and four kinds of serving angles,and the effect of these factors on the performance and subjective feelings in moving target selection in VR,is *** The results show that the moving speed of the shuttlecock has a significant impact on the user *** angle of service has a significant impact on hitting rate,but has no significant impact on the hitting *** acquisition of the user performance by the moving target is mainly influenced by vision under the combined modalities;adding additional modalities can improve user *** the hitting distance of the target is increased in the trimodal condition,the hitting rate *** This study analyses the results of user performance and subjective perception,and then provides suggestions on the combination of modality clues in different scenarios.

关键词： Multimodal Moving target selection Virtual reality

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