Outputs (23)

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?.

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..

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..

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..

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..

Failure-driven transformational case reuse of explanation strategies in CloodCBR.
Presentation / Conference Contribution
NKISI-ORJI, I., PALIHAWADANA, C., WIRATUNGA, N., WIJEKOON, A. and CORSAR, D. 2023. Failure-driven transformational case reuse of explanation strategies in CloodCBR. In Massie, S. and Chakraborti, S. (eds.) Case-based reasoning research and development: proceedings of the 31st International conference on case-based reasoning 2023 (ICCBR 2023), 17-20 July 2023, Aberdeen, UK. Lecture notes in computer science (LNCS), 14141. Cham: Springer [online], pages 279-293. Available from: https://doi.org/10.1007/978-3-031-40177-0_18

In this paper, we propose a novel approach to improve problem-solving efficiency through the reuse of case solutions. Specifically, we introduce the concept of failure-driven transformational case reuse of explanation strategies, which involves trans... Read More about Failure-driven transformational case reuse of explanation strategies in CloodCBR..

Case-based reasoning research and development: proceedings of the 31st International conference on case-based reasoning (ICCBR 2023).
Presentation / Conference Contribution
MASSIE, S. and CHAKRABORTI, S. (eds.) 2023. Case-based reasoning research and development: proceedings of the 31st International conference on case-based reasoning (ICCBR 2023), 17-20 July 2023, Aberdeen, UK. Lecture notes in computer science, 14141. Cham: Springer [online]. Available from: https://doi.org/10.1007/978-3-031-40177-0

This volume contains the papers presented at the 31st International Conference on Case-Based Reasoning (ICCBR 2023), which was held on July 17–20, 2023, at Robert Gordon University in Aberdeen, Scotland, UK. ICCBR is the premier annual meeting of the... Read More about Case-based reasoning research and development: proceedings of the 31st International conference on case-based reasoning (ICCBR 2023)..

CBR assisted context-aware surface realisation for data-to-text generation.
Presentation / Conference Contribution
UPADHYAY, A. and MASSIE, S. 2023. CBR assisted context-aware surface realisation for data-to-text generation. In MASSIE, S. and CHAKRABORTI, S. (eds.) 2023. Case-based reasoning research and development: proceedings of the 31st International conference on case-based reasoning 2023, (ICCBR 2023), 17-20 July 2023, Aberdeen, UK. Lecture notes in computer science (LNCS), 14141. Cham: Springer [online], pages 34-49. Available from: https://doi.org/10.1007/978-3-031-40177-0_3

Current state-of-the-art neural systems for Data-to-Text Generation (D2T) struggle to generate content from past events with interesting insights. This is because these systems have limited access to historic data and can also hallucinate inaccurate... Read More about CBR assisted context-aware surface realisation for data-to-text generation..

Machine learning for risk stratification of diabetic foot ulcers using biomarkers.
Presentation / Conference Contribution
MARTIN, K., UPADHYAY, A., WIJEKOON, A., WIRATUNGA, N. and MASSIE, S. 2023. Machine learning for risk stratification of diabetic foot ulcers using biomarkers. In Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational science: proceedings of the 23rd International conference on computational science 2023 (ICCS 2023): computing at the cutting edge of science (ICCS 2023), 3-5 July 2023, Prague, Czech Republic: [virtual event]. Lecture notes in computer science, 14075. Cham: Springer [online], part III, pages 153-161. Available from: https://doi.org/10.1007/978-3-031-36024-4_11

Development of a Diabetic Foot Ulcer (DFU) causes a sharp decline in a patient's health and quality of life. The process of risk stratification is crucial for informing the care that a patient should receive to help manage their Diabetes before an ul... Read More about Machine learning for risk stratification of diabetic foot ulcers using biomarkers..

Content type profiling of data-to-text generation datasets.
Presentation / Conference Contribution
UPADHYAY, A. and MASSIE, S. 2022. Content type profiling of data-to-text generation datasets. In N. Calzolari, C.-R. Huang, H. Kim. et al. (eds.) Proceedings of the 29th International conference on computational linguistics (COLING 2022), 12-17 October 2022, Gyeongju, Republic of Korea. Stroudsburg, PA: International Committee on Computational Linguistics [online], 29(1), pages 5770–5782. Available from: https://aclanthology.org/2022.coling-1.pdf

Data-to-Text Generation (D2T) problems can be considered as a stream of time-stamped events with a text summary being produced for each. The problem becomes more challenging when event summaries contain complex insights derived from multiple records... Read More about Content type profiling of data-to-text generation datasets..

A case-based approach for content planning in data-to-text generation.
Presentation / Conference Contribution
UPADHYAY, A. and MASSIE, S. 2022. A case-based approach for content planning in data-to-text generation. In Keane, M.T. and Wiratunga, N. (eds.) Case-based reasoning research and development: proceedings of the 30th International conference on case-based reasoning (ICCBR 2022), 12-15 September 2022, Nancy, France. Lecture notes in computer science, 13405. Cham: Springer [online], pages 380-394. Available from: https://doi.org/10.1007/978-3-031-14923-8_25

The problem of Data-to-Text Generation (D2T) is usually solved using a modular approach by breaking the generation process into some variant of planning and realisation phases. Traditional methods have been very good at producing high quality texts b... Read More about A case-based approach for content planning in data-to-text generation..

A case-based approach to data-to-text generation.
Presentation / Conference Contribution
UPADHYAY, A., MASSIE, S., SINGH, R.K., GUPTA, G. and OJHA, M. 2021. A case-based approach to data-to-text generation. In Sánchez-Ruiz, A.A. and Floyd, M.W. (eds.) Case-based reasoning research and development: proceedings of 29th International conference case-based reasoning 2021 (ICCBR 2021), 13-16 September 2021, Salamanca, Spain. Lecture notes in computer science (LNCS), 12877. Cham: Springer [online], pages 232-247. Available from: https://doi.org/10.1007/978-3-030-86957-1_16

Traditional Data-to-Text Generation (D2T) systems utilise carefully crafted domain specific rules and templates to generate high quality accurate texts. More recent approaches use neural systems to learn domain rules from the training data to produce... Read More about A case-based approach to data-to-text generation..

Case-based approach to automated natural language generation for obituaries.
Presentation / Conference Contribution
UPADHYAY, A., MASSIE, S. and CLOGHER, S. 2020. Case-based approach to automated natural language generation for obituaries. 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 279-294. Available from: https://doi.org/10.1007/978-3-030-58342-2_18

Automated generation of human readable text from structured information is challenging because grammatical rules are complex making good quality outputs difficult to achieve. Textual Case-Based Reasoning provides one approach in which the text from p... Read More about Case-based approach to automated natural language generation for obituaries..

Wifi-based human activity recognition using Raspberry Pi.
Presentation / Conference Contribution
FORBES, G., MASSIE, S. and CRAW, S. 2020. Wifi-based human activity recognition using Raspberry Pi. In Alamaniotis, M. and Pan, S. (eds.) Proceedings of Institute of Electrical and Electronics Engineers (IEEE) 32nd Tools with artificial intelligence international conference 2020 (ICTAI 2020), 9-11 Nov 2020, [virtual conference]. Piscataway: IEEE [online], pages 722-730. Available from: https://doi.org/10.1109/ICTAI50040.2020.00115

Ambient, non-intrusive approaches to smart home health monitoring, while limited in capability, are preferred by residents. More intrusive methods of sensing, such as video and wearables, can offer richer data but at the cost of lower resident uptake... Read More about Wifi-based human activity recognition using Raspberry Pi..

Representing temporal dependencies in smart home activity recognition for health monitoring.
Presentation / Conference Contribution
FORBES, G., MASSIE, S., CRAW, S., FRASER, L. and HAMILTON, G. 2020. Representing temporal dependencies in smart home activity recognition for health monitoring. 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 9207480. Available from: https://doi.org/10.1109/IJCNN48605.2020.9207480

Long term health conditions, such as fall risk, are traditionally diagnosed through testing performed in hospital environments. Smart Homes offer the opportunity to perform continuous, long-term behavioural and vitals monitoring of residents, which m... Read More about Representing temporal dependencies in smart home activity recognition for health monitoring..

Representing temporal dependencies in human activity recognition.
Presentation / Conference Contribution
FORBES, G., MASSIE, S., CRAW, S., FRASER, L. and HAMILTON, G. 2019. Representing temporal dependencies in human activity recognition. 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 29-38. Available from: http://ceur-ws.org/Vol-2567/paper3.pdf

Smart Homes offer the opportunity to perform continuous, long-term behavioural and vitals monitoring of residents, which may be employed to aid diagnosis and management of chronic conditions without placing additional strain on health services. A pro... Read More about Representing temporal dependencies in human activity recognition..

Informed pair selection for self-paced metric learning in Siamese neural networks.
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..

FITsense: employing multi-modal sensors in smart homes to predict falls.
Presentation / Conference Contribution
MASSIE, S., FORBES, G., CRAW, S., FRASER, L. and HAMILTON, G. 2018. FITsense: employing multi-modal sensors in smart homes to predict falls. 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 249-263. Available from: https://doi.org/10.1007/978-3-030-01081-2_17

As people live longer, the increasing average age of the population places additional strains on our health and social services. There are widely recognised benefits to both the individual and society from supporting people to live independently for... Read More about FITsense: employing multi-modal sensors in smart homes to predict falls..

Personalised human activity recognition using matching networks.
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..

Opinion context extraction for aspect sentiment analysis.
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..