Domain-specific visual languages for big data analytics applications. This project aims to invent domain-specific visual languages and support model-driven engineering based infrastructure so domain experts can specify, generate and apply complex data analytics and visualisation techniques. Many domains, including intelligent transport, business intelligence, and population health, need more effective “big data” analytics and visualisation. A challenge is to combine detailed domain knowledge – w ....Domain-specific visual languages for big data analytics applications. This project aims to invent domain-specific visual languages and support model-driven engineering based infrastructure so domain experts can specify, generate and apply complex data analytics and visualisation techniques. Many domains, including intelligent transport, business intelligence, and population health, need more effective “big data” analytics and visualisation. A challenge is to combine detailed domain knowledge – what the data means and what it can be used for – with sophisticated, scalable computational techniques to mine and present information from the huge volumes of raw data. This project is expected to improve productivity and quality of big data analytics and visualisation in critical domains.Read moreRead less
Australian Laureate Fellowships - Grant ID: FL190100035
Funder
Australian Research Council
Funding Amount
$3,009,457.00
Summary
Human-centric Model-driven Software Engineering. This project aims to find fundamentally new ways to capture and use human-centric software requirements during model-driven software engineering and verifying that systems meet these requirements. There are major issues with misaligned software applications in terms of accessibility, usability, emotions, personality, age, gender, and culture. This project aims to address these through new conceptual foundations and modelling techniques for their s ....Human-centric Model-driven Software Engineering. This project aims to find fundamentally new ways to capture and use human-centric software requirements during model-driven software engineering and verifying that systems meet these requirements. There are major issues with misaligned software applications in terms of accessibility, usability, emotions, personality, age, gender, and culture. This project aims to address these through new conceptual foundations and modelling techniques for their support during software engineering. The intended outcomes are enhanced theory, models, tools and capability for next-generation software engineering with these critical elements. Significant benefits are expected to include greatly improved software quality, developer productivity and cost savings.Read moreRead less
Values-oriented Defect Fixing for Mobile Software Applications. This project aims to address critical problems with mobile applications that exhibit human values-based defects, by advancing our understanding, detection and fixing of such defects. Many mobile apps do not operate according to the essential values of their human users - e.g. inclusivity, accessibility, privacy, ethical behaviour, due care, emotions, etc - making them ineffective, underused, unfit for purpose or even dangerous. Exp ....Values-oriented Defect Fixing for Mobile Software Applications. This project aims to address critical problems with mobile applications that exhibit human values-based defects, by advancing our understanding, detection and fixing of such defects. Many mobile apps do not operate according to the essential values of their human users - e.g. inclusivity, accessibility, privacy, ethical behaviour, due care, emotions, etc - making them ineffective, underused, unfit for purpose or even dangerous. Expected outcomes include new theories, techniques and prototype tools for developers and end users to detect and help fix values-based defects in mobile apps. Benefits include better, safer mobile apps for people and organisations and improved app developer productivity and competitiveness.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE220101057
Funder
Australian Research Council
Funding Amount
$424,140.00
Summary
Practical Automated Software Bug Fixing via Syntactic and Semantic Analyses. This proposal aims to advance the practical adoption of automated software bug repair, which has recently been adopted by industry, e.g., Facebook. It will produce novel methods that use mining software repositories, program analysis, and human-guided search to help automated repair to scale and be accurate. Expected outcomes include a publicly available automated bug repair framework. This project will help the softwar ....Practical Automated Software Bug Fixing via Syntactic and Semantic Analyses. This proposal aims to advance the practical adoption of automated software bug repair, which has recently been adopted by industry, e.g., Facebook. It will produce novel methods that use mining software repositories, program analysis, and human-guided search to help automated repair to scale and be accurate. Expected outcomes include a publicly available automated bug repair framework. This project will help the software industry deliver to users high quality software with improved reliability and safety, and increase education quality for students learning to code via automated feedback generation.Read moreRead less
Next-generation spatial keyword search. Spatial keyword search is like a normal web search plus additional location information, which helps users to obtain a better ranking of results by considering the spatial proximity. The success of this project will deliver a next-generation spatial keyword search system that overcomes the severe usability limitations experienced by users today.
Formal modelling and analysis of software requirements for air traffic management systems for improved integrity assurance. This project will significantly reduce the time and cost of developing software for critical applications such as aviation and air traffic management. As well as improving the trustworthiness of safety-critical computer-based systems, this project will also enable system improvements to be deployed faster and more reliably.
Learning to see in 3D. The project aims to endow machine vision with an ability we, as humans, use almost constantly: to judge 3D properties from a 2D image. This extremely useful ability will be applied to digital images to obtain 3D measurements and aid in automating tasks such as mining, surveying, medical diagnosis, and visual effects in movies.
Recognising and reconstructing objects in real time from a moving camera. This project will use a moving camera to estimate the three-dimensional shape and identity of objects and surfaces it can see. This ability, which we humans use all the time, has wide application in automation including driver assistance, exploring hazardous environments, robotics, remote collaboration, and the creation of three-dimensional models for entertainment.
Optimal Robust Fitting under the Framework of LP-Type Problems. The project aims to develop algorithms to support the development of robust and accurate computer vision systems. Real-world visual data (images, videos) is inherently noisy and outlier prone. To build computer vision systems that work reliably in the real world, it is necessary to ensure that the underlying algorithms are robust and efficient. The project aims to devise novel algorithms that can compute the best possible result giv ....Optimal Robust Fitting under the Framework of LP-Type Problems. The project aims to develop algorithms to support the development of robust and accurate computer vision systems. Real-world visual data (images, videos) is inherently noisy and outlier prone. To build computer vision systems that work reliably in the real world, it is necessary to ensure that the underlying algorithms are robust and efficient. The project aims to devise novel algorithms that can compute the best possible result given the input data in a short amount of time. The expected outcomes would support the construction of reliable and accurate computer vision-based systems, such as large-scale 3-D reconstruction from photo collections, self-driving cars and domestic robots.Read moreRead less
Australian Laureate Fellowships - Grant ID: FL130100102
Funder
Australian Research Council
Funding Amount
$3,179,946.00
Summary
Lifelong computer vision systems. This project will create a computer vision system that can produce a detailed environmental map in real time, turning standard video cameras into sensors that 'understand' a scene with basic semantic tools. This high-level sensing will unlock a wide range of applications for autonomous systems.