Insight Research Collection

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At Insight we undertake high impact research in data analytics that has significant impact on industry and society by enabling better decision making.

Insight brings together leading Irish academics from 5 of Ireland's leading research centres (DERI, CLARITY, CLIQUE, 4C, TRIL), previously established by Science Foundation Ireland (SFI) and the Irish Industrial Development Authority (IDA), in key areas of priority research including:

  • The Semantic Web
  • Sensors and the Sensor Web
  • Social network analysis
  • Decision Support and Optimization
  • Connected Health

For more information, please visit the official website.

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Recent Submissions

Now showing 1 - 5 of 578
  • Publication
    Gaussian parsimonious clustering models with covariates and a noise component
    (Springer, 2019-09-20) ;
    We consider model-based clustering methods for continuous, correlated data that account for external information available in the presence of mixed-type fixed covariates by proposing the MoEClust suite of models. These models allow different subsets of covariates to influence the component weights and/or component densities by modelling the parameters of the mixture as functions of the covariates. A familiar range of constrained eigen-decomposition parameterisations of the component covariance matrices are also accommodated. This paper thus addresses the equivalent aims of including covariates in Gaussian parsimonious clustering models and incorporating parsimonious covariance structures into all special cases of the Gaussian mixture of experts framework. The MoEClust models demonstrate significant improvement from both perspectives in applications to both univariate and multivariate data sets. Novel extensions to include a uniform noise component for capturing outliers and to address initialisation of the EM algorithm, model selection, and the visualisation of results are also proposed.
    Scopus© Citations 22  10
  • Publication
    Multi-objective Clustering Algorithm with Parallel Games
    (IEEE, 2020-02-08) ;
    Data mining and knowledge discovery are two important growing research fields in the last few decades due to the abundance of data collected from various sources. The exponentially growing volumes of generated data urge the development of several mining techniques to feed the needs for automatically derived knowledge. Clustering analysis (finding similar groups of data) is a well-established and widely used approach in data mining and knowledge discovery. In this paper, we introduce a clustering technique that uses game theory models to tackle multi-objective application problems. The main idea is to exploit a specific type of simultaneous move games, called congestion games. Congestion games offer numerous advantages ranging from being succinctly represented to possessing a Nash equilibrium that is reachable in a polynomial-time. The proposed algorithm has three main steps: 1) it starts by identifying the initial players (or the cluster-heads); 2) then, it establishes the initial clusters' composition by constructing the game to play and try to find the equilibrium of the game. The third step consists of merging close clusters to obtain the final clusters. The experiment results show that the proposed clustering approach obtains good results and it is very promising in terms of scalability, and performance.
      12Scopus© Citations 1
  • Publication
    Recommendations for determining the validity of consumer wearable and smartphone step-count: Expert statement and checklist of the INTERLIVE Network
    Consumer wearable and smartphone devices provide an accessible means to objectively measure physical activity (PA) through step counts. With the increasing proliferation of this technology, consumers, practitioners and researchers are interested in leveraging these devices as a means to track and facilitate PA behavioural change. However, while the acceptance of these devices is increasing, the validity of many consumer devices have not been rigorously and transparently evaluated. The Towards Intelligent Health and Well-Being Network of Physical Activity Assessment (INTERLIVE) is a joint European initiative of six universities and one industrial partner. The consortium was founded in 2019 and strives to develop best-practice recommendations for evaluating the validity of consumer wearables and smartphones. This expert statement presents a best-practice consumer wearable and smartphone step counter validation protocol. A two-step process was used to aggregate data and form a scientific foundation for the development of an optimal and feasible validation protocol: (1) a systematic literature review and (2) additional searches of the wider literature pertaining to factors that may introduce bias during the validation of these devices. The systematic literature review process identified 2897 potential articles, with 85 articles deemed eligible for the final dataset. From the synthesised data, we identified a set of six key domains to be considered during design and reporting of validation studies: target population, criterion measure, index measure, validation conditions, data processing and statistical analysis. Based on these six domains, a set of key variables of interest were identified and a ‘basic’ and ‘advanced’ multistage protocol for the validation of consumer wearable and smartphone step counters was developed. The INTERLIVE consortium recommends that the proposed protocol is used when considering the validation of any consumer wearable or smartphone step counter. Checklists have been provided to guide validation protocol development and reporting. The network also provide guidance for future research activities, highlighting the imminent need for the development of feasible alternative ‘gold-standard’ criterion measures for free-living validation. Adherence to these validation and reporting standards will help ensure methodological and reporting consistency, facilitating comparison between consumer devices. Ultimately, this will ensure that as these devices are integrated into standard medical care, consumers, practitioners, industry and researchers can use this technology safely and to its full potential.
      13Scopus© Citations 36
  • Publication
    Overlapping community finding with noisy pairwise constraints
    In many real applications of semi-supervised learning, the guidance provided by a human oracle might be “noisy” or inaccurate. Human annotators will often be imperfect, in the sense that they can make subjective decisions, they might only have partial knowledge of the task at hand, or they may simply complete a labeling task incorrectly due to the burden of annotation. Similarly, in the context of semi-supervised community finding in complex networks, information encoded as pairwise constraints may be unreliable or conflicting due to the human element in the annotation process. This study aims to address the challenge of handling noisy pairwise constraints in overlapping semi-supervised community detection, by framing the task as an outlier detection problem. We propose a general architecture which includes a process to “clean” or filter noisy constraints. Furthermore, we introduce multiple designs for the cleaning process which use different type of outlier detection models, including autoencoders. A comprehensive evaluation is conducted for each proposed methodology, which demonstrates the potential of the proposed architecture for reducing the impact of noisy supervision in the context of overlapping community detection.
      11
  • Publication
    Hyperalgesia and central sensitization in subjects with chronic orofacial pain: analysis of pain thresholds and EEG biomarkers
    Introduction: The presence of a temporomandibular disorder is one of the most frequent causes of orofacial pain (OFP). When pain continues beyond tissue healing time, it becomes chronic and may be caused, among other factors, by the sensitization of higher-order neurons. The aim of this study is to describe psychological characteristics of patients with chronic OFP, their peripheral pain threshold, and electroencephalography (EEG) recording, looking for possible signs of central sensitization (CS). Materials and methods: Twenty-four subjects with chronic OFP caused by temporomandibular disorder were evaluated using the Research Diagnostic Criteria for Temporomandibular Disorders Axis I and Axis II. Pain intensity, catastrophizing, and presence of CS were assessed through self-reported questionnaires. Pressure pain threshold (PPT) was recorded in facial and peripheral sites; EEG activity was recorded during open and closed eyes resting state and also during the pain threshold assessment. Pain thresholds and EEG recordings were compared with a cohort of pain-free age- and sex-matched healthy subjects. Results: Patients with chronic OFP showed a significant reduction in their pain threshold compared to healthy subjects in all sites assessed. Greater reduction in pain threshold was recorded in patients with more severe psychological symptoms. Decreased alpha and increased gamma activity was recorded in central and frontal regions of all subjects, although no significant differences were observed between groups. Discussion: A general reduction in PPT was recorded in people who suffer from chronic OFP. This result may be explained by sensitization of the central nervous system due to chronic pain conditions. Abnormal EEG activity was recorded during painful stimulation compared to the relaxed condition in both chronic OFP subjects and healthy controls.
      10Scopus© Citations 11