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Dr Stewart Massie's Outputs (63)

CBR-RAG: case-based reasoning for retrieval augmented generation in LLMs for legal question answering. (2024)
Presentation / Conference Contribution
WIRATUNGA, N., ABEYRATNE, R., JAYAWARDENA, L., MARTIN, K., MASSIE, S., NKISI-ORJI, I., WEERASINGHE, R., LIRET, A. and FLEISCH, B. 2024. CBR-RAG: case-based reasoning for retrieval augmented generation in LLMs for legal question answering. In Recio-Garcia, J.A., Orozco-del-Castillo, M.G. and Bridge, D (eds.) Case-based reasoning research and development: proceedings of the 32nd International conference of case-based reasoning research and development 2024 (ICCBR 2024), 1-4 July 2024, Merida, Mexico. Lecture notes in computer science, 14775. Cham: Springer [online], pages 445-460. Available from: https://doi.org/10.1007/978-3-031-63646-2_29

Retrieval-Augmented Generation (RAG) enhances Large Language Model (LLM) output by providing prior knowledge as context to input. This is beneficial for knowledge-intensive and expert reliant tasks, including legal question-answering, which require e... Read More about CBR-RAG: case-based reasoning for retrieval augmented generation in LLMs for legal question answering..

Context-aware data-to-text generation. (2024)
Thesis
UPADHYAY, A. 2024. Context-aware data-to-text generation. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-2571408

Data-to-Text Generation (D2T) is the subfield of Artificial Intelligence (AI) and Natural Language Processing (NLP) that aims to build systems capable of summarising nonlinguistic structured data into textual reports. D2T systems extract important in... Read More about Context-aware data-to-text generation..

Employing multi-modal sensors for personalised smart home health monitoring. (2022)
Thesis
FORBES, G. 2022. Employing multi-modal sensors for personalised smart home health monitoring. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-2071646

Smart home systems are employed worldwide for a variety of automated monitoring tasks. FITsense is a system that performs personalised smart home health monitoring using sensor data. In this thesis, we expand upon this system by identifying the limit... Read More about Employing multi-modal sensors for personalised smart home health monitoring..

Angles of vision: digital storytelling on the cosmic tide? (2021)
Report
IRONSIDE, R., HEDDLE, D. and MASSIE, S. 2021. Angles of vision: digital storytelling on the cosmic tide? Edinburgh: Royal Society of Edinburgh. Hosted on Orkney Digital Storytelling [online]. Available from: https://www.orkneydigitalstorytelling.com/project-report.html

In this report, a collaboration between Robert Gordon University and the University of the Highlands and Islands Institute for Northern Studies, the authors bring together findings from four workshops hosted as part of the My Orkney Story project.... Read More about Angles of vision: digital storytelling on the cosmic tide?.

Personalised exercise recognition towards improved self-management of musculoskeletal disorders. (2021)
Thesis
WIJEKOON, A. 2021. Personalised exercise recognition towards improved self-management of musculoskeletal disorders. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-1358224

Musculoskeletal Disorders (MSD) have been the primary contributor to the global disease burden, with increased years lived with disability. Such chronic conditions require self-management, typically in the form of maintaining an active lifestyle whil... Read More about Personalised exercise recognition towards improved self-management of musculoskeletal disorders..

The folklore-centric gaze: a relational approach to landscape, folklore and tourism. (2020)
Journal Article
IRONSIDE, R. and MASSIE, S. 2020. The folklore-centric gaze: a relational approach to landscape, folklore and tourism. Time and mind [online], 13(3), pages 227-244. Available from: https://doi.org/10.1080/1751696X.2020.1809862

Supernatural folktales have a long oral tradition in Scotland, embedded in local communities and the landscapes of the region. Recently, these folktales have been utilised by destinations as a form of place-making, and a driver for increasing tourist... Read More about The folklore-centric gaze: a relational approach to landscape, folklore and tourism..

Representation and learning schemes for argument stance mining. (2019)
Thesis
CLOS, J. 2019. Representation and learning schemes for argument stance mining. Robert Gordon University [online], PhD thesis. Available from: https://openair.rgu.ac.uk

Argumentation is a key part of human interaction. Used introspectively, it searches for the truth, by laying down argument for and against positions. As a mediation tool, it can be used to search for compromise between multiple human agents. For this... Read More about Representation and learning schemes for argument stance mining..

Ontology driven information retrieval. (2019)
Thesis
NKISI-ORJI, I. 2019. Ontology driven information retrieval. Robert Gordon University [online], PhD thesis. Available from: https://openair.rgu.ac.uk

Ontology-driven information retrieval deals with the use of entities specified in domain ontologies to enhance search and browse. The entities or concepts of lightweight ontological resources are traditionally used to index resources in specialised d... Read More about Ontology driven information retrieval..

Fall prediction using behavioural modelling from sensor data in smart homes. (2019)
Journal Article
FORBES, G., MASSIE, S. and CRAW, S. 2020. Fall prediction using behavioural modelling from sensor data in smart homes. Artificial intelligence review [online], 53(2), pages 1071-1091. Available from: https://doi.org/10.1007/s10462-019-09687-7

The number of methods for identifying potential fall risk is growing as the rate of elderly fallers continues to rise in the UK. Assessments for identifying risk of falling are usually performed in hospitals and other laboratory environments, however... Read More about Fall prediction using behavioural modelling from sensor data in smart homes..

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..

Knowledge driven approaches to e-learning recommendation. (2018)
Thesis
MBIPOM, B. 2018. Knowledge driven approaches to e-learning recommendation. Robert Gordon University, PhD thesis.

Learners often have difficulty finding and retrieving relevant learning materials to support their learning goals because of two main challenges. The vocabulary learners use to describe their goals is different from that used by domain experts in tea... Read More about Knowledge driven approaches to e-learning recommendation..

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..

Improving e-learning recommendation by using background knowledge. (2018)
Journal Article
MBIPOM, B., CRAW, S. and MASSIE, S. 2021. Improving e-learning recommendation by using background knowledge. Expert systems [online], 38(7): artificial intelligence/EDMA 2017, article e12265. Available from: https://doi.org/10.1111/exsy.12265

There is currently a large amount of e-Learning resources available to learners on the Web. However, learners often have difficulty finding and retrieving relevant materials to support their learning goals because they lack the domain knowledge to cr... Read More about Improving e-learning recommendation by using background knowledge..

Accuracy of physical activity recognition from a wrist-worn sensor. (2017)
Presentation / Conference Contribution
COOPER, K., SANI, S., CORRIGAN, L., MACDONALD, H., PRENTICE, C., VARETA, R., MASSIE, S. and WIRATUNGA, N. 2017. Accuracy of physical activity recognition from a wrist-worn sensor. Presented at the 2017 Physiotherapy UK conference and trade exhibition: transform lives, maximise independence and empower populations, 10-11 November 2017, Birmingham, UK.

The EU-funded project 'selfBACK' (http://www.selfback.eu) will utilise continuous objective monitoring of physical activity (PA) by a wrist-mounted wearable, combined with self-monitoring of symptoms and case-based reasoning. Together these will prov... Read More about Accuracy of physical activity recognition from a wrist-worn sensor..

mHealth optimisation for education and physical activity in Type 1 diabetes: MEDPAT1. (2017)
Presentation / Conference Contribution
HALL, J., STEPHEN, K., CROALL, A., MACMILLAN, J., MURRAY, L., WIRATUNGA, N., MASSIE, S. and MACRURY, S. 2017. mHealth optimisation for education and physical activity in Type 1 diabetes: MEDPAT1. Presented at the 2017 Diabetes UK professional conference, 8-10 March 2017, Manchester, UK.

Aims: To develop and evaluate usability of prototype personalised prediction algorithms for people with Type 1 diabetes to optimise blood glucose control associated with physical activity using smart phone technology. To explore the potential to buil... Read More about mHealth optimisation for education and physical activity in Type 1 diabetes: MEDPAT1..

Lexicon generation for emotion detection from text. (2017)
Journal Article
BANDHAKAVI, A., WIRATUNGA, N., MASSIE, S. and PADMANABHAN, D. 2017. Lexicon generation for emotion detection from text. IEEE intelligent systems [online], 32(1), pages 102-108. Available from: https://doi.org/10.1109/MIS.2017.22

General-purpose emotion lexicons (GPELs) that associate words with emotion categories remain a valuable resource for emotion detection. However, the static and formal nature of their vocabularies make them an inadequate resource for detecting emotion... Read More about Lexicon generation for emotion detection from text..

Lexicon based feature extraction for emotion text classification. (2016)
Journal Article
BANDHAKAVI, A., WIRATUNGA, N., DEEPAK, P. and MASSIE, S. 2017. Lexicon based feature extraction for emotion text classification. Pattern recognition letters [online], 93, pages 133-142. Available from: https://doi.org/10.1016/j.patrec.2016.12.009

General Purpose Emotion Lexicons (GPELs) that associate words with emotion categories remain a valuable resource for emotion analysis of text. However the static and formal nature of their vocabularies make them inadequate for extracting effective fe... Read More about Lexicon based feature extraction for emotion text classification..

Case-base maintenance with multi-objective evolutionary algorithms. (2015)
Journal Article
LUPIANI, E., MASSIE, S., CRAW, S., JUAREZ, J.M. and PALMA, J. 2016. Case-base maintenance with multi-objective evolutionary algorithms. Journal of intelligent information systems [online], 46(2), pages 259-284. Available from: https://doi.org/10.1007/s10844-015-0378-z

Case-Base Reasoning is a problem-solving methodology that uses old solved problems, called cases, to solve new problems. The case-base is the knowledge source where the cases are stored, and the amount of stored cases is critical to the problem-solvi... Read More about Case-base maintenance with multi-objective evolutionary algorithms..

Learning pseudo-tags to augment sparse tagging in hybrid music recommender systems. (2014)
Journal Article
HORSBURGH, B., CRAW, S. and MASSIE, S. 2015. Learning pseudo-tags to augment sparse tagging in hybrid music recommender systems. Artificial intelligence [online], 219, pages 25-39. Available from: https://doi.org/10.1016/j.artint.2014.11.004

Online recommender systems are an important tool that people use to find new music. To generate recommendations, many systems rely on tag representations of music. Such systems however suffer from tag sparsity, whereby tracks lack a strong tag repres... Read More about Learning pseudo-tags to augment sparse tagging in hybrid music recommender systems..