Computing and Information Systems - Theses
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ItemOntologies in neuroscience and their application in processing questions( 2019)Neuroscience is a vast, multi-dimensional and complex field of study based on both its medical importance and unresolved issues regarding how brain and the nervous system work. This is because of the huge amount of brain disorders and their burden on people and society. Furthermore, scientist have been excited about the function and structure of brain, ever since it was discovered to be responsible for all our emotions, thoughts and behaviour. Ontologies are concepts whose origins go back to philosophy and the concern with the nature and relation of being. They have emerged as promising tools for assistance with neuroscience research recently and provide additional data on a field of study. They connect each entity or element to other ones through descriptive relationships. Ontologies seem to suit the complex, multi-dimensional and still incomplete nature of neuroscience very well because of their characteristics. The first study shines light on applications of ontologies in neuroscience. It incorporated a systematic literature review and methodically reviewed over 1000 research papers from eight databases and three journals. After scanning all documents, 208 of them were selected. Then, a full text analysis was performed on the selected documents. This study found eight major applications for ontologies in neuroscience, most of them consisted of several subcategories. The analysis not only demonstrated the current applications of ontologies in neuroscience, but also their potential future in this field. The second study was set to represent neuroscience questions and then, classify them using ontologies. For this purpose, a questions set was gathered from two research teams and analysed. This, results in a set of dimensions which represents questions. Then, a question hierarchy was formed based on dimensions and questions were classified according to that hierarchy. Two different approaches were used for the classification including an ontology-based approach and a statistical approach. The ontology-based approach exceeded the statistical approach by 15.73% better classification results. The last study was designed to tackle and resolve questions with the assistance of ontologies. It first proposed a set of templates that acted as a translation mechanism for changing questions into machine readable code. Templates were based on the question hierarchy presented in the previous study. Second, this study created an integrated collection of resources including two domain ontologies (NIFSTD and NeuroFMA) and a neuroimaging annotation application (Freesurfer). Subsequently, the code created using templates was executed upon the integrated resource (knowledge base) to find the appropriate answer. While processing the questions, ontologies were used for disambiguation purposes too. At the end, all parts created in this study along with the question classification method created in the previous study were merged as different modules of a question processing model. In conclusion, this thesis reviewed all current ontology applications in neuroscience in detail and demonstrated the extent to which they can assist scientists in classifying and resolving questions. The results of this thesis show that applications of ontologies in neuroscience are diverse and cover a wide range; they are steadily becoming more used in this field; and they can be powerful semantic tools in performing different tasks in neuroscience.
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ItemDauphin A Programming Language for Statistical Signal Processing - from principles to practice( 2018)This dissertation describes the design and implementation of a new programming language called Dauphin for the signal processing domain. Dauphin's focus is on the primitive concepts and algorithmic structures of signal processing. In this language, random variables and probability distributions are as fundamental and easy to use as the numeric types of other languages. The basic algorithms of signal processing --- estimation, detection, classification and so on --- become the standard function calls. Too much time is expended by researchers in re-writing these basic algorithms for each application. Dauphin allows you to code these algorithms directly, so they can be coded once and put into libraries for future use. Ultimately, Dauphin aims to extend the power of the researcher by allowing them to focus on the real problems and simplify the process of implementing their ideas. The first half of this dissertation describes Dauphin and the design issues of existing languages used for signal processing that motivated its development. It includes a general investigation into programming language design and the identification of specific design criteria that impact signal processing programming. These criteria directed the features in Dauphin that support writing signal processing algorithms. Of equal importance, the criteria also provide a means to compare, with some objectivity, the suitability of different languages for signal processing. Following the discussion on language design, Dauphin's features are described in detail, then details related to Dauphin's implementation are presented, including a description of Dauphin's semantics and type system. The second half of the dissertation presents practical applications of the Dauphin language, focussing on three broad areas associated with signal processing: classification, estimation and Monte Carlo methods. These non-trivial applications, combined with examples throughout the dissertation, demonstrate that Dauphin is simple and natural to use, easy to learn and has sufficient expressiveness for general programming in the signal processing domain.
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ItemThe use of clinical decision support systems for the development of medical students’ diagnostic reasoning skills( 2018)Computer-aided learning systems (e-learning systems) can help medical students gain more experience with diagnostic reasoning and decision-making. Within this context, providing feedback that matches student needs (i.e. personalised feedback) is both critical and challenging. Prior research showed that using Clinical Decision Support System (CDSS) to assist doctors improves the effectiveness and efficiency of diagnostic and treatment processes. However, the application of CDSS to the developmental process of clinical reasoning in a clinical teaching environment is still limited. In this research, we developed a new diagnostic decision support system embedded in a learning tool, called DrKnow. Students interact with twenty virtual patients to investigate though the learning steps similar to bedside teaching to arrive at a proper final diagnosis. DrKnow with CDSS-based design monitors students’ activities and provide personalised feedback to support students’ diagnostic decisions. We developed expert knowledge within DrKnow based on the machine learning models trained on 208 realworld clinical cases presenting with abdominal pain, to predict 5 diagnoses (appendicitis, gastroenteritis, urinary tract infection, ectopic pregnancy, and pelvic inflammatory disease). We assessed which of these models are likely to be most effective by predictive accuracy and clinical appropriateness when the model prediction was transformed to feedback. These models were leveraged to generate different kinds of feedback provided during along the process of decision making (interim feedback) and at the end of scenario (final feedback) based on the specific information requested by students from the virtual patients and their active diagnostic hypotheses. Students used this tool to explore one or more common clinical presentations, assessing patient histories, selecting and evaluating appropriate investigations and integrating these findings to select the most appropriate diagnosis. Based on the clinical information they request and prioritise, DrKnow presents key decision points and suggest three provisional diagnoses as they work through the virtual cases. Once students make a final diagnosis, DrKnow presents students with information about their overall diagnostic performance as well as recommendations for diagnosing similar cases. The analysis of the decisions of students as compared to those of DrKnow shows that DrKnow provided appropriate feedback on supporting students to select appropriate differential diagnoses and effective assessment of students diagnostic performance. Although DrKnow still has some limitations, we argue that the implementation of CDSS-based learning support for the development of diagnostic reasoning skills represented by DrKnow provides an effective learning process enabling positive student learning outcomes, while simultaneously overcoming the resource challenges of expert clinician supported bedside teaching.
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ItemMachine learning with adversarial perturbations and noisy labels( 2018)Machine learning models such as traditional random forests (RFs) and modern deep neural networks (DNNs) have been successfully used to solve complex learning problems in many applications such as speech recognition, image classification, face recognition, gaming agents and self-driving cars. For example, DNNs have demonstrated near or even surpassing human-level performance in image classification tasks. Despite their current success, these models are still vulnerable to noisy real-world situations where illegitimate or noisy data may exist to corrupt learning. Studies have shown that by adding small, human imperceptible (in the case of images) adversarial perturbations, normal samples can be perturbed into "adversarial examples'', and DNNs can be made to misclassify adversarial examples with a high level of confidence. This arouses security concerns when employing DNNs in security-sensitive applications such as fingerprint recognition, face verification and autonomous cars. Studies have also found that DNNs can overfit to noisy (incorrect) labels and as a result, generalize poorly. This has been one of the key challenges when applying DNNs in noisy real-world scenarios where even high-quality datasets tend to contain noisy labels. Another open question in machine learning is whether actionable knowledge (or "feedback") can be generated from prediction models to support decision making towards some long-term learning goals (for example, mastering a certain type of skills in a simulation-based learning (SBL) environment). We view the feedback generation problem from a new perspective of adversarial perturbation, and explore the possibility of using adversarial techniques to generate feedback. In this thesis, we investigate machine learning models including DNNs and RFs, and their learning behavior through the lens of adversarial perturbations and noisy labels, with the aim of achieving more secure and robust machine learning. We also explore the possibility of using adversarial techniques in a real-world application: to support skill acquisition in SBL environments through the provision of performance feedback. The first part of our work is on the investigation of DNNs and their vulnerability to adversarial perturbations, in the context of image classification. In contrast to existing work, we develop new understandings of adversarial perturbations by exploring DNN representation space with the Local Intrinsic Dimensionality (LID) measure. In particular, we characterize adversarial subspaces in the vicinity of adversarial examples using LID, and find that adversarial subspaces are of higher intrinsic dimensionality than normal data subspaces. We not only provide a theoretical explanation of the high dimensionality of adversarial subspaces, but also empirically demonstrate that such properties can be used to effectively discriminate adversarial examples generated using state-of-the-art attacking methods. The second part of our work is to explore the possibility of using adversarial techniques in a beneficial way to generate interactive feedback for intelligent tutoring in SBL environments. Feedback is actions (in the form of feature changes) generated from a pre-trained prediction model that can be delivered to a leaner in an SBL environment to correct mistakes or improve skills. We demonstrate that such feedback can be generated accurately and efficiently using properly constrained adversarial techniques with DNNs. In addition to DNNs, we also explore, in the third part of our work, adversarial feedback generation from RF models. Adversarial perturbations can be easily generated from DNNs using gradient descent and backpropagation, however, it is still an open question whether such perturbations can be generated from models such as RFs that do not work with gradients. This part of our work confirms that adversarial perturbations can also be crafted from RFs for the provision of feedback in SBL. In particular, we propose a perturbation method that can find the optimal space transition from one undesired class (e.g. 'novice') to the desired class (e.g. 'expert'), based on a geometric view of the RF decision space as overlapping high dimensional rectangles. We demonstrate empirically that our proposed method has high effectiveness as well as high efficiency when compared to existing methods, making it possible to be used for real-time feedback generation in SBL. The fourth part of our work focuses on DNNs and noisy label learning: training accurate DNNs on data with noisy labels. In this work, we investigate the learning behaviours of DNNs, and show that DNNs exhibit two distinct learning styles when trained on clean versus noisy labels. A LID-based characterization of the intrinsic dimensionality of DNN subspace (inspired by the first part of our work) allows us to identify the two stages of learning from dimensionality compression to dimensionality expansion on datasets with noisy labels. Based on the observation that dimensionality expansion is associated with overfitting to noisy labels, we further propose a heuristic learning strategy to avoid the later stage of dimensionality expansion, so as to robustly train DNNs in the presence of noisy labels. In summary, this work has contributed significantly to existing knowledge through: novel dimensional characterization of DNNs, effective discrimination of adversarial attacks, robust deep learning strategies against noisy labels, and novel approaches to feedback generation. All work is supported by theoretical analysis, empirical results and publications.
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ItemIndoor localization supported by landmark graph and locomotion activity recognition( 2018)Indoor localization is important for a variety of applications such as location-based services, mobile social networks, and emergency response. Although a number of indoor localization systems have been proposed in recent years, they have different limitations in terms of accuracy, cost, coverage, complexity, and applicability. In order to achieve a higher accuracy with relatively low cost, hybrid methods combining multiple positioning techniques have been used. However, hybrid methods usually require an infrastructure of beacons or transmitters, which may not be available in many environments or it may be available at a high cost. Spatial knowledge is available in many scenarios, and can be used to assist localization without additional cost. Landmarks are one of the spatial constraints useful for indoor localization. Indoor localization systems that use landmarks have been proposed in the literature, but they are usually applied for tracking robots by using laser scanners or/and cameras. The systems using these devices are economically or/and computationally expensive, and hence are not suitable for indoor pedestrian localization. Although landmarks based on the built-in smartphone sensors are also used in some indoor localization systems, the performance of these systems relies highly on the completeness of landmarks. A mismatch of landmarks may cause a large localization error and even lead to the failure of localization. The advent of sensor-equipped smart devices has enabled a variety of activity recognition and inference, including locomotion (e.g., walking, running, standing). The sensors built in the smart devices can capture the intensity and duration of activity, and even are able to sense the activity context. Such information can be used to enhance the localization accuracy or reduce the energy consumption and deployment cost while maintaining the accuracy. For example, the knowledge of locomotion activities can be used to optimize the step length estimation of people, which will contribute to the improvement of localization accuracy. However, it is challenging to precisely recognize activities related to indoor localization with smartphones. The hypothesis of this research is that accurate and reliable indoor localization can be achieved by fusing smartphone sensor data with locomotion activities and a landmark graph. This hypothesis is tested using the novel algorithms proposed and developed in this research. The proposed framework consists of four main phases, namely recognizing locomotion activities related to indoor localization from sensor data, improving the accuracy of step counting and step length estimation for pedestrian dead reckoning method, developing a landmark graph-based indoor localization method, and implementing quick WiFi fingerprint collection. The main contributions of this research are as follows. First, a novel method is proposed for locomotion activity recognition by automatically learning useful features from sensor data using a deep learning model. Second, robust and accurate algorithms are proposed for step counting and step length estimation to improve the performance of pedestrian dead reckoning, which will be fused with spatial information. Third, the concept of sensory landmarks and the landmark graph is proposed, and a landmark graph-based method is developed for indoor localization. Fourth, a practical, fast, and reliable fingerprint collection method is designed, which uses the landmark graph-based localization method for automatically estimating the location of reference points used to associate the collected fingerprints.
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ItemAnalysing the interplay of location, language and links utilising geotagged Twitter content( 2018)Language use and interactions on social media are geographically biased. In this work we utilise this bias in predictive models of user geolocation and lexical dialectology. User geolocation is an important component of applications such as personalised search and recommendation systems. We propose text-based and network-based geolocation models, and compare them over benchmark datasets yielding state-of-the- art performance. We also propose hybrid and joint text and network geolocation models that improve upon text or network only models and show that the joint models are able to achieve reasonable performance in minimal supervision scenarios, as often happens in real world datasets. Finally, we also propose the use of continuous representations of location, which enables regression modelling of geolocation and lexical dialectology. We show that our proposed data-driven lexical dialectology model provides qualitative insights in studying geographical lexical variation.
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ItemScaling learning algorithms using locality sensitive hashing( 2018)With increasing volumes of data, it is imperative that data analysis can appropriately scale. However, many common machine learning algorithms, e.g., metric learning, manifold landmark learning, and processing trajectories, suffer poor computational complexity in the size of training data. In this thesis, we propose generic methods for scaling up learning algorithms by utilizing locality sensitive hashing. First, finding representative samples utilizing locality sensitive hashing is proposed. The usefulness of these samples is demonstrated on large-scale supervised metric learning. Our methods achieve quadratic speed up with only minimal decrease in accuracy. Second, representative samples are leveraged for adaptive minibatch selection for fitting Gaussian processes for landmarking manifolds. Our methods exploit the compatibility of locality sensitive hashing and the manifold assumption in high-dimensional data, thereby limiting expensive optimization to relevant regions of the data. Training the state-of-the-art learner with our compressed dataset achieves superior accuracy compared to training with randomly selected samples. We also demonstrate that our methods can be used to find manifold landmarks without learning Gaussian processes at all, which leads to orders-of-magnitude speed up with only minimal decrease in accuracy. And finally, we propose locality sensitive hashing based feature hashing methods which map variable length trajectories to constant length trajectories for efficient similarity computation in Euclidean space. Our methods can accelerate trajectory clustering while achieving competitive accuracy in comparison to clustering using more complicated distance function, such as Dynamic Time Warping.
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ItemSemi-supervised community detection and clustering( 2017)Data clustering and community detection in networks are two important tasks in machine learning which aim to group the data into similar objects or densely connected sub-graphs. However, applying an appropriate similarity measure to obtain the highest accuracy is always a challenge. Furthermore, in some real- world applications, some background knowledge and information exists about the true or desired assignments or properties of clusters and communities. The side-information could be obtained by experiments, domain knowledge or user preferences in different applications. Some constraints may also be imposed to the system due to natural complexity of the problem or resource limitations. Community detection (clustering) in the presence of side-information represented as supervision constraints is called semi-supervised community detection (clustering). However, finding efficient approaches to take the most advantage of this pre-existing information to improve quality of the solutions is still a challenge. In this thesis, we study community detection and clustering problems with and without incorporating domain knowledge for which we propose a similarity measure and exact and approximate optimization techniques to improve the accuracy of the results. In this thesis, we address limitations of a popular community detection measure called modularity and propose an improved measure called generalized modularity which quantifies similarity of network vertices more realistically and comprehensively by incorporating vertex similarity concepts. The pro- posed generalized modularity outperforms the state of the art modularity optimization approach in community detection. In addition, to incorporate background knowledge and user preferences into community detection process, two semi-supervised approaches are proposed in this thesis: First we address the modelling flexibility issue in the literature of semi- supervised community detection to simultaneously model instance level and community level constraints. We propose a generic framework using constraint programming technology which enables incorporating a variety of instance level, community level and complex supervision constraints at the same time. The framework also enables modelling local community definitions as constraints to a global community detection scheme and is able to incorporate a variety of similarity measures and community detection objective functions. Using a high level modelling language enables the proposed semi-supervised community detection framework to have the flexibility of applying both complete and incomplete techniques and has the advantage of proving optimality of the solutions found when using complete techniques. Second, a new algorithm for semi-supervised community detection is pro- posed based on discrete Lagrange multipliers which incorporates pairwise constraints. Unlike most of the existing semi-supervised community detection schemes that modify the graph adjacency based on the supervision constraints, the pro- posed algorithm works with quality measures such as modularity or generalized modularity and guides the community detection process by systematically modifying the similarity matrix only for hard-to satisfy constraints. The pro- posed algorithm commits to satisfy (almost) all of the constraints to take the most advantage of the existing supervision. It outperforms the existing semi- supervised community detection algorithms in terms of satisfying the supervision constraints and noise resistance. Another contribution of this thesis is to incorporate instance level supervision constraints into clustering problem. In this regard, a k-means type semi- supervised clustering algorithm is proposed which can take the most advantage of the pre-existing information to achieve high quality solutions satisfying the constraints. The proposed algorithm is based on continuous Lagrange multipliers and penalizes the constraint violations in a systematic manner which guides the cluster centroids and cluster assignments towards satisfying all of the constraints. The achievements of this thesis are supported by several experiments and publications.
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ItemAutomatic optical coherence tomography imaging analysis for retinal disease screening( 2017)The retina and the choroid are two important structures of the eye and on which the quality of eye sight depends. They have many tissue layers which are very important for monitoring the health and the progression of the eye disease from an early stage. These layers can be visualised using Optical Coherence Tomography (OCT) imaging. The abnormalities in these layers are indications of several eye diseases that can lead to blindness, such as Diabetic Macular Edema (DME), Age-related Macular Degeneration (AMD) and Glaucoma. If the retina and the choroid are damaged there is little chance to recover normal sight. Moreover, any damage in them will lead to blindness if no or late treatment is administered. With eye diseases, early detection and treatment are more effective and cheaper. Biomarkers extracted from these tissue layers, such as changes in thickness of the layers, will note the presence of abnormalities called pathologies such as drusen and hyper-reflective intra-retinal spots, and are very effective in the early detection and monitoring the progression of eye disease. Large scale and reliable biomarker extraction by manual grading for early detection is infeasible and prone to error due to subjective bias and are also cost ineffective. Automatic biomarker extraction is the best solution. However, OCT image analysis for extracting biomarkers is very challenging because of noisy images, low contrast, extremely thin retinal layers, the presence of pathologies and complex anatomical structures such as the optic disc and macula. In this thesis, a robust, efficient and accurate automated 3D segmentation algorithm for OCT images is proposed for the retinal tissue layers and the choroid, thus overcoming those challenges. By mapping OCT image segmentation problem as a graph problem, we converted the detection of layer boundaries to the problem of finding the shortest paths in the mapped graph. The proposed method exploits layer-oriented small regions of interest, edge pixels from canny edge detections as nodes of the graph, and incorporates prior knowledge of the structures into edge weight computation for finding the shortest path using Dijkstra’s shortest path algorithm as a boundary of the layers. Using this segmentation scheme, we were able to segment all the retinal and choroid tissue layers very accurately and extract eight novel biomarkers such as attenuation of the retinal nerve fibre layer, relative intensity of the ellipsoid zone, thickness of the retinal layers, and volume of pathologies i.e. drusen, etc. In addition, we demonstrated that using these biomarkers provides a very accurate (98%) classification model for classifying eye patients into those with normal, DME and AMD diseases which can be built using a Random Forest classifier. The proposed segmentation method and classification method have been evaluated on several datasets collected locally at the Center for Eye Research Australia and from the public domain. In total, the dataset contains 56 patients for the evaluation of the segmentation algorithms and 72 patients for the classification model. The method developed from this study has shown high accuracy for all layers of the retina and the choroid over eight state-of-the-art methods. The root means square error between manually delineated and automatically segmented boundaries is as low as 0.01 pixels. The quantification of biomarkers has also shown a low margin of error from the manually quantified values. Furthermore, the classification model has shown more than 98% accuracy, which outperformed four state-of-the-art methods with an area under the receiver operating characteristic curve (AUC) of 0.99. The classification model can also be used in the early detection of diseases which allows significant prevention of blindness as well as providing a score/index for the condition or prediction of the eye diseases. In this thesis, we have also developed a fully automated prototype system, OCTInspector, for OCT image analysis using these proposed algorithms and methods.
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ItemMachine learning for feedback in massive open online courses( 2016)Massive Open Online Courses (MOOCs) have received widespread attention for their potential to scale higher education, with multiple platforms such as Coursera, edX and Udacity recently appearing. Online courses from elite universities around the world are offered for free, so that anyone with internet access can learn anywhere. Enormous enrolments and diversity of students have been widely observed in MOOCs. Despite their popularity, MOOCs are limited in reaching their full potential by a number of issues. One of the major problems is the notoriously low completion rates. A number of studies have focused on identifying the factors leading to this problem. One of the factors is the lack of interactivity and support. There is broad agreement in the literature that interaction and communication play an important role in improving student learning. It has been indicated that interaction in MOOCs helps students ease their feelings of isolation and frustration, develop their own knowledge, and improve learning experience. A natural way of improving interactivity is providing feedback to students on their progress and problems. MOOCs give rise to vast amounts of student engagement data, bringing opportunities to gain insights into student learning and provide feedback. This thesis focuses on applying and designing new machine learning algorithms to assist instructors in providing student feedback. In particular, we investigate three main themes: i) identifying at-risk students not completing courses as a step towards timely intervention; ii) exploring the suitability of using automatically discovered forum topics as instruments for modelling students' ability; iii) similarity search in heterogeneous information networks. The first theme can be helpful for assisting instructors to design interventions for at-risk students to improve retention. The second theme is inspired by recent research on measurement of student learning in education research communities. Educators explore the suitability of using latent complex patterns of engagement instead of traditional visible assessment tools (e.g. quizzes and assignments), to measure a hypothesised distinctive and complex learning skill of promoting learning in MOOCs. This process is often human-intensive and time-consuming. Inspired by this research, together with the importance of MOOC discussion forums for understanding student learning and providing feedback, we investigate whether students' participation across forum discussion topics can indicate their academic ability. The third theme is a generic study of utilising the rich semantic information in heterogeneous information networks to help find similar objects. MOOCs contain diverse and complex student engagement data, which is a typical example of heterogeneous information networks, and so could benefit from this study. We make the following contributions for solving the above problems. Firstly, we propose transfer learning algorithms based on regularised logistic regression, to identify students who are at risk of not completing courses weekly. Predicted probabilities with well-calibrated and smoothed properties can not only be used for the identification of at-risk students but also for subsequent interventions. We envision an intervention that presents probability of success/failure to borderline students with the hypothesis that they can be motivated by being classified as "nearly there". Secondly, we combine topic models with measurement models to discover topics from students' online forum postings. The topics are enforced to fit measurement models as statistical evidence of instruments for measuring student ability. In particular, we focus on two measurement models, the Guttman scale and the Rasch model. To the best our knowledge, this is the first study to explore the suitability of using discovered topics from MOOC forum content as instruments for measuring student ability, by combining topic models with psychometric measurement models in this way. Furthermore, these scaled topics imply a range of difficulty levels, which can be useful for monitoring the health of a course and refining curricula, student assessment, and providing personalised feedback based on student ability levels and topic difficulty levels. Thirdly, we extend an existing meta path-based similarity measure by incorporating transitive similarity and temporal dynamics in heterogeneous information networks, evaluated using the DBLP bibliographic network. The proposed similarity measure might apply to MOOC settings to find similar students or threads, or thread recommendation in MOOC forums, by modelling student interactions in MOOC forums as a heterogeneous information network.