@proceedings {168,
	title = {Developing Robust and Lightweight Adversarial Defenders by Enforcing Orthogonality on Attack-Agnostic Denoising Autoencoders},
	journal = {International Conference on Computer Vision, Workshop on Resource Efficient Deep Learning for Computer Vision},
	year = {2023},
	abstract = {<span style="caret-color: $\#$282828; color: $\#$282828; font-family: {\textquoteright}Helvetica Neue{\textquoteright}, Helvetica, Arial, sans-serif; font-size: 12px; background-color: $\#$98d4ee;">Adversarial attacks have become a critical threat to the security and reliability of machine learning models. We propose a solution to the problem of defending against adversarial attacks using a deep Denoising Auto Encoder (DAE). The proposed DAE is trained to enforce orthogonality between the noise and the range space of its output in each layer of the encoder{\textquoteright}s chain. Furthermore, the pseudoinverse decoder of the DAE is designed to ensure that the reconstructed image and the null space of its intermediate representations in each layer of the chain maintain orthogonality as it progresses from the target space to the latent space. The denoising problem is formulated as an equality constrained optimization problem, which is solved by finding the stationary points of the Lagrangian function. The noisy data are generated by adding realizations of multiple random noise distributions to pristine data during DAE training, resulting in excellent denoising performance. We compare the performance of our full weights and tied-weights DAEs, showing that the latter not only has half the complexity of the former, but also outperforms the former in denoising and in strong adversarial attacks. To demonstrate the effectiveness of the proposed solution we evaluate our networks against recent works in the literature, specifically those focusing on defending against adversarial attacks.</span>},
	author = {Aristeidis Bifis and Emmanuel Psarakis and Dimitrios Kosmopoulos}
}
@proceedings {135,
	title = {A Dataset of Mycenaean Linear B Sequences},
	journal = {Language Resources and Evaluation Conference - LREC},
	year = {2020},
	pages = {2552-2561},
	abstract = {[size= 13.008px]We present a dataset of Mycenaean Linear B sequences gathered from the Mycenaean inscriptions written in the 13th and 14th century B.C. (c. 1400-1200 B.C.). The dataset contains sequences of Mycenaean words and ideograms according to the rules of the Mycenaean Greek language in the Late Bronze Age. Our ultimate goal is to contribute to the study, reading and understanding of ancient scripts and languages. Focusing on sequences, we seek to exploit the structure of the entire language, not just the Mycenaean vocabulary, to analyse sequential patterns. We present an initial experiment on estimating the missing symbols in damaged inscriptions using the dataset.[/size]},
	author = {K. Papavasileiou and G. Owens and D. Kosmopoulos}
}
@proceedings {90,
	title = {Developing visual competencies for  socially assistive robots: the HOBBIT approach},
	journal = {6th International Conference on Pervasive Technologies for Assistive Environments},
	year = {2013},
	abstract = {In this paper, we present our approach towards developing visual competencies for socially assistive robots within the framework of the HOBBIT project. We show how we integrated several vision modules using a layered architectural scheme. Our goal is to endow the mobile robot with visual perception capabilities so that it can interact with the users. We present the key modules of independent motion detection, object detection, body localization, person tracking, head pose estimation and action recognition and we explain how they serve the goal of natural integration of robots in social environments.},
	author = {Kostas Papoutsakis and Pashalis Panteleris and Antonis Ntelidakis and Stavros Stefanou and Xenofon Zabulis and Dimitrios Kosmopoulos and Antonis Argyros}
}
@proceedings {10,
	title = {A Dataset for Workflow Recognition in Industrial Scenes},
	journal = {IEEE International Conference on Image Processing},
	year = {2011},
	pages = {3310-3313},
	author = {A. Voulodimos and D. Kosmopoulos and Vasileiou, G. and Sardis, E. and Doulamis, A.D. and Anagnostopoulos, V. and Lalos, C. and Varvarigou, T.A.}
}
@article {28,
	title = {Detecting abnormal human behaviour using multiple cameras},
	journal = {Signal Processing},
	volume = {89},
	year = {2009},
	pages = {1723 - 1738},
	keywords = {Behaviour understanding, hidden Markov model, Homography, Support vector machine, Trajectory},
	issn = {0165-1684},
	doi = {10.1016/j.sigpro.2009.03.016},
	author = {Panagiota Antonakaki and Dimitrios Kosmopoulos and Stavros J. Perantonis}
}
