All Outputs (46)

Towards a declarative approach to constructing dialogue games. (2021)
Conference Proceeding
SNAITH, M. and WELLS, S. 2021. Towards a declarative approach to constructing dialogue games. In Grasso, F., Green, N.L., Schneider, J. and Wells, S. (eds.) Proceedings of the 21st Workshop on computational models on natural argument (CMNA 2021), 2-3 September 2021, [virtual conference]. CEUR workshop proceedings, 2937. Aachen: CEUR-WS [online], pages 9-18. Available from: http://ceur-ws.org/Vol-2937/paper2.pdf

In this paper we sketch a new approach to the development of dialogue games that builds upon the knowledge gained from several decades of dialogue game research across a variety of communities and which leverages the capabilities of the Dialogue Game... Read More about Towards a declarative approach to constructing dialogue games..

Cost-effective and efficient detection of autism from screening test data using light gradient boosting machine. (2021)
Conference Proceeding
KAMMA, S.P., BANO, S., NIHARIKA, G.L., CHILUKURI, G.S. and GHANTA, D. 2022. Cost-effective and efficient detection of autism from screening test data using light gradient boosting machine. In Raj, J.S., Palanisamy, R., Perikos, I. and Shi, Y. (eds.) Proceedings of the 4th International conference on intelligent sustainable systems (ICISS 2021), 26-27 February 2021, Tirunelveli, India. Lecture notes in networks and systems, 213. Singapore: Springer [online], pages 777-789. Available from: https://doi.org/10.1007/978-981-16-2422-3_61

Autism spectrum disorder (ASD) is a developmental disorder that affects the brain. Autism constrains a person’s ability to interact and communicate with others. The cause of autism, in general, is unknown though genetics does play a role in the manif... Read More about Cost-effective and efficient detection of autism from screening test data using light gradient boosting machine..

Deep neural networks based error level analysis for lossless image compression based forgery detection. (2021)
Conference Proceeding
SRI, C.G., BANO, S., DEEPIKA, T., KOLA, N. and PRANATHI, Y.L. 2021. Deep neural networks based error level analysis for lossless image compression based forgery detection. In Proceedings of the 2021 International conference on intelligent technologies (CONIT 2021), 25-27 June 2021, Hubli, India. Piscataway: IEEE [online]. Available from: https://doi.org/10.1109/CONIT51480.2021.9498357

The proposed model is implemented in deep learning based on counterfeit feature extraction and Error Level Analysis (ELA) techniques. Error level analysis is used to improve the efficiency of distinguishing copy-move images produced by Deep Fake from... Read More about Deep neural networks based error level analysis for lossless image compression based forgery detection..

An interactive evolution strategy based deep convolutional generative adversarial network for 2D video game level procedural content generation. (2021)
Conference Proceeding
JIANG, M. and ZHANG, L. 2021. An interactive evolution strategy based deep convolutional generative adversarial network for 2D video game level procedural content generation. In Proceedings of 2021 International joint conference on neural networks (IJCNN 2021), 18-22 July 2021, [virtual conference]. Piscataway: IEEE [online], article 9533847. Available from: https://doi.org/10.1109/IJCNN52387.2021.9533847

The generation of desirable video game contents has been a challenge of games level design and production. In this research, we propose a game player flow experience driven interactive latent variable evolution strategy incorporated with a Deep Convo... Read More about An interactive evolution strategy based deep convolutional generative adversarial network for 2D video game level procedural content generation..

Class-decomposition and augmentation for imbalanced data sentiment analysis. (2021)
Conference Proceeding
MORENO-GARCIA, C.F., JAYNE, C. and ELYAN, E. 2021. Class-decomposition and augmentation for imbalanced data sentiment analysis. In Proceedings of 2021 International joint conference on neural networks (IJCNN 2021), 18-22 July 2021, [virtual conference]. Piscataway: IEEE [online], article 9533603. Available from: https://doi.org/10.1109/IJCNN52387.2021.9533603

Significant progress has been made in the area of text classification and natural language processing. However, like many other datasets from across different domains, text-based datasets may suffer from class-imbalance. This problem leads to model's... Read More about Class-decomposition and augmentation for imbalanced data sentiment analysis..

Deep recurrent neural networks with attention mechanisms for respiratory anomaly classification. (2021)
Conference Proceeding
WALL, C., ZHANG, L., YU, Y. and MISTRY, K. 2021. Deep recurrent neural networks with attention mechanisms for respiratory anomaly classification. In Proceedings of 2021 International joint conference on neural networks (IJCNN 2021), 18-22 July 2021, [virtual conference]. Piscataway: IEEE [online], article 9533966. Available from: https://doi.org/10.1109/IJCNN52387.2021.9533966

In recent years, a variety of deep learning techniques and methods have been adopted to provide AI solutions to issues within the medical field, with one specific area being audio-based classification of medical datasets. This research aims to create... Read More about Deep recurrent neural networks with attention mechanisms for respiratory anomaly classification..

Towards the landscape rotation as a perturbation strategy on the quadratic assignment problem. (2021)
Conference Proceeding
ALZA, J., BARTLETT, M., CEBERIO, J. and MCCALL, J. 2021. Towards the landscape rotation as a perturbation strategy on the quadratic assignment problem. In Chicano, F. (ed.) GECCO '21: proceedings of 2021 Genetic and evolutionary computation conference companion, 10-14 July 2021, [virtual conference]. New York: ACM [online], pages 1405-1413. Available from: https://doi.org/10.1145/3449726.3463139

Recent work in combinatorial optimisation have demonstrated that neighbouring solutions of a local optima may belong to more favourable attraction basins. In this sense, the perturbation strategy plays a critical role on local search based algorithms... Read More about Towards the landscape rotation as a perturbation strategy on the quadratic assignment problem..

Experiences of piloting the learning by developing action model in a computing science context. (2021)
Conference Proceeding
LINTILÄ, T. and ZARB, M. 2021. Experiences of piloting the learning by developing action model in a computing science context. In Gómez Chova, L., López Martínez, A. and Candel Torres, I. (eds.) EDULEARN 21: proceedings of the 13th international conference on Education and new learning technologies 2021 (EDULEARN 2021), 5-6 July 2021, [virtual conference]. Valencia: International Academy of Technology, Education and Development (IATED) [online], pages 3036-3042. To be available from: https://doi.org/10.21125/edulearn.2021

This article describes the piloting of the Learning by Developing (LbD) action model in the UK. The purpose of the pilot is to study how a pedagogical method based on the LbD can be introduced in computing students in the UK. The LbD action model has... Read More about Experiences of piloting the learning by developing action model in a computing science context..

Proceedings of the 2021 SICSA explainable artificial intelligence workshop (SICSA XAI 2021) (2021)
Conference Proceeding
MARTIN, K., WIRATUNGA, N. and WIJEKOON, A. (eds.) 2021. Proceedings of the 2021 SICSA explainable artificial intelligence workshop (SICSA XAI 2021), 1 June 2021, Aberdeen, UK. CEUR workshop proceedings, 2894. Aachen: CEUR-WS [online]. Available from: https://ceur-ws.org/Vol-2894/

The SICSA Workshop 2021 was designed to present a forum for the dissemination of ideas on domains relating to the explainability of Artificial Intelligence and Machine Learning methods. The event was organised into several themed sessions: Session 1... Read More about Proceedings of the 2021 SICSA explainable artificial intelligence workshop (SICSA XAI 2021).

Image pre-processing and segmentation for real-time subsea corrosion inspection. (2021)
Conference Proceeding
PIRIE, C. and MORENO-GARCIA, C.F. 2021. Image pre-processing and segmentation for real-time subsea corrosion inspection. In Iliadis, L., Macintyre, J., Jayne, C. and Pimenidis, E. (eds.). Proceedings of the 22nd Engineering applications of neural networks conference (EANN2021), 25-27 June 2021, Halkidiki, Greece. Proceedings of the International Neural Networks Society (INNS), 3. Cham: Springer [online], pages 220-231. Available from: https://doi.org/10.1007/978-3-030-80568-5_19

Inspection engineering is a highly important field in the Oil & Gas sector for analysing the health of offshore assets. Corrosion, a naturally occurring phenomenon, arises as a result of a chemical reaction between a metal and its environment, causin... Read More about Image pre-processing and segmentation for real-time subsea corrosion inspection..

Weighted ensemble of deep learning models based on comprehensive learning particle swarm optimization for medical image segmentation. (2021)
Conference Proceeding
DANG, T., NGUYEN, T.T., MORENO-GARCIA, C.F., ELYAN, E. and MCCALL, J. 2021. Weighted ensemble of deep learning models based on comprehensive learning particle swarm optimization for medical image segmentation. In Proceeding of 2021 IEEE (Institute of electrical and electronics engineers) Congress on evolutionary computation (CEC 2021), 28 June - 1 July 2021, Kraków, Poland : [virtual conference]. Piscataway: IEEE [online], pages 744-751. Available from: https://doi.org/10.1109/CEC45853.2021.9504929

In recent years, deep learning has rapidly become a method of choice for segmentation of medical images. Deep neural architectures such as UNet and FPN have achieved high performances on many medical datasets. However, medical image analysis algorith... Read More about Weighted ensemble of deep learning models based on comprehensive learning particle swarm optimization for medical image segmentation..

Face detection with YOLO on edge. (2021)
Conference Proceeding
ALI-GOMBE, A., ELYAN, E., MORENO-GARCIA, C.F. and ZWIEGELAAR, J. 2021. Face detection with YOLO on edge. In Iliadis, L., Macintyre, J., Jayne, C. and Pimenidis, E. (eds.). Proceedings of the 22nd Enginering applications of neural networks conference (EANN2021), 25-27 June 2021, Halkidiki, Greece. Proceedings of the International Neural Networks Society (INNS), 3. Cham: Springer [online], pages 284-292. Available from: https://doi.org/10.1007/978-3-030-80568-5_24

Significant progress has been achieved in objects detection applications such as Face Detection. This mainly due to the latest development in deep learning-based approaches and especially in the computer vision domain. However, deploying deep-learnin... Read More about Face detection with YOLO on edge..

Weighted ensemble of gross error detection methods based on particle swarm optimization. (2021)
Conference Proceeding
DOBOS, D., NGUYEN, T.T., MCCALL, J., WILSON, A., STOCKTON, P. and CORBETT, H. 2021. Weighted ensemble of gross error detection methods based on particle swarm optimization. In Chicano, F. (ed) Proceedings of the 2021 Genetic and evolutionary computation conference (GECCO 2021), 10-14 July 2021, [virtual conference]. New York: ACM [online], pages 307-308. Available from: https://doi.org/10.1145/3449726.3459415

Gross errors, a kind of non-random error caused by process disturbances or leaks, can make reconciled estimates can be very inaccurate and even infeasible. Detecting gross errors thus prevents financial loss from incorrectly accounting and also ident... Read More about Weighted ensemble of gross error detection methods based on particle swarm optimization..

Evaluating the learning by development action model with CS students. (2021)
Conference Proceeding
LINTILÄ, T. 2021. Evaluating the learning by development action model with CS students. In Proceedings of the 26th Association for Computing Machinery (ACM) Innovation and technology in computer science education conference 2021 (ITiCSE '21), 26 June - 1 July 2021, [virtual conference]. New York: ACM [online], pages 670-671. Available from: https://doi.org/10.1145/3456565.3460020

The purpose of the study is to find out how the competence of computing students develops throughout a study module as they are exposed to a Learning by Developing (LbD) Action Model. Furthermore, their perception of the model is evaluated against ex... Read More about Evaluating the learning by development action model with CS students..

Educational landscapes during and after COVID-19. (2021)
Conference Proceeding
SIEGEL, A.A., ZARB, M., ALSHAIGY, B., BLANCHARD, J., CRICK, T., GLASSEY, R., HOTT, J.R., LATULIPE, C., RIEDESEL, C., SENAPATHI, M., SIMON. and WILLIAMS, D. 2021. Educational landscapes during and after COVID-19. In Proceedings of the 26th Association for Computing Machinery (ACM) Innovation and technology in computer science education conference 2021 (ITiCSE '21), 26 June - 1 July 2021, [virtual conference]. New York: ACM [online], pages 597-598. Available from: https://doi.org/10.1145/3456565.3461439

The coronavirus (COVID-19) pandemic has forced an unprecedented global shift within higher education in the ways that we communicate with and educate students. This necessary paradigm shift has compelled educators to take a critical look at their tea... Read More about Educational landscapes during and after COVID-19..

Landscape features and automated algorithm selection for multi-objective interpolated continuous optimisation problems. (2021)
Conference Proceeding
LIEFOOGHE, A., VEREL, S., LACROIX, B., ZĂVOIANU, A.-C. and MCCALL, J. 2021. Landscape features and automated algorithm selection for multi-objective interpolated continuous optimisation problems. In Chicano, F. (ed) Proceedings of 2021 Genetic and evolutionary computation conference (GECCO 2021), 10-14 July 2021, [virtual conference]. New York: ACM [online], pages 421-429. Available from: https://doi.org/10.1145/3449639.3459353

In this paper, we demonstrate the application of features from landscape analysis, initially proposed for multi-objective combinatorial optimisation, to a benchmark set of 1 200 randomly-generated multiobjective interpolated continuous optimisation p... Read More about Landscape features and automated algorithm selection for multi-objective interpolated continuous optimisation problems..

Performance analysis of vertical handover using predictable LGD event based on IEEE 802.21. (2021)
Conference Proceeding
HAJAR, M.S., CHAHINE, M.K., HAMDAN, R. and QDAH, M.S. 2021. Performance analysis of vertical handover using predictable LGD event based on IEEE 802.21. In Proceedings of the 2021 IEEE International conference on communications workshops (ICC workshops 2021), 14-23 June 2021, Montreal, Canada. Piscataway: IEEE [online], 9473639. Available from: https://doi.org/10.1109/ICCWorkshops50388.2021.9473639

Next Generation Wireless Networks (NGWN) aim to provide any service at any time and anywhere with seamless mobility between homogeneous and heterogeneous networks. IEEE defines the IEEE 802.21 standard to facilitate seamless handover, namely, Media I... Read More about Performance analysis of vertical handover using predictable LGD event based on IEEE 802.21..

Non-deterministic solvers and explainable AI through trajectory mining. (2021)
Conference Proceeding
FYVIE, M., MCCALL, J.A.W. and CHRISTIE, L.A. 2021. Non-deterministic solvers and explainable AI through trajectory mining. In Martin, K., Wiratunga, N. and Wijekoon, A. (eds.) SICSA XAI workshop 2021: proceedings of 2021 SICSA (Scottish Informatics and Computer Science Alliance) eXplainable artificial intelligence workshop (SICSA XAI 2021), 1st June 2021, [virtual conference]. CEUR workshop proceedings, 2894. Aachen: CEUR-WS [online], session 4, pages 75-78. Available from: http://ceur-ws.org/Vol-2894/poster2.pdf

Traditional methods of creating explanations from complex systems involving the use of AI have resulted in a wide variety of tools available to users to generate explanations regarding algorithm and network designs. This however has traditionally bee... Read More about Non-deterministic solvers and explainable AI through trajectory mining..

Counterfactual explanations for student outcome prediction with Moodle footprints. (2021)
Conference Proceeding
WIJEKOON, A., WIRATUNGA, N., NKILSI-ORJI, I., MARTIN, K., PALIHAWADANA, C. and CORSAR, D. 2021. Counterfactual explanations for student outcome prediction with Moodle footprints. In Martin, K., Wiratunga, N. and Wijekoon, A. (eds.) SICSA XAI workshop 2021: proceedings of 2021 SICSA (Scottish Informatics and Computer Science Alliance) eXplainable artificial intelligence workshop (SICSA XAI 2021), 1st June 2021, [virtual conference]. CEUR workshop proceedings, 2894. Aachen: CEUR-WS [online], session 1, pages 1-8. Available from: http://ceur-ws.org/Vol-2894/short1.pdf

Counterfactual explanations focus on “actionable knowledge” to help end-users understand how a machine learning outcome could be changed to one that is more desirable. For this purpose a counterfactual explainer needs to be able to reason with simila... Read More about Counterfactual explanations for student outcome prediction with Moodle footprints..

ConfIs: a tool for privacy and security analysis and conflict resolution for supporting GDPR compliance through privacy-by-design. (2021)
Conference Proceeding
ALKUBAISY, D., PIRAS, L., AL-OBEIDALLAH, M.G., COX, K. and MOURATIDIS, H. 2021. ConfIs: a tool for privacy and security analysis and conflict resolution for supporting GDPR compliance through privacy-by-design. In Ali, R., Kaindl, H. and Maciaszek, L. (eds.) Proceedings of 16th Evaluation of novel approaches to software engineering international conference 2021 (ENASE 2021), 26-27 April 2021, [virtual conference]. Setúbal, Portugal: SciTePress [online], pages 80-91. Available from: https://doi.org/10.5220/0010406100800091

Privacy and security requirements, and their potential conflicts, are increasingly having more and more importance. It is becoming a necessary part to be considered, starting from the very early stages of requirements engineering, and in the entire s... Read More about ConfIs: a tool for privacy and security analysis and conflict resolution for supporting GDPR compliance through privacy-by-design..