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Actionable feature discovery in counterfactuals using feature relevance explainers. (2021)
Conference Proceeding
WIRATUNGA, N., WIJEKOON, A., NKISI-ORJI, I., MARTIN, K., PALIHAWADANA, C. and CORSAR, D. 2021. Actionable feature discovery in counterfactuals using feature relevance explainers. In Borck, H., Eisenstadt, V., Sánchez-Ruiz, A. and Floyd, M. (eds.) Workshop proceedings of the 29th International conference on case-based reasoning (ICCBR-WS 2021), 13-16 September 2021, [virtual event]. CEUR workshop proceedings, 3017. Aachen: CEUR-WS [online], pages 63-74. Available from: http://ceur-ws.org/Vol-3017/101.pdf

Counterfactual explanations focus on 'actionable knowledge' to help end-users understand how a Machine Learning model outcome could be changed to a more desirable outcome. For this purpose a counterfactual explainer needs to be able to reason with si... Read More about Actionable feature discovery in counterfactuals using feature relevance explainers..

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).

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..

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..

Personalised meta-learning for human activity recognition with few-data. (2020)
Conference Proceeding
WIJEKOON, A. and WIRATUNGA, N. 2020. Personalised meta-learning for human activity recognition with few-data. In Bramer, M. and Ellis, R. (eds.) Artificial intelligence XXXVII: proceedings of 40th British Computer Society's Specialist Group on Artificial Intelligence (SGAI) Artificial intelligence international conference 2020 (AI-2020), 15-17 December 2020, [virtual conference]. Lecture notes in artificial intelligence, 12498. Cham: Springer [online], pages 79-93. Available from: https://doi.org/10.1007/978-3-030-63799-6_6

State-of-the-art methods of Human Activity Recognition(HAR) rely on a considerable amount of labelled data to train deep architectures. This becomes prohibitive when tasked with creating models that are sensitive to personal nuances in human movement... Read More about Personalised meta-learning for human activity recognition with few-data..

Clood CBR: towards microservices oriented case-based reasoning. (2020)
Conference Proceeding
NKISI-ORJI, I., WIRATUNGA, N., PALIHAWADANA, C., RECIO-GARCIA, J.A. and CORSAR, D. 2020. Clood CBR: towards microservices oriented case-based reasoning. In Watson, I and Weber, R. (eds.) Case-based reasoning research and development: proceedings of the 28th International conference on case-based reasoning research and development (ICCBR 2020), 8-12 June 2020, Salamanca, Spain [virtual conference]. Lecture notes in computer science, 12311. Cham: Springer [online], pages 129-143. Available from: https://doi.org/10.1007/978-3-030-58342-2_9

CBR applications have been deployed in a wide range of sectors, from pharmaceuticals; to defence and aerospace to IoT and transportation, to poetry and music generation; for example. However, a majority of these have been built using monolithic archi... Read More about Clood CBR: towards microservices oriented case-based reasoning..

Learning to compare with few data for personalised human activity recognition. (2020)
Conference Proceeding
WIRATUNGA, N., WIJEKOON, A. and COOPER, K. 2020. Learning to compare with few data for personalised human activity recognition. In Watson, I and Weber, R. (eds.) Case-based reasoning research and development: proceedings of the 28th International conference on case-based reasoning research and development (ICCBR 2020), 8-12 June 2020, Salamanca, Spain [virtual conference]. Lecture notes in computer science, 12311. Cham: Springer [online], pages 3-14. Available from: https://doi.org/10.1007/978-3-030-58342-2_1

Recent advances in meta-learning provides interesting opportunities for CBR research, in similarity learning, case comparison and personalised recommendations. Rather than learning a single model for a specific task, meta-learners adopt a generalist... Read More about Learning to compare with few data for personalised human activity recognition..

Assessing the clinicians’ pathway to embed artificial intelligence for assisted diagnostics of fracture detection. (2020)
Conference Proceeding
MORENO-GARCÍA, C.F., DANG, T., MARTIN, K., PATEL, M., THOMPSON, A., LEISHMAN, L. and WIRATUNGA, N. 2020. Assessing the clinicians’ pathway to embed artificial intelligence for assisted diagnostics of fracture detection. In Bach, K., Bunescu, R., Marling, C. and Wiratunga, N. (eds.) Knowledge discovery in healthcare data 2020: proceedings of the 5th Knowledge discovery in healthcare data international workshop 2020 (KDH 2020), co-located with 24th European Artificial intelligence conference (ECAI 2020), 29-30 August 2020, [virtual conference]. CEUR workshop proceedings, 2675. Aachen: CEUR-WS [online], pages 63-70. Available from: http://ceur-ws.org/Vol-2675/paper10.pdf

Fracture detection has been a long-standingparadigm on the medical imaging community. Many algo-rithms and systems have been presented to accurately detectand classify images in terms of the presence and absence offractures in different parts of the... Read More about Assessing the clinicians’ pathway to embed artificial intelligence for assisted diagnostics of fracture detection..

Heterogeneous multi-modal sensor fusion with hybrid attention for exercise recognition. (2020)
Conference Proceeding
WIJEKOON, A., WIRATUNGA, N. and COOPER, K. 2020. Heterogeneous multi-modal sensor fusion with hybrid attention for exercise recognition. 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 9206941. Available from: https://doi.org/10.1109/IJCNN48605.2020.9206941

Exercise adherence is a key component of digital behaviour change interventions for the self-management of musculoskeletal pain. Automated monitoring of exercise adherence requires sensors that can capture patients performing exercises and Machine Le... Read More about Heterogeneous multi-modal sensor fusion with hybrid attention for exercise recognition..

Locality sensitive batch selection for triplet networks. (2020)
Conference Proceeding
MARTIN, K., WIRATUNGA, N. and SANI, S. 2020. Locality sensitive batch selection for triplet 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 9207538. Available from: https://doi.org/10.1109/IJCNN48605.2020.9207538

Triplet networks are deep metric learners which learn to optimise a feature space using similarity knowledge gained from training on triplets of data simultaneously. The architecture relies on the triplet loss function to optimise its weights based u... Read More about Locality sensitive batch selection for triplet networks..

Evaluating the transferability of personalised exercise recognition models. (2020)
Conference Proceeding
WIJEKOON, A. and WIRATUNGA, N. 2020. Evaluating the transferability of personalised exercise recognition models. 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 32-44. Available from: https://doi.org/10.1007/978-3-030-48791-1_3

Exercise Recognition (ExR) is relevant in many high impact domains, from health care to recreational activities to sports sciences. Like Human Activity Recognition (HAR), ExR faces many challenges when deployed in the real-world. For instance, typica... Read More about Evaluating the transferability of personalised exercise recognition models..

Learning to recognise exercises for the self-management of low back pain. (2020)
Conference Proceeding
WIJEKOON, A., WIRATUNGA, N., COOPER, K. and BACH, K. 2020. Learning to recognise exercises for the self-management of low back pain. In Barták, R. and Bell, E. (eds.). Proceedings of the 33rd International Florida Artificial Intelligence Research Society (FLAIRS) 2020 conference (FLAIRS-33), 17-20 May 2020, Miami Beach, USA. Palo Alto: AAAI Press [online], pages 347-352. Available from: https://aaai.org/ocs/index.php/FLAIRS/FLAIRS20/paper/view/18460

Globally, Low back pain (LBP) is one of the top three contributors to years lived with disability. Self-management with an active lifestyle is the cornerstone for preventing and managing LBP. Digital interventions are introduced in the recent past to... Read More about Learning to recognise exercises for the self-management of low back pain..

Human activity recognition with deep metric learners. (2020)
Conference Proceeding
MARTIN, K., WIJEKOON, A. and WIRATUNGA, N. 2019. Human activity recognition with deep metric learners. In Kapetanakis, S. and Borck, H. (eds.) Proceedings of the 27th International conference on case-based reasoning workshop (ICCBR-WS19), co-located with the 27th International conference on case-based reasoning (ICCBR19), 8-12 September 2019, Otzenhausen, Germany. CEUR workshop proceedings, 2567. Aachen: CEUR-WS [online], pages 8-17. Available from: http://ceur-ws.org/Vol-2567/paper1.pdf

Establishing a strong foundation for similarity-based return is a top priority in Case-Based Reasoning (CBR) systems. Deep Metric Learners (DMLs) are a group of neural network architectures which learn to optimise case representations for similarity-... Read More about Human activity recognition with deep metric learners..

Developing a catalogue of explainability methods to support expert and non-expert users. (2019)
Conference Proceeding
MARTIN, K., LIRET, A., WIRATUNGA, N., OWUSU, G. and KERN, M. 2019. Developing a catalogue of explainability methods to support expert and non-expert users. In Bramer, M. and Petridis, M. (eds.) Artificial intelligence XXXVI: proceedings of the 39th British Computer Society's Specialist Group on Artificial Intelligence (SGAI) international Artificial intelligence conference 2019 (AI 2019), 17-19 December 2019, Cambridge, UK. Lecture notes in computer science, 11927. Cham: Springer [online], pages 309-324. Available from: https://doi.org/10.1007/978-3-030-34885-4_24

Organisations face growing legal requirements and ethical responsibilities to ensure that decisions made by their intelligent systems are explainable. However, provisioning of an explanation is often application dependent, causing an extended design... Read More about Developing a catalogue of explainability methods to support expert and non-expert users..

Learning to self-manage by intelligent monitoring, prediction and intervention. (2019)
Conference Proceeding
WIRATUNGA, N., CORSAR, D., MARTIN, K., WIJEKOON, A., ELYAN, E., COOPER, K., IBRAHIM, Z., CELIKTUTAN, O., DOBSON, R.J., MCKENNA, S., MORRIS, J., WALLER, A., ABD-ALHAMMED, R., QAHWAJI, R. and CHAUDHURI, R. 2019. Learning to self-manage by intelligent monitoring, prediction and intervention. In Wiratunga, N., Coenen, F. and Sani, S. (eds.) Proceedings of the 4th International workshop on knowledge discovery in healthcare data (KDH 2019), co-located with the 28th International joint conference on artificial intelligence (IJCAI-19), 10-11 August 2019, Macao, China. CEUR workshop proceedings, 2429. Aachen: CEUR-WS [online], pages 60-67. Available from: http://ceur-ws.org/Vol-2429/paper10.pdf

Despite the growing prevalence of multimorbidities, current digital self-management approaches still prioritise single conditions. The future of out-of-hospital care requires researchers to expand their horizons; integrated assistive technologies sho... Read More about Learning to self-manage by intelligent monitoring, prediction and intervention..

Ontology alignment based on word embedding and random forest classification. (2019)
Conference Proceeding
NKISI-ORJI, I., WIRATUNGA, N., MASSIE, S., HUI, K.-Y. and HEAVEN, R. 2019. Ontology alignment based on word embedding and random forest classification. In Berlingerio, M., Bonchi, F., Gärtner, T., Hurley, N. and Ifrim, G. (eds.) Machine learning and knowledge discovery in databases: proceedings of the 2018 European conference on machine learning and principles and practice of knowledge discovery in databases (ECML PKDD 2018), 10-14 September 2018, Dublin, Ireland. Lecture notes in computer science, 11051. Cham: Springer [online], part I, pages 557-572. Available from: https://doi.org/10.1007/978-3-030-10925-7_34

Ontology alignment is crucial for integrating heterogeneous data sources and forms an important component for realising the goals of the semantic web. Accordingly, several ontology alignment techniques have been proposed and used for discovering corr... Read More about Ontology alignment based on word embedding and random forest classification..

Informed pair selection for self-paced metric learning in Siamese neural networks. (2018)
Conference Proceeding
MARTIN, K., WIRATUNGA, N., MASSIE, S. and CLOS, J. 2018. Informed pair selection for self-paced metric learning in Siamese neural networks. In Bramer, M. and Petridis, M. (eds.) Artificial intelligence XXXV: proceedings of the 38th British Computer Society's Specialist Group on Artificial Intelligence (SGAI) International conference on innovative techniques and applications of artificial intelligence (AI-2018), 11-13 December 2018, Cambridge, UK. Lecture notes in computer science, 11311. Cham: Springer [online], pages 34-49. Available from: https://doi.org/10.1007/978-3-030-04191-5_3

Siamese Neural Networks (SNNs) are deep metric learners that use paired instance comparisons to learn similarity. The neural feature maps learnt in this way provide useful representations for classification tasks. Learning in SNNs is not reliant on e... Read More about Informed pair selection for self-paced metric learning in Siamese neural networks..

Risk information recommendation for engineering workers. (2018)
Conference Proceeding
MARTIN, K., LIRET, A., WIRATUNGA, N., OWUSU, G. and KERN, M. 2018. Risk information recommendation for engineering workers. In Bramer, M. and Petridis, M. (eds.) Artificial intelligence XXXV: proceedings of the 38th British Computer Society's Specialist Group on Artificial Intelligence (SGAI) International conference on innovative techniques and applications of artificial intelligence (AI-2018), 11-13 December 2018, Cambridge, UK. Lecture notes in computer science, 11311. Cham: Springer [online], pages 311-325. Available from: https://doi.org/10.1007/978-3-030-04191-5_27

Within any sufficiently expertise-reliant and work-driven domain there is a requirement to understand the similarities between specific work tasks. Though mechanisms to develop similarity models for these areas do exist, in practice they have been cr... Read More about Risk information recommendation for engineering workers..

GramError: a quality metric for machine generated songs. (2018)
Conference Proceeding
DAVIES, C., WIRATUNGA, N. and MARTIN, K. 2018. GramError: a quality metric for machine generated songs. In Bramer, M. and Petridis, M. (eds.) Artificial intelligence XXXV: proceedings of the 38th British Computer Society's Specialist Group on Artificial Intelligence (SGAI) International conference on innovative techniques and applications of artificial intelligence (AI-2018), 11-13 December 2018, Cambridge, UK. Lecture notes in computer science, 11311. Cham: Springer [online], pages 184-190. Available from: https://doi.org/10.1007/978-3-030-04191-5_16

This paper explores whether a simple grammar-based metric can accurately predict human opinion of machine-generated song lyrics quality. The proposed metric considers the percentage of words written in natural English and the number of grammatical er... Read More about GramError: a quality metric for machine generated songs..

Context extraction for aspect-based sentiment analytics: combining syntactic, lexical and sentiment knowledge. (2018)
Conference Proceeding
BANDHAKAVI, A., WIRATUNGA, N., MASSIE, S. and LUHAR, R. 2018. Context extraction for aspect-based sentiment analytics: combining syntactic, lexical and sentiment knowledge. In Bramer, M. and Petridis, M. (eds.) Artificial intelligence xxxv: proceedings of the 38th British Computer Society's Specialist Group on Artificial Intelligence (SGAI) International conference on innovative techniques and applications of artificial intelligence (AI-2018), 11-13 December 2018, Cambridge, UK. Lecture notes in artificial intelligence, 11311. Cham: Springer [online], pages 357-371. Available from: https://doi.org/10.1007/978-3-030-04191-5_30

Aspect-level sentiment analysis of customer feedback data when done accurately can be leveraged to understand strong and weak performance points of businesses and services and also formulate critical action steps to improve their performance. In this... Read More about Context extraction for aspect-based sentiment analytics: combining syntactic, lexical and sentiment knowledge..