Now showing items 10-29 of 36

  • Finding the hidden gems: recommending untagged music. 

    Horsburgh, Ben; Craw, Susan; Massie, Stewart; Boswell, Robin (AAAI Press/ International Joint Conferences on Artificial Intelligence., 2011)
    HORSBURGH, B., CRAW, S., MASSIE, S. and BOSWELL, R., 2011. Finding the hidden gems: recommending untagged music. In: WALSH, T., ed. Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. 16-22 July 2011. Menlo Park, California: AAAI Press/ International Joint Conferences on Artificial Intelligence. Pp. 2256-2261
    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 ...
  • FITsense: employing multi-modal sensors in smart homes to predict falls. 

    Massie, Stewart; Forbes, Glenn; Craw, Susan; Fraser, Lucy; Hamilton, Graeme (Springer https://doi.org/10.1007/978-3-030-01081-2_17, 2018-10-09)
    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.) Lecture notes in computer science, 11156. Case-based reasoning research and development; proceedings of the 26th International conference on case-based reasoning (ICCBR-18), 9-12 July 2018, Stockholm, Sweden. 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 ...
  • Harnessing background knowledge for e-learning recommendation. 

    Mbipom, Blessing; Craw, Susan; Massie, Stewart (Springer https://dx.doi.org/10.1007/978-3-319-47175-4_1, 2016-11-05)
    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 (AI-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 ...
  • Improving e-learning recommendation by using background knowledge. 

    Mbipom, Blessing; Craw, Susan; Massie, Stewart (Wiley https://doi.org/10.1111/exsy.12265, 2018-01-26)
    MBIPOM, B., CRAW, S. and MASSIE, S. 2018. Improving e-learning recommendation by using background knowledge. Expert systems [online], Early View. 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 ...
  • Improving kNN for human activity recognition with privileged learning using translation models. 

    Wijekoon, Anjana; Wiratunga, Nirmalie; Sani, Sadiq; Massie, Stewart; Cooper, Kay (Springer https://doi.org/10.1007/978-3-030-01081-2_30, 2018-10-09)
    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.) Lecture notes in computer science, 11156. Case-based reasoning research and development; proceedings of the 26th International conference on case-based reasoning (ICCBR-18), 9-12 July 2018, Stockholm, Sweden. 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 ...
  • Integrating content and semantic representations for music recommendation. 

    Horsburgh, Ben (Robert Gordon University School of Computing Science, 2013-07)
    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. ...
  • kNN sampling for personalised human recognition. 

    Sani, Sadiq; Wiratunga, Nirmalie; Massie, Stewart; Cooper, Kay (Springer https://doi.org/10.1007/978-3-319-61030-6_23, 2017-06-21)
    SANI, S., WIRATUNGA, N., MASSIE, S. and COOPER, K. 2017. kNN sampling for personalised human recognition. Lecture notes in computer science, 10339, case-based reasoning research and development: proceedings of the 25th international case-based reasoning conference (ICCBR 2017), 26-28 June 2017, Trondheim, Norway. 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 ...
  • Knowledge driven approaches to e-learning recommendation. 

    Mbipom, Blessing (Robert Gordon University School of Computing Science and Digital Media, 2018-05-01)
    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 ...
  • Learning deep and shallow features for human activity recognition. 

    Sani, Sadiq; Massie, Stewart; Wiratunga, Nirmalie; Cooper, Kay (Springer https://doi.org/10.1007/978-3-319-63558-3_40, 2017-07-19)
    SANI, S., MASSIE, S., WIRATUNGA, N. and COOPER, K. 2017. Learning deep and shallow features for human activity recognition. Lecture notes in artificial intelligence, 10412: proceedings of the 10th international knowledge science engineering and management conference (KESEM 2017), 19-20 August 2017, Melbourne, Australia. 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 ...
  • Learning deep features for kNN-based human activity recognition. 

    Sani, Sadiq; Wiratunga, Nirmalie; Massie, Stewart (ICCBR (Organisers) http://ceur-ws.org/Vol-2028/, 2017-06-26)
    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.) Proceedings of the 25th International case-based reasoning conference (ICCBR 2017): case-based reasoning and deep learning workshop (CBRDL 2017), 26-28 June 2017, Trondheim, Norway. Trondheim: ICCBR [online], 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 ...
  • Learning pseudo-tags to augment sparse tagging in hybrid music recommender systems. 

    Horsburgh, Ben; Craw, Susan; Massie, Stewart (Elsevier http://dx.doi.org/10.1016/j.artint.2014.11.004, 2015-02)
    HORSBURGH, B., CRAW, S. and MASSIE, S., 2015. Learning pseudo-tags to augment sparse tagging in hybrid music recommender systems. Artificial Intelligence, 219, pp. 25-39.
    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 ...
  • Lexicon based feature extraction for emotion text classification. 

    Bandhakavi, Anil; Wiratunga, Nirmalie; Padmanabhan, Deepak; Massie, Stewart (Elsevier https://doi.org/10.1016/j.patrec.2016.12.009, 2016-12-15)
    BANDHAKAVI, A., WIRATUNGA, N., DEEPAK, P. and MASSIE, S. 2017. Lexicon based feature extraction for emotion text classification. Pattern recognition letters [online], 93, pages 133-143. 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 ...
  • Lexicon generation for emotion detection from text. 

    Bandhakavi, Anil; Wiratunga, Nirmalie; Massie, Stewart; Padmanabhan, Deepak (IEEE https://doi.org/10.1109/MIS.2017.22, 2017-02-13)
    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 ...
  • Matching networks for personalised human activity recognition. 

    Sani, Sadiq; Wiratunga, Nirmalie; Massie, Stewart; Cooper, Kay (CEUR http://ceur-ws.org/Vol-2142/, 2018-07-13)
    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 17th International autonomous agents and multiagent systems conference (AAMAS 2018), 35th International conference on machine learning (ICML 2018), 27th International joint conference on artificial intelligence (IJCAI 2018) and 26th International conference on case-based reasoning (ICCBR 2018), 13-19 July 2018, Stockholm, Sweden. Stockholm: CEUR [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 ...
  • mHealth optimisationfor education and physical activity in type 1 diabetes: MEDPAT1. 

    Hall, J.; Stephen, K.; Croall, A.; MacMillan, J.; Murray, L.; Wiratunga, Nirmalie; Massie, Stewart; MacRury, S. (University of Highlands and Islands/RGU/Diabetes Scotland, 2017-03-08)
    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 Diabetes UK professional conference 2017, 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 ...
  • Monitoring health in smart homes using simple sensors. 

    Massie, Stewart; Forbes, Glenn; Craw, Susan; Fraser, Lucy; Hamilton, Graeme (ICCBR (Organisers) http://ceur-ws.org/Vol-2148/, 2018-07-13)
    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 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. Stockholm: CEUR [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 ...
  • A multi-objective evolutionary algorithm fitness function for case-base maintenance. 

    Lupiani, Eduardo; Craw, Susan; Massie, Stewart; Juarez, Jose M.; Palma, Jose T. (Springer. http://dx.doi.org/10.1007/978-3-642-39056-2_16, 2013-07)
    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: S. J. DELANY and S. ONTANON, eds. Case-Based Reasoning Research and Development: Proceedings of the 21st International Conference, ICCBR 2013. 8-11 July 2013. Berlin: Springer. Pp. 218-232.
    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 ...
  • Music recommendation: audio neighbourhoods to discover music in the long tail. 

    Craw, Susan; Horsburgh, Ben; Massie, Stewart (Springer https://dx.doi.org/10.1007/978-3-319-24586-7_6, 2015-11-26)
    CRAW, S., HORSBURGH, B. and MASSIE, S. 2015. Music recommendation: audio neighbourhoods to discover music in the long tail. Lecture notes in computer science [online], 9343, Proceedings of the 23rd international conference on case-based reasoning (ICCBR 2015), pages 73-87. Available from: https://dx.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 ...
  • Music recommenders: user evaluation without real users? 

    Craw, Susan; Horsburgh, Ben; Massie, Stewart (AAAI/International Joint Conferences on Artificial Intelligence (IJCAI) http://ijcai.org/papers15/Papers/IJCAI15-249.pdf, 2015-07)
    CRAW, S., HORSBURGH, B. and MASSIE, S., 2015. Music recommenders: user evaluation without real users? In: Q. YANG and M. WOOLDRIDGE, eds. Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. 25-31 July 2015. [online] Palo Alto: AAAI/IJCAI pp. 1749-1755. Available from: http://ijcai.org/papers15/contents.php [Accessed 10 August 2015]
    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 ...
  • Music-inspired texture representation. 

    Horsburgh, Ben; Craw, Susan; Massie, Stewart (AAAI Press. http://www.aaai.org/Press/Proceedings/aaai12.php, 2012-07)
    HORSBURGH, B., CRAW, S. and MASSIE, S., 2012. Music-inspired texture representation. In: Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12). 22-26 July 2012. Palo Alto, CA: AAAI Press. Pp. 52-58.
    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 ...