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Case-based reasoning research and development: proceedings of the 31st International conference on case-based reasoning (ICCBR 2023). (2023)
Conference Proceeding
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. (2023)
Conference Proceeding
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. (2023)
Conference Proceeding
MARTIN, K., UPHADYAY, A., WIJEKOON, A., WIRATUNGA, N. and MASSIE, S. [2023]. Machine learning for risk stratification of diabetic foot ulcers using biomarkers. To be presented at the 2023 International conference on computational science (ICCS 2023): computing at the cutting edge of science, 3-5 July 2023, Prague, Czech Republic: [virtual event].

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..

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..

Content type profiling of data-to-text generation datasets. (2022)
Conference Proceeding
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. (2022)
Conference Proceeding
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. (2021)
Conference Proceeding
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..

Visualisation to explain personal health trends in smart homes. (2021)
Presentation / Conference
FORBES, G., MASSIE, S. and CRAW, S. 2021. Visualisation to explain personal health trends in smart homes. Presented at 1st eXplainable artificial intelligence (XAI) in healthcare international workshop 2021 (XAI-Healthcare 2021), 16 June 2021, co-located with 19th Artificial intelligence in medicine (AIME) international conference 2021 (AIME 2021), 15-17 June 2021, [virtual conference]. Hosted on ArXiv [online], article 2109.15125. Available from: https://arxiv.org/abs/2109.15125

An ambient sensor network is installed in Smart Homes to identify low-level events taking place by residents, which are then analysed to generate a profile of activities of daily living. These profiles are compared to both the resident's typical prof... Read More about Visualisation to explain personal health trends in smart homes..

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..

Wifi-based human activity recognition using Raspberry Pi. (2020)
Conference Proceeding
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..

Case-based approach to automated natural language generation for obituaries. (2020)
Conference Proceeding
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..

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..

WEC: weighted ensemble of text classifiers. (2020)
Conference Proceeding
UPADHYAY, A., NGUYEN, T.T., MASSIE, S. and MCCALL, J. 2020. WEC: weighted ensemble of text classifiers. In Proceedings of 2020 Institute of Electrical and Electronics Engineers (IEEE) congress on evolutionary computation (IEEE CEC 2020), part of the 2020 (IEEE) World congress on computational intelligence (IEEE WCCI 2020) and co-located with the 2020 International joint conference on neural networks (IJCNN 2020) and the 2020 IEEE International fuzzy systems conference (FUZZ-IEEE 2020), 19-24 July 2020, Glasgow, UK [virtual conference]. Piscataway: IEEE [online], article ID 9185641. Available from: https://doi.org/10.1109/CEC48606.2020.9185641

Text classification is one of the most important tasks in the field of Natural Language Processing. There are many approaches that focus on two main aspects: generating an effective representation; and selecting and refining algorithms to build the c... Read More about WEC: weighted ensemble of text classifiers..

Representing temporal dependencies in smart home activity recognition for health monitoring. (2020)
Conference Proceeding
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..

Preface: case-based reasoning and deep learning. (2020)
Conference Proceeding
MARTIN, K., KAPETANAKIS, S., WIJEKOON, A., AMIN, K. and MASSIE, S. 2019. Preface: case-based reasoning and deep learning. 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 6-7. Available from: http://ceur-ws.org/Vol-2567/cbr_dl_preface.pdf

Recent advances in deep learning (DL) have helped to usher in a new wave of confidence in the capability of artificial intelligence. Increasingly, we are seeing DL architectures out perform long established state-of-the-art algorithms in a numb... Read More about Preface: case-based reasoning and deep learning..

Representing temporal dependencies in human activity recognition. (2020)
Conference Proceeding
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..

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..

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..

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..

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..

Personalised human activity recognition using matching networks. (2018)
Conference Proceeding
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..

FITsense: employing multi-modal sensors in smart homes to predict falls. (2018)
Conference Proceeding
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..

Improving kNN for human activity recognition with privileged learning using translation models. (2018)
Conference Proceeding
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..

Monitoring health in smart homes using simple sensors. (2018)
Conference Proceeding
MASSIE, S., FORBES, G., CRAW, S., FRASER, L. and HAMILTON, G. 2018. Monitoring health in smart homes using simple sensors. In Bach, K., Bunescu, R., Farri, O., Guo, A., Hasan, S., Ibrahim, Z.M., Marling, C., Raffa, J., Rubin, J. and Wu, H. (eds.) Proceedings of the 3rd International workshop on knowledge discovery in healthcare data (KDH), co-located with the 27th International joint conference on artificial intelligence and the 23rd European conference on artificial intelligence (IJCAI-ECAI 2018), 13 July 2018, Stockholm, Sweden. CEUR workshop proceedings, 2148. Aachen: CEUR-WS [online], pages 33-37. Available from: http://ceur-ws.org/Vol-2148/paper05.pdf

We consider use of an ambient sensor network, installed in Smart Homes, to identify low level events taking place which can then be analysed to generate a resident's profile of activities of daily living (ADLs). These ADL profiles are compared to bot... Read More about Monitoring health in smart homes using simple sensors..

Matching networks for personalised human activity recognition. (2018)
Conference Proceeding
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..

Study of similarity metrics for matching network-based personalised human activity recognition. (2018)
Presentation / Conference
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..

Opinion context extraction for aspect sentiment analysis. (2018)
Conference Proceeding
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..

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..

An e-learning recommender that helps learners find the right materials. (2018)
Conference Proceeding
MBIPO, B., MASSIE, S. and CRAW, S. 2018. An e-learning recommender that helps learners find the right materials. In Zilberstein, S., McIlraith, S., Weinberger, K., Youngblood, G.M., Myers, K., Eaton, E. and Wollowski, M. (eds.) Proceedings of the 32nd Association for the Advancement of Artificial Intelligence (AAAI) Artificial intelligence conference (AAAI18), co-located with the 30th Innovative applications of artificial intelligence conference (IAAI18) and the 8th AAAI Educational advances in artificial intelligence (EAAI-18), 2-7 February 2018, New Orleans, Louisiana, USA. Palo Alto: AAAI Press [online], pages 7928-7933. Available from: https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16253

Learning materials are increasingly available on the Web making them an excellent source of information for building e-Learning recommendation systems. However, learners often have difficulty finding the right materials to support their learning goal... Read More about An e-learning recommender that helps learners find the right materials..

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
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..

Taxonomic corpus-based concept summary generation for document annotation. (2017)
Conference Proceeding
NKISI-ORJI, I., WIRATUNGA, N., HUI, K.-Y., HEAVEN, R. and MASSIE, S. 2017. Taxonomic corpus-based concept summary generation for document annotation. In Kampus, J., Tsakonas, G., Manolopoulos, Y., Iliadis, L. and Karydis, I. (eds.) Proceedings of the 21st International conference on theory and practice of digital libraries (TPDL 2017): research and advanced technology for digital libraries, 18-21 September 2017, Thessaloniki, Greece. Lecture notes in computer science, 10450. Cham: Springer [online], pages 49-60. Available from: https://doi.org/10.1007/978-3-319-67008-9_5

Semantic annotation is an enabling technology which links documents to concepts that unambiguously describe their content. Annotation improves access to document contents for both humans and software agents. However, the annotation process is a chall... Read More about Taxonomic corpus-based concept summary generation for document annotation..

Learning deep and shallow features for human activity recognition. (2017)
Conference Proceeding
SANI, S., MASSIE, S., WIRATUNGA, N. and COOPER, K. 2017. Learning deep and shallow features for human activity recognition. In Li, G., Ge, Y, Zhang, Z., Jin, Z. and Blumenstein, M. (eds.) Knowledge science, engineering and management: proceedings of the 10th International knowledge science, engineering and management conference (KSEM 2017), 19-20 August 2017, Melbourne, Australia. Lecture notes in computer science, 10412. Cham: Springer [online], pages 469-482. Available from: https://doi.org/10.1007/978-3-319-63558-3_40

selfBACK is an mHealth decision support system used by patients for the self-management of Lower Back Pain. It uses Human Activity Recognition from wearable sensors to monitor user activity in order to measure their adherence to prescribed physical a... Read More about Learning deep and shallow features for human activity recognition..

Learning deep features for kNN-based human activity recognition. (2017)
Conference Proceeding
SANI, S., WIRATUNGA, N. and MASSIE, S. 2017. Learning deep features for kNN-based human activity recognition. In Sanchez-Ruiz, A.A. and Kofod-Petersen, A. (eds.) Workshop proceedings of the 25th International conference on case-based reasoning (ICCBR 2017), 26-29 June 2017, Trondheim, Norway. CEUR workshop proceedings, 2028. Aachen: CEUR-WS [online], session 2: case-based reasoning and deep learning workshop (CBRDL-2017), pages 95-103. Available from: http://ceur-ws.org/Vol-2028/paper9.pdf

A CBR approach to Human Activity Recognition (HAR) uses the kNN algorithm to classify sensor data into different activity classes. Different feature representation approaches have been proposed for sensor data for the purpose of HAR. These include sh... Read More about Learning deep features for kNN-based human activity recognition..

A convolutional Siamese network for developing similarity knowledge in the SelfBACK dataset. (2017)
Conference Proceeding
MARTIN, K., WIRATUNGA, N., SANI, S., MASSIE, S. and CLOS, J. 2017. A convolutional Siamese network for developing similarity knowledge in the SelfBACK dataset. In Sanchez-Ruiz, A.A. and Kofod-Petersen, A. (eds.) Workshop proceedings of the 25th International conference on case-based reasoning (ICCBR 2017), 26-29 June 2017, Trondheim, Norway. CEUR workshop proceedings, 2028. Aachen: CEUR-WS [online], session 2: case-based reasoning and deep learning workshop (CBRDL-2017), pages 85-94. Available from: https://ceur-ws.org/Vol-2028/paper8.pdf

The Siamese Neural Network (SNN) is a neural network architecture capable of learning similarity knowledge between cases in a case base by receiving pairs of cases and analysing the differences between their features to map them to a multi-dimensiona... Read More about A convolutional Siamese network for developing similarity knowledge in the SelfBACK dataset..

kNN sampling for personalised human recognition. (2017)
Conference Proceeding
SANI, S., WIRATUNGA, N., MASSIE, S. and COOPER, K. 2017. kNN sampling for personalised human recognition. In Aha, D.W. and Lieber, J. (eds.) Case-based reasoning research and development: proceedings of the 25th International case-based reasoning conference (ICCBR 2017), 26-28 June 2017, Trondheim, Norway. Lecture notes in computer science, 10339. Cham: Springer [online], pages 330-344. Available from: https://doi.org/10.1007/978-3-319-61030-6_23

The need to adhere to recommended physical activity guidelines for a variety of chronic disorders calls for high precision Human Activity Recognition (HAR) systems. In the SelfBACK system, HAR is used to monitor activity types and intensities to enab... Read More about kNN sampling for personalised human recognition..

mHealth optimisation for education and physical activity in Type 1 diabetes: MEDPAT1. (2017)
Presentation / Conference
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..

SelfBACK: Activity recognition for self-management of low back pain. (2016)
Conference Proceeding
SANI, S., WIRATUNGA, N., MASSIE, S. and COOPER, K. 2016. SelfBACK: Activity recognition for self-management of low back pain. In Bramer, M. and Petridis, M. (eds.) 2016. Research and development in intelligent systems XXXIII: incorporating applications and innovations in intelligent systems XXIV: proceedings of the 36th SGAI nternational conference on innovative techniques and applications of artificial intelligence (SGAI 2016), 13-15 December 2016, Cambridge, UK. Cham: Springer [online], pages 281-294. Available from: https://doi.org/10.1007/978-3-319-47175-4_21

Low back pain (LBP) is the most significant contributor to years lived with disability in Europe and results in significant financial cost to European economies. Guidelines for the management of LBP have self-management at their cornerstone, where pa... Read More about SelfBACK: Activity recognition for self-management of low back pain..

Harnessing background knowledge for e-learning recommendation. (2016)
Conference Proceeding
MBIPOM, B., CRAW, S. and MASSIE, S. 2016. Harnessing background knowledge for e-learning recommendation. In Bramer, M. and Petridis, M. (eds.) 2016. Research and development in intelligent systems XXXIII: incorporating applications and innovations in intelligent systems XXIV: proceedings of the 36th SGAI nternational conference on innovative techniques and applications of artificial intelligence (SGAI 2016), 13-15 December 2016, Cambridge, UK. Cham: Springer [online], pages 3-17. Available from: https://dx.doi.org/10.1007/978-3-319-47175-4_1

The growing availability of good quality, learning-focused content on the Web makes it an excellent source of resources for e-learning systems. However, learners can find it hard to retrieve material well-aligned with their learning goals because of... Read More about Harnessing background knowledge for e-learning recommendation..

Emotion-corpus guided lexicons for sentiment analysis on Twitter. (2016)
Conference Proceeding
BANDHAKAVI, A., WIRATUNGA, N. and MASSIE, S. 2016. Emotion-corpus guided lexicons for sentiment analysis on Twitter. In Bramer, M. and Petridis, M. (eds.) 2016. Research and development in intelligent systems XXXIII: incorporating applications and innovations in intelligent systems XXIV: proceedings of the 36th SGAI nternational conference on innovative techniques and applications of artificial intelligence (SGAI 2016), 13-15 December 2016, Cambridge, UK. Cham: Springer [online], pages 71-86. Available from: https://doi.org/10.10007/978-3-319-47175-4_5

Research in Psychology have proposed frameworks that map emotion concepts with sentiment concepts. In this paper we study this mapping from a computational modelling perspective with a view to establish the role of an emotion-rich corpus for lexicon-... Read More about Emotion-corpus guided lexicons for sentiment analysis on Twitter..

Music recommendation: audio neighbourhoods to discover music in the long tail. (2015)
Conference Proceeding
CRAW, S., HORSBURGH, B. and MASSIE, S. 2015. Music recommendation: audio neighbourhoods to discover music in the long tail. In Hüllermeier, E. and Minor, M. (eds.) Proceedings of the 23rd international conference on case-based reasoning research and development (ICCBR 2015), 28-30 September 2015, Frankfurt am Main, Germany. Lecture notes in computer science, 9343. Cham: Springer [online], pages 73-87. Available from: https://doi.org/10.1007/978-3-319-24586-7_6

Millions of people use online music services every day and recommender systems are essential to browse these music collections. Users are looking for high quality recommendations, but also want to discover tracks and artists that they do not already... Read More about Music recommendation: audio neighbourhoods to discover music in the long tail..

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..

Music recommenders: user evaluation without real users? (2015)
Conference Proceeding
CRAW, S., HORSBURGH, B. and MASSIE, S. 2015. Music recommenders: user evaluation without real users? In Yang, Q. and Woolridge, M. (eds.) Proceedings of the 24th International joint conference on artificial intelligence (IJCAI-15), 25-31 July 2015, Buenos Aires, Argentina. Palo Alto: AAAI Press [online], pages 1749-1755. Available from: https://www.ijcai.org/Proceedings/15/Papers/249.pdf

Good music recommenders should not only suggest quality recommendations, but should also allow users to discover new/niche music. User studies capture explicit feedback on recommendation quality and novelty, but can be expensive, and may have difficu... Read More about Music recommenders: user evaluation without real users?.

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..

A multi-objective evolutionary algorithm fitness function for case-base maintenance. (2013)
Conference Proceeding
LUPIANI, E., CRAW, S., MASSIE, S., JUAREZ, J.M. and PALMA, J.T. 2013. A multi-objective evolutionary algorithm fitness function for case-base maintenance. In Delany, S.J. and Ontañón, S. (eds.) Case-based reasoning research and development: proceedings of the 21st International conference on case-based reasoning (ICCBR 2013), 8-11 July 2013, Saratoga Springs, USA. Lecture notes in computer science, 7969. Berlin: Springer [online], pages 218-232. Available from: https://doi.org/10.1007/978-3-642-39056-2_16

Case-Base Maintenance (CBM) has two important goals. On the one hand, it aims to reduce the size of the case-base. On the other hand, it has to improve the accuracy of the CBR system. CBM can be represented as a multi-objective optimization problem t... Read More about A multi-objective evolutionary algorithm fitness function for case-base maintenance..

Integrating content and semantic representations for music recommendation. (2013)
Thesis
HORSBURGH, B. 2013. Integrating content and semantic representations for music recommendation. Robert Gordon University, PhD thesis.

Music recommender systems are used by millions of people every day to discover new and exciting music. Central to making recommendations is the representation of each track, which may be used to calculate similarity. Content representations capture t... Read More about Integrating content and semantic representations for music recommendation..

Cold-start music recommendation using a hybrid representation. (2012)
Presentation / Conference
HORSBURGH, B., CRAW, S. and MASSIE, S. 2012. Cold-start music recommendation using a hybrid representation. Presented at the 3rd Annual digital economy 'all hands' conference (Digital Futures 2012), 23-25 October 2012, Aberdeen, UK.

Digital music systems are a new and exciting way to dis- cover, share, and listen to new music. Their success is so great, that digital downloads are now included alongside tra- ditional record sales in many o cial music charts [10]. In the past list... Read More about Cold-start music recommendation using a hybrid representation..

Music-inspired texture representation. (2012)
Conference Proceeding
HORSBURGH, B., CRAW, S. and MASSIE, S. 2012. Music-inspired texture representation. In Proceedings of the 26th Association for the Advancement of Artificial Intelligence conference on artificial intelligence (AAAI-12), co-located with the 2012 Symposium on educational advances in artificial intelligence (EAAI-12), 22-26 July 2012, Toronto, Canada. Palo Alto: AAAI Press [online], pages 52-58. Available from: https://www.aaai.org/ocs/index.php/AAAI/AAAI12/paper/view/5041

Techniques for music recommendation are increasingly relying on hybrid representations to retrieve new and exciting music. A key component of these representations is musical content, with texture being the most widely used feature. Current technique... Read More about Music-inspired texture representation..

Finding the hidden gems: recommending untagged music. (2011)
Conference Proceeding
HORSBURGH, B., CRAW, S., MASSIE, S. and BOSWELL, R. 2011. Finding the hidden gems: recommending untagged music. In Proceedings of the 22nd International joint conference on artificial intelligence (IJCAI-11), 16-22 July 2011, Barcelona, Spain. Palo Alto: AAAI Press [online], pages 2256-2261. Available from: https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-376

We have developed a novel hybrid representation for Music Information Retrieval. Our representation is built by incorporating audio content into the tag space in a tag-track matrix, and then learning hybrid concepts using latent semantic analysis. We... Read More about Finding the hidden gems: recommending untagged music..

Plan recommendation for well engineering. (2011)
Conference Proceeding
THOMSON, R., MASSIE, S., CRAW, S., AHRIZ, H. and MILLS, I. 2011. Plan recommendation for well engineering. In Mehrotra, K.G., Mohan, C.K., Oh, J.C., Varshney, P.K. and Ali, M. (eds.) Modern approaches in applied intelligence: proceedings of the 24th International conference on industrial, engineering and other applications of applied intelligent systems (IEA/AIE 2011), 28 June - 1 July 2011, Syracuse, USA. Lecture notes in computer science, 6704. Berlin: Springer [online], part II, pages 436-445. Available from: https://doi.org/10.1007/978-3-642-21827-9_45

Good project planning provides the basis for successful offshore well drilling projects. In this domain, planning occurs in two phases: an onshore phase develops a project plan; and an offshore phase implements the plan and tracks progress. The Perfo... Read More about Plan recommendation for well engineering..

Complexity modelling for case knowledge maintenance in case-based reasoning. (2006)
Thesis
MASSIE. S. 2006. Complexity modelling for case knowledge maintenance in case-based reasoning. Robert Gordon University, PhD thesis.

Case-based reasoning solves new problems by re-using the solutions of previously solved similar problems and is popular because many of the knowledge engineering demands of conventional knowledge-based systems are removed. The content of the case kno... Read More about Complexity modelling for case knowledge maintenance in case-based reasoning..

A case-based approach to data-to-text generation. [Software]
Digital Artefact
UPADHYAY, A., MASSIE, S., SINGH, R.K., GUPTA, G. and OJHA, M. 2021. A case-based approach to data-to-text generation. [Software]. Hosted on GitHub [online]. Available from: https://github.com/ashishu007/data2text-cbr

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. [Software].