Asymptotic Geometric Analysis and Learning Theory. Learning Theory is used in various real-world applications in diverse research areas, ranging from Biology (e.g. DNA sequencing) to Information Sciences. Therefore, having a deep understanding of fundamental questions in Learning Theory, and in particular, pin-pointing the parameters that make a learning problem hard would have a significant practical impact. This projects aims to achieve this goal, and in addition, we expect it would have a hig ....Asymptotic Geometric Analysis and Learning Theory. Learning Theory is used in various real-world applications in diverse research areas, ranging from Biology (e.g. DNA sequencing) to Information Sciences. Therefore, having a deep understanding of fundamental questions in Learning Theory, and in particular, pin-pointing the parameters that make a learning problem hard would have a significant practical impact. This projects aims to achieve this goal, and in addition, we expect it would have a high theoretical value, as the questions we shall address are of independent interest to pure mathematicians.Read moreRead less
Asymptotic Geometric Analysis and Learning Theory. Learning Theory is used in various real-world applications in diverse research areas, ranging from Biology (e.g. DNA sequencing) to Information Sciences. Therefore, having a deep understanding of fundamental questions in Learning Theory, and in particular, pin-pointing the parameters that make a learning problem hard would have a significant practical impact. This projects aims to achieve this goal, and in addition, we expect it would have a hig ....Asymptotic Geometric Analysis and Learning Theory. Learning Theory is used in various real-world applications in diverse research areas, ranging from Biology (e.g. DNA sequencing) to Information Sciences. Therefore, having a deep understanding of fundamental questions in Learning Theory, and in particular, pin-pointing the parameters that make a learning problem hard would have a significant practical impact. This projects aims to achieve this goal, and in addition, we expect it would have a high theoretical value, as the questions we shall address are of independent interest to pure mathematicians.
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Learning to Pinpoint Emerging Software Vulnerabilities. This project aims to develop learning-based software vulnerability detection techniques to improve the reliability and security of modern software systems. The existing techniques relying on conventional yet rigid software analysis and testing techniques are ineffective and/or inefficient when detecting a wide variety of emerging software vulnerabilities. The outcomes of this project will be a deep-learning-based detection approach and an ....Learning to Pinpoint Emerging Software Vulnerabilities. This project aims to develop learning-based software vulnerability detection techniques to improve the reliability and security of modern software systems. The existing techniques relying on conventional yet rigid software analysis and testing techniques are ineffective and/or inefficient when detecting a wide variety of emerging software vulnerabilities. The outcomes of this project will be a deep-learning-based detection approach and an open-source tool that can capture precision correlations between deep code features and diverse vulnerabilities to pinpoint emerging vulnerabilities without the need for bug specifications. Significant benefits include greatly improved quality, reliability and security for modern software systems.Read moreRead less
Individual Differences in Orientations to Risk and Uncertainty. The main goal of this research project is to extend and integrate three individual-differences approaches to predicting and explaining human judgement and decision making (JDM) and risk-taking behaviours (RTB) under uncertainty: Cognitive-capacity, preferences and dispositions, and dual cognitive process approaches. It will achieve this by studying the joint impact of cognitive style, capacities, and RTB/JDM dispositions on performa ....Individual Differences in Orientations to Risk and Uncertainty. The main goal of this research project is to extend and integrate three individual-differences approaches to predicting and explaining human judgement and decision making (JDM) and risk-taking behaviours (RTB) under uncertainty: Cognitive-capacity, preferences and dispositions, and dual cognitive process approaches. It will achieve this by studying the joint impact of cognitive style, capacities, and RTB/JDM dispositions on performance in appropriate JDM tasks. JDM and RTB are at the root of managing uncertainty, human adaptiveness and rationality. This project will also extend our knowledge of gender differences in JDM and RTB, and lay foundations for systematic cross-cultural studies on this topic.Read moreRead less
Face recognition: Properties and origins of whole-face processing. Humans identify other individuals almost entirely by their faces. Correspondingly, research has demonstrated a "special" style of cognitive processing that is unique to faces (at least in ordinary adults). The present project will address two major theoretical issues: (1) the exact nature of the special processing for faces, and (2) the extent to which it is innate, or learned. New progress in understanding these issues will be m ....Face recognition: Properties and origins of whole-face processing. Humans identify other individuals almost entirely by their faces. Correspondingly, research has demonstrated a "special" style of cognitive processing that is unique to faces (at least in ordinary adults). The present project will address two major theoretical issues: (1) the exact nature of the special processing for faces, and (2) the extent to which it is innate, or learned. New progress in understanding these issues will be made using a series of novel experimental techniques. These techniques isolate the specific contribution of the face recognition system, independent of contributions from object recognition, and from early visual processing.Read moreRead less
Special cognitive processing for faces: Expertise effects, and links to neural mechanisms. Humans identify other individuals primarily by their faces. Evidence from cognitive psychology indicates a special 'whole-face' (as opposed to part-based) style of processing for upright faces. This project will provide new insights into two long-standing issues about the origin of special face processing: (1) whether it derives from generic expert recognition processes or has some face-specific innate co ....Special cognitive processing for faces: Expertise effects, and links to neural mechanisms. Humans identify other individuals primarily by their faces. Evidence from cognitive psychology indicates a special 'whole-face' (as opposed to part-based) style of processing for upright faces. This project will provide new insights into two long-standing issues about the origin of special face processing: (1) whether it derives from generic expert recognition processes or has some face-specific innate component; and (2) the extent to which it can be distinguished from part-based processing at the neural level using both functional brain imaging (fMRI) and adaptation to distorted faces.Read moreRead less
Geometric parameters in Learning Theory. We aim to investigate the behaviour of geometric parameters which appear naturally in Statistical Learning Theory. Those parameters are used to control the sample complexity, which is the size of a random sample needed to produce an accurate prediction. They are also of independent interest in the local theory of Banach spaces. We shall use geometric methods originating in the local theory of Banach spaces to investigate the parameters and the way they in ....Geometric parameters in Learning Theory. We aim to investigate the behaviour of geometric parameters which appear naturally in Statistical Learning Theory. Those parameters are used to control the sample complexity, which is the size of a random sample needed to produce an accurate prediction. They are also of independent interest in the local theory of Banach spaces. We shall use geometric methods originating in the local theory of Banach spaces to investigate the parameters and the way they influence sample complexity. All the problems we focus on are not only important from the Machine Learning point of view, but are intriguing in their theoretical implications.Read moreRead less
DeepHoney: Automatic Honey Data Generation for Active Cyber Defence . This project aims to enhance the security of networks and information systems by empowering them with intelligent deception techniques to achieve proactive attack detection and defence. In recent times, the fictitious environment – honeypot designed by human experience becomes popular to attract attackers and capture their interactions. However, rules-based construction of honeypots fails in preserving the privacy, boosting th ....DeepHoney: Automatic Honey Data Generation for Active Cyber Defence . This project aims to enhance the security of networks and information systems by empowering them with intelligent deception techniques to achieve proactive attack detection and defence. In recent times, the fictitious environment – honeypot designed by human experience becomes popular to attract attackers and capture their interactions. However, rules-based construction of honeypots fails in preserving the privacy, boosting the attractiveness and evolving the system. The project expects to advance deep learning and yield novel DeepHoney technologies with associated publications and open-source software. This should benefit science, society, and the economy by building the next generation of active cyber defence systems. Read moreRead less
Using data mining methods to remove uncertainties in sensor data streams. This project will develop key techniques for removing uncertainties in sensor data streams and thus improve the monitoring quality of sensor networks. The expected outcomes will benefit Australia by enabling improved, lower-cost monitoring of natural resources and management of stock raising.
Reconceiving Machine Learning. The proposed research will develop a new way to consider problems to which machine learning can be applied. Machine learning is crucial enabler of the digital economy. The research will provide better opportunities for Australian industry to gain a competitive advantage with machine learning technology. The framework developed will enable better opportunities for collaborative research and will build and strengthen international linkages.