@article{
 title = {When trust collides: Exploring human-LLM cooperation intention through the prisoner’s dilemma},
 type = {article},
 year = {2026},
 keywords = {Large language model,Game theory,Human-AI interact,large language model},
 pages = {103740},
 volume = {209},
 websites = {https://doi.org/10.1016/j.ijhcs.2026.103740},
 publisher = {Elsevier Ltd},
 id = {f4873fdb-3e35-36c2-ad38-a5d5c5038c9a},
 created = {2026-06-09T08:12:19.424Z},
 file_attached = {true},
 profile_id = {4b66b327-35ad-3956-a9a2-307331dd9988},
 last_modified = {2026-06-12T06:38:44.939Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Jiang, Guanxuan and Yang, Shirao and Wang, Yuyang and Hui, Pan},
 doi = {10.1016/j.ijhcs.2026.103740},
 journal = {International Journal of Human-Computer Studies},
 number = {December 2025}
}

@article{
 title = {J NeuroEngineering Rehabil Article in Press Multimodal colour modulation for cognitive enhancement in intelligent rehabilitation : a systematic review and translational guidance IN Cognitive Enhancement in Intelligent Rehabilitation : A Systematic Review },
 type = {article},
 year = {2026},
 id = {101fe567-a558-3665-be61-6beac5956f22},
 created = {2026-06-09T08:12:19.626Z},
 file_attached = {true},
 profile_id = {4b66b327-35ad-3956-a9a2-307331dd9988},
 last_modified = {2026-06-09T08:14:28.112Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 bibtype = {article},
 author = {Xu, Lina and Wang, Xingkai and Li, Wenan and Jiang, Yuyang and Cheng, Chen and Li, Zhenhong and Yu, Luwen and Xu, Lina}
}

@book{
 title = {GuideMe: A VLM-Based System Assisting Independent Smartphone Learning for Older Adults},
 type = {book},
 year = {2026},
 source = {Conference on Human Factors in Computing Systems - Proceedings },
 keywords = {Accessibility Design,Independent Learning,Interaction Design,Older Adults,Smartphone,Vision-language-model},
 volume = {1},
 issue = {1},
 publisher = {Association for Computing Machinery},
 id = {2d7bcef5-5717-3610-a483-e36cad1d6478},
 created = {2026-06-09T08:12:19.933Z},
 file_attached = {true},
 profile_id = {4b66b327-35ad-3956-a9a2-307331dd9988},
 last_modified = {2026-06-12T06:38:52.547Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Due to age-related cognitive and physical decline, older adults face numerous difficulties when learning new functions of smartphone applications. However, older adults often struggle to ask questions clearly and follow instructions independently. Through a formative study (N=16), we identified the behaviors and challenges of older adults seeking help independently and analyzed the effective mechanism of in-person instruction. Based on these findings, we proposed GuideMe, an in-situ conversational instruction system for older adults' application learning. GuideMe utilizes Vision-Language-Models to analyze multimodal context in users' situations, then assists users in confirming their intentions by asking clarifying questions, and finally provides step-by-step instructions using in-situ highlight and deictic gestures. We conducted a user study (N=18) that demonstrated that GuideMe significantly reduced users' cognitive load during learning, helped them ask questions and follow instructions efficiently, and achieved performance comparable to that of in-person instruction.},
 bibtype = {book},
 author = {Fang, Kairong and Zhang, Jiesi and Ni, Shi Ting and Hui, Pan and Wang, Yuyang},
 doi = {10.1145/3772318.3791448}
}

Due to age-related cognitive and physical decline, older adults face numerous difficulties when learning new functions of smartphone applications. However, older adults often struggle to ask questions clearly and follow instructions independently. Through a formative study (N=16), we identified the behaviors and challenges of older adults seeking help independently and analyzed the effective mechanism of in-person instruction. Based on these findings, we proposed GuideMe, an in-situ conversational instruction system for older adults' application learning. GuideMe utilizes Vision-Language-Models to analyze multimodal context in users' situations, then assists users in confirming their intentions by asking clarifying questions, and finally provides step-by-step instructions using in-situ highlight and deictic gestures. We conducted a user study (N=18) that demonstrated that GuideMe significantly reduced users' cognitive load during learning, helped them ask questions and follow instructions efficiently, and achieved performance comparable to that of in-person instruction.

@article{
 title = {Aligning Gamification with Learner Motivation: Insights from VR-Based Learning Tasks},
 type = {article},
 year = {2026},
 keywords = {Extrinsic Motivation,Gamification,Intrinsic Motivation,Learning Environments,User Experience,Virtual Reality},
 pages = {3411-3421},
 volume = {32},
 publisher = {IEEE},
 id = {aa27a58a-4da0-3ac9-ae4b-4a9427dbed6f},
 created = {2026-06-09T08:12:19.958Z},
 file_attached = {true},
 profile_id = {4b66b327-35ad-3956-a9a2-307331dd9988},
 last_modified = {2026-06-12T06:38:46.731Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Virtual reality is increasingly adopted in education for its potential to create immersive, interactive learning experiences. Among various instructional approaches, gamification has emerged as a promising strategy to enhance learner engagement and motivation. While VR naturally provides a platform for game-based learning, current applications often overlook the underlying motivational mechanisms that influence learner behavior in these environments. Building on Self-Determination Theory, we examined how gamification and motivational framing influence VR learning. We designed a VR system featuring both gamified and non-gamified versions of two learning tasks—one culturally expressive (batik-based task) and one technically focused (code-based task)—to reflect different motivational framings. Our findings show that gamification selectively influenced intrinsic motivation components and played a dominant role in shaping learner satisfaction. A trend-level interaction suggested that combining extrinsic framing with gamification may increase satisfaction. Gamification improved overall experience and engagement, even though it elevated perceived workload. These results highlight the importance of aligning gamification strategies with learner motivation and underscore the need for more dynamic approaches to capture motivational processes in VR, offering design insights for more effective and psychologically attuned VR learning environments.},
 bibtype = {article},
 author = {Bai, Yuxuan and Wang, Yuzhu and Hui, Pan and Wang, Yuyang},
 doi = {10.1109/TVCG.2026.3680716},
 journal = {IEEE Transactions on Visualization and Computer Graphics},
 number = {5}
}

Virtual reality is increasingly adopted in education for its potential to create immersive, interactive learning experiences. Among various instructional approaches, gamification has emerged as a promising strategy to enhance learner engagement and motivation. While VR naturally provides a platform for game-based learning, current applications often overlook the underlying motivational mechanisms that influence learner behavior in these environments. Building on Self-Determination Theory, we examined how gamification and motivational framing influence VR learning. We designed a VR system featuring both gamified and non-gamified versions of two learning tasks—one culturally expressive (batik-based task) and one technically focused (code-based task)—to reflect different motivational framings. Our findings show that gamification selectively influenced intrinsic motivation components and played a dominant role in shaping learner satisfaction. A trend-level interaction suggested that combining extrinsic framing with gamification may increase satisfaction. Gamification improved overall experience and engagement, even though it elevated perceived workload. These results highlight the importance of aligning gamification strategies with learner motivation and underscore the need for more dynamic approaches to capture motivational processes in VR, offering design insights for more effective and psychologically attuned VR learning environments.

@article{
 title = {Identity, crimes, and law enforcement in the Metaverse},
 type = {article},
 year = {2025},
 pages = {1-15},
 volume = {12},
 websites = {http://dx.doi.org/10.1057/s41599-024-04266-w},
 publisher = {Springer US},
 id = {34aef4d8-5798-3c1e-8f9a-cbac5cc87165},
 created = {2026-06-09T08:12:18.873Z},
 file_attached = {true},
 profile_id = {4b66b327-35ad-3956-a9a2-307331dd9988},
 last_modified = {2026-06-12T06:38:56.264Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {The recent Metaverse technology boom in major areas of the public’s life makes the safety of users a pressing concern. Though the nature of the Metaverse as a geographically unbounded space blending the physical and the virtual presents new challenges for law enforcement and governance. To tackle these, this paper supports the establishment of a unified international legal framework. Specifically, from a law enforcer’s perspective, it provides the first comprehensive discussion in the past five years on legal concerns related to identity, various types of potential crimes, and challenges to unified law enforcement in the Metaverse based on prior incidents.},
 bibtype = {article},
 author = {Qin, Hua Xuan and Wang, Yuyang and Hui, Pan},
 doi = {10.1057/s41599-024-04266-w},
 journal = {Humanities and Social Sciences Communications},
 number = {1}
}

The recent Metaverse technology boom in major areas of the public’s life makes the safety of users a pressing concern. Though the nature of the Metaverse as a geographically unbounded space blending the physical and the virtual presents new challenges for law enforcement and governance. To tackle these, this paper supports the establishment of a unified international legal framework. Specifically, from a law enforcer’s perspective, it provides the first comprehensive discussion in the past five years on legal concerns related to identity, various types of potential crimes, and challenges to unified law enforcement in the Metaverse based on prior incidents.

@article{
 title = {Balancing Exploration and Cybersickness: Investigating Curiosity-Driven Behavior in Virtual Environments},
 type = {article},
 year = {2025},
 keywords = {Curiosity-driven behavior,cybersickness,exploration,virtual navigation},
 pages = {1043-1052},
 volume = {55},
 publisher = {IEEE},
 id = {7c4fe632-cf5f-3516-ac75-df53983cfb28},
 created = {2026-06-09T08:12:18.883Z},
 file_attached = {true},
 profile_id = {4b66b327-35ad-3956-a9a2-307331dd9988},
 last_modified = {2026-06-12T06:39:01.467Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Virtual reality offers the opportunity for immersive exploration, yet it is often undermined by cybersickness. However, how individuals strike a balance between exploration and discomfort remains unclear. Existing method (e.g., reinforcement learning (RL)) often fail to fully capture the complexities of navigation and decision-making patterns. This study investigates how curiosity influences users’ navigation behavior, particularly how users strike a balance between exploration and discomfort. We propose curiosity as a key factor driving irrational decision-making and apply the free energy principle to model the relationship between curiosity and user behavior quantitatively. Our findings indicate that users generally adopt conservative strategies when navigating. Also, curiosity levels tend to rise when the virtual environment changes. These results illustrate the dynamic interplay between exploration and discomfort. In addition, it offers a new perspective on how curiosity drives behavior in immersive environments, providing a foundation for designing adaptive VR environments. Future research will further refine this model by incorporating additional psychological and environmental factors to improve prediction accuracy.},
 bibtype = {article},
 author = {Li, Tangyao and Wang, Yuyang},
 doi = {10.1109/THMS.2025.3602125},
 journal = {IEEE Transactions on Human-Machine Systems},
 number = {6}
}

Virtual reality offers the opportunity for immersive exploration, yet it is often undermined by cybersickness. However, how individuals strike a balance between exploration and discomfort remains unclear. Existing method (e.g., reinforcement learning (RL)) often fail to fully capture the complexities of navigation and decision-making patterns. This study investigates how curiosity influences users’ navigation behavior, particularly how users strike a balance between exploration and discomfort. We propose curiosity as a key factor driving irrational decision-making and apply the free energy principle to model the relationship between curiosity and user behavior quantitatively. Our findings indicate that users generally adopt conservative strategies when navigating. Also, curiosity levels tend to rise when the virtual environment changes. These results illustrate the dynamic interplay between exploration and discomfort. In addition, it offers a new perspective on how curiosity drives behavior in immersive environments, providing a foundation for designing adaptive VR environments. Future research will further refine this model by incorporating additional psychological and environmental factors to improve prediction accuracy.

@article{
 title = {VRtalk: Real-Time Interactive Intelligent Anime Avatars in Virtual Reality},
 type = {article},
 year = {2025},
 keywords = {Anime Research,Avatar,Human-Computer Interaction,LLM,Live Streaming,Persona,Virtual Reality},
 pages = {1191-1201},
 publisher = {IEEE},
 id = {1699d400-a11b-3470-913b-145bdd4b03c8},
 created = {2026-06-09T08:12:19.100Z},
 file_attached = {true},
 profile_id = {4b66b327-35ad-3956-a9a2-307331dd9988},
 last_modified = {2026-06-12T06:38:58.269Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {The convergence of virtual reality live streaming and AI-driven avatars has emerged as a significant technological trend. However, current integration attempts remain in the proof-of-concept stage, with the primary challenge of automatic interaction system establishment. To build interactive intelligence anime avatars within VR frameworks, we have developed a multimodal interaction architecture centered on dialogue agents, realizing comprehensive understanding, reasoning, and response. Our approach 1).proposes high granularity explicit-implicit understanding and a dual-center switchable reasoning mechanism to support flexible responses. 2).innovates a dual-source animation mechanism for co-speech face-body visualization and a textual command module for supervising crossmodal animation, and 3).enhances expressiveness through mapping persona, content, voice, and motion to anime style. Experimental results demonstrate the state-of-the-art performance of VRtalk, highlighting its practical significance and future potential.},
 bibtype = {article},
 author = {Yu, Yuan and Xu, Chunlei and Yang, Shirao and Cao, Yu and Wang, Yuyang and Lee, Boon Giin},
 doi = {10.1109/ISMAR67309.2025.00125},
 journal = {Proceedings - 2025 IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2025}
}

The convergence of virtual reality live streaming and AI-driven avatars has emerged as a significant technological trend. However, current integration attempts remain in the proof-of-concept stage, with the primary challenge of automatic interaction system establishment. To build interactive intelligence anime avatars within VR frameworks, we have developed a multimodal interaction architecture centered on dialogue agents, realizing comprehensive understanding, reasoning, and response. Our approach 1).proposes high granularity explicit-implicit understanding and a dual-center switchable reasoning mechanism to support flexible responses. 2).innovates a dual-source animation mechanism for co-speech face-body visualization and a textual command module for supervising crossmodal animation, and 3).enhances expressiveness through mapping persona, content, voice, and motion to anime style. Experimental results demonstrate the state-of-the-art performance of VRtalk, highlighting its practical significance and future potential.

@book{
 title = {Towards Consumer-Grade Cybersickness Prediction: Multi-Model Alignment for Real-Time Vision-Only Inference},
 type = {book},
 year = {2025},
 source = {MM 2025 - Proceedings of the 33rd ACM International Conference on Multimedia, Co-Located with MM 2025},
 keywords = {consumer-grade deployment,cross-modal alignment,cybersickness prediction,difference attention},
 pages = {6859-6867},
 volume = {1},
 issue = {1},
 publisher = {Association for Computing Machinery},
 id = {7fdbe78d-5bda-3836-8f74-28b58490b47f},
 created = {2026-06-09T08:12:19.137Z},
 file_attached = {true},
 profile_id = {4b66b327-35ad-3956-a9a2-307331dd9988},
 last_modified = {2026-06-12T06:38:55.158Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Cybersickness remains a major obstacle to the widespread adoption of immersive virtual reality (VR), particularly in consumer-grade environments. While prior methods rely on invasive signals such as electroencephalography (EEG) for high predictive accuracy, these approaches require specialized hardware and are impractical for real-world applications. In this work, we propose a scalable, deployable framework for personalized cybersickness prediction leveraging only non-invasive signals readily available from commercial VR headsets, including head motion, eye tracking, and physiological responses. Our model employs a modality-specific graph neural network enhanced with a Difference Attention Module to extract temporal-spatial embeddings capturing dynamic changes across modalities. A cross-modal alignment module jointly trains the video encoder to learn personalized traits by aligning video features with sensor-derived representations. Consequently, the model accurately predicts individual cybersickness using only video input during inference. Experimental results show our model achieves 88.4% accuracy, closely matching EEG-based approaches (89.16%), while reducing deployment complexity. With an average inference latency of 90ms, our framework supports real-time applications, ideal for integration into consumer-grade VR platforms without compromising personalization or performance. The code will be relesed at https://github.com/U235-Aurora/PTGNN.},
 bibtype = {book},
 author = {Zhu, Yitong and Liang, Zhuowen and Wu, Yiming and Li, Tangyao and Wang, Yuyang},
 doi = {10.1145/3746027.3755115}
}

Cybersickness remains a major obstacle to the widespread adoption of immersive virtual reality (VR), particularly in consumer-grade environments. While prior methods rely on invasive signals such as electroencephalography (EEG) for high predictive accuracy, these approaches require specialized hardware and are impractical for real-world applications. In this work, we propose a scalable, deployable framework for personalized cybersickness prediction leveraging only non-invasive signals readily available from commercial VR headsets, including head motion, eye tracking, and physiological responses. Our model employs a modality-specific graph neural network enhanced with a Difference Attention Module to extract temporal-spatial embeddings capturing dynamic changes across modalities. A cross-modal alignment module jointly trains the video encoder to learn personalized traits by aligning video features with sensor-derived representations. Consequently, the model accurately predicts individual cybersickness using only video input during inference. Experimental results show our model achieves 88.4% accuracy, closely matching EEG-based approaches (89.16%), while reducing deployment complexity. With an average inference latency of 90ms, our framework supports real-time applications, ideal for integration into consumer-grade VR platforms without compromising personalization or performance. The code will be relesed at https://github.com/U235-Aurora/PTGNN.

@article{
 title = {Flow-Aware Diffusion for Real-Time VR Restoration: Enhancing Spatiotemporal Coherence and Efficiency},
 type = {article},
 year = {2025},
 pages = {1-15},
 volume = {PP},
 websites = {http://arxiv.org/abs/2506.18786},
 publisher = {IEEE},
 id = {bb2d232b-7db8-363a-be57-e8febd5fe33f},
 created = {2026-06-09T08:12:20.251Z},
 file_attached = {true},
 profile_id = {4b66b327-35ad-3956-a9a2-307331dd9988},
 last_modified = {2026-06-09T08:13:01.042Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Cybersickness remains a critical barrier to the widespread adoption of Virtual Reality (VR), particularly in scenarios involving intense or artificial motion cues. Among the key contributors is excessive optical flow-perceived visual motion that, when unmatched by vestibular input, leads to sensory conflict and discomfort. While previous efforts have explored geometric or hardware based mitigation strategies, such methods often rely on predefined scene structures, manual tuning, or intrusive equipment. In this work, we propose U-MAD, a lightweight, real-time, AI-based solution that suppresses perceptually disruptive optical flow directly at the image level. Unlike prior handcrafted approaches, this method learns to attenuate high-intensity motion patterns from rendered frames without requiring mesh-level editing or scene specific adaptation. Designed as a plug and play module, U-MAD integrates seamlessly into existing VR pipelines and generalizes well to procedurally generated environments. The experiments show that U-MAD consistently reduces average optical flow and enhances temporal stability across diverse scenes. A user study further confirms that reducing visual motion leads to improved perceptual comfort and alleviated cybersickness symptoms. These findings demonstrate that perceptually guided modulation of optical flow provides an effective and scalable approach to creating more user-friendly immersive experiences. The code will be released at https://github.com/XXXXX (upon publication).},
 bibtype = {article},
 author = {Zhu, Yitong and Jiang, Guanxuan and Liang, Zhuowen and Wang, Yuyang},
 doi = {10.1109/TVCG.2026.3697013},
 journal = {IEEE Transactions on Visualization and Computer Graphics}
}

Cybersickness remains a critical barrier to the widespread adoption of Virtual Reality (VR), particularly in scenarios involving intense or artificial motion cues. Among the key contributors is excessive optical flow-perceived visual motion that, when unmatched by vestibular input, leads to sensory conflict and discomfort. While previous efforts have explored geometric or hardware based mitigation strategies, such methods often rely on predefined scene structures, manual tuning, or intrusive equipment. In this work, we propose U-MAD, a lightweight, real-time, AI-based solution that suppresses perceptually disruptive optical flow directly at the image level. Unlike prior handcrafted approaches, this method learns to attenuate high-intensity motion patterns from rendered frames without requiring mesh-level editing or scene specific adaptation. Designed as a plug and play module, U-MAD integrates seamlessly into existing VR pipelines and generalizes well to procedurally generated environments. The experiments show that U-MAD consistently reduces average optical flow and enhances temporal stability across diverse scenes. A user study further confirms that reducing visual motion leads to improved perceptual comfort and alleviated cybersickness symptoms. These findings demonstrate that perceptually guided modulation of optical flow provides an effective and scalable approach to creating more user-friendly immersive experiences. The code will be released at https://github.com/XXXXX (upon publication).

@article{
 title = {Using a virtual reality interview simulator to explore factors influencing people’s behavior},
 type = {article},
 year = {2024},
 pages = {56},
 volume = {28},
 websites = {https://link.springer.com/10.1007/s10055-023-00934-5},
 month = {3},
 day = {28},
 id = {12dec4f3-705a-3902-a8fd-7b620d7ed955},
 created = {2023-05-18T02:58:43.475Z},
 file_attached = {true},
 profile_id = {4b66b327-35ad-3956-a9a2-307331dd9988},
 last_modified = {2024-04-14T08:26:11.885Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Virtual reality interview simulator (VRIS) is an effective and valid tool that uses virtual reality technology to train people’s interview skills. Typically, it offers candidates prone to being very nervous during interviews the opportunity to practice interviews in a safe and manageable virtual environment and realistic settings, providing real-time feedback from a virtual interviewer on their performance. It helps interviewees improve their skills, reduce their fears, gain confidence, and minimize the cost and time associated with traditional interview preparation. Yet, the major anxiety-inducing elements remain unknown. During an interview, the anxiety levels, overall experience, and performance of interviewees might be affected by various circumstances. By analyzing electrodermal activity and questionnaire, we investigated the influence of five variables: (I) Realism ; (II) Question type ; (III) Interviewer attitude ; (IV) Timing ; and (V) Preparation . As such, an orthogonal design $$L_8(4^1 \times 2^4)$$ L 8 ( 4 1 × 2 4 ) with eight experiments ( $$O A_8$$ O A 8 matrix) was implemented, in which 19 college students took part in the experiments. Considering the anxiety, overall experience, and performance of the interviewees, we found that Question type plays a major role; secondly, Realism , Preparation , and Interviewer attitude all have middle influence; lastly, Timing has little to no impact. Specifically, professional interview questions elicited a greater degree of anxiety than personal ones among the categories of interview questions. This work contributes to our understanding of anxiety-stimulating factors during job interviews in virtual reality and provides cues for designing future VRIS.},
 bibtype = {article},
 author = {Luo, Xinyi and Wang, Yuyang and Lee, Lik-Hang and Xing, Zihan and Jin, Shan and Dong, Boya and Hu, Yuanyi and Chen, Zeming and Yan, Jing and Hui, Pan},
 doi = {10.1007/s10055-023-00934-5},
 journal = {Virtual Reality},
 number = {1}
}

Virtual reality interview simulator (VRIS) is an effective and valid tool that uses virtual reality technology to train people’s interview skills. Typically, it offers candidates prone to being very nervous during interviews the opportunity to practice interviews in a safe and manageable virtual environment and realistic settings, providing real-time feedback from a virtual interviewer on their performance. It helps interviewees improve their skills, reduce their fears, gain confidence, and minimize the cost and time associated with traditional interview preparation. Yet, the major anxiety-inducing elements remain unknown. During an interview, the anxiety levels, overall experience, and performance of interviewees might be affected by various circumstances. By analyzing electrodermal activity and questionnaire, we investigated the influence of five variables: (I) Realism ; (II) Question type ; (III) Interviewer attitude ; (IV) Timing ; and (V) Preparation . As such, an orthogonal design $$L_8(4^1 \times 2^4)$$ L 8 ( 4 1 × 2 4 ) with eight experiments ( $$O A_8$$ O A 8 matrix) was implemented, in which 19 college students took part in the experiments. Considering the anxiety, overall experience, and performance of the interviewees, we found that Question type plays a major role; secondly, Realism , Preparation , and Interviewer attitude all have middle influence; lastly, Timing has little to no impact. Specifically, professional interview questions elicited a greater degree of anxiety than personal ones among the categories of interview questions. This work contributes to our understanding of anxiety-stimulating factors during job interviews in virtual reality and provides cues for designing future VRIS.

@inbook{
 type = {inbook},
 year = {2024},
 pages = {325-346},
 websites = {https://link.springer.com/10.1007/978-981-99-8141-0_25},
 id = {544d0e58-3576-3a38-bd7d-e63a34d65abe},
 created = {2023-11-30T01:57:24.357Z},
 file_attached = {false},
 profile_id = {4b66b327-35ad-3956-a9a2-307331dd9988},
 last_modified = {2023-11-30T01:57:24.357Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {inbook},
 author = {Wang, Yuyang and Chardonnet, Jean-Rémy and Merienne, Frédéric},
 doi = {10.1007/978-981-99-8141-0_25},
 chapter = {Modeling Online Adaptive Navigation in Virtual Environments Based on PID Control}
}

@inproceedings{
 title = {Text2VRScene: Exploring the Framework of Automated Text-driven Generation System for VR Experience},
 type = {inproceedings},
 year = {2024},
 keywords = {Index Terms},
 pages = {701-711},
 websites = {https://github.com/Williamy946/Text2VRScene,https://ieeexplore.ieee.org/document/10494137/},
 month = {3},
 publisher = {IEEE},
 day = {16},
 id = {48697da0-ede0-34a2-b461-85fe47498a5f},
 created = {2024-04-15T09:13:18.633Z},
 file_attached = {true},
 profile_id = {4b66b327-35ad-3956-a9a2-307331dd9988},
 last_modified = {2024-04-18T04:33:30.580Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {With the recent development of the Virtual Reality (VR) industry, the increasing number of VR users pushes the demand for the massive production of immersive and expressive VR scenes in related industries. However, creating expressive VR scenes involves the reasonable organization of various digital content to express a coherent and logical theme, which is time-consuming and labor-intensive. In recent years, Large Language Models (LLMs) such as ChatGPT 3.5 and generative models such as stable diffusion have emerged as powerful tools for comprehending natural language and generating digital contents such as text, code, images, and 3D objects. In this paper, we have explored how we can generate VR scenes from text by incorporating LLMs and various generative models into an automated system. To achieve this, we first identify the possible limitations of LLMs for an automated system and propose a systematic framework to mitigate them. Subsequently, we developed Text2VRScene, a VR scene generation system, based on our proposed framework with well-designed prompts. To validate the effectiveness of our proposed framework and the designed prompts, we carry out a series of test cases. The results show that the proposed framework contributes to improving the reliability of the system and the quality of the generated VR scenes. The results also illustrate the promising performance of the Text2VRScene in generating satisfying VR scenes with a clear theme regularized by our well-designed prompts. This paper ends with a discussion about the limitations of the current system and the potential of developing similar generation systems based on our framework.},
 bibtype = {inproceedings},
 author = {Yin, Zhizhuo and Wang, Yuyang and Papatheodorou, Theodoros and Hui, Pan},
 doi = {10.1109/VR58804.2024.00090},
 booktitle = {2024 IEEE Conference Virtual Reality and 3D User Interfaces (VR)}
}

With the recent development of the Virtual Reality (VR) industry, the increasing number of VR users pushes the demand for the massive production of immersive and expressive VR scenes in related industries. However, creating expressive VR scenes involves the reasonable organization of various digital content to express a coherent and logical theme, which is time-consuming and labor-intensive. In recent years, Large Language Models (LLMs) such as ChatGPT 3.5 and generative models such as stable diffusion have emerged as powerful tools for comprehending natural language and generating digital contents such as text, code, images, and 3D objects. In this paper, we have explored how we can generate VR scenes from text by incorporating LLMs and various generative models into an automated system. To achieve this, we first identify the possible limitations of LLMs for an automated system and propose a systematic framework to mitigate them. Subsequently, we developed Text2VRScene, a VR scene generation system, based on our proposed framework with well-designed prompts. To validate the effectiveness of our proposed framework and the designed prompts, we carry out a series of test cases. The results show that the proposed framework contributes to improving the reliability of the system and the quality of the generated VR scenes. The results also illustrate the promising performance of the Text2VRScene in generating satisfying VR scenes with a clear theme regularized by our well-designed prompts. This paper ends with a discussion about the limitations of the current system and the potential of developing similar generation systems based on our framework.

@inproceedings{
 title = {Jump Cut Effects in Cinematic Virtual Reality: Editing with the 30-degree Rule and 180-degree Rule},
 type = {inproceedings},
 year = {2024},
 pages = {51-60},
 websites = {https://ieeexplore.ieee.org/document/10494084/},
 month = {3},
 publisher = {IEEE},
 day = {16},
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 author = {Zhang, Junjie and Lee, Lik-hang and Wang, Yuyang and Jin, Shan and Fei, Dan-Lu and Hui, Pan},
 doi = {10.1109/VR58804.2024.00029},
 booktitle = {2024 IEEE Conference Virtual Reality and 3D User Interfaces (VR)}
}

@article{
 title = {OmniColor: A Global Camera Pose Optimization Approach of LiDAR-360Camera Fusion for Colorizing Point Clouds},
 type = {article},
 year = {2024},
 pages = {6396-6402},
 publisher = {IEEE},
 id = {fa24e437-2e78-30b5-a203-c185a998902e},
 created = {2026-06-09T08:12:18.765Z},
 file_attached = {true},
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 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {A Colored point cloud, as a simple and efficient 3D representation, has many advantages in various fields, including robotic navigation and scene reconstruction. This representation is now commonly used in 3D reconstruction tasks relying on cameras and LiDARs. However, fusing data from these two types of sensors is poorly performed in many existing frameworks, leading to unsatisfactory mapping results, mainly due to inaccurate camera poses. This paper presents Omni-Color, a novel and efficient algorithm to colorize point clouds using an independent 360-degree camera. Given a LiDAR-based point cloud and a sequence of panorama images with initial coarse camera poses, our objective is to jointly optimize the poses of all frames for mapping images onto geometric reconstructions. Our pipeline works in an off-the-shelf manner that does not require any feature extraction or matching process. Instead, we find optimal poses by directly maximizing the photometric consistency of LiDAR maps. In experiments, we show that our method can overcome the severe visual distortion of omnidirectional images and greatly benefit from the wide field of view (FOV) of 360-degree cameras to reconstruct various scenarios with accuracy and stability. The code will be released at https://github.com/liubonan123/OmniColor/.},
 bibtype = {article},
 author = {Liu, Bonan and Zhao, Guoyang and Jiao, Jianhao and Cai, Guang and Li, Chengyang and Yin, Handi and Wang, Yuyang and Liu, Ming and Hui, Pan},
 doi = {10.1109/ICRA57147.2024.10610292},
 journal = {Proceedings - IEEE International Conference on Robotics and Automation}
}

A Colored point cloud, as a simple and efficient 3D representation, has many advantages in various fields, including robotic navigation and scene reconstruction. This representation is now commonly used in 3D reconstruction tasks relying on cameras and LiDARs. However, fusing data from these two types of sensors is poorly performed in many existing frameworks, leading to unsatisfactory mapping results, mainly due to inaccurate camera poses. This paper presents Omni-Color, a novel and efficient algorithm to colorize point clouds using an independent 360-degree camera. Given a LiDAR-based point cloud and a sequence of panorama images with initial coarse camera poses, our objective is to jointly optimize the poses of all frames for mapping images onto geometric reconstructions. Our pipeline works in an off-the-shelf manner that does not require any feature extraction or matching process. Instead, we find optimal poses by directly maximizing the photometric consistency of LiDAR maps. In experiments, we show that our method can overcome the severe visual distortion of omnidirectional images and greatly benefit from the wide field of view (FOV) of 360-degree cameras to reconstruct various scenarios with accuracy and stability. The code will be released at https://github.com/liubonan123/OmniColor/.

@article{
 title = {A Study of Partisan News Sharing in the Russian Invasion of Ukraine},
 type = {article},
 year = {2024},
 keywords = {ICWSM: Analysis of the relationship between social,ICWSM: Measuring predictability of real world phen,ICWSM: Organizational and group behavior mediated,interpersonal communication mediated by social med},
 pages = {1847-1858},
 volume = {18},
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 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Since the Russian invasion of Ukraine, a large volume of biased and partisan news has been spread via social media platforms. As this may lead to wider societal issues, we argue that understanding how partisan news sharing impacts users' communication is crucial for better governance of online communities. In this paper, we perform a measurement study of partisan news sharing. We aim to characterize the role of such sharing in influencing users' communications. Our analysis covers an eight-month dataset across six Reddit communities related to the Russian invasion. We first perform an analysis of the temporal evolution of partisan news sharing. We confirm that the invasion stimulates discussion in the observed communities, accompanied by an increased volume of partisan news sharing. Next, we characterize users' response to such sharing. We observe that partisan bias plays a role in narrowing its propagation. More biased media is less likely to be spread across multiple subreddits. However, we find that partisan news sharing attracts more users to engage in the discussion, by generating more comments. We then built a predictive model to identify users likely to spread partisan news. The prediction is challenging though, with 61.57% accuracy on average. Our centrality analysis on the commenting network further indicates that the users who disseminate partisan news possess lower network influence in comparison to those who propagate neutral news.},
 bibtype = {article},
 author = {Zhu, Yiming and Haq, Ehsan-Ul and Tyson, Gareth and Lee, Lik-Hang and Wang, Yuyang and Hui, Pan},
 doi = {10.1609/icwsm.v18i1.31430},
 journal = {Proceedings of the International AAAI Conference on Web and Social Media},
 number = {Icwsm}
}

Since the Russian invasion of Ukraine, a large volume of biased and partisan news has been spread via social media platforms. As this may lead to wider societal issues, we argue that understanding how partisan news sharing impacts users' communication is crucial for better governance of online communities. In this paper, we perform a measurement study of partisan news sharing. We aim to characterize the role of such sharing in influencing users' communications. Our analysis covers an eight-month dataset across six Reddit communities related to the Russian invasion. We first perform an analysis of the temporal evolution of partisan news sharing. We confirm that the invasion stimulates discussion in the observed communities, accompanied by an increased volume of partisan news sharing. Next, we characterize users' response to such sharing. We observe that partisan bias plays a role in narrowing its propagation. More biased media is less likely to be spread across multiple subreddits. However, we find that partisan news sharing attracts more users to engage in the discussion, by generating more comments. We then built a predictive model to identify users likely to spread partisan news. The prediction is challenging though, with 61.57% accuracy on average. Our centrality analysis on the commenting network further indicates that the users who disseminate partisan news possess lower network influence in comparison to those who propagate neutral news.

@article{
 title = {Dataset for predicting cybersickness from a virtual navigation task},
 type = {article},
 year = {2023},
 websites = {http://arxiv.org/abs/2303.13527},
 month = {2},
 day = {6},
 id = {4b260738-6e30-3ea6-afdc-4971ce6e330d},
 created = {2023-04-13T03:35:33.350Z},
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 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {This work presents a dataset collected to predict cybersickness in virtual reality environments. The data was collected from navigation tasks in a virtual environment designed to induce cybersickness. The dataset consists of many data points collected from diverse participants, including physiological responses (EDA and Heart Rate) and self-reported cybersickness symptoms. The paper will provide a detailed description of the dataset, including the arranged navigation task, the data collection procedures, and the data format. The dataset will serve as a valuable resource for researchers to develop and evaluate predictive models for cybersickness and will facilitate more research in cybersickness mitigation.},
 bibtype = {article},
 author = {Wang, Yuyang and Li, Ruichen and Chardonnet, Jean-Rémy and Hui, Pan}
}

This work presents a dataset collected to predict cybersickness in virtual reality environments. The data was collected from navigation tasks in a virtual environment designed to induce cybersickness. The dataset consists of many data points collected from diverse participants, including physiological responses (EDA and Heart Rate) and self-reported cybersickness symptoms. The paper will provide a detailed description of the dataset, including the arranged navigation task, the data collection procedures, and the data format. The dataset will serve as a valuable resource for researchers to develop and evaluate predictive models for cybersickness and will facilitate more research in cybersickness mitigation.

@inproceedings{
 title = {ARCam: A User-Defined Camera for AR Photographic Art Creation},
 type = {inproceedings},
 year = {2023},
 pages = {999-1000},
 websites = {https://ieeexplore.ieee.org/document/10108810/},
 month = {3},
 publisher = {IEEE},
 id = {2d35d885-509b-3265-9595-5e1b682d4df8},
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 bibtype = {inproceedings},
 author = {Luo, Xinyi and Zhu, Zihao and Wang, Yuyang and Hui, Pan},
 doi = {10.1109/VRW58643.2023.00342},
 booktitle = {2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)}
}

@inproceedings{
 title = {An immersive simulator for improving chemistry learning efficiency},
 type = {inproceedings},
 year = {2023},
 pages = {841-842},
 websites = {https://ieeexplore.ieee.org/document/10108889/},
 month = {3},
 publisher = {IEEE},
 id = {b4a20964-b3e8-36bc-ae6c-2cd8f167ba94},
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 authored = {true},
 confirmed = {true},
 hidden = {false},
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 bibtype = {inproceedings},
 author = {Shan, Jin and Wang, Yuyang and Lee, Lik-Hang and Wang, Xian and Chen, Zeming and Dong, Boya and Luo, Xinyi and Hui, Pan},
 doi = {10.1109/VRW58643.2023.00263},
 booktitle = {2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)}
}

@inproceedings{
 title = {Development and penta-metric evaluation of a virtual interview simulator},
 type = {inproceedings},
 year = {2023},
 pages = {917-918},
 websites = {https://ieeexplore.ieee.org/document/10108628/},
 month = {3},
 publisher = {IEEE},
 id = {61aa4169-50ba-3e54-8940-f193582ff6b8},
 created = {2023-04-13T03:40:27.558Z},
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 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Luo, Xinyi and Wang, Yuyang and Lee, Lik-Hang and Xing, Zihan and Jin, Shan and Dong, Boya and Hu, Yuanyi and Chen, Zeming and Yan, Jing and Hui, Pan},
 doi = {10.1109/VRW58643.2023.00301},
 booktitle = {2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)}
}

@inproceedings{
 title = {VR/AR/MR in the Electricity Industry: Concepts, Techniques, and Applications},
 type = {inproceedings},
 year = {2023},
 pages = {82-88},
 websites = {https://ieeexplore.ieee.org/document/10108819/},
 month = {3},
 publisher = {IEEE},
 id = {0d7dec2b-26a3-3674-b9ec-cf87284da056},
 created = {2023-04-15T01:37:02.134Z},
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 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
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 bibtype = {inproceedings},
 author = {Xiao, Jiakai and Qian, Yang and Du, Wei and Wang, Yuyang and Jiang, Yuanchun and Liu, Yezheng},
 doi = {10.1109/VRW58643.2023.00022},
 booktitle = {2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)}
}

@inproceedings{
 title = {IEEE VR 2023 Workshop: Datasets for developing intelligent XR applications (DATA4XR)},
 type = {inproceedings},
 year = {2023},
 pages = {67-68},
 websites = {https://ieeexplore.ieee.org/document/10108730/},
 month = {3},
 publisher = {IEEE},
 id = {f6c6dd21-c16f-3a11-a9ad-2021027398d3},
 created = {2023-05-03T08:18:34.549Z},
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 bibtype = {inproceedings},
 author = {Wang, Yuyang and Chardonnet, Jean-Rémy and Lee, Lik-Hang and Hui, Pan},
 doi = {10.1109/VRW58643.2023.00019},
 booktitle = {2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)}
}

@article{
 title = {Efficient Task Offloading Algorithm for Digital Twin in Edge/Cloud Computing Environment},
 type = {article},
 year = {2023},
 websites = {http://arxiv.org/abs/2307.05888},
 month = {7},
 day = {11},
 id = {cbccb76f-5301-35e2-8700-3468e74c3821},
 created = {2023-08-10T12:19:58.134Z},
 file_attached = {false},
 profile_id = {4b66b327-35ad-3956-a9a2-307331dd9988},
 last_modified = {2023-08-10T12:19:58.134Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {In the era of Internet of Things (IoT), Digital Twin (DT) is envisioned to empower various areas as a bridge between physical objects and the digital world. Through virtualization and simulation techniques, multiple functions can be achieved by leveraging computing resources. In this process, Mobile Cloud Computing (MCC) and Mobile Edge Computing (MEC) have become two of the key factors to achieve real-time feedback. However, current works only considered edge servers or cloud servers in the DT system models. Besides, The models ignore the DT with not only one data resource. In this paper, we propose a new DT system model considering a heterogeneous MEC/MCC environment. Each DT in the model is maintained in one of the servers via multiple data collection devices. The offloading decision-making problem is also considered and a new offloading scheme is proposed based on Distributed Deep Learning (DDL). Simulation results demonstrate that our proposed algorithm can effectively and efficiently decrease the system's average latency and energy consumption. Significant improvement is achieved compared with the baselines under the dynamic environment of DTs.},
 bibtype = {article},
 author = {Zhang, Ziru and Zhang, Xuling and Zhu, Guangzhi and Wang, Yuyang and Hui, Pan}
}

In the era of Internet of Things (IoT), Digital Twin (DT) is envisioned to empower various areas as a bridge between physical objects and the digital world. Through virtualization and simulation techniques, multiple functions can be achieved by leveraging computing resources. In this process, Mobile Cloud Computing (MCC) and Mobile Edge Computing (MEC) have become two of the key factors to achieve real-time feedback. However, current works only considered edge servers or cloud servers in the DT system models. Besides, The models ignore the DT with not only one data resource. In this paper, we propose a new DT system model considering a heterogeneous MEC/MCC environment. Each DT in the model is maintained in one of the servers via multiple data collection devices. The offloading decision-making problem is also considered and a new offloading scheme is proposed based on Distributed Deep Learning (DDL). Simulation results demonstrate that our proposed algorithm can effectively and efficiently decrease the system's average latency and energy consumption. Significant improvement is achieved compared with the baselines under the dynamic environment of DTs.

@inproceedings{
 title = {VR PreM+: An Immersive Pre-learning Branching Visualization System for Museum Tours},
 type = {inproceedings},
 year = {2023},
 pages = {374-385},
 websites = {https://dl.acm.org/doi/10.1145/3629606.3629643},
 month = {11},
 publisher = {ACM},
 day = {13},
 city = {New York, NY, USA},
 id = {fca92808-2073-3d2d-baac-61de1182f6e0},
 created = {2024-03-05T07:51:39.542Z},
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 bibtype = {inproceedings},
 author = {Gao, Ze and Li, Xiang and Liu, Changkun and Wang, Xian and Wang, Anqi and Yang, Liang and Wang, Yuyang and Hui, Pan and Braud, Tristan},
 doi = {10.1145/3629606.3629643},
 booktitle = {Proceedings of the Eleventh International Symposium of Chinese CHI}
}

@inproceedings{
 title = {A Deep Cybersickness Predictor through Kinematic Data with Encoded Physiological Representation},
 type = {inproceedings},
 year = {2023},
 keywords = {Cybersickness Prediction,Deep Neural Classifiers,Kinematic data,Physiological Representation,VR},
 pages = {1132-1141},
 websites = {https://ieeexplore.ieee.org/document/10316407/},
 month = {10},
 publisher = {IEEE},
 day = {16},
 id = {9a3113ac-8f48-34e9-beac-68808b90539c},
 created = {2024-03-05T07:53:31.355Z},
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 last_modified = {2024-03-05T07:53:35.628Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Users would experience individually different sickness symptoms during or after navigating through an immersive virtual environment, generally known as cybersickness. Previous studies have predicted the severity of cybersickness based on physiological and/or kinematic data. However, compared with kinematic data, physiological data rely heavily on biosensors during the collection, which is inconvenient and limited to a few affordable VR devices. In this work, we proposed a deep neural network to predict cybersickness through kinematic data. We introduced the encoded physiological representation to characterize the individual susceptibility; therefore, the predictor could predict cybersickness only based on a user's kinematic data without counting on biosensors. Fifty-three participants were recruited to attend the user study to collect multimodal data, including kinematic data (navigation speed, head tracking), physiological signals (e.g., electrodermal activity, heart rate), and Simulator Sickness Questionnaire (SSQ). The predictor achieved an accuracy of 97.8% for cybersickness prediction by involving the pre-computed physiological representation to characterize individual differences, providing much convenience for the current cybersickness measurement.},
 bibtype = {inproceedings},
 author = {Li, Ruichen and Wang, Yuyang and Yin, Handi and Chardonnet, Jean-Rémy and Hui, Pan},
 doi = {10.1109/ISMAR59233.2023.00130},
 booktitle = {2023 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)}
}

Users would experience individually different sickness symptoms during or after navigating through an immersive virtual environment, generally known as cybersickness. Previous studies have predicted the severity of cybersickness based on physiological and/or kinematic data. However, compared with kinematic data, physiological data rely heavily on biosensors during the collection, which is inconvenient and limited to a few affordable VR devices. In this work, we proposed a deep neural network to predict cybersickness through kinematic data. We introduced the encoded physiological representation to characterize the individual susceptibility; therefore, the predictor could predict cybersickness only based on a user's kinematic data without counting on biosensors. Fifty-three participants were recruited to attend the user study to collect multimodal data, including kinematic data (navigation speed, head tracking), physiological signals (e.g., electrodermal activity, heart rate), and Simulator Sickness Questionnaire (SSQ). The predictor achieved an accuracy of 97.8% for cybersickness prediction by involving the pre-computed physiological representation to characterize individual differences, providing much convenience for the current cybersickness measurement.

@inproceedings{
 title = {Re-shaping Post-COVID-19 Teaching and Learning: A Blueprint of Virtual-Physical Blended Classrooms in the Metaverse Era},
 type = {inproceedings},
 year = {2022},
 pages = {241-247},
 websites = {http://arxiv.org/abs/2203.09228,https://ieeexplore.ieee.org/document/9951355/},
 month = {7},
 publisher = {IEEE},
 day = {17},
 id = {419c6903-244f-3cb2-ac74-ec43f747e988},
 created = {2022-03-19T11:01:03.156Z},
 file_attached = {true},
 profile_id = {4b66b327-35ad-3956-a9a2-307331dd9988},
 last_modified = {2023-01-13T06:12:06.889Z},
 read = {true},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {During the COVID-19 pandemic, most countries have experienced some form of remote education through video conferencing software platforms. However, these software platforms fail to reduce immersion and replicate the classroom experience. The currently emerging Metaverse addresses many of such limitations by offering blended physical-digital environments. This paper aims to assess how the Metaverse can support and improve e-learning. We first survey the latest applications of blended environments in education and highlight the primary challenges and opportunities. Accordingly, we derive our proposal for a virtual-physical blended classroom configuration that brings students and teachers into a shared educational Metaverse. We focus on the system architecture of the Metaverse classroom to achieve real-time synchronization of a large number of participants and activities across physical (mixed reality classrooms) and virtual (remote VR platform) learning spaces. Our proposal attempts to transform the traditional physical classroom into virtual-physical cyberspace as a new social network of learners and educators connected at an unprecedented scale.},
 bibtype = {inproceedings},
 author = {Wang, Yuyang and Lee, Lik-Hang and Braud, Tristan and Hui, Pan},
 doi = {10.1109/ICDCSW56584.2022.00053},
 booktitle = {2022 IEEE 42nd International Conference on Distributed Computing Systems Workshops (ICDCSW)}
}

During the COVID-19 pandemic, most countries have experienced some form of remote education through video conferencing software platforms. However, these software platforms fail to reduce immersion and replicate the classroom experience. The currently emerging Metaverse addresses many of such limitations by offering blended physical-digital environments. This paper aims to assess how the Metaverse can support and improve e-learning. We first survey the latest applications of blended environments in education and highlight the primary challenges and opportunities. Accordingly, we derive our proposal for a virtual-physical blended classroom configuration that brings students and teachers into a shared educational Metaverse. We focus on the system architecture of the Metaverse classroom to achieve real-time synchronization of a large number of participants and activities across physical (mixed reality classrooms) and virtual (remote VR platform) learning spaces. Our proposal attempts to transform the traditional physical classroom into virtual-physical cyberspace as a new social network of learners and educators connected at an unprecedented scale.

@article{
 title = {Identification of Key Features for VR Applications with VREVIEW : A Topic Model Approach},
 type = {article},
 year = {2022},
 keywords = {feature identification,hci,human computer,human-centered computing,index terms,interaction,interaction paradigms,topic model,user reviews,virtual reality,vr game},
 pages = {183-188},
 id = {3d05bf80-4715-3886-a601-d422cd068ecb},
 created = {2022-03-26T02:28:18.100Z},
 file_attached = {true},
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 last_modified = {2022-03-26T02:28:33.019Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
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 bibtype = {article},
 author = {Qian, Yang and Xiong, Yingqiu and Wang, Yuyang and Jiang, Yuanchun and Liu, Yezheng and Chai, Yidong},
 doi = {10.1109/VRW55335.2022.00046},
 journal = {2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)}
}

@article{
 title = {Identity, Crimes, and Law Enforcement in the Metaverse},
 type = {article},
 year = {2022},
 websites = {http://arxiv.org/abs/2210.06134},
 month = {10},
 day = {12},
 id = {530f9525-f526-367d-be21-3dd6136b877a},
 created = {2022-10-30T13:22:00.340Z},
 file_attached = {true},
 profile_id = {4b66b327-35ad-3956-a9a2-307331dd9988},
 last_modified = {2022-10-31T08:41:58.458Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {With the boom in metaverse-related projects in major areas of the public's life, the safety of users becomes a pressing concern. We believe that an international legal framework should be established to promote collaboration among nations, facilitate crime investigation, and support democratic governance. In this paper, we discuss the legal concerns of identity, crimes that could occur based on incidents in existing virtual worlds, and challenges to unified law enforcement in the metaverse.},
 bibtype = {article},
 author = {Qin, Hua Xuan and Wang, Yuyang and Hui, Pan}
}

With the boom in metaverse-related projects in major areas of the public's life, the safety of users becomes a pressing concern. We believe that an international legal framework should be established to promote collaboration among nations, facilitate crime investigation, and support democratic governance. In this paper, we discuss the legal concerns of identity, crimes that could occur based on incidents in existing virtual worlds, and challenges to unified law enforcement in the metaverse.

@article{
 title = {Prediction of cybersickness in virtual environments using topological data analysis and machine learning},
 type = {article},
 year = {2022},
 keywords = {TDA,cybersickness,machine learing,navigation,persistent homology,virtual reality},
 volume = {3},
 websites = {https://www.frontiersin.org/articles/10.3389/frvir.2022.973236/full},
 month = {10},
 publisher = {Frontiers Media S.A.},
 day = {11},
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 citation_key = {Hadadi2022},
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 abstract = {Recent significant progress in Virtual Reality (VR) applications and environments raised several challenges. They proved to have side effects on specific users, thus reducing the usability of the VR technology in some critical domains, such as flight and car simulators. One of the common side effects is cybersickness. Some significant commonly reported symptoms are nausea, oculomotor discomfort, and disorientation. To mitigate these symptoms and consequently improve the usability of VR systems, it is necessary to predict the incidence of cybersickness. This paper proposes a machine learning approach to VR’s cybersickness prediction based on physiological and subjective data. We investigated combinations of topological data analysis with a range of classifier algorithms and assessed classification performance. The highest performance of Topological Data Analysis (TDA) based methods was achieved in combination with SVMs with Gaussian RBF kernel, indicating that Gaussian RBF kernels provide embeddings of physiological time series data into spaces that are rich enough to capture the essential geometric features of this type of data. Comparing several combinations with feature descriptors for physiological time series, the performance of the TDA + SVM combination is in the top group, statistically being on par or outperforming more complex and less interpretable methods. Our results show that heart rate does not seem to correlate with cybersickness.},
 bibtype = {article},
 author = {Hadadi, Azadeh and Guillet, Christophe and Chardonnet, Jean-Rémy and Langovoy, Mikhail and Wang, Yuyang and Ovtcharova, Jivka},
 doi = {10.3389/frvir.2022.973236},
 journal = {Frontiers in Virtual Reality}
}

Recent significant progress in Virtual Reality (VR) applications and environments raised several challenges. They proved to have side effects on specific users, thus reducing the usability of the VR technology in some critical domains, such as flight and car simulators. One of the common side effects is cybersickness. Some significant commonly reported symptoms are nausea, oculomotor discomfort, and disorientation. To mitigate these symptoms and consequently improve the usability of VR systems, it is necessary to predict the incidence of cybersickness. This paper proposes a machine learning approach to VR’s cybersickness prediction based on physiological and subjective data. We investigated combinations of topological data analysis with a range of classifier algorithms and assessed classification performance. The highest performance of Topological Data Analysis (TDA) based methods was achieved in combination with SVMs with Gaussian RBF kernel, indicating that Gaussian RBF kernels provide embeddings of physiological time series data into spaces that are rich enough to capture the essential geometric features of this type of data. Comparing several combinations with feature descriptors for physiological time series, the performance of the TDA + SVM combination is in the top group, statistically being on par or outperforming more complex and less interpretable methods. Our results show that heart rate does not seem to correlate with cybersickness.

@inproceedings{
 title = {Explore and Interpret the Correlations Among VR Applications},
 type = {inproceedings},
 year = {2022},
 pages = {22-26},
 websites = {https://ieeexplore.ieee.org/document/9974282/},
 month = {10},
 publisher = {IEEE},
 id = {c777ffee-857f-33f3-b214-93e6688bc6d2},
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 author = {Qian, Yang and Xu, Huahua and Wang, Yuyang and Liu, Yezheng and Jiang, Yuanchun},
 doi = {10.1109/ISMAR-Adjunct57072.2022.00015},
 booktitle = {2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)}
}

@article{
 title = {Enhanced cognitive workload evaluation in 3D immersive environments with TOPSIS model},
 type = {article},
 year = {2021},
 pages = {102572},
 volume = {147},
 websites = {https://linkinghub.elsevier.com/retrieve/pii/S1071581920301749},
 month = {3},
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 author = {Wang, Yuyang and Chardonnet, Jean-Rémy and Merienne, Frédéric},
 doi = {10.1016/j.ijhcs.2020.102572},
 journal = {International Journal of Human-Computer Studies}
}

@article{
 title = {Development of a speed protector to optimize user experience in 3D virtual environments},
 type = {article},
 year = {2021},
 pages = {102578},
 volume = {147},
 websites = {https://linkinghub.elsevier.com/retrieve/pii/S1071581920301804},
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 author = {Wang, Yuyang and Chardonnet, Jean-Rémy and Merienne, Frédéric},
 doi = {10.1016/j.ijhcs.2020.102578},
 journal = {International Journal of Human-Computer Studies}
}

@inproceedings{
 title = {Using Fuzzy Logic to Involve Individual Differences for Predicting Cybersickness during VR Navigation},
 type = {inproceedings},
 year = {2021},
 pages = {373-381},
 websites = {https://ieeexplore.ieee.org/document/9417785/},
 month = {3},
 publisher = {IEEE},
 city = {Lisbon, Portugal},
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 authored = {true},
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 hidden = {false},
 citation_key = {Wang2021},
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 abstract = {There have been many studies about how individual differences can affect users' susceptibility to cybersickness in a VR application. However, the lack of strategy to integrate the influence of each factor on cybersickness makes it difficult to utilize the results of existing research. Based on the fuzzy logic theory that can represent the effect of different factors as a single value containing integrated information, we developed two approaches including the knowledge-based Mamdani-type fuzzy inference system and the data-driven Adaptive neuro-fuzzy inference system (ANFIS) to involve three individual differences (Age, Gaming experience and Ethnicity) and we correlated the corresponding outputs with the scores obtained from the simulator sickness questionnaire (SSQ) in a simple navigation scenario. The correlation coefficients obtained through a 4-fold cross validation were found statistically significant with both fuzzy logic approaches, indicating their effectiveness to influence the occurrence and the level of cybersickness. Our work provides insights to establish customized experiences for VR navigation by involving individual differences.},
 bibtype = {inproceedings},
 author = {Wang, Yuyang and Chardonnet, Jean-Remy and Merienne, Frederic and Ovtcharova, Jivka},
 doi = {10.1109/VR50410.2021.00060},
 booktitle = {2021 IEEE Virtual Reality and 3D User Interfaces (VR)}
}

There have been many studies about how individual differences can affect users' susceptibility to cybersickness in a VR application. However, the lack of strategy to integrate the influence of each factor on cybersickness makes it difficult to utilize the results of existing research. Based on the fuzzy logic theory that can represent the effect of different factors as a single value containing integrated information, we developed two approaches including the knowledge-based Mamdani-type fuzzy inference system and the data-driven Adaptive neuro-fuzzy inference system (ANFIS) to involve three individual differences (Age, Gaming experience and Ethnicity) and we correlated the corresponding outputs with the scores obtained from the simulator sickness questionnaire (SSQ) in a simple navigation scenario. The correlation coefficients obtained through a 4-fold cross validation were found statistically significant with both fuzzy logic approaches, indicating their effectiveness to influence the occurrence and the level of cybersickness. Our work provides insights to establish customized experiences for VR navigation by involving individual differences.

@inproceedings{
 title = {VR Sickness Prediction for Navigation in Immersive Virtual Environments using a Deep Long Short Term Memory Model},
 type = {inproceedings},
 year = {2019},
 pages = {1874-1881},
 websites = {https://ieeexplore.ieee.org/document/8798213/},
 month = {3},
 publisher = {IEEE},
 city = {Osaka, Japan},
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 citation_key = {Wang2019},
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 bibtype = {inproceedings},
 author = {Wang, Yuyang and Chardonnet, Jean-Remy and Merienne, Frederic},
 doi = {10.1109/VR.2019.8798213},
 booktitle = {2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)}
}

@inproceedings{
 title = {Design of a Semiautomatic Travel Technique in VR Environments},
 type = {inproceedings},
 year = {2019},
 pages = {1223-1224},
 websites = {https://ieeexplore.ieee.org/document/8798004/},
 month = {3},
 publisher = {IEEE},
 city = {Osaka, Japan},
 id = {f9515e01-fc0e-3d21-97b6-facadf9137b6},
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 bibtype = {inproceedings},
 author = {Wang, Yuyang and Chardonnet, Jean-Remy and Merienne, Frederic},
 doi = {10.1109/VR.2019.8798004},
 booktitle = {2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)}
}

@article{
 title = {Knowledge-Based Open Performance Measurement System (KBO-PMS) for a Garment Product Development Process in Big Data Environment},
 type = {article},
 year = {2019},
 pages = {129910-129929},
 volume = {7},
 websites = {https://ieeexplore.ieee.org/document/8805317/},
 publisher = {IEEE},
 id = {25509913-b3f3-3b51-bf31-6130eb80f51d},
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 bibtype = {article},
 author = {Hong, Yan and Wu, Tianyu and Zeng, Xianyi and Wang, Yuyang and Yang, Wen and Pan, Zhijuan},
 doi = {10.1109/ACCESS.2019.2936294},
 journal = {IEEE Access}
}

@article{
 title = {CBCRS: An open case-based color recommendation system},
 type = {article},
 year = {2018},
 keywords = {Case-based l,Color recommendation,Dynamical system,color recommendation},
 pages = {113-128},
 volume = {141},
 websites = {http://linkinghub.elsevier.com/retrieve/pii/S095070511730535X},
 month = {2},
 publisher = {Elsevier B.V.},
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 author = {Hong, Yan and Zeng, Xianyi and Wang, Yuyang and Bruniaux, Pascal and Chen, Yan},
 doi = {10.1016/j.knosys.2017.11.014},
 journal = {Knowledge-Based Systems}
}

@inproceedings{
 title = {Speed Profile Optimization for Enhanced Passenger Comfort: An Optimal Control Approach},
 type = {inproceedings},
 year = {2018},
 pages = {723-728},
 websites = {https://ieeexplore.ieee.org/document/8569420/},
 month = {11},
 publisher = {IEEE},
 city = {Maui, HI, USA},
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 author = {Wang, Yuyang and Chardonnet, Jean-Remy and Merienne, Frederic},
 doi = {10.1109/ITSC.2018.8569420},
 booktitle = {2018 21st International Conference on Intelligent Transportation Systems (ITSC)}
}

@inproceedings{
 title = {A Semiautomatic Navigation Interface to Reduce Visually Induced Motion Sickness in Virtual Reality},
 type = {inproceedings},
 year = {2018},
 pages = {47-52},
 websites = {https://jrv2018.sciencesconf.org/program/details},
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 author = {Wang, Yuyang and Chardonnet, Jean-Rémy and Merienne, Frédéric},
 booktitle = {Journées de la Réalité Virtuelle}
}