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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Liu, Weimin Li, Qingkun Wang, Wenjun Wang, Zhenyuan Zeng, Chao Cheng, Bo State Key Laboratory of Automotive Safety and Energy Center for Intelligent Connected Vehicles and Transportation School of Vehicle and Mobility Tsinghua University Beijing China Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing China College of Information Science and Engineering Henan University of Technology Zhengzhou China

Take-over performance plays a significant role in evaluating drivers' state, and serves as a crucial reference for enhancing control transitions in the context of conditionally automated driving. In this study, we aim to predict minimum anticipated collision time (min ACT), an indicator of drivers' take-over performance, in expectation of promoting safer take-overs via deep learning, so that drivers' state detriment of take-over safety could be adjusted accordingly with intelligent human-machine interaction algorithms predictably. By incorporating multi-source information including drivers' state, drivers' demographics, surrounding traffic features as well as driver-vehicle interaction characteristics, network model “ACTNet” was proposed to facilitate continuous estimation. Depthwise separable convolution and non-local self-attention were utilized to prevent overfitting and establish spatial dependency over fixation heatmap, respectively. To overcome data distribution imbalance, class balanced loss was used in conjunction with regression loss to realize more accurate predictions. Driving simulator experiment was conducted with dataset collected for the subsequent verification of the proposed algorithm. Potentialities of deep learning methods were highlighted for take-over studies, contributing to the design of intelligent human-machine interaction systems in conditional automation. Our findings present a valid method of deep learning in predicting drivers' take-over performance and meanwhile have implications for the development of intelligent adaptive take-over time budget regulation and dynamic drivers' state adjustment algorithms. IEEE

关键词： Automation

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DRDM: A Disentangled Representations Diffusion Model for Synthesizing Realistic Person Images

DRDM: A Disentangled Representations Diffusion Model for Syn...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Enbo Zhang, Yuan Huang, Faliang Zhang, Guangyu Liu, Yang Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China School of Computer and Information Engineering Nanning Normal University Nanning China College of Mathematics and Informatics South China Agricultural University Guangzhou China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (纸本)9798350368741

Person image synthesis with controllable body poses and appearances is an essential task owing to the practical needs in the context of virtual try-on, image editing and video production. However, existing methods face significant challenges with details missing, limbs distortion and the garment style deviation. To address these issues, we propose a Disentangled Representations Diffusion Model (DRDM) to generate photo-realistic images from source portraits in specific desired poses and appearances. First, a pose encoder is responsible for encoding pose features into a high-dimensional space to guide the generation of person images. Second, a body-part subspace decoupling block (BSDB) disentangles features from the different body parts of a source figure and feeds them to the various layers of the noise prediction block, thereby supplying the network with rich disentangled features for generating a realistic target image. Moreover, during inference, we develop a parsing map-based disentangled classifier-free guided sampling method, which amplifies the conditional signals of texture and pose. Extensive experimental results on the Deepfashion dataset demonstrate the effectiveness of our approach in achieving pose transfer and appearance control. The associated project can be found at https://***/lovemusiceb/DRDM. © 2025 IEEE.

关键词： Diffusion Model Disentangled Representation human Parsing Person image synthesis

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Velocity-Based LOD Reduction in Virtual Reality: A Psychophysical Approach

Velocity-Based LOD Reduction in Virtual Reality: A Psychophy...

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44th Annual Conference on European Association for computer Graphics, EUROGRAPHICS 2023

作者： Petrescu, David Warren, Paul A. Montazeri, Zahra Pettifer, Steve Human Computer Systems Department of Computer Science University of Manchester United Kingdom Facility Division of Psychology Communication and Human Neuroscience University of Manchester United Kingdom

ISBN: (纸本)9783038682097

Virtual Reality headsets enable users to explore the environment by performing self-induced movements. The retinal velocity produced by such motion reduces the visual system's ability to resolve fine detail. We measured the impact of self-induced head rotations on the ability to detect quality changes of a realistic 3D model in an immersive virtual reality environment. We varied the Level of Detail (LOD) as a function of rotational head velocity with different degrees of severity. Using a psychophysical method, we asked 17 participants to identify which of the two presented intervals contained the higher quality model under two different maximum velocity conditions. After fitting psychometric functions to data relating the percentage of correct responses to the aggressiveness of LOD manipulations, we identified the threshold severity for which participants could reliably (75%) detect the lower LOD model. Participants accepted an approximately four-fold LOD reduction even in the low maximum velocity condition without a significant impact on perceived quality, suggesting that there is considerable potential for optimisation when users are moving (increased range of perceptual uncertainty). Moreover, LOD could be degraded significantly more (around 84%) in the maximum head velocity condition, suggesting these effects are indeed speed-dependent. © 2023 Authors.

关键词： Virtual reality

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EIROB : An Emotionally Intelligent ROBot for Empathetic Support

EIROB : An Emotionally Intelligent ROBot for Empathetic Supp...

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2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2024

作者： Ismail, Safad Vasudev, Harsha Saintgits College of Engineering Department of Computer Science and Engineering Kerala India University of Padua Human Inspired Technology Research Centre Department of Mathematics Italy

ISBN: (纸本)9798350376135

Social assistive robots enhance social interaction and well-being by providing companionship and support. Emotional Intelligence in these robots refers to their ability to identify, understand, and respond to human emotions. This study investigates how well a social robot named 'EIROB' can engage elderly persons by using Artificial Emotional Intelligence. EIROB employs a Multimodal Emotion Recognition Algorithm and a Multimodal Emotion Expression System to detect the user's emotional state and generate appropriate responses through an effective dialogue manager. interaction with EIROB is facilitated by an Android app, which uses the mobile phone's inbuilt camera and microphone for facial expression and speech sentiment detection. The captured data is processed in the cloud that enables the model to be deployed remotely. This study aims to create an empathetic interaction model for senior citizens particularly during pandemics like COVID-19 to prevent feelings of isolation. The performance of the emotion recognition models is evaluated via accuracy, precision, recall, and F1-score to ensure the effectiveness of the system in recognizing and responding to user's emotional states. © 2024 IEEE.

关键词： Microrobots

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Intelligent Predictive Maintenance RAG framework for Power Plants: Enhancing QA with StyleDFS and Domain Specific Instruction Tuning

Intelligent Predictive Maintenance RAG framework for Power P...

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2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, EMNLP 2024

作者： Hong, Seongtae Shin, Joongmin Seo, Jaehyung Lee, Taemin Park, Jeongbae Cho, Manyoung Choi, Byeongho Lim, Heuiseok Department of Computer Science and Engineering Korea University Korea Republic of Human-inspired AI Research Korea Republic of GaonPlatform Korea Republic of

ISBN: (纸本)9798891761667

Process plants are complex large-scale industrial facilities that convert raw materials or intermediate products into final products, requiring continuous processes with high safety and efficiency standards. In particular, in nuclear process plants, Predictive Maintenance System (PMS) plays a critical role in predicting equipment anomalies and performing preventive maintenance. However, current PMS relies heavily on the experience of a few experts, leading to knowledge loss upon their retirement and difficulty in swift response. Existing off-premise Question-Answering (QA) systems based on Large Language Models (LLM) face issues such as data leakage and challenges in domain-specific tuning. To address these problems, this study proposes an on-premise intelligent PMS framework utilizing a new chunking method, StyleDFS, which effectively reflects the structural information of documents. Additionally, we demonstrate that Instruction tuning using relevant domain-specific data improves LLM performance even under limited data conditions. © 2024 Association for Computational Linguistics.

关键词： Predictive maintenance

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Design Visions for Future Energy Systems: Towards Aligning Developers' Assumptions and Householders' Expectations 22

Design Visions for Future Energy Systems: Towards Aligning D...

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12th Nordic Conference on human-computer interaction: Participative Computing for Sustainable Futures, NordiCHI 2022

作者： Jensen, Rikke Hagensby Raptis, Dimitrios Siksnys, Laurynas Pedersen, Torben Skov, Mikael B. Department of Computer Science Aalborg University Denmark Department of Computer Science Human-Centred Computing Group Aalborg University Denmark

ISBN: (纸本)9781450396998

We increasingly see smart energy technology moving into peoples' homes, designed to support householders in strategically managing different forms of energy conservation. Often, design visions for such technologies assume householders will accept smart energy systems as long as the interaction remains effortless, efficient, and convenient. Likewise, developers rely on assumptions that smart energy technologies meeting such usability goals may urge householders to become active players in a future energy market. Yet, a growing body of HCI research illustrates that actively engaging people with sustainability issues through technology requires more than merely meeting usability goals. Against this backdrop, our study investigates the design vision of a cutting-edge home energy management system created as part of an EU project and householders' expectations of it. Our findings highlight (mis)alignments that emerged from a questionnaire study with 167 households participating in a field trial of the developed technology. We use the insights to discuss the alignment of sustainable visions, HCI, and the design of future energy systems. © 2022 ACM.

关键词： Sustainable development

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Postphenomenological Dimensions of Digitally Mediated Domestic Heating 22

Postphenomenological Dimensions of Digitally Mediated Domest...

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12th Nordic Conference on human-computer interaction: Participative Computing for Sustainable Futures, NordiCHI 2022

作者： Svangren, Michael Kvist Raptis, Dimitrios Ananjeva, Alisa Persson, John Stouby Nielsen, Peter Axel Aalborg University Department of Computer Science Denmark Department of Computer Science Human-Centred Computing Group Aalborg University Denmark

ISBN: (纸本)9781450396998

Designing interactive technology to support and reduce domestic energy consumption has occupied HCI researchers for decades. This paper extends this body of HCI research by using postphenomenology as an analytical lens to understand how the digital energy assistant' Watts' mediates domestic district heating. We base our inquiry on empirical insights from a two-year study. We conducted informal meetings, focus groups with seven households before Watts' distribution, two rounds of semi-structured interviews with seven household representatives using Watts, and 304 questionnaire respondents. We illustrate findings in three postphenomenological dimensions - practical, ontological, and epistemological - showing a two-sidedness of technology-mediated domestic district heating. For instance, Watts enables some to reduce heating by magnifying certain new aspects of consumption, but also constrains others from doing the same by concealing the "raw"consumption data. Finally, we discuss four tensions of Watts mediation and how postphenomenology can complement HCI studies on domestic energy consumption. © 2022 ACM.

关键词： Surveys

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Focus Cat: Designing Idle Games to Promote Intermittent Practice and On-Going Adherence of Breathing Exercise for ADHD 22

Focus Cat: Designing Idle Games to Promote Intermittent Prac...

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9th ACM SIGCHI Annual Symposium on computer-human interaction in Play, CHI PLAY 2022

作者： Sadprasid, Book Tabor, Aaron Scheme, Erik Bateman, Scott Faculty Of Computer Science University Of New Brunswick Canada Human Computer Interaction Lab University Of New Brunswick Canada Institute Of Biomedical Engineering University Of New Brunswick Canada

ISBN: (纸本)9781450392112

Adherence and frequency of play are well-established challenges for serious games that target breathing exercises, because exercises are most effective when practiced in short and frequent sessions. Researchers have recognized that elements of idle games ideally align with many therapeutic use cases because idle games have a frequently repeating gameplay cycle that draws people in for short play sessions. However, there is little research about how idle games can be used to motivate consistent, frequent practice of therapies like breathing exercises that are often recommended for chronic conditions like ADHD. This paper describes the design and implementation of a therapeutic idle game, Focus Cat. This game is designed to help people with ADHD incorporate breathing exercises into their symptom management routine. Our work demonstrates how the unique qualities of idle game design - including short, frequent gameplay sessions, simple mechanics that make mundane tasks engaging and mechanics that pull and push players into and out of active gameplay - can be used for ADHD breathing exercises to improve adherence and frequency of practice. © 2022 Owner/Author.

关键词： Game design

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In Time and Space: Towards Usable Adaptive Control for Assistive Robotic Arms

In Time and Space: Towards Usable Adaptive Control for Assis...

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IEEE International Workshop on Robot and human Communication (ROMAN)

作者： Max Pascher Kirill Kronhardt Felix Ferdinand Goldau Udo Frese Jens Gerken Human-Computer Interaction Westphalian University of Applied Sciences Gelsenkirchen Germany Human-Computer Interaction University of Duisburg-Essen Essen Germany Mathematics & Computer Science University of Bremen Bremen Germany

Robotic solutions, in particular robotic arms, are becoming more frequently deployed for close collaboration with humans, for example in manufacturing or domestic care environments. These robotic arms require the user to control several Degrees-of-Freedom (DoFs) to perform tasks, primarily involving grasping and manipulating objects. Standard input devices predominantly have two DoFs, requiring time-consuming and cognitively demanding mode switches to select individual DoFs. Contemporary Adaptive DoF Mapping Controls (ADMCs) have shown to decrease the necessary number of mode switches but were up to now not able to significantly reduce the perceived workload. Users still bear the mental workload of incorporating abstract mode switching into their workflow. We address this by providing feed-forward multimodal feedback using updated recommendations of ADMC, allowing users to visually compare the current and the suggested mapping in real-time. We contrast the effectiveness of two new approaches that a) continuously recommend updated DoF combinations or b) use discrete thresholds between current robot movements and new recommendations. Both are compared in a Virtual Reality (VR) in-person study against a classic control method. Significant results for lowered task completion time, fewer mode switches, and reduced perceived workload conclusively establish that in combination with feedforward, ADMC methods can indeed outperform classic mode switching. A lack of apparent quantitative differences between Continuous and Threshold reveals the importance of user-centered customization options. Including these implications in the development process will improve usability, which is essential for successfully implementing robotic technologies with high user acceptance.

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MiniNet: Dense squeeze with depthwise separable convolutions for image classification in resource-constrained autonomous systems

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ISA Transactions 2023年 132卷 120-130页

作者： Tseng, Fan-Hsun Yeh, Kuo-Hui Kao, Fan-Yi Chen, Chi-Yuan Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Department of Information Management National Dong Hwa University Hualien Taiwan Computer Science and Engineering Department of National Sun Yat-sen University Kaohsiung804 Taiwan Department of Technology Application and Human Resource Development National Taiwan Normal University Taipei Taiwan Department of Computer Science and Information Engineering National Ilan University Yilan Taiwan

In recent years, artificial intelligence (AI) has been developed vigorously, and a great number of AI autonomous applications have been proposed. However, how to decrease computations and shorten training time with high accuracy under the limited hardware resource is a vital issue. In this paper, on the basis of MobileNet architecture, the dense squeeze with depthwise separable convolutions model is proposed, viz. MiniNet. MiniNet utilizes depthwise and pointwise convolutions, and is composed of the dense connection technique and the Squeeze-and-Excitation operations. The proposed MiniNet model is implemented and experimented with Keras. In experimental results, MiniNet is compared with three existing models, i.e., DenseNet, MobileNet, and SE-Inception-Resnet-v1. To validate that the proposed MiniNet model is provided with less computation and shorter training time, two types as well as large and small datasets are used. The experimental results showed that the proposed MiniNet model significantly reduces the number of parameters and shortens training time efficiently. MiniNet is superior to other models in terms of the lowest parameters, shortest training time, and highest accuracy when the dataset is small, especially. © 2022 ISA

关键词： Convolution

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