Publications

@article{lymperaiou2022towards,
title={Towards explainable evaluation of language models on the semantic similarity of visual concepts},
author={Lymperaiou, Maria and Manoliadis, George and Mastromichalakis, Orfeas Menis and Dervakos, Edmund G and Stamou, Giorgos},
journal={arXiv preprint arXiv:2209.03723},
year={2022}
}

@article{dervakos2022computing,
title={Computing Rule-Based Explanations of Machine Learning Classifiers using Knowledge Graphs},
author={Dervakos, Edmund and Menis-Mastromichalakis, Orfeas and Chortaras, Alexandros and Stamou, Giorgos},
journal={arXiv preprint arXiv:2202.03971},
year={2022}
}

@inproceedings{ralli2020crowdheritage,
title={CrowdHeritage: Improving the quality of Cultural Heritage through crowdsourcing methods},
author={Ralli, Maria and Bekiaris, Spyros and Kaldeli, Eirini and Menis-Mastromichalakis, Orfeas and Sofou, Natasa and Tzouvaras, Vassilis and Stamou, Giorgos},
booktitle={2020 15th International Workshop on Semantic and Social Media Adaptation and Personalization (SMA},
pages={1--6},
year={2020},
organization={IEEE}
}

@inproceedings{liartis2021semantic,
title={Semantic Queries Explaining Opaque Machine Learning Classifiers.},
author={Liartis, Jason and Dervakos, Edmund and Menis-Mastromichalakis, Orfeas and Chortaras, Alexandros and Stamou, Giorgos},
booktitle={DAO-XAI},
year={2021}
}

@inproceedings{menis2020deep,
title={Deep ensemble art style recognition},
author={Menis-Mastromichalakis, Orfeas and Sofou, Natasa and Stamou, Giorgos},
booktitle={2020 International Joint Conference on Neural Networks (IJCNN)},
pages={1--8},
year={2020},
organization={IEEE}
}

@article{kaldeli2021crowdheritage,
title={CrowdHeritage: crowdsourcing for improving the quality of cultural heritage metadata},
author={Kaldeli, Eirini and Menis-Mastromichalakis, Orfeas and Bekiaris, Spyros and Ralli, Maria and Tzouvaras, Vassilis and Stamou, Giorgos},
journal={Information},
volume={12},
number={02},
pages={64},
year={2021},
publisher={MDPI}
}

@inproceedings{10.1145/3555858.3555914,
author = {Papagiannis, Tasos and Alexandridis, Georgios and Stafylopatis, Andreas},
title = {State Similarity Based Rapid Action Value Estimation for General Game Playing MCTS Agents},
year = {2022},
isbn = {9781450397957},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3555858.3555914},
doi = {10.1145/3555858.3555914},
abstract = {As Monte Carlo Tree Search has been established as one of the most promising algorithms in the field of Game AI, several approaches have been proposed in an attempt to exploit as much information as possible during the tree search, most important of which include Rapid Action Value Estimation and its variants. These techniques estimate for each action in a node an additional value (AMAF), based on statistics of all simulations where the action was selected deeper in the search tree. In this study, a methodology for determining the most suitable node for using its AMAF scores during the selection phase is presented. Two different approaches are proposed under the scope of discovering similar nodes’ states based on the actions selected towards their paths; in the first one, N-grams are employed to detect similar paths, while in the second one a vectorized representation of the actions taken is used. The suggested algorithms are tested in the context of general game playing achieving quite satisfactory results in terms of both win rate and overall score.},
booktitle = {FDG '22: Proceedings of the 17th International Conference on the Foundations of Digital Games},
articleno = {59},
numpages = {4},
keywords = {MCTS, general game playing, state similarity, N-grams, RAVE},
location = {Athens, Greece},
series = {FDG22}
}