User-task co-adaptation for effective interactive simulation environments. This project aims to deliver smart interactive simulation environments in which users and simulation tasks work together. This project aims to create novel adaptive algorithms to automatically discover those user and task features that vary together to smartly adapt users and simulation tasks to work together harmoniously, seamlessly and effectively. Interactive simulation environments are the backbone for evaluating conc ....User-task co-adaptation for effective interactive simulation environments. This project aims to deliver smart interactive simulation environments in which users and simulation tasks work together. This project aims to create novel adaptive algorithms to automatically discover those user and task features that vary together to smartly adapt users and simulation tasks to work together harmoniously, seamlessly and effectively. Interactive simulation environments are the backbone for evaluating concepts, designs, products and advanced training systems in industry and government organisations. By bringing the user naturally inside the simulation as a task's component, users can improve their experience while task performance is simultaneously optimised. Intended outcomes include novel dynamic user-task profiling algorithms and new adaptive algorithms for user-task co-adaptation. Practical outcomes may include robust and highly effective simulation environments.Read moreRead less
Rigorous Privacy Compliance in Modern Application Ecosystems. Modern network applications such as mobile applications and browser extensions have become the primary gateways for consumers to access the Internet in today’s digital landscape. This project aims to address privacy issues in these ecosystems by developing a new privacy-compliance assessment framework. The framework will evaluate the current privacy practices of application ecosystems, enabling users and developers in Australia and wo ....Rigorous Privacy Compliance in Modern Application Ecosystems. Modern network applications such as mobile applications and browser extensions have become the primary gateways for consumers to access the Internet in today’s digital landscape. This project aims to address privacy issues in these ecosystems by developing a new privacy-compliance assessment framework. The framework will evaluate the current privacy practices of application ecosystems, enabling users and developers in Australia and worldwide to reliably identify potential privacy risks and issues on their applications. The intended outcomes should endow data controllers with the capability of evidencing their compliance of data protection legislations such as Australia Privacy Act 1988 and EU General Data Protection Regulation (GDPR).Read moreRead less
Finding and exploiting interesting paths in multidimensional information spaces. This project will invent a new approach for searching within a large complex information space, finding interesting paths between points within the space, visualising the results, and supporting rich, human-centric user interaction with queries and results. This project will embody these techniques in a novel, internet-scale framework to support rapid development of large path search and visualisation applications. ....Finding and exploiting interesting paths in multidimensional information spaces. This project will invent a new approach for searching within a large complex information space, finding interesting paths between points within the space, visualising the results, and supporting rich, human-centric user interaction with queries and results. This project will embody these techniques in a novel, internet-scale framework to support rapid development of large path search and visualisation applications. Evaluation will be via development of several exemplar applications. The techniques and framework will be applicable to a broad range of economically important problems in areas as diverse as health, travel, scientific publication search, product marketing and software engineering.Read moreRead less
Development and implementation of efficient new models for electron correlation. The two new approaches will allow researchers in the chemical, pharmaceutical and materials sciences to predict the physical and chemical behaviour of moderately large molecular systems with an accuracy and efficiency that has not previously been possible. The software that will result will enable cost and time savings in the design of advanced materials in the medical and agricultural contexts.
Exploiting Structure in AI Planning. The research will improve our ability to build generic, automated planning systems, which can efficiently select effective courses of actions in a range of situations such as crisis management, project planning, military operations planning, and transportation. It will help reduce the cost of building software to more efficiently solve important problems occurring in validating, controlling, and diagnosing complex systems. More generally, it will advance our ....Exploiting Structure in AI Planning. The research will improve our ability to build generic, automated planning systems, which can efficiently select effective courses of actions in a range of situations such as crisis management, project planning, military operations planning, and transportation. It will help reduce the cost of building software to more efficiently solve important problems occurring in validating, controlling, and diagnosing complex systems. More generally, it will advance our understanding of how machines can intelligently solve complex problems by identifying and exploiting their relevant structure.Read moreRead less
Robust Preference Inference from Spatial-Temporal Interaction Networks. This project aims to develop innovative techniques for effectively and efficiently managing user preference profiles from less labelled, sparse and noisy interaction data. A unified novel learning framework along with a set of data analysis techniques are expected to be developed from this project, which will provide a non-intrusive way of conducting predictive analysis on user preference profiling via discovering human expl ....Robust Preference Inference from Spatial-Temporal Interaction Networks. This project aims to develop innovative techniques for effectively and efficiently managing user preference profiles from less labelled, sparse and noisy interaction data. A unified novel learning framework along with a set of data analysis techniques are expected to be developed from this project, which will provide a non-intrusive way of conducting predictive analysis on user preference profiling via discovering human explicit and implicit interest domains. The expected results of this application will not only maintain Australia's leadership in this frontier research area, but also support many important applications that safeguard Australian people and economy such as cyber security, healthcare, and e-Commerce.Read moreRead less
Practical Automated Deduction. This project will develop, implement and validate improved methods for automated deduction in decidable fragments of first order logic, also incorporating reasoning in special theories such as arithmetic. It will significantly extend previous work on the model evolution calculus and dynamic semantic resolution, and introduce new techniques that combine these reasoning methods. This work has direct application to reasoning about business rules and about industrial o ....Practical Automated Deduction. This project will develop, implement and validate improved methods for automated deduction in decidable fragments of first order logic, also incorporating reasoning in special theories such as arithmetic. It will significantly extend previous work on the model evolution calculus and dynamic semantic resolution, and introduce new techniques that combine these reasoning methods. This work has direct application to reasoning about business rules and about industrial optimisation problems, and it will motivate and test our systems by means of case studies from both of these areas.Read moreRead less
AI Planning: The Next Generation. This is a project in Artificial Intelligence. It aims at extending and integrating automated planning (and other forms of reasoning) with learning to produce a new generation of planning systems that are robust, safe, scalable, and trusted. These are some of the most significant issues to address to accelerate the adoption of planning systems in industry. Expected outcomes include a pipeline to learn rich symbolic planning models from narrated demonstration vide ....AI Planning: The Next Generation. This is a project in Artificial Intelligence. It aims at extending and integrating automated planning (and other forms of reasoning) with learning to produce a new generation of planning systems that are robust, safe, scalable, and trusted. These are some of the most significant issues to address to accelerate the adoption of planning systems in industry. Expected outcomes include a pipeline to learn rich symbolic planning models from narrated demonstration videos, new ways to represent, learn, and search for generalised policies that are scalable and robust, and approaches to verify and explain generalised policies. The new systems should benefit the aerospace industry by assisting humans in assembling and delivering aerospace products.Read moreRead less
Automatic Training Data Search and Model Evaluation by Measuring Domain Gap. We aim to investigate computer vision training data and test data, using automatically generated data sets for facial expression recognition and object re-identification. This project expects to quantify and understand the domain gap, the distribution difference between training and test data sets. Expected outcomes of this project are insights on measuring the domain gap, the ability to estimate model performance witho ....Automatic Training Data Search and Model Evaluation by Measuring Domain Gap. We aim to investigate computer vision training data and test data, using automatically generated data sets for facial expression recognition and object re-identification. This project expects to quantify and understand the domain gap, the distribution difference between training and test data sets. Expected outcomes of this project are insights on measuring the domain gap, the ability to estimate model performance without accessing expensive test labels and improvements to system generalisation. This should provide significant benefits for computer vision applications that currently require expensive labelling, and commercial and economic benefits across sectors such as transportation, security and manufacturing.Read moreRead less
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