AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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Here you can find the complete list of our publications.
You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2025
Hu, H.; Wan, Y.; Tang, K. Y.; Li, Q.; Wang, X.
Affective-Computing-Driven Personalized Display of Cultural Information for Commercial Heritage Architecture Journal Article
In: Applied Sciences (Switzerland), vol. 15, no. 7, 2025, ISSN: 20763417 (ISSN).
Abstract | Links | BibTeX | Tags: Affective Computing, Cultural informations, Cultural value, Data fusion, Information display, Information fusion, Information presentation, Language Model, Large language model, Multimodal information fusion, User-generated, User-generated content, Virtual environments
@article{hu_affective-computing-driven_2025,
title = {Affective-Computing-Driven Personalized Display of Cultural Information for Commercial Heritage Architecture},
author = {H. Hu and Y. Wan and K. Y. Tang and Q. Li and X. Wang},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105002467183&doi=10.3390%2fapp15073459&partnerID=40&md5=1dc611258248d58a2bf5f44b6a0e890b},
doi = {10.3390/app15073459},
issn = {20763417 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Applied Sciences (Switzerland)},
volume = {15},
number = {7},
abstract = {The display methods for traditional cultural heritage lack personalization and emotional interaction, making it difficult to stimulate the public’s deep cultural awareness. This is especially true in commercialized historical districts, where cultural value is easily overlooked. Balancing cultural value and commercial value in information display has become one of the challenges that needs to be addressed. To solve the above problems, this article focuses on the identification of deep cultural values and the optimization of the information display in Beijing’s Qianmen Street, proposing a framework for cultural information mining and display based on affective computing and large language models. The pre-trained models QwenLM and RoBERTa were employed to analyze text and image data from user-generated content on social media, identifying users’ emotional tendencies toward various cultural value dimensions and quantifying their multilayered understanding of architectural heritage. This study further constructed a multimodal information presentation model driven by emotional feedback, mapping it into virtual reality environments to enable personalized, multilayered cultural information visualization. The framework’s effectiveness was validated through an eye-tracking experiment that assessed how different presentation styles impacted users’ emotional engagement and cognitive outcomes. The results show that the affective computing and multimodal data fusion approach to cultural heritage presentation accurately captures users’ emotions, enhancing their interest and emotional involvement. Personalized presentations of information significantly improve users’ engagement, historical understanding, and cultural experience, thereby fostering a deeper comprehension of historical contexts and architectural details. © 2025 by the authors.},
keywords = {Affective Computing, Cultural informations, Cultural value, Data fusion, Information display, Information fusion, Information presentation, Language Model, Large language model, Multimodal information fusion, User-generated, User-generated content, Virtual environments},
pubstate = {published},
tppubtype = {article}
}
The display methods for traditional cultural heritage lack personalization and emotional interaction, making it difficult to stimulate the public’s deep cultural awareness. This is especially true in commercialized historical districts, where cultural value is easily overlooked. Balancing cultural value and commercial value in information display has become one of the challenges that needs to be addressed. To solve the above problems, this article focuses on the identification of deep cultural values and the optimization of the information display in Beijing’s Qianmen Street, proposing a framework for cultural information mining and display based on affective computing and large language models. The pre-trained models QwenLM and RoBERTa were employed to analyze text and image data from user-generated content on social media, identifying users’ emotional tendencies toward various cultural value dimensions and quantifying their multilayered understanding of architectural heritage. This study further constructed a multimodal information presentation model driven by emotional feedback, mapping it into virtual reality environments to enable personalized, multilayered cultural information visualization. The framework’s effectiveness was validated through an eye-tracking experiment that assessed how different presentation styles impacted users’ emotional engagement and cognitive outcomes. The results show that the affective computing and multimodal data fusion approach to cultural heritage presentation accurately captures users’ emotions, enhancing their interest and emotional involvement. Personalized presentations of information significantly improve users’ engagement, historical understanding, and cultural experience, thereby fostering a deeper comprehension of historical contexts and architectural details. © 2025 by the authors.
2024
He, Z.; Li, S.; Song, Y.; Cai, Z.
Towards Building Condition-Based Cross-Modality Intention-Aware Human-AI Cooperation under VR Environment Proceedings Article
In: Conf Hum Fact Comput Syst Proc, Association for Computing Machinery, 2024, ISBN: 979-840070330-0 (ISBN).
Abstract | Links | BibTeX | Tags: Action Generation, Building conditions, Condition, Critical challenges, Cross modality, Human-AI Cooperation, Information presentation, Intention Detection, Language Model, Multi-modal, Purchasing, User interfaces, Virtual Reality
@inproceedings{he_towards_2024,
title = {Towards Building Condition-Based Cross-Modality Intention-Aware Human-AI Cooperation under VR Environment},
author = {Z. He and S. Li and Y. Song and Z. Cai},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194829231&doi=10.1145%2f3613904.3642360&partnerID=40&md5=44d237a6e2a686af74ffb684ef887ab6},
doi = {10.1145/3613904.3642360},
isbn = {979-840070330-0 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Conf Hum Fact Comput Syst Proc},
publisher = {Association for Computing Machinery},
abstract = {To address critical challenges in effectively identifying user intent and forming relevant information presentations and recommendations in VR environments, we propose an innovative condition-based multi-modal human-AI cooperation framework. It highlights the intent tuples (intent, condition, intent prompt, action prompt) and 2-Large-Language-Models (2-LLMs) architecture. This design, utilizes “condition” as the core to describe tasks, dynamically match user interactions with intentions, and empower generations of various tailored multi-modal AI responses. The architecture of 2-LLMs separates the roles of intent detection and action generation, decreasing the prompt length and helping with generating appropriate responses. We implemented a VR-based intelligent furniture purchasing system based on the proposed framework and conducted a three-phase comparative user study. The results conclusively demonstrate the system's superiority in time efficiency and accuracy, intention conveyance improvements, effective product acquisitions, and user satisfaction and cooperation preference. Our framework provides a promising approach towards personalized and efficient user experiences in VR. © 2024 Copyright held by the owner/author(s)},
keywords = {Action Generation, Building conditions, Condition, Critical challenges, Cross modality, Human-AI Cooperation, Information presentation, Intention Detection, Language Model, Multi-modal, Purchasing, User interfaces, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
To address critical challenges in effectively identifying user intent and forming relevant information presentations and recommendations in VR environments, we propose an innovative condition-based multi-modal human-AI cooperation framework. It highlights the intent tuples (intent, condition, intent prompt, action prompt) and 2-Large-Language-Models (2-LLMs) architecture. This design, utilizes “condition” as the core to describe tasks, dynamically match user interactions with intentions, and empower generations of various tailored multi-modal AI responses. The architecture of 2-LLMs separates the roles of intent detection and action generation, decreasing the prompt length and helping with generating appropriate responses. We implemented a VR-based intelligent furniture purchasing system based on the proposed framework and conducted a three-phase comparative user study. The results conclusively demonstrate the system's superiority in time efficiency and accuracy, intention conveyance improvements, effective product acquisitions, and user satisfaction and cooperation preference. Our framework provides a promising approach towards personalized and efficient user experiences in VR. © 2024 Copyright held by the owner/author(s)