All Outputs (16)

Who consumes anthocyanins and anthocyanidins? Mining national retail data to reveal the influence of socioeconomic deprivation and seasonality on polyphenol dietary intake (2023)
Conference Proceeding
Harvey, J., Long, G., Welham, S., Mansilla, R., Rose, P., Thomas, M., …Goulding, J. (2023). Who consumes anthocyanins and anthocyanidins? Mining national retail data to reveal the influence of socioeconomic deprivation and seasonality on polyphenol dietary intake. In Proceedings: 2023 IEEE International Conference on Big Data (BigData) (4530-4538). https://doi.org/10.1109/BigData59044.2023.10386220

Anthocyanins are a class of polyphenols that have received widespread recent attention due to their potential health benefits. However, estimating the dietary intake of anthocyanins at a population level is a challenging task, due to the difficulty o... Read More about Who consumes anthocyanins and anthocyanidins? Mining national retail data to reveal the influence of socioeconomic deprivation and seasonality on polyphenol dietary intake.

Assessing relative contribution of Environmental, Behavioural and Social factors on Life Satisfaction via mobile app data (2023)
Conference Proceeding
Milligan, G., Harvey, J., Dowthwaite, L., Vallejos, E. P., Nica-Avram, G., & Goulding, J. (2023). Assessing relative contribution of Environmental, Behavioural and Social factors on Life Satisfaction via mobile app data. In Proceedings: 2023 IEEE International Conference on Big Data: Dec 15 - Dec 18, 2023 Sorrento, Italy. https://doi.org/10.1109/BigData59044.2023.10386740

Life satisfaction significantly contributes to wellbe-ing and is linked to positive outcomes for individual people and society more broadly. However, previous research demonstrates that many factors contribute to the life satisfaction of an individua... Read More about Assessing relative contribution of Environmental, Behavioural and Social factors on Life Satisfaction via mobile app data.

Bundle entropy as an optimized measure of consumers' systematic product choice combinations in mass transactional data (2022)
Conference Proceeding
Mansilla, R., Smith, G., Smith, A., & Goulding, J. (2022). Bundle entropy as an optimized measure of consumers' systematic product choice combinations in mass transactional data. In Proceedings 2022 IEEE International Conference on Big Data (1044-1053). https://doi.org/10.1109/BigData55660.2022.10021062

Understanding and measuring the predictability of consumer purchasing (basket) behaviour is of significant value. While predictability measures such as entropy have been well studied and leveraged in other sectors, their development and application t... Read More about Bundle entropy as an optimized measure of consumers' systematic product choice combinations in mass transactional data.

Towards Idea Mining: Problem-Solution Phrase Extraction fromText (2022)
Conference Proceeding
Liu, H., Brailsford, T., Goulding, J., Maul, T., Tan, T., & Chaudhuri, D. (2022). Towards Idea Mining: Problem-Solution Phrase Extraction fromText. In Advanced Data Mining and Applications 18th International Conference, ADMA 2022, Brisbane, QLD, Australia, November 28–30, 2022: Proceedings, Part II (3-14). https://doi.org/10.1007/978-3-031-22137-8_1

This paper investigates the feasibility of problem-solution phrases extraction from scientific publications using neural network approaches. Bidirectional Long Short-Term Memory with Conditional Random Fields (Bi-LSTM-CRFs) and Bidirectional Encoder... Read More about Towards Idea Mining: Problem-Solution Phrase Extraction fromText.

Model Class Reliance for Random Forests (2020)
Conference Proceeding
Smith, G., Mansilla, R., & Goulding, J. (2020). Model Class Reliance for Random Forests. In Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020)

Variable Importance (VI) has traditionally been cast as the process of estimating each variable's contribution to a predictive model's overall performance. Analysis of a single model instance, however, guarantees no insight into a variables relevance... Read More about Model Class Reliance for Random Forests.

FIMS: Identifying, Predicting and Visualising Food Insecurity (2020)
Conference Proceeding
Lucas, B., Smith, A., Smith, G., Perrat, B., Nica-Avram, G., Harvey, J., & Goulding, J. (2020). FIMS: Identifying, Predicting and Visualising Food Insecurity. In WWW '20: Companion Proceedings of the Web Conference 2020 (190-193). https://doi.org/10.1145/3366424.3383538

Food insecurity is a persistent and pernicious problem in the UK. Due to logistical challenges, national food insecurity statistics are unmeasured by government bodies - and this lack of data leads to any local estimates that do exist being routinely... Read More about FIMS: Identifying, Predicting and Visualising Food Insecurity.

The unbanked and poverty: predicting area-level socio-economic vulnerability from M-Money transactions (2018)
Conference Proceeding
Engelmann, G., Smith, G., & Goulding, J. (2018). The unbanked and poverty: predicting area-level socio-economic vulnerability from M-Money transactions

Emerging economies around the world are often characterized by governments and institutions struggling to keep key demographic data streams up to date. A demographic of interest particularly linked to social vulnerability is that of poverty and socio... Read More about The unbanked and poverty: predicting area-level socio-economic vulnerability from M-Money transactions.

Seasonal Variation in Collective Mood via Twitter Content and Medical Purchases (2017)
Conference Proceeding
Dzogang, F., Goulding, J., Lightman, S., & Cristianini, N. (2017). Seasonal Variation in Collective Mood via Twitter Content and Medical Purchases. In Advances in Intelligent Data Analysis XVI (63-74). https://doi.org/10.1007/978-3-319-68765-0_6

The analysis of sentiment contained in vast amounts of Twitter messages has reliably shown seasonal patterns of variation in multiple studies, a finding that can have great importance in the understanding of seasonal affective disorders, particularly... Read More about Seasonal Variation in Collective Mood via Twitter Content and Medical Purchases.

Exploring the capabilities of Projection Augmented Relief Models (PARM) (2017)
Conference Proceeding
Priestnall, G., Goulding, J., Smith, A., & Arss, N. (2017). Exploring the capabilities of Projection Augmented Relief Models (PARM).

This paper explores the broad capabilities of physical landscape models when augmented by projection, termed Projection Augmented Relief Models (PARM). This includes experiences of developing PARM displays in public settings such as museums and visit... Read More about Exploring the capabilities of Projection Augmented Relief Models (PARM).

Event series prediction via non-homogeneous Poisson process modelling (2016)
Conference Proceeding
Goulding, J., Preston, S. P., & Smith, G. (2016). Event series prediction via non-homogeneous Poisson process modelling. In 2016 IEEE 16th International Conference on Data Mining (ICDM). https://doi.org/10.1109/ICDM.2016.0027

Data streams whose events occur at random arrival times rather than at the regular, tick-tock intervals of traditional time series are increasingly prevalent. Event series are continuous, irregular and often highly sparse, differing greatly in nature... Read More about Event series prediction via non-homogeneous Poisson process modelling.

Cross-system Recommendation: User-modelling via Social Media versus Self-Declared Preferences (2016)
Conference Proceeding
Alanazi, S., Goulding, J., & McAuley, D. (2016). Cross-system Recommendation: User-modelling via Social Media versus Self-Declared Preferences. In HT '16: Proceedings of the 27th ACM Conference on Hypertext and Social Media (183-188). https://doi.org/10.1145/2914586.2914640

© 2016 ACM. It is increasingly rare to encounter aWeb service that doesn't engage in some form of automated recommendation, with Collaborative Filtering (CF) techniques being virtually ubiquitous as the means for delivering relevant content. Yet seve... Read More about Cross-system Recommendation: User-modelling via Social Media versus Self-Declared Preferences.

A novel symbolization technique for time-series outlier detection (2015)
Conference Proceeding
Smith, G., & Goulding, J. (2015). A novel symbolization technique for time-series outlier detection. In 2015 IEEE International Conference on Big Data (Big Data). https://doi.org/10.1109/BigData.2015.7364037

The detection of outliers in time series data is a core component of many data-mining applications and broadly applied in industrial applications. In large data sets algorithms that are efficient in both time and space are required. One area where sp... Read More about A novel symbolization technique for time-series outlier detection.

AMP: a new time-frequency feature extraction method for intermittent time-series data (2015)
Conference Proceeding
Barrack, D. S., Goulding, J., Hopcraft, K., Preston, S., & Smith, G. (2015). AMP: a new time-frequency feature extraction method for intermittent time-series data.

The characterisation of time-series data via their most salient features is extremely important in a range of machine learning task, not least of all with regards to classification and clustering. While there exist many feature extraction techniques... Read More about AMP: a new time-frequency feature extraction method for intermittent time-series data.

A refined limit on the predictability of human mobility (2014)
Conference Proceeding
Smith, G., Wieser, R., Goulding, J., & Barrack, D. (2014). A refined limit on the predictability of human mobility. In 2014 IEEE International Conference on Pervasive Computing and Communications (PerCom). https://doi.org/10.1109/PerCom.2014.6813948

It has been recently claimed that human movement is highly predictable. While an upper bound of 93% predictability was shown, this was based upon human movement trajectories of very high spatiotemporal granularity. Recent studies reduced this spatiot... Read More about A refined limit on the predictability of human mobility.

A data driven approach to mapping urban neighbourhoods (2014)
Conference Proceeding
Brindley, P., Goulding, J., & Wilson, M. L. (2014). A data driven approach to mapping urban neighbourhoods.

Neighbourhoods have been described by the UK Secretary of State for Communities and Local Government as the “building blocks of public service society”. Despite this, difficulties in data collection combined with the concept’s subjective nature have... Read More about A data driven approach to mapping urban neighbourhoods.

Towards optimal symbolization for time series comparisons (2013)
Conference Proceeding
Smith, G., Goulding, J., & Barrack, D. (2013). Towards optimal symbolization for time series comparisons. In 2013 IEEE 13th International Conference on Data Mining Workshops. https://doi.org/10.1109/ICDMW.2013.59

The abundance and value of mining large time series data sets has long been acknowledged. Ubiquitous in fields ranging from astronomy, biology and web science the size and number of these datasets continues to increase, a situation exacerbated by the... Read More about Towards optimal symbolization for time series comparisons.