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Student interaction with a virtual learning environment: an empirical study of online engagement behaviours during and since the time of COVID-19. (2023)
Conference Proceeding
JOHNSTON, P., ZARB, M. and MORENO-GARCIA, C.F. 2023. Student interaction with a virtual learning environment: an empirical study of online engagement behaviours during and since the time of COVID-19. In Proceedings of the 2023 IEEE (Institute of Electrical and Electronics Engineers) Frontiers in education conference (FIE 2023),18-21 October 2023, College Station, TX, USA. Piscataway: IEEE [online], article number 10343048. Available from: https://doi.org/10.1109/fie58773.2023.10343048

This paper presents an experience report of online attendance and associated behavioural patterns during a module in the first complete semester undertaken fully online in the autumn of 2020, and the corresponding module deliveries in 2021 and 2022.... Read More about Student interaction with a virtual learning environment: an empirical study of online engagement behaviours during and since the time of COVID-19..

Unmasking the imposters: task-specific feature learning for face presentation attack detection. (2023)
Conference Proceeding
ABDULLAKUTTY, F., ELYAN, E. and JOHNSTON, P. 2023. Unmasking the imposters: task-specific feature learning for face presentation attack detection. In Proceedings of the 2023 International joint conference on neural networks (IJCNN2023), 18-23 June 2023, Gold Coast, Australia. Piscataway: IEEE [online], 10191953. Available from: https://doi.org/10.1109/IJCNN54540.2023.10191953

Presentation attacks pose a threat to the reliability of face recognition systems. A photograph, a video, or a mask representing an authorised user can be used to circumvent the face recognition system. Recent research has demonstrated high accuracy... Read More about Unmasking the imposters: task-specific feature learning for face presentation attack detection..

Pixel-based layer segmentation of complex engineering drawings using convolutional neural networks. (2020)
Conference Proceeding
MORENO-GARCÍA, C.F., JOHNSTON, P. and GARKUWA, B. 2020. Pixel-based layer segmentation of complex engineering drawings using convolutional neural networks. In Proceedings of the 2020 Institute of Electrical and Electronics Engineers (IEEE) International joint conference on neural networks (IEEE IJCNN 2020), part of the 2020 IEEE World congress on computational intelligence (IEEE WCCI 2020) and co-located with the 2020 IEEE congress on evolutionary computation (IEEE CEC 2020) and the 2020 IEEE International fuzzy systems conference (FUZZ-IEEE 2020), 19-24 July 2020, [virtual conference]. Piscataway: IEEE [online], article ID 9207479. Available from: https://doi.org/10.1109/IJCNN48605.2020.9207479

One of the key features of most document image digitisation systems is the capability of discerning between the main components of the printed representation at hand. In the case of engineering drawings, such as circuit diagrams, telephone exchanges... Read More about Pixel-based layer segmentation of complex engineering drawings using convolutional neural networks..

Symbols in engineering drawings (SiED): an imbalanced dataset benchmarked by convolutional neural networks. (2020)
Conference Proceeding
ELYAN, E., MORENO-GARCÍA, C.F. and JOHNSTON, P. 2020. Symbols in engineering drawings (SiED): an imbalanced dataset benchmarked by convolutional neural networks. In Iliadis, L., Angelov, P.P., Jayne, C. and Pimenidis, E. (eds.) Proceedings of the 21st Engineering applications of neural networks conference 2020 (EANN 2020); proceedings of the EANN 2020, 5-7 June 2020, Halkidiki, Greece. Proceedings of the International Neural Networks Society, 2. Cham: Springer [online], pages 215-224. Available from: https://doi.org/10.1007/978-3-030-48791-1_16

Engineering drawings are common across different domains such as Oil & Gas, construction, mechanical and other domains. Automatic processing and analysis of these drawings is a challenging task. This is partly due to the complexity of these documents... Read More about Symbols in engineering drawings (SiED): an imbalanced dataset benchmarked by convolutional neural networks..

Toward video tampering exposure: inferring compression parameters from pixels. (2018)
Conference Proceeding
JOHNSTON, P., ELYAN, E. and JAYNE, C. 2018. Toward video tampering exposure: inferring compression parameters from pixels. In Pimenidis, E. and Jayne, C. (eds.) Proceedings of the 19th International conference on engineering applications of neural networks (EANN 2018), 3-5 September 2018, Bristol, UK. Communications in computer and information science, 893. Cham: Springer [online], pages 44-57, Available from: https://doi.org/10.1007/978-3-319-98204-5_4

Video tampering detection remains an open problem in the field of digital media forensics. Some existing methods focus on recompression detection because any changes made to the pixels of a video will require recompression of the complete stream. Rec... Read More about Toward video tampering exposure: inferring compression parameters from pixels..

Spatial effects of video compression on classification in convolutional neural networks. (2018)
Conference Proceeding
JOHNSTON, P., ELYAN, E. and JAYNE, C. 2018. Spatial effects of video compression on classification in convolutional neural networks. In Proceedings of the 2018 International joint conference on neural networks (IJCNN 2018), 8-13 July 2018, Rio de Janeiro, Brazil. Piscataway, NJ: IEEE [online], article number 8489370. Available from: https://doi.org/10.1109/IJCNN.2018.8489370

A collection of Computer Vision application reuse pre-learned features to analyse video frame-by-frame. Those features are classically learned by Convolutional Neural Networks (CNN) trained on high quality images. However, available video content is... Read More about Spatial effects of video compression on classification in convolutional neural networks..