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.
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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
Michael, R.; Kutza, J. -O.; Werth, P.; Liebe, J. -D.; Schöning, J.
Simulating Vulnerability: An Examination of Ethical, Legal and Social Aspects in the Context of Training Child Protection Workers Proceedings Article
In: Cheong, M.; Herkert, J.; Zhu, Q.; Love, H. A. (Ed.): Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 9798331532284 (ISBN).
Abstract | Links | BibTeX | Tags: and social aspect), Artificial intelligence, artificial intelligence (AI), Case files, Child Protection, Child protections, Ethical, Ethical aspects, Information Management, Language Model, Large language model, large language model (LLM), Laws and legislation, Legal, Personnel training, Social aspect (ethical, Social aspects, Social Aspects (ELSA), Virtual Reality, Virtual Reality (VR) Training, Virtual reality training, Workers'
@inproceedings{michael_simulating_2025,
title = {Simulating Vulnerability: An Examination of Ethical, Legal and Social Aspects in the Context of Training Child Protection Workers},
author = {R. Michael and J. -O. Kutza and P. Werth and J. -D. Liebe and J. Schöning},
editor = {M. Cheong and J. Herkert and Q. Zhu and H. A. Love},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105014522871&doi=10.1109%2FETHICS65148.2025.11098302&partnerID=40&md5=c5cba2cca188ceb263701952c2f386ea},
doi = {10.1109/ETHICS65148.2025.11098302},
isbn = {9798331532284 (ISBN)},
year = {2025},
date = {2025-01-01},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The success of artificial intelligence (AI), particularly large language models (LLMs), has led to innovative applications and numerous attempts to find a use for this tool in different fields. One of these fields is in the work fielded by child protection offices. The AId4Children project investigates the use of AI-generated virtual reality (VR) training environments for child protection workers. This VR training application simulates home visits and is generated on synthetically constructed cases of child abuse and neglect that are based on real case files. Using real case files for AI-based generation of VR training raises several ethical, legal, and social aspects (ELSA) and questions. One question Q1) is how to acquire and create an open data set on child endangerment case files. A second question Q2) is how to represent the vulnerable characters in VR respectfully. A third question Q3) is if the use of AI, incl. LLM within the context of child abuse and neglect is acceptable at all. This paper will discuss and provide the first answers to the considerations on ELSA raised by the explorative concept development of this VR training application. © 2025 Elsevier B.V., All rights reserved.},
keywords = {and social aspect), Artificial intelligence, artificial intelligence (AI), Case files, Child Protection, Child protections, Ethical, Ethical aspects, Information Management, Language Model, Large language model, large language model (LLM), Laws and legislation, Legal, Personnel training, Social aspect (ethical, Social aspects, Social Aspects (ELSA), Virtual Reality, Virtual Reality (VR) Training, Virtual reality training, Workers'},
pubstate = {published},
tppubtype = {inproceedings}
}
The success of artificial intelligence (AI), particularly large language models (LLMs), has led to innovative applications and numerous attempts to find a use for this tool in different fields. One of these fields is in the work fielded by child protection offices. The AId4Children project investigates the use of AI-generated virtual reality (VR) training environments for child protection workers. This VR training application simulates home visits and is generated on synthetically constructed cases of child abuse and neglect that are based on real case files. Using real case files for AI-based generation of VR training raises several ethical, legal, and social aspects (ELSA) and questions. One question Q1) is how to acquire and create an open data set on child endangerment case files. A second question Q2) is how to represent the vulnerable characters in VR respectfully. A third question Q3) is if the use of AI, incl. LLM within the context of child abuse and neglect is acceptable at all. This paper will discuss and provide the first answers to the considerations on ELSA raised by the explorative concept development of this VR training application. © 2025 Elsevier B.V., All rights reserved.
2024
Chaccour, C.; Saad, W.; Debbah, M.; Poor, H. V. Vincent
Joint Sensing, Communication, and AI: A Trifecta for Resilient THz User Experiences Journal Article
In: IEEE Transactions on Wireless Communications, vol. 23, no. 9, pp. 11444–11460, 2024, ISSN: 15361276 (ISSN); 15582248 (ISSN), (Publisher: Institute of Electrical and Electronics Engineers Inc.).
Abstract | Links | BibTeX | Tags: Artificial intelligence, artificial intelligence (AI), Behavioral Research, Channel state information, Computer hardware, Cramer-Rao bounds, Extended reality (XR), Hardware, Joint sensing and communication, Learning systems, machine learning, machine learning (ML), Machine-learning, Multi agent systems, reliability, Resilience, Sensor data fusion, Tera Hertz, Terahertz, terahertz (THz), Terahertz communication, Wireless communications, Wireless sensor networks, X reality
@article{chaccour_joint_2024,
title = {Joint Sensing, Communication, and AI: A Trifecta for Resilient THz User Experiences},
author = {C. Chaccour and W. Saad and M. Debbah and H. V. Vincent Poor},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190170739&doi=10.1109%2FTWC.2024.3382192&partnerID=40&md5=561f4cdf229d462bb636c787487201bd},
doi = {10.1109/TWC.2024.3382192},
issn = {15361276 (ISSN); 15582248 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {IEEE Transactions on Wireless Communications},
volume = {23},
number = {9},
pages = {11444–11460},
abstract = {In this paper a novel joint sensing, communication, and artificial intelligence (AI) framework is proposed so as to optimize extended reality (XR) experiences over terahertz (THz) wireless systems. Within this framework, active reconfigurable intelligent surfaces (RISs) are incorporated as pivotal elements, serving as enhanced base stations in the THz band to enhance Line-of-Sight (LoS) communication. The proposed framework consists of three main components. First, a tensor decomposition framework is proposed to extract unique sensing parameters for XR users and their environment by exploiting the THz channel sparsity. Essentially, the THz band's quasi-opticality is exploited and the sensing parameters are extracted from the uplink communication signal, thereby allowing for the use of the same waveform, spectrum, and hardware for both communication and sensing functionalities. Then, the Cramér-Rao lower bound is derived to assess the accuracy of the estimated sensing parameters. Second, a non-autoregressive multi-resolution generative AI framework integrated with an adversarial transformer is proposed to predict missing and future sensing information. The proposed framework offers robust and comprehensive historical sensing information and anticipatory forecasts of future environmental changes, which are generalizable to fluctuations in both known and unforeseen user behaviors and environmental conditions. Third, a multi-agent deep recurrent hysteretic Q-neural network is developed to control the handover policy of RIS subarrays, leveraging the informative nature of sensing information to minimize handover cost, maximize the individual quality of personal experiences (QoPEs), and improve the robustness and resilience of THz links. Simulation results show a high generalizability of the proposed unsupervised generative artificial intelligence (AI) framework to fluctuations in user behavior and velocity, leading to a 61% improvement in instantaneous reliability compared to schemes with known channel state information. © 2024 Elsevier B.V., All rights reserved.},
note = {Publisher: Institute of Electrical and Electronics Engineers Inc.},
keywords = {Artificial intelligence, artificial intelligence (AI), Behavioral Research, Channel state information, Computer hardware, Cramer-Rao bounds, Extended reality (XR), Hardware, Joint sensing and communication, Learning systems, machine learning, machine learning (ML), Machine-learning, Multi agent systems, reliability, Resilience, Sensor data fusion, Tera Hertz, Terahertz, terahertz (THz), Terahertz communication, Wireless communications, Wireless sensor networks, X reality},
pubstate = {published},
tppubtype = {article}
}
In this paper a novel joint sensing, communication, and artificial intelligence (AI) framework is proposed so as to optimize extended reality (XR) experiences over terahertz (THz) wireless systems. Within this framework, active reconfigurable intelligent surfaces (RISs) are incorporated as pivotal elements, serving as enhanced base stations in the THz band to enhance Line-of-Sight (LoS) communication. The proposed framework consists of three main components. First, a tensor decomposition framework is proposed to extract unique sensing parameters for XR users and their environment by exploiting the THz channel sparsity. Essentially, the THz band's quasi-opticality is exploited and the sensing parameters are extracted from the uplink communication signal, thereby allowing for the use of the same waveform, spectrum, and hardware for both communication and sensing functionalities. Then, the Cramér-Rao lower bound is derived to assess the accuracy of the estimated sensing parameters. Second, a non-autoregressive multi-resolution generative AI framework integrated with an adversarial transformer is proposed to predict missing and future sensing information. The proposed framework offers robust and comprehensive historical sensing information and anticipatory forecasts of future environmental changes, which are generalizable to fluctuations in both known and unforeseen user behaviors and environmental conditions. Third, a multi-agent deep recurrent hysteretic Q-neural network is developed to control the handover policy of RIS subarrays, leveraging the informative nature of sensing information to minimize handover cost, maximize the individual quality of personal experiences (QoPEs), and improve the robustness and resilience of THz links. Simulation results show a high generalizability of the proposed unsupervised generative artificial intelligence (AI) framework to fluctuations in user behavior and velocity, leading to a 61% improvement in instantaneous reliability compared to schemes with known channel state information. © 2024 Elsevier B.V., All rights reserved.
Wu, J.; Gan, W.; Chao, H. -C.; Yu, P. S.
Geospatial Big Data: Survey and Challenges Journal Article
In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 17007–17020, 2024, ISSN: 21511535 (ISSN); 19391404 (ISSN), (Publisher: Institute of Electrical and Electronics Engineers Inc.).
Abstract | Links | BibTeX | Tags: Artificial intelligence, artificial intelligence (AI), Behavioral Research, Big Data, Data challenges, Data Mining, Data surveys, Data visualization, Earth observation data, Environmental management, environmental protection, Geo-spatial, Geo-spatial analysis, Geo-spatial data, Geospatial big data, geospatial big data (GBD), geospatial data, GIS, Green products, Human behaviors, Knowledge graph, Knowledge graphs, satellite, sensor, spatial data, Sustainable development, urban planning
@article{wu_geospatial_2024,
title = {Geospatial Big Data: Survey and Challenges},
author = {J. Wu and W. Gan and H. -C. Chao and P. S. Yu},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200804056&doi=10.1109%2FJSTARS.2024.3438376&partnerID=40&md5=63c39a7c302e6d9ff055633efab0349a},
doi = {10.1109/JSTARS.2024.3438376},
issn = {21511535 (ISSN); 19391404 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
volume = {17},
pages = {17007–17020},
abstract = {In recent years, geospatial big data (GBD) has obtained attention across various disciplines, categorized into big Earth observation data and big human behavior data. Identifying geospatial patterns from GBD has been a vital research focus in the fields of urban management and environmental sustainability. This article reviews the evolution of GBD mining and its integration with advanced artificial intelligence techniques. GBD consists of data generated by satellites, sensors, mobile devices, and geographical information systems, and we categorize geospatial data based on different perspectives. We outline the process of GBD mining and demonstrate how it can be incorporated into a unified framework. In addition, we explore new technologies, such as large language models, the metaverse, and knowledge graphs, and how they could make GBD even more useful. We also share examples of GBD helping with city management and protecting the environment. Finally, we discuss the real challenges that come up when working with GBD, such as issues with data retrieval and security. Our goal is to give readers a clear view of where GBD mining stands today and where it might go next. © 2024 Elsevier B.V., All rights reserved.},
note = {Publisher: Institute of Electrical and Electronics Engineers Inc.},
keywords = {Artificial intelligence, artificial intelligence (AI), Behavioral Research, Big Data, Data challenges, Data Mining, Data surveys, Data visualization, Earth observation data, Environmental management, environmental protection, Geo-spatial, Geo-spatial analysis, Geo-spatial data, Geospatial big data, geospatial big data (GBD), geospatial data, GIS, Green products, Human behaviors, Knowledge graph, Knowledge graphs, satellite, sensor, spatial data, Sustainable development, urban planning},
pubstate = {published},
tppubtype = {article}
}
In recent years, geospatial big data (GBD) has obtained attention across various disciplines, categorized into big Earth observation data and big human behavior data. Identifying geospatial patterns from GBD has been a vital research focus in the fields of urban management and environmental sustainability. This article reviews the evolution of GBD mining and its integration with advanced artificial intelligence techniques. GBD consists of data generated by satellites, sensors, mobile devices, and geographical information systems, and we categorize geospatial data based on different perspectives. We outline the process of GBD mining and demonstrate how it can be incorporated into a unified framework. In addition, we explore new technologies, such as large language models, the metaverse, and knowledge graphs, and how they could make GBD even more useful. We also share examples of GBD helping with city management and protecting the environment. Finally, we discuss the real challenges that come up when working with GBD, such as issues with data retrieval and security. Our goal is to give readers a clear view of where GBD mining stands today and where it might go next. © 2024 Elsevier B.V., All rights reserved.