Publications – AI-Lab

The Semantic Web: Research and Applications: First European Semantic Web Symposium (ESWS 2004), Springer-Verlag Lecture Notes in Computer Science Springer-Verlag Lecture Notes in Computer Science, Crete, Greece, 2004.

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Vouros, G; Panayiotopoulos, T

Methods and Applications of Artificial Intelligence Third Helenic Conference on AI, SETN 2004 Book

Springer Verlag, 2004, ISBN: 978-3-540-21937-8.

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Partsakoulakis, I; Vouros, G

Personal Representative Agents for Reliable Participation in Social Contexts Conference

In Proceedings of the CEAS workshop – ECAI 04 Workshop, 2004.

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Aivaloglou, E; Vouros, G

Shoplet and the Personal Market Place model for e-commerce Journal Article

The electronic journal of e-commerce tools and applications, 1 , 2004.

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Partsakoulakis, I; Kourakos-Mavromichalis, V; Vouros, G

Socially Deliberating Agents for Human-Centered Knowledge Management Conference

In Proc of the IEEE International Conference on Systems, Man and Cybernetics, 2004.

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2003

Karakapilidis, N; Vouros, G; Darzentas, J

Applying Intelligent Agents Technology in a Collaborative Work Environment Journal Article

International Transactions in Operations Research, 10 , pp. 13-31, 2003.

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Kotis, K; Vouros, G; Kotis, K

Human Centered Ontology Management with HCONE Conference

ODS 2003 Workshop on Ontologies and Distributed Systems,International Joint Conference in Artificial Intelligence(IJCAI-03), CEUR Workshop Proceedings (CEUR-WS.org) CEUR Workshop Proceedings (CEUR-WS.org), Acapulco, Mexico, 2003.

BibTeX

Vouros, G; Partsakoulakis, I; Kourakos-Mavromichalis, V

Realizing Human-Centered Systems via Socially Deliberating Agents Conference

In Proceedings of the 10th International Conference in Human-Computer Interaction, Crete, Greece, 2003.

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Vouros, G

Technological Issues towards Knowledge Powered Organizations Journal Article

Knowledge Management Journal, 7 , pp. 114-127, 2003.

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2002

Vouros, G

Discrete Mathematics Book

Hellenic Open University, Patras, 2002.

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Partsakoulakis, I; Vouros, G

Helping Young Students Reach Valid Decision Through Model Checking Conference

3rd Hellenic Conference on Technology of Information and Communication in Education, Rhodes, Greece, 2002.

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Partsakoulakis, I; Vouros, G

Importance and Properties of Roles in MAS Organization: A review of methodologies and systems Conference

in Proc. of the workshop on MAS Problem Spaces and Their Implications to Achieving Globally Coherent Behavior (AAMAS 02), Bologna, Italy, 2002.

BibTeX

Partsakoulakis, I; Vouros, G; Partsakoulakis, I

Roles in Collaborative Activity Conference

Second Hellenic Conference on Artificial Intelligence, SETN 02, LNCS – Springer Verlag LNCS – Springer Verlag, Thessaloniki, Greece, 2002.

BibTeX

Vouros, G; Eumeridou, E

Simple and EuroWordNet: Towards the Prometheus Ontological Framework Journal Article

Terminology – International journal of Theoretical and applied issues in Specialized Communication, 8 , pp. 245-281 (37), 2002.

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Vouros, G

Towards a Generic Architecture for Cooperative Learning Environments Journal Article

International Journal of Continuous education and Life-Long Learning, special issue in Intelligent Agents for education and Training Systems, 12 , pp. 331-347, 2002.

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2001

Kourakos-Mavromichalis, V; Vouros, G

Balancing Between Reactivity and Deliberation in the ICAGENT Framework Book Chapter

Balancing Reactivity and Social Deliberation in Multi-Agent Systems, From RoboCup to Real-World Applications (selected papers from the ECAI 2000 Workshop and additional contributions), 2103 , pp. 53-75, LNAI Volume 2103, Springer-Verlag, 2001.

BibTeX

Kourakos-Mavromichalis, V; Vouros, G; Kourakos-Mavromichalis, V

“Building Intelligent Collaborative Interface Agents with the ICagent Development Framework” Conference

Proceedings of 8th Panhellenic Conference on Informatics (with international participation), 2001.

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Vouros, G

Conceptual Modeling of Multimedia Objects for User-Tailored Information Presentations Journal Article

Applied Artificial Intelligence, 15 , pp. 521-560, 2001.

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Vouros, G; Kourakos-Mavromichalis, V

Towards a Generic Framework for Building Intelligent Collaborative Agents Journal Article

ERCIM News, 2001.

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2000

Vouros, G; Vidalis, M I; Papadopoulos, H T

Heuristic Algorithm for Buffer Allocation in Unreliable Production Lines Journal Article

International Journal of Operations and Quantitative Management, 6 , pp. 23-44, 2000.

BibTeX

Kourakos-Mavromichalis, V; Vouros, G

ICAGENT : Balancing between Reactivity and Deliberation Conference

Balancing Reactivity and Social Deliberation in Multi-Agent Systems, From RoboCup to Real-World Applications, ECAI 2000 Workshop, Springer Springer, 2000.

BibTeX

Vouros, G; Pantelakis, J; Lekkas, T D

Knowledge Representation in an activated sludge plant diagnosis system Journal Article

International Journal Expert Systems with Applications, 17 , pp. 226-240, 2000.

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P.Tselios, ; Platis, A; Vouros, G

Providing Advice to Website Designers towards effective Websites Structure Re-Organisation Conference

4th European Conference PKDD 2000, Lyon, France, 2000.

BibTeX

Vouros, G

Representing, adapting and reasoning with uncertain, imprecise and vague information Journal Article

Journal Expert Systems with Applications, 19 , pp. 167-192, 2000.

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Vouros, G; Kotis, K; Tselios, P; Vouros, G

Retrieval and Exploration of Terminological Knowledge over the World Wide Web Conference

COMLEX 2000, Greece, 2000.

BibTeX

1999

Karacapilidis, N; Vouros, G; J.Darzentas, ; Karacapilidis, N

Enhancing Collaborative Work and Human – Computer Interaction with Intelligent Agents Conference

IFORS, SPC-9, Intelligent Systems and Active DSS, Turku, Finland, 1999.

BibTeX

Aarnisalo, J; Makela, K; Bourgeois, B; Spyropoulos, C; Varoufakis, S; Morten, E; K.Maglaras, ; Soininen, H; Angelopοulos, A; et al,

Integrated technologies for mineral exploration:pilot project for Ni ore deposits Journal Article

Transactions of the Institution of Mining and Metallurgy, Section B, Applied Earth Science, 108 , pp. 151-163, 1999.

BibTeX

Vouros, G; Vouros, G

Knowledge-Based and Layout-Driven Adaptive Information Presentations on the WWW Conference

5th ERCIM Workshop UI4ALL, 1999.

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1998

Vouros, G; Papadopoulos, H T

Buffer Allocation in Unreliable Production Lines Using a Knowledge Based System Journal Article

Computers and Operations Research. An International Journal, 25 , pp. 1055-1067, 1998.

BibTeX

Vouros, G; Vouros, G

Collaborative Multimedia Systems Conference

KRIMS II Workshop, Trento Italy, 1998.

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Vouros, G

Conceptualisation of Device Structure and Function Journal Article

International Journal of Expert Systems, 10 , pp. 137-176, 1998.

BibTeX

L.Bardis, ; G.Grigoropoulos, ; S.Kokkotos, ; T.Loukakis, ; C.D.Spyropoulos, ; Vouros, G

An Intelligent Chemical and Product Carrier Loadmaster Journal Article

New Review of Applied Expert Systems, 4 , pp. 47-60, 1998.

BibTeX

E.Karkaletsis, ; Spyropoulos, C D; Vouros, G

A knowledge-based methodology for supporting multilingual and user-tailored interfaces Journal Article

Interacting with Computers, 9 , pp. 311-333, 1998.

BibTeX

Vouros, G; Vouros, G

Supporting Intelligent Information Presentation and Navigation on the Web Conference

Πανελλήνιο Συνέδριο ΕΕΕΕ, Samos, Greece, 1998.

BibTeX

1997

Karkaletsis, V; Spyropoulos, C D; Vouros, G; Honkela, T; Lagus, K; Lehtola, A; Karkaletsis, V

Commercial tools to support localisation Book Chapter

Software Without Frontiers, pp. 289-298, Wiley and Sons, 1997.

BibTeX

Vouros, G; Karkaletsis, V; C.D.Spyropoulos, ; Vouros, G

Documentation and Translation Book Chapter

Software Without Frontiers, pp. 167-202, J.Willey and Sons, 1997.

BibTeX

Karkaletsis, V; Spyropoulos, C D; Vouros, G; Honkela, T; Lagus, K; Lehtola, A; Karkaletsis, V

Message Generation Book Chapter

Software Without Frontiers, pp. 203-218, J.Willey and Sons, 1997.

BibTeX

Papadopoulos, H T; Vouros, G

A Model Management System for Production Lines Journal Article

International Journal of Production Research, 35 , pp. 2213-2236, 1997.

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202 entries « ‹ 4 of 5 › »

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202 entries « ‹ 4 of 11 › »

61.	Nikolaos Zafeiropoulos Pavlos Bitilis, Konstantinos Kotis : Wear4PDmove: an ontology for knowledge-based personalized health monitoring of PD patients. The 22nd International Semantic Web Conference (ISWC), 2023. (Type: Conference \| Abstract \| Links \| BibTeX) @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} } Close 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. Close https://hozo.jp/ISWC2023_PD-Industry-proc/ISWC2023_paper_433.pdf Close
62.	Manolis Remountakis Konstantinos Kotis, Babis Kourtzis George Tsekouras E: Using ChatGPT and persuasive technology for personalized recommendation messages in hotel upselling. In: Information, 14 (9), pp. 504, 2023. (Type: Journal Article \| Abstract \| Links \| BibTeX) @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} } Close 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. Close doi:https://doi.org/10.3390/info14090504 Close
63.	S. Bentos S. Spirou, Kotis Tsekouras K G: Bias Assessment in AI-Based Predictions of Recidivism. 13th Beyond Humanism Conference (BHC), 2023. (Type: Conference \| Abstract \| Links \| BibTeX) @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} } Close 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. Close https://metabody.eu/wp-content/uploads/2023/06/13thBHC_ABSTRACT_BOOKLET.pdf Close
64.	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 : Tissue classification and diagnosis of colorectal cancer histopathology images using deep learning algorithms. Is the time ripe for clinical practice implementation?. In: Gastroenterology Review, 18 (4), pp. 353-367, 2023. (Type: Journal Article \| Abstract \| Links \| BibTeX) @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} } Close 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. Close https://www.termedia.pl/Tissue-classification-and-diagnosis-of-colorectal-cancer[…] doi:https://doi.org/10.5114/pg.2023.130337 Close
65.	Manolis Remountakis Konstantinos Kotis, Babis Kourtzis George Tsekouras E: ChatGPT and persuasive technologies for the management and delivery of personalized recommendations in hotel hospitality. In: arXiv, 2023. (Type: Journal Article \| Abstract \| Links \| BibTeX) @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} } Close 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. Close https://arxiv.org/pdf/2307.14298 doi:https://doi.org/10.48550/arXiv.2307.14298 Close
66.	Georgios Batsis Ioannis Mademlis, Georgios Th Papadopoulos : Illicit item detection in X-ray images for security applications. 2023 IEEE Ninth International Conference on Big Data Computing Service and Applications (BigDataService), 2023, ISBN: 979-8-3503-3379-4. (Type: Conference \| Abstract \| Links \| BibTeX) @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} } Close 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. Close https://ieeexplore.ieee.org/abstract/document/10233969 doi:https://doi.org/10.1109/BigDataService58306.2023.00016 Close
67.	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. In: Gastroenterology Review, 18 (3), pp. 266-273, 2023. (Type: Journal Article \| Abstract \| Links \| BibTeX) @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} } Close 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. Close doi:https://doi.org/10.5114/pg.2023.129494 Close
68.	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. In: Algorithms, 16 (7), pp. 339, 2023. (Type: Journal Article \| Abstract \| Links \| BibTeX) @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} } Close 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. Close https://www.mdpi.com/1999-4893/16/7/339 doi:https://doi.org/10.3390/a16070339 Close
69.	Pavlos Bitilis Nikolaos Zafeiropoulos, Adam Koletis Konstantinos Kotis : Uncovering the semantics of PD patients’ movement data collected via off-the-shelf wearables. The 14th International Conference on Information, Intelligence, Systems and Applications (IISA), Volos, 2023, 2023. (Type: Conference \| Abstract \| Links \| BibTeX) @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} } Close 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. Close https://ieeexplore.ieee.org/abstract/document/10345958 doi:https://doi.org/10.1109/IISA59645.2023.10345958 Close
70.	Alexandros Karakikes Panagiotis Alexiadis, Theocharis Theocharopoulos Nikolaos Skoulidas Konstantinos Kotis : Understanding bias in Twitter-based intelligence analysis. 2023 14th International Conference on Information, Intelligence, Systems & Applications (IISA), 2023, ISBN: 979-8-3503-1806-7. (Type: Conference \| Abstract \| Links \| BibTeX) @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} } Close 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. Close https://ieeexplore.ieee.org/abstract/document/10345941 doi:https://doi.org/10.1109/IISA59645.2023.10345941 Close
71.	Georgios Papadopoulos Marko Kokol, Maria Dagioglou Georgios Petasis : Andronicus of rhodes at SemEval-2023 task 4: Transformer-based human value detection using four different neural network architectures. Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), Association for Computational Linguistics, 2023. (Type: Conference \| Abstract \| Links \| BibTeX) @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} } Close 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. Close https://aclanthology.org/2023.semeval-1.75.pdf doi:https://doi.org/10.18653/v1/2023.semeval-1.75 Close
72.	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. DASC 2023, 2023. (Type: Conference \| Abstract \| BibTeX) @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} } Close 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. Close
73.	Georgios Santipantakis Christos Doulkeridis, George Vouros A: An Ontology for Representing and Querying Semantic Trajectories in the Maritime Domain. ADBIS 2023, 2023. (Type: Conference \| BibTeX) @conference{Santipantakis2023, title = {An Ontology for Representing and Querying Semantic Trajectories in the Maritime Domain}, author = {Georgios Santipantakis, Christos Doulkeridis, George A. Vouros}, year = {2023}, date = {2023-06-20}, booktitle = {ADBIS 2023}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Close
74.	Piyabhum Chaysri Christos Spatharis, Konstantinos Blekas Kostas Vlachos : Unmanned surface vehicle navigation through generative adversarial imitation learning. In: Ocean Engineering, 282 , pp. 114989, 2023, ISSN: 0029-8018. (Type: Journal Article \| Abstract \| Links \| BibTeX) @article{Chaysri2023, title = {Unmanned surface vehicle navigation through generative adversarial imitation learning}, author = {Piyabhum Chaysri, Christos Spatharis, Konstantinos Blekas, Kostas Vlachos}, url = {https://www.sciencedirect.com/science/article/pii/S0029801823013732}, doi = {https://doi.org/10.1016/j.oceaneng.2023.114989}, issn = {0029-8018}, year = {2023}, date = {2023-06-13}, journal = {Ocean Engineering}, volume = {282}, pages = {114989}, abstract = {In the artificial intelligent and big data technology era, the marine industry among others is inevitably developing in this direction, aiming at becoming autonomous and completing tasks without relying on human involvement while providing safety. The technology of small unmanned surface vehicles (USVs) is relatively mature but with a large development potential and wide research interest expecting significant benefits such as safety and high efficiency in shipping and transportation systems. This article addresses these issues and utilizes an imitation learning algorithm to resolve autonomous navigation for USVs even in complex environmental conditions. We formulate the trajectory modeling as a data-driven imitation learning problem where we employ a state of the art imitation learning algorithm. Experiments are performed in a particular simulated environment tailored to match the specific weather conditions of the local area. The simulation results show the potential of the proposed imitation learning scheme to create advanced intelligent agents for USVs under real-world environmental settings, and USV actuation constraints that allow to predict trajectories with high accuracy and safety. In addition, we evaluated the method’s robustness in generating successful trajectories under environmental conditions that differed from those encountered during training, thereby promoting knowledge reusing without the need for retraining.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close In the artificial intelligent and big data technology era, the marine industry among others is inevitably developing in this direction, aiming at becoming autonomous and completing tasks without relying on human involvement while providing safety. The technology of small unmanned surface vehicles (USVs) is relatively mature but with a large development potential and wide research interest expecting significant benefits such as safety and high efficiency in shipping and transportation systems. This article addresses these issues and utilizes an imitation learning algorithm to resolve autonomous navigation for USVs even in complex environmental conditions. We formulate the trajectory modeling as a data-driven imitation learning problem where we employ a state of the art imitation learning algorithm. Experiments are performed in a particular simulated environment tailored to match the specific weather conditions of the local area. The simulation results show the potential of the proposed imitation learning scheme to create advanced intelligent agents for USVs under real-world environmental settings, and USV actuation constraints that allow to predict trajectories with high accuracy and safety. In addition, we evaluated the method’s robustness in generating successful trajectories under environmental conditions that differed from those encountered during training, thereby promoting knowledge reusing without the need for retraining. Close https://www.sciencedirect.com/science/article/pii/S0029801823013732 doi:https://doi.org/10.1016/j.oceaneng.2023.114989 Close
75.	Alevizos Bastas, George Vouros A: Data-Driven Modeling of Air Traffic Controllers’ Policy to Resolve Conflicts. In: Aerospace, 10 (6), 2023, ISSN: 2226-4310. (Type: Journal Article \| Abstract \| Links \| BibTeX) @article{Bastas2023, title = {Data-Driven Modeling of Air Traffic Controllers’ Policy to Resolve Conflicts}, author = {Alevizos Bastas, George A. Vouros}, doi = {https://doi.org/10.3390/aerospace10060557}, issn = {2226-4310}, year = {2023}, date = {2023-06-13}, journal = {Aerospace}, volume = {10}, number = {6}, abstract = {With the aim to enhance automation in conflict detection and resolution (CD&R) tasks in the air traffic management (ATM) domain, this article studies the use of artificial intelligence and machine learning (AI/ML) methods to learn air traffic controllers’ (ATCOs) policy in resolving conflicts among aircraft assessed to violate separation minimum constraints during the en route phase of flights, in the tactical phase of operations. The objective is to model ℎ𝑜𝑤 conflicts are being resolved by ATCOs. Towards this goal, the article formulates the ATCO policy learning problem for conflict resolution, addresses the challenging issue of an inherent lack of information in real-world data, and presents AI/ML methods that learn models of ATCOs’ behavior. The methods are evaluated using real-world datasets. The results show that AI/ML methods can achieve good accuracy on predicting ATCOs’ actions given specific conflicts, revealing the preferences of ATCOs for resolution actions in specific circumstances. However, the high accuracy of predictions is hindered by real-world data-inherent limitations.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close With the aim to enhance automation in conflict detection and resolution (CD&R) tasks in the air traffic management (ATM) domain, this article studies the use of artificial intelligence and machine learning (AI/ML) methods to learn air traffic controllers’ (ATCOs) policy in resolving conflicts among aircraft assessed to violate separation minimum constraints during the en route phase of flights, in the tactical phase of operations. The objective is to model ℎ𝑜𝑤 conflicts are being resolved by ATCOs. Towards this goal, the article formulates the ATCO policy learning problem for conflict resolution, addresses the challenging issue of an inherent lack of information in real-world data, and presents AI/ML methods that learn models of ATCOs’ behavior. The methods are evaluated using real-world datasets. The results show that AI/ML methods can achieve good accuracy on predicting ATCOs’ actions given specific conflicts, revealing the preferences of ATCOs for resolution actions in specific circumstances. However, the high accuracy of predictions is hindered by real-world data-inherent limitations. Close doi:https://doi.org/10.3390/aerospace10060557 Close
76.	Alevizos Bastas, George Vouros A: Data-Driven Modeling of Air Traffic Controllers’ Policy to Resolve Conflicts. In: Aerospace, 10 (6), pp. 557, 2023. (Type: Journal Article \| Abstract \| Links \| BibTeX) @article{Bastas2023b, title = {Data-Driven Modeling of Air Traffic Controllers’ Policy to Resolve Conflicts}, author = {Alevizos Bastas, George A Vouros}, doi = {https://doi.org/10.3390/aerospace10060557}, year = {2023}, date = {2023-06-13}, journal = {Aerospace}, volume = {10}, number = {6}, pages = {557}, abstract = {With the aim to enhance automation in conflict detection and resolution (CD&R) tasks in the air traffic management (ATM) domain, this article studies the use of artificial intelligence and machine learning (AI/ML) methods to learn air traffic controllers’ (ATCOs) policy in resolving conflicts among aircraft assessed to violate separation minimum constraints during the en route phase of flights, in the tactical phase of operations. The objective is to model how conflicts are being resolved by ATCOs. Towards this goal, the article formulates the ATCO policy learning problem for conflict resolution, addresses the challenging issue of an inherent lack of information in real-world data, and presents AI/ML methods that learn models of ATCOs’ behavior. The methods are evaluated using real-world datasets. The results show that AI/ML methods can achieve good accuracy on predicting ATCOs’ actions given specific conflicts, revealing the preferences of ATCOs for resolution actions in specific circumstances. However, the high accuracy of predictions is hindered by real-world data-inherent limitations.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close With the aim to enhance automation in conflict detection and resolution (CD&R) tasks in the air traffic management (ATM) domain, this article studies the use of artificial intelligence and machine learning (AI/ML) methods to learn air traffic controllers’ (ATCOs) policy in resolving conflicts among aircraft assessed to violate separation minimum constraints during the en route phase of flights, in the tactical phase of operations. The objective is to model how conflicts are being resolved by ATCOs. Towards this goal, the article formulates the ATCO policy learning problem for conflict resolution, addresses the challenging issue of an inherent lack of information in real-world data, and presents AI/ML methods that learn models of ATCOs’ behavior. The methods are evaluated using real-world datasets. The results show that AI/ML methods can achieve good accuracy on predicting ATCOs’ actions given specific conflicts, revealing the preferences of ATCOs for resolution actions in specific circumstances. However, the high accuracy of predictions is hindered by real-world data-inherent limitations. Close doi:https://doi.org/10.3390/aerospace10060557 Close
77.	Christos Tzouvaras Asimina Dimara, Alexios Papaioannou Christos-Nikolaos Anagnostopoulos Konstantinos Kotis Stelios Krinidis Dimosthenis Ioannidis Dimitrios Tzovaras : Semantic interoperability for managing energy-efficiency and ieq: A short review. Artificial Intelligence Applications and Innovations. AIAI 2023 IFIP WG 12.5 International Workshops, 2023, ISBN: 978-3-031-34171-7. (Type: Conference \| Abstract \| Links \| BibTeX) @conference{Tzouvaras2023, title = {Semantic interoperability for managing energy-efficiency and ieq: A short review}, author = {Christos Tzouvaras, Asimina Dimara, Alexios Papaioannou, Christos-Nikolaos Anagnostopoulos, Konstantinos Kotis, Stelios Krinidis, Dimosthenis Ioannidis, Dimitrios Tzovaras}, doi = {https://doi.org/10.1007/978-3-031-34171-7_19}, isbn = {978-3-031-34171-7}, year = {2023}, date = {2023-06-02}, booktitle = {Artificial Intelligence Applications and Innovations. AIAI 2023 IFIP WG 12.5 International Workshops}, abstract = {With the rise of the Internet of Things and Smart Home industries, there is a real opportunity to increase the energy efficiency of buildings and improve the indoor experience of their occupants. However, as these industries continue to grow, so does the number of data sources in the energy sector in recent years. This can lead to suboptimal exploitation of these data and even to dualities and misunderstandings. As a result, semantic interoperability in the energy sector is now more necessary than ever. Combining event processing to handle data quantities, semantics to manage numerous data streams, and background ontologies will increase prompt identification of all information. In this context, this short review aims to explore state-of-the-art semantic ontologies and their utilization in the energy sector, with an additional emphasis on the indoor environment and air quality. Furthermore, a semantically enriched framework for a smart home will be proposed.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Close With the rise of the Internet of Things and Smart Home industries, there is a real opportunity to increase the energy efficiency of buildings and improve the indoor experience of their occupants. However, as these industries continue to grow, so does the number of data sources in the energy sector in recent years. This can lead to suboptimal exploitation of these data and even to dualities and misunderstandings. As a result, semantic interoperability in the energy sector is now more necessary than ever. Combining event processing to handle data quantities, semantics to manage numerous data streams, and background ontologies will increase prompt identification of all information. In this context, this short review aims to explore state-of-the-art semantic ontologies and their utilization in the energy sector, with an additional emphasis on the indoor environment and air quality. Furthermore, a semantically enriched framework for a smart home will be proposed. Close doi:https://doi.org/10.1007/978-3-031-34171-7_19 Close
78.	Andreas Kontogiannis, George Vouros : Inherently Interpretable Deep Reinforcement Learning through Online Mimicking. In: EXTRAAMAS @AAMAS 2023, 2023. (Type: Inproceedings \| Abstract \| Links \| BibTeX) @inproceedings{Kontogiannis2023, title = {Inherently Interpretable Deep Reinforcement Learning through Online Mimicking}, author = {Andreas Kontogiannis, George Vouros}, url = {https://www.researchgate.net/profile/Andreas-Kontogiannis/publication/373676887_Inherently_Interpretable_Deep_Reinforcement_Learning_Through_Online_Mimicking/links/67d93edb78221c759f4b1cc1/Inherently-Interpretable-Deep-Reinforcement-Learning-Through-Online-Mimicking.pdf}, year = {2023}, date = {2023-05-29}, booktitle = {EXTRAAMAS @AAMAS 2023}, abstract = {Although deep reinforcement learning (DRL) methods have been successfully applied in challenging tasks, their application in real-world operational settings – where transparency and accountability play important roles in automation – is challenged by methods’ limited ability to provide explanations. Among the paradigms for explainability in DRL is the interpretable box design paradigm, where interpretable models substitute inner closed constituent models of the DRL method, thus making the DRL method “inherently” interpretable. In this paper we propose a generic paradigm where interpretable policy models are trained following an online mimicking paradigm. We exemplify this paradigm through XDQN, an explainable variation of DQN that uses an interpretable policy model trained online with the deep policy model. XDQN is challenged in a complex, real-world operational multi-agent problem pertaining to the demand-capacity balancing problem of air traffic management (ATM), where human operators need to master complexity and understand the factors driving decision making. XDQN is shown to achieve performance similar to that of its DQN counterpart, while its abilities to provide global models’ interpretations and interpretations of local decisions are demonstrated.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Close Although deep reinforcement learning (DRL) methods have been successfully applied in challenging tasks, their application in real-world operational settings – where transparency and accountability play important roles in automation – is challenged by methods’ limited ability to provide explanations. Among the paradigms for explainability in DRL is the interpretable box design paradigm, where interpretable models substitute inner closed constituent models of the DRL method, thus making the DRL method “inherently" interpretable. In this paper we propose a generic paradigm where interpretable policy models are trained following an online mimicking paradigm. We exemplify this paradigm through XDQN, an explainable variation of DQN that uses an interpretable policy model trained online with the deep policy model. XDQN is challenged in a complex, real-world operational multi-agent problem pertaining to the demand-capacity balancing problem of air traffic management (ATM), where human operators need to master complexity and understand the factors driving decision making. XDQN is shown to achieve performance similar to that of its DQN counterpart, while its abilities to provide global models’ interpretations and interpretations of local decisions are demonstrated. Close https://www.researchgate.net/profile/Andreas-Kontogiannis/publication/373676887_[…] Close
79.	Andreas Kontogiannis, George Vouros A: Inherently Interpretable Deep Reinforcement Learning Through Online Mimicking. Explainable and Transparent AI and Multi-Agent Systems. EXTRAAMAS 2023, 14127 , Springer Nature Switzerland, 2023, ISBN: 978-3-031-40877-9. (Type: Conference \| Links \| BibTeX) @conference{Kontogiannis2023c, title = {Inherently Interpretable Deep Reinforcement Learning Through Online Mimicking}, author = {Andreas Kontogiannis, George A Vouros}, doi = {https://doi.org/10.1007/978-3-031-40878-6_10}, isbn = {978-3-031-40877-9}, year = {2023}, date = {2023-05-29}, booktitle = {Explainable and Transparent AI and Multi-Agent Systems. EXTRAAMAS 2023}, volume = {14127}, pages = {160-179}, publisher = {Springer Nature Switzerland}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Close doi:https://doi.org/10.1007/978-3-031-40878-6_10 Close
80.	Adam Koletis Pavlos Bitilis, Nikolaos Zafeiropoulos Konstantinos Kotis : Can Semantics Uncover Hidden Relations between Neurodegenerative Diseases and Artistic Behaviors?. In: Applied Sciences, 13 (7), pp. 4287, 2023. (Type: Journal Article \| Abstract \| Links \| BibTeX) @article{Koletis2023, title = {Can Semantics Uncover Hidden Relations between Neurodegenerative Diseases and Artistic Behaviors?}, author = {Adam Koletis, Pavlos Bitilis, Nikolaos Zafeiropoulos, Konstantinos Kotis}, url = {https://www.mdpi.com/2076-3417/13/7/4287}, doi = {https://doi.org/10.3390/app13074287}, year = {2023}, date = {2023-03-28}, journal = {Applied Sciences}, volume = {13}, number = {7}, pages = {4287}, abstract = {Semantics play a crucial role in organizing domain knowledge, schematizing it, and modeling it into classes of objects and relationships between them. Knowledge graphs (KGs) use semantic models to integrate and represent different types of data. This study aimed to systematically review related work on the topics of ontologies for neurodegenerative diseases (NDs), ontology-based expert systems for NDs, and the artistic behavior of ND patients. The utilization of ontologies allows for a more comprehensive understanding of the progression and etiology of NDs, the structure and function of the brain, and the artistic expression associated with these diseases. The data collected from ND patients highlights the presence of cases where artistic expression can be linked to the disease. By developing fuzzy ontologies for NDs and incorporating them into expert systems, early detection and monitoring can be supported. Through our systematic review, we identify and discuss open issues and challenges in understanding the relationship between ND patients and their artistic behavior. We also conclude that ontology-based expert systems hold immense potential in uncovering hidden correlations between these two. Further research in this area has the potential to address key research questions and provide deeper insights.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Close Semantics play a crucial role in organizing domain knowledge, schematizing it, and modeling it into classes of objects and relationships between them. Knowledge graphs (KGs) use semantic models to integrate and represent different types of data. This study aimed to systematically review related work on the topics of ontologies for neurodegenerative diseases (NDs), ontology-based expert systems for NDs, and the artistic behavior of ND patients. The utilization of ontologies allows for a more comprehensive understanding of the progression and etiology of NDs, the structure and function of the brain, and the artistic expression associated with these diseases. The data collected from ND patients highlights the presence of cases where artistic expression can be linked to the disease. By developing fuzzy ontologies for NDs and incorporating them into expert systems, early detection and monitoring can be supported. Through our systematic review, we identify and discuss open issues and challenges in understanding the relationship between ND patients and their artistic behavior. We also conclude that ontology-based expert systems hold immense potential in uncovering hidden correlations between these two. Further research in this area has the potential to address key research questions and provide deeper insights. Close https://www.mdpi.com/2076-3417/13/7/4287 doi:https://doi.org/10.3390/app13074287 Close

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