Chrisina Jayne

Effects of Response-Driven Feedback in Computer Science Learning

This paper presents the results of a project on generating diagnostic feedback for guided learning in a first-year course on programming and a Masters course on software quality. An online multiple-choice questions (MCQs) system is integrated with neural network-based data analysis. Findings about how students use the system suggest that the feedback is effective in addressing the level of knowledge of the individual and guiding him/her toward a greater understanding of particular concepts. In contrast, there is no evidence that learning required in programming problems, where students develop higher-level thinking according to Blooms taxonomy, was exercised by using MCQs.

Imitation Learning: A Survey of Learning Methods

Imitation learning techniques aim to mimic human behavior in a given task. An agent (a learning machine) is trained to perform a task from demonstrations by learning a mapping between observations and actions. The idea of teaching by imitation has been around for many years; however, the field is gaining attention recently due to advances in computing and sensing as well as rising demand for intelligent applications. The paradigm of learning by imitation is gaining popularity because it facilitates teaching complex tasks with minimal expert knowledge of the tasks. Generic imitation learning methods could potentially reduce the problem of teaching a task to that of providing demonstrations, without the need for explicit programming or designing reward functions specific to the task. Modern sensors are able to collect and transmit high volumes of data rapidly, and processors with high computational power allow fast processing that maps the sensory data to actions in a timely manner. This opens the door for many potential AI applications that require real-time perception and reaction such as humanoid robots, self-driving vehicles, human computer interaction, and computer games, to name a few. However, specialized algorithms are needed to effectively and robustly learn models as learning by imitation poses its own set of challenges. In this article, we survey imitation learning methods and present design options in different steps of the learning process. We introduce a background and motivation for the field as well as highlight challenges specific to the imitation problem. Methods for designing and evaluating imitation learning tasks are categorized and reviewed. Special attention is given to learning methods in robotics and games as these domains are the most popular in the literature and provide a wide array of problems and methodologies. We extensively discuss combining imitation learning approaches using different sources and methods, as well as incorporating other motion learning methods to enhance imitation. We also discuss the potential impact on industry, present major applications, and highlight current and future research directions.

Automatic screening and classification of diabetic retinopathy and maculopathy using fuzzy image processing

Digital retinal imaging is a challenging screening method for which effective, robust and cost-effective approaches are still to be developed. Regular screening for diabetic retinopathy and diabetic maculopathy diseases is necessary in order to identify the group at risk of visual impairment. This paper presents a novel automatic detection of diabetic retinopathy and maculopathy in eye fundus images by employing fuzzy image processing techniques. The paper first introduces the existing systems for diabetic retinopathy screening, with an emphasis on the maculopathy detection methods. The proposed medical decision support system consists of four parts, namely: image acquisition, image preprocessing including four retinal structures localisation, feature extraction and the classification of diabetic retinopathy and maculopathy. A combination of fuzzy image processing techniques, the Circular Hough Transform and several feature extraction methods are implemented in the proposed system. The paper also presents a novel technique for the macula region localisation in order to detect the maculopathy. In addition to the proposed detection system, the paper highlights a novel online dataset and it presents the dataset collection, the expert diagnosis process and the advantages of our online database compared to other public eye fundus image databases for diabetic retinopathy purposes.

Automated Sound Signalling Device Quality Assurance Tool for Embedded Industrial Control Applications

This paper presents a novel system for automatic detection and recognition of faulty audio signaling devices as part of an automated industrial manufacturing process. The system uses historical data labeled by human experts in detecting faulty signaling devices to train an artificial neural network based classifier for modeling their decision making process. The neural network is implemented on a real time embedded micro controller which can be more efficiently incorporated into an automated production line eliminating the need for a manual inspection within the manufacturing process. We present real world experiments based on data pertaining to the production and manufacture of audio signaling components used in car instrument clusters. Our results show that the proposed expert system is able to successfully classify faulty audio signaling devices to a high degree of accuracy. The results can be generalized to other signaling devices where an output signal is represented by a complex and changing frequency spectrum even with significant environmental noise.

Automatic detection of microaneurysms in colour fundus images for diabetic retinopathy screening

Abstract Regular eye screening is essential for the early detection and treatment of the diabetic retinopathy. This paper presents a novel automatic screening system for diabetic retinopathy that focuses on the detection of the earliest visible signs of retinopathy, which are microaneurysms. Microaneurysms are small dots on the retina, formed by ballooning out of a weak part of the capillary wall. The detection of the microaneurysms at an early stage is vital, and it is the first step in preventing the diabetic retinopathy. The paper first explores the existing systems and applications related to diabetic retinopathy screening, with a focus on the microaneurysm detection methods. The proposed decision support system consists of an automatic acquisition, screening and classification of diabetic retinopathy colour fundus images, which could assist in the detection and management of the diabetic retinopathy. Several feature extraction methods and the circular Hough transform have been employed in the proposed microaneurysm detection system, alongside the fuzzy histogram equalisation method. The latter method has been applied in the preprocessing stage of the diabetic retinopathy eye fundus images and provided improved results for detecting the microaneurysms.

Neural network methods for one-to-many multi-valued mapping problems

An investigation of the applicability of neural network-based methods in predicting the values of multiple parameters, given the value of a single parameter within a particular problem domain is presented. In this context, the input parameter may be an important source of variation that is related with a complex mapping function to the remaining sources of variation within a multivariate distribution. The definition of the relationship between the variables of a multivariate distribution and a single source of variation allows the estimation of the values of multiple variables given the value of the single variable, addressing in that way an ill-conditioned one-to-many mapping problem. As part of our investigation, two problem domains are considered: predicting the values of individual stock shares, given the value of the general index, and predicting the grades received by high school pupils, given the grade for a single course or the average grade. With our work, the performance of standard neural network-based methods and in particular multilayer perceptrons (MLPs), radial basis functions (RBFs), mixture density networks (MDNs) and a latent variable method, the general topographic mapping (GTM), is compared. According to the results, MLPs and RBFs outperform MDNs and the GTM for these one-to-many mapping problems.

Perceived Realism of Crowd Behaviour with Social Forces

This paper investigates the development of an urban crowd simulation for the purposes of psychophysical experimentation. Whilst artificial intelligence (AI) is advancing to produce more concise and interesting crowd behaviours, the number or sophistication of the algorithms implemented within a system does not necessarily guarantee its perceptual realism. Human perception is highly subjective and does not always conform to the reality of the situation. Therefore it is important to consider this aspect when dealing with A implementations within a crowd system aimed at humans. In this research an initial two-alternative forced choice (2AFC) with constant stimuli psychophysical experiment is presented. The purpose of the experiment is to assess whether human participants perceive crowd behaviour with a social forces model to be more realistic. Results from the experiment suggest that participants do consider crowd behaviour with social forces to be more realistic. This research could inform the development of crowd-based systems, especially those that consider viewer perception to be important, such as for example video games and other media.

Automatic Screening and Classification of Diabetic Retinopathy Fundus Images

Eye screening is essential for the early detection and treatment of the diabetic retinopathy. This paper presents an automatic screening system for diabetic retinopathy to be used in the field of retinal ophthalmology. The paper first explores the existing systems and applications related to diabetic retinopathy screening and detection methods that have been previously reported in the literature. The proposed ophthalmic decision support system consists of an automatic acquisition, screening and classification of diabetic retinopathy fundus images, which will assist in the detection and management of the diabetic retinopathy. The developed system contains four main parts, namely the image acquisition, the image preprocessing, the feature extraction, and the classification by using several machine learning techniques.

Evaluation of hyperbox neural network learning for classification

This paper evaluates the performance of a number of novel extensions of the hyperbox neural network algorithm, a method which uses different modes of learning for supervised classification problems. One hyperbox per class is defined that covers the full range of attribute values in the class. Each hyperbox has one or more neurons associated with it, which model the class distribution. During prediction, points falling into only one hyperbox can be classified immediately, with the neural outputs used only when points lie in overlapping regions of hyperboxes. Decomposing the learning problem into easier and harder regions allows extremely efficient classification. We introduce an unsupervised clustering stage in each hyperbox followed by supervised learning of a neuron per cluster. Both random and heuristic-driven initialisation of the cluster centres and initial weight vectors are considered. We also consider an adaptive activation function for use in the neural mode. The performance and computational efficiency of the hyperbox methods is evaluated on artificial datasets and publically available real datasets and compared with results obtained on the same datasets using Support Vector Machine, Decision tree, K-nearest neighbour, and Multilayer Perceptron (with Back Propagation) classifiers. We conclude that the method is competitively performing, computationally efficient and provide recommendations for best usage of the method based on results on artificial datasets, and evaluation of sensitivity to initialisation.

Engineering Applications of Neural Networks

Proceeedings of the 13th International Conference, EANN 2012, London, UK, September 20-23, 2012