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
Afzal, M. Z.; Ali, S. K. A.; Stricker, D.; Eisert, P.; Hilsmann, A.; Pérez-Marcos, D.; Bianchi, M.; Crottaz-Herbette, S.; Ioris, R.; Mangina, E.; Sanguineti, M.; Salaberria, A.; de Lacalle, O. Lopez; García-Pablos, A.; Cuadros, M.
Next Generation XR Systems - Large Language Models Meet Augmented and Virtual Reality Journal Article
In: IEEE Computer Graphics and Applications, vol. 45, no. 1, pp. 43–55, 2025, ISSN: 02721716 (ISSN); 15581756 (ISSN), (Publisher: IEEE Computer Society).
Abstract | Links | BibTeX | Tags: adult, Article, Augmented and virtual realities, Augmented Reality, Awareness, Context-Aware, human, Information Retrieval, Knowledge model, Knowledge reasoning, Knowledge retrieval, Language Model, Large language model, Mixed reality, neurorehabilitation, Position papers, privacy, Real- time, Reasoning, Situational awareness, Virtual environments, Virtual Reality
@article{afzal_next_2025,
title = {Next Generation XR Systems - Large Language Models Meet Augmented and Virtual Reality},
author = {M. Z. Afzal and S. K. A. Ali and D. Stricker and P. Eisert and A. Hilsmann and D. Pérez-Marcos and M. Bianchi and S. Crottaz-Herbette and R. Ioris and E. Mangina and M. Sanguineti and A. Salaberria and O. Lopez de Lacalle and A. García-Pablos and M. Cuadros},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003598602&doi=10.1109%2FMCG.2025.3548554&partnerID=40&md5=94e7efe987708afc9f066b906ce232b1},
doi = {10.1109/MCG.2025.3548554},
issn = {02721716 (ISSN); 15581756 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Computer Graphics and Applications},
volume = {45},
number = {1},
pages = {43–55},
abstract = {Extended reality (XR) is evolving rapidly, offering new paradigms for human-computer interaction. This position paper argues that integrating large language models (LLMs) with XR systems represents a fundamental shift toward more intelligent, context-aware, and adaptive mixed-reality experiences. We propose a structured framework built on three key pillars: first, perception and situational awareness, second, knowledge modeling and reasoning, and third, visualization and interaction. We believe leveraging LLMs within XR environments enables enhanced situational awareness, real-time knowledge retrieval, and dynamic user interaction, surpassing traditional XR capabilities. We highlight the potential of this integration in neurorehabilitation, safety training, and architectural design while underscoring ethical considerations, such as privacy, transparency, and inclusivity. This vision aims to spark discussion and drive research toward more intelligent, human-centric XR systems. © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: IEEE Computer Society},
keywords = {adult, Article, Augmented and virtual realities, Augmented Reality, Awareness, Context-Aware, human, Information Retrieval, Knowledge model, Knowledge reasoning, Knowledge retrieval, Language Model, Large language model, Mixed reality, neurorehabilitation, Position papers, privacy, Real- time, Reasoning, Situational awareness, Virtual environments, Virtual Reality},
pubstate = {published},
tppubtype = {article}
}
Extended reality (XR) is evolving rapidly, offering new paradigms for human-computer interaction. This position paper argues that integrating large language models (LLMs) with XR systems represents a fundamental shift toward more intelligent, context-aware, and adaptive mixed-reality experiences. We propose a structured framework built on three key pillars: first, perception and situational awareness, second, knowledge modeling and reasoning, and third, visualization and interaction. We believe leveraging LLMs within XR environments enables enhanced situational awareness, real-time knowledge retrieval, and dynamic user interaction, surpassing traditional XR capabilities. We highlight the potential of this integration in neurorehabilitation, safety training, and architectural design while underscoring ethical considerations, such as privacy, transparency, and inclusivity. This vision aims to spark discussion and drive research toward more intelligent, human-centric XR systems. © 2025 Elsevier B.V., All rights reserved.
Anvitha, K.; Durjay, T.; Sathvika, K.; Gnanendra, G.; Annamalai, S.; Natarajan, S. K.
EduBot: A Compact AI-Driven Study Assistant for Contextual Knowledge Retrieval Proceedings Article
In: Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 9798331507756 (ISBN).
Abstract | Links | BibTeX | Tags: Chatbots, Computer aided instruction, Contextual knowledge, Curricula, Digital Education, E-Learning, Education computing, Educational Technology, Engineering education, Indexing (of information), Information Retrieval, Intelligent systems, Knowledge retrieval, LangChain Framework, Language Model, Large language model, learning experience, Learning experiences, Learning systems, LLM, PDF - Driven Chatbot, Query processing, Students, Teaching, Traditional learning, Virtual Reality
@inproceedings{anvitha_edubot_2025,
title = {EduBot: A Compact AI-Driven Study Assistant for Contextual Knowledge Retrieval},
author = {K. Anvitha and T. Durjay and K. Sathvika and G. Gnanendra and S. Annamalai and S. K. Natarajan},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105013615976&doi=10.1109%2FGINOTECH63460.2025.11077097&partnerID=40&md5=b08377283f2ea2ee406d38d1d23f1e42},
doi = {10.1109/GINOTECH63460.2025.11077097},
isbn = {9798331507756 (ISBN)},
year = {2025},
date = {2025-01-01},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {In the evolving landscape of educational technology, intelligent systems are redefining traditional learning methods by enhancing accessibility, adaptability, and engagement in instructional processes. This paper presents EduBot, a PDF-Driven Chatbot developed using advanced Large Language Models (LLMs) and leveraging frameworks like LangChain, OpenAI's Chat-Gpt, and Pinecone. EduBot is designed as an interactive educational assistant, responding to student queries based on faculty-provided guidelines embedded in PDF documents. Through natural language processing, EduBot streamlines information retrieval, providing accurate, context-aware responses that foster a self- directed learning experience. By aligning with specific academic requirements and enhancing clarity in information delivery, EduBot stands as a promising tool in personalized digital learning support. This paper explores the design, implementation, and impact of EduBot, offering insights into its potential as a scalable solution for academic institutions The demand for accessible and adaptive educational tools is increasing as students seek more personalized and efficient ways to enhance their learning experience. EduBot is a cutting- edge PDF-driven chatbot designed to act as a virtual educational assistant, helping students to navigate and understand course materials by answering queries directly based on faculty guidelines. Built upon Large Language Models (LLMs), specifically utilizing frameworks such as LangChain and OpenAI's GPT-3.5, EduBot provides a sophisticated solution for integrating curated academic content into interactive learning. With its backend support from Pinecone for optimized data indexing, EduBot offers accurate and context-specific responses, facilitating a deeper level of engagement and comprehension. The average relevancy score is 80%. This paper outlines the design and deployment of EduBot, emphasizing its architecture, adaptability, and contributions to the educational landscape, where such AI- driven tools are poised to become indispensable in fostering autonomous, personalized learning environments. © 2025 Elsevier B.V., All rights reserved.},
keywords = {Chatbots, Computer aided instruction, Contextual knowledge, Curricula, Digital Education, E-Learning, Education computing, Educational Technology, Engineering education, Indexing (of information), Information Retrieval, Intelligent systems, Knowledge retrieval, LangChain Framework, Language Model, Large language model, learning experience, Learning experiences, Learning systems, LLM, PDF - Driven Chatbot, Query processing, Students, Teaching, Traditional learning, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
In the evolving landscape of educational technology, intelligent systems are redefining traditional learning methods by enhancing accessibility, adaptability, and engagement in instructional processes. This paper presents EduBot, a PDF-Driven Chatbot developed using advanced Large Language Models (LLMs) and leveraging frameworks like LangChain, OpenAI's Chat-Gpt, and Pinecone. EduBot is designed as an interactive educational assistant, responding to student queries based on faculty-provided guidelines embedded in PDF documents. Through natural language processing, EduBot streamlines information retrieval, providing accurate, context-aware responses that foster a self- directed learning experience. By aligning with specific academic requirements and enhancing clarity in information delivery, EduBot stands as a promising tool in personalized digital learning support. This paper explores the design, implementation, and impact of EduBot, offering insights into its potential as a scalable solution for academic institutions The demand for accessible and adaptive educational tools is increasing as students seek more personalized and efficient ways to enhance their learning experience. EduBot is a cutting- edge PDF-driven chatbot designed to act as a virtual educational assistant, helping students to navigate and understand course materials by answering queries directly based on faculty guidelines. Built upon Large Language Models (LLMs), specifically utilizing frameworks such as LangChain and OpenAI's GPT-3.5, EduBot provides a sophisticated solution for integrating curated academic content into interactive learning. With its backend support from Pinecone for optimized data indexing, EduBot offers accurate and context-specific responses, facilitating a deeper level of engagement and comprehension. The average relevancy score is 80%. This paper outlines the design and deployment of EduBot, emphasizing its architecture, adaptability, and contributions to the educational landscape, where such AI- driven tools are poised to become indispensable in fostering autonomous, personalized learning environments. © 2025 Elsevier B.V., All rights reserved.
de Oliveira, E. A. Masasi; Sousa, R. T.; Bastos, A. A.; de Freitas Cintra, L. Martins; Filho, A. R. G. Galvão
Immersive Virtual Museums with Spatially-Aware Retrieval-Augmented Generation Proceedings Article
In: IMX - Proc. ACM Int. Conf. Interact. Media Experiences, pp. 437–440, Association for Computing Machinery, Inc, 2025, ISBN: 9798400713910 (ISBN).
Abstract | Links | BibTeX | Tags: Association reactions, Behavioral Research, Generation systems, Geographics, Human computer interaction, Human engineering, Immersive, Information Retrieval, Interactive computer graphics, Language Model, Large language model, large language models, Museums, Retrieval-Augmented Generation, Search engines, Spatially aware, User interfaces, Virtual environments, Virtual museum, Virtual museum., Virtual Reality, Visual Attention, Visual languages
@inproceedings{masasi_de_oliveira_immersive_2025,
title = {Immersive Virtual Museums with Spatially-Aware Retrieval-Augmented Generation},
author = {E. A. Masasi de Oliveira and R. T. Sousa and A. A. Bastos and L. Martins de Freitas Cintra and A. R. G. Galvão Filho},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105007979183&doi=10.1145%2F3706370.3731643&partnerID=40&md5=47a47f3408a0e6cb35c16dd6101a15b0},
doi = {10.1145/3706370.3731643},
isbn = {9798400713910 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {IMX - Proc. ACM Int. Conf. Interact. Media Experiences},
pages = {437–440},
publisher = {Association for Computing Machinery, Inc},
abstract = {Virtual Reality has significantly expanded possibilities for immersive museum experiences, overcoming traditional constraints such as space, preservation, and geographic limitations. However, existing virtual museum platforms typically lack dynamic, personalized, and contextually accurate interactions. To address this, we propose Spatially-Aware Retrieval-Augmented Generation (SA-RAG), an innovative framework integrating visual attention tracking with Retrieval-Augmented Generation systems and advanced Large Language Models. By capturing users' visual attention in real time, SA-RAG dynamically retrieves contextually relevant data, enhancing the accuracy, personalization, and depth of user interactions within immersive virtual environments. The system's effectiveness is initially demonstrated through our preliminary tests within a realistic VR museum implemented using Unreal Engine. Although promising, comprehensive human evaluations involving broader user groups are planned for future studies to rigorously validate SA-RAG's effectiveness, educational enrichment potential, and accessibility improvements in virtual museums. The framework also presents opportunities for broader applications in immersive educational and storytelling domains. © 2025 Elsevier B.V., All rights reserved.},
keywords = {Association reactions, Behavioral Research, Generation systems, Geographics, Human computer interaction, Human engineering, Immersive, Information Retrieval, Interactive computer graphics, Language Model, Large language model, large language models, Museums, Retrieval-Augmented Generation, Search engines, Spatially aware, User interfaces, Virtual environments, Virtual museum, Virtual museum., Virtual Reality, Visual Attention, Visual languages},
pubstate = {published},
tppubtype = {inproceedings}
}
Virtual Reality has significantly expanded possibilities for immersive museum experiences, overcoming traditional constraints such as space, preservation, and geographic limitations. However, existing virtual museum platforms typically lack dynamic, personalized, and contextually accurate interactions. To address this, we propose Spatially-Aware Retrieval-Augmented Generation (SA-RAG), an innovative framework integrating visual attention tracking with Retrieval-Augmented Generation systems and advanced Large Language Models. By capturing users' visual attention in real time, SA-RAG dynamically retrieves contextually relevant data, enhancing the accuracy, personalization, and depth of user interactions within immersive virtual environments. The system's effectiveness is initially demonstrated through our preliminary tests within a realistic VR museum implemented using Unreal Engine. Although promising, comprehensive human evaluations involving broader user groups are planned for future studies to rigorously validate SA-RAG's effectiveness, educational enrichment potential, and accessibility improvements in virtual museums. The framework also presents opportunities for broader applications in immersive educational and storytelling domains. © 2025 Elsevier B.V., All rights reserved.
2024
Karabiyik, M. A.; Tan, F. G.; Yüksel, A. S.
Application of Prompt Engineering Techniques to Optimize Information Retrieval in the Metaverse Journal Article
In: Journal of Metaverse, vol. 4, no. 2, pp. 157–164, 2024, ISSN: 27920232 (ISSN), (Publisher: Izmir Academy Association).
Abstract | Links | BibTeX | Tags: Information Retrieval, large language models, Metaverse, Prompt engineering, response generation
@article{karabiyik_application_2024,
title = {Application of Prompt Engineering Techniques to Optimize Information Retrieval in the Metaverse},
author = {M. A. Karabiyik and F. G. Tan and A. S. Yüksel},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214488898&doi=10.57019%2Fjmv.1543077&partnerID=40&md5=047505e9f633d4e197f2a20b3843ba67},
doi = {10.57019/jmv.1543077},
issn = {27920232 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Journal of Metaverse},
volume = {4},
number = {2},
pages = {157–164},
abstract = {Prompt engineering techniques are instructions that enable large language models (LLMs) to solve real-world problems more effectively. These techniques enhance the capabilities of LLMs to generate accurate and efficient responses. Our study examines the challenge of acquiring comprehensive and efficient information in the metaverse through the application of various prompt engineering techniques. The main objective is to improve the accuracy and effectiveness of metaverse-related responses by leveraging LLM capabilities. In this study, 100 questions were generated using GPT, GEMINI, QWEN, and MISTRAL language models focusing on the metaverse. Our experiments indicated that responses often included unrelated information, highlighting the need for prompt engineering techniques. We applied knowledge-based, rule-based, few-shot, and template-based prompt engineering techniques to refine the responses. The performance of GPT, GEMINI, QWEN, and MISTRAL models were evaluated based on criteria including accuracy, timeliness, comprehensiveness, and consistency. Our findings reveal that prompt engineering techniques significantly enhance the efficacy of LLMs in providing improved information retrieval and response generation, aiding users in efficiently acquiring information in complex environments like the metaverse. © 2025 Elsevier B.V., All rights reserved.},
note = {Publisher: Izmir Academy Association},
keywords = {Information Retrieval, large language models, Metaverse, Prompt engineering, response generation},
pubstate = {published},
tppubtype = {article}
}
Prompt engineering techniques are instructions that enable large language models (LLMs) to solve real-world problems more effectively. These techniques enhance the capabilities of LLMs to generate accurate and efficient responses. Our study examines the challenge of acquiring comprehensive and efficient information in the metaverse through the application of various prompt engineering techniques. The main objective is to improve the accuracy and effectiveness of metaverse-related responses by leveraging LLM capabilities. In this study, 100 questions were generated using GPT, GEMINI, QWEN, and MISTRAL language models focusing on the metaverse. Our experiments indicated that responses often included unrelated information, highlighting the need for prompt engineering techniques. We applied knowledge-based, rule-based, few-shot, and template-based prompt engineering techniques to refine the responses. The performance of GPT, GEMINI, QWEN, and MISTRAL models were evaluated based on criteria including accuracy, timeliness, comprehensiveness, and consistency. Our findings reveal that prompt engineering techniques significantly enhance the efficacy of LLMs in providing improved information retrieval and response generation, aiding users in efficiently acquiring information in complex environments like the metaverse. © 2025 Elsevier B.V., All rights reserved.
2012
Augello, Agnese; Gentile, Manuel; Pilato, Giovanni; Vassallo, Giorgio
Geometric Encoding of Sentences Based on Clifford Algebra Proceedings Article
In: KDIR 2012 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval, pp. 457–462, 2012, ISBN: 978-989-8565-29-7.
Abstract | BibTeX | Tags: Geometric algebra, Information Retrieval, Knowledge Representation, Natural Language Processing, Semantic Computing, Semantic Spaces
@inproceedings{augelloGeometricEncodingSentences2012,
title = {Geometric Encoding of Sentences Based on Clifford Algebra},
author = { Agnese Augello and Manuel Gentile and Giovanni Pilato and Giorgio Vassallo},
isbn = {978-989-8565-29-7},
year = {2012},
date = {2012-01-01},
booktitle = {KDIR 2012 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval},
pages = {457--462},
abstract = {Natural language sentences can be represented as vectors in a high dimensional vector space. Generally, these models are based on bag of words approaches, and therefore they do not fully capture the semantics of sentences which depends both by the semantics of the words, and their order in in the phrase. In this work we propose a sub-symbolic methodology to encode natural language sentences considering both these two aspects. The proposed approach exploits the properties of Geometric Algebra rotation operators, called rotors, to code sentences through the rotation of an orthogonal basis of a semantic space. The methodology is based on three main steps: the construction of a semantic space, the association of ad-hoc rotors to sentence bigrams, and finally the coding of the sentence through the application of the obtained rotors to a standard basis in the semantic space. Copyright textcopyright 2012 SciTePress - Science and Technology Publications.},
keywords = {Geometric algebra, Information Retrieval, Knowledge Representation, Natural Language Processing, Semantic Computing, Semantic Spaces},
pubstate = {published},
tppubtype = {inproceedings}
}
Natural language sentences can be represented as vectors in a high dimensional vector space. Generally, these models are based on bag of words approaches, and therefore they do not fully capture the semantics of sentences which depends both by the semantics of the words, and their order in in the phrase. In this work we propose a sub-symbolic methodology to encode natural language sentences considering both these two aspects. The proposed approach exploits the properties of Geometric Algebra rotation operators, called rotors, to code sentences through the rotation of an orthogonal basis of a semantic space. The methodology is based on three main steps: the construction of a semantic space, the association of ad-hoc rotors to sentence bigrams, and finally the coding of the sentence through the application of the obtained rotors to a standard basis in the semantic space. Copyright textcopyright 2012 SciTePress - Science and Technology Publications.
Augello, Agnese; Gentile, Manuel; Pilato, Giovanni; Vassallo, Giorgio
Geometric encoding of sentences based on clifford algebra Proceedings Article
In: KDIR 2012 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval, pp. 457–462, 2012, ISBN: 978-989-8565-29-7.
Abstract | Links | BibTeX | Tags: Geometric algebra, Information Retrieval, Knowledge Representation, Natural Language Processing, Semantic Computing, Semantic Spaces
@inproceedings{augello_geometric_2012,
title = {Geometric encoding of sentences based on clifford algebra},
author = {Agnese Augello and Manuel Gentile and Giovanni Pilato and Giorgio Vassallo},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84881535463&partnerID=40&md5=b6d0cd52d69a3dfc0a811c6390bf0d6c},
isbn = {978-989-8565-29-7},
year = {2012},
date = {2012-01-01},
booktitle = {KDIR 2012 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval},
pages = {457–462},
abstract = {Natural language sentences can be represented as vectors in a high dimensional vector space. Generally, these models are based on bag of words approaches, and therefore they do not fully capture the semantics of sentences which depends both by the semantics of the words, and their order in in the phrase. In this work we propose a sub-symbolic methodology to encode natural language sentences considering both these two aspects. The proposed approach exploits the properties of Geometric Algebra rotation operators, called rotors, to code sentences through the rotation of an orthogonal basis of a semantic space. The methodology is based on three main steps: the construction of a semantic space, the association of ad-hoc rotors to sentence bigrams, and finally the coding of the sentence through the application of the obtained rotors to a standard basis in the semantic space. Copyright © 2012 SciTePress - Science and Technology Publications.},
keywords = {Geometric algebra, Information Retrieval, Knowledge Representation, Natural Language Processing, Semantic Computing, Semantic Spaces},
pubstate = {published},
tppubtype = {inproceedings}
}
Natural language sentences can be represented as vectors in a high dimensional vector space. Generally, these models are based on bag of words approaches, and therefore they do not fully capture the semantics of sentences which depends both by the semantics of the words, and their order in in the phrase. In this work we propose a sub-symbolic methodology to encode natural language sentences considering both these two aspects. The proposed approach exploits the properties of Geometric Algebra rotation operators, called rotors, to code sentences through the rotation of an orthogonal basis of a semantic space. The methodology is based on three main steps: the construction of a semantic space, the association of ad-hoc rotors to sentence bigrams, and finally the coding of the sentence through the application of the obtained rotors to a standard basis in the semantic space. Copyright © 2012 SciTePress - Science and Technology Publications.
2007
Sorce, Salvatore; Augello, Agnese; Santangelo, Antonella; Pilato, Giovanni; Gentile, Antonio; Genco, Alessandro; Gaglio, Salvatore
A Multimodal Guide for the Augmented Campus Proceedings Article
In: Proceedings ACM SIGUCCS User Services Conference, pp. 325–331, 2007, ISBN: 978-1-59593-634-9.
Abstract | Links | BibTeX | Tags: Human computer interaction, Information Retrieval, Multimodal Interaction
@inproceedings{sorceMultimodalGuideAugmented2007,
title = {A Multimodal Guide for the Augmented Campus},
author = { Salvatore Sorce and Agnese Augello and Antonella Santangelo and Giovanni Pilato and Antonio Gentile and Alessandro Genco and Salvatore Gaglio},
doi = {10.1145/1294046.1294123},
isbn = {978-1-59593-634-9},
year = {2007},
date = {2007-01-01},
booktitle = {Proceedings ACM SIGUCCS User Services Conference},
pages = {325--331},
abstract = {The use of Personal Digital Assistants (PDAs) with ad-hoc built-in information retrieval and auto-localization functionalities can help people navigating an environment in a more natural manner compared to traditional audio/visual pre-recorded guides. In this work we propose and discuss a user-friendly, multi-modal guide system for pervasive context-aware service provision within augmented environments. The proposed system is adaptable to the user needs of mobility within a given environment; it is usable on different mobile devices and in particular on PDAs, which are used as advanced adaptive HEI (human-environment interaction) interfaces. An information retrieval service is provided that is easily accessible through spoken language interaction in cooperation with an auto-localization service. The interaction is enabled by speech recognition and synthesis technologies, and by a ChatBot system, endowed with common sense reasoning capabilities to properly interpret user speech and provide him with the requested information. This interaction mode turns to be more natural, and users are required to have only basic skills on the use of PDAs. The auto-localization service relies on a RFID-based framework, which resides partly in the mobile side of the entire system (PDAs), and partly in the environment side. In particular, RFID technology allows the system to provide users with context-related information. An implemented case study is showed that illustrates service provision in an augmented environment within university campus settings (termed "Augmented Campus"). Lastly, a discussion about user experiences while using trial services within the Augmented Campus is given. textcopyright Copyright 2007 ACM.},
keywords = {Human computer interaction, Information Retrieval, Multimodal Interaction},
pubstate = {published},
tppubtype = {inproceedings}
}
The use of Personal Digital Assistants (PDAs) with ad-hoc built-in information retrieval and auto-localization functionalities can help people navigating an environment in a more natural manner compared to traditional audio/visual pre-recorded guides. In this work we propose and discuss a user-friendly, multi-modal guide system for pervasive context-aware service provision within augmented environments. The proposed system is adaptable to the user needs of mobility within a given environment; it is usable on different mobile devices and in particular on PDAs, which are used as advanced adaptive HEI (human-environment interaction) interfaces. An information retrieval service is provided that is easily accessible through spoken language interaction in cooperation with an auto-localization service. The interaction is enabled by speech recognition and synthesis technologies, and by a ChatBot system, endowed with common sense reasoning capabilities to properly interpret user speech and provide him with the requested information. This interaction mode turns to be more natural, and users are required to have only basic skills on the use of PDAs. The auto-localization service relies on a RFID-based framework, which resides partly in the mobile side of the entire system (PDAs), and partly in the environment side. In particular, RFID technology allows the system to provide users with context-related information. An implemented case study is showed that illustrates service provision in an augmented environment within university campus settings (termed "Augmented Campus"). Lastly, a discussion about user experiences while using trial services within the Augmented Campus is given. textcopyright Copyright 2007 ACM.
Sorce, Salvatore; Augello, Agnese; Santangelo, Antonella; Pilato, Giovanni; Gentile, Antonio; Genco, Alessandro; Gaglio, Salvatore
A multimodal guide for the augmented campus Proceedings Article
In: Proceedings ACM SIGUCCS User Services Conference, pp. 325–331, 2007, ISBN: 978-1-59593-634-9.
Abstract | Links | BibTeX | Tags: Human computer interaction, Information Retrieval, Multimodal Interaction
@inproceedings{sorce_multimodal_2007,
title = {A multimodal guide for the augmented campus},
author = {Salvatore Sorce and Agnese Augello and Antonella Santangelo and Giovanni Pilato and Antonio Gentile and Alessandro Genco and Salvatore Gaglio},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-41149136230&doi=10.1145%2f1294046.1294123&partnerID=40&md5=629ffe213d018d4e0f6f8383f8eb7ecb},
doi = {10.1145/1294046.1294123},
isbn = {978-1-59593-634-9},
year = {2007},
date = {2007-01-01},
booktitle = {Proceedings ACM SIGUCCS User Services Conference},
pages = {325–331},
abstract = {The use of Personal Digital Assistants (PDAs) with ad-hoc built-in information retrieval and auto-localization functionalities can help people navigating an environment in a more natural manner compared to traditional audio/visual pre-recorded guides. In this work we propose and discuss a user-friendly, multi-modal guide system for pervasive context-aware service provision within augmented environments. The proposed system is adaptable to the user needs of mobility within a given environment; it is usable on different mobile devices and in particular on PDAs, which are used as advanced adaptive HEI (human-environment interaction) interfaces. An information retrieval service is provided that is easily accessible through spoken language interaction in cooperation with an auto-localization service. The interaction is enabled by speech recognition and synthesis technologies, and by a ChatBot system, endowed with common sense reasoning capabilities to properly interpret user speech and provide him with the requested information. This interaction mode turns to be more natural, and users are required to have only basic skills on the use of PDAs. The auto-localization service relies on a RFID-based framework, which resides partly in the mobile side of the entire system (PDAs), and partly in the environment side. In particular, RFID technology allows the system to provide users with context-related information. An implemented case study is showed that illustrates service provision in an augmented environment within university campus settings (termed "Augmented Campus"). Lastly, a discussion about user experiences while using trial services within the Augmented Campus is given. © Copyright 2007 ACM.},
keywords = {Human computer interaction, Information Retrieval, Multimodal Interaction},
pubstate = {published},
tppubtype = {inproceedings}
}
The use of Personal Digital Assistants (PDAs) with ad-hoc built-in information retrieval and auto-localization functionalities can help people navigating an environment in a more natural manner compared to traditional audio/visual pre-recorded guides. In this work we propose and discuss a user-friendly, multi-modal guide system for pervasive context-aware service provision within augmented environments. The proposed system is adaptable to the user needs of mobility within a given environment; it is usable on different mobile devices and in particular on PDAs, which are used as advanced adaptive HEI (human-environment interaction) interfaces. An information retrieval service is provided that is easily accessible through spoken language interaction in cooperation with an auto-localization service. The interaction is enabled by speech recognition and synthesis technologies, and by a ChatBot system, endowed with common sense reasoning capabilities to properly interpret user speech and provide him with the requested information. This interaction mode turns to be more natural, and users are required to have only basic skills on the use of PDAs. The auto-localization service relies on a RFID-based framework, which resides partly in the mobile side of the entire system (PDAs), and partly in the environment side. In particular, RFID technology allows the system to provide users with context-related information. An implemented case study is showed that illustrates service provision in an augmented environment within university campus settings (termed "Augmented Campus"). Lastly, a discussion about user experiences while using trial services within the Augmented Campus is given. © Copyright 2007 ACM.
2005
Pilato, Giovanni; Vassallo, Giorgio; Vasile, Maria; Augello, Agnese; Gaglio, Salvatore
A Simple Solution for Improving the Effectiveness of Traditional Information Retrieval Systems Proceedings Article
In: Proceedings of the 4th WSEAS International Conference on Software Engineering, Parallel & Distributed Systems, World Scientific and Engineering Academy and Society (WSEAS), Stevens Point, Wisconsin, USA, 2005, ISBN: 960-8457-09-2.
Abstract | BibTeX | Tags: Human computer interaction, Information Retrieval, Latent Semantic Analysis, Semantic Spaces
@inproceedings{pilatoSimpleSolutionImproving2005,
title = {A Simple Solution for Improving the Effectiveness of Traditional Information Retrieval Systems},
author = { Giovanni Pilato and Giorgio Vassallo and Maria Vasile and Agnese Augello and Salvatore Gaglio},
isbn = {960-8457-09-2},
year = {2005},
date = {2005-01-01},
booktitle = {Proceedings of the 4th WSEAS International Conference on Software Engineering, Parallel & Distributed Systems},
publisher = {World Scientific and Engineering Academy and Society (WSEAS)},
address = {Stevens Point, Wisconsin, USA},
series = {SEPADS'05},
abstract = {In this paper we present a system based on the LSA paradigm to improve the performance of a traditional information retrieval system. The proposed system aims to improve both the recall and the precision capabilities of traditional search engines thanks to a semantic query expansion and a subsequent semantic results filtering. A collection of 650 documents has been used to compare the performances of the proposed system with a traditional search engine. Experimental trials show the effectiveness of the proposed solution.},
keywords = {Human computer interaction, Information Retrieval, Latent Semantic Analysis, Semantic Spaces},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper we present a system based on the LSA paradigm to improve the performance of a traditional information retrieval system. The proposed system aims to improve both the recall and the precision capabilities of traditional search engines thanks to a semantic query expansion and a subsequent semantic results filtering. A collection of 650 documents has been used to compare the performances of the proposed system with a traditional search engine. Experimental trials show the effectiveness of the proposed solution.
Pilato, Giovanni; Vassallo, Giorgio; Vasile, Maria; Augello, Agnese; Gaglio, Salvatore
A Simple Solution for Improving the Effectiveness of Traditional Information Retrieval Systems Proceedings Article
In: Proceedings of the 4th WSEAS International Conference on Software Engineering, Parallel & Distributed Systems, World Scientific and Engineering Academy and Society (WSEAS), Stevens Point, Wisconsin, USA, 2005, ISBN: 960-8457-09-2.
Abstract | BibTeX | Tags: Human computer interaction, Information Retrieval, Latent Semantic Analysis, Semantic Spaces
@inproceedings{pilato_simple_2005,
title = {A Simple Solution for Improving the Effectiveness of Traditional Information Retrieval Systems},
author = {Giovanni Pilato and Giorgio Vassallo and Maria Vasile and Agnese Augello and Salvatore Gaglio},
isbn = {960-8457-09-2},
year = {2005},
date = {2005-01-01},
booktitle = {Proceedings of the 4th WSEAS International Conference on Software Engineering, Parallel & Distributed Systems},
publisher = {World Scientific and Engineering Academy and Society (WSEAS)},
address = {Stevens Point, Wisconsin, USA},
series = {SEPADS'05},
abstract = {In this paper we present a system based on the LSA paradigm to improve the performance of a traditional information retrieval system. The proposed system aims to improve both the recall and the precision capabilities of traditional search engines thanks to a semantic query expansion and a subsequent semantic results filtering. A collection of 650 documents has been used to compare the performances of the proposed system with a traditional search engine. Experimental trials show the effectiveness of the proposed solution.},
keywords = {Human computer interaction, Information Retrieval, Latent Semantic Analysis, Semantic Spaces},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper we present a system based on the LSA paradigm to improve the performance of a traditional information retrieval system. The proposed system aims to improve both the recall and the precision capabilities of traditional search engines thanks to a semantic query expansion and a subsequent semantic results filtering. A collection of 650 documents has been used to compare the performances of the proposed system with a traditional search engine. Experimental trials show the effectiveness of the proposed solution.