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.
2025
Espinal, W. Y. Arevalo; Jimenez, J.; Corneo, L.
An eXtended Reality Data Transformation Framework for Internet of Things Devices Integration Proceedings Article
In: IoT - Proc. Int. Conf. Internet Things, pp. 10–18, Association for Computing Machinery, Inc, 2025, ISBN: 979-840071285-2 (ISBN).
Abstract | Links | BibTeX | Tags: Application programs, Comprehensive evaluation, Data integration, Data Transformation, Device and Data Integration, Devices integration, Extended reality, Generative AI, Interactive objects, Internet of Things, Language Model, Software runtime, Time-consuming tasks
@inproceedings{arevalo_espinal_extended_2025,
title = {An eXtended Reality Data Transformation Framework for Internet of Things Devices Integration},
author = {W. Y. Arevalo Espinal and J. Jimenez and L. Corneo},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105002862430&doi=10.1145%2f3703790.3703792&partnerID=40&md5=6ba7d70e00e3b0803149854b5744e55e},
doi = {10.1145/3703790.3703792},
isbn = {979-840071285-2 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {IoT - Proc. Int. Conf. Internet Things},
pages = {10–18},
publisher = {Association for Computing Machinery, Inc},
abstract = {The multidisciplinary nature of XR applications makes device and data integration a resource-intensive and time-consuming task, especially in the context of the Internet of Things (IoT). This paper presents Visualize Interactive Objects, VIO for short, a data transformation framework aimed at simplifying visualization and interaction of IoT devices and their data into XR applications. VIO comprises a software runtime (VRT) running on XR headsets, and a JSON-based syntax for defining VIO Descriptions (VDs). The VRT interprets VDs to facilitate visualization and interaction within the application. By raising the level of abstraction, VIO enhances interoperability among XR experiences and enables developers to integrate IoT data with minimal coding effort. A comprehensive evaluation demonstrated that VIO is lightweight, incurring in negligible overhead compared to native implementations. Ten Large Language Models (LLM) were used to generate VDs and native source-code from user intents. The results showed that LLMs have superior syntactical and semantical accuracy in generating VDs compared to native XR application development code, thus indicating that the task of creating VDs can be effectively automated using LLMs. Additionally, a user study with 12 participants found that VIO is developer-friendly and easily extensible. © 2024 Copyright held by the owner/author(s).},
keywords = {Application programs, Comprehensive evaluation, Data integration, Data Transformation, Device and Data Integration, Devices integration, Extended reality, Generative AI, Interactive objects, Internet of Things, Language Model, Software runtime, Time-consuming tasks},
pubstate = {published},
tppubtype = {inproceedings}
}
The multidisciplinary nature of XR applications makes device and data integration a resource-intensive and time-consuming task, especially in the context of the Internet of Things (IoT). This paper presents Visualize Interactive Objects, VIO for short, a data transformation framework aimed at simplifying visualization and interaction of IoT devices and their data into XR applications. VIO comprises a software runtime (VRT) running on XR headsets, and a JSON-based syntax for defining VIO Descriptions (VDs). The VRT interprets VDs to facilitate visualization and interaction within the application. By raising the level of abstraction, VIO enhances interoperability among XR experiences and enables developers to integrate IoT data with minimal coding effort. A comprehensive evaluation demonstrated that VIO is lightweight, incurring in negligible overhead compared to native implementations. Ten Large Language Models (LLM) were used to generate VDs and native source-code from user intents. The results showed that LLMs have superior syntactical and semantical accuracy in generating VDs compared to native XR application development code, thus indicating that the task of creating VDs can be effectively automated using LLMs. Additionally, a user study with 12 participants found that VIO is developer-friendly and easily extensible. © 2024 Copyright held by the owner/author(s).
2024
Ding, P.; Liu, J.; Sun, M.; Li, L.; Liu, H.
Enhancing Computational Processing Performance for Generative AI Large Models with Autonomous Decision-Making in Metaverse Applications Proceedings Article
In: Proc. - IEEE Int. Conf. Metaverse Comput., Netw., Appl., MetaCom, pp. 253–258, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-833151599-7 (ISBN).
Abstract | Links | BibTeX | Tags: Adversarial machine learning, AGI (Artificial General Intelligence), Artificial general intelligence, Artificial general intelligences, Autonomous decision, Autonomous Decision-Making, Data assimilation, Data integration, Decisions makings, Digital Twin Technology, Emotion Recognition, Generative adversarial networks, Generative AI large model, Generative AI Large Models, Large models, Metaverse, Metaverses, Model Acceleration, Model Compression, Multi agent systems, Multi-agent systems, Multi-modal data, Multi-Modal Data Integration, Multiagent systems (MASs), Reinforcement Learning, Reinforcement learnings, Spatio-temporal data
@inproceedings{ding_enhancing_2024,
title = {Enhancing Computational Processing Performance for Generative AI Large Models with Autonomous Decision-Making in Metaverse Applications},
author = {P. Ding and J. Liu and M. Sun and L. Li and H. Liu},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211489063&doi=10.1109%2fMetaCom62920.2024.00048&partnerID=40&md5=ae085a7d90b12c9090f5bf7a274bc7ce},
doi = {10.1109/MetaCom62920.2024.00048},
isbn = {979-833151599-7 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Proc. - IEEE Int. Conf. Metaverse Comput., Netw., Appl., MetaCom},
pages = {253–258},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {We explore how to enhance the computational processing performance for generative AI large models with autonomous decision-making in metaverse applications. We first introduce the relationship between AI large models and the Metaverse. We elaborate on the application scenarios of generative AI large models in Metaverse, including real-time weather simulation, embodied intelligence of agents, dynamic environment interaction, and user emotion recognition. We then propose the method of Multi-Dimensional Optimization Generation Framework (MDOGF) to improve computational processing performance. The experiment results show great improvement in computational processing performance. © 2024 IEEE.},
keywords = {Adversarial machine learning, AGI (Artificial General Intelligence), Artificial general intelligence, Artificial general intelligences, Autonomous decision, Autonomous Decision-Making, Data assimilation, Data integration, Decisions makings, Digital Twin Technology, Emotion Recognition, Generative adversarial networks, Generative AI large model, Generative AI Large Models, Large models, Metaverse, Metaverses, Model Acceleration, Model Compression, Multi agent systems, Multi-agent systems, Multi-modal data, Multi-Modal Data Integration, Multiagent systems (MASs), Reinforcement Learning, Reinforcement learnings, Spatio-temporal data},
pubstate = {published},
tppubtype = {inproceedings}
}
We explore how to enhance the computational processing performance for generative AI large models with autonomous decision-making in metaverse applications. We first introduce the relationship between AI large models and the Metaverse. We elaborate on the application scenarios of generative AI large models in Metaverse, including real-time weather simulation, embodied intelligence of agents, dynamic environment interaction, and user emotion recognition. We then propose the method of Multi-Dimensional Optimization Generation Framework (MDOGF) to improve computational processing performance. The experiment results show great improvement in computational processing performance. © 2024 IEEE.
Guo, Y.; Hou, K.; Yan, Z.; Chen, H.; Xing, G.; Jiang, X.
Sensor2Scene: Foundation Model-Driven Interactive Realities Proceedings Article
In: Proc. - IEEE Int. Workshop Found. Model. Cyber-Phys. Syst. Internet Things, FMSys, pp. 13–19, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-835036345-6 (ISBN).
Abstract | Links | BibTeX | Tags: 3D modeling, Augmented Reality, Computational Linguistics, Data integration, Data visualization, Foundation models, Generative model, Language Model, Large language model, large language models, Model-driven, Sensor Data Integration, Sensors data, Text-to-3d generative model, Text-to-3D Generative Models, Three dimensional computer graphics, User interaction, User Interaction in AR, User interaction in augmented reality, User interfaces, Virtual Reality, Visualization
@inproceedings{guo_sensor2scene_2024,
title = {Sensor2Scene: Foundation Model-Driven Interactive Realities},
author = {Y. Guo and K. Hou and Z. Yan and H. Chen and G. Xing and X. Jiang},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199893762&doi=10.1109%2fFMSys62467.2024.00007&partnerID=40&md5=c3bf1739e8c1dc6227d61609ddc66910},
doi = {10.1109/FMSys62467.2024.00007},
isbn = {979-835036345-6 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Proc. - IEEE Int. Workshop Found. Model. Cyber-Phys. Syst. Internet Things, FMSys},
pages = {13–19},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Augmented Reality (AR) is acclaimed for its potential to bridge the physical and virtual worlds. Yet, current integration between these realms often lacks a deep under-standing of the physical environment and the subsequent scene generation that reflects this understanding. This research introduces Sensor2Scene, a novel system framework designed to enhance user interactions with sensor data through AR. At its core, an AI agent leverages large language models (LLMs) to decode subtle information from sensor data, constructing detailed scene descriptions for visualization. To enable these scenes to be rendered in AR, we decompose the scene creation process into tasks of text-to-3D model generation and spatial composition, allowing new AR scenes to be sketched from the descriptions. We evaluated our framework using an LLM evaluator based on five metrics on various datasets to examine the correlation between sensor readings and corresponding visualizations, and demonstrated the system's effectiveness with scenes generated from end-to-end. The results highlight the potential of LLMs to understand IoT sensor data. Furthermore, generative models can aid in transforming these interpretations into visual formats, thereby enhancing user interaction. This work not only displays the capabilities of Sensor2Scene but also lays a foundation for advancing AR with the goal of creating more immersive and contextually rich experiences. © 2024 IEEE.},
keywords = {3D modeling, Augmented Reality, Computational Linguistics, Data integration, Data visualization, Foundation models, Generative model, Language Model, Large language model, large language models, Model-driven, Sensor Data Integration, Sensors data, Text-to-3d generative model, Text-to-3D Generative Models, Three dimensional computer graphics, User interaction, User Interaction in AR, User interaction in augmented reality, User interfaces, Virtual Reality, Visualization},
pubstate = {published},
tppubtype = {inproceedings}
}
Augmented Reality (AR) is acclaimed for its potential to bridge the physical and virtual worlds. Yet, current integration between these realms often lacks a deep under-standing of the physical environment and the subsequent scene generation that reflects this understanding. This research introduces Sensor2Scene, a novel system framework designed to enhance user interactions with sensor data through AR. At its core, an AI agent leverages large language models (LLMs) to decode subtle information from sensor data, constructing detailed scene descriptions for visualization. To enable these scenes to be rendered in AR, we decompose the scene creation process into tasks of text-to-3D model generation and spatial composition, allowing new AR scenes to be sketched from the descriptions. We evaluated our framework using an LLM evaluator based on five metrics on various datasets to examine the correlation between sensor readings and corresponding visualizations, and demonstrated the system's effectiveness with scenes generated from end-to-end. The results highlight the potential of LLMs to understand IoT sensor data. Furthermore, generative models can aid in transforming these interpretations into visual formats, thereby enhancing user interaction. This work not only displays the capabilities of Sensor2Scene but also lays a foundation for advancing AR with the goal of creating more immersive and contextually rich experiences. © 2024 IEEE.