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Francesk Mulita Chrysa Apostoloumi, Admir Mulita Georgios-Ioannis Verras Michail Pitiakoudis Konstantinos Kotis Christos-Nikolaos Anagnostopoulos The use of artificial intelligence in surgical oncology simulation Journal Article European Journal of Surgical Oncology, 50 , 2024. @article{Mulita2024, title = {The use of artificial intelligence in surgical oncology simulation}, author = {Francesk Mulita, Chrysa Apostoloumi, Admir Mulita, Georgios-Ioannis Verras, Michail Pitiakoudis, Konstantinos Kotis, Christos-Nikolaos Anagnostopoulos}, url = {https://www.sciencedirect.com/science/article/abs/pii/S0748798324015063}, doi = {https://doi.org/10.1016/j.ejso.2024.109438}, year = {2024}, date = {2024-12-01}, journal = {European Journal of Surgical Oncology}, volume = {50}, abstract = {We systematically searched PubMed from inception to 10 July 2024. Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines were followed for this systematic review. Keywords used for search were (“Artificial intelligence”) AND (“Surgical education” OR “Surgical training”). The search was conducted on 5 August 2024.}, keywords = {}, pubstate = {published}, tppubtype = {article} } We systematically searched PubMed from inception to 10 July 2024. Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines were followed for this systematic review. Keywords used for search were (“Artificial intelligence”) AND (“Surgical education” OR “Surgical training”). The search was conducted on 5 August 2024. |
Admir Mulita Francesk Mulita, Georgios-Ioannis Verras Konstantinos Kotis Christos-Nikolaos Anagnostopoulos Using Deep Learning to Predict Head-Neck and Lung Cancer Treatment Response from Serial Medical Imaging Journal Article European Journal of Surgical Oncology, 50 , pp. 109437, 2024, ISSN: 0748-7983. @article{Mulita2024b, title = {Using Deep Learning to Predict Head-Neck and Lung Cancer Treatment Response from Serial Medical Imaging}, author = {Admir Mulita, Francesk Mulita, Georgios-Ioannis Verras, Konstantinos Kotis, Christos-Nikolaos Anagnostopoulos}, doi = {https://doi.org/10.1016/j.ejso.2024.109437}, issn = {0748-7983}, year = {2024}, date = {2024-12-01}, journal = {European Journal of Surgical Oncology}, volume = {50}, pages = {109437}, abstract = {Tumors are continuously evolving biological systems, and medical imaging is uniquely positioned to monitor changes throughout treatment. Although qualitatively tracking lesions over space and time may be trivial, the development of clinically relevant, automated radiomics methods that incorporate serial imaging data is far more challenging. This review evaluates deep learning networks for predicting clinical outcomes through analyzing time series Positron Emission tomography – PET and CT images of patients with head -neck and lung cancer.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Tumors are continuously evolving biological systems, and medical imaging is uniquely positioned to monitor changes throughout treatment. Although qualitatively tracking lesions over space and time may be trivial, the development of clinically relevant, automated radiomics methods that incorporate serial imaging data is far more challenging. This review evaluates deep learning networks for predicting clinical outcomes through analyzing time series Positron Emission tomography – PET and CT images of patients with head -neck and lung cancer. |
Andreas Sideras Konstantinos Bougiatiotis, Elias Zavitsanos Georgios Paliouras George Vouros Bankruptcy Prediction: Data Augmentation, LLMs and the Need for Auditor’s Opinion Conference ICAIF ’24: Proceedings of the 5th ACM International Conference on AI in Finance, 2024. @conference{Sideras2024, title = {Bankruptcy Prediction: Data Augmentation, LLMs and the Need for Auditor’s Opinion}, author = {Andreas Sideras, Konstantinos Bougiatiotis, Elias Zavitsanos, Georgios Paliouras, George Vouros}, url = {https://dl.acm.org/doi/epdf/10.1145/3677052.3698627}, doi = {https://doi.org/10.1145/3677052.3698627}, year = {2024}, date = {2024-11-14}, booktitle = {ICAIF ’24: Proceedings of the 5th ACM International Conference on AI in Finance}, abstract = {Predicting bankruptcy is crucial for managing financial risk in corporations. This study emphasizes incorporating the auditor’s opinion text into prediction models to improve their ability to assess financial health. These opinions provide essential insights as they offer an independent assessment, complementing other predictive inputs like the management’s discussion and analysis. However, the rarity of bankruptcy cases in the data introduces a challenging issue due to severe class imbalance. To address this, we propose a method to generate synthetic positive samples using a variational autoencoder and integrate the multi-source input in a late fusion setting. We showcase that both data augmentation and using multiple textual sources improve the performance of existing models on a related benchmark dataset. Additionally, we evaluate LLMs when used for data augmentation in the proposed method and in a zero-shot prediction setting, discussing important aspects to consider when incorporating them in a predictive pipeline.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Predicting bankruptcy is crucial for managing financial risk in corporations. This study emphasizes incorporating the auditor’s opinion text into prediction models to improve their ability to assess financial health. These opinions provide essential insights as they offer an independent assessment, complementing other predictive inputs like the management’s discussion and analysis. However, the rarity of bankruptcy cases in the data introduces a challenging issue due to severe class imbalance. To address this, we propose a method to generate synthetic positive samples using a variational autoencoder and integrate the multi-source input in a late fusion setting. We showcase that both data augmentation and using multiple textual sources improve the performance of existing models on a related benchmark dataset. Additionally, we evaluate LLMs when used for data augmentation in the proposed method and in a zero-shot prediction setting, discussing important aspects to consider when incorporating them in a predictive pipeline. |
George Vouros Ioannis Ioannidis, Georgios Santipantakis Theodore Tranos Konstantinos Blekas Marc Melgosa Xavier Prats Machine-learning methods estimating flights’ hidden parameters for the prediction of KPIs Journal Article Aerospace, 11 (11), pp. 937, 2024. @article{Vouros2024, title = {Machine-learning methods estimating flights’ hidden parameters for the prediction of KPIs}, author = {George Vouros, Ioannis Ioannidis, Georgios Santipantakis, Theodore Tranos, Konstantinos Blekas, Marc Melgosa, Xavier Prats}, url = {https://www.mdpi.com/2226-4310/11/11/937}, doi = {https://doi.org/10.3390/aerospace11110937}, year = {2024}, date = {2024-11-12}, journal = {Aerospace}, volume = {11}, number = {11}, pages = {937}, abstract = {Complex microscopic simulation models of strategic Air Traffic Management (ATM) performance assessment and decision-making are hindered by several factors. One of the most important is the existence of hidden parameters—such as aircraft take-off weight (TOW) and the selected cost index (CI)—which, if known, would allow for more effective performance modeling methodologies for assessing Key Performance Indicators (KPIs) at various levels of abstraction/detail, e.g., system-wide, or at the level of individual flights. This research proposes a data-driven methodology for the estimation of flights’ hidden parameters combining mechanistic and advanced Artificial Intelligence/Machine Learning (AI/ML) models. Aiming at microsimulation models, our goal is to study the effect of these estimations on the prediction of flights’ KPIs. In so doing, we propose a novel methodology according to which data-driven methods are trained given optimal trajectories (produced by mechanistic models) corresponding to known hidden parameter values, with the aim of predicting hidden parameters’ values of unseen trajectories. The results show that estimations of hidden parameters support the accurate prediction of KPIs regarding the efficiency of flights: fuel consumption, gate-to-gate time and distance flown.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Complex microscopic simulation models of strategic Air Traffic Management (ATM) performance assessment and decision-making are hindered by several factors. One of the most important is the existence of hidden parameters—such as aircraft take-off weight (TOW) and the selected cost index (CI)—which, if known, would allow for more effective performance modeling methodologies for assessing Key Performance Indicators (KPIs) at various levels of abstraction/detail, e.g., system-wide, or at the level of individual flights. This research proposes a data-driven methodology for the estimation of flights’ hidden parameters combining mechanistic and advanced Artificial Intelligence/Machine Learning (AI/ML) models. Aiming at microsimulation models, our goal is to study the effect of these estimations on the prediction of flights’ KPIs. In so doing, we propose a novel methodology according to which data-driven methods are trained given optimal trajectories (produced by mechanistic models) corresponding to known hidden parameter values, with the aim of predicting hidden parameters’ values of unseen trajectories. The results show that estimations of hidden parameters support the accurate prediction of KPIs regarding the efficiency of flights: fuel consumption, gate-to-gate time and distance flown. |
Fotis Assimakopoulos Costas Vassilakis, Dionisis Margaris Konstantinos Kotis Dimitris Spiliotopoulos Artificial intelligence tools for the agriculture value chain: Status and prospects Journal Article Electronics, 13 (22), pp. 4362, 2024. @article{Assimakopoulos2024b, title = {Artificial intelligence tools for the agriculture value chain: Status and prospects}, author = {Fotis Assimakopoulos, Costas Vassilakis, Dionisis Margaris, Konstantinos Kotis, Dimitris Spiliotopoulos}, url = {https://www.mdpi.com/2079-9292/13/22/4362}, doi = {https://doi.org/10.3390/electronics13224362}, year = {2024}, date = {2024-11-07}, journal = {Electronics}, volume = {13}, number = {22}, pages = {4362}, abstract = {This article explores the transformative potential of artificial intelligence (AI) tools across the agricultural value chain, highlighting their applications, benefits, challenges, and future prospects. With global food demand projected to increase by 70% by 2050, AI technologies—including machine learning, big data analytics, and the Internet of things (IoT)—offer critical solutions for enhancing agricultural productivity, sustainability, and resource efficiency. The study provides a comprehensive review of AI applications at multiple stages of the agricultural value chain, including land use planning, crop selection, resource management, disease detection, yield prediction, and market integration. It also discusses the significant challenges to AI adoption, such as data accessibility, technological infrastructure, and the need for specialized skills. By examining case studies and empirical evidence, the article demonstrates how AI-driven solutions can optimize decision-making and operational efficiency in agriculture. The findings underscore AI’s pivotal role in addressing global agricultural challenges, with implications for farmers, agribusinesses, policymakers, and researchers. This article aims to advance the evolving research and discussions on sustainable agriculture, contributing insights that promote the adoption of AI technologies and influence the future of farming.}, keywords = {}, pubstate = {published}, tppubtype = {article} } This article explores the transformative potential of artificial intelligence (AI) tools across the agricultural value chain, highlighting their applications, benefits, challenges, and future prospects. With global food demand projected to increase by 70% by 2050, AI technologies—including machine learning, big data analytics, and the Internet of things (IoT)—offer critical solutions for enhancing agricultural productivity, sustainability, and resource efficiency. The study provides a comprehensive review of AI applications at multiple stages of the agricultural value chain, including land use planning, crop selection, resource management, disease detection, yield prediction, and market integration. It also discusses the significant challenges to AI adoption, such as data accessibility, technological infrastructure, and the need for specialized skills. By examining case studies and empirical evidence, the article demonstrates how AI-driven solutions can optimize decision-making and operational efficiency in agriculture. The findings underscore AI’s pivotal role in addressing global agricultural challenges, with implications for farmers, agribusinesses, policymakers, and researchers. This article aims to advance the evolving research and discussions on sustainable agriculture, contributing insights that promote the adoption of AI technologies and influence the future of farming. |
Alexandros Troupiotis-Kapeliaris Nikolaos Sapountzis, Giannis Spiliopoulos Thomas Kogias Piyabhum Chaysri Bernardo AntÓnio Correia Gabriel Miguel Filipe Santos Silva Elias Xidias Konstantinos Blekas Dimitris Zissis Collection: The Aegean Ro-Boat Race 2023 Journal Article IEEE Data Descriptions, 1 , pp. 87-94, 2024, ISSN: 2995-4274. @article{Troupiotis-Kapeliaris2024, title = {Collection: The Aegean Ro-Boat Race 2023}, author = {Alexandros Troupiotis-Kapeliaris, Nikolaos Sapountzis, Giannis Spiliopoulos, Thomas Kogias, Piyabhum Chaysri, Bernardo AntÓnio Correia Gabriel, Miguel Filipe Santos Silva, Elias Xidias, Konstantinos Blekas, Dimitris Zissis}, url = {https://ieeexplore.ieee.org/abstract/document/10707278}, doi = {https://doi.org/10.1109/IEEEDATA.2024.3475332}, issn = {2995-4274}, year = {2024}, date = {2024-10-08}, journal = {IEEE Data Descriptions}, volume = {1}, pages = {87-94}, abstract = {In this article, we introduce a publicly available real-world dataset collected during the Aegean Ro-Boat Race 2023, which took place at the University of the Aegean in Syros, Greece. The Aegean Ro-Boat Race represents an international competition at the university level, challenging teams to innovate and develop autonomous marine robotic systems capable of performing in unknown dynamic maritime environments under real-world conditions. The 2023 competition featured three primary mission tasks, each designed to test different aspects of the robotic systems: 1) high-speed performance for evaluating the speed and agility of the autonomous vessels; 2) collision avoidance for assessing the systems’ ability to detect and avoid obstacles in real-time; and 3) endurance for testing the operational longevity and efficiency of the robotic systems over extended periods. In total, seven teams registered for the competition, with five of them being from Greece and two from the countries of Portugal and Latvia. Due to several technical difficulties, three vessels were able to complete all races, and data were recorded during their entire participation. The spatiotemporal data for the “Aegean Ro-Boat Race” was gathered through an onboard data logging system that continuously monitored various sensors, including global positioning system (GPS), for all vessels during the entire competition. The dataset includes positional reports from the vessels during all three races (totaling over 6500 records), the positions of the external track and obstacle buoys, together with a file regarding the weather conditions during the race day.}, keywords = {}, pubstate = {published}, tppubtype = {article} } In this article, we introduce a publicly available real-world dataset collected during the Aegean Ro-Boat Race 2023, which took place at the University of the Aegean in Syros, Greece. The Aegean Ro-Boat Race represents an international competition at the university level, challenging teams to innovate and develop autonomous marine robotic systems capable of performing in unknown dynamic maritime environments under real-world conditions. The 2023 competition featured three primary mission tasks, each designed to test different aspects of the robotic systems: 1) high-speed performance for evaluating the speed and agility of the autonomous vessels; 2) collision avoidance for assessing the systems’ ability to detect and avoid obstacles in real-time; and 3) endurance for testing the operational longevity and efficiency of the robotic systems over extended periods. In total, seven teams registered for the competition, with five of them being from Greece and two from the countries of Portugal and Latvia. Due to several technical difficulties, three vessels were able to complete all races, and data were recorded during their entire participation. The spatiotemporal data for the “Aegean Ro-Boat Race” was gathered through an onboard data logging system that continuously monitored various sensors, including global positioning system (GPS), for all vessels during the entire competition. The dataset includes positional reports from the vessels during all three races (totaling over 6500 records), the positions of the external track and obstacle buoys, together with a file regarding the weather conditions during the race day. |
Theodore Tranos Piyabhum Chaysri, Christos Spatharis Konstantinos Blekas SETN ’24: Proceedings of the 13th Hellenic Conference on Artificial Intelligence , 2024. @conference{Tranos2024b, title = {An Advanced Deep Reinforcement Learning Framework for Docking Unmanned Surface Vessels in Variable Environmental Conditions and Amid Moving Ships}, author = {Theodore Tranos, Piyabhum Chaysri, Christos Spatharis, Konstantinos Blekas}, url = {https://dl.acm.org/doi/full/10.1145/3688671.3688779}, doi = {https://doi.org/10.1145/3688671.3688779}, year = {2024}, date = {2024-09-11}, booktitle = {SETN ’24: Proceedings of the 13th Hellenic Conference on Artificial Intelligence }, pages = {1-10}, abstract = {This work utilizes advanced reinforcement learning techniques to optimize the docking process of unmanned surface vessels (USVs) in challenging maritime environments. The proposed methodology accounts for fluctuating environmental conditions, such as wind, as well as the dynamic presence of moving ships as objects and varying traffic densities. It integrates learning models and rich state spaces with predictive information to enhance decision-making capabilities that can anticipate and react to environmental changes and vessel movements, ensuring safe and efficient docking procedures. Experiments were conducted in a simulated environment of the port of Piraeus using real data on environmental conditions and ship movements. In addition, we tried to evaluate the proposed method in conditions different from those it was trained, in order to measure the generalization ability of the USV agent’s policy and also to achieve more plausibility in the results.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } This work utilizes advanced reinforcement learning techniques to optimize the docking process of unmanned surface vessels (USVs) in challenging maritime environments. The proposed methodology accounts for fluctuating environmental conditions, such as wind, as well as the dynamic presence of moving ships as objects and varying traffic densities. It integrates learning models and rich state spaces with predictive information to enhance decision-making capabilities that can anticipate and react to environmental changes and vessel movements, ensuring safe and efficient docking procedures. Experiments were conducted in a simulated environment of the port of Piraeus using real data on environmental conditions and ship movements. In addition, we tried to evaluate the proposed method in conditions different from those it was trained, in order to measure the generalization ability of the USV agent’s policy and also to achieve more plausibility in the results. |
Fotis Assimakopoulos Costas Vassilakis, Dionisis Margaris Konstantinos Kotis Dimitris Spiliotopoulos The implementation of “smart” technologies in the agricultural sector: a review Journal Article Information, 15 (8), pp. 466, 2024. @article{Assimakopoulos2024, title = {The implementation of “smart” technologies in the agricultural sector: a review}, author = {Fotis Assimakopoulos, Costas Vassilakis, Dionisis Margaris, Konstantinos Kotis, Dimitris Spiliotopoulos}, url = {https://www.mdpi.com/2078-2489/15/8/466}, doi = {https://doi.org/10.3390/info15080466}, year = {2024}, date = {2024-08-06}, journal = {Information}, volume = {15}, number = {8}, pages = {466}, abstract = {The growing global population demands an increase in agricultural production and the promotion of sustainable practices. Smart agriculture, driven by advanced technologies, is crucial to achieving these goals. These technologies provide real-time information for crop monitoring, yield prediction, and essential farming functions. However, adopting intelligent farming systems poses challenges, including learning new systems and dealing with installation costs. Robust support is crucial for integrating smart farming into practices. Understanding the current state of agriculture, technology trends, and the challenges in technology acceptance is essential for a smooth transition to Agriculture 4.0. This work reports on the pivotal synergy of IoT technology with other research trends, such as weather forecasting and robotics. It also presents the applications of smart agriculture worldwide, with an emphasis on government initiatives to support farmers and promote global adoption. The aim of this work is to provide a comprehensive review of smart technologies for precision agriculture and especially of their adoption level and results on the global scale; to this end, this review examines three important areas of smart agriculture, namely field, greenhouse, and livestock monitoring.}, keywords = {}, pubstate = {published}, tppubtype = {article} } The growing global population demands an increase in agricultural production and the promotion of sustainable practices. Smart agriculture, driven by advanced technologies, is crucial to achieving these goals. These technologies provide real-time information for crop monitoring, yield prediction, and essential farming functions. However, adopting intelligent farming systems poses challenges, including learning new systems and dealing with installation costs. Robust support is crucial for integrating smart farming into practices. Understanding the current state of agriculture, technology trends, and the challenges in technology acceptance is essential for a smooth transition to Agriculture 4.0. This work reports on the pivotal synergy of IoT technology with other research trends, such as weather forecasting and robotics. It also presents the applications of smart agriculture worldwide, with an emphasis on government initiatives to support farmers and promote global adoption. The aim of this work is to provide a comprehensive review of smart technologies for precision agriculture and especially of their adoption level and results on the global scale; to this end, this review examines three important areas of smart agriculture, namely field, greenhouse, and livestock monitoring. |
Theodore Tranos Christos Spatharis, Konstantinos Blekas Andreas-Giorgios Stafylopatis Large-Scale Urban Traffic Management Using Zero-Shot Knowledge Transfer in Multi-Agent Reinforcement Learning for Intersection Patterns Journal Article Robotics, 13 (7), pp. 109, 2024. @article{Tranos2024, title = {Large-Scale Urban Traffic Management Using Zero-Shot Knowledge Transfer in Multi-Agent Reinforcement Learning for Intersection Patterns}, author = {Theodore Tranos, Christos Spatharis, Konstantinos Blekas, Andreas-Giorgios Stafylopatis}, url = {https://www.mdpi.com/2218-6581/13/7/109}, doi = {https://doi.org/10.3390/robotics13070109}, year = {2024}, date = {2024-07-19}, journal = {Robotics}, volume = {13}, number = {7}, pages = {109}, abstract = {The automatic control of vehicle traffic in large urban networks constitutes one of the most serious challenges to modern societies, with an impact on improving the quality of human life and saving energy and time. Intersections are a special traffic structure of pivotal importance as they accumulate a large number of vehicles that should be served in an optimal manner. Constructing intelligent models that manage to automatically coordinate and steer vehicles through intersections is a key point in the fragmentation of traffic control, offering active solutions through the flexibility of automatically adapting to a variety of traffic conditions. Responding to this call, this work aims to propose an integrated active solution of automatic traffic management. We introduce a multi-agent reinforcement learning framework that effectively models traffic flow at individual unsignalized intersections. It relies on a compact agent definition, a rich information state space, and a learning process characterized not only by depth and quality, but also by substantial degrees of freedom and variability. The resulting driving profiles are further transferred to larger road networks to integrate their individual elements and compose an effective automatic traffic control platform. Experiments are conducted on simulated road networks of variable complexity, demonstrating the potential of the proposed method.}, keywords = {}, pubstate = {published}, tppubtype = {article} } The automatic control of vehicle traffic in large urban networks constitutes one of the most serious challenges to modern societies, with an impact on improving the quality of human life and saving energy and time. Intersections are a special traffic structure of pivotal importance as they accumulate a large number of vehicles that should be served in an optimal manner. Constructing intelligent models that manage to automatically coordinate and steer vehicles through intersections is a key point in the fragmentation of traffic control, offering active solutions through the flexibility of automatically adapting to a variety of traffic conditions. Responding to this call, this work aims to propose an integrated active solution of automatic traffic management. We introduce a multi-agent reinforcement learning framework that effectively models traffic flow at individual unsignalized intersections. It relies on a compact agent definition, a rich information state space, and a learning process characterized not only by depth and quality, but also by substantial degrees of freedom and variability. The resulting driving profiles are further transferred to larger road networks to integrate their individual elements and compose an effective automatic traffic control platform. Experiments are conducted on simulated road networks of variable complexity, demonstrating the potential of the proposed method. |
Sideras, Andreas Multimodal pretraining for music audio Masters Thesis Department of Digital Systems, School of Information Technologies and Communication, University of Piraeus, 2024. @mastersthesis{Sideras2024b, title = {Multimodal pretraining for music audio}, author = {Andreas Sideras}, url = {https://dione.lib.unipi.gr/xmlui/handle/unipi/16697 https://dione.lib.unipi.gr/xmlui/bitstream/handle/unipi/16697/Andreas-Sideras-MSc-Thesis.pdf?sequence=1&isAllowed=y}, doi = {http://dx.doi.org/10.26267/unipi_dione/4119}, year = {2024}, date = {2024-07-01}, school = {Department of Digital Systems, School of Information Technologies and Communication, University of Piraeus}, abstract = {Data can be expressed in various forms, each potentially encoded through diverse means. For instance, we might encounter audio data paired with descriptive texts about their lyrics. Modern systems leverage, if available, the different sources of information and outperform, under certain conditions, their single-modal counterparts. In such multimodal settings, each modality encapsulates a distinct aspect of the underlying semantics of the data and has a supplementary role. Data can also be limited and without annotations related to the task at hand. In such cases, transfer learning and pretraining could be two techniques that enhance the performance of the models. In this thesis, we explore various unsupervised pretraining techniques while evaluating them on a supervised downstream task. Our goal is to train a model that can extract meaningful features and be further finetuned to any new task. We use LLMs to create pseudo-captions that describe the sentiment and the theme of the lyrics, from a large pool of non-annotated audio. We then perform a pretraining step, where we learn a multimodal coordinated space between the audio signals and these pseudo-captions. Then, we finetune our model on an annotated dataset, where only the audio modality is available. We highlight the ability of such models to deliver adequate performance in few-shot learning settings, the incorporation of LLMs into the pretraining step, and the importance of learning a shared semantic space for information originating from different modalities.}, keywords = {}, pubstate = {published}, tppubtype = {mastersthesis} } Data can be expressed in various forms, each potentially encoded through diverse means. For instance, we might encounter audio data paired with descriptive texts about their lyrics. Modern systems leverage, if available, the different sources of information and outperform, under certain conditions, their single-modal counterparts. In such multimodal settings, each modality encapsulates a distinct aspect of the underlying semantics of the data and has a supplementary role. Data can also be limited and without annotations related to the task at hand. In such cases, transfer learning and pretraining could be two techniques that enhance the performance of the models. In this thesis, we explore various unsupervised pretraining techniques while evaluating them on a supervised downstream task. Our goal is to train a model that can extract meaningful features and be further finetuned to any new task. We use LLMs to create pseudo-captions that describe the sentiment and the theme of the lyrics, from a large pool of non-annotated audio. We then perform a pretraining step, where we learn a multimodal coordinated space between the audio signals and these pseudo-captions. Then, we finetune our model on an annotated dataset, where only the audio modality is available. We highlight the ability of such models to deliver adequate performance in few-shot learning settings, the incorporation of LLMs into the pretraining step, and the importance of learning a shared semantic space for information originating from different modalities. |
Mariana Ziku Konstantinos Kotis, Gerasimos Pavlogeorgatos Evangelia Kavakli Chara Zeeri George Caridakis Evaluating crowdsourcing applications with map-based storytelling capabilities in cultural heritage Journal Article Heritage, 7 (7), pp. 3429-3454, 2024. @article{Ziku2024, title = {Evaluating crowdsourcing applications with map-based storytelling capabilities in cultural heritage}, author = {Mariana Ziku, Konstantinos Kotis, Gerasimos Pavlogeorgatos, Evangelia Kavakli, Chara Zeeri, George Caridakis}, url = {https://www.mdpi.com/2571-9408/7/7/162}, doi = {https://doi.org/10.3390/heritage7070162}, year = {2024}, date = {2024-06-28}, journal = {Heritage}, volume = {7}, number = {7}, pages = {3429-3454}, abstract = {Crowdsourcing applications that integrate storytelling and geotagging capabilities offer new avenues for engaging the public in cultural heritage. However, standardised evaluation frameworks are lacking. This paper presents an applied evaluation methodology involving the analysis of relevant web-based tools. Towards this goal, this paper presents the development of crowdsourcing applications using, as a case study, the collection of myths and legends associated with the monumental heritage site of the Palace of the Grand Master of the Knights of Rhodes in Greece. Additionally, the paper presents an evaluation conducted through a criteria-based approach and user-based survey. The study reviews the concepts of crowdsourcing and digital storytelling within digital heritage, along with current concepts of living heritage and folklore, and examines relevant initiatives. The evaluation follows a four-stage methodology: (i) initial web-based tool selection based on the minimum requirements of web compatibility, crowdsourced data display, and map-based storytelling capability; (ii) application development using the selected web-based tools; (iii) a five-criteria assessment, based on the factors of open access, usability/tool support, participatory content/story creation, metrics provision and metadata model usage; and (iv) a crowd-based survey, indicating the most effective option. Findings from 100 respondents reveal limited exposure to participatory storytelling applications but interest in contributing content. Social media and influential figures serve as key channels for promoting crowdsourcing open calls. The results highlight gaps in understanding user expectations and perceptions, suggesting future research for gaining insights into engagement rates.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Crowdsourcing applications that integrate storytelling and geotagging capabilities offer new avenues for engaging the public in cultural heritage. However, standardised evaluation frameworks are lacking. This paper presents an applied evaluation methodology involving the analysis of relevant web-based tools. Towards this goal, this paper presents the development of crowdsourcing applications using, as a case study, the collection of myths and legends associated with the monumental heritage site of the Palace of the Grand Master of the Knights of Rhodes in Greece. Additionally, the paper presents an evaluation conducted through a criteria-based approach and user-based survey. The study reviews the concepts of crowdsourcing and digital storytelling within digital heritage, along with current concepts of living heritage and folklore, and examines relevant initiatives. The evaluation follows a four-stage methodology: (i) initial web-based tool selection based on the minimum requirements of web compatibility, crowdsourced data display, and map-based storytelling capability; (ii) application development using the selected web-based tools; (iii) a five-criteria assessment, based on the factors of open access, usability/tool support, participatory content/story creation, metrics provision and metadata model usage; and (iv) a crowd-based survey, indicating the most effective option. Findings from 100 respondents reveal limited exposure to participatory storytelling applications but interest in contributing content. Social media and influential figures serve as key channels for promoting crowdsourcing open calls. The results highlight gaps in understanding user expectations and perceptions, suggesting future research for gaining insights into engagement rates. |
Dimitrios Doumanas Andreas Soularidis, Konstantinos Kotis George Vouros Integrating LLMs in the Engineering of a SAR Ontology Conference Artificial Intelligence Applications and Innovations, Springer, Cham, 2024, ISBN: 978-3-031-63223-5. @conference{Doumanas2024, title = {Integrating LLMs in the Engineering of a SAR Ontology}, author = {Dimitrios Doumanas, Andreas Soularidis, Konstantinos Kotis, George Vouros}, doi = {https://doi.org/10.1007/978-3-031-63223-5_27}, isbn = {978-3-031-63223-5}, year = {2024}, date = {2024-06-21}, booktitle = { Artificial Intelligence Applications and Innovations}, pages = {360-374}, publisher = {Springer, Cham}, abstract = {In Search and Rescue (SAR) missions, the integration of multiple sources of information may enhance operational efficiency and increase responsiveness significantly, improving situation awareness and aiding decision-making to save lives and mitigate incident impact. Ontologies are crucial for integrating and reasoning with data from diverse sources. Engineering a domain ontology for SAR can be better supported from an agile, collaborative, and iterative ontology engineering methodology (OEM), incorporating the interests of several stakeholders. Large Language Models (LLMs) can play a significant role in completing OEM processes. The goal of this work is to identify how ontology engineering (OE) tasks can be completed with the collaboration of LLMs and humans. The objectives of this paper are, a) to present preliminary exploration of LLMs to generate domain ontologies for the modeling of SAR missions in wildfire incidents b) to propose and evaluate an LLM-enhanced OE approach. In overall, the main contribution of the work presented in this paper is the analysis of LLMs capabilities to ontology engineering, and the evaluation of the synergy between humans and machines to efficiently represent knowledge, with specific focus in the SAR domain.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } In Search and Rescue (SAR) missions, the integration of multiple sources of information may enhance operational efficiency and increase responsiveness significantly, improving situation awareness and aiding decision-making to save lives and mitigate incident impact. Ontologies are crucial for integrating and reasoning with data from diverse sources. Engineering a domain ontology for SAR can be better supported from an agile, collaborative, and iterative ontology engineering methodology (OEM), incorporating the interests of several stakeholders. Large Language Models (LLMs) can play a significant role in completing OEM processes. The goal of this work is to identify how ontology engineering (OE) tasks can be completed with the collaboration of LLMs and humans. The objectives of this paper are, a) to present preliminary exploration of LLMs to generate domain ontologies for the modeling of SAR missions in wildfire incidents b) to propose and evaluate an LLM-enhanced OE approach. In overall, the main contribution of the work presented in this paper is the analysis of LLMs capabilities to ontology engineering, and the evaluation of the synergy between humans and machines to efficiently represent knowledge, with specific focus in the SAR domain. |
Christos Spatharis Konstantinos Blekas, George Vouros A Modelling flight trajectories with multi-modal generative adversarial imitation learning Journal Article Applied Intelligence, 54 (11), pp. 7118-7134, 2024. @article{Spatharis2024c, title = {Modelling flight trajectories with multi-modal generative adversarial imitation learning}, author = {Christos Spatharis, Konstantinos Blekas, George A Vouros}, doi = {https://doi.org/10.1007/s10489-024-05519-6}, year = {2024}, date = {2024-06-03}, journal = {Applied Intelligence}, volume = {54}, number = {11}, pages = {7118-7134}, abstract = {Models of aircraft trajectories become important components of systems supporting the trajectory based operations paradigm: trajectory predictability is considered to be the main driver to enhance operational key performance areas, such as capacity of the airspace, effectiveness regarding all stakeholders’ objectives, and, of course, safety. This article formulates the trajectory modelling problem as a data-driven imitation learning problem addressing multi-modality. To solve this problem we study the use of state-of-the-art multi-modal imitation learning methods Info-GAIL and Triple-GAIL operating in a supervised way, with the aim of (a) disentangling modalities representing patterns of trajectory evolution, and (b) predicting trajectories. Experiments are performed using a real-world dataset of long flights with origin Paris and destination Istanbul. Results show the potential of imitation learning methods to disentangle multi-modal trajectories in real-world settings and predict trajectories with high accuracy.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Models of aircraft trajectories become important components of systems supporting the trajectory based operations paradigm: trajectory predictability is considered to be the main driver to enhance operational key performance areas, such as capacity of the airspace, effectiveness regarding all stakeholders’ objectives, and, of course, safety. This article formulates the trajectory modelling problem as a data-driven imitation learning problem addressing multi-modality. To solve this problem we study the use of state-of-the-art multi-modal imitation learning methods Info-GAIL and Triple-GAIL operating in a supervised way, with the aim of (a) disentangling modalities representing patterns of trajectory evolution, and (b) predicting trajectories. Experiments are performed using a real-world dataset of long flights with origin Paris and destination Istanbul. Results show the potential of imitation learning methods to disentangle multi-modal trajectories in real-world settings and predict trajectories with high accuracy. |
Christos Spatharis Konstantinos Blekas, George Vouros A Modelling flight trajectories with multi-modal generative adversarial imitation learning Journal Article Applied Intelligence, 54 , pp. 7118-7134, 2024. @article{Spatharis2024, title = {Modelling flight trajectories with multi-modal generative adversarial imitation learning}, author = {Christos Spatharis, Konstantinos Blekas, George A. Vouros }, url = {https://link.springer.com/article/10.1007/s10489-024-05519-6}, doi = {https://doi.org/10.1007/s10489-024-05519-6}, year = {2024}, date = {2024-06-03}, journal = {Applied Intelligence}, volume = {54}, pages = {7118-7134}, abstract = {Models of aircraft trajectories become important components of systems supporting the trajectory based operations paradigm: trajectory predictability is considered to be the main driver to enhance operational key performance areas, such as capacity of the airspace, effectiveness regarding all stakeholders’ objectives, and, of course, safety. This article formulates the trajectory modelling problem as a data-driven imitation learning problem addressing multi-modality. To solve this problem we study the use of state-of-the-art multi-modal imitation learning methods Info-GAIL and Triple-GAIL operating in a supervised way, with the aim of (a) disentangling modalities representing patterns of trajectory evolution, and (b) predicting trajectories. Experiments are performed using a real-world dataset of long flights with origin Paris and destination Istanbul. Results show the potential of imitation learning methods to disentangle multi-modal trajectories in real-world settings and predict trajectories with high accuracy.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Models of aircraft trajectories become important components of systems supporting the trajectory based operations paradigm: trajectory predictability is considered to be the main driver to enhance operational key performance areas, such as capacity of the airspace, effectiveness regarding all stakeholders’ objectives, and, of course, safety. This article formulates the trajectory modelling problem as a data-driven imitation learning problem addressing multi-modality. To solve this problem we study the use of state-of-the-art multi-modal imitation learning methods Info-GAIL and Triple-GAIL operating in a supervised way, with the aim of (a) disentangling modalities representing patterns of trajectory evolution, and (b) predicting trajectories. Experiments are performed using a real-world dataset of long flights with origin Paris and destination Istanbul. Results show the potential of imitation learning methods to disentangle multi-modal trajectories in real-world settings and predict trajectories with high accuracy. |
Georgios Bouchouras Pavlos Bitilis, Konstantinos Kotis George Vouros A LLMs for the Engineering of a Parkinson Disease Monitoring and Alerting Ontology. Conference GeNeSy’24: First International Workshop on Generative Neuro-Symbolic Artificial Intelligence, co-located with ESWC 2024, 2024. @conference{Bouchouras2024, title = {LLMs for the Engineering of a Parkinson Disease Monitoring and Alerting Ontology.}, author = {Georgios Bouchouras, Pavlos Bitilis, Konstantinos Kotis, George A Vouros}, url = {https://www.researchgate.net/profile/Giorgos-Bouchouras/publication/383431620_LLMs_for_the_Engineering_of_a_Parkinson_Disease_Monitoring_and_Alerting_Ontology/links/67a859dd4c479b26c9dac1f5/LLMs-for-the-Engineering-of-a-Parkinson-Disease-Monitoring-and-Alerting-Ontology.pdf}, year = {2024}, date = {2024-05-26}, booktitle = {GeNeSy’24: First International Workshop on Generative Neuro-Symbolic Artificial Intelligence, co-located with ESWC 2024}, abstract = {This paper investigates the integration of Large Language Models (LLMs) in the engineering of a Parkinson’s Disease (PD) monitoring and alerting ontology. The focus is on the ontology engineering methodology which combines the capabilities of LLMs and human expertise to develop more robust and comprehensive domain ontologies, faster than humans do alone. Evaluating models like ChatGPT-3.5, ChatGPT4, Gemini, and Llama2, this study explores various LLM based ontology engineering methods. The findings reveal that the proposed hybrid approach (both LLM and human involvement), namely X-HCOME, consistently excelled in class generation and F-1 score, indicating its efficiency in creating valid and comprehensive ontologies faster than humans do alone. The study underscores the potential of the combined LLMs and human intelligence to enrich PD domain knowledge and enhance expert-generated PD ontologies. In overall, the presented approach exemplifies a promising collaboration between machine capabilities and human expertise in developing ontologies for complex domains.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } This paper investigates the integration of Large Language Models (LLMs) in the engineering of a Parkinson’s Disease (PD) monitoring and alerting ontology. The focus is on the ontology engineering methodology which combines the capabilities of LLMs and human expertise to develop more robust and comprehensive domain ontologies, faster than humans do alone. Evaluating models like ChatGPT-3.5, ChatGPT4, Gemini, and Llama2, this study explores various LLM based ontology engineering methods. The findings reveal that the proposed hybrid approach (both LLM and human involvement), namely X-HCOME, consistently excelled in class generation and F-1 score, indicating its efficiency in creating valid and comprehensive ontologies faster than humans do alone. The study underscores the potential of the combined LLMs and human intelligence to enrich PD domain knowledge and enhance expert-generated PD ontologies. In overall, the presented approach exemplifies a promising collaboration between machine capabilities and human expertise in developing ontologies for complex domains. |
Tania Litaina Andreas Soularidis, Georgios Bouchouras Konstantinos Kotis Evangelia Kavakli Towards LLM-based semantic analysis of historical legal documents Workshop First International Workshop of Semantic Digital Humanities, co-located with ESWC 2024, 2024. @workshop{Litaina2024, title = {Towards LLM-based semantic analysis of historical legal documents}, author = {Tania Litaina, Andreas Soularidis, Georgios Bouchouras, Konstantinos Kotis, Evangelia Kavakli}, url = {https://www.researchgate.net/profile/Giorgos-Bouchouras/publication/383431412_Towards_LLM-based_Semantic_Analysis_of_Historical_Legal_Documents/links/66cd70cec2eaa500231aeeb6/Towards-LLM-based-Semantic-Analysis-of-Historical-Legal-Documents.pdf}, year = {2024}, date = {2024-05-26}, booktitle = {First International Workshop of Semantic Digital Humanities, co-located with ESWC 2024}, abstract = {The preservation of legal documents such as notarial ones is of vital importance as they are evidence of legal transactions between the involved entities through the years, serving as historical legal knowledge bases. The emergence of Large Language Models (LLMs) and their ability to analyze big data and generate content (much faster and relatively better than humans do alone) has created new perspectives in many fields, including law. Motivated by the significant potential of LLMs, we investigate the capabilities and limitations of using them in semantically analyzing legal documents through experimentation with two most prevalent LLMs i.e., ChatGPT-3.5 and Gemini/Bard. The goal is to emphasize automated and faster semantic analysis of documents, placing questions (prompts) concerning the type and subject of contracts, the recognition of the involved named entities and their relationship(s) e.g., landlord-tenant or family relationships. The experiments conducted with digitized contract documents that have been converted from handwritten Greek originals into plain text (LLM input) using Transkribus, an AI-powered platform for text recognition and transcription. The LLM responses were evaluated against the results obtained from a human expert, performing better in terms of precision but not in recall.}, keywords = {}, pubstate = {published}, tppubtype = {workshop} } The preservation of legal documents such as notarial ones is of vital importance as they are evidence of legal transactions between the involved entities through the years, serving as historical legal knowledge bases. The emergence of Large Language Models (LLMs) and their ability to analyze big data and generate content (much faster and relatively better than humans do alone) has created new perspectives in many fields, including law. Motivated by the significant potential of LLMs, we investigate the capabilities and limitations of using them in semantically analyzing legal documents through experimentation with two most prevalent LLMs i.e., ChatGPT-3.5 and Gemini/Bard. The goal is to emphasize automated and faster semantic analysis of documents, placing questions (prompts) concerning the type and subject of contracts, the recognition of the involved named entities and their relationship(s) e.g., landlord-tenant or family relationships. The experiments conducted with digitized contract documents that have been converted from handwritten Greek originals into plain text (LLM input) using Transkribus, an AI-powered platform for text recognition and transcription. The LLM responses were evaluated against the results obtained from a human expert, performing better in terms of precision but not in recall. |
Alexandros Karakikes Panagiotis Alexiadis, Konstantinos Kotis Bias in X (Twitter) and telegram based intelligence analysis: exploring challenges and potential mitigating roles of AI Journal Article SN Computer Science, 5 (5), pp. 574, 2024. @article{Karakikes2024, title = {Bias in X (Twitter) and telegram based intelligence analysis: exploring challenges and potential mitigating roles of AI}, author = {Alexandros Karakikes, Panagiotis Alexiadis, Konstantinos Kotis}, url = {https://link.springer.com/article/10.1007/s42979-024-02935-w}, doi = {https://doi.org/10.1007/s42979-024-02935-w}, year = {2024}, date = {2024-05-23}, journal = {SN Computer Science}, volume = {5}, number = {5}, pages = {574}, abstract = {Bias identification and mitigation in the social media ecosystem has been lately researched towards achieving a more efficient utilization of social media platforms for different stakeholders and purposes. Among these stakeholders, intelligence services worldwide, collectively called the Intelligence Community (IC), tend to use social media, supplementarily to their pre-extant disciplines, for monitoring areas of interest and identifying emerging social, political and security trends/threats. Over time, the IC has identified bias as the major impediment in information analysis, thus it has developed scientific and empirical methods for bias mitigation, in parallel to those developed by the information and communication technology (ICT) and artificial intelligence (AI) community. As it becomes apparent, it is to both communities’ interest to accurately trace bias and ideally eradicate or moderate its effects. This paper is an extension of a previously presented academic work, in which we drew systemic parallels between Intelligence Analysis (IA) and Twitter Analytics (TA), comparatively examined existing bias mitigating methodologies to identify similarities/dissimilarities, and subsequently investigated the viability of adopting and attuning methodologies from the first field to the latter. Furthermore, we proposed a novel framework for AI-augmented bias mitigation in the IC and simultaneously recommended on a theoretical level, methods and tools, already adapted by the ICT community, for efficiently supporting bias mitigation in each phase of the aforementioned framework. In the current paper, we extend our previous work by implementing the collection phase of the proposed framework on a real-world use case utilizing Telegram as a collection platform. We contribute new insights resulted from our experimentation with a tri-modal source selection approach in which human agents and Large Language Models (LLMs) are involved. The experiments were performed with data collected using one of the correspondingly suggested tools, engineering an equally represented, balanced dataset for the working case.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Bias identification and mitigation in the social media ecosystem has been lately researched towards achieving a more efficient utilization of social media platforms for different stakeholders and purposes. Among these stakeholders, intelligence services worldwide, collectively called the Intelligence Community (IC), tend to use social media, supplementarily to their pre-extant disciplines, for monitoring areas of interest and identifying emerging social, political and security trends/threats. Over time, the IC has identified bias as the major impediment in information analysis, thus it has developed scientific and empirical methods for bias mitigation, in parallel to those developed by the information and communication technology (ICT) and artificial intelligence (AI) community. As it becomes apparent, it is to both communities’ interest to accurately trace bias and ideally eradicate or moderate its effects. This paper is an extension of a previously presented academic work, in which we drew systemic parallels between Intelligence Analysis (IA) and Twitter Analytics (TA), comparatively examined existing bias mitigating methodologies to identify similarities/dissimilarities, and subsequently investigated the viability of adopting and attuning methodologies from the first field to the latter. Furthermore, we proposed a novel framework for AI-augmented bias mitigation in the IC and simultaneously recommended on a theoretical level, methods and tools, already adapted by the ICT community, for efficiently supporting bias mitigation in each phase of the aforementioned framework. In the current paper, we extend our previous work by implementing the collection phase of the proposed framework on a real-world use case utilizing Telegram as a collection platform. We contribute new insights resulted from our experimentation with a tri-modal source selection approach in which human agents and Large Language Models (LLMs) are involved. The experiments were performed with data collected using one of the correspondingly suggested tools, engineering an equally represented, balanced dataset for the working case. |
Efthymia Moraitou Yannis Christodoulou, Konstantinos Kotis George Caridakis An ontology-based framework for supporting decision-making in conservation and restoration interventions for cultural heritage Journal Article ACM Journal on Computing and Cultural Heritage, 17 (3), pp. 1-24, 2024. @article{Moraitou2024, title = {An ontology-based framework for supporting decision-making in conservation and restoration interventions for cultural heritage}, author = {Efthymia Moraitou, Yannis Christodoulou, Konstantinos Kotis, George Caridakis}, url = {https://dl.acm.org/doi/full/10.1145/3653977}, doi = {https://doi.org/10.1145/3653977}, year = {2024}, date = {2024-05-22}, journal = {ACM Journal on Computing and Cultural Heritage}, volume = {17}, number = {3}, pages = {1-24}, abstract = {Decision-making (DM) is the backbone of the Conservation and Restoration (CnR) of Cultural Heritage (CH). The demands of the DM process for information organization and management have raised issues that the CnR community attempts to solve by creating DM-support tools and systems, which, among others, exploit Semantic Web (SW) technologies. Regarding the tools and systems that focus on the DM process of selecting an intervention option (CnR-DM-I), they present benefits, as well as limitations, regarding the (1) completeness of representation of the relevant knowledge in a unified manner, (2) facilitation of recording the CnR-DM-I process per se, in terms of the problem at hand as well as the intervention parameters, requirements, and criteria, and (3) recommendation and further exploration of CnR intervention options in a systematic manner. This work proposes an ontology-based framework as a means to overcome those limitations. The proposed framework (DS-CnRI) sets at its core a formal ontology which provides the necessary entities to represent expert knowledge related to CnR-DM-I. The ontology also includes rules which provide useful inferences to assist the CnR-DM-I process. The proposed framework has been deployed and evaluated in collaboration with conservators. Initial evaluation results show that the framework assists conservators in CnR-DM-I to detect and select the most suitable intervention options, to better understand the limitations of different options, and to document the process of reaching their decision.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Decision-making (DM) is the backbone of the Conservation and Restoration (CnR) of Cultural Heritage (CH). The demands of the DM process for information organization and management have raised issues that the CnR community attempts to solve by creating DM-support tools and systems, which, among others, exploit Semantic Web (SW) technologies. Regarding the tools and systems that focus on the DM process of selecting an intervention option (CnR-DM-I), they present benefits, as well as limitations, regarding the (1) completeness of representation of the relevant knowledge in a unified manner, (2) facilitation of recording the CnR-DM-I process per se, in terms of the problem at hand as well as the intervention parameters, requirements, and criteria, and (3) recommendation and further exploration of CnR intervention options in a systematic manner. This work proposes an ontology-based framework as a means to overcome those limitations. The proposed framework (DS-CnRI) sets at its core a formal ontology which provides the necessary entities to represent expert knowledge related to CnR-DM-I. The ontology also includes rules which provide useful inferences to assist the CnR-DM-I process. The proposed framework has been deployed and evaluated in collaboration with conservators. Initial evaluation results show that the framework assists conservators in CnR-DM-I to detect and select the most suitable intervention options, to better understand the limitations of different options, and to document the process of reaching their decision. |
Piyabhum Chaysri Christos Spatharis, Kostas Vlachos Konstantinos Blekas Design and implementation of a low-cost intelligent unmanned surface vehicle Journal Article Sensors, 24 (10), pp. 3254, 2024. @article{Chaysri2024, title = {Design and implementation of a low-cost intelligent unmanned surface vehicle}, author = {Piyabhum Chaysri, Christos Spatharis, Kostas Vlachos, Konstantinos Blekas}, url = {https://www.mdpi.com/1424-8220/24/10/3254}, doi = {https://doi.org/10.3390/s24103254}, year = {2024}, date = {2024-05-20}, journal = {Sensors}, volume = {24}, number = {10}, pages = {3254}, abstract = {This article describes the design and construction journey of a self-developed unmanned surface vehicle (USV). In order to increase the accessibility and lower the barrier of entry we propose a low-cost (under EUR 1000) approach to the vessel construction with great adaptability and customizability. This design prioritizes minimal power consumption as a key objective. It focuses on elucidating the intricacies of both the design and assembly processes involved in creating an economical USV. Utilizing easily accessible components, the boat outlined in this study has been already participated in the 1st Aegean Ro-boat Race 2023 competition and is tailored for entry into similar robotic competitions. Its primary functionalities encompass autonomous sea navigation coupled with sophisticated collision avoidance capabilities. Finally, we studied reinforcement learning strategies for constructing a robust intelligent controller for the task of USV navigation under disturbances and we show some preliminary simulation results we have obtained.}, keywords = {}, pubstate = {published}, tppubtype = {article} } This article describes the design and construction journey of a self-developed unmanned surface vehicle (USV). In order to increase the accessibility and lower the barrier of entry we propose a low-cost (under EUR 1000) approach to the vessel construction with great adaptability and customizability. This design prioritizes minimal power consumption as a key objective. It focuses on elucidating the intricacies of both the design and assembly processes involved in creating an economical USV. Utilizing easily accessible components, the boat outlined in this study has been already participated in the 1st Aegean Ro-boat Race 2023 competition and is tailored for entry into similar robotic competitions. Its primary functionalities encompass autonomous sea navigation coupled with sophisticated collision avoidance capabilities. Finally, we studied reinforcement learning strategies for constructing a robust intelligent controller for the task of USV navigation under disturbances and we show some preliminary simulation results we have obtained. |
Sotirios Bentos Eleftherios Bailis, Fotini Oikonomou Stamatis Spirou Emmanouil Mavrikos Stamatis Chatzistamatis Konstantinos Kotis George Tsekouras E 2024 4th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), 2024, ISBN: 979-8-3503-0950-8. @conference{Bentos2024, title = {Evaluation of fairness in machine learning-based recidivism predictions: The case of Greek female prison system}, author = {Sotirios Bentos, Eleftherios Bailis, Fotini Oikonomou, Stamatis Spirou, Emmanouil Mavrikos, Stamatis Chatzistamatis, Konstantinos Kotis, George E Tsekouras}, url = {https://ieeexplore.ieee.org/abstract/document/10548202}, doi = {https://doi.org/10.1109/IRASET60544.2024.10548202}, isbn = {979-8-3503-0950-8}, year = {2024}, date = {2024-05-16}, booktitle = {2024 4th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)}, pages = {1-8}, abstract = {Recidivism refers to a person’s relapse into criminal behavior, often after receiving some form of punishment or undergoing intervention for a previous crime. Machine learning (ML) algorithms are commonly used for quantitatively predicting recidivism by assessing a criminal defendant’s likelihood of committing a crime thus, guiding decisions and imposing choices for criminal justice officers in managing the criminal population. Beyond the prediction adequacy of these algorithms, an important issue is whether they are capable of making fair decisions. It has been stated that attributes such as gender, race, age, ethnicity, and unemployment appear to affect the fair decision-making of ML systems upon recidivism. In this paper, we study the recidivism predictions obtained by several supervised ML algorithms over a dataset that has been extracted from a Greek female prison data record. The main points addressed by the current contribution concern the study of the resulting recidivism predictions from the perspective of fairness assessment that is related to certain data attributes such as age at exiting the first imprisonment, and employment status at the moment of the first imprisonment. To accomplish that task, several criteria are applied to analyze the ML-based predictions in terms of statistical analysis.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Recidivism refers to a person’s relapse into criminal behavior, often after receiving some form of punishment or undergoing intervention for a previous crime. Machine learning (ML) algorithms are commonly used for quantitatively predicting recidivism by assessing a criminal defendant’s likelihood of committing a crime thus, guiding decisions and imposing choices for criminal justice officers in managing the criminal population. Beyond the prediction adequacy of these algorithms, an important issue is whether they are capable of making fair decisions. It has been stated that attributes such as gender, race, age, ethnicity, and unemployment appear to affect the fair decision-making of ML systems upon recidivism. In this paper, we study the recidivism predictions obtained by several supervised ML algorithms over a dataset that has been extracted from a Greek female prison data record. The main points addressed by the current contribution concern the study of the resulting recidivism predictions from the perspective of fairness assessment that is related to certain data attributes such as age at exiting the first imprisonment, and employment status at the moment of the first imprisonment. To accomplish that task, several criteria are applied to analyze the ML-based predictions in terms of statistical analysis. |
Andreas Kontogiannis Vasilis Pollatos, Sotiris Kanellopoulos Panayotis Mertikopoulos Aris Pagourtzis Ioannis Panageas The computational complexity of finding second-order stationary points Conference Forty-first International Conference on Machine Learning, 2024. @conference{Kontogiannis*2024, title = {The computational complexity of finding second-order stationary points}, author = {Andreas Kontogiannis, Vasilis Pollatos, Sotiris Kanellopoulos, Panayotis Mertikopoulos, Aris Pagourtzis, Ioannis Panageas}, url = {https://openreview.net/forum?id=t8WDBcegae}, year = {2024}, date = {2024-05-02}, booktitle = {Forty-first International Conference on Machine Learning}, abstract = {Non-convex minimization problems are universally considered hard, and even guaranteeing that a computed solution is locally minimizing is known to be NP-hard. In this general context, our paper focuses on the problem of finding stationary points that satisfy an approximate second-order optimality condition, which serves to exclude strict saddles and other non-minimizing stationary points. Our main result is that the problem of finding approximate second-order stationary points (SOSPs) is PLS-complete, i.e., of the same complexity as the problem of finding first-order stationary points (FOSPs), thus resolving an open question in the field. In particular, our results imply that, under the widely believed complexity conjecture that PLS FNP, finding approximate SOSPs in unconstrained domains is *easier* than in constrained domains, which is known to be NP-hard. This comes in stark contrast with earlier results which implied that, unless PLS = CLS, finding approximate FOSPs in unconstrained domains is *harder* than in constrained domains.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Non-convex minimization problems are universally considered hard, and even guaranteeing that a computed solution is locally minimizing is known to be NP-hard. In this general context, our paper focuses on the problem of finding stationary points that satisfy an approximate second-order optimality condition, which serves to exclude strict saddles and other non-minimizing stationary points. Our main result is that the problem of finding approximate second-order stationary points (SOSPs) is PLS-complete, i.e., of the same complexity as the problem of finding first-order stationary points (FOSPs), thus resolving an open question in the field. In particular, our results imply that, under the widely believed complexity conjecture that PLS FNP, finding approximate SOSPs in unconstrained domains is *easier* than in constrained domains, which is known to be NP-hard. This comes in stark contrast with earlier results which implied that, unless PLS = CLS, finding approximate FOSPs in unconstrained domains is *harder* than in constrained domains. |
Konstantinos Bougiatiotis Andreas Sideras, Elias Zavitsanos Georgios Paliouras Proceedings of the Joint Workshop of the 7th Financial Technology and Natural Language Processing, the 5th Knowledge Discovery from Unstructured Data in Financial Services, and the 4th Workshop on Economics and Natural Language Processing, 2024. @conference{Bougiatiotis2024, title = {Dice@ ml-esg-3: Esg impact level and duration inference using llms for augmentation and contrastive learning}, author = {Konstantinos Bougiatiotis, Andreas Sideras, Elias Zavitsanos, Georgios Paliouras}, url = {https://aclanthology.org/2024.finnlp-1.24/ https://aclanthology.org/2024.finnlp-1.24.pdf}, year = {2024}, date = {2024-05-01}, booktitle = {Proceedings of the Joint Workshop of the 7th Financial Technology and Natural Language Processing, the 5th Knowledge Discovery from Unstructured Data in Financial Services, and the 4th Workshop on Economics and Natural Language Processing}, pages = {234-243}, abstract = {We present the submission of team DICE for ML-ESG-3, the 3rd Shared Task on Multilingual ESG impact duration inference in the context of the joint FinNLP-KDF workshop series. The task provides news articles and seeks to determine the impact and duration of an event in the news article may have on a company. We experiment with various baselines and discuss the results of our best-performing submissions based on contrastive pre-training and a stacked model based on the bag-of-words assumption and sentence embeddings. We also explored the label correlations among events stemming from the same news article and the correlations between impact level and impact length. Our analysis shows that even simple classifiers trained in this task can achieve comparable performance with more complex models, under certain conditions.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } We present the submission of team DICE for ML-ESG-3, the 3rd Shared Task on Multilingual ESG impact duration inference in the context of the joint FinNLP-KDF workshop series. The task provides news articles and seeks to determine the impact and duration of an event in the news article may have on a company. We experiment with various baselines and discuss the results of our best-performing submissions based on contrastive pre-training and a stacked model based on the bag-of-words assumption and sentence embeddings. We also explored the label correlations among events stemming from the same news article and the correlations between impact level and impact length. Our analysis shows that even simple classifiers trained in this task can achieve comparable performance with more complex models, under certain conditions. |
Sotiris Angelis Efthymia Moraitou, George Caridakis Konstantinos Kotis CHEKG: a collaborative and hybrid methodology for engineering modular and fair domain-specific knowledge graphs Journal Article Knowledge and Information Systems, 66 , pp. 4899–4925, 2024. @article{Angelis2024, title = {CHEKG: a collaborative and hybrid methodology for engineering modular and fair domain-specific knowledge graphs}, author = {Sotiris Angelis, Efthymia Moraitou, George Caridakis, Konstantinos Kotis}, url = {https://link.springer.com/content/pdf/10.1007/s10115-024-02110-w.pdf}, doi = {https://doi.org/10.1007/s10115-024-02110-w}, year = {2024}, date = {2024-04-20}, journal = {Knowledge and Information Systems}, volume = {66}, pages = {4899–4925}, abstract = {Ontologies constitute the semantic model of Knowledge Graphs (KGs). This structural association indicates the potential existence of methodological analogies in the development of ontologies and KGs. The deployment of fully and well-defined methodologies for KG development based on existing ontology engineering methodologies (OEMs) has been suggested and efficiently applied. However, most of the modern/recent OEMs may not include tasks that (i) empower knowledge workers and domain experts to closely collaborate with ontology engineers and KG specialists for the development and maintenance of KGs, (ii) satisfy special requirements of KG development, such as (a) ensuring modularity and agility of KGs, (b) assessing and mitigating bias at schema and data levels. Toward this aim, the paper presents a methodology for the Collaborative and Hybrid Engineering of Knowledge Graphs (CHEKG), which constitutes a hybrid (schema-centric/top-down and data-driven/bottom-up), collaborative, agile, and iterative approach for developing modular and fair domain-specific KGs. CHEKG contributes to all phases of the KG engineering lifecycle: from the specification of a KG to its exploitation, evaluation, and refinement. The CHEKG methodology is based on the main phases of the extended Human-Centered Collaborative Ontology Engineering Methodology (ext-HCOME), while it adjusts and expands the individual processes and tasks of each phase according to the specialized requirements of KG development. Apart from the presentation of the methodology per se, the paper presents recent work regarding the deployment and evaluation of the CHEKG methodology for the engineering of semantic trajectories as KGs generated from unmanned aerial vehicles (UAVs) data during real cultural heritage documentation scenarios.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Ontologies constitute the semantic model of Knowledge Graphs (KGs). This structural association indicates the potential existence of methodological analogies in the development of ontologies and KGs. The deployment of fully and well-defined methodologies for KG development based on existing ontology engineering methodologies (OEMs) has been suggested and efficiently applied. However, most of the modern/recent OEMs may not include tasks that (i) empower knowledge workers and domain experts to closely collaborate with ontology engineers and KG specialists for the development and maintenance of KGs, (ii) satisfy special requirements of KG development, such as (a) ensuring modularity and agility of KGs, (b) assessing and mitigating bias at schema and data levels. Toward this aim, the paper presents a methodology for the Collaborative and Hybrid Engineering of Knowledge Graphs (CHEKG), which constitutes a hybrid (schema-centric/top-down and data-driven/bottom-up), collaborative, agile, and iterative approach for developing modular and fair domain-specific KGs. CHEKG contributes to all phases of the KG engineering lifecycle: from the specification of a KG to its exploitation, evaluation, and refinement. The CHEKG methodology is based on the main phases of the extended Human-Centered Collaborative Ontology Engineering Methodology (ext-HCOME), while it adjusts and expands the individual processes and tasks of each phase according to the specialized requirements of KG development. Apart from the presentation of the methodology per se, the paper presents recent work regarding the deployment and evaluation of the CHEKG methodology for the engineering of semantic trajectories as KGs generated from unmanned aerial vehicles (UAVs) data during real cultural heritage documentation scenarios. |
Batsis, Georgios Machine learning for children’s music emotion recognition Masters Thesis Department of Digital Systems, School of Information Technologies and Communication, University of Piraeus, 2024. @mastersthesis{Batsis2024, title = {Machine learning for children’s music emotion recognition}, author = {Georgios Batsis}, url = {https://dione.lib.unipi.gr/xmlui/handle/unipi/16460}, doi = {http://dx.doi.org/10.26267/unipi_dione/3882}, year = {2024}, date = {2024-04-01}, school = {Department of Digital Systems, School of Information Technologies and Communication, University of Piraeus}, abstract = {This work focuses on the application of Machine Learning techniques for Music Emotion Recognition, particularly focusing on children’s music. The first step was to create a specialized dataset for children’s music, which includes songs of varied emotions and cultural backgrounds, annotated by experts in child psychology, education, and Machine Learning Engineers. A Support Vector Machine was employed as a baseline model for the prediction task, processing a range of handcrafted audio features. Concerning more advanced models, Convolutional Neural Networks and a Dual-Stream architecture model, integrating both Convolutional and attention-based Long Short-Term Memory networks were evaluated. This approach offers a comprehensive analysis of children’s music by examining both spectrograms and music transcription sequences. Models were evaluated using the Probabilistic Emotion Alignment to compare model posteriors with the probability distribution of expert annotations. Moreover, models evaluated using the established Machine Learning metrics, indicating that different modalities are able to enhance the predictive capacity for emotion recognition.}, keywords = {}, pubstate = {published}, tppubtype = {mastersthesis} } This work focuses on the application of Machine Learning techniques for Music Emotion Recognition, particularly focusing on children’s music. The first step was to create a specialized dataset for children’s music, which includes songs of varied emotions and cultural backgrounds, annotated by experts in child psychology, education, and Machine Learning Engineers. A Support Vector Machine was employed as a baseline model for the prediction task, processing a range of handcrafted audio features. Concerning more advanced models, Convolutional Neural Networks and a Dual-Stream architecture model, integrating both Convolutional and attention-based Long Short-Term Memory networks were evaluated. This approach offers a comprehensive analysis of children’s music by examining both spectrograms and music transcription sequences. Models were evaluated using the Probabilistic Emotion Alignment to compare model posteriors with the probability distribution of expert annotations. Moreover, models evaluated using the established Machine Learning metrics, indicating that different modalities are able to enhance the predictive capacity for emotion recognition. |
Elias Alevizos Georgios M Santipantakis, Christos Doulkeridis Alexander Artikis Online Integration of Spatial Reasoning in Complex Event Recognition Conference 6th International Workshop on Big Mobility Data Analytics (BMDA), Joint Conference of the EDBT/ICDT 2024, 3651 , 2024, ISSN: 1613-0073. @conference{Alevizos2024, title = {Online Integration of Spatial Reasoning in Complex Event Recognition}, author = {Elias Alevizos, Georgios M Santipantakis, Christos Doulkeridis, Alexander Artikis}, url = {https://ceur-ws.org/Vol-3651/BMDA-10.pdf}, issn = {1613-0073}, year = {2024}, date = {2024-03-25}, booktitle = {6th International Workshop on Big Mobility Data Analytics (BMDA), Joint Conference of the EDBT/ICDT 2024}, volume = {3651}, abstract = {Complex Event Recognition (CER) systems have the ability to process streams of events in real time by detecting event patterns with minimal latency. Typically, these patterns have a temporal structure, often resembling the sequential structure of regular expressions. A pattern advances to the next state by checking various conditions on the current state and possibly previous events of the stream. CER systems are very efficient in tracking all the possible paths that a pattern may follow (since these are often non-deterministic) and report when a path is complete and a complex event must be reported. However, the conditions that need to be checked may be very demanding. For example, in the maritime monitoring domain, a condition may need to check whether a vessel is close to any other vessel. Such conditions are not easily expressed directly as regular expressions. For such spatio-temporal tasks, there exist dedicated engines which can evaluate this type of conditions very efficiently. Thus, we can integrate such a spatial reasoning engine within a CER engine in order to take advantage of both worlds: the CER engine can accommodate and process complex regular expressions and delegate the evaluation of expensive spatial tasks to the dedicated spatial reasoning engine. In this paper, we present an approach towards such an integration. We show how a CER engine can take advantage of a spatial reasoning engine. We describe two different communication schemes between the CER engine and the spatial reasoning engine (blocking and lazy) and explore when each one should be preferred.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Complex Event Recognition (CER) systems have the ability to process streams of events in real time by detecting event patterns with minimal latency. Typically, these patterns have a temporal structure, often resembling the sequential structure of regular expressions. A pattern advances to the next state by checking various conditions on the current state and possibly previous events of the stream. CER systems are very efficient in tracking all the possible paths that a pattern may follow (since these are often non-deterministic) and report when a path is complete and a complex event must be reported. However, the conditions that need to be checked may be very demanding. For example, in the maritime monitoring domain, a condition may need to check whether a vessel is close to any other vessel. Such conditions are not easily expressed directly as regular expressions. For such spatio-temporal tasks, there exist dedicated engines which can evaluate this type of conditions very efficiently. Thus, we can integrate such a spatial reasoning engine within a CER engine in order to take advantage of both worlds: the CER engine can accommodate and process complex regular expressions and delegate the evaluation of expensive spatial tasks to the dedicated spatial reasoning engine. In this paper, we present an approach towards such an integration. We show how a CER engine can take advantage of a spatial reasoning engine. We describe two different communication schemes between the CER engine and the spatial reasoning engine (blocking and lazy) and explore when each one should be preferred. |
N. Zafeiropoulos P. Bitilis, Tsekouras Kotis G E K Evaluating Ontology-Based PD Monitoring and Alerting in Personal Health Knowledge Graphs and Graph Neural Networks Journal Article Information, 15 (2), pp. 100, 2024. @article{Zafeiropoulos2024, title = {Evaluating Ontology-Based PD Monitoring and Alerting in Personal Health Knowledge Graphs and Graph Neural Networks}, author = {N. Zafeiropoulos, P. Bitilis, G.E. Tsekouras, K. Kotis}, url = {https://www.mdpi.com/2078-2489/15/2/100/pdf?version=1707998600}, doi = {https://doi.org/10.3390/info15020100}, year = {2024}, date = {2024-02-08}, journal = {Information}, volume = {15}, number = {2}, pages = {100}, abstract = {In the realm of Parkinson’s Disease (PD) research, the integration of wearable sensor data with personal health records (PHR) has emerged as a pivotal avenue for patient alerting and monitoring. This study delves into the complex domain of PD patient care, with a specific emphasis on harnessing the potential of wearable sensors to capture, represent and semantically analyze crucial movement data and knowledge. The primary objective is to enhance the assessment of PD patients by establishing a robust foundation for personalized health insights through the development of Personal Health Knowledge Graphs (PHKGs) and the employment of personal health Graph Neural Networks (PHGNNs) that utilize PHKGs. The objective is to formalize the representation of related integrated data, unified sensor and PHR data in higher levels of abstraction, i.e., in a PHKG, to facilitate interoperability and support rule-based high-level event recognition such as patient’s missing dose or falling. This paper, extending our previous related work, presents the Wear4PDmove ontology in detail and evaluates the ontology within the development of an experimental PHKG. Furthermore, this paper focuses on the integration and evaluation of PHKG within the implementation of a Graph Neural Network (GNN). This work emphasizes the importance of integrating PD-related data for monitoring and alerting patients with appropriate notifications. These notifications offer health experts precise and timely information for the continuous evaluation of personal health-related events, ultimately contributing to enhanced patient care and well-informed medical decision-making. Finally, the paper concludes by proposing a novel approach for integrating personal health KGs and GNNs for PD monitoring and alerting solutions.}, keywords = {}, pubstate = {published}, tppubtype = {article} } In the realm of Parkinson’s Disease (PD) research, the integration of wearable sensor data with personal health records (PHR) has emerged as a pivotal avenue for patient alerting and monitoring. This study delves into the complex domain of PD patient care, with a specific emphasis on harnessing the potential of wearable sensors to capture, represent and semantically analyze crucial movement data and knowledge. The primary objective is to enhance the assessment of PD patients by establishing a robust foundation for personalized health insights through the development of Personal Health Knowledge Graphs (PHKGs) and the employment of personal health Graph Neural Networks (PHGNNs) that utilize PHKGs. The objective is to formalize the representation of related integrated data, unified sensor and PHR data in higher levels of abstraction, i.e., in a PHKG, to facilitate interoperability and support rule-based high-level event recognition such as patient’s missing dose or falling. This paper, extending our previous related work, presents the Wear4PDmove ontology in detail and evaluates the ontology within the development of an experimental PHKG. Furthermore, this paper focuses on the integration and evaluation of PHKG within the implementation of a Graph Neural Network (GNN). This work emphasizes the importance of integrating PD-related data for monitoring and alerting patients with appropriate notifications. These notifications offer health experts precise and timely information for the continuous evaluation of personal health-related events, ultimately contributing to enhanced patient care and well-informed medical decision-making. Finally, the paper concludes by proposing a novel approach for integrating personal health KGs and GNNs for PD monitoring and alerting solutions. |
George Papadopoulos Alevizos Bastas, George Vouros Ian Crook Natalia Andrienko Gennady Andrienko Jose Manuel Cordero A Deep reinforcement learning in service of air traffic controllers to resolve tactical conflicts Journal Article Expert Systems with Applications, 236 , 2024, ISBN: 0957-4174. @article{Papadopoulos2024, title = {Deep reinforcement learning in service of air traffic controllers to resolve tactical conflicts}, author = {George Papadopoulos, Alevizos Bastas, George A. Vouros, Ian Crook, Natalia Andrienko, Gennady Andrienko, Jose Manuel Cordero }, doi = {https://doi.org/10.1016/j.eswa.2023.121234}, isbn = { 0957-4174}, year = {2024}, date = {2024-02-01}, journal = {Expert Systems with Applications}, volume = {236}, abstract = {Dense and complex air traffic requires higher levels of automation than those exhibited by tactical conflict detection and resolution (CD&R) tools that air traffic controllers (ATCOs) use today: AI tools can act on their own initiative, increasing the capacity of ATCOs to control higher volumes of traffic. However, given that the air traffic control (ATC) domain is safety critical, requires AI systems to which ATCOs are comfortable to relinquishing control, guaranteeing operational integrity and automation adoption. Two major factors towards this goal are quality of solutions and operational transparency. ResoLver, the system that this article presents, addresses these challenges using an enhanced graph convolutional reinforcement learning method operating in a multiagent setting where each agent – representing a flight – performs a CD&R task, jointly with other agents. We show that ResoLver can provide high-quality solutions with respect to stakeholders interests (air traffic controllers and airspace users), addressing also operational transparency issues, which have been validated by ATCOs in simulated real-world settings.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Dense and complex air traffic requires higher levels of automation than those exhibited by tactical conflict detection and resolution (CD&R) tools that air traffic controllers (ATCOs) use today: AI tools can act on their own initiative, increasing the capacity of ATCOs to control higher volumes of traffic. However, given that the air traffic control (ATC) domain is safety critical, requires AI systems to which ATCOs are comfortable to relinquishing control, guaranteeing operational integrity and automation adoption. Two major factors towards this goal are quality of solutions and operational transparency. ResoLver, the system that this article presents, addresses these challenges using an enhanced graph convolutional reinforcement learning method operating in a multiagent setting where each agent – representing a flight – performs a CD&R task, jointly with other agents. We show that ResoLver can provide high-quality solutions with respect to stakeholders interests (air traffic controllers and airspace users), addressing also operational transparency issues, which have been validated by ATCOs in simulated real-world settings. |
George Papadopoulos Alevizos Bastas, George Vouros Ian Crook Natalia Andrienko Gennady Andrienko Jose Manuel Cordero A Deep reinforcement learning in service of air traffic controllers to resolve tactical conflicts Journal Article Expert Systems with Applications, 236 , pp. 121234, 2024, ISSN: 0957-4174. @article{Papadopoulos2024b, title = {Deep reinforcement learning in service of air traffic controllers to resolve tactical conflicts}, author = {George Papadopoulos, Alevizos Bastas, George A Vouros, Ian Crook, Natalia Andrienko, Gennady Andrienko, Jose Manuel Cordero}, doi = {https://doi.org/10.1016/j.eswa.2023.121234}, issn = {0957-4174}, year = {2024}, date = {2024-02-01}, journal = {Expert Systems with Applications}, volume = {236}, pages = {121234}, abstract = {Dense and complex air traffic requires higher levels of automation than those exhibited by tactical conflict detection and resolution (CD&R) tools that air traffic controllers (ATCOs) use today: AI tools can act on their own initiative, increasing the capacity of ATCOs to control higher volumes of traffic. However, given that the air traffic control (ATC) domain is safety critical, requires AI systems to which ATCOs are comfortable to relinquishing control, guaranteeing operational integrity and automation adoption. Two major factors towards this goal are quality of solutions and operational transparency. ResoLver, the system that this article presents, addresses these challenges using an enhanced graph convolutional reinforcement learning method operating in a multiagent setting where each agent – representing a flight – performs a CD&R task, jointly with other agents. We show that ResoLver can provide high-quality solutions with respect to stakeholders interests (air traffic controllers and airspace users), addressing also operational transparency issues, which have been validated by ATCOs in simulated real-world settings.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Dense and complex air traffic requires higher levels of automation than those exhibited by tactical conflict detection and resolution (CD&R) tools that air traffic controllers (ATCOs) use today: AI tools can act on their own initiative, increasing the capacity of ATCOs to control higher volumes of traffic. However, given that the air traffic control (ATC) domain is safety critical, requires AI systems to which ATCOs are comfortable to relinquishing control, guaranteeing operational integrity and automation adoption. Two major factors towards this goal are quality of solutions and operational transparency. ResoLver, the system that this article presents, addresses these challenges using an enhanced graph convolutional reinforcement learning method operating in a multiagent setting where each agent – representing a flight – performs a CD&R task, jointly with other agents. We show that ResoLver can provide high-quality solutions with respect to stakeholders interests (air traffic controllers and airspace users), addressing also operational transparency issues, which have been validated by ATCOs in simulated real-world settings. |
Andreas Soularidis Konstantinos Ι Kotis, George Vouros A Real-time semantic data integration and reasoning in life-and time-critical decision support systems Journal Article Electronics, 13 (3), pp. 526, 2024. @article{Soularidis2024, title = {Real-time semantic data integration and reasoning in life-and time-critical decision support systems}, author = {Andreas Soularidis, Konstantinos Ι Kotis, George A Vouros}, url = {https://www.mdpi.com/2079-9292/13/3/526/pdf?version=1706421011}, doi = {https://doi.org/10.3390/electronics13030526}, year = {2024}, date = {2024-01-28}, journal = {Electronics}, volume = {13}, number = {3}, pages = {526}, abstract = {Natural disasters such as earthquakes, floods, and forest fires involve critical situations in which human lives and infrastructures are in jeopardy. People are often injured and/or trapped without being able to be assisted by first responders on time. Moreover, in most cases, the harsh environment jeopardizes first responders by significantly increasing the difficulty of their mission. In such scenarios, time is crucial and often of vital importance. First responders must have a clear and complete view of the current situation every few seconds/minutes to efficiently and timely tackle emerging challenges, ensuring the safety of both victims and personnel. Advances in related technology including robots, drones, and Internet of Things (IoT)-enabled equipment have increased their usability and importance in life- and time-critical decision support systems such as the ones designed and developed for Search and Rescue (SAR) missions. Such systems depend on efficiency in their ability to integrate large volumes of heterogeneous and streaming data and reason with this data in (near) real time. In addition, real-time critical data integration and reasoning need to be performed on edge devices that reside near the missions, instead of using cloud infrastructure. The aim of this paper is twofold: (a) to review technologies and approaches related to real-time semantic data integration and reasoning on IoT-enabled collaborative entities and edge devices in life- and time-critical decision support systems, with a focus on systems designed for SAR missions and (b) to identify open issues and challenges focusing on the specific topic. In addition, this paper proposes a novel approach that will go beyond the state-of-the-art in efficiently recognizing time-critical high-level events, supporting commanders and first responders with meaningful and life-critical insights about the current and predicted state of the environment in which they operate.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Natural disasters such as earthquakes, floods, and forest fires involve critical situations in which human lives and infrastructures are in jeopardy. People are often injured and/or trapped without being able to be assisted by first responders on time. Moreover, in most cases, the harsh environment jeopardizes first responders by significantly increasing the difficulty of their mission. In such scenarios, time is crucial and often of vital importance. First responders must have a clear and complete view of the current situation every few seconds/minutes to efficiently and timely tackle emerging challenges, ensuring the safety of both victims and personnel. Advances in related technology including robots, drones, and Internet of Things (IoT)-enabled equipment have increased their usability and importance in life- and time-critical decision support systems such as the ones designed and developed for Search and Rescue (SAR) missions. Such systems depend on efficiency in their ability to integrate large volumes of heterogeneous and streaming data and reason with this data in (near) real time. In addition, real-time critical data integration and reasoning need to be performed on edge devices that reside near the missions, instead of using cloud infrastructure. The aim of this paper is twofold: (a) to review technologies and approaches related to real-time semantic data integration and reasoning on IoT-enabled collaborative entities and edge devices in life- and time-critical decision support systems, with a focus on systems designed for SAR missions and (b) to identify open issues and challenges focusing on the specific topic. In addition, this paper proposes a novel approach that will go beyond the state-of-the-art in efficiently recognizing time-critical high-level events, supporting commanders and first responders with meaningful and life-critical insights about the current and predicted state of the environment in which they operate. |
Apostolos Glenis, George Vouros A Scale-boss-mr: scalable time series classification using multiple symbolic representations Journal Article Applied Sciences, 14 (2), pp. 689, 2024. @article{Glenis2024, title = {Scale-boss-mr: scalable time series classification using multiple symbolic representations}, author = {Apostolos Glenis, George A Vouros}, url = {https://www.mdpi.com/2076-3417/14/2/689/pdf?version=1705147852}, doi = {https://doi.org/10.3390/app14020689}, year = {2024}, date = {2024-01-13}, journal = {Applied Sciences}, volume = {14}, number = {2}, pages = {689}, abstract = {Time-Series-Classification (TSC) is an important machine learning task for many branches of science. Symbolic representations of time series, especially Symbolic Fourier Approximation (SFA), have been proven very effective for this task, given their abilities to reduce noise. In this paper, we improve upon SCALE-BOSS using multiple symbolic representations of time series. More specifically, the proposed SCALE-BOSS-MR incorporates into the process a variety of window sizes combined with multiple dilation parameters applied to the original and to first-order differences’ time series, with the latter modeling trend information. SCALE-BOSS-MR has been evaluated using the eight datasets with the largest training size of the UCR time series repository. The results indicate that SCALE-BOSS-MR can be instantiated to classifiers that are able to achieve state-of-the-art accuracy and can be tuned for scalability.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Time-Series-Classification (TSC) is an important machine learning task for many branches of science. Symbolic representations of time series, especially Symbolic Fourier Approximation (SFA), have been proven very effective for this task, given their abilities to reduce noise. In this paper, we improve upon SCALE-BOSS using multiple symbolic representations of time series. More specifically, the proposed SCALE-BOSS-MR incorporates into the process a variety of window sizes combined with multiple dilation parameters applied to the original and to first-order differences’ time series, with the latter modeling trend information. SCALE-BOSS-MR has been evaluated using the eight datasets with the largest training size of the UCR time series repository. The results indicate that SCALE-BOSS-MR can be instantiated to classifiers that are able to achieve state-of-the-art accuracy and can be tuned for scalability. |
Christos Spatharis, Konstantinos Blekas Multiagent reinforcement learning for autonomous driving in traffic zones with unsignalized intersections Journal Article Journal of Intelligent Transportation Systems, 28 (1), pp. 103-119, 2024. @article{Spatharis2024b, title = {Multiagent reinforcement learning for autonomous driving in traffic zones with unsignalized intersections}, author = {Christos Spatharis, Konstantinos Blekas}, url = {https://www.tandfonline.com/doi/abs/10.1080/15472450.2022.2109416}, doi = {https://doi.org/10.1080/15472450.2022.2109416}, year = {2024}, date = {2024-01-02}, journal = {Journal of Intelligent Transportation Systems}, volume = {28}, number = {1}, pages = {103-119}, abstract = {In this work we present a multiagent deep reinforcement learning approach for autonomous driving vehicles that is able to operate in traffic networks with unsignalized intersections. The key aspects of the proposed study are the introduction of route-agents as the main building block of the system, as well as a collision term that allows the cooperation among vehicles and the construction of an efficient reward function. These have the advantage of establishing an enhanced collaborative multiagent deep reinforcement learning scheme that manages to control multiple vehicles and navigate them safely and efficiently-economically to their destination. In addition, it provides the beneficial flexibility to lay down a platform for transfer learning and reusing knowledge from the agents’ policies in handling unknown traffic scenarios. We provide several experimental results in simulated road traffic networks of variable complexity and diverse characteristics using the SUMO environment that empirically illustrate the efficiency of the proposed multiagent framework.}, keywords = {}, pubstate = {published}, tppubtype = {article} } In this work we present a multiagent deep reinforcement learning approach for autonomous driving vehicles that is able to operate in traffic networks with unsignalized intersections. The key aspects of the proposed study are the introduction of route-agents as the main building block of the system, as well as a collision term that allows the cooperation among vehicles and the construction of an efficient reward function. These have the advantage of establishing an enhanced collaborative multiagent deep reinforcement learning scheme that manages to control multiple vehicles and navigate them safely and efficiently-economically to their destination. In addition, it provides the beneficial flexibility to lay down a platform for transfer learning and reusing knowledge from the agents’ policies in handling unknown traffic scenarios. We provide several experimental results in simulated road traffic networks of variable complexity and diverse characteristics using the SUMO environment that empirically illustrate the efficiency of the proposed multiagent framework. |
2023 |
Ioannis Mademlis Georgios Batsis, Adamantia Anna Rebolledo Chrysochoou Georgios Th Papadopoulos Visual inspection for illicit items in x-ray images using deep learning Conference 2023 IEEE International Conference on Big Data (BigData), 2023, ISBN: 979-8-3503-2445-7. @conference{Mademlis2023, title = {Visual inspection for illicit items in x-ray images using deep learning}, author = {Ioannis Mademlis, Georgios Batsis, Adamantia Anna Rebolledo Chrysochoou, Georgios Th Papadopoulos}, url = {https://ieeexplore.ieee.org/abstract/document/10386207/keywords#keywords}, doi = {https://doi.org/10.1109/BigData59044.2023.10386207}, isbn = {979-8-3503-2445-7}, year = {2023}, date = {2023-12-15}, booktitle = {2023 IEEE International Conference on Big Data (BigData)}, pages = {4081-4089}, abstract = {Automated detection of contraband items in X-ray images can significantly increase public safety, by enhancing the productivity and alleviating the mental load of security officers in airports, subways, customs/post offices, etc. The large volume and high throughput of passengers, mailed parcels, etc., during rush hours practically make it a Big Data problem. Modern computer vision algorithms relying on Deep Neural Networks (DNNs) have proven capable of undertaking this task even under resource-constrained and embedded execution scenarios, e.g., as is the case with fast, single-stage object detectors. However, no comparative experimental assessment of the various relevant DNN components/methods has been performed under a common evaluation protocol, which means that reliable cross-method comparisons are missing. This paper presents exactly such a comparative assessment, utilizing a public relevant dataset and a well-defined methodology for selecting the specific DNN components/modules that are being evaluated. The results indicate the superiority of Transformer detectors, the obsolete nature of auxiliary neural modules that have been developed in the past few years for security applications and the efficiency of the CSP-DarkNet backbone CNN.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Automated detection of contraband items in X-ray images can significantly increase public safety, by enhancing the productivity and alleviating the mental load of security officers in airports, subways, customs/post offices, etc. The large volume and high throughput of passengers, mailed parcels, etc., during rush hours practically make it a Big Data problem. Modern computer vision algorithms relying on Deep Neural Networks (DNNs) have proven capable of undertaking this task even under resource-constrained and embedded execution scenarios, e.g., as is the case with fast, single-stage object detectors. However, no comparative experimental assessment of the various relevant DNN components/methods has been performed under a common evaluation protocol, which means that reliable cross-method comparisons are missing. This paper presents exactly such a comparative assessment, utilizing a public relevant dataset and a well-defined methodology for selecting the specific DNN components/modules that are being evaluated. The results indicate the superiority of Transformer detectors, the obsolete nature of auxiliary neural modules that have been developed in the past few years for security applications and the efficiency of the CSP-DarkNet backbone CNN. |
Christos Doulkeridis Georgios M Santipantakis, Nikolaos Koutroumanis George Makridis Vasilis Koukos George Theodoropoulos Yannis Theodoridis Dimosthenis Kyriazis Pavlos Kranas Diego Burgos Ricardo Jimenez-Peris Mariana MG Duarte Mahmoud Sakr Esteban Zimányi Anita Graser Clemens Heistracher Kristian Torp Ioannis Chrysakis Theofanis Orphanoudakis Evgenia Kapassa Marios Touloupou Jürgen Neises Petros Petrou Sophia Karagiorgou Rosario Catelli Domenico Messina Marcelo Corrales Compagnucci Matteo Falsetta S MobiSpaces: An architecture for energy-efficient data spaces for mobility data Conference 2023 IEEE International Conference on Big Data (BigData), 2023, ISBN: 979-8-3503-2445-7. @conference{Doulkeridis2023, title = {MobiSpaces: An architecture for energy-efficient data spaces for mobility data}, author = {Christos Doulkeridis, Georgios M Santipantakis, Nikolaos Koutroumanis, George Makridis, Vasilis Koukos, George S Theodoropoulos, Yannis Theodoridis, Dimosthenis Kyriazis, Pavlos Kranas, Diego Burgos, Ricardo Jimenez-Peris, Mariana MG Duarte, Mahmoud Sakr, Esteban Zimányi, Anita Graser, Clemens Heistracher, Kristian Torp, Ioannis Chrysakis, Theofanis Orphanoudakis, Evgenia Kapassa, Marios Touloupou, Jürgen Neises, Petros Petrou, Sophia Karagiorgou, Rosario Catelli, Domenico Messina, Marcelo Corrales Compagnucci, Matteo Falsetta}, url = {https://ieeexplore.ieee.org/abstract/document/10386539}, doi = {https://doi.org/10.1109/BigData59044.2023.10386539}, isbn = {979-8-3503-2445-7}, year = {2023}, date = {2023-12-15}, booktitle = {2023 IEEE International Conference on Big Data (BigData)}, pages = {1487-1494}, abstract = {In this paper, we present an architecture for mobility data spaces enabling trustworthy and reliable data operations along with its main constituent parts. The architecture makes use of a data lake for scalable storage of diverse mobility data sets, on top of which separate computing and storage layers are implemented to allow independent scaling with a data operations toolbox providing all data operations. Furthermore, to cater for mobility analytics, machine learning and artificial intelligence support, an edge analytics suite is provided that encompasses distributed algorithms for mobility analytics and federated learning, thereby exploiting edge computing technologies. In turn, this is supported by a resource allocator that monitors the energy consumption of data-intensive operations and provides this information to the platform for intelligent task placement in edge devices, aiming at energy-efficient operations. As a result, an end-to-end platform is proposed that combines data services and infrastructure services towards supporting mobility application domains, such as urban and maritime.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } In this paper, we present an architecture for mobility data spaces enabling trustworthy and reliable data operations along with its main constituent parts. The architecture makes use of a data lake for scalable storage of diverse mobility data sets, on top of which separate computing and storage layers are implemented to allow independent scaling with a data operations toolbox providing all data operations. Furthermore, to cater for mobility analytics, machine learning and artificial intelligence support, an edge analytics suite is provided that encompasses distributed algorithms for mobility analytics and federated learning, thereby exploiting edge computing technologies. In turn, this is supported by a resource allocator that monitors the energy consumption of data-intensive operations and provides this information to the platform for intelligent task placement in edge devices, aiming at energy-efficient operations. As a result, an end-to-end platform is proposed that combines data services and infrastructure services towards supporting mobility application domains, such as urban and maritime. |
Andreas Chitos Merkouris Karaliopoulos, Sabine Pelka Maria Halkidi Iordanis Koutsopoulos Nudging households for energy savings via smartphone apps and web portals: An empirical study Conference BEHAVE 2023: 7th European Conference on Behaviour Change for Energy Efficiency, 2023. @conference{Chitos2023, title = {Nudging households for energy savings via smartphone apps and web portals: An empirical study}, author = {Andreas Chitos, Merkouris Karaliopoulos, Sabine Pelka, Maria Halkidi, Iordanis Koutsopoulos}, url = {https://mm.aueb.gr/publications/197aa2ac-aede-4bd2-8764-edc35cbbd101.pdf}, doi = {https://resolver.tudelft.nl/uuid:b1b7680d-8470-4aa2-905c-df77a16de685}, year = {2023}, date = {2023-11-28}, booktitle = {BEHAVE 2023: 7th European Conference on Behaviour Change for Energy Efficiency}, pages = {293-304}, abstract = {In this paper, we report evidence collected in the context of the Horizon 2020 NUDGE project about the effectiveness of digital tools such as smartphone apps and web portals to realize nudging interventions towards different energy efficiency goals: from the reduction of heating energy and electricity to the increase of self-consumption in energy prosumer households. We analyse recorded events from the interaction of participants with those tools in the context of three different pilot experiments. We first assess the level of end user engagement with the apps and the portal, counting the number of distinct days that they interact with them. We find it to be highly heterogeneous, with up to 25% of participants in the Greek pilot and 12% in the Portuguese pilot not using the mobile app at all, and the rest forming three distinct groups of low, medium and high engagement. The interaction with the apps almost always lasts fractions of a minute and involves accessing a few app screens. We next turn to the actual users’ exposure to the nudging features of the digital tools to find out that high percentages of users (up to 50%) exhibit zero or very occasional exposure to the app screens that implement nudges. The mobile app users, in particular, can be grouped into four clusters depending on the level of engagement with the app and their exposure to its nudging features. Disappointingly, more than half the pilot participants belong to the cluster combining low engagement with low exposure to nudging. Combining these data with self-statements of participants in postintervention surveys, we find no significant correlation between the level of nudging exposure and the (self-stated) motivation/ intentions to save energy.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } In this paper, we report evidence collected in the context of the Horizon 2020 NUDGE project about the effectiveness of digital tools such as smartphone apps and web portals to realize nudging interventions towards different energy efficiency goals: from the reduction of heating energy and electricity to the increase of self-consumption in energy prosumer households. We analyse recorded events from the interaction of participants with those tools in the context of three different pilot experiments. We first assess the level of end user engagement with the apps and the portal, counting the number of distinct days that they interact with them. We find it to be highly heterogeneous, with up to 25% of participants in the Greek pilot and 12% in the Portuguese pilot not using the mobile app at all, and the rest forming three distinct groups of low, medium and high engagement. The interaction with the apps almost always lasts fractions of a minute and involves accessing a few app screens. We next turn to the actual users’ exposure to the nudging features of the digital tools to find out that high percentages of users (up to 50%) exhibit zero or very occasional exposure to the app screens that implement nudges. The mobile app users, in particular, can be grouped into four clusters depending on the level of engagement with the app and their exposure to its nudging features. Disappointingly, more than half the pilot participants belong to the cluster combining low engagement with low exposure to nudging. Combining these data with self-statements of participants in postintervention surveys, we find no significant correlation between the level of nudging exposure and the (self-stated) motivation/ intentions to save energy. |
Georgios M Santipantakis, Christos Doulkeridis An RDF Benchmark for Enriched Maritime Data Conference Proceedings of the 1st ACM SIGSPATIAL International Workshop on Methods for Enriched Mobility Data: Emerging issues and Ethical perspectives 2023, 2023, ISBN: 9798400703478. @conference{Santipantakis2023b, title = {An RDF Benchmark for Enriched Maritime Data}, author = {Georgios M Santipantakis, Christos Doulkeridis}, url = {https://dl.acm.org/doi/abs/10.1145/3615885.3628007}, doi = {https://doi.org/10.1145/3615885.3628007}, isbn = {9798400703478}, year = {2023}, date = {2023-11-13}, booktitle = {Proceedings of the 1st ACM SIGSPATIAL International Workshop on Methods for Enriched Mobility Data: Emerging issues and Ethical perspectives 2023}, pages = {1-10}, abstract = {This paper provides an RDF benchmark for triple stores for querying maritime data. The benchmark comes with an integrated data set of real-world data from diverse sources, which has been transformed to RDF triples, and a set of 15 SPARQL queries of varying complexity. Differently from previous available benchmarks that have focused on spatial RDF data, our work targets RDF data about trajectories of moving vessels, therefore the focus is on spatio-temporal RDF data. We present the process to generate the integrated data set, we delve into the details of the benchmark queries, and we provide evaluation results using a selected RDF triple store as a showcase. Furthermore, we make all data, queries and results publicly available to stimulate further research in the field.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } This paper provides an RDF benchmark for triple stores for querying maritime data. The benchmark comes with an integrated data set of real-world data from diverse sources, which has been transformed to RDF triples, and a set of 15 SPARQL queries of varying complexity. Differently from previous available benchmarks that have focused on spatial RDF data, our work targets RDF data about trajectories of moving vessels, therefore the focus is on spatio-temporal RDF data. We present the process to generate the integrated data set, we delve into the details of the benchmark queries, and we provide evaluation results using a selected RDF triple store as a showcase. Furthermore, we make all data, queries and results publicly available to stimulate further research in the field. |
Efthymia Moraitou Yannis Christodoulou, Konstantinos Kotis George Caridakis 3rd International Workshop on Semantic Web and Ontology Design for Cultural Heritage, International Semantic Web Conference (ISWC) 2023, 2023. @workshop{Moraitou2023, title = {An ontology to support decision-making in conservation and restoration interventions of cultural heritage}, author = {Efthymia Moraitou, Yannis Christodoulou, Konstantinos Kotis, George Caridakis}, url = {https://ceur-ws.org/Vol-3540/paper3.pdf}, year = {2023}, date = {2023-11-07}, booktitle = {3rd International Workshop on Semantic Web and Ontology Design for Cultural Heritage, International Semantic Web Conference (ISWC) 2023}, abstract = {The Conservation and Restoration (CnR) of Cultural Heritage (CH) community has exploited Semantic Web (SW) technologies to facilitate the representation and share of knowledge and data that the experts of the domain collect and produce. The different developed models represent aspects of knowledge of the domain, while they have been employed for implementing semantic services that support CnR practice. Furthermore, to some extent, the models represent and support the decision-making process of the CnR, facilitating the organization and management of information that could lead to concrete CnR intervention decisions. However, the decision-making regarding the intervention selection (CnR-DM-I) per se, has not been modelled yet. Furthermore, the support of the experts in a more assistive way, regarding the selection of the most suitable intervention option for different cases at hand, constitutes a field of interest that can be further explored. This work proposes a formal ontology which represents the expert’s knowledge related to CnR-DM-I. The ontology includes the necessary classes, properties, and individuals. The individuals represent specialized knowledge regarding the intervention problem, options, requirements, and criteria of two specific categories of CnR interventions: i) the cleaning of superficial deposits and ii) the consolidation of flaking gouache. Additionally, the ontology incorporates a set of rules, which generate necessary inferences which supplementally support the representation of the domain of interest. The ontology has been deployed in collaboration with and evaluated by conservators. Evaluation results show that the developed ontology successfully represents the domain of interest, while it provides useful inferences and queries answering which assist conservators in CnRDM-I processes. Thus, the incorporation of the ontology in a framework could lead to the detection and selection of the most suitable intervention options, as well as the full documentation of the context of the CnR-DM-I process.}, keywords = {}, pubstate = {published}, tppubtype = {workshop} } The Conservation and Restoration (CnR) of Cultural Heritage (CH) community has exploited Semantic Web (SW) technologies to facilitate the representation and share of knowledge and data that the experts of the domain collect and produce. The different developed models represent aspects of knowledge of the domain, while they have been employed for implementing semantic services that support CnR practice. Furthermore, to some extent, the models represent and support the decision-making process of the CnR, facilitating the organization and management of information that could lead to concrete CnR intervention decisions. However, the decision-making regarding the intervention selection (CnR-DM-I) per se, has not been modelled yet. Furthermore, the support of the experts in a more assistive way, regarding the selection of the most suitable intervention option for different cases at hand, constitutes a field of interest that can be further explored. This work proposes a formal ontology which represents the expert’s knowledge related to CnR-DM-I. The ontology includes the necessary classes, properties, and individuals. The individuals represent specialized knowledge regarding the intervention problem, options, requirements, and criteria of two specific categories of CnR interventions: i) the cleaning of superficial deposits and ii) the consolidation of flaking gouache. Additionally, the ontology incorporates a set of rules, which generate necessary inferences which supplementally support the representation of the domain of interest. The ontology has been deployed in collaboration with and evaluated by conservators. Evaluation results show that the developed ontology successfully represents the domain of interest, while it provides useful inferences and queries answering which assist conservators in CnRDM-I processes. Thus, the incorporation of the ontology in a framework could lead to the detection and selection of the most suitable intervention options, as well as the full documentation of the context of the CnR-DM-I process. |
Alexandros Karakikes Panagiotis Alexiadis, Theocharis Thecharopoulos Nikolaos Skoulidas Dimitris Spiliotopoulos Konstantinos Kotis Towards Handling Bias in Intelligence Analysis with Twitter Conference 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA), 2023, ISBN: 979-8-3503-4503-2. @conference{Karakikes2023b, title = {Towards Handling Bias in Intelligence Analysis with Twitter}, author = {Alexandros Karakikes, Panagiotis Alexiadis, Theocharis Thecharopoulos, Nikolaos Skoulidas, Dimitris Spiliotopoulos, Konstantinos Kotis}, url = {https://ieeexplore.ieee.org/abstract/document/10302618/}, doi = {https://doi.org/10.1109/DSAA60987.2023.10302618}, isbn = {979-8-3503-4503-2}, year = {2023}, date = {2023-11-06}, booktitle = {2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)}, abstract = {Bias identification and mitigation in the Twitter ecosystem has been lately researched towards achieving a more efficient utilization of the application by different stakeholders and for a wide area of purposes. Among these stakeholders, intelligence services worldwide, collectively called the Intelligence Community (IC), tend to use Twitter, supplementarily to their pre-existent disciplines, for monitoring areas of interest and identifying emerging social, political and security trends/threats. Over time, the IC has identified bias as the major obstacle in information analysis, thus it has developed scientific and empirical methods for bias mitigation, in parallel to those developed by the information and communication technology (ICT) and artificial intelligence (AI) community. As it becomes apparent, it is to both communities’ interest to accurately trace bias and ideally eradicate or moderate its effects. In this paper we draw systemic parallels between Intelligence Analysis (IA) and Twitter Analytics (TA), comparatively examine existing bias mitigating methodologies to pinpoint similarities/dissimilarities, and utterly investigate the feasibility of adapting and adjusting methodologies from the first field to the latter. Furthermore, we propose a novel framework for AI-augmented bias mitigation in the IC. Finally, we propose methods and tools, already adapted by the ICT community, for efficiently supporting bias mitigation methodologies adapted by the IC.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Bias identification and mitigation in the Twitter ecosystem has been lately researched towards achieving a more efficient utilization of the application by different stakeholders and for a wide area of purposes. Among these stakeholders, intelligence services worldwide, collectively called the Intelligence Community (IC), tend to use Twitter, supplementarily to their pre-existent disciplines, for monitoring areas of interest and identifying emerging social, political and security trends/threats. Over time, the IC has identified bias as the major obstacle in information analysis, thus it has developed scientific and empirical methods for bias mitigation, in parallel to those developed by the information and communication technology (ICT) and artificial intelligence (AI) community. As it becomes apparent, it is to both communities’ interest to accurately trace bias and ideally eradicate or moderate its effects. In this paper we draw systemic parallels between Intelligence Analysis (IA) and Twitter Analytics (TA), comparatively examine existing bias mitigating methodologies to pinpoint similarities/dissimilarities, and utterly investigate the feasibility of adapting and adjusting methodologies from the first field to the latter. Furthermore, we propose a novel framework for AI-augmented bias mitigation in the IC. Finally, we propose methods and tools, already adapted by the ICT community, for efficiently supporting bias mitigation methodologies adapted by the IC. |
Nikolaos Zafeiropoulos Pavlos Bitilis, George Tsekouras Konstantinos Kotis E Graph neural networks for parkinson’s disease monitoring and alerting Journal Article Sensors, 23 (21), pp. 8936, 2023. @article{Zafeiropoulos2023b, title = {Graph neural networks for parkinson’s disease monitoring and alerting}, author = {Nikolaos Zafeiropoulos, Pavlos Bitilis, George E Tsekouras, Konstantinos Kotis}, url = {https://www.mdpi.com/1424-8220/23/21/8936}, doi = {https://doi.org/10.3390/s23218936}, year = {2023}, date = {2023-11-02}, journal = {Sensors}, volume = {23}, number = {21}, pages = {8936}, abstract = {Graph neural networks (GNNs) have been increasingly employed in the field of Parkinson’s disease (PD) research. The use of GNNs provides a promising approach to address the complex relationship between various clinical and non-clinical factors that contribute to the progression of PD. This review paper aims to provide a comprehensive overview of the state-of-the-art research that is using GNNs for PD. It presents PD and the motivation behind using GNNs in this field. Background knowledge on the topic is also presented. Our research methodology is based on PRISMA, presenting a comprehensive overview of the current solutions using GNNs for PD, including the various types of GNNs employed and the results obtained. In addition, we discuss open issues and challenges that highlight the limitations of current GNN-based approaches and identify potential paths for future research. Finally, a new approach proposed in this paper presents the integration of new tasks for the engineering of GNNs for PD monitoring and alert solutions.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Graph neural networks (GNNs) have been increasingly employed in the field of Parkinson’s disease (PD) research. The use of GNNs provides a promising approach to address the complex relationship between various clinical and non-clinical factors that contribute to the progression of PD. This review paper aims to provide a comprehensive overview of the state-of-the-art research that is using GNNs for PD. It presents PD and the motivation behind using GNNs in this field. Background knowledge on the topic is also presented. Our research methodology is based on PRISMA, presenting a comprehensive overview of the current solutions using GNNs for PD, including the various types of GNNs employed and the results obtained. In addition, we discuss open issues and challenges that highlight the limitations of current GNN-based approaches and identify potential paths for future research. Finally, a new approach proposed in this paper presents the integration of new tasks for the engineering of GNNs for PD monitoring and alert solutions. |
Nikolaos Zafeiropoulos Pavlos Bitilis, Konstantinos Kotis Wear4PDmove: an ontology for knowledge-based personalized health monitoring of PD patients Conference The 22nd International Semantic Web Conference (ISWC), 2023. @conference{Zafeiropoulos2023, title = {Wear4PDmove: an ontology for knowledge-based personalized health monitoring of PD patients}, author = {Nikolaos Zafeiropoulos, Pavlos Bitilis, Konstantinos Kotis}, url = {https://hozo.jp/ISWC2023_PD-Industry-proc/ISWC2023_paper_433.pdf}, year = {2023}, date = {2023-11-01}, booktitle = {The 22nd International Semantic Web Conference (ISWC)}, abstract = {In the field of Parkinson’s Disease (PD), wearable sensors are commonly used to collect movement data from patients for various purposes such as analysis, monitoring, and alerting. To ensure interoperability with other personal health data, such as PHR data, it is crucial to semantically describe this data. Our work focuses on reusing existing ontologies and introducing new conceptualizations to engineer Personal Health Knowledge Graph (PHKG) for PD patient monitoring and doctor alerting. We aim to address the specific knowledge requirements in personal health for PD and support rule-based high-level event recognition. Developing a PHKG can greatly assist health specialists in efficiently assessing patients’ conditions, providing timely and cost-effective care for PD patients.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } In the field of Parkinson’s Disease (PD), wearable sensors are commonly used to collect movement data from patients for various purposes such as analysis, monitoring, and alerting. To ensure interoperability with other personal health data, such as PHR data, it is crucial to semantically describe this data. Our work focuses on reusing existing ontologies and introducing new conceptualizations to engineer Personal Health Knowledge Graph (PHKG) for PD patient monitoring and doctor alerting. We aim to address the specific knowledge requirements in personal health for PD and support rule-based high-level event recognition. Developing a PHKG can greatly assist health specialists in efficiently assessing patients’ conditions, providing timely and cost-effective care for PD patients. |
Manolis Remountakis Konstantinos Kotis, Babis Kourtzis George Tsekouras E Using ChatGPT and persuasive technology for personalized recommendation messages in hotel upselling Journal Article Information, 14 (9), pp. 504, 2023. @article{Remountakis2023b, title = {Using ChatGPT and persuasive technology for personalized recommendation messages in hotel upselling}, author = {Manolis Remountakis, Konstantinos Kotis, Babis Kourtzis, George E Tsekouras}, doi = {https://doi.org/10.3390/info14090504}, year = {2023}, date = {2023-09-13}, journal = {Information}, volume = {14}, number = {9}, pages = {504}, abstract = {Recommender systems have become indispensable tools in the hotel hospitality industry, enabling personalized and tailored experiences for guests. Recent advancements in large language models (LLMs), such as ChatGPT, and persuasive technologies have opened new avenues for enhancing the effectiveness of those systems. This paper explores the potential of integrating ChatGPT and persuasive technologies for automating and improving hotel hospitality recommender systems. First, we delve into the capabilities of ChatGPT, which can understand and generate human-like text, enabling more accurate and context-aware recommendations. We discuss the integration of ChatGPT into recommender systems, highlighting the ability to analyze user preferences, extract valuable insights from online reviews, and generate personalized recommendations based on guest profiles. Second, we investigate the role of persuasive technology in influencing user behavior and enhancing the persuasive impact of hotel recommendations. By incorporating persuasive techniques, such as social proof, scarcity, and personalization, recommender systems can effectively influence user decision making and encourage desired actions, such as booking a specific hotel or upgrading their room. To investigate the efficacy of ChatGPT and persuasive technologies, we present pilot experiments with a case study involving a hotel recommender system. Our inhouse commercial hotel marketing platform, eXclusivi, was extended with a new software module working with ChatGPT prompts and persuasive ads created for its recommendations. In particular, we developed an intelligent advertisement (ad) copy generation tool for the hotel marketing platform. The proposed approach allows for the hotel team to target all guests in their language, leveraging the integration with the hotel’s reservation system. Overall, this paper contributes to the field of hotel hospitality by exploring the synergistic relationship between ChatGPT and persuasive technology in recommender systems, ultimately influencing guest satisfaction and hotel revenue.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Recommender systems have become indispensable tools in the hotel hospitality industry, enabling personalized and tailored experiences for guests. Recent advancements in large language models (LLMs), such as ChatGPT, and persuasive technologies have opened new avenues for enhancing the effectiveness of those systems. This paper explores the potential of integrating ChatGPT and persuasive technologies for automating and improving hotel hospitality recommender systems. First, we delve into the capabilities of ChatGPT, which can understand and generate human-like text, enabling more accurate and context-aware recommendations. We discuss the integration of ChatGPT into recommender systems, highlighting the ability to analyze user preferences, extract valuable insights from online reviews, and generate personalized recommendations based on guest profiles. Second, we investigate the role of persuasive technology in influencing user behavior and enhancing the persuasive impact of hotel recommendations. By incorporating persuasive techniques, such as social proof, scarcity, and personalization, recommender systems can effectively influence user decision making and encourage desired actions, such as booking a specific hotel or upgrading their room. To investigate the efficacy of ChatGPT and persuasive technologies, we present pilot experiments with a case study involving a hotel recommender system. Our inhouse commercial hotel marketing platform, eXclusivi, was extended with a new software module working with ChatGPT prompts and persuasive ads created for its recommendations. In particular, we developed an intelligent advertisement (ad) copy generation tool for the hotel marketing platform. The proposed approach allows for the hotel team to target all guests in their language, leveraging the integration with the hotel’s reservation system. Overall, this paper contributes to the field of hotel hospitality by exploring the synergistic relationship between ChatGPT and persuasive technology in recommender systems, ultimately influencing guest satisfaction and hotel revenue. |
S. Bentos S. Spirou, Kotis Tsekouras K G Bias Assessment in AI-Based Predictions of Recidivism Conference 13th Beyond Humanism Conference (BHC), 2023. @conference{Bentos2023, title = {Bias Assessment in AI-Based Predictions of Recidivism}, author = {S. Bentos, S. Spirou, K. Kotis, G. Tsekouras}, url = {https://metabody.eu/wp-content/uploads/2023/06/13thBHC_ABSTRACT_BOOKLET.pdf}, year = {2023}, date = {2023-09-01}, booktitle = {13th Beyond Humanism Conference (BHC)}, abstract = {Recidivism refers to a person’s relapse into criminal behavior after receiving some form of punishment or undergoes intervention for a previous crime [1, 2]. Numerous individual factors and criminal justice processes (e.g., age, prior arrests, etc.) contribute to the construction of risk assessment instruments. As such, predicting recidivism has significant impact in terms of allocating and managing resources such as in social services, in policy-making decisions, in sentencing planning and probation, in bail options, and in obtaining valuable and prompt insights of the risk posed by various individuals involved in the system [2]. During the last decades, artificial intelligence (AI) algorithms have been used to predict recidivism and guide decisions and choices in managing criminal population by assessing a criminal defendant’s likelihood of committing a crime. Two well-known AI algorithmic frameworks are the COMPAS [3] and the OxRec [4]. Both capture and use certain personal aspects relating to a natural person such as income, marital status, prior alcohol abuse, drug use, and psychological illness of the suspect or alleged offender. Beyond the adequacy of AI systems in terms of prediction, an important obstacle is the bias that is encoded in the data and/or in the algorithms predicting delinquency or recidivism analysis [1, 5, 6]. Often it has been stated that bias in terms of gender, race and nationality are some of the most sensitive variables that affect the fair decision of AI systems on recidivism of the offenders. These sensitive variables are usually called protected variables. As an example, it has been proved that the COMPAS system obtains biased decisions against black defendants (i.e., the protected variable in this case is race) by classifying them as having twice higher risk of recidivism than white defendants [1, 7]. This study contributes a framework towards assessing the bias related to AI-based recidivism predictions using a set of open-source methods and tools such as the Weka-based machine-learning (ML) algorithms [8], evaluated with a Greek female prison recidivism data set. The data set includes a sample of 6000 females with the following features: (1) year of first release, (2) age of exiting first imprisonment, (3) country of origin, (4) profession before the first imprisonment, (5) education, (6) marital status, (7) number of children. To conduct our experiments, five different ML classification algorithms running on WEKA platform [8] were used: (a) decision tree (J48), (b) naïve Bayes, (c) knearest-neighbors (iBk), (d) logistic regression, and (e) neural network. By observing and analyzing the classification results of each algorithm, we investigated which attributes associated with re-incarceration are subject to bias. Thus, the goal was to figure out whether AI, in the simplest form of ML, is biased when deciding the fate of a past offender, and ultimately, which algorithm is most effective according to AI models. To obtain our results we quantified the disparate impact [2, 9] of the above seven features. The results showed a significant bias estimation related to the unemployment status before the first imprisonment. In this case, the offender’s profession before the first imprisonment was the protected variable. Future research involves the development of sophisticated algorithms to mitigate and eliminate the bias resulting from the implementation of the above classification algorithms.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Recidivism refers to a person’s relapse into criminal behavior after receiving some form of punishment or undergoes intervention for a previous crime [1, 2]. Numerous individual factors and criminal justice processes (e.g., age, prior arrests, etc.) contribute to the construction of risk assessment instruments. As such, predicting recidivism has significant impact in terms of allocating and managing resources such as in social services, in policy-making decisions, in sentencing planning and probation, in bail options, and in obtaining valuable and prompt insights of the risk posed by various individuals involved in the system [2]. During the last decades, artificial intelligence (AI) algorithms have been used to predict recidivism and guide decisions and choices in managing criminal population by assessing a criminal defendant’s likelihood of committing a crime. Two well-known AI algorithmic frameworks are the COMPAS [3] and the OxRec [4]. Both capture and use certain personal aspects relating to a natural person such as income, marital status, prior alcohol abuse, drug use, and psychological illness of the suspect or alleged offender. Beyond the adequacy of AI systems in terms of prediction, an important obstacle is the bias that is encoded in the data and/or in the algorithms predicting delinquency or recidivism analysis [1, 5, 6]. Often it has been stated that bias in terms of gender, race and nationality are some of the most sensitive variables that affect the fair decision of AI systems on recidivism of the offenders. These sensitive variables are usually called protected variables. As an example, it has been proved that the COMPAS system obtains biased decisions against black defendants (i.e., the protected variable in this case is race) by classifying them as having twice higher risk of recidivism than white defendants [1, 7]. This study contributes a framework towards assessing the bias related to AI-based recidivism predictions using a set of open-source methods and tools such as the Weka-based machine-learning (ML) algorithms [8], evaluated with a Greek female prison recidivism data set. The data set includes a sample of 6000 females with the following features: (1) year of first release, (2) age of exiting first imprisonment, (3) country of origin, (4) profession before the first imprisonment, (5) education, (6) marital status, (7) number of children. To conduct our experiments, five different ML classification algorithms running on WEKA platform [8] were used: (a) decision tree (J48), (b) naïve Bayes, (c) knearest-neighbors (iBk), (d) logistic regression, and (e) neural network. By observing and analyzing the classification results of each algorithm, we investigated which attributes associated with re-incarceration are subject to bias. Thus, the goal was to figure out whether AI, in the simplest form of ML, is biased when deciding the fate of a past offender, and ultimately, which algorithm is most effective according to AI models. To obtain our results we quantified the disparate impact [2, 9] of the above seven features. The results showed a significant bias estimation related to the unemployment status before the first imprisonment. In this case, the offender’s profession before the first imprisonment was the protected variable. Future research involves the development of sophisticated algorithms to mitigate and eliminate the bias resulting from the implementation of the above classification algorithms. |
David Dimitris Chlorogiannis Georgios-Ioannis Verras, Vasiliki Tzelepi Anargyros Chlorogiannis Anastasios Apostolos Konstantinos Kotis Christos-Nikolaos Anagnostopoulos Andreas Antzoulas Michail Vailas Dimitrios Schizas Francesk Mulita Gastroenterology Review, 18 (4), pp. 353-367, 2023. @article{Chlorogiannis2023, title = {Tissue classification and diagnosis of colorectal cancer histopathology images using deep learning algorithms. Is the time ripe for clinical practice implementation?}, author = {David Dimitris Chlorogiannis, Georgios-Ioannis Verras, Vasiliki Tzelepi, Anargyros Chlorogiannis, Anastasios Apostolos, Konstantinos Kotis, Christos-Nikolaos Anagnostopoulos, Andreas Antzoulas, Michail Vailas, Dimitrios Schizas, Francesk Mulita}, url = {https://www.termedia.pl/Tissue-classification-and-diagnosis-of-colorectal-cancer-histopathology-images-using-deep-learning-algorithms-Is-the-time-ripe-for-clinical-practice-implementation-,41,51207,0,1.html}, doi = {https://doi.org/10.5114/pg.2023.130337}, year = {2023}, date = {2023-08-07}, journal = {Gastroenterology Review}, volume = {18}, number = {4}, pages = {353-367}, abstract = {Colorectal cancer is one of the most prevalent types of cancer, with histopathologic examination of biopsied tissue samples remaining the gold standard for diagnosis. During the past years, artificial intelligence (AI) has steadily found its way into the field of medicine and pathology, especially with the introduction of whole slide imaging (WSI). The main outcome of interest was the composite balanced accuracy (ACC) as well as the F1 score. The average reported ACC from the collected studies was 95.8±3.8%. Reported F1 scores reached as high as 0.975, with an average of 89.7±9.8%, indicating that existing deep learning algorithms can achieve in silico distinction between malignant and benign. Overall, the available state-of-the-art algorithms are non-inferior to pathologists for image analysis and classification tasks. However, due to their inherent uniqueness in their training and lack of widely accepted external validation datasets, their generalization potential is still limited.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Colorectal cancer is one of the most prevalent types of cancer, with histopathologic examination of biopsied tissue samples remaining the gold standard for diagnosis. During the past years, artificial intelligence (AI) has steadily found its way into the field of medicine and pathology, especially with the introduction of whole slide imaging (WSI). The main outcome of interest was the composite balanced accuracy (ACC) as well as the F1 score. The average reported ACC from the collected studies was 95.8±3.8%. Reported F1 scores reached as high as 0.975, with an average of 89.7±9.8%, indicating that existing deep learning algorithms can achieve in silico distinction between malignant and benign. Overall, the available state-of-the-art algorithms are non-inferior to pathologists for image analysis and classification tasks. However, due to their inherent uniqueness in their training and lack of widely accepted external validation datasets, their generalization potential is still limited. |
Manolis Remountakis Konstantinos Kotis, Babis Kourtzis George Tsekouras E ChatGPT and persuasive technologies for the management and delivery of personalized recommendations in hotel hospitality Journal Article arXiv, 2023. @article{Remountakis2023, title = {ChatGPT and persuasive technologies for the management and delivery of personalized recommendations in hotel hospitality}, author = {Manolis Remountakis, Konstantinos Kotis, Babis Kourtzis, George E Tsekouras}, url = {https://arxiv.org/pdf/2307.14298}, doi = {https://doi.org/10.48550/arXiv.2307.14298}, year = {2023}, date = {2023-07-26}, journal = {arXiv}, abstract = {Recommender systems have become indispensable tools in the hotel hospitality industry, enabling personalized and tailored experiences for guests. Recent advancements in large language models (LLMs), such as ChatGPT, and persuasive technologies, have opened new avenues for enhancing the effectiveness of those systems. This paper explores the potential of integrating ChatGPT and persuasive technologies for automating and improving hotel hospitality recommender systems. First, we delve into the capabilities of ChatGPT, which can understand and generate human-like text, enabling more accurate and context-aware recommendations. We discuss the integration of ChatGPT into recommender systems, highlighting the ability to analyze user preferences, extract valuable insights from online reviews, and generate personalized recommendations based on guest profiles. Second, we investigate the role of persuasive technology in influencing user behavior and enhancing the persuasive impact of hotel recommendations. By incorporating persuasive techniques, such as social proof, scarcity and personalization, recommender systems can effectively influence user decision-making and encourage desired actions, such as booking a specific hotel or upgrading their room. To investigate the efficacy of ChatGPT and persuasive technologies, we present a pilot experi-ment with a case study involving a hotel recommender system. We aim to study the impact of integrating ChatGPT and persua-sive techniques on user engagement, satisfaction, and conversion rates. The preliminary results demonstrate the potential of these technologies in enhancing the overall guest experience and business performance. Overall, this paper contributes to the field of hotel hospitality by exploring the synergistic relationship between LLMs and persuasive technology in recommender systems, ultimately influencing guest satisfaction and hotel revenue.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Recommender systems have become indispensable tools in the hotel hospitality industry, enabling personalized and tailored experiences for guests. Recent advancements in large language models (LLMs), such as ChatGPT, and persuasive technologies, have opened new avenues for enhancing the effectiveness of those systems. This paper explores the potential of integrating ChatGPT and persuasive technologies for automating and improving hotel hospitality recommender systems. First, we delve into the capabilities of ChatGPT, which can understand and generate human-like text, enabling more accurate and context-aware recommendations. We discuss the integration of ChatGPT into recommender systems, highlighting the ability to analyze user preferences, extract valuable insights from online reviews, and generate personalized recommendations based on guest profiles. Second, we investigate the role of persuasive technology in influencing user behavior and enhancing the persuasive impact of hotel recommendations. By incorporating persuasive techniques, such as social proof, scarcity and personalization, recommender systems can effectively influence user decision-making and encourage desired actions, such as booking a specific hotel or upgrading their room. To investigate the efficacy of ChatGPT and persuasive technologies, we present a pilot experi-ment with a case study involving a hotel recommender system. We aim to study the impact of integrating ChatGPT and persua-sive techniques on user engagement, satisfaction, and conversion rates. The preliminary results demonstrate the potential of these technologies in enhancing the overall guest experience and business performance. Overall, this paper contributes to the field of hotel hospitality by exploring the synergistic relationship between LLMs and persuasive technology in recommender systems, ultimately influencing guest satisfaction and hotel revenue. |
Georgios Batsis Ioannis Mademlis, Georgios Th Papadopoulos Illicit item detection in X-ray images for security applications Conference 2023 IEEE Ninth International Conference on Big Data Computing Service and Applications (BigDataService), 2023, ISBN: 979-8-3503-3379-4. @conference{Batsis2023, title = {Illicit item detection in X-ray images for security applications}, author = {Georgios Batsis, Ioannis Mademlis, Georgios Th Papadopoulos}, url = {https://ieeexplore.ieee.org/abstract/document/10233969}, doi = {https://doi.org/10.1109/BigDataService58306.2023.00016}, isbn = {979-8-3503-3379-4}, year = {2023}, date = {2023-07-17}, booktitle = {2023 IEEE Ninth International Conference on Big Data Computing Service and Applications (BigDataService)}, pages = {63-70}, abstract = {Automated detection of contraband items in X-ray images can significantly increase public safety, by enhancing the productivity and alleviating the mental load of security officers in airports, subways, customs/post offices, etc. The large volume and high throughput of passengers, mailed parcels, etc., during rush hours make it a Big Data analysis task. Modern computer vision algorithms relying on Deep Neural Networks (DNNs) have proven capable of undertaking this task even under resource-constrained and embedded execution scenarios, e.g., as is the case with fast, single-stage, anchor-based object detectors. This paper proposes a two-fold improvement of such algorithms for the X-ray analysis domain, introducing two complementary novelties. Firstly, more efficient anchors are obtained by hierarchical clustering the sizes of the ground-truth training set bounding boxes; thus, the resulting anchors follow a natural hierarchy aligned with the semantic structure of the data. Secondly, the default Non-Maximum Suppression (NMS) algorithm at the end of the object detection pipeline is modified to better handle occluded object detection and to reduce the number of false predictions, by inserting the Efficient Intersection over Union (E-IoU) metric into the Weighted Cluster NMS method. E-IoU provides more discriminative geometrical correlations between the candidate bounding boxes/Regions-of-Interest (RoIs). The proposed method is implemented on a common single-stage object detector (YOLOv5) and its experimental evaluation on a relevant public dataset indicates significant accuracy gains over both the baseline and competing approaches. This highlights the potential of Big Data analysis in enhancing public safety.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Automated detection of contraband items in X-ray images can significantly increase public safety, by enhancing the productivity and alleviating the mental load of security officers in airports, subways, customs/post offices, etc. The large volume and high throughput of passengers, mailed parcels, etc., during rush hours make it a Big Data analysis task. Modern computer vision algorithms relying on Deep Neural Networks (DNNs) have proven capable of undertaking this task even under resource-constrained and embedded execution scenarios, e.g., as is the case with fast, single-stage, anchor-based object detectors. This paper proposes a two-fold improvement of such algorithms for the X-ray analysis domain, introducing two complementary novelties. Firstly, more efficient anchors are obtained by hierarchical clustering the sizes of the ground-truth training set bounding boxes; thus, the resulting anchors follow a natural hierarchy aligned with the semantic structure of the data. Secondly, the default Non-Maximum Suppression (NMS) algorithm at the end of the object detection pipeline is modified to better handle occluded object detection and to reduce the number of false predictions, by inserting the Efficient Intersection over Union (E-IoU) metric into the Weighted Cluster NMS method. E-IoU provides more discriminative geometrical correlations between the candidate bounding boxes/Regions-of-Interest (RoIs). The proposed method is implemented on a common single-stage object detector (YOLOv5) and its experimental evaluation on a relevant public dataset indicates significant accuracy gains over both the baseline and competing approaches. This highlights the potential of Big Data analysis in enhancing public safety. |
Dimitrios Bousis Georgios-Ioannis Verras, Konstantinos Bouchagier Andreas Antzoulas Ioannis Panagiotopoulos Anastasia Katinioti Dimitrios Kehagias Charalampos Kaplanis Konstantinos Kotis Christos-Nikolaos Anagnostopoulos Francesk Mulita The role of deep learning in diagnosing colorectal cancer Journal Article Gastroenterology Review, 18 (3), pp. 266-273, 2023. @article{Bousis2023, title = {The role of deep learning in diagnosing colorectal cancer}, author = {Dimitrios Bousis, Georgios-Ioannis Verras, Konstantinos Bouchagier, Andreas Antzoulas, Ioannis Panagiotopoulos, Anastasia Katinioti, Dimitrios Kehagias, Charalampos Kaplanis, Konstantinos Kotis, Christos-Nikolaos Anagnostopoulos, Francesk Mulita}, doi = {https://doi.org/10.5114/pg.2023.129494}, year = {2023}, date = {2023-07-17}, journal = {Gastroenterology Review}, volume = {18}, number = {3}, pages = {266-273}, abstract = {Colon cancer is a major public health issue, affecting a growing number of individuals worldwide. Proper and early diagnosis of colon cancer is the necessary first step toward effective treatment and/or prevention of future disease relapse. Artificial intelligence and its subtypes, deep learning in particular, tend nowadays to have an expanding role in all fields of medicine, and diagnosing colon cancer is no exception. This report aims to summarize the entire application spectrum of deep learning in all diagnostic tests regarding colon cancer, from endoscopy and histologic examination to medical imaging and screening serologic tests.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Colon cancer is a major public health issue, affecting a growing number of individuals worldwide. Proper and early diagnosis of colon cancer is the necessary first step toward effective treatment and/or prevention of future disease relapse. Artificial intelligence and its subtypes, deep learning in particular, tend nowadays to have an expanding role in all fields of medicine, and diagnosing colon cancer is no exception. This report aims to summarize the entire application spectrum of deep learning in all diagnostic tests regarding colon cancer, from endoscopy and histologic examination to medical imaging and screening serologic tests. |
Vasilis Bouras Dimitris Spiliotopoulos, Dionisis Margaris Costas Vassilakis Konstantinos Kotis Angeliki Antoniou George Lepouras Manolis Wallace Vassilis Poulopoulos Chatbots for cultural venues: a topic-based approach Journal Article Algorithms, 16 (7), pp. 339, 2023. @article{Bouras2023, title = {Chatbots for cultural venues: a topic-based approach}, author = {Vasilis Bouras, Dimitris Spiliotopoulos, Dionisis Margaris, Costas Vassilakis, Konstantinos Kotis, Angeliki Antoniou, George Lepouras, Manolis Wallace, Vassilis Poulopoulos}, url = {https://www.mdpi.com/1999-4893/16/7/339}, doi = {https://doi.org/10.3390/a16070339}, year = {2023}, date = {2023-07-14}, journal = {Algorithms}, volume = {16}, number = {7}, pages = {339}, abstract = {Digital assistants—such as chatbots—facilitate the interaction between persons and machines and are increasingly used in web pages of enterprises and organizations. This paper presents a methodology for the creation of chatbots that offer access to museum information. The paper introduces an information model that is offered through the chatbot, which subsequently maps the museum’s modeled information to structures of DialogFlow, Google’s chatbot engine. Means for automating the chatbot generation process are also presented. The evaluation of the methodology is illustrated through the application of a real case, wherein we developed a chatbot for the Archaeological Museum of Tripolis, Greece.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Digital assistants—such as chatbots—facilitate the interaction between persons and machines and are increasingly used in web pages of enterprises and organizations. This paper presents a methodology for the creation of chatbots that offer access to museum information. The paper introduces an information model that is offered through the chatbot, which subsequently maps the museum’s modeled information to structures of DialogFlow, Google’s chatbot engine. Means for automating the chatbot generation process are also presented. The evaluation of the methodology is illustrated through the application of a real case, wherein we developed a chatbot for the Archaeological Museum of Tripolis, Greece. |
Pavlos Bitilis Nikolaos Zafeiropoulos, Adam Koletis Konstantinos Kotis Uncovering the semantics of PD patients’ movement data collected via off-the-shelf wearables Conference The 14th International Conference on Information, Intelligence, Systems and Applications (IISA), Volos, 2023, 2023. @conference{Bitilis2023, title = {Uncovering the semantics of PD patients’ movement data collected via off-the-shelf wearables}, author = {Pavlos Bitilis, Nikolaos Zafeiropoulos, Adam Koletis, Konstantinos Kotis}, url = {https://ieeexplore.ieee.org/abstract/document/10345958}, doi = {https://doi.org/10.1109/IISA59645.2023.10345958}, year = {2023}, date = {2023-07-10}, booktitle = {The 14th International Conference on Information, Intelligence, Systems and Applications (IISA), Volos, 2023}, abstract = {Wearable sensors are used in monitoring patients with neurodegenerative diseases (ND), such as Parkinson Disease (PD), to collect movement data for the analysis and the assessment of patients’ symptoms. To become interoperable and interlinked with other related personal health data, collected data through sensors embedded in wearable devices need to be semantically described in a commonly agreed, explicit, and formal way. Personal health records (PHRs) including patients’ Magnetic Resonance Imaging (MRIs), medical prescriptions, and medical advice, can provide a unified view of personal health to health specialists, decreasing their efforts to constantly assess patients’ condition via traditional methods. This study aims to present our work for collecting movement data of PD patients through wearables, analyzing them to uncover their inherent semantics, and employing these semantic insights to annotate data in a formal and explicit way to facilitate interlinking with other related heterogeneous data. The movement data was collected via unobstructive wearable technology for health monitoring, and existing formal semantic models were examined for their suitability to be reused and extended for the semantic annotation of the collected movement data. Furthermore, this paper reports early work towards representing such knowledge in the form of a Knowledge Graph (KG) to support rule-based high-level event recognition, such as a missing dose event, for monitoring PD patients and alerting their doctors.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Wearable sensors are used in monitoring patients with neurodegenerative diseases (ND), such as Parkinson Disease (PD), to collect movement data for the analysis and the assessment of patients’ symptoms. To become interoperable and interlinked with other related personal health data, collected data through sensors embedded in wearable devices need to be semantically described in a commonly agreed, explicit, and formal way. Personal health records (PHRs) including patients’ Magnetic Resonance Imaging (MRIs), medical prescriptions, and medical advice, can provide a unified view of personal health to health specialists, decreasing their efforts to constantly assess patients’ condition via traditional methods. This study aims to present our work for collecting movement data of PD patients through wearables, analyzing them to uncover their inherent semantics, and employing these semantic insights to annotate data in a formal and explicit way to facilitate interlinking with other related heterogeneous data. The movement data was collected via unobstructive wearable technology for health monitoring, and existing formal semantic models were examined for their suitability to be reused and extended for the semantic annotation of the collected movement data. Furthermore, this paper reports early work towards representing such knowledge in the form of a Knowledge Graph (KG) to support rule-based high-level event recognition, such as a missing dose event, for monitoring PD patients and alerting their doctors. |
Alexandros Karakikes Panagiotis Alexiadis, Theocharis Theocharopoulos Nikolaos Skoulidas Konstantinos Kotis Understanding bias in Twitter-based intelligence analysis Conference 2023 14th International Conference on Information, Intelligence, Systems & Applications (IISA), 2023, ISBN: 979-8-3503-1806-7. @conference{Karakikes2023, title = {Understanding bias in Twitter-based intelligence analysis}, author = {Alexandros Karakikes, Panagiotis Alexiadis, Theocharis Theocharopoulos, Nikolaos Skoulidas, Konstantinos Kotis}, url = {https://ieeexplore.ieee.org/abstract/document/10345941}, doi = {https://doi.org/10.1109/IISA59645.2023.10345941}, isbn = {979-8-3503-1806-7}, year = {2023}, date = {2023-07-10}, booktitle = {2023 14th International Conference on Information, Intelligence, Systems & Applications (IISA)}, abstract = {Twitter has been lately engaged by the community of intelligence services worldwide that for monitoring areas of interest and identifying emerging social, political and security trends/threats. Over time, the Intelligence Community (IC) has identified bias as the major obstacle in information analysis, thus it has developed scientific and empirical methods for bias mitigation, in parallel to those developed by the information and communication technology (ICT) and artificial intelligence (AI) community. Both communities share the interest to accurately trace bias and ideally eradicate or moderate its effects. In this paper we introduce systemic parallels between Intelligence Analysis (IA) and Twitter Analytics (TA), to highlight similarities/dissimilarities, and investigate the feasibility of adapting and adjusting methodologies from the first field to the latter. Furthermore, we briefly introduce a novel framework for AI-augmented bias mitigation in the IC, proposing methods and tools, already adapted by the ICT community, for efficiently supporting bias mitigation methodologies adapted by the IC.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Twitter has been lately engaged by the community of intelligence services worldwide that for monitoring areas of interest and identifying emerging social, political and security trends/threats. Over time, the Intelligence Community (IC) has identified bias as the major obstacle in information analysis, thus it has developed scientific and empirical methods for bias mitigation, in parallel to those developed by the information and communication technology (ICT) and artificial intelligence (AI) community. Both communities share the interest to accurately trace bias and ideally eradicate or moderate its effects. In this paper we introduce systemic parallels between Intelligence Analysis (IA) and Twitter Analytics (TA), to highlight similarities/dissimilarities, and investigate the feasibility of adapting and adjusting methodologies from the first field to the latter. Furthermore, we briefly introduce a novel framework for AI-augmented bias mitigation in the IC, proposing methods and tools, already adapted by the ICT community, for efficiently supporting bias mitigation methodologies adapted by the IC. |
Georgios Papadopoulos Marko Kokol, Maria Dagioglou Georgios Petasis Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), Association for Computational Linguistics, 2023. @conference{Papadopoulos2023, title = {Andronicus of rhodes at SemEval-2023 task 4: Transformer-based human value detection using four different neural network architectures}, author = {Georgios Papadopoulos, Marko Kokol, Maria Dagioglou, Georgios Petasis}, url = {https://aclanthology.org/2023.semeval-1.75.pdf}, doi = {https://doi.org/10.18653/v1/2023.semeval-1.75}, year = {2023}, date = {2023-07-01}, booktitle = {Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)}, pages = {542–548}, publisher = {Association for Computational Linguistics}, abstract = {This paper presents our participation to the “Human Value Detection shared task (Kiesel et al., 2023), as “Andronicus of Rhodes. We describe the approaches behind each entry in the official evaluation, along with the motivation behind each approach. Our best-performing approach has been based on BERT large, with 4 classification heads, implementing two different classification approaches (with different activation and loss functions), and two different partitioning of the training data, to handle class imbalance. Classification is performed through majority voting. The proposed approach outperforms the BERT baseline, ranking in the upper half of the competition.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } This paper presents our participation to the “Human Value Detection shared task (Kiesel et al., 2023), as “Andronicus of Rhodes. We describe the approaches behind each entry in the official evaluation, along with the motivation behind each approach. Our best-performing approach has been based on BERT large, with 4 classification heads, implementing two different classification approaches (with different activation and loss functions), and two different partitioning of the training data, to handle class imbalance. Classification is performed through majority voting. The proposed approach outperforms the BERT baseline, ranking in the upper half of the competition. |
Antonio Gracia-Berna Jose Manuel Cordero, Natividad Valle Ruben Rodriguez Gennady Andrienko Natalia Andrienko George Vouros Ian Crook Sandrine Molton A Framework for transparent, explainable and trustworthy automation of ATM Conference DASC 2023, 2023. @conference{Gracia-Berna´2023, title = {Framework for transparent, explainable and trustworthy automation of ATM}, author = {Antonio Gracia-Berna, Jose Manuel Cordero, Natividad Valle, Ruben Rodriguez, Gennady Andrienko, Natalia Andrienko, George A. Vouros, Ian Crook, Sandrine Molton}, year = {2023}, date = {2023-06-20}, booktitle = {DASC 2023}, abstract = {Scientific studies before the COVID-19 pandemic indicated that Air Traffic Management (ATM) was close to saturation. The integration of Artificial Intelligence (AI) into ATM has been identified as crucial to achieve higher levels of automation, and the need for trustworthy and explainable automation systems in safety-critical domains is essential. Boeing Aerospace Spain (BAS) has conducted pioneering research on achieving high levels of automation while ensuring transparency and explainability. BAS, along with other major ATM players, has developed a framework for implementing transparent and explainable automation using Explainable Artificial Intelligence (XAI) for Air Traffic Flow and Capacity Management (ATFCM), and Conflict Detection and Resolution (CDR) scenarios. The framework provides a set of principles for the transparent application of XAI technology in ATM to ensure that different types of audiences can trust the AI system’s decisions. The principles have been developed based on feedback from experts in ATM, human factors, and AI. This framework can be considered the first attempt to pave the way for AI techniques to achieve higher levels of automation in accordance with the European ATM Master Plan.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Scientific studies before the COVID-19 pandemic indicated that Air Traffic Management (ATM) was close to saturation. The integration of Artificial Intelligence (AI) into ATM has been identified as crucial to achieve higher levels of automation, and the need for trustworthy and explainable automation systems in safety-critical domains is essential. Boeing Aerospace Spain (BAS) has conducted pioneering research on achieving high levels of automation while ensuring transparency and explainability. BAS, along with other major ATM players, has developed a framework for implementing transparent and explainable automation using Explainable Artificial Intelligence (XAI) for Air Traffic Flow and Capacity Management (ATFCM), and Conflict Detection and Resolution (CDR) scenarios. The framework provides a set of principles for the transparent application of XAI technology in ATM to ensure that different types of audiences can trust the AI system’s decisions. The principles have been developed based on feedback from experts in ATM, human factors, and AI. This framework can be considered the first attempt to pave the way for AI techniques to achieve higher levels of automation in accordance with the European ATM Master Plan. |
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| 21. | Eleftherios Efkleidis Stefanou Pavlos Bitilis, Georgios Bouchouras Konstantinos Kotis : Collecting, Integrating and Processing IoT Sensor Data on Edge Devices for PD Monitoring: A Scoping Review. In: Applied Sciences, 15 (19), pp. 10541, 2025, ISSN: 2076-3417. (Type: Journal Article | Abstract | Links | BibTeX) @article{Stefanou2025b, title = {Collecting, Integrating and Processing IoT Sensor Data on Edge Devices for PD Monitoring: A Scoping Review}, author = {Eleftherios Efkleidis Stefanou, Pavlos Bitilis, Georgios Bouchouras, Konstantinos Kotis}, url = {https://www.mdpi.com/2076-3417/15/19/10541}, doi = {https://doi.org/10.3390/app151910541}, issn = {2076-3417}, year = {2025}, date = {2025-09-29}, journal = {Applied Sciences}, volume = {15}, number = {19}, pages = {10541}, abstract = {Bradykinesia and tremor are critical motor symptoms in diagnosing and monitoring Parkinson’s disease (PD), a progressive neurodegenerative disorder. The integration of IoT sensors, smartwatch technology, and edge computing has facilitated real-time collection, processing, and analysis of data related to these impairments, enabling continuous monitoring of PD beyond traditional clinical settings. This survey provides a comprehensive review of recent technological advancements in data collection from wearable IoT sensors and its semantic integration and processing on edge devices, emphasizing methods optimized for efficient and low-latency processing. Additionally, this survey explores AI-driven techniques for detecting and analyzing bradykinesia and tremor symptoms on edge devices. By leveraging localized computation on edge devices, these approaches facilitate energy efficiency, data privacy, and scalability, making them suitable for deployment in real environments. This paper also examines related open-source tools and datasets, assessing their roles in improving reproducibility and integration into these environments. Furthermore, key challenges, including variability in real environments, model generalization, and computational constraints, are discussed, along with potential strategies to enhance detection accuracy and system robustness. By bridging the gap between sensor data collection and integration, and AI-based detection of bradykinesia and tremor on edge devices, this survey intends to contribute to the development of efficient, scalable, and privacy-preserving healthcare solutions for continuous PD monitoring.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Bradykinesia and tremor are critical motor symptoms in diagnosing and monitoring Parkinson’s disease (PD), a progressive neurodegenerative disorder. The integration of IoT sensors, smartwatch technology, and edge computing has facilitated real-time collection, processing, and analysis of data related to these impairments, enabling continuous monitoring of PD beyond traditional clinical settings. This survey provides a comprehensive review of recent technological advancements in data collection from wearable IoT sensors and its semantic integration and processing on edge devices, emphasizing methods optimized for efficient and low-latency processing. Additionally, this survey explores AI-driven techniques for detecting and analyzing bradykinesia and tremor symptoms on edge devices. By leveraging localized computation on edge devices, these approaches facilitate energy efficiency, data privacy, and scalability, making them suitable for deployment in real environments. This paper also examines related open-source tools and datasets, assessing their roles in improving reproducibility and integration into these environments. Furthermore, key challenges, including variability in real environments, model generalization, and computational constraints, are discussed, along with potential strategies to enhance detection accuracy and system robustness. By bridging the gap between sensor data collection and integration, and AI-based detection of bradykinesia and tremor on edge devices, this survey intends to contribute to the development of efficient, scalable, and privacy-preserving healthcare solutions for continuous PD monitoring. |
| 22. | Andreas Kontogiannis Vasilis Pollatos, Gabriele Farina Panayotis Mertikopoulos Ioannis Panageas : Efficient kernelized learning in polyhedral games beyond full-information: From Colonel Blotto to congestion games. The Thirty-ninth Annual Conference on Neural Information Processing Systems (NeurIPS 2025 poster), 2025. (Type: Conference | Abstract | Links | BibTeX) @conference{Kontogiannis2025b, title = {Efficient kernelized learning in polyhedral games beyond full-information: From Colonel Blotto to congestion games}, author = {Andreas Kontogiannis, Vasilis Pollatos, Gabriele Farina, Panayotis Mertikopoulos, Ioannis Panageas}, url = {https://openreview.net/attachment?id=FUBaZDMOFj&name=pdf https://arxiv.org/pdf/2509.20919}, doi = {https://doi.org/10.48550/arXiv.2509.20919}, year = {2025}, date = {2025-09-25}, booktitle = {The Thirty-ninth Annual Conference on Neural Information Processing Systems (NeurIPS 2025 poster)}, journal = {The Thirty-ninth Annual Conference on Neural Information Processing Systems (NeurIPS 2025 poster)}, abstract = {We examine the problem of efficiently learning coarse correlated equilibria (CCE) in polyhedral games, that is, normal-form games with an exponentially large number of actions per player and an underlying combinatorial structure—such as the classic Colonel Blotto game or congestion games. Achieving computational efficiency in this setting requires learning algorithms whose regret and per-iteration complexity scale at most polylogarithmically with the size of the players’ action sets. This challenge has recently been addressed in the full-information setting, primarily through the use of kernelization; however, in the more realistic partial information setting, the situation is much more challenging, and existing approaches result in suboptimal and impractical runtime complexity to learn CCE. We address this gap via a novel kernelization-based framework for payoff-based learning in polyhedral games, which we then apply to certain key classes of polyhedral games—namely Colonel Blotto, graphic matroid and network congestion games. In so doing, we obtain a range of computationally efficient payoff-based learning algorithms which significantly improve upon prior work in terms of the runtime for learning CCE.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } We examine the problem of efficiently learning coarse correlated equilibria (CCE) in polyhedral games, that is, normal-form games with an exponentially large number of actions per player and an underlying combinatorial structure—such as the classic Colonel Blotto game or congestion games. Achieving computational efficiency in this setting requires learning algorithms whose regret and per-iteration complexity scale at most polylogarithmically with the size of the players’ action sets. This challenge has recently been addressed in the full-information setting, primarily through the use of kernelization; however, in the more realistic partial information setting, the situation is much more challenging, and existing approaches result in suboptimal and impractical runtime complexity to learn CCE. We address this gap via a novel kernelization-based framework for payoff-based learning in polyhedral games, which we then apply to certain key classes of polyhedral games—namely Colonel Blotto, graphic matroid and network congestion games. In so doing, we obtain a range of computationally efficient payoff-based learning algorithms which significantly improve upon prior work in terms of the runtime for learning CCE. |
| 23. | Adam Koletis Pavlos Bitilis, Georgios Bouchouras Konstantinos Kotis : A Comparative Analysis of Parkinson’s Disease Diagnosis Approaches Using Drawing-Based Datasets: Utilizing Large Language Models, Machine Learning, and Fuzzy Ontologies. In: Information, 16 (9), pp. 820, 2025, ISSN: 2078-2489. (Type: Journal Article | Abstract | Links | BibTeX) @article{Koletis2025, title = {A Comparative Analysis of Parkinson’s Disease Diagnosis Approaches Using Drawing-Based Datasets: Utilizing Large Language Models, Machine Learning, and Fuzzy Ontologies}, author = {Adam Koletis, Pavlos Bitilis, Georgios Bouchouras, Konstantinos Kotis}, url = {https://www.mdpi.com/2078-2489/16/9/820}, doi = {https://doi.org/10.3390/info16090820}, issn = {2078-2489}, year = {2025}, date = {2025-09-22}, journal = {Information}, volume = {16}, number = {9}, pages = {820}, abstract = {Parkinson’s disease (PD) is a progressive neurodegenerative disorder that impairs motor function, often causing tremors and difficulty with movement control. A promising diagnostic method involves analyzing hand-drawn patterns, such as spirals and waves, which show characteristic distortions in individuals with PD. This study compares three computational approaches for classifying individuals as Parkinsonian or healthy based on drawing-derived features: (1) Large Language Models (LLMs), (2) traditional machine learning (ML) algorithms, and (3) a fuzzy ontology-based method using fuzzy sets and Fuzzy-OWL2. Each method offers unique strengths: LLMs leverage pre-trained knowledge for subtle pattern detection, ML algorithms excel in feature extraction and predictive accuracy, and fuzzy ontologies provide interpretable, logic-based reasoning under uncertainty. Using three structured handwriting datasets of varying complexity, we assessed performance in terms of accuracy, interpretability, and generalization. Among the approaches, the fuzzy ontology-based method showed the strongest performance on complex tasks, achieving a high F1-score, while ML models demonstrated strong generalization and LLMs offered a reliable, interpretable baseline. These findings suggest that combining symbolic and statistical AI may improve drawing-based PD diagnosis.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Parkinson’s disease (PD) is a progressive neurodegenerative disorder that impairs motor function, often causing tremors and difficulty with movement control. A promising diagnostic method involves analyzing hand-drawn patterns, such as spirals and waves, which show characteristic distortions in individuals with PD. This study compares three computational approaches for classifying individuals as Parkinsonian or healthy based on drawing-derived features: (1) Large Language Models (LLMs), (2) traditional machine learning (ML) algorithms, and (3) a fuzzy ontology-based method using fuzzy sets and Fuzzy-OWL2. Each method offers unique strengths: LLMs leverage pre-trained knowledge for subtle pattern detection, ML algorithms excel in feature extraction and predictive accuracy, and fuzzy ontologies provide interpretable, logic-based reasoning under uncertainty. Using three structured handwriting datasets of varying complexity, we assessed performance in terms of accuracy, interpretability, and generalization. Among the approaches, the fuzzy ontology-based method showed the strongest performance on complex tasks, achieving a high F1-score, while ML models demonstrated strong generalization and LLMs offered a reliable, interpretable baseline. These findings suggest that combining symbolic and statistical AI may improve drawing-based PD diagnosis. |
| 24. | Theocharis Kravaris, George Vouros A: Transferable aircraft trajectory prediction with generative deep imitation learning. In: International Journal of Data Science and Analytics, 20 (3), pp. 1977-1999, 2025. (Type: Journal Article | Abstract | Links | BibTeX) @article{Kravaris2025, title = {Transferable aircraft trajectory prediction with generative deep imitation learning}, author = {Theocharis Kravaris, George A Vouros}, url = {https://link.springer.com/article/10.1007/s41060-024-00574-1}, doi = {https://doi.org/10.1007/s41060-024-00574-1}, year = {2025}, date = {2025-09-01}, journal = {International Journal of Data Science and Analytics}, volume = {20}, number = {3}, pages = {1977-1999}, abstract = {Trajectory-oriented transformations to air traffic management operations require high fidelity aircraft trajectory prediction capabilities. Data-driven trajectory prediction approaches provide promising results, albeit with important limitations that hinder seriously the efficient and effective deployment of trajectory prediction methods: They need abundant training effort with a large amount of training samples and require training distinct models for different origin–destination (OD) airport pairs. In this paper, we address the problem of building transferable trajectory prediction models, casting the prediction problem as a transferable imitation task, introducing a novel formulation which (a) provides the capability to utilize trained models, in new OD pairs, offering a warm starting for computationally efficient training, and (b) improves the efficacy of data-driven trajectory prediction. The proposed approach provides very accurate results for large look-ahead time predictions, even if transferable models have been trained with few samples.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Trajectory-oriented transformations to air traffic management operations require high fidelity aircraft trajectory prediction capabilities. Data-driven trajectory prediction approaches provide promising results, albeit with important limitations that hinder seriously the efficient and effective deployment of trajectory prediction methods: They need abundant training effort with a large amount of training samples and require training distinct models for different origin–destination (OD) airport pairs. In this paper, we address the problem of building transferable trajectory prediction models, casting the prediction problem as a transferable imitation task, introducing a novel formulation which (a) provides the capability to utilize trained models, in new OD pairs, offering a warm starting for computationally efficient training, and (b) improves the efficacy of data-driven trajectory prediction. The proposed approach provides very accurate results for large look-ahead time predictions, even if transferable models have been trained with few samples. |
| 25. | Dimitrios Doumanas Alexandros Karakikes, Andreas Soularidis Efstathios Mainas Konstantinos Kotis : Emerging Threat Vectors: How Malicious Actors Exploit LLMs to Undermine Border Security. In: AI, 6 (9), pp. 232, 2025, ISSN: 2673-2688. (Type: Journal Article | Abstract | Links | BibTeX) @article{Doumanas2025d, title = {Emerging Threat Vectors: How Malicious Actors Exploit LLMs to Undermine Border Security}, author = {Dimitrios Doumanas, Alexandros Karakikes, Andreas Soularidis, Efstathios Mainas, Konstantinos Kotis}, url = {https://www.mdpi.com/2673-2688/6/9/232}, doi = {https://doi.org/10.3390/ai6090232}, issn = {2673-2688}, year = {2025}, date = {2025-09-01}, journal = {AI}, volume = {6}, number = {9}, pages = {232}, abstract = {The rapid proliferation of Large Language Models (LLMs) has democratized access to advanced generative capabilities while raising urgent concerns about misuse in sensitive security domains. Border security, in particular, represents a high-risk environment where malicious actors may exploit LLMs for document forgery, synthetic identity creation, logistics planning, or disinformation campaigns. Existing studies often highlight such risks in theory, yet few provide systematic empirical evidence of how state-of-the-art LLMs can be exploited. This paper introduces the Silent Adversary Framework (SAF), a structured pipeline that models the sequential stages by which obfuscated prompts can covertly bypass safeguards. We evaluate ten high-risk scenarios using five leading models—GPT-4o, Claude 3.7 Sonnet, Gemini 2.5 Flash, Grok 3, and Runway Gen-2—and assess outputs through three standardized metrics: Bypass Success Rate (BSR), Output Realism Score (ORS), and Operational Risk Level (ORL). Results reveal that, while all models exhibited some susceptibility, vulnerabilities were heterogeneous. Claude showed greater resistance in chemistry-related prompts, whereas GPT-4o and Gemini generated highly realistic outputs in identity fraud and logistics optimization tasks. Document forgery attempts produced only partially successful templates that lacked critical security features. These findings highlight the uneven distribution of risks across models and domains. By combining a reproducible adversarial framework with empirical testing, this study advances the evidence base on LLM misuse and provides actionable insights for policymakers and border security agencies, underscoring the need for stronger safeguards and oversight in the deployment of generative AI.}, keywords = {}, pubstate = {published}, tppubtype = {article} } The rapid proliferation of Large Language Models (LLMs) has democratized access to advanced generative capabilities while raising urgent concerns about misuse in sensitive security domains. Border security, in particular, represents a high-risk environment where malicious actors may exploit LLMs for document forgery, synthetic identity creation, logistics planning, or disinformation campaigns. Existing studies often highlight such risks in theory, yet few provide systematic empirical evidence of how state-of-the-art LLMs can be exploited. This paper introduces the Silent Adversary Framework (SAF), a structured pipeline that models the sequential stages by which obfuscated prompts can covertly bypass safeguards. We evaluate ten high-risk scenarios using five leading models—GPT-4o, Claude 3.7 Sonnet, Gemini 2.5 Flash, Grok 3, and Runway Gen-2—and assess outputs through three standardized metrics: Bypass Success Rate (BSR), Output Realism Score (ORS), and Operational Risk Level (ORL). Results reveal that, while all models exhibited some susceptibility, vulnerabilities were heterogeneous. Claude showed greater resistance in chemistry-related prompts, whereas GPT-4o and Gemini generated highly realistic outputs in identity fraud and logistics optimization tasks. Document forgery attempts produced only partially successful templates that lacked critical security features. These findings highlight the uneven distribution of risks across models and domains. By combining a reproducible adversarial framework with empirical testing, this study advances the evidence base on LLM misuse and provides actionable insights for policymakers and border security agencies, underscoring the need for stronger safeguards and oversight in the deployment of generative AI. |
| 26. | Konstantinos Kotis Eleni Angoura, Eleni-Ioanna Lyngri : Emerging technologies in smart libraries for visually impaired people: challenges and design considerations. In: ACM Journal on Computing and Cultural Heritage, 18 (3), pp. 1-37, 2025, ISSN: 1556-4673. (Type: Journal Article | Abstract | Links | BibTeX) @article{Kotis2025, title = {Emerging technologies in smart libraries for visually impaired people: challenges and design considerations}, author = {Konstantinos Kotis, Eleni Angoura, Eleni-Ioanna Lyngri}, url = {https://dl.acm.org/doi/full/10.1145/3727965}, doi = {https://doi.org/10.1145/3727965}, issn = {1556-4673}, year = {2025}, date = {2025-07-24}, journal = {ACM Journal on Computing and Cultural Heritage}, volume = {18}, number = {3}, pages = {1-37}, abstract = {Emerging technologies are transforming cultural spaces in a variety of ways, presenting opportunities and challenges. Autonomous robots, eXtended Reality, AI, Digital Twins, and Internet of Things are only a few examples of such technologies, with accessibility and inclusivity of people to these technologies to be considered key challenges. In general, the use of emerging technologies in cultural spaces presents exciting opportunities for enhancing visitors’ experience and engaging new participants. However, it is important to also consider the inclusion ability of people with special needs and to ensure that these emerging technologies are used in an accessible-to-all and inclusive way. The aim of this article is to review the state-of-the-art and current trends in approaches that use emerging technologies in the domain of smart libraries designed to include visually impaired people in a common innovative way for the whole community of visitors, discuss open issues and challenges identified in such a cultural environment/case, and propose a novel approach based on specific design considerations of the specific domain.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Emerging technologies are transforming cultural spaces in a variety of ways, presenting opportunities and challenges. Autonomous robots, eXtended Reality, AI, Digital Twins, and Internet of Things are only a few examples of such technologies, with accessibility and inclusivity of people to these technologies to be considered key challenges. In general, the use of emerging technologies in cultural spaces presents exciting opportunities for enhancing visitors’ experience and engaging new participants. However, it is important to also consider the inclusion ability of people with special needs and to ensure that these emerging technologies are used in an accessible-to-all and inclusive way. The aim of this article is to review the state-of-the-art and current trends in approaches that use emerging technologies in the domain of smart libraries designed to include visually impaired people in a common innovative way for the whole community of visitors, discuss open issues and challenges identified in such a cultural environment/case, and propose a novel approach based on specific design considerations of the specific domain. |
| 27. | Sotiris Angelis Joana Pinho, Athanasia Sykiotou Dimitar Markov Stamatis Chatzistamatis Stamatis Spirou George Tsekouras Konstantinos Kotis : RRAO: An Ontology for the Representation of Reoffending Risk Assessment Knowledge. 16th International Conference on Information, Intelligence, Systems & Applications (IISA), 2025, ISBN: 979-8-3315-5636-5. (Type: Conference | Abstract | Links | BibTeX) @conference{Angelis2025, title = {RRAO: An Ontology for the Representation of Reoffending Risk Assessment Knowledge}, author = {Sotiris Angelis, Joana Pinho, Athanasia Sykiotou, Dimitar Markov, Stamatis Chatzistamatis, Stamatis Spirou, George Tsekouras, Konstantinos Kotis}, doi = {https://doi.org/10.1109/IISA66859.2025.11311249}, isbn = {979-8-3315-5636-5}, year = {2025}, date = {2025-07-10}, booktitle = {16th International Conference on Information, Intelligence, Systems & Applications (IISA)}, pages = {1-9}, abstract = {Judicial decision making related to parole, sentencing, rehabilitation, reintegration, and public safety is often supported by the assessment of the risk of reoffending. AI prediction systems can introduce bias in the analysis of reoffending riskrelated data. Several studies criticize the fairness of such AI systems. This paper presents the Reoffending Risk Assessment Ontology (RRAO) which aims to provide a comprehensive representation of reoffending risk and recidivism knowledge integrated into ontology-based AI systems. RRAO is engineered following the X-HCOME ontology engineering (OE) methodology, which provides a hybrid bottom-up (data driven), top-down (expert knowledge) OE approach, including tasks designed to assess and mitigate bias at the schema level. By developing a bias-free risk assessment ontology, our objective is to enhance the fairness of AI-driven ontology-based reoffending risk prediction systems, ultimately contributing to more fair and effective criminal justice practices.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Judicial decision making related to parole, sentencing, rehabilitation, reintegration, and public safety is often supported by the assessment of the risk of reoffending. AI prediction systems can introduce bias in the analysis of reoffending riskrelated data. Several studies criticize the fairness of such AI systems. This paper presents the Reoffending Risk Assessment Ontology (RRAO) which aims to provide a comprehensive representation of reoffending risk and recidivism knowledge integrated into ontology-based AI systems. RRAO is engineered following the X-HCOME ontology engineering (OE) methodology, which provides a hybrid bottom-up (data driven), top-down (expert knowledge) OE approach, including tasks designed to assess and mitigate bias at the schema level. By developing a bias-free risk assessment ontology, our objective is to enhance the fairness of AI-driven ontology-based reoffending risk prediction systems, ultimately contributing to more fair and effective criminal justice practices. |
| 28. | Eleftherios-Efkleidis Stefanou Pavlos Bitilis, Konstantinos Kotis : Current Status, Trends and Challenges in AI-Based Bradykinesia and Tremor Detection on Edge Devices. 16th International Conference on Information, Intelligence, Systems & Applications (IISA), 2025, ISBN: 979-8-3315-5636-5. (Type: Conference | Abstract | Links | BibTeX) @conference{Stefanou2025, title = {Current Status, Trends and Challenges in AI-Based Bradykinesia and Tremor Detection on Edge Devices}, author = {Eleftherios-Efkleidis Stefanou, Pavlos Bitilis, Konstantinos Kotis}, url = {https://ieeexplore.ieee.org/abstract/document/11311304}, doi = {https://doi.org/10.1109/IISA66859.2025.11311304}, isbn = {979-8-3315-5636-5}, year = {2025}, date = {2025-07-10}, booktitle = {16th International Conference on Information, Intelligence, Systems & Applications (IISA)}, pages = {1-4}, abstract = {Bradykinesia and tremor are pivotal indicators in diagnosing and managing Parkinson’s disease (PD), a progressive neurodegenerative disorder. Advances in wearable sensor technologies and AI methods have enabled real-time monitoring of these motor impairments, facilitating continuous assessment outside traditional clinical settings. This short paper focuses on recent advancements in bradykinesia and tremor detection using machine learning (ML) and deep learning (DL) techniques, while also exploring their applicability on edge devices. By leveraging inertial data, these techniques enhance the detection and analysis of movement patterns associated with PD. The paper emphasizes techniques optimized for edge deployment, which enable localized data processing, reduce latency, and enhance privacy. In addition, open-source tools and datasets are highlighted for their role in improving reproducibility and supporting system integration efforts. Finally, challenges such as variability in real-world conditions are discussed, along with opportunities for enhancing wearable-based healthcare systems through accurate and reliable motion pattern recognition on edge platforms.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Bradykinesia and tremor are pivotal indicators in diagnosing and managing Parkinson’s disease (PD), a progressive neurodegenerative disorder. Advances in wearable sensor technologies and AI methods have enabled real-time monitoring of these motor impairments, facilitating continuous assessment outside traditional clinical settings. This short paper focuses on recent advancements in bradykinesia and tremor detection using machine learning (ML) and deep learning (DL) techniques, while also exploring their applicability on edge devices. By leveraging inertial data, these techniques enhance the detection and analysis of movement patterns associated with PD. The paper emphasizes techniques optimized for edge deployment, which enable localized data processing, reduce latency, and enhance privacy. In addition, open-source tools and datasets are highlighted for their role in improving reproducibility and supporting system integration efforts. Finally, challenges such as variability in real-world conditions are discussed, along with opportunities for enhancing wearable-based healthcare systems through accurate and reliable motion pattern recognition on edge platforms. |
| 29. | George Papadopoulos, George Vouros A: Learning safe, constrained policies via imitation learning: Connection to Probabilistic Inference and a Naive Algorithm. In: arXiv, 2025. (Type: Journal Article | Abstract | Links | BibTeX) @article{Papadopoulos2025b, title = {Learning safe, constrained policies via imitation learning: Connection to Probabilistic Inference and a Naive Algorithm}, author = {George Papadopoulos, George A Vouros}, url = {https://arxiv.org/pdf/2507.06780}, doi = {https://doi.org/10.48550/arXiv.2507.06780}, year = {2025}, date = {2025-07-09}, journal = {arXiv}, abstract = {This article introduces an imitation learning method for learning maximum entropy policies that comply with constraints demonstrated by expert trajectories executing a task. The formulation of the method takes advantage of results connecting performance to bounds for the KL-divergence between demonstrated and learned policies, and its objective is rigorously justified through a connection to a probabilistic inference framework for reinforcement learning, incorporating the reinforcement learning objective and the objective to abide by constraints in an entropy maximization setting. The proposed algorithm optimizes the learning objective with dual gradient descent, supporting effective and stable training. Experiments show that the proposed method can learn effective policy models for constraints-abiding behaviour, in settings with multiple constraints of different types, accommodating different modalities of demonstrated behaviour, and with abilities to generalize.}, keywords = {}, pubstate = {published}, tppubtype = {article} } This article introduces an imitation learning method for learning maximum entropy policies that comply with constraints demonstrated by expert trajectories executing a task. The formulation of the method takes advantage of results connecting performance to bounds for the KL-divergence between demonstrated and learned policies, and its objective is rigorously justified through a connection to a probabilistic inference framework for reinforcement learning, incorporating the reinforcement learning objective and the objective to abide by constraints in an entropy maximization setting. The proposed algorithm optimizes the learning objective with dual gradient descent, supporting effective and stable training. Experiments show that the proposed method can learn effective policy models for constraints-abiding behaviour, in settings with multiple constraints of different types, accommodating different modalities of demonstrated behaviour, and with abilities to generalize. |
| 30. | Piyabhum Chaysri Theodoros Tranos, George Papadopoulos George Vouros Konstantinos Blekas A: Efficient Autonomous Marine Vessel Navigation with Safe Deep Reinforcement Learning. 2025 Symposium on Maritime Informatics and Robotics (MARIS), 2025. (Type: Conference | Abstract | Links | BibTeX) @conference{Chaysri2025, title = { Efficient Autonomous Marine Vessel Navigation with Safe Deep Reinforcement Learning}, author = {Piyabhum Chaysri, Theodoros Tranos, George Papadopoulos, George A Vouros, Konstantinos Blekas}, doi = {https://doi.org/10.1109/MARIS64137.2025.11139786}, year = {2025}, date = {2025-06-26}, booktitle = {2025 Symposium on Maritime Informatics and Robotics (MARIS)}, abstract = {The rise of automation and self-driving systems brings a strong focus on safety-centric decision-making, especially in complex environments with large degree of uncertainty where unpredictable interactions occur at high frequency. In this study we address the challenge of safe and efficient maritime navigation by proposing a safe Deep Reinforcement Learning scheme for training Unmanned Surface Vehicle (USV) agents. Our approach leverages the Lagrangian relaxation framework to effectively handle safety constraints, ensuring that the learned navigation policies balance goal achievement with obstacle avoidance. We address each type of static and moving obstacle separately with the aim of achieving more effective management of their impact on safe navigation. This enables the design of a more advanced constraint-aware optimization framework, enhancing USV’s ability to navigate complex maritime environment, adapt to changing traffic conditions and maintain a minimal risk of collision. Experiments were conducted in a simulated environment tailored to match realistic weather and traffic density conditions. The simulation results highlight the potential of the proposed method in developing advanced USV navigation policies that achieve high accuracy and enhanced safety.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } The rise of automation and self-driving systems brings a strong focus on safety-centric decision-making, especially in complex environments with large degree of uncertainty where unpredictable interactions occur at high frequency. In this study we address the challenge of safe and efficient maritime navigation by proposing a safe Deep Reinforcement Learning scheme for training Unmanned Surface Vehicle (USV) agents. Our approach leverages the Lagrangian relaxation framework to effectively handle safety constraints, ensuring that the learned navigation policies balance goal achievement with obstacle avoidance. We address each type of static and moving obstacle separately with the aim of achieving more effective management of their impact on safe navigation. This enables the design of a more advanced constraint-aware optimization framework, enhancing USV’s ability to navigate complex maritime environment, adapt to changing traffic conditions and maintain a minimal risk of collision. Experiments were conducted in a simulated environment tailored to match realistic weather and traffic density conditions. The simulation results highlight the potential of the proposed method in developing advanced USV navigation policies that achieve high accuracy and enhanced safety. |
| 31. | Dimitrios Doumanas Efthalia Ntalouka, Costas Vassilakis Manolis Wallace Konstantinos Kotis : Stitching History into Semantics: LLM-Supported Knowledge Graph Engineering for 19th-Century Greek Bookbinding. In: Machine Learning and Knowledge Extraction, 7 (3), pp. 59, 2025, ISSN: 2504-4990. (Type: Journal Article | Abstract | Links | BibTeX) @article{Doumanas2025c, title = {Stitching History into Semantics: LLM-Supported Knowledge Graph Engineering for 19th-Century Greek Bookbinding}, author = {Dimitrios Doumanas, Efthalia Ntalouka, Costas Vassilakis, Manolis Wallace, Konstantinos Kotis}, url = {https://www.mdpi.com/2504-4990/7/3/59}, doi = {https://doi.org/10.3390/make7030059}, issn = {2504-4990}, year = {2025}, date = {2025-06-24}, journal = {Machine Learning and Knowledge Extraction}, volume = {7}, number = {3}, pages = {59}, abstract = {Preserving cultural heritage can be efficiently supported by structured and semantic representation of historical artifacts. Bookbinding, a critical aspect of book history, provides valuable insights into past craftsmanship, material use, and conservation practices. However, existing bibliographic records often lack the depth needed to analyze bookbinding techniques, provenance, and preservation status. This paper presents a proof-of-concept system that explores how Large Language Models (LLMs) can support knowledge graph engineering within the context of 19th-century Greek bookbinding (1830–1900), and as a result, generate a domain-specific ontology and a knowledge graph. Our ontology encapsulates materials, binding techniques, artistic styles, and conservation history, integrating metadata standards like MARC and Dublin Core to ensure interoperability with existing library and archival systems. To validate its effectiveness, we construct a Neo4j knowledge graph, based on the generated ontology and utilize Cypher Queries—including LLM-generated queries—to extract insights about bookbinding practices and trends. This study also explores how semantic reasoning over the knowledge graph can identify historical binding patterns, assess book conservation needs, and infer relationships between bookbinding workshops. Unlike previous bibliographic ontologies, our approach provides a comprehensive, semantically rich representation of bookbinding history, methods and techniques, supporting scholars, conservators, and cultural heritage institutions. By demonstrating how LLMs can assist in ontology/KG creation and query generation, we introduce and evaluate a semi-automated pipeline as a methodological demonstration for studying historical bookbinding, contributing to digital humanities, book conservation, and cultural informatics. Finally, the proposed approach can be used in other domains, thus, being generally applicable in knowledge engineering.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Preserving cultural heritage can be efficiently supported by structured and semantic representation of historical artifacts. Bookbinding, a critical aspect of book history, provides valuable insights into past craftsmanship, material use, and conservation practices. However, existing bibliographic records often lack the depth needed to analyze bookbinding techniques, provenance, and preservation status. This paper presents a proof-of-concept system that explores how Large Language Models (LLMs) can support knowledge graph engineering within the context of 19th-century Greek bookbinding (1830–1900), and as a result, generate a domain-specific ontology and a knowledge graph. Our ontology encapsulates materials, binding techniques, artistic styles, and conservation history, integrating metadata standards like MARC and Dublin Core to ensure interoperability with existing library and archival systems. To validate its effectiveness, we construct a Neo4j knowledge graph, based on the generated ontology and utilize Cypher Queries—including LLM-generated queries—to extract insights about bookbinding practices and trends. This study also explores how semantic reasoning over the knowledge graph can identify historical binding patterns, assess book conservation needs, and infer relationships between bookbinding workshops. Unlike previous bibliographic ontologies, our approach provides a comprehensive, semantically rich representation of bookbinding history, methods and techniques, supporting scholars, conservators, and cultural heritage institutions. By demonstrating how LLMs can assist in ontology/KG creation and query generation, we introduce and evaluate a semi-automated pipeline as a methodological demonstration for studying historical bookbinding, contributing to digital humanities, book conservation, and cultural informatics. Finally, the proposed approach can be used in other domains, thus, being generally applicable in knowledge engineering. |
| 32. | Myrto Stogia Asimina Dimara, Alexios Papaioannou Christos-Nikolaos Anagnostopoulos Konstantinos Kotis Stelios Krinidis : The Role of IoT and 3D Modeling in Shaping Industry 5.0. IFIP International Conference on Artificial Intelligence Applications and Innovations, 2025, ISBN: 978-3-031-97313-0. (Type: Conference | Abstract | Links | BibTeX) @conference{Stogia2025, title = {The Role of IoT and 3D Modeling in Shaping Industry 5.0}, author = {Myrto Stogia, Asimina Dimara, Alexios Papaioannou, Christos-Nikolaos Anagnostopoulos, Konstantinos Kotis, Stelios Krinidis}, url = {https://link.springer.com/chapter/10.1007/978-3-031-97313-0_27}, doi = {https://doi.org/10.1007/978-3-031-97313-0_27}, isbn = {978-3-031-97313-0}, year = {2025}, date = {2025-06-23}, booktitle = {IFIP International Conference on Artificial Intelligence Applications and Innovations}, pages = {353-366}, abstract = {The shift from Industry 4.0 to Industry 5.0 represents a significant transformation in industrial ecosystems, prioritizing human-machine collaboration, sustainability, and ethical Artificial Intelligence (AI). This paper provides a concise overview of the crucial role played by the Internet of Things (IoT) in advancing Digital Twin (DT) technology, particularly in improving three-dimensional modeling capabilities. IoT-driven DTs facilitate adaptive, efficient, and sustainable industrial operations by integrating real-time data, utilizing predictive analytics, and supporting smart manufacturing. Unlike Industry 4.0, which focuses on automation and cyber-physical systems, Industry 5.0 reintroduces human intelligence to ensure that technological progress aligns with ethical, social, and environmental considerations. This survey examines challenges such as scalability, interoperability, energy efficiency, and cybersecurity while exploring innovations like cognitive DTs, 5G-powered IoT networks, and AI-driven decision-making. Additionally, it highlights key technological advancements, including edge computing, neuro-symbolic and conversational AI, blockchain for secure data management, and eco-friendly IoT solutions, paving the way for a resilient, human-centric industrial future.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } The shift from Industry 4.0 to Industry 5.0 represents a significant transformation in industrial ecosystems, prioritizing human-machine collaboration, sustainability, and ethical Artificial Intelligence (AI). This paper provides a concise overview of the crucial role played by the Internet of Things (IoT) in advancing Digital Twin (DT) technology, particularly in improving three-dimensional modeling capabilities. IoT-driven DTs facilitate adaptive, efficient, and sustainable industrial operations by integrating real-time data, utilizing predictive analytics, and supporting smart manufacturing. Unlike Industry 4.0, which focuses on automation and cyber-physical systems, Industry 5.0 reintroduces human intelligence to ensure that technological progress aligns with ethical, social, and environmental considerations. This survey examines challenges such as scalability, interoperability, energy efficiency, and cybersecurity while exploring innovations like cognitive DTs, 5G-powered IoT networks, and AI-driven decision-making. Additionally, it highlights key technological advancements, including edge computing, neuro-symbolic and conversational AI, blockchain for secure data management, and eco-friendly IoT solutions, paving the way for a resilient, human-centric industrial future. |
| 33. | George Giannakopoulos Andreas Sideras, Konstantinos Stamatakis Nikolaos Melanitis : NAVMAT: An AI-supported naval failures knowledge management system. In: Expert Systems with Applications, 277 , pp. 127117, 2025. (Type: Journal Article | Abstract | Links | BibTeX) @article{Giannakopoulos2025, title = {NAVMAT: An AI-supported naval failures knowledge management system}, author = {George Giannakopoulos, Andreas Sideras, Konstantinos Stamatakis, Nikolaos Melanitis}, doi = {https://doi.org/10.1016/j.eswa.2025.127117}, year = {2025}, date = {2025-06-05}, journal = {Expert Systems with Applications}, volume = {277}, pages = {127117}, abstract = {We present “NAVMAT”, an intelligent, multilingual knowledge management platform designed to record and categorize material failure incidents reported in naval operations. This paper provides an overview of the platform, identifying its key software components and highlighting the information retrieval approach used to support user workflows. The platform primarily facilitates real-time, multilingual search and intelligent indexing, streamlining the incident management process while offering valuable insights from past incidents and knowledge resources. To achieve this, it employs a customized natural language processing pipeline integrated with a carefully engineered ontology. The ontology, regularly updated by domain experts, enriches the retrieval mechanism by instilling domain specific knowledge. This approach aims to reduce the significant variability in specialized terminology by promoting convergence towards a unified vocabulary.}, keywords = {}, pubstate = {published}, tppubtype = {article} } We present “NAVMAT”, an intelligent, multilingual knowledge management platform designed to record and categorize material failure incidents reported in naval operations. This paper provides an overview of the platform, identifying its key software components and highlighting the information retrieval approach used to support user workflows. The platform primarily facilitates real-time, multilingual search and intelligent indexing, streamlining the incident management process while offering valuable insights from past incidents and knowledge resources. To achieve this, it employs a customized natural language processing pipeline integrated with a carefully engineered ontology. The ontology, regularly updated by domain experts, enriches the retrieval mechanism by instilling domain specific knowledge. This approach aims to reduce the significant variability in specialized terminology by promoting convergence towards a unified vocabulary. |
| 34. | Foteini Oikonomou Eleftherios Bailis, Sotiris Bentos Stamatis Chatzistamatis Marianna Tzortzi Konstantinos Kotis Stamatis Spirou George Tsekouras E: Towards Fair Recidivism Prediction: Addressing Bias in Machine Learning for the Greek Prison System. 5th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), 2025, ISBN: 979-8-3315-3297-0. (Type: Conference | Abstract | Links | BibTeX) @conference{Oikonomou2025, title = {Towards Fair Recidivism Prediction: Addressing Bias in Machine Learning for the Greek Prison System}, author = {Foteini Oikonomou, Eleftherios Bailis, Sotiris Bentos, Stamatis Chatzistamatis, Marianna Tzortzi, Konstantinos Kotis, Stamatis Spirou, George E Tsekouras}, url = {https://ieeexplore.ieee.org/abstract/document/11008007}, doi = {https://doi.org/10.1109/IRASET64571.2025.11008007}, isbn = {979-8-3315-3297-0}, year = {2025}, date = {2025-05-15}, booktitle = {5th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET)}, pages = {1-7}, abstract = {Recidivism prediction has become an essential tool in criminal justice systems, aiding decision-making in areas such as sentencing, parole, and rehabilitation. Machine learning (ML) algorithms have been widely employed to improve the accuracy of recidivism risk assessments. However, concerns about fairness and algorithmic bias have been raised, particularly in high-stakes applications. This study focuses on the Greek prison system, utilizing a dataset from Greek prisons to analyze and mitigate biases in ML-based recidivism predictions. The study primarily investigates the impact of age as a sensitive attribute and employs fairness-aware optimization techniques to reduce disparities in predictive outcomes. By incorporating fairness constraints into the training process, we demonstrate that balancing fairness and accuracy is possible. The results indicate that implementing fairness-aware ML models can significantly reduce bias, particularly against younger offenders, while maintaining acceptable predictive performance. Our findings contribute to ongoing discussions on the ethical application of AI in criminal justice and highlight the necessity of fairness-aware methodologies for equitable decision-making.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Recidivism prediction has become an essential tool in criminal justice systems, aiding decision-making in areas such as sentencing, parole, and rehabilitation. Machine learning (ML) algorithms have been widely employed to improve the accuracy of recidivism risk assessments. However, concerns about fairness and algorithmic bias have been raised, particularly in high-stakes applications. This study focuses on the Greek prison system, utilizing a dataset from Greek prisons to analyze and mitigate biases in ML-based recidivism predictions. The study primarily investigates the impact of age as a sensitive attribute and employs fairness-aware optimization techniques to reduce disparities in predictive outcomes. By incorporating fairness constraints into the training process, we demonstrate that balancing fairness and accuracy is possible. The results indicate that implementing fairness-aware ML models can significantly reduce bias, particularly against younger offenders, while maintaining acceptable predictive performance. Our findings contribute to ongoing discussions on the ethical application of AI in criminal justice and highlight the necessity of fairness-aware methodologies for equitable decision-making. |
| 35. | Andreas Kontogiannis Konstantinos Papathanasiou, Yi Shen Giorgos Stamou Michael Zavlanos George Vouros M: Enhancing cooperative multi-agent reinforcement learning with state modelling and adversarial exploration. In: arXiv, 2025. (Type: Journal Article | Abstract | Links | BibTeX) @article{Kontogiannis2025, title = {Enhancing cooperative multi-agent reinforcement learning with state modelling and adversarial exploration}, author = {Andreas Kontogiannis, Konstantinos Papathanasiou, Yi Shen, Giorgos Stamou, Michael M Zavlanos, George Vouros}, url = {https://arxiv.org/pdf/2505.05262}, doi = {https://doi.org/10.48550/arXiv.2505.05262}, year = {2025}, date = {2025-05-08}, journal = {arXiv}, abstract = {Learning to cooperate in distributed partially observable environments with no communication abilities poses significant challenges for multi-agent deep reinforcement learning (MARL). This paper addresses key concerns in this domain, focusing on inferring state representations from individual agent observations and leveraging these representations to enhance agents’ exploration and collaborative task execution policies. To this end, we propose a novel state modelling framework for cooperative MARL, where agents infer meaningful belief representations of the non-observable state, with respect to optimizing their own policies, while filtering redundant and less informative joint state information. Building upon this framework, we propose the MARL SMPE algorithm. In SMPE, agents enhance their own policy’s discriminative abilities under partial observability, explicitly by incorporating their beliefs into the policy network, and implicitly by adopting an adversarial type of exploration policies which encourages agents to discover novel, high-value states while improving the discriminative abilities of others. Experimentally, we show that SMPE outperforms state-of-the-art MARL algorithms in complex fully cooperative tasks from the MPE, LBF, and RWARE benchmarks.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Learning to cooperate in distributed partially observable environments with no communication abilities poses significant challenges for multi-agent deep reinforcement learning (MARL). This paper addresses key concerns in this domain, focusing on inferring state representations from individual agent observations and leveraging these representations to enhance agents’ exploration and collaborative task execution policies. To this end, we propose a novel state modelling framework for cooperative MARL, where agents infer meaningful belief representations of the non-observable state, with respect to optimizing their own policies, while filtering redundant and less informative joint state information. Building upon this framework, we propose the MARL SMPE algorithm. In SMPE, agents enhance their own policy’s discriminative abilities under partial observability, explicitly by incorporating their beliefs into the policy network, and implicitly by adopting an adversarial type of exploration policies which encourages agents to discover novel, high-value states while improving the discriminative abilities of others. Experimentally, we show that SMPE outperforms state-of-the-art MARL algorithms in complex fully cooperative tasks from the MPE, LBF, and RWARE benchmarks. |
| 36. | Dimitris Kostadimas Vlasios Kasapakis, Konstantinos Kotis : A systematic review on the combination of VR, IoT and AI technologies, and their integration in applications. In: Future Internet, 17 (4), pp. 163, 2025, ISSN: 1999-5903. (Type: Journal Article | Abstract | Links | BibTeX) @article{Kostadimas2025, title = {A systematic review on the combination of VR, IoT and AI technologies, and their integration in applications}, author = {Dimitris Kostadimas, Vlasios Kasapakis, Konstantinos Kotis}, url = {https://www.mdpi.com/1999-5903/17/4/163}, doi = {https://doi.org/10.3390/fi17040163}, issn = {1999-5903}, year = {2025}, date = {2025-04-07}, journal = {Future Internet}, volume = {17}, number = {4}, pages = {163}, abstract = {The convergence of Virtual Reality (VR), Artificial Intelligence (AI), and the Internet of Things (IoT) offers transformative potential across numerous sectors. However, existing studies often examine these technologies independently or in limited pairings, which overlooks the synergistic possibilities of their combined usage. This systematic review adheres to the PRISMA guidelines in order to critically analyze peer-reviewed literature from highly recognized academic databases related to the intersection of VR, AI, and IoT, and identify application domains, methodologies, tools, and key challenges. By focusing on real-life implementations and working prototypes, this review highlights state-of-the-art advancements and uncovers gaps that hinder practical adoption, such as data collection issues, interoperability barriers, and user experience challenges. The findings reveal that digital twins (DTs), AIoT systems, and immersive XR environments are promising as emerging technologies (ET), but require further development to achieve scalability and real-world impact, while in certain fields a limited amount of research is conducted until now. This review bridges theory and practice, providing a targeted foundation for future interdisciplinary research aimed at advancing practical, scalable solutions across domains such as healthcare, smart cities, industry, education, cultural heritage, and beyond. The study found that the integration of VR, AI, and IoT holds significant potential across various domains, with DTs, IoT systems, and immersive XR environments showing promising applications, but challenges such as data interoperability, user experience limitations, and scalability barriers hinder widespread adoption.}, keywords = {}, pubstate = {published}, tppubtype = {article} } The convergence of Virtual Reality (VR), Artificial Intelligence (AI), and the Internet of Things (IoT) offers transformative potential across numerous sectors. However, existing studies often examine these technologies independently or in limited pairings, which overlooks the synergistic possibilities of their combined usage. This systematic review adheres to the PRISMA guidelines in order to critically analyze peer-reviewed literature from highly recognized academic databases related to the intersection of VR, AI, and IoT, and identify application domains, methodologies, tools, and key challenges. By focusing on real-life implementations and working prototypes, this review highlights state-of-the-art advancements and uncovers gaps that hinder practical adoption, such as data collection issues, interoperability barriers, and user experience challenges. The findings reveal that digital twins (DTs), AIoT systems, and immersive XR environments are promising as emerging technologies (ET), but require further development to achieve scalability and real-world impact, while in certain fields a limited amount of research is conducted until now. This review bridges theory and practice, providing a targeted foundation for future interdisciplinary research aimed at advancing practical, scalable solutions across domains such as healthcare, smart cities, industry, education, cultural heritage, and beyond. The study found that the integration of VR, AI, and IoT holds significant potential across various domains, with DTs, IoT systems, and immersive XR environments showing promising applications, but challenges such as data interoperability, user experience limitations, and scalability barriers hinder widespread adoption. |
| 37. | Georgios Bouchouras Georgios Sofianidis, Konstantinos Kotis : Predicting freezing of gait in parkinson’s disease: A machine-learning-based approach in on and off medication states. In: Journal of Clinical Medicine, 14 (6), pp. 2120, 2025, ISSN: 2077-0383. (Type: Journal Article | Abstract | Links | BibTeX) @article{Bouchouras2025c, title = {Predicting freezing of gait in parkinson’s disease: A machine-learning-based approach in on and off medication states}, author = {Georgios Bouchouras, Georgios Sofianidis, Konstantinos Kotis}, url = {https://www.mdpi.com/2077-0383/14/6/2120}, doi = {https://doi.org/10.3390/jcm14062120}, issn = {2077-0383}, year = {2025}, date = {2025-03-20}, journal = {Journal of Clinical Medicine}, volume = {14}, number = {6}, pages = {2120}, abstract = {Freezing of gait (FoG) is a debilitating motor symptom of Parkinson’s disease (PD), characterized by sudden episodes where patients struggle to initiate or sustain movement, often describing a sensation of their feet being “glued to the ground.” This study investigates the potential of machine-learning (ML) models to predict FoG severity in PD patients, focusing on the influence of dopaminergic medication by comparing gait parameters in ON and OFF medication states. Methods: Specifically, this study employed spatiotemporal gait features to develop a predictive model for FoG severity, leveraging a random forest regressor to identify the most influential gait parameters associated with this in each medication state. The results indicate that the model achieved higher predictive performance in the OFF-medication condition (R² = 0.82, MAE = 2.25, MSE = 15.23) compared to the ON-medication condition (R² = 0.52, MAE = 4.16, MSE = 42.00). Results: These findings suggest that dopaminergic treatment alters gait dynamics, potentially reducing the reliability of FoG predictions when patients are medicated. Feature importance analysis revealed distinct gait characteristics associated with FoG severity across medication states. In the OFF condition, step length parameters, particularly left step length mean, were the most dominant predictors, alongside swing time and stride width, indicating the role of spatial and temporal gait control in FoG severity without medication. In contrast, under the ON medication condition, stride width and gait speed emerged as the most influential predictors, followed by stepping frequency, reflecting how medication influences stability and movement rhythm. Conclusions: These findings highlight the need for predictive models that account for medication-induced gait variability, ensuring more reliable FoG detection. By integrating spatiotemporal gait analysis and ML-based prediction, this study contributes to the development of personalized intervention strategies for PD patients experiencing FoG episodes.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Freezing of gait (FoG) is a debilitating motor symptom of Parkinson’s disease (PD), characterized by sudden episodes where patients struggle to initiate or sustain movement, often describing a sensation of their feet being “glued to the ground.” This study investigates the potential of machine-learning (ML) models to predict FoG severity in PD patients, focusing on the influence of dopaminergic medication by comparing gait parameters in ON and OFF medication states. Methods: Specifically, this study employed spatiotemporal gait features to develop a predictive model for FoG severity, leveraging a random forest regressor to identify the most influential gait parameters associated with this in each medication state. The results indicate that the model achieved higher predictive performance in the OFF-medication condition (R² = 0.82, MAE = 2.25, MSE = 15.23) compared to the ON-medication condition (R² = 0.52, MAE = 4.16, MSE = 42.00). Results: These findings suggest that dopaminergic treatment alters gait dynamics, potentially reducing the reliability of FoG predictions when patients are medicated. Feature importance analysis revealed distinct gait characteristics associated with FoG severity across medication states. In the OFF condition, step length parameters, particularly left step length mean, were the most dominant predictors, alongside swing time and stride width, indicating the role of spatial and temporal gait control in FoG severity without medication. In contrast, under the ON medication condition, stride width and gait speed emerged as the most influential predictors, followed by stepping frequency, reflecting how medication influences stability and movement rhythm. Conclusions: These findings highlight the need for predictive models that account for medication-induced gait variability, ensuring more reliable FoG detection. By integrating spatiotemporal gait analysis and ML-based prediction, this study contributes to the development of personalized intervention strategies for PD patients experiencing FoG episodes. |
| 38. | Despoina P Kiouri Georgios C Batsis, Thomas Mavromoustakos Alessandro Giuliani Christos Chasapis T: Structure-Based Modeling of the Gut Bacteria–Host Interactome Through Statistical Analysis of Domain–Domain Associations Using Machine Learning. In: BioTech, 14 (1), pp. 13, 2025. (Type: Journal Article | Abstract | Links | BibTeX) @article{Kiouri2025c, title = {Structure-Based Modeling of the Gut Bacteria–Host Interactome Through Statistical Analysis of Domain–Domain Associations Using Machine Learning}, author = {Despoina P Kiouri, Georgios C Batsis, Thomas Mavromoustakos, Alessandro Giuliani, Christos T Chasapis}, url = {https://www.mdpi.com/2673-6284/14/1/13/pdf?version=1740479495}, doi = {https://doi.org/10.3390/biotech14010013}, year = {2025}, date = {2025-02-25}, journal = {BioTech}, volume = {14}, number = {1}, pages = {13}, abstract = {The gut microbiome, a complex ecosystem of microorganisms, plays a pivotal role in human health and disease. The gut microbiome’s influence extends beyond the digestive system to various organs, and its imbalance is linked to a wide range of diseases, including cancer and neurodevelopmental, inflammatory, metabolic, cardiovascular, autoimmune, and psychiatric diseases. Despite its significance, the interactions between gut bacteria and human proteins remain understudied, with less than 20,000 experimentally validated protein interactions between the host and any bacteria species. This study addresses this knowledge gap by predicting a protein–protein interaction network between gut bacterial and human proteins. Using statistical associations between Pfam domains, a comprehensive dataset of over one million experimentally validated pan-bacterial–human protein interactions, as well as inter- and intra-species protein interactions from various organisms, were used for the development of a machine learning-based prediction method to uncover key regulatory molecules in this dynamic system. This study’s findings contribute to the understanding of the intricate gut microbiome–host relationship and pave the way for future experimental validation and therapeutic strategies targeting the gut microbiome interplay.}, keywords = {}, pubstate = {published}, tppubtype = {article} } The gut microbiome, a complex ecosystem of microorganisms, plays a pivotal role in human health and disease. The gut microbiome’s influence extends beyond the digestive system to various organs, and its imbalance is linked to a wide range of diseases, including cancer and neurodevelopmental, inflammatory, metabolic, cardiovascular, autoimmune, and psychiatric diseases. Despite its significance, the interactions between gut bacteria and human proteins remain understudied, with less than 20,000 experimentally validated protein interactions between the host and any bacteria species. This study addresses this knowledge gap by predicting a protein–protein interaction network between gut bacterial and human proteins. Using statistical associations between Pfam domains, a comprehensive dataset of over one million experimentally validated pan-bacterial–human protein interactions, as well as inter- and intra-species protein interactions from various organisms, were used for the development of a machine learning-based prediction method to uncover key regulatory molecules in this dynamic system. This study’s findings contribute to the understanding of the intricate gut microbiome–host relationship and pave the way for future experimental validation and therapeutic strategies targeting the gut microbiome interplay. |
| 39. | Natalia Koliou, George Vouros : Ranking Joint Policies in Dynamic Games using Evolutionary Dynamics. In: arXiv, 2025. (Type: Journal Article | Abstract | Links | BibTeX) @article{Koliou2025, title = {Ranking Joint Policies in Dynamic Games using Evolutionary Dynamics}, author = {Natalia Koliou, George Vouros}, url = {https://arxiv.org/pdf/2502.14724}, doi = {https://doi.org/10.48550/arXiv.2502.14724}, year = {2025}, date = {2025-02-20}, journal = {arXiv}, abstract = {Game-theoretic solution concepts, such as the Nash equilibrium, have been key to finding stable joint actions in multi-player games. However, it has been shown that the dynamics of agents’ interactions, even in simple two-player games with few strategies, are incapable of reaching Nash equilibria, exhibiting complex and unpredictable behavior. Instead, evolutionary approaches can describe the long-term persistence of strategies and filter out transient ones, accounting for the long-term dynamics of agents’ interactions. Our goal is to identify agents’ joint strategies that result in stable behavior, being resistant to changes, while also accounting for agents’ payoffs, in dynamic games. Towards this goal, and building on previous results, this paper proposes transforming dynamic games into their empirical forms by considering agents’ strategies instead of agents’ actions, and applying the evolutionary methodology -Rank to evaluate and rank strategy profiles according to their long-term dynamics. This methodology not only allows us to identify joint strategies that are strong through agents’ long-term interactions, but also provides a descriptive, transparent framework regarding the high ranking of these strategies. Experiments report on agents that aim to collaboratively solve a stochastic version of the graph coloring problem. We consider different styles of play as strategies to define the empirical game, and train policies realizing these strategies, using the DQN algorithm. Then we run simulations to generate the payoff matrix required by -Rank to rank joint strategies.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Game-theoretic solution concepts, such as the Nash equilibrium, have been key to finding stable joint actions in multi-player games. However, it has been shown that the dynamics of agents’ interactions, even in simple two-player games with few strategies, are incapable of reaching Nash equilibria, exhibiting complex and unpredictable behavior. Instead, evolutionary approaches can describe the long-term persistence of strategies and filter out transient ones, accounting for the long-term dynamics of agents’ interactions. Our goal is to identify agents’ joint strategies that result in stable behavior, being resistant to changes, while also accounting for agents’ payoffs, in dynamic games. Towards this goal, and building on previous results, this paper proposes transforming dynamic games into their empirical forms by considering agents’ strategies instead of agents’ actions, and applying the evolutionary methodology -Rank to evaluate and rank strategy profiles according to their long-term dynamics. This methodology not only allows us to identify joint strategies that are strong through agents’ long-term interactions, but also provides a descriptive, transparent framework regarding the high ranking of these strategies. Experiments report on agents that aim to collaboratively solve a stochastic version of the graph coloring problem. We consider different styles of play as strategies to define the empirical game, and train policies realizing these strategies, using the DQN algorithm. Then we run simulations to generate the payoff matrix required by -Rank to rank joint strategies. |
| 40. | Dimitrios Doumanas Andreas Soularidis, Dimitris Spiliotopoulos Costas Vassilakis Konstantinos Kotis : Fine-tuning large language models for ontology engineering: A comparative analysis of GPT-4 and Mistral. In: Applied Sciences, 15 (4), pp. 2146, 2025, ISSN: 2076-3417. (Type: Journal Article | Abstract | Links | BibTeX) @article{Doumanas2025b, title = {Fine-tuning large language models for ontology engineering: A comparative analysis of GPT-4 and Mistral}, author = {Dimitrios Doumanas, Andreas Soularidis, Dimitris Spiliotopoulos, Costas Vassilakis, Konstantinos Kotis}, doi = {https://doi.org/10.3390/app15042146}, issn = {2076-3417}, year = {2025}, date = {2025-02-18}, journal = {Applied Sciences}, volume = {15}, number = {4}, pages = {2146}, abstract = {Ontology engineering (OE) plays a critical role in modeling and managing structured knowledge across various domains. This study examines the performance of fine-tuned large language models (LLMs), specifically GPT-4 and Mistral 7B, in efficiently automating OE tasks. Foundational OE textbooks are used as the basis for dataset creation and for feeding the LLMs. The methodology involved segmenting texts into manageable chapters, generating question–answer pairs, and translating visual elements into description logic to curate fine-tuned datasets in JSONL format. This research aims to enhance the models’ abilities to generate domain-specific ontologies, with hypotheses asserting that fine-tuned LLMs would outperform base models, and that domain-specific datasets would significantly improve their performance. Comparative experiments revealed that GPT-4 demonstrated superior accuracy and adherence to ontology syntax, albeit with higher computational costs. Conversely, Mistral 7B excelled in speed and cost efficiency but struggled with domain-specific tasks, often generating outputs that lacked syntactical precision and relevance. The presented results highlight the necessity of integrating domain-specific datasets to improve contextual understanding and practical utility in specialized applications, such as Search and Rescue (SAR) missions in wildfire incidents. Both models, despite their limitations, exhibited potential in understanding OE principles. However, their performance underscored the importance of aligning training data with domain-specific knowledge to emulate human expertise effectively. This study, based on and extending our previous work on the topic, concludes that fine-tuned LLMs with targeted datasets enhance their utility in OE, offering insights into improving future models for domain-specific applications. The findings advocate further exploration of hybrid solutions to balance accuracy and efficiency.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Ontology engineering (OE) plays a critical role in modeling and managing structured knowledge across various domains. This study examines the performance of fine-tuned large language models (LLMs), specifically GPT-4 and Mistral 7B, in efficiently automating OE tasks. Foundational OE textbooks are used as the basis for dataset creation and for feeding the LLMs. The methodology involved segmenting texts into manageable chapters, generating question–answer pairs, and translating visual elements into description logic to curate fine-tuned datasets in JSONL format. This research aims to enhance the models’ abilities to generate domain-specific ontologies, with hypotheses asserting that fine-tuned LLMs would outperform base models, and that domain-specific datasets would significantly improve their performance. Comparative experiments revealed that GPT-4 demonstrated superior accuracy and adherence to ontology syntax, albeit with higher computational costs. Conversely, Mistral 7B excelled in speed and cost efficiency but struggled with domain-specific tasks, often generating outputs that lacked syntactical precision and relevance. The presented results highlight the necessity of integrating domain-specific datasets to improve contextual understanding and practical utility in specialized applications, such as Search and Rescue (SAR) missions in wildfire incidents. Both models, despite their limitations, exhibited potential in understanding OE principles. However, their performance underscored the importance of aligning training data with domain-specific knowledge to emulate human expertise effectively. This study, based on and extending our previous work on the topic, concludes that fine-tuned LLMs with targeted datasets enhance their utility in OE, offering insights into improving future models for domain-specific applications. The findings advocate further exploration of hybrid solutions to balance accuracy and efficiency. |