AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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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.
2024
Asadi, A. R.; Appiah, J.; Muntaka, S. A.; Kropczynski, J.
Actions, Not Apps: Toward Using LLMs to Reshape Context Aware Interactions in Mixed Reality Systems Proceedings Article
In: C., Stephanidis; M., Antona; S., Ntoa; G., Salvendy (Ed.): Commun. Comput. Info. Sci., pp. 166–176, Springer Science and Business Media Deutschland GmbH, 2024, ISBN: 18650929 (ISSN); 978-303162109-3 (ISBN).
Abstract | Links | BibTeX | Tags: Computation theory, Context Aware System, Context-aware interaction, Context-aware systems, Decision making, Digital information, Flat-screens, Interaction Design, Language Model, Mixed reality, Mixed reality systems, User interaction, User interfaces, User perceptions
@inproceedings{asadi_actions_2024,
title = {Actions, Not Apps: Toward Using LLMs to Reshape Context Aware Interactions in Mixed Reality Systems},
author = {A. R. Asadi and J. Appiah and S. A. Muntaka and J. Kropczynski},
editor = {Stephanidis C. and Antona M. and Ntoa S. and Salvendy G.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196733497&doi=10.1007%2f978-3-031-62110-9_17&partnerID=40&md5=9cd702ff979c7f111a5172df8f155ddf},
doi = {10.1007/978-3-031-62110-9_17},
isbn = {18650929 (ISSN); 978-303162109-3 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Commun. Comput. Info. Sci.},
volume = {2120 CCIS},
pages = {166–176},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Mixed reality computing merges user perception of the environment with digital information. As we move from flatscreen computing toward head-mounted computing, the necessity for developing alternative interactions and user flows becomes more evident. Activity theory provides a holistic overview of user interactions and motives. In this work in progress, we propose Action Sandbox Workspace as an interaction framework for the future of MR systems by focusing on action-centric interactions rather than application-centric interactions, aiming to bridge the gap between user goals and system functionalities in everyday tasks. By integrating the ontology of actions, user intentions, and context and connecting it to spatial data mapping, this forward-looking framework aims to create a contextually adaptive user interaction environment. The recent development in large language models (LLMs) has made the implementation of this interaction flow feasible by enabling inference and decision-making based on text-based descriptions of a user’s state and intentions with data and actions users have access to. We propose this approach as a future direction for developing mixed reality platforms and integrating AI in interacting with computers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},
keywords = {Computation theory, Context Aware System, Context-aware interaction, Context-aware systems, Decision making, Digital information, Flat-screens, Interaction Design, Language Model, Mixed reality, Mixed reality systems, User interaction, User interfaces, User perceptions},
pubstate = {published},
tppubtype = {inproceedings}
}
Mixed reality computing merges user perception of the environment with digital information. As we move from flatscreen computing toward head-mounted computing, the necessity for developing alternative interactions and user flows becomes more evident. Activity theory provides a holistic overview of user interactions and motives. In this work in progress, we propose Action Sandbox Workspace as an interaction framework for the future of MR systems by focusing on action-centric interactions rather than application-centric interactions, aiming to bridge the gap between user goals and system functionalities in everyday tasks. By integrating the ontology of actions, user intentions, and context and connecting it to spatial data mapping, this forward-looking framework aims to create a contextually adaptive user interaction environment. The recent development in large language models (LLMs) has made the implementation of this interaction flow feasible by enabling inference and decision-making based on text-based descriptions of a user’s state and intentions with data and actions users have access to. We propose this approach as a future direction for developing mixed reality platforms and integrating AI in interacting with computers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.