Basic Filters
2018 |
Santipantakis, G; Doulkeridis, C; Vouros, G; Vlachou, A MaskLink: Efficient Link Discovery for Spatial Relations via Masking Areas Journal Article arxiv, 2018. @article{336, title = {MaskLink: Efficient Link Discovery for Spatial Relations via Masking Areas}, author = {G Santipantakis and C Doulkeridis and G Vouros and A Vlachou}, url = {http://arxiv.org/abs/1803.01135}, year = {2018}, date = {2018-03-01}, journal = {arxiv}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Vouros, George A; et al., Big Data Analytics for Time Critical Mobility Forecasting: Recent Progress and Research Challenges Proceeding 2018. @proceedings{329, title = {Big Data Analytics for Time Critical Mobility Forecasting: Recent Progress and Research Challenges}, author = {George A Vouros and et al.}, year = {2018}, date = {2018-01-01}, journal = {EDBT 2018}, keywords = {}, pubstate = {published}, tppubtype = {proceedings} } |
Nikitopoulos, P; Vlachou, A; Doulkeridis, C; Vouros, G A DiStRDF: Distributed Spatio-temporal RDF Queries on Spark Proceeding 2018. @proceedings{335, title = {DiStRDF: Distributed Spatio-temporal RDF Queries on Spark}, author = {P Nikitopoulos and A Vlachou and C Doulkeridis and G A Vouros}, year = {2018}, date = {2018-01-01}, journal = {Proceedings of the Workshops of the EDBT/ICDT 2018 Joint Conference}, keywords = {}, pubstate = {published}, tppubtype = {proceedings} } |
Santipantakis, Georgios M; et al., FAIMUSS: Flexible Data Transformation to RDF from Multiple Streaming Sources Proceeding 2018. @proceedings{331, title = {FAIMUSS: Flexible Data Transformation to RDF from Multiple Streaming Sources}, author = {Georgios M Santipantakis and et al.}, year = {2018}, date = {2018-01-01}, journal = {EDBT 2018}, keywords = {}, pubstate = {published}, tppubtype = {proceedings} } |
Kofinas, Panagiotis; Dounis, Anastasios; Vouros, George A Fuzzy Q-Learning for Multi-Agent Decentralized Energy Management in Microgrids Journal Article Applied Energy, (accepted) , 2018. @article{332, title = {Fuzzy Q-Learning for Multi-Agent Decentralized Energy Management in Microgrids}, author = {Panagiotis Kofinas and Anastasios Dounis and George A Vouros}, year = {2018}, date = {2018-01-01}, journal = {Applied Energy}, volume = {(accepted)}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Vouros, George A; et al., Increasing Maritime Situation Awareness via Trajectory Detection, Enrichment and Recognition of Events Proceeding 2018. @proceedings{330, title = {Increasing Maritime Situation Awareness via Trajectory Detection, Enrichment and Recognition of Events}, author = {George A Vouros and et al.}, year = {2018}, date = {2018-01-01}, journal = {W2GIS 2018}, keywords = {}, pubstate = {published}, tppubtype = {proceedings} } |
Doulkeridis, Christos; Vouros, George A; Qu, Qiang; Wang, Shuhui Springer, 10731 , 2018, ISBN: 978-3-319-73520-7. @proceedings{333, title = {Mobility Analytics for Spatio-Temporal and Social Data – First International Workshop, MATES 2017, Munich, Germany, September 1, 2017, Revised Selected Papers.}, author = {Christos Doulkeridis and George A Vouros and Qiang Qu and Shuhui Wang}, url = {http://dblp.uni-trier.de/db/conf/vldb/mates2017.html}, isbn = {978-3-319-73520-7}, year = {2018}, date = {2018-01-01}, volume = {10731}, publisher = {Springer}, keywords = {}, pubstate = {published}, tppubtype = {proceedings} } |
Calvo, Esther; Cordero, Jose Manuel; Chalkiadakis, Georgios; Spatharis, Christos; Kravaris, Theocharis; Vouros, George; Blekas, Konstantinos Multiagent Reinforcement Learning Methods to Resolve Demand Capacity Balance Problems Proceeding 2018. @proceedings{334, title = {Multiagent Reinforcement Learning Methods to Resolve Demand Capacity Balance Problems}, author = {Esther Calvo and Jose Manuel Cordero and Georgios Chalkiadakis and Christos Spatharis and Theocharis Kravaris and George Vouros and Konstantinos Blekas}, year = {2018}, date = {2018-01-01}, journal = {Hellenic Artificial Intelligence Conference (SETN)}, keywords = {}, pubstate = {published}, tppubtype = {proceedings} } |
Santipantakis, Georgios; Doulkeridis, Christos; Kotis, Konstantinos; Vouros, George A RDF-Gen: Generating RDF from streaming and archival data Proceeding Novi Sad, Serbia, 2018. @proceedings{338, title = {RDF-Gen: Generating RDF from streaming and archival data}, author = {Georgios Santipantakis and Christos Doulkeridis and Konstantinos Kotis and George A Vouros}, year = {2018}, date = {2018-01-01}, journal = {WIMS 2018}, address = {Novi Sad, Serbia}, keywords = {}, pubstate = {published}, tppubtype = {proceedings} } |
Vouros, George A; Kontopoulos, Ioannis; Santipantakis, George M; Vlachou, Akrivi; Doulkeridis, Christos; Artikis, Alexander A Stream Reasoning System for Maritime Monitoring Proceeding 2018. @proceedings{341, title = {A Stream Reasoning System for Maritime Monitoring}, author = {George A Vouros and Ioannis Kontopoulos and George M Santipantakis and Akrivi Vlachou and Christos Doulkeridis and Alexander Artikis}, year = {2018}, date = {2018-01-01}, journal = {TIME 2018}, keywords = {}, pubstate = {published}, tppubtype = {proceedings} } |
Karampelas, Andreas; Vouros, George A Time and Space Efficient Large-Scale Link Discovery using String Similarities Conference WIMS 2018, Novi Sad, Serbia, 2018. @conference{339, title = {Time and Space Efficient Large-Scale Link Discovery using String Similarities}, author = {Andreas Karampelas and George A Vouros}, year = {2018}, date = {2018-01-01}, booktitle = {WIMS 2018}, address = {Novi Sad, Serbia}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
Spatharis, Christos; Kravaris, Theocharis; Blekas, Konstantinos; Vouros, George A Multiagent Reinforcement Learning Methods for Resolving Demand – Capacity Imbalances Conference DASC 2018, London, UK, 2018. @conference{337, title = {Multiagent Reinforcement Learning Methods for Resolving Demand – Capacity Imbalances}, author = {Christos Spatharis and Theocharis Kravaris and Konstantinos Blekas and George A Vouros}, year = {2018}, date = {2018-00-01}, booktitle = {DASC 2018}, address = {London, UK}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
Koubarakis, Manolis; Vouros, George; et. al., AI in Greece: The Case of Research on Linked Geospatial Data Journal Article AI Magazine, 39 , 2018. @article{340, title = {AI in Greece: The Case of Research on Linked Geospatial Data}, author = {Manolis Koubarakis and George Vouros and et. al.}, year = {2018}, date = {2018-00-01}, journal = {AI Magazine}, volume = {39}, chapter = {91}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Cordero, J M; Herranz, R; Scarlatti, D; Marcos, R; Vouros, G; Andrienko, Gennady; Andrienko, Natalia; Fuchs, Georg; Rueping, S Visual Analytics of Flight Trajectories for Uncovering Decision Making Strategies Conference SESAR Innovation Days (SID) 2018, 2018. @conference{343, title = {Visual Analytics of Flight Trajectories for Uncovering Decision Making Strategies}, author = {J M Cordero and R Herranz and D Scarlatti and R Marcos and G Vouros and Gennady Andrienko and Natalia Andrienko and Georg Fuchs and S Rueping}, year = {2018}, date = {2018-00-01}, booktitle = {SESAR Innovation Days (SID) 2018}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
2017 |
Santipantakis, Georgios; et al., The datAcron Ontology for Semantic Trajectories Conference ESWC 2017, 2017. @conference{323, title = {The datAcron Ontology for Semantic Trajectories}, author = {Georgios Santipantakis and et al.}, year = {2017}, date = {2017-01-01}, booktitle = {ESWC 2017}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
Kofinas, Panagiotis; Vouros, George A; Dounis, Anastasios Energy Management in Solar Microgrid via Reinforcement Learning Using Fuzzy Reward Journal Article Advances in Building Energy Research, 2017. @article{317, title = {Energy Management in Solar Microgrid via Reinforcement Learning Using Fuzzy Reward}, author = {Panagiotis Kofinas and George A Vouros and Anastasios Dounis}, year = {2017}, date = {2017-01-01}, journal = {Advances in Building Energy Research}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Vouros, George A; et al., Taming big maritime data to support analytics Conference IF & GIS 2017, Springer Springer, Shanghai, China, 2017. @conference{324, title = {Taming big maritime data to support analytics}, author = {George A Vouros and et al.}, year = {2017}, date = {2017-01-01}, booktitle = {IF & GIS 2017}, publisher = {Springer}, address = {Shanghai, China}, organization = {Springer}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
Doulkeridis, Christos; Pelekis, Nikos; Theodoridis, Yannis; Vouros, George A Big Data Management and Analytics for Mobility Forecasting in datAcron Proceeding 1810 , 2017. @proceedings{322, title = {Big Data Management and Analytics for Mobility Forecasting in datAcron}, author = {Christos Doulkeridis and Nikos Pelekis and Yannis Theodoridis and George A Vouros}, url = {http://ceur-ws.org/Vol-1810/}, year = {2017}, date = {2017-01-01}, journal = {EuroPro 2017 Workshop @ EDBT 2017}, volume = {1810}, keywords = {}, pubstate = {published}, tppubtype = {proceedings} } |
Vlachou, Akrivi; Santipantakis, Georgios; Vouros, George A; Glenis, Apostolos; Doulkeridis, Christos Generating linked RDF data from heterogeneous streaming and archival data sources: Populating the datAcron ontology Conference Semantics 2017, Amstrerdam, Holland, 2017. @conference{328, title = {Generating linked RDF data from heterogeneous streaming and archival data sources: Populating the datAcron ontology}, author = {Akrivi Vlachou and Georgios Santipantakis and George A Vouros and Apostolos Glenis and Christos Doulkeridis}, year = {2017}, date = {2017-01-01}, booktitle = {Semantics 2017}, address = {Amstrerdam, Holland}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
Garcia, Jose Manuel Cordero; Chalkiadakis, Georgios; Kravaris, Theocharis; Vouros, George A; Spatharis, Christos; Blekas, Konstantinos Learning Policies for Resolving Demand-Capacity Imbalances during Pre-tactical Air Traffic Management Proceeding Springer, 2017. @proceedings{325, title = {Learning Policies for Resolving Demand-Capacity Imbalances during Pre-tactical Air Traffic Management}, author = {Jose Manuel Cordero Garcia and Georgios Chalkiadakis and Theocharis Kravaris and George A Vouros and Christos Spatharis and Konstantinos Blekas}, year = {2017}, date = {2017-01-01}, journal = {MATES 2017}, publisher = {Springer}, keywords = {}, pubstate = {published}, tppubtype = {proceedings} } |
Claramunt, Christophe; et al, Maritime data integration and analysis: recent progress and research challenges Proceeding Open Proceedings , 2017. @proceedings{321, title = {Maritime data integration and analysis: recent progress and research challenges}, author = {Christophe Claramunt and et al}, url = {https://openproceedings.org/2017/conf/edbt/proceedings.pdf}, year = {2017}, date = {2017-01-01}, journal = {20th International Conference on Extending Database Technology (EDBT 2017)}, volume = {Open Proceedings}, keywords = {}, pubstate = {published}, tppubtype = {proceedings} } |
Santipantakis, Georgios; Kotis, Konstantinos; Vouros, George A OBDAIR: Ontology-Based Distributed Framework for Accessing, Integrating and Reasoning with Data in Disparate Data Sources Journal Article Expert Systems With Applications, 2017. @article{327, title = {OBDAIR: Ontology-Based Distributed Framework for Accessing, Integrating and Reasoning with Data in Disparate Data Sources}, author = {Georgios Santipantakis and Konstantinos Kotis and George A Vouros}, doi = {https://doi.org/10.1016/j.eswa.2017.08.031}, year = {2017}, date = {2017-01-01}, journal = {Expert Systems With Applications}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Santipantakis, Georgios M; Vouros, George A; Andrienko, Natalia; Martinez, Miguel Garcia; Fuchs, Georg; Andrienko, Gennady; Garcia, Jose Manuel Cordero; Doulkeridis, Christos; Vlachou, Akrivi Specification of Semantic Trajectories Supporting Data Transformations for Analytics: The datAcron Ontology Proceeding 2017. @proceedings{326, title = {Specification of Semantic Trajectories Supporting Data Transformations for Analytics: The datAcron Ontology}, author = {Georgios M Santipantakis and George A Vouros and Natalia Andrienko and Miguel Garcia Martinez and Georg Fuchs and Gennady Andrienko and Jose Manuel Cordero Garcia and Christos Doulkeridis and Akrivi Vlachou}, year = {2017}, date = {2017-01-01}, journal = {Semantics 2017}, keywords = {}, pubstate = {published}, tppubtype = {proceedings} } |
Andrienko, Natalia; et al., Visual exploration of movement and event data with interactive time masks Journal Article Visual Informatics, On Line , 2017. @article{319, title = {Visual exploration of movement and event data with interactive time masks}, author = {Natalia Andrienko and et al.}, url = {http://www.sciencedirect.com/science/article/pii/S2468502X17300049}, doi = {http://dx.doi.org/10.1016/j.visinf.2017.01.004}, year = {2017}, date = {2017-01-01}, journal = {Visual Informatics}, volume = {On Line}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Vouros, George A Learning Conventions via Social Reinforcement Learning in Complex and Open Settings Proceeding Sao Paulo, Brazil, 2017. @proceedings{320, title = {Learning Conventions via Social Reinforcement Learning in Complex and Open Settings}, author = {George A Vouros}, year = {2017}, date = {2017-00-01}, journal = {AAMAS 2017}, address = {Sao Paulo, Brazil}, keywords = {}, pubstate = {published}, tppubtype = {proceedings} } |
Kofinas, Panagiotis; Doltsinis, Stefanos; Dounis, Anastasios; Vouros, George A A Reinforcement Learning Approach for MPPT Control Method of Photovoltaic Sources Renewable Energy Journal Article Reniewable Energy, 108 , 2017. @article{318, title = {A Reinforcement Learning Approach for MPPT Control Method of Photovoltaic Sources Renewable Energy}, author = {Panagiotis Kofinas and Stefanos Doltsinis and Anastasios Dounis and George A Vouros}, url = {http://www.sciencedirect.com/science/article/pii/S0960148117301891}, doi = {http://dx.doi.org/10.1016/j.renene.2017.03.008}, year = {2017}, date = {2017-00-01}, journal = {Reniewable Energy}, volume = {108}, chapter = {461}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
2016 |
Kofinas, Panagiotis; Vouros, George A; Dounis, Anastasios I Energy Management in Solar Microgrid via Reinforcement Learning. Conference SETN 2016, Springer Springer, Thessaloniki, Greece, 2016, ISBN: 978-145033734-2. @conference{316, title = {Energy Management in Solar Microgrid via Reinforcement Learning.}, author = {Panagiotis Kofinas and George A Vouros and Anastasios I Dounis}, url = {http://dl.acm.org/citation.cfm?doid=2903220.2903257}, doi = {10.1145/2903220.2903257}, isbn = {978-145033734-2}, year = {2016}, date = {2016-01-01}, booktitle = {SETN 2016}, publisher = {Springer}, address = {Thessaloniki, Greece}, organization = {Springer}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
Rovatsos, Michael; Vouros, George A; á, Vicente Juli Multi-Agent Systems and Agreement Technologies – 13th European Conference, EUMAS 2015, and Third International Conference, AT 2015 Book Springer, Athens, 2016, ISSN: ISBN 978-3-319-33508-7. @book{315, title = {Multi-Agent Systems and Agreement Technologies – 13th European Conference, EUMAS 2015, and Third International Conference, AT 2015}, author = {Michael Rovatsos and George A Vouros and Vicente Juli á}, issn = {ISBN 978-3-319-33508-7}, year = {2016}, date = {2016-01-01}, volume = {9571}, publisher = {Springer}, address = {Athens}, organization = {Springer}, series = {Lecture Notes in Computer Science}, keywords = {}, pubstate = {published}, tppubtype = {book} } |
Vouros:, George A; Bassiliades, Nick; Bikakis, Antonis; Vrakas, Dimitris; Vlahavas, Ioannis P Proceedings of the 9th Hellenic Conference on Artificial Intelligence Book Thessaloniki, Greece, 2016, ISBN: 978-1-4503-3734-2. @book{314, title = {Proceedings of the 9th Hellenic Conference on Artificial Intelligence}, author = {George A Vouros: and Nick Bassiliades and Antonis Bikakis and Dimitris Vrakas and Ioannis P Vlahavas}, isbn = {978-1-4503-3734-2}, year = {2016}, date = {2016-01-01}, address = {Thessaloniki, Greece}, series = {ACM 2016}, keywords = {}, pubstate = {published}, tppubtype = {book} } |
2015 |
Vouros, George The Emergence of Norms via Contextual Agreements in Open Societies Journal Article CoRR, abs/1503.07017 , 2015. @article{306b, title = {The Emergence of Norms via Contextual Agreements in Open Societies}, author = {George Vouros}, url = {http://arxiv.org/abs/1503.07017}, year = {2015}, date = {2015-03-01}, journal = {CoRR}, volume = {abs/1503.07017}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Skarlatidis, Anastasios; Paliouras, Georgios; Artikis, Alexander; Vouros, George A Probabilistic Event Calculus for Event Recognition Journal Article Transactions on Computational Logic, 16 , 2015. @article{285, title = {Probabilistic Event Calculus for Event Recognition}, author = {Anastasios Skarlatidis and Georgios Paliouras and Alexander Artikis and George A Vouros}, url = {http://dl.acm.org/citation.cfm?doid=2737801.2699916}, year = {2015}, date = {2015-02-01}, journal = {Transactions on Computational Logic}, volume = {16}, chapter = {11}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Santipantakis, Georgios; Kotis, Konstantinos; Vouros, George A Accessing and Reasoning with Data from Disparate Data Sources Using Modular Ontologies and OBDA Conference SEMANTiCS 2015, Vienna, 2015. @conference{310, title = {Accessing and Reasoning with Data from Disparate Data Sources Using Modular Ontologies and OBDA}, author = {Georgios Santipantakis and Konstantinos Kotis and George A Vouros}, year = {2015}, date = {2015-01-01}, booktitle = {SEMANTiCS 2015}, address = {Vienna}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
Santipantakis, Georgios; Vouros, George A Decomposing Ontologies for the Construction of Distributed Knowledge Bases: The mONT ul method Journal Article IJAIT, 24 , 2015. @article{303b, title = {Decomposing Ontologies for the Construction of Distributed Knowledge Bases: The mONT ul method}, author = {Georgios Santipantakis and George A Vouros}, url = {http://dx.doi.org/10.1142/S0218213015400229}, doi = {10.1142/S0218213015400229}, year = {2015}, date = {2015-01-01}, journal = {IJAIT}, volume = {24}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
G.Vouros, The Emergence of Norms via Contextual Agreements in Open Societies Book Chapter Fernando, Guttmann Christian Busquets Didac Koch (Ed.): Advances in Social Computing and Multiagent Systems, 541 , 2015. @inbook{311b, title = {The Emergence of Norms via Contextual Agreements in Open Societies}, author = {G.Vouros}, editor = {Guttmann Christian Busquets Didac Koch Fernando}, url = {http://www.springer.com/gp/book/9783319248035}, year = {2015}, date = {2015-01-01}, booktitle = {Advances in Social Computing and Multiagent Systems}, volume = {541}, series = {Communications in Computer and Information Science}, keywords = {}, pubstate = {published}, tppubtype = {inbook} } |
Santipantakis, Georgios; Kotis, Konstantinos; Vouros, George Ontology-Based Data Integration for Event Recognition in the Maritime Domain Conference WIMS 2015, Limassol, Cyprus, 2015. @conference{309, title = {Ontology-Based Data Integration for Event Recognition in the Maritime Domain}, author = {Georgios Santipantakis and Konstantinos Kotis and George Vouros}, year = {2015}, date = {2015-01-01}, booktitle = {WIMS 2015}, address = {Limassol, Cyprus}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
Santipantakis, George M; Kotis, Konstantinos; Vouros, George A Ontology-based Data Sourcestextquoteleft Integration for Maritime Event Recognition Conference Workshop on Modeling, Computing and Data Handling for Marine Transportation (MCDMT 2015) @ IISA 2015, IEEE IEEE, Corfu – Greece, 2015. @conference{307, title = {Ontology-based Data Sourcestextquoteleft Integration for Maritime Event Recognition}, author = {George M Santipantakis and Konstantinos Kotis and George A Vouros}, year = {2015}, date = {2015-01-01}, booktitle = {Workshop on Modeling, Computing and Data Handling for Marine Transportation (MCDMT 2015) @ IISA 2015}, publisher = {IEEE}, address = {Corfu – Greece}, organization = {IEEE}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
~n, Carlos Iván Ches; Onaindia, Eva; Ossowski, Sascha; Vouros, George A Special Issue on Agreement Technologies Journal Article Information Systems Frontiers Journal, Springer, 2015. @article{302b, title = {Special Issue on Agreement Technologies}, author = {Carlos Iván Ches ~n and Eva Onaindia and Sascha Ossowski and George A Vouros}, doi = {10.1007/s10796-015-9584-z}, year = {2015}, date = {2015-01-01}, journal = {Information Systems Frontiers Journal, Springer}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
2014 |
Santipantakis, Georgios; Vouros, George A Distributed Reasoning with Coupled Ontologies: The E-SHIQ Representation Framework Journal Article KAIS, OnLine , 2014, ISSN: 0219-3116. @article{305, title = {Distributed Reasoning with Coupled Ontologies: The E-SHIQ Representation Framework}, author = {Georgios Santipantakis and George A Vouros}, url = {http://link.springer.com/article/10.1007%2Fs10115-014-0807-2}, doi = {10.1007/s10115-014-0807-2}, issn = {0219-3116}, year = {2014}, date = {2014-11-01}, journal = {KAIS}, volume = {OnLine}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Santipantakis, Georgios; Vouros, George A Constructing E-SHIQ Distributed Knowledge Bases via Ontology Modularization: The mONTul method Conference WoMO 2014, 2014. @conference{304, title = {Constructing E-SHIQ Distributed Knowledge Bases via Ontology Modularization: The mONTul method}, author = {Georgios Santipantakis and George A Vouros}, url = {http://ceur-ws.org/Vol-1248/WoMO14-Paper1.pdf}, year = {2014}, date = {2014-01-01}, booktitle = {WoMO 2014}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
Vouros, George Decentralized Semantic Coordination of Interconnected Entities via Belief Propagation Journal Article AI Communications, On Line , 2014, ISSN: 1875-8452. @article{293b, title = {Decentralized Semantic Coordination of Interconnected Entities via Belief Propagation}, author = {George Vouros}, url = {http://iospress.metapress.com/content/l4x5730110004278/}, doi = {10.3233/AIC-140624}, issn = {1875-8452}, year = {2014}, date = {2014-01-01}, journal = {AI Communications}, volume = {On Line}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Santipantakis, Georgios; Vouros, George A Modularizing Ontologies for the Construction of E-SHIQ Distributed Knowledge Bases Conference SETN 2014 (Hellenic Conference on AI), Springer Springer, Ioannina, Greece, 2014. @conference{301b, title = {Modularizing Ontologies for the Construction of E-SHIQ Distributed Knowledge Bases}, author = {Georgios Santipantakis and George A Vouros}, year = {2014}, date = {2014-01-01}, booktitle = {SETN 2014 (Hellenic Conference on AI)}, publisher = {Springer}, address = {Ioannina, Greece}, organization = {Springer}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
2004 |
Kotis, K; Vouros, G HCONE approach to Ontology Merging Conference 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. @conference{2265, title = {HCONE approach to Ontology Merging}, author = {K. Kotis and G. Vouros}, doi = {10.1007/b97867}, year = {2004}, date = {2004-01-01}, booktitle = {The Semantic Web: Research and Applications: First European Semantic Web Symposium (ESWS 2004)}, publisher = {Springer-Verlag Lecture Notes in Computer Science}, address = {Crete, Greece}, organization = {Springer-Verlag Lecture Notes in Computer Science}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
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. @book{2214, title = {Methods and Applications of Artificial Intelligence Third Helenic Conference on AI, SETN 2004}, author = {G. Vouros and T. Panayiotopoulos}, url = {http://www.springer.com/computer/artificial/book/978-3-540-21937-8}, isbn = {978-3-540-21937-8}, year = {2004}, date = {2004-01-01}, volume = {3025}, publisher = {Springer Verlag}, organization = {Springer Verlag}, series = {Lecture Notes in Computer Science}, keywords = {}, pubstate = {published}, tppubtype = {book} } |
Partsakoulakis, I; Vouros, G Personal Representative Agents for Reliable Participation in Social Contexts Conference In Proceedings of the CEAS workshop – ECAI 04 Workshop, 2004. @conference{2258, title = {Personal Representative Agents for Reliable Participation in Social Contexts}, author = {I. Partsakoulakis and G. Vouros}, year = {2004}, date = {2004-01-01}, booktitle = {In Proceedings of the CEAS workshop – ECAI 04 Workshop}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
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. @article{2288, title = {Shoplet and the Personal Market Place model for e-commerce}, author = {E. Aivaloglou and G. Vouros}, year = {2004}, date = {2004-01-01}, journal = {The electronic journal of e-commerce tools and applications}, volume = {1}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
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. @conference{2259, title = {Socially Deliberating Agents for Human-Centered Knowledge Management}, author = {I. Partsakoulakis and V. Kourakos-Mavromichalis and G. Vouros}, doi = {10.1109/ICSMC.2004.1401085}, year = {2004}, date = {2004-01-01}, booktitle = {In Proc of the IEEE International Conference on Systems, Man and Cybernetics}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
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. @article{2312, title = {Applying Intelligent Agents Technology in a Collaborative Work Environment}, author = {N. Karakapilidis and G. Vouros and J. Darzentas}, year = {2003}, date = {2003-01-01}, journal = {International Transactions in Operations Research}, volume = {10}, pages = {13-31}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
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. @conference{2266, title = {Human Centered Ontology Management with HCONE}, author = {K. Kotis and G. Vouros and K. Kotis}, year = {2003}, date = {2003-01-01}, booktitle = {ODS 2003 Workshop on Ontologies and Distributed Systems,International Joint Conference in Artificial Intelligence(IJCAI-03)}, publisher = {CEUR Workshop Proceedings (CEUR-WS.org)}, address = {Acapulco, Mexico}, organization = {CEUR Workshop Proceedings (CEUR-WS.org)}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
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. @conference{2240, title = {Realizing Human-Centered Systems via Socially Deliberating Agents}, author = {G. Vouros and I. Partsakoulakis and V. Kourakos-Mavromichalis}, year = {2003}, date = {2003-01-01}, booktitle = {In Proceedings of the 10th International Conference in Human-Computer Interaction}, address = {Crete, Greece}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
Vouros, G Technological Issues towards Knowledge Powered Organizations Journal Article Knowledge Management Journal, 7 , pp. 114-127, 2003. @article{2301, title = {Technological Issues towards Knowledge Powered Organizations}, author = {G. Vouros}, doi = {10.1108/13673270310477324}, year = {2003}, date = {2003-01-01}, journal = {Knowledge Management Journal}, volume = {7}, pages = {114-127}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Search with keywords
| 61. | Mariana Ziku Konstantinos Kotis, Gerasimos Pavlogeorgatos Evangelia Kavakli Chara Zeeri George Caridakis : Evaluating crowdsourcing applications with map-based storytelling capabilities in cultural heritage. In: Heritage, 7 (7), pp. 3429-3454, 2024. (Type: Journal Article | Abstract | Links | BibTeX) @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. |
| 62. | Dimitrios Doumanas Andreas Soularidis, Konstantinos Kotis George Vouros : Integrating LLMs in the Engineering of a SAR Ontology. Artificial Intelligence Applications and Innovations, Springer, Cham, 2024, ISBN: 978-3-031-63223-5. (Type: Conference | Abstract | Links | BibTeX) @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. |
| 63. | Christos Spatharis Konstantinos Blekas, George Vouros A: Modelling flight trajectories with multi-modal generative adversarial imitation learning. In: Applied Intelligence, 54 (11), pp. 7118-7134, 2024. (Type: Journal Article | Abstract | Links | BibTeX) @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. |
| 64. | Christos Spatharis Konstantinos Blekas, George Vouros A: Modelling flight trajectories with multi-modal generative adversarial imitation learning. In: Applied Intelligence, 54 , pp. 7118-7134, 2024. (Type: Journal Article | Abstract | Links | BibTeX) @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. |
| 65. | Georgios Bouchouras Pavlos Bitilis, Konstantinos Kotis George Vouros A: LLMs for the Engineering of a Parkinson Disease Monitoring and Alerting Ontology.. GeNeSy’24: First International Workshop on Generative Neuro-Symbolic Artificial Intelligence, co-located
with ESWC 2024, 2024. (Type: Conference | Abstract | Links | BibTeX) @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. |
| 66. | Tania Litaina Andreas Soularidis, Georgios Bouchouras Konstantinos Kotis Evangelia Kavakli : Towards LLM-based semantic analysis of historical legal documents. First International Workshop of Semantic Digital Humanities, co-located with ESWC 2024, 2024. (Type: Workshop | Abstract | Links | BibTeX) @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. |
| 67. | Alexandros Karakikes Panagiotis Alexiadis, Konstantinos Kotis : Bias in X (Twitter) and telegram based intelligence analysis: exploring challenges and potential mitigating roles of AI. In: SN Computer Science, 5 (5), pp. 574, 2024. (Type: Journal Article | Abstract | Links | BibTeX) @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. |
| 68. | Efthymia Moraitou Yannis Christodoulou, Konstantinos Kotis George Caridakis : An ontology-based framework for supporting decision-making in conservation and restoration interventions for cultural heritage. In: ACM Journal on Computing and Cultural Heritage, 17 (3), pp. 1-24, 2024. (Type: Journal Article | Abstract | Links | BibTeX) @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. |
| 69. | Piyabhum Chaysri Christos Spatharis, Kostas Vlachos Konstantinos Blekas : Design and implementation of a low-cost intelligent unmanned surface vehicle. In: Sensors, 24 (10), pp. 3254, 2024. (Type: Journal Article | Abstract | Links | BibTeX) @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. |
| 70. | Sotirios Bentos Eleftherios Bailis, Fotini Oikonomou Stamatis Spirou Emmanouil Mavrikos Stamatis Chatzistamatis Konstantinos Kotis George Tsekouras E: Evaluation of fairness in machine learning-based recidivism predictions: The case of Greek female prison system. 2024 4th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), 2024, ISBN: 979-8-3503-0950-8. (Type: Conference | Abstract | Links | BibTeX) @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. |
| 71. | Andreas Kontogiannis Vasilis Pollatos, Sotiris Kanellopoulos Panayotis Mertikopoulos Aris Pagourtzis Ioannis Panageas : The computational complexity of finding second-order stationary points. Forty-first International Conference on Machine Learning, 2024. (Type: Conference | Abstract | Links | BibTeX) @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. |
| 72. | Konstantinos Bougiatiotis Andreas Sideras, Elias Zavitsanos Georgios Paliouras : Dice@ ml-esg-3: Esg impact level and duration inference using llms for augmentation and contrastive learning. 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. (Type: Conference | Abstract | Links | BibTeX) @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. |
| 73. | Sotiris Angelis Efthymia Moraitou, George Caridakis Konstantinos Kotis : CHEKG: a collaborative and hybrid methodology for engineering modular and fair domain-specific knowledge graphs. In: Knowledge and Information Systems, 66 , pp. 4899–4925, 2024. (Type: Journal Article | Abstract | Links | BibTeX) @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. |
| 74. | Batsis, Georgios: Machine learning for children’s music emotion recognition. Department of Digital Systems, School of Information Technologies and Communication, University of Piraeus, 2024. (Type: Masters Thesis | Abstract | Links | BibTeX) @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. |
| 75. | Elias Alevizos Georgios M Santipantakis, Christos Doulkeridis Alexander Artikis : Online Integration of Spatial Reasoning in Complex Event Recognition. 6th International Workshop on Big Mobility Data Analytics (BMDA), Joint Conference of the EDBT/ICDT 2024, 3651 , 2024, ISSN: 1613-0073. (Type: Conference | Abstract | Links | BibTeX) @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. |
| 76. | 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. In: Information, 15 (2), pp. 100, 2024. (Type: Journal Article | Abstract | Links | BibTeX) @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. |
| 77. | 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. In: Expert Systems with Applications, 236 , 2024, ISBN: 0957-4174. (Type: Journal Article | Abstract | Links | BibTeX) @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. |
| 78. | 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. In: Expert Systems with Applications, 236 , pp. 121234, 2024, ISSN: 0957-4174. (Type: Journal Article | Abstract | Links | BibTeX) @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. |
| 79. | Andreas Soularidis Konstantinos Ι Kotis, George Vouros A: Real-time semantic data integration and reasoning in life-and time-critical decision support systems. In: Electronics, 13 (3), pp. 526, 2024. (Type: Journal Article | Abstract | Links | BibTeX) @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. |
| 80. | Apostolos Glenis, George Vouros A: Scale-boss-mr: scalable time series classification using multiple symbolic representations. In: Applied Sciences, 14 (2), pp. 689, 2024. (Type: Journal Article | Abstract | Links | BibTeX) @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. |