Professor Nirmalie Wiratunga's Outputs (15)

Informed pair selection for self-paced metric learning in Siamese neural networks. (2018)
Presentation / Conference Contribution
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)
Presentation / Conference Contribution
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)
Presentation / Conference Contribution
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)
Presentation / Conference Contribution
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..

Emotion-aware polarity lexicons for Twitter sentiment analysis. (2018)
Journal Article
BANDHAKAVI, A., WIRATUNGA, N., MASSIE, S. and P, D. 2021. Emotion-aware polarity lexicons for Twitter sentiment analysis. Expert systems [online], 38(7): artificial intelligence/EDMA 2017, article e12332. Available from: https://doi.org/10.1111/exsy.12332

Theoretical frameworks in psychology map the relationships between emotions and sentiments. In this paper we study the role of such mapping for computational emotion detection from text (e.g. social media) with a aim to understand the usefulness of a... Read More about Emotion-aware polarity lexicons for Twitter sentiment analysis..

Improving kNN for human activity recognition with privileged learning using translation models. (2018)
Presentation / Conference Contribution
WIJEKOON, A., WIRATUNGA, N., SANI, S., MASSIE, S. and COOPER, K. 2018. Improving kNN for human activity recognition with privileged learning using translation models. In Cox, M.T., Funk, P. and Begum, S. (eds.) Case-based reasoning research and development: proceedings of the 26th International conference on case-based reasoning (ICCBR 2018), 9-12 July 2018, Stockholm, Sweden. Lecture notes in computer science, 11156. Cham: Springer [online], pages 448-463. Available from: https://doi.org/10.1007/978-3-030-01081-2_30

Multiple sensor modalities provide more accurate Human Activity Recognition (HAR) compared to using a single modality, yet the latter is preferred by consumers as it is more convenient and less intrusive. This presents a challenge to researchers, as... Read More about Improving kNN for human activity recognition with privileged learning using translation models..

Personalised human activity recognition using matching networks. (2018)
Presentation / Conference Contribution
SANI, S., WIRATUNGA, N., MASSIE, S. and COOPER, K. 2018. Personalised human activity recognition using matching networks. In Cox, M.T., Funk, P. and Begum, S. (eds.) Case-based reasoning research and development: proceedings of the 26th International conference on case-based reasoning (ICCBR 2018), 9-12 July 2018, Stockholm, Sweden. Lecture notes in computer science, 11156. Cham: Springer [online], pages 339-353. Available from: https://doi.org/10.1007/978-3-030-01081-2_23

Human Activity Recognition (HAR) is typically modelled as a classification task where sensor data associated with activity labels are used to train a classifier to recognise future occurrences of these activities. An important consideration when trai... Read More about Personalised human activity recognition using matching networks..

Matching networks for personalised human activity recognition. (2018)
Presentation / Conference Contribution
SANI, S., WIRATUNGA, N., MASSIE, S. and COOPER, K. 2018. Matching networks for personalised human activity recognition. In Bichindaritz, I., Guttmann, C., Herrero, P., Koch, F., Koster, A., Lenz, R., López Ibáñez, B., Marling, C., Martin, C., Montagna, S., Montani, S., Reichert, M., Riaño, D., Schumacher, M.I., ten Teije, A. and Wiratunga, N. (eds.) Proceedings of the 1st Joint workshop on artificial intelligence in health, organized as part of the Federated AI meeting (FAIM 2018), co-located with the 17th International conference on autonomous agents and multiagent systems (AAMAS 2018), the 35th International conference on machine learning (ICML 2018), the 27th International joint conference on artificial intelligence (IJCAI 2018), and the 26th International conference on case-based reasoning (ICCBR 2018), 13-19 July 2018, Stockholm, Sweden. CEUR workshop proceedings, 2142. Aachen: CEUR-WS [online], pages 61-64. Available from: http://ceur-ws.org/Vol-2142/short4.pdf

Human Activity Recognition (HAR) has many important applications in health care which include management of chronic conditions and patient rehabilitation. An important consideration when training HAR models is whether to use training data from a gene... Read More about Matching networks for personalised human activity recognition..

Improving human activity recognition with neural translator models. (2018)
Presentation / Conference Contribution
WIJEKOON, A., WIRATUNGA, N. and SANI, S. 2018. Improving human activity recognition with neural translator models. In Minor, M. (ed.) Workshop proceedings for the 26th International conference on case-based reasoning (ICCBR 2018), 9-12 July 2018, Stockholm, Sweden. Stockholm: ICCBR [online], pages 96-100. Available from: http://iccbr18.com/wp-content/uploads/ICCBR-2018-V3.pdf#page=96

Multiple sensor modalities provide more accurate Human Activity Recognition (HAR) compared to using a single modality, yet the latter is more convenient and less intrusive. It is advantages to create a model which learns from all available sensors; a... Read More about Improving human activity recognition with neural translator models..

Explainability through transparency and user control: a case-based recommender for engineering workers. (2018)
Presentation / Conference Contribution
MARTIN, K., LIRET, A., WIRATUNGA, N., OWUSU, G. and KERN, M. 2018. Explainability through transparency and user control: a case-based recommender for engineering workers. In Minor, M. (ed.) Workshop proceedings for the 26th International conference on case-based reasoning (ICCBR 2018), 9-12 July 2018, Stockholm, Sweden. Stockholm: ICCBR [online], pages 22-31. Available from: http://iccbr18.com/wp-content/uploads/ICCBR-2018-V3.pdf#page=22

Within the service providing industries, field engineers can struggle to access tasks which are suited to their individual skills and experience. There is potential for a recommender system to improve access to information while being on site. Howeve... Read More about Explainability through transparency and user control: a case-based recommender for engineering workers..

Study of similarity metrics for matching network-based personalised human activity recognition. (2018)
Presentation / Conference Contribution
SANI, S., WIRATUNGA, N., MASSIE, S. and COOPER, K. 2018. Study of similarity metrics for matching network-based personalised human activity recognition. In Minor, M. (ed.) Workshop proceedings for the 26th International conference on case-based reasoning (ICCBR 2018), 9-12 July 2018, Stockholm, Sweden, pages 91-95. Available from: http://iccbr18.com/wp-content/uploads/ICCBR-2018-V3.pdf#page=91

Personalised Human Activity Recognition (HAR) models trained using data from the target user (subject-dependent) have been shown to be superior to non personalised models that are trained on data from a general population (subject-independent). Howev... Read More about Study of similarity metrics for matching network-based personalised human activity recognition..

Zero-shot learning with matching networks for open-ended human activity recognition. (2018)
Presentation / Conference Contribution
WIJEKOON, A., WIRATUNGA, N. and SANI, S. 2018. Zero-shot learning with matching networks for open-ended human activity recognition. In Martin, K., Wiratunga, N. and Smith, L.S. (eds.) Proceedings of the 2018 Scottish Informatics and Computer Science Alliance (SCISA) workshop on reasoning, learning and explainability (ReaLX 2018), 27 June 2018, Aberdeen, UK. CEUR workshop proceedings, 2151. Aachen: CEUR-WS [online], session 2, paper 4. Available from: http://ceur-ws.org/Vol-2151/Paper_S9.pdf

A real-world solution for Human Activity Recognition (HAR) should cover a variety of activities. However training a model to cover each and every possible activity is not practical. Instead we need a solution that can adapt its learning to unseen act... Read More about Zero-shot learning with matching networks for open-ended human activity recognition..

Digital interpretation of sensor-equipment diagrams. (2018)
Presentation / Conference Contribution
MORENO-GARCÍA, C.F. 2018. Digital interpretation of sensor-equipment diagrams. In Martin, K., Wiratunga, N. and Smith, L.S. (eds.) Proceedings of the 2018 Scottish Informatics and Computer Science Alliance (SCISA) workshop on reasoning, learning and explainability (ReaLX 2018), 27 June 2018, Aberdeen, UK. CEUR workshop proceedings, 2151. Aachen: CEUR-WS [online], session 2, paper 1. Available from: http://ceur-ws.org/Vol-2151/Paper_s2.pdf

A sensor-equipment diagram is a type of engineering drawing used in the industrial practice that depicts the interconnectivity between a group of sensors and a portion of an Oil & Gas facility. The interpretation of these documents is not a straightf... Read More about Digital interpretation of sensor-equipment diagrams..

Opinion context extraction for aspect sentiment analysis. (2018)
Presentation / Conference Contribution
BANDHAKAVI, A., WIRATUNGA, N., MASSIE, S. and LUHAR, R. 2018. Opinion context extraction for aspect sentiment analysis. In Proceedings of the 12th Association for the Advancement of Artificial Intelligence (AAAI) international conference on web and social media (ICWSM 2018), 25-28 June 2018, Palo Alto, USA. Palo Alto: AAAI Press [online], pages 564-567. Available from: https://www.aaai.org/ocs/index.php/ICWSM/ICWSM18/paper/view/17859

Sentiment analysis is the computational study of opinionated text and is becoming increasing important to online commercial applications. However, the majority of current approaches determine sentiment by attempting to detect the overall polarity of... Read More about Opinion context extraction for aspect sentiment analysis..

Domain-specific lexicon generation for emotion detection from text. (2018)
Thesis
BANDHAKAVI, A. 2018. Domain-specific lexicon generation for emotion detection from text. Robert Gordon University, PhD thesis.

Emotions play a key role in effective and successful human communication. Text is popularly used on the internet and social media websites to express and share emotions, feelings and sentiments. However useful applications and services built to under... Read More about Domain-specific lexicon generation for emotion detection from text..