Publications

@unknown{unknown,
author = {Thota, Mamatha and Kollias, Stefanos and Swainson, Mark and Leontidis, Georgios},
year = {2020},
month = {01},
pages = {},
title = {Multi-Source Deep Domain Adaptation for Quality Control in Retail Food Packaging}
}

@article{DBLP:journals/corr/abs-1911-10653,
author = {James Wingate and
Ilianna Kollia and
Luc Bidaut and
Stefanos D. Kollias},
title = {A Unified Deep Learning Approach for Prediction of Parkinson's Disease},
journal = {CoRR},
volume = {abs/1911.10653},
year = {2019},
url = {http://arxiv.org/abs/1911.10653},
archivePrefix = {arXiv},
eprint = {1911.10653},
timestamp = {Tue, 03 Dec 2019 14:15:54 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1911-10653.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{d20f97865b024860a764b993cb131ec5,
title = "Deep Bayesian Self-Training",
abstract = "Supervised deep learning has been highly successful in recent years, achieving state-of-the-art results in most tasks. However, with the ongoing uptake of such methods in industrial applications, the requirement for large amounts of annotated data is often a challenge. In most real-world problems, manual annotation is practically intractable due to time/labour constraints; thus, the development of automated and adaptive data annotation systems is highly sought after. In this paper, we propose both a (1) deep Bayesian self-training methodology for automatic data annotation, by leveraging predictive uncertainty estimates using variational inference and modern neural network (NN) architectures, as well as (2) a practical adaptation procedure for handling high label variability between different dataset distributions through clustering of NN latent variable representations. An experimental study on both public and private datasets is presented illustrating the superior performance of the proposed approach over standard self-training baselines, highlighting the importance of predictive uncertainty estimates in safety-critical domains.",
keywords = "Machine Learning, Deep Learning, Deep learning, Representation learning, Bayesian CNN, Variational inference, Clustering, Self-training, Adaptation, Uncertainty weighting",
author = "Ribeiro, {Fabio De Sousa} and Francesco Caliv{\'a} and Mark Swainson and Kjartan Gudmundsson and Georgios Leontidis and Stefanos Kollias",
note = "Acknowledgements The authors would like to thank Mr. George Marandianos, Mrs. Mamatha Thota and Mr. Samuel Bond-Taylor for manually annotating datasets used in this study and of course the reviewers for their constructive feedback that helped to improve the manuscript. We would also like to thank Professor Luc Bidaut for enabling this collaboration. Funding The research presented in this paper was funded by Engineering and Physical Sciences Research Council (Reference Number EP/R005524/1) and Innovate UK (Reference Number 102908), in collaboration with the Olympus Automation Limited Company, for the project Automated Robotic Food Manufacturing System.",
year = "2020",
month = may,
doi = "10.1007/s00521-019-04332-4",
language = "English",
volume = "32",
pages = "4275--4291",
journal = "Neural Computing and Applications",
issn = "0941-0643",
publisher = "Springer London",

}

@inproceedings{Yu2019AMC,
title={A monocular camera based person-specific fall detection system exploiting deep neural network aided unsupervised},
author={M. Yu and L. Gong and R. Clifford and Carol Duff and Xujiong Ye and S. Kollias},
year={2019}
}

@article{760f1c40554947a3a2ced73834a1c13f,
title = "3D convolutional and recurrent neural networks for reactor perturbation unfolding and anomaly detection",
abstract = "With Europe's ageing fleet of nuclear reactors operating closer to their safety limits, the monitoring of such reactors through complex models has become of great interest to maintain a high level of availability and safety. Therefore, we propose an extended Deep Learning framework as part of the CORTEX Horizon 2020 EU project for the unfolding of reactor transfer functions from induced neutron noise sources. The unfolding allows for the identification and localisation of reactor core perturbation sources from neutron detector readings in Pressurised Water Reactors. A 3D Convolutional Neural Network (3D-CNN) and Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) have been presented, each to study the signals presented in frequency and time domain respectively. The proposed approach achieves state-of-the-art results with the classification of perturbation type in the frequency domain reaching 99.89% accuracy and localisation of the classified perturbation source being regressed to 0.2902 Mean Absolute Error (MAE).",
keywords = "Machine Learning, Deep Learning, nuclear reactors, signal processing",
author = "Aiden Durrant and Georgios Leontidis and Stefanos Kollias",
note = "The research conducted was made possible through funding from the Euratom research and training programme 2014-2018 under grant agreement No 754316 for the {\textquoteleft}CORe Monitoring Techniques And EXperimental Validation And Demonstration (CORTEX){\textquoteright} Horizon 2020 project, 2017-2021. We would like to thank the Chalmers University of Technology, particularly Dr C. Demaziere, Dr P. Vinai, Dr A. Milonakis and the Paul Scherrer Institute, particularly Dr A. Dokhane and Dr V. Verma for providing the frequency and domain data respectively, for assisting us with their understanding and for collaborating with us in the analysis process.",
year = "2019",
doi = "10.1051/epjn/2019047",
language = "English",
volume = "5",
journal = "EPJ Nuclear Sciences & Technologies",

}

@inproceedings{AILS:conf/dlog/ChortarasGS19,
author = {Alexandros Chortaras and
Michalis Giazitzoglou and
Giorgos Stamou},
title = {Inside the Query Space of {DL} Knowledge Bases},
booktitle = {Proceedings of the 32nd International Workshop on Description Logics,
Oslo, Norway, June 18-21, 2019.},
year = {2019},
url = {http://ceur-ws.org/Vol-2373/paper-11.pdf}
}