@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} } @article {184, title = {A cross-domain recommender system using deep coupled autoencoders}, journal = {ACM Transactions on Recommender Systems}, year = {2025}, abstract = {Long-standing data sparsity and cold-start constitute thorny and perplexing problems for the recommendation systems. Cross-domain recommendation as a domain adaptation framework has been utilized to effectively address these challenging issues, by exploiting information from multiple domains. In this study, an item-level relevance cross-domain recommendation task is explored, where two related domains, that is, the source and the target domain contain common items. Additionally, a user-level relevance scenario is considered, where the two related domains contain common users. In light of these scenarios, two novel coupled autoencoder-based deep learning methods are proposed for cross-domain recommendation. The first method aims to simultaneously learn a pair of autoencoders in order to reveal the intrinsic representations in the source and target domains, along with a coupled mapping function to model the non-linear relationships between these representations. The second method is derived based on a new joint regularized optimization problem, which employs two autoencoders to generate in a deep and non-linear manner the user and item-latent factors, while at the same time a data-driven function is learnt to map the latent factors across domains. Extensive numerical experiments are conducted illustrating the superior performance of our proposed methods compared to several state-of-the-art cross-domain recommendation frameworks for the extreme cold start scenario.}, author = {Alexandros Gkillas and Dimitrios Kosmopoulos} } @article {166, title = {Cost-efficient coupled learning methods for recovering near-infrared information from RGB signals: Application in precision agriculture}, journal = {Computers and Electronics in Agriculture}, volume = {209}, year = {2023}, abstract = {[size= 16px; box-sizing: border-box; margin: 0px; padding: 0px; caret-color: $\#$2e2e2e; color: $\#$2e2e2e; font-family: ElsevierGulliver, Georgia, {\textquoteright}Times New Roman{\textquoteright}, STIXGeneral, {\textquoteright}Cambria Math{\textquoteright}, Arial, Helvetica, {\textquoteright}Lucida Sans Unicode{\textquoteright}, {\textquoteright}Microsoft Sans Serif{\textquoteright}, {\textquoteright}Segoe UI Symbol{\textquoteright}, {\textquoteright}Arial Unicode MS{\textquoteright}, serif][url=https://www.sciencedirect.com/topics/computer-science/multispectral-imaging]Multispectral imaging[/url] and the derived [/size][url=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/spectral-analysis]spectral analysis[/url] offer useful tools for revealing beneficial information for a variety of applications, e.g., precision agriculture, [url=https://www.sciencedirect.com/topics/computer-science/medical-imaging]medical imaging[/url] and [url=https://www.sciencedirect.com/topics/computer-science/autonomous-driving]autonomous driving[/url]. Contrary to mainstream RGB cameras that can capture information derived only from three bands within the visible spectrum, the multispectral cameras can offer better spectral resolution by utilizing the underlying information in the visible and the near-infrared spectrum. However, the cost of the multispectral cameras is very high and their mobility is limited due to their weight and their need for special hardware equipment. Considering the aforementioned limitations, we propose two low-cost and efficient methods to infer detailed [url=https://www.sciencedirect.com/topics/computer-science/spectral-information]spectral information[/url] outside the visible spectrum range by employing only an RGB camera. The proposed methods require significantly less training data, containing approximately [size= 16px; box-sizing: border-box; margin: 0px; padding: 0px; caret-color: $\#$2e2e2e; color: $\#$2e2e2e; font-family: ElsevierGulliver, Georgia, {\textquoteright}Times New Roman{\textquoteright}, STIXGeneral, {\textquoteright}Cambria Math{\textquoteright}, Arial, Helvetica, {\textquoteright}Lucida Sans Unicode{\textquoteright}, {\textquoteright}Microsoft Sans Serif{\textquoteright}, {\textquoteright}Segoe UI Symbol{\textquoteright}, {\textquoteright}Arial Unicode MS{\textquoteright}, serif]99.8\%[/size][size= 16px; box-sizing: border-box; margin: 0px; padding: 0px; caret-color: $\#$2e2e2e; color: $\#$2e2e2e; font-family: ElsevierGulliver, Georgia, {\textquoteright}Times New Roman{\textquoteright}, STIXGeneral, {\textquoteright}Cambria Math{\textquoteright}, Arial, Helvetica, {\textquoteright}Lucida Sans Unicode{\textquoteright}, {\textquoteright}Microsoft Sans Serif{\textquoteright}, {\textquoteright}Segoe UI Symbol{\textquoteright}, {\textquoteright}Arial Unicode MS{\textquoteright}, serif] less parameters compared to the competing [url=https://www.sciencedirect.com/topics/computer-science/deep-learning]deep learning[/url] approaches and can be deployed on various edge devices with computational and power constraints, e.g., mobile phones or unmanned drones for addressing problems in precision agriculture under real-field settings. Extensive numerical results demonstrate the efficacy of the proposed models to reconstruct images outside the visible spectrum. Additionally, the reconstructed images can be utilized to estimate the [url=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/normalized-difference-vegetation-index]normalized difference vegetation index[/url] (NDVI), which can reveal valuable information concerning the health of the monitored plants without the need of a multispectral camera.[/size]}, author = {Alexandros Gkillas and Dimitrios Kosmopoulos and Kostas Berberidis} } @article {156, title = {Context-Aware Automatic Sign Language Video Transcription in Psychiatric Interviews}, journal = {Sensors}, volume = {22}, year = {2022}, pages = {2656}, abstract = {Sign language (SL) translation constitutes an extremely challenging task when undertaken in a general unconstrained setup, especially in the absence of vast training datasets that enable the use of end-to-end solutions employing deep architectures. In such cases, the ability to incorporate prior information can yield a significant improvement in the translation results by greatly restricting the search space of the potential solutions. In this work, we treat the translation problem in the limited confines of psychiatric interviews involving doctor-patient diagnostic sessions for deaf and hard of hearing patients with mental health problems.To overcome the lack of extensive training data and be able to improve the obtained translation performance, we follow a domain-specific approach combining data-driven feature extraction with the incorporation of prior information drawn from the available domain knowledge. This knowledge enables us to model the context of the interviews by using an appropriately defined hierarchical ontology for the contained dialogue, allowing for the classification of the current state of the interview, based on the doctor{\textquoteright}s question. Utilizing this information, video transcription is treated as a sentence retrieval problem. The goal is predicting the patient{\textquoteright}s sentence that has been signed in the SL video based on the available pool of possible responses, given the context of the current exchange. Our experimental evaluation using simulated scenarios of psychiatric interviews demonstrate the significant gains of incorporating context awareness in the system{\textquoteright}s decisions.}, author = {Erion-Vasilis Pikoulis and Aristeidis Bifis and Maria Trigka and Constantinos Constantinopoulos and Dimitrios Kosmopoulos} } @article {154, title = {A cross-domain recommender system using deep coupled autoencoders}, year = {2021}, month = {8 December 2021}, abstract = {Long-standing data sparsity and cold-start constitute thorny and perplexing problems for the recommendation systems. Cross-domain recommendation as a domain adaptation framework has been utilized to efficiently address these challenging issues, by exploiting information from multiple domains. In this study, an item-level relevance cross-domain recommendation task is explored, where two related domains, that is, the source and the target domain contain common items without sharing sensitive information regarding the users{\textquoteright} behavior, and thus avoiding the leak of user privacy. In light of this scenario, two novel coupled autoencoder-based deep learning methods are proposed for cross-domain recommendation. The first method aims to simultaneously learn a pair of autoencoders in order to reveal the intrinsic representations of the items in the source and target domains, along with a coupled mapping function to model the non-linear relationships between these representations, thus transferring beneficial information from the source to the target domain. The second method is derived based on a new joint regularized optimization problem, which employs two autoencoders to generate in a deep and non-linear manner the user and item latent factors, while at the same time a data-driven function is learnt to map the item-latent factors across domains. Extensive numerical experiments on two publicly available benchmark datasets are conducted illustrating the superior performance of our proposed methods compared to several state-of-the-art cross-domain recommendation frameworks.}, author = {Alexandros Gkillas and Dimitrios Kosmopoulos} } @article {38, title = {A Conditional Random Field-Based Model for Joint Sequence Segmentation and Classification}, journal = {Pattern Recognition}, volume = {46}, year = {2013}, month = {06/2013}, pages = {1569-1578}, author = {Sotirios P. Chatzis and D. Kosmopoulos and P. Doliotis} } @proceedings {13, title = {Content-Based Time Sampling for Efficient Video Delivery over Networks of Low and Variable Bandwidth}, journal = {Digital Telecommunications, , 2006. ICDT {\textquoteright}06. International Conference on}, year = {2006}, month = {Aug.}, keywords = {bandwidth allocation, computational complexity, content management, content-based time sampling, data compression, decoding, image sampling, image sequences, linear frame skipping method, low bandwidth, MPEG compressed domain, multimedia communication, multimedia information, objective evaluation scheme, variable bandwidth, video coding, video communication, video delivery, video sequences}, doi = {10.1109/ICDT.2006.26}, author = {Doulamis, A.D. and D. Kosmopoulos and Doulamis, N.D.} } @proceedings {20, title = {Content-based decomposition of gesture videos}, journal = {Signal Processing Systems Design and Implementation, 2005. IEEE Workshop on}, year = {2005}, month = {Nov.}, pages = {319-324}, keywords = {adapted video summary, binary trees, content-based decomposition, depicted gestures, feature extraction, gesture video decomposition, hierarchically structured video, human computer interaction, human-computer interface applications, key-frame extraction, video browsing, video signal processing, video transmission}, doi = {10.1109/SIPS.2005.1579886}, author = {Doulamis, N.D. and Doulamis, A.D. and D. Kosmopoulos} }