The use of general descriptive names, registered names, trademarks, etc. in this publication does... more The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
Behavioural coding is time-intensive and laborious. Thin slice sampling provides an alternative a... more Behavioural coding is time-intensive and laborious. Thin slice sampling provides an alternative approach, aiming to alleviate the coding burden. However, little is understood about whether different behaviours coded over thin slices are comparable to those same behaviours over entire interactions. To provide quantitative evidence for the value of thin slice sampling for a variety of behaviours. We used data from three populations of parent-infant interactions: mother-infant dyads from the Grown in Wales (GiW) cohort (n = 31), mother-infant dyads from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort (n = 14), and father-infant dyads from the ALSPAC cohort (n = 11). Mean infant ages were 13.8, 6.8, and 7.1 months, respectively. Interactions were coded using a comprehensive coding scheme comprised of 11–14 behavioural groups, with each group comprised of 3–13 mutually exclusive behaviours. We calculated frequencies of verbal and non-verbal behaviours, transition matr...
2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), 2019
This paper presents an industrial system to read text on tire sidewalls. Images of vehicle tires ... more This paper presents an industrial system to read text on tire sidewalls. Images of vehicle tires in motion are acquired using roadside cameras. Firstly, the tire circularity is detected using Circular Hough Transform (CHT) with dynamic radius detection. The tire is then unwarped into a rectangular patch and a cascade of convolutional neural network (CNN) classifiers is applied for text recognition. We introduce a novel proposal generator for localizing the tire code by combining Histogram of Oriented Gradients (HOG) with a CNN. The proposals are then filtered using a deep network. After the code is localized, character detection and recognition are carried out using two separate deep CNNs. The system presents impressive accuracy and efficiency proving its suitability for the intended industrial application.
We present a novel method for prediction of the onset of a spontaneous (paroxysmal) atrial fibril... more We present a novel method for prediction of the onset of a spontaneous (paroxysmal) atrial fibrilation episode by representing the electrocardiograph (ECG) output as two time series corresponding to the interbeat intervals and the lengths of the atrial component of the ECG. We will then show how different entropy measures can be calulated from both of these series and then combined in a neural network trained using the Bayesian evidence procedure to form and effective predictive classifier.
Most conventional techniques for estimating conditional probability densities are inappropriate f... more Most conventional techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we introduce three related techniques for tackling such problems, and test them using synthetic data. We then apply them to the problem of extracting the distribution of wind vector directions from radar scatterometer data.
Intelligent decision support agents work with heterogeneous information that is constantly changi... more Intelligent decision support agents work with heterogeneous information that is constantly changing. They are expected to process that information autonomously and produce results in a form that is easy to understand by a human user. Similarly, online career information is published by different authors in various formats and is subject to continuous editing. Online career advice seekers should be informed by an intuitive, easy to explore view of available career resources. We propose Aviator, a career guidance agent that collates relevant data from different sources, allows viewers to conveniently explore as well as edit that data and provides a means for personalising the users’ interaction with the system. We describe, justify and evaluate Aviator’s features to highlight their usefulness with providing efficient support for career decisions.
Background Since the era of smartphones started in early 2007, they have steadily turned into an ... more Background Since the era of smartphones started in early 2007, they have steadily turned into an accepted part of our lives. Poor sleep is a health problem that needs to be closely monitored before it causes severe mental health problems, such as anxiety or depression. Sleep disorders (eg, acute insomnia) can also develop to chronic insomnia if not treated early. More specifically, mental health problems have been recognized to have casual links to anxiety, depression, heart disease, obesity, dementia, diabetes, and cancer. Several researchers have used mobile sensors to monitor sleep and to study changes in individual mood that may cause depression and anxiety. Objective Extreme sleepiness and insomnia not only influence physical health, they also have a significant impact on mental health, such as by causing depression, which has a prevalence of 18% to 21% among young adults aged 16 to 24 in the United Kingdom. The main body of this narrative review explores how passive data colle...
Diabetes patients might suffer from an unhealthy life, long-term treatment and chronic complicate... more Diabetes patients might suffer from an unhealthy life, long-term treatment and chronic complicated diseases. The decreasing hospitalization rate is a crucial problem for health care centers. This study combines the bagging method with base classifier decision tree and costs-sensitive analysis for diabetes patients' classification purpose. Real patients' data collected from a regional hospital in Thailand were analyzed. The relevance factors were selected and used to construct base classifier decision tree models to classify diabetes and non-diabetes patients. The bagging method was then applied to improve accuracy. Finally, asymmetric classification cost matrices were used to give more alternative models for diabetes data analysis.
Highlights We outline new and improved approach to determining MHC supertype. This methodolog... more Highlights We outline new and improved approach to determining MHC supertype. This methodology combines Poisson Boltzmann electrostatics with state-of-the-art data reduction. The clustering we observe reconstructs known structures with supertypes purely from the projected potential. This approach should allow us to rationalise peptide binding and TCR recognition and thus facilitate epitope identification and optimise transplantation strategies.
Radial Basis Function networks with linear outputs are often used in regression problems because ... more Radial Basis Function networks with linear outputs are often used in regression problems because they can be substantially faster to train than Multi-layer Perceptrons. For classification problems, the use of linear outputs is less appropriate as the outputs are not guaranteed to represent probabilities. We show how RBFs with logistic and softmax outputs can be trained efficiently using the Fisher scoring algorithm. This approach can be used with any model which consists of a generalised linear output function applied to a model which is linear in its parameters. We compare this approach with standard non-linear optimisation algorithms on a number of datasets.
Visual analytics systems combine machine learning or other analytic techniques with interactive d... more Visual analytics systems combine machine learning or other analytic techniques with interactive data visualization to promote sensemaking and analytical reasoning. It is through such techniques that people can make sense of large, complex data. While progress has been made, the tactful combination of machine learning and data visualization is still under-explored. This state-ofthe-art report presents a summary of the progress that has been made by highlighting and synthesizing select research advances. Further, it presents opportunities and challenges to enhance the synergy between machine learning and visual analytics for impactful future research directions.
ABSTRACT Hierarchical visualization systems are desirable because a single twodimensional visuali... more ABSTRACT Hierarchical visualization systems are desirable because a single twodimensional visualization plot may not be sufficient to capture all of the interesting aspects of complex high-dimensional data sets. We extend an existing locally linear hierarchical visualization system PhiVis [1] in several directions: (1) we allow for non-linear projection manifolds (the basic building block is the Generative Topographic Mapping -- GTM), (2) we introduce a general formulation of hierarchical probabilistic models consisting of local probabilistic models organized in a hierarchical tree, (3) we describe folding patterns of low-dimensional projection manifold in high-dimensional data space by computing and visualizing the manifold 's local directional curvatures. Quantities such as magnification factors [3] and directional curvatures are helpful for understanding the layout of the nonlinear projection manifold in the data space and for further refinement of the hierarchical visualization plot. Like PhiVis, our system is statistically principled and is built interactively in a top-down fashion using the EM algorithm. We demonstrate the visualization system principle of the approach on a complex 12-dimensional data set and mention possible applications in the pharmaceutical industry.
The use of general descriptive names, registered names, trademarks, etc. in this publication does... more The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.
Behavioural coding is time-intensive and laborious. Thin slice sampling provides an alternative a... more Behavioural coding is time-intensive and laborious. Thin slice sampling provides an alternative approach, aiming to alleviate the coding burden. However, little is understood about whether different behaviours coded over thin slices are comparable to those same behaviours over entire interactions. To provide quantitative evidence for the value of thin slice sampling for a variety of behaviours. We used data from three populations of parent-infant interactions: mother-infant dyads from the Grown in Wales (GiW) cohort (n = 31), mother-infant dyads from the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort (n = 14), and father-infant dyads from the ALSPAC cohort (n = 11). Mean infant ages were 13.8, 6.8, and 7.1 months, respectively. Interactions were coded using a comprehensive coding scheme comprised of 11–14 behavioural groups, with each group comprised of 3–13 mutually exclusive behaviours. We calculated frequencies of verbal and non-verbal behaviours, transition matr...
2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), 2019
This paper presents an industrial system to read text on tire sidewalls. Images of vehicle tires ... more This paper presents an industrial system to read text on tire sidewalls. Images of vehicle tires in motion are acquired using roadside cameras. Firstly, the tire circularity is detected using Circular Hough Transform (CHT) with dynamic radius detection. The tire is then unwarped into a rectangular patch and a cascade of convolutional neural network (CNN) classifiers is applied for text recognition. We introduce a novel proposal generator for localizing the tire code by combining Histogram of Oriented Gradients (HOG) with a CNN. The proposals are then filtered using a deep network. After the code is localized, character detection and recognition are carried out using two separate deep CNNs. The system presents impressive accuracy and efficiency proving its suitability for the intended industrial application.
We present a novel method for prediction of the onset of a spontaneous (paroxysmal) atrial fibril... more We present a novel method for prediction of the onset of a spontaneous (paroxysmal) atrial fibrilation episode by representing the electrocardiograph (ECG) output as two time series corresponding to the interbeat intervals and the lengths of the atrial component of the ECG. We will then show how different entropy measures can be calulated from both of these series and then combined in a neural network trained using the Bayesian evidence procedure to form and effective predictive classifier.
Most conventional techniques for estimating conditional probability densities are inappropriate f... more Most conventional techniques for estimating conditional probability densities are inappropriate for applications involving periodic variables. In this paper we introduce three related techniques for tackling such problems, and test them using synthetic data. We then apply them to the problem of extracting the distribution of wind vector directions from radar scatterometer data.
Intelligent decision support agents work with heterogeneous information that is constantly changi... more Intelligent decision support agents work with heterogeneous information that is constantly changing. They are expected to process that information autonomously and produce results in a form that is easy to understand by a human user. Similarly, online career information is published by different authors in various formats and is subject to continuous editing. Online career advice seekers should be informed by an intuitive, easy to explore view of available career resources. We propose Aviator, a career guidance agent that collates relevant data from different sources, allows viewers to conveniently explore as well as edit that data and provides a means for personalising the users’ interaction with the system. We describe, justify and evaluate Aviator’s features to highlight their usefulness with providing efficient support for career decisions.
Background Since the era of smartphones started in early 2007, they have steadily turned into an ... more Background Since the era of smartphones started in early 2007, they have steadily turned into an accepted part of our lives. Poor sleep is a health problem that needs to be closely monitored before it causes severe mental health problems, such as anxiety or depression. Sleep disorders (eg, acute insomnia) can also develop to chronic insomnia if not treated early. More specifically, mental health problems have been recognized to have casual links to anxiety, depression, heart disease, obesity, dementia, diabetes, and cancer. Several researchers have used mobile sensors to monitor sleep and to study changes in individual mood that may cause depression and anxiety. Objective Extreme sleepiness and insomnia not only influence physical health, they also have a significant impact on mental health, such as by causing depression, which has a prevalence of 18% to 21% among young adults aged 16 to 24 in the United Kingdom. The main body of this narrative review explores how passive data colle...
Diabetes patients might suffer from an unhealthy life, long-term treatment and chronic complicate... more Diabetes patients might suffer from an unhealthy life, long-term treatment and chronic complicated diseases. The decreasing hospitalization rate is a crucial problem for health care centers. This study combines the bagging method with base classifier decision tree and costs-sensitive analysis for diabetes patients' classification purpose. Real patients' data collected from a regional hospital in Thailand were analyzed. The relevance factors were selected and used to construct base classifier decision tree models to classify diabetes and non-diabetes patients. The bagging method was then applied to improve accuracy. Finally, asymmetric classification cost matrices were used to give more alternative models for diabetes data analysis.
Highlights We outline new and improved approach to determining MHC supertype. This methodolog... more Highlights We outline new and improved approach to determining MHC supertype. This methodology combines Poisson Boltzmann electrostatics with state-of-the-art data reduction. The clustering we observe reconstructs known structures with supertypes purely from the projected potential. This approach should allow us to rationalise peptide binding and TCR recognition and thus facilitate epitope identification and optimise transplantation strategies.
Radial Basis Function networks with linear outputs are often used in regression problems because ... more Radial Basis Function networks with linear outputs are often used in regression problems because they can be substantially faster to train than Multi-layer Perceptrons. For classification problems, the use of linear outputs is less appropriate as the outputs are not guaranteed to represent probabilities. We show how RBFs with logistic and softmax outputs can be trained efficiently using the Fisher scoring algorithm. This approach can be used with any model which consists of a generalised linear output function applied to a model which is linear in its parameters. We compare this approach with standard non-linear optimisation algorithms on a number of datasets.
Visual analytics systems combine machine learning or other analytic techniques with interactive d... more Visual analytics systems combine machine learning or other analytic techniques with interactive data visualization to promote sensemaking and analytical reasoning. It is through such techniques that people can make sense of large, complex data. While progress has been made, the tactful combination of machine learning and data visualization is still under-explored. This state-ofthe-art report presents a summary of the progress that has been made by highlighting and synthesizing select research advances. Further, it presents opportunities and challenges to enhance the synergy between machine learning and visual analytics for impactful future research directions.
ABSTRACT Hierarchical visualization systems are desirable because a single twodimensional visuali... more ABSTRACT Hierarchical visualization systems are desirable because a single twodimensional visualization plot may not be sufficient to capture all of the interesting aspects of complex high-dimensional data sets. We extend an existing locally linear hierarchical visualization system PhiVis [1] in several directions: (1) we allow for non-linear projection manifolds (the basic building block is the Generative Topographic Mapping -- GTM), (2) we introduce a general formulation of hierarchical probabilistic models consisting of local probabilistic models organized in a hierarchical tree, (3) we describe folding patterns of low-dimensional projection manifold in high-dimensional data space by computing and visualizing the manifold 's local directional curvatures. Quantities such as magnification factors [3] and directional curvatures are helpful for understanding the layout of the nonlinear projection manifold in the data space and for further refinement of the hierarchical visualization plot. Like PhiVis, our system is statistically principled and is built interactively in a top-down fashion using the EM algorithm. We demonstrate the visualization system principle of the approach on a complex 12-dimensional data set and mention possible applications in the pharmaceutical industry.
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