Acoustic Filter Functions from Morphological Measurements. This project works toward enabling the broadcast of high-fidelity 3D audio. By developing an efficient technique for deriving personalised acoustic filter functions based on image data of the listener's head and ears, we hope to enable high-fidelity 3D audio decoding and rendering. Morphological parameterisation techniques and databases recently developed at the University of York will be combined with the statistical synthesis technique ....Acoustic Filter Functions from Morphological Measurements. This project works toward enabling the broadcast of high-fidelity 3D audio. By developing an efficient technique for deriving personalised acoustic filter functions based on image data of the listener's head and ears, we hope to enable high-fidelity 3D audio decoding and rendering. Morphological parameterisation techniques and databases recently developed at the University of York will be combined with the statistical synthesis techniques and databases developed at the University of Sydney to substantially reduce the development time required for either group alone. Psychoacoustic experiments carried out at the University of Sydney will demonstrate the fidelity of the delivered 3D audio.Read moreRead less
Tensor and Hypergraph Methods in Fitting Visual Data. This proposal will put an important class of clustering (extracting data that should fit a geometric model) on a more solid theoretical foundation. This will lead to better understanding of how to certify outcomes, efficiency, reliability etc. The type of clustering under consideration is relevant to many problems in machine learning and computer vision, as well as data mining and a wide variety of other settings.
Improved image analysis: maximised statistical use of geometry/shape constraints. This project will improve image analysis to apply such applications as 3D street-scape reconstruction, synthetic inserts into video for special effects, autonomous navigation, and scene understanding. It will do so by maximally exploiting the geometry of planar surfaces (e.g. walls) and straight lines and other simple geometric shapes.
Promoting fairness in online attention. This project aims to design mechanisms for fairness of attention to online digital items, by promoting diversity and reducing biases. Attention is one of the most valuable, yet scarce resources in the modern world. Biased attention fuels the propagation of fake information, hurts democratic debate in society and leads to public trust crisis of online media, which could result in unpleasant surprises in individual and group decisions. This project builds ....Promoting fairness in online attention. This project aims to design mechanisms for fairness of attention to online digital items, by promoting diversity and reducing biases. Attention is one of the most valuable, yet scarce resources in the modern world. Biased attention fuels the propagation of fake information, hurts democratic debate in society and leads to public trust crisis of online media, which could result in unpleasant surprises in individual and group decisions. This project builds upon recent breakthroughs in social dynamics, and expects to design new methods for measuring the extent of (un)fairness and (mis)trust, validate novel intervention strategies in a series of online experiments promoting unbiased information consumption and fair decision-making.Read moreRead less
Efficient causal discovery from observational data. Discovering cause-effect relationships is the ultimate goal for many applications. Randomised control trial is the gold standard for discovering causal relationships. However, conducting such trials is impossible in many cases due to cost and/or ethical concerns. In contrast, a large amount of data has been accumulated in all areas. It is desirable to infer causal relationships from data directly and automatically. This project aims to develop ....Efficient causal discovery from observational data. Discovering cause-effect relationships is the ultimate goal for many applications. Randomised control trial is the gold standard for discovering causal relationships. However, conducting such trials is impossible in many cases due to cost and/or ethical concerns. In contrast, a large amount of data has been accumulated in all areas. It is desirable to infer causal relationships from data directly and automatically. This project aims to develop fast and scalable data mining methods for identifying causal relationships from large and/or high dimensional data sets. The developed methods will mainly be evaluated in real world biological applications. The research outcomes will be useful in many areas for causal reasoning and decision making.Read moreRead less
Developing novel data mining methods to reveal complex group relationships from heterogeneous data. This project aims to develop novel and effective data mining methods that will enable us to unravel the relationships between multiple, rather than individual, components of complex systems (such as genes, gene regulators and cancer), which is crucial to understanding how such systems work. Potential applications for such methods are extensive.
A Computer-Aided Cartooning System. This project is aimed at developing a computer-aided system to accelerate main image-related processes in cartoon production. Using such a system, many of the tedious and repetitive tasks can be performed semi-automatically. The project is focused on accurate representation and matching of shapes. New vectorization methods based on projection onto convex sets (POCS), and new matching methods based on multi-stage hierarchical structures will be developed. The t ....A Computer-Aided Cartooning System. This project is aimed at developing a computer-aided system to accelerate main image-related processes in cartoon production. Using such a system, many of the tedious and repetitive tasks can be performed semi-automatically. The project is focused on accurate representation and matching of shapes. New vectorization methods based on projection onto convex sets (POCS), and new matching methods based on multi-stage hierarchical structures will be developed. The targeted applications include entertainment, next generation mobile services, and the internet.Read moreRead less
Frequency-related features derived from phase spectrum for robust speech recognition. Though the currently available speech recognizers work reasonably well in noise-free environments, their performance deteriorates drastically even in the presence of a small amount of noise. In order to overcome this problem, new frequency-related features are proposed in this project for speech recognition. These features are derived from the phase spectrum of the speech signal, and are expected to be robust t ....Frequency-related features derived from phase spectrum for robust speech recognition. Though the currently available speech recognizers work reasonably well in noise-free environments, their performance deteriorates drastically even in the presence of a small amount of noise. In order to overcome this problem, new frequency-related features are proposed in this project for speech recognition. These features are derived from the phase spectrum of the speech signal, and are expected to be robust to the additive noise distortion. These features will make the speech recognizer less sensitive to noise and will enhance its utility in a number of applications in the telecommunication and business world.Read moreRead less
A computational theory of strategic deception. This artificial project aims to develop a theory of strategic deception and test it through an Artificial Intelligence model. The project will combine computational Theory-of-Mind concepts with recent scientific findings to allow us to better understand whether and how intelligent technologies of the future might deceive humans. The findings will provide new insights into how Artificial Intelligence technologies of the future will impact applied are ....A computational theory of strategic deception. This artificial project aims to develop a theory of strategic deception and test it through an Artificial Intelligence model. The project will combine computational Theory-of-Mind concepts with recent scientific findings to allow us to better understand whether and how intelligent technologies of the future might deceive humans. The findings will provide new insights into how Artificial Intelligence technologies of the future will impact applied areas of computing, where simulating advanced forms of social behaviour and cognition, including deception, will become increasingly significant.Read moreRead less
Management of Complex Assets Using Smart Information Systems. The aim is to develop an integrative framework and a smart system for holistic asset management in order to manage and optimise asset decisions vis-a-vis shifts in customer and market needs. It will develop an advanced conceptual framework, a dedicated smart system that is capable of handling uncertainty often associated with decision making in large complex operations. The benefits of this research are: a more competitive indusry wit ....Management of Complex Assets Using Smart Information Systems. The aim is to develop an integrative framework and a smart system for holistic asset management in order to manage and optimise asset decisions vis-a-vis shifts in customer and market needs. It will develop an advanced conceptual framework, a dedicated smart system that is capable of handling uncertainty often associated with decision making in large complex operations. The benefits of this research are: a more competitive indusry with focus on markets; a smart system to demonstrate business-based decision making and generally a much more sophisticated approach to asset management.Read moreRead less