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- PublicationGaussian 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.
- PublicationOverlapping community finding with noisy pairwise constraints(Springer, 2020-12-11)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.
- PublicationDevelopment of a Speech Quality Database Under Uncontrolled Conditions(2020-10-24)Objective audio quality assessment is preferred to avoid timeconsuming and costly listening tests. The development of objective quality metrics depends on the availability of datasets appropriate to the application under study. Currently, a suitable human-annotated dataset for developing quality metrics in archive audio is missing. Given the online availability of archival recordings, we propose to develop a real-world audio quality dataset. We present a methodology used to curate a speech quality database using the archive recordings from the Apollo Space Program. The proposed procedure is based on two steps: a pilot listening test and an exploratory data analysis. The pilot listening test shows that we can extract audio clips through the control of speech-to-text performance metrics to prevent data repetition. Through unsupervised exploratory data analysis, we explore the characteristics of the degradations. We classify distinct degradations and we study spectral, intensity, tonality and overall quality properties of the data through clustering techniques. These results provide the necessary foundation to support the subsequent development of large-scale crowdsourced datasets for audio quality.
Scopus© Citations 3 7
- PublicationAnalytical View on Non-Invasive Measurement of Moving Charge by Position Dependent Semiconductor Qubit(Springer, 2020-11-01)Detection of moving charge in free space is presented in the framework of single electron CMOS devices. It opens the perspective for construction of new type detectors for beam diagnostic in accelerators. General phenomenological model of noise acting on position based qubit implemented in semiconductor quantum dots is given in the framework of simplistic tight-binding model.
Scopus© Citations 3 7
- PublicationEffect of Combination of HBM and Certainty Sampling on Workload of Semi-Automated Grey Literature Screening(2021-07-24)With the rapid increase of unstructured text data, grey literature has become an important source of information to support research and innovation activities. In this paper, we propose a novel semiautomated grey literature screening approach that combines a Hierarchical BERT Model (HBM) with active learning to reduce the human workload in grey literature screening. Evaluations over three real-world grey literature datasets demonstrate that the proposed approach can save up to 64.88% of the human screening workload, while maintaining high screening accuracy. We also demonstrate how the use of the HBM model allows salient sentences within grey literature documents to be selected and highlighted to support workers in screening tasks.