@conference {180,
	title = {Cross-Domain Recommendations Using Attention and~Multitask Learning},
	booktitle = {Artificial Intelligence Applications and Innovations},
	year = {2025},
	publisher = {Springer Nature Switzerland},
	organization = {Springer Nature Switzerland},
	address = {Limassol},
	abstract = {In this study, we propose a novel deep learning method for cross-domain recommendations that effectively combines attention mechanisms, autoencoders, and multitask learning. Our approach leverages multiple datasets from diverse domains and incorporates domain-specific encoders, a shared self-attention mechanism, and a multilayer perceptron (MLP) to capture both intra-domain and inter-domain relationships. By jointly modeling these interactions, we improve recommendation accuracy across domains. Experimental results using the MovieLens dataset demonstrate that our proposed cross-domain recommendation system outperforms traditional approaches including matrix factorization, standard MLPs, and self-attention-based baselines.},
	isbn = {978-3-031-96228-8},
	doi = {https://doi.org/10.1007/978-3-031-96228-8_29},
	author = {Papachronopoulos, Gerasimos and Gkillas, Alexandros and Kosmopoulos, Dimitrios},
	editor = {Maglogiannis, Ilias and Iliadis, Lazaros and Andreou, Andreas and Papaleonidas, Antonios}
}
@conference {179,
	title = {Geometric Knowledge Distillation via Procrustes Analysis for Efficient Motion Sequence Classification},
	booktitle = {2025 25th International Conference on Digital Signal Processing (DSP)},
	year = {2025},
	month = {June 25{\textendash}27},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Costa Navarino, Messinia, Greece},
	abstract = {Motion sequence classification methods that rely on geometric approaches{\textemdash}such as Procrustes analysis and Dynamic Time Warping (DTW){\textemdash}offer high accuracy but are often unsuitable for real-time applications due to their computational cost. In this paper, we present a novel geometric knowledge distillation framework that bridges the gap between accuracy and efficiency by transferring rich geometric insights from a Procrustes-DTW-based distance metric into a transformer-based neural network. By generating soft probability distributions from pre-computed Procrustes-DTW distances, we effectively guide the student model{\textquoteright}s training to preserve essential geometric properties like shape similarity, temporal alignment, and spatial transformation invariance. Our method enables fast and scalable motion sequence classification while retaining the benefits of geometric interpretability. We evaluate our framework on two benchmark tasks: sign language recognition using the SIGNUM dataset and human action recognition on UTD-MHAD. Results show that our distillation approach significantly improves classification accuracy over standard supervised learning and achieves dramatically lower inference time compared to traditional geometric methods{\textemdash}making it ideal for real-time motion understanding in wearable, robotic, and interactive systems.},
	keywords = {Action Recognition, Efficient Inference, Geometric Distance Learning, Knowledge Distillation, Procrustes Analysis, Sign Language Recognition, Skeletal Sequences},
	author = {De, Bikram and Blekos, Kostas and Pikoulis, Vasilis and Kosmopoulos, Dimitrios and Metsis, Vangelis}
}
@article {173,
	title = {Guest Editorial: New Frontiers in Image and Video Processing for Sustainable Agriculture},
	journal = {IET Image Processing},
	volume = {19},
	year = {2025},
	pages = {e70032},
	keywords = {agricultural engineering, computer vision, image classification, image processing, object detection, remote sensing},
	doi = {https://doi.org/10.1049/ipr2.70032},
	url = {https://ietresearch.onlinelibrary.wiley.com/doi/abs/10.1049/ipr2.70032},
	author = {Moroni, Davide and Kosmopoulos, Dimitrios}
}
@article {183,
	title = {Towards more Inclusive Museums Using AI: Guidance in Sign Language},
	journal = {ERCIM News},
	volume = {141},
	year = {2025},
	month = {April},
	pages = {21{\textendash}22},
	author = {Kosmopoulos, Dimitrios and Papadopoulou, Evanthia and Argyros, Antonis}
}
@conference {181,
	title = {Automated classification of olive fruit for enhanced olive oil production using computer vision},
	booktitle = {Optics, Photonics, and Digital Technologies for Imaging Applications VIII},
	year = {2024},
	publisher = {SPIE},
	organization = {SPIE},
	address = {Strasbourg, France},
	doi = {10.1117/12.3023019},
	author = {Pikoulis, Erion-Vasilis and Blekos, Kostas and Kosmopoulos, Dimitrios}
}
@conference {167,
	title = {Procrustes-DTW: Dynamic Time Warping Variant for the Recognition of Sign Language Utterances},
	booktitle = {2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)},
	year = {2023},
	abstract = {[color=$\#$333333; font-family: {\textquoteright}HelveticaNeue Regular{\textquoteright}, sans-serif; font-size: 18px; font-variant-ligatures: normal; orphans: 2; widows: 2; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial]In this paper we present a method for classifying sign language videos using a variation of dynamic time warping with the Procrustes distance, which is a shape similarity measure appropriate for handshape recognition. We initially extract features from the dominant hand. We then classify the signs using a nearest-neighbor scheme which includes a dynamic time warping variant, which we dub Procrustes-DTW and which is suitable for extracting a similarity measure of sequences of handshapes only. To reduce computational costs it is combined with a curvature-based summarization scheme with a reasonable sacrifice in accuracy. We verify experimentally our approach on a custom dataset and we show its merit compared to the conventional dynamic time warping approach.[/color]},
	doi = {10.1109/ICASSPW59220.2023.10193012},
	author = {Arvanitis, Nikolaos and Sartinas, Evangelos and Kosmopoulos, Dimitrios}
}
@proceedings {145,
	title = {Variational Bayesian Sequence-to-Sequence Networks for Memory-Efficient Sign Language Translation},
	journal = {International Symposium on Visual Computing},
	year = {2020},
	pages = {251-262},
	publisher = {Springer International Publishing},
	address = {Cham},
	abstract = {Memory-efficient continuous Sign Language Translation is a significant challenge for the development of assisted technologies with real-time applicability for the deaf. In this work, we introduce a paradigm of designing recurrent deep networks whereby the output of the recurrent layer is derived from appropriate arguments from nonparametric statistics. A novel variational Bayesian sequence-to-sequence network architecture is proposed that consists of a) a full Gaussian posterior distribution for data-driven memory compression and b) a nonparametric Indian Buffet Process prior for regularization applied on the Gated Recurrent Unit non-gate weights. We dub our approach Stick-Breaking Recurrent network and show that it can achieve a substantial weight compression without diminishing modeling performance.},
	isbn = {978-3-030-64559-5},
	author = {Partaourides, Harris and Voskou, Andreas and Kosmopoulos, Dimitrios and Chatzis, Sotirios and N. Metaxas, Dimitris}
}
@conference {85,
	title = {Violence content classification using audio features},
	booktitle = {Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence},
	year = {2006},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	address = {Berlin, Heidelberg},
	isbn = {3-540-34117-X, 978-3-540-34117-8},
	author = {Giannakopoulos, Theodoros and Kosmopoulos, Dimitrios and Aristidou, Andreas and Theodoridis, Sergios}
}
