@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}
}
@proceedings {185,
	title = {PhishBENCH: A benchmark dataset for legal tasks related to phishing attacks},
	journal = {International Conference on Artificial Intelligence Tools},
	year = {2025},
	abstract = {Phishing attacks constitute a significant cyber-threat, causing financial losses and undermining digital trust. Despite advancements in awareness campaigns and regulatory efforts, many individuals and organizations struggle to understand their legal rights and obligations when victimized by phishing. This paper presents a benchmark framework designed to support researchers in the analysis of legal texts and cases, with the goal of helping users navigate the complex legal landscape surrounding phishing attacks. We evaluate BERT-based models, large language models, and retrieval-augmented generation using a curated knowledge base of phishing laws, regulations, and common scenarios. We provide baseline evaluations across multiple task categories, including true-false questions, phishing attack cases, penal code analysis, legal cases with judicial decisions, and question-answering tasks, all within the Greek legal jurisdiction.},
	keywords = {benchmark, large language model, phishing law, retrieval augmented generation},
	author = {Ioannis Katoikos and Dimitrios Kosmopoulos and Ioannis Iglezakis and Panagiota Kiortsi and George Thomopoulos and Christos Fidas}
}
