Discovery Early Career Researcher Award - Grant ID: DE200101323
Funder
Australian Research Council
Funding Amount
$427,098.00
Summary
Structure guided mapping of protein interactions and their perturbation. Protein interactions are central to most biological processes, and significant effort has been devoted to trying to unravel these complicated networks. This project aims to develop new approaches to better understand these interactions, and the consequences of their perturbation. The main expected contributions will be: (i) methods to identify likely protein interaction sites using population conservation; (ii) computationa ....Structure guided mapping of protein interactions and their perturbation. Protein interactions are central to most biological processes, and significant effort has been devoted to trying to unravel these complicated networks. This project aims to develop new approaches to better understand these interactions, and the consequences of their perturbation. The main expected contributions will be: (i) methods to identify likely protein interaction sites using population conservation; (ii) computational approaches to assess the effects of any type of mutation on the interaction; and (iii) an understanding of how disruption of a specific interaction can affect the complicated biological network within a cell. Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE170101415
Funder
Australian Research Council
Funding Amount
$365,000.00
Summary
Life-long learning in the understanding of big data. This project aims to design and develop computational systems and algorithms that learn as humans do (life-long learning). This will enable systems to automatically interpret Big Data from social media, social network and public surveillance. This project will apply the knowledge learned in auxiliary Big Data sources to effectively interpret target tasks and analyse the communication network (one variant of social network). This project is exp ....Life-long learning in the understanding of big data. This project aims to design and develop computational systems and algorithms that learn as humans do (life-long learning). This will enable systems to automatically interpret Big Data from social media, social network and public surveillance. This project will apply the knowledge learned in auxiliary Big Data sources to effectively interpret target tasks and analyse the communication network (one variant of social network). This project is expected to benefit science, society and the economy, and help governments to better serve the public by improving transport logistics, modelling and regulation, and preventing crime and terrorism.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE170100641
Funder
Australian Research Council
Funding Amount
$305,754.00
Summary
Priced attribute-based encryption and its applications. This project aims to develop Priced Attribute-Based Encryption (PABE), a security mechanism. Access control is important for secure online information access. Access to encrypted data requires both private key and payment from earmarked funds specified by the access policy of encrypted data. This research will enable both authorisation and restriction of users while they access protected data anonymously. Expected outcomes include new model ....Priced attribute-based encryption and its applications. This project aims to develop Priced Attribute-Based Encryption (PABE), a security mechanism. Access control is important for secure online information access. Access to encrypted data requires both private key and payment from earmarked funds specified by the access policy of encrypted data. This research will enable both authorisation and restriction of users while they access protected data anonymously. Expected outcomes include new models, theories, techniques and PABE constructions. This research project is expected to contribute to cyber security in anonymous access control with advanced management for all Australians.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200100166
Funder
Australian Research Council
Funding Amount
$424,709.00
Summary
Enabling Energy Self-Sufficient and Secure Internet of Things. This project aims to develop novel resource management and transmission techniques to enable an energy self-sufficient and secure Internet of Things by utilising energy harvesting technology and robust physical-layer security approach. This project expects to generate new knowledge to address current challenges around energy self-sufficiency and data confidentiality protection capabilities. Expected outcomes include efficient algorit ....Enabling Energy Self-Sufficient and Secure Internet of Things. This project aims to develop novel resource management and transmission techniques to enable an energy self-sufficient and secure Internet of Things by utilising energy harvesting technology and robust physical-layer security approach. This project expects to generate new knowledge to address current challenges around energy self-sufficiency and data confidentiality protection capabilities. Expected outcomes include efficient algorithms and prototypes for long-lasting Internet of Things systems. This should provide significant benefits, including the improved self-sustainability and security critical to realising the Internet of Things’ potential to contribute to enhanced health service delivery and factory automation for Industry 4.0.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE170101259
Funder
Australian Research Council
Funding Amount
$360,000.00
Summary
Zero-shot and few-shot learning with deep knowledge transfer. This project aims to develop few-shot and zero-shot learning, visual recognition techniques that can learn a visual concept with few or no visual examples. Visual recognition is a major component in Artificial Intelligence and used in cybernetic security, robotic vision and medical image analysis. This project will use deep learning to enable the zero/few-shot learning to use and model previously unexplored information, making zero/fe ....Zero-shot and few-shot learning with deep knowledge transfer. This project aims to develop few-shot and zero-shot learning, visual recognition techniques that can learn a visual concept with few or no visual examples. Visual recognition is a major component in Artificial Intelligence and used in cybernetic security, robotic vision and medical image analysis. This project will use deep learning to enable the zero/few-shot learning to use and model previously unexplored information, making zero/few-shot learning more practical, scalable and flexible. The project is expected to advance the applicability of visual recognition in many challenging scenarios and provide effective tools to analyse the online visual data for supporting Australia’s cybernetic security.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240100730
Funder
Australian Research Council
Funding Amount
$455,000.00
Summary
Hybrid Technologies for Tabletop Games . This project aims to develop design tools for hybrid games that combine technology with tabletop play. Through a detailed examination of successful hybrid boardgames and an iterative, human-centered design and evaluation process that explores embedding novel sensors and tools into boardgames, it will explore the design, use, and experience of hybrid games. Expected outcomes include design of innovative and reusable components, a framework for understandin ....Hybrid Technologies for Tabletop Games . This project aims to develop design tools for hybrid games that combine technology with tabletop play. Through a detailed examination of successful hybrid boardgames and an iterative, human-centered design and evaluation process that explores embedding novel sensors and tools into boardgames, it will explore the design, use, and experience of hybrid games. Expected outcomes include design of innovative and reusable components, a framework for understanding technologies that enable hybrid play, and a theory-based design methodology. Benefits include innovation in the tabletop game sector, fostering social connections for distanced families, and new applications of games for simulations in health, defence, and logistics.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE230100477
Funder
Australian Research Council
Funding Amount
$421,554.00
Summary
Advancing Human Perception: Countering Evolving Malicious Fake Visual Data. The aim of this project is to provide new effective and generalisable deepfake detection methods for automatically detecting maliciously manipulated visual data generated by misused artificial intelligence (AI) techniques. It will present innovative computer vision and image processing knowledge and techniques, enabling the developed methods to advance human perception in recognising fake data, enhance cybersecurity, and ....Advancing Human Perception: Countering Evolving Malicious Fake Visual Data. The aim of this project is to provide new effective and generalisable deepfake detection methods for automatically detecting maliciously manipulated visual data generated by misused artificial intelligence (AI) techniques. It will present innovative computer vision and image processing knowledge and techniques, enabling the developed methods to advance human perception in recognising fake data, enhance cybersecurity, and protect privacy in AI applications. The anticipated outcomes should provide significant benefits to a wide range of applications, such as providing timely alerts to the media, government organisations, and the industry about misleading fake visual data, and preventing financial crimes on synthetic identity fraud.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE220100595
Funder
Australian Research Council
Funding Amount
$416,400.00
Summary
Efficient privacy-preserving proofs for secure e-government and e-voting. Electronic systems are becoming increasingly widespread and crucial to social and economic wellbeing. This project aims to ensure that e-government, e-health, e-commerce and e-voting are secure and trustworthy by inventing new ways to verify these systems without infringing privacy. This project expects to use innovative techniques from cryptography to support development of trustworthy systems. Expected outcomes of this p ....Efficient privacy-preserving proofs for secure e-government and e-voting. Electronic systems are becoming increasingly widespread and crucial to social and economic wellbeing. This project aims to ensure that e-government, e-health, e-commerce and e-voting are secure and trustworthy by inventing new ways to verify these systems without infringing privacy. This project expects to use innovative techniques from cryptography to support development of trustworthy systems. Expected outcomes of this project include better support for organisations to build trustworthy systems that will maximise benefit to Australian business and society. This should provide significant commercial, reputational, and societal benefits by avoiding disruptions to the organisations and their clients if and when they are attacked. 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
Discovery Early Career Researcher Award - Grant ID: DE200101061
Funder
Australian Research Council
Funding Amount
$424,848.00
Summary
Single particle imaging: x-ray imaging of individual dynamic biomolecules. X-ray lasers produce powerful ultra-short pulses of light that can take temporal snap shots of small radiation-sensitive biological complexes. Thanks to superconducting technology, the next generation of x-ray lasers will be able to produce x-ray pulses at greater rates than ever before. But because of the sheer number of possible molecular configurations, these molecular movies will have only a small amount of data per f ....Single particle imaging: x-ray imaging of individual dynamic biomolecules. X-ray lasers produce powerful ultra-short pulses of light that can take temporal snap shots of small radiation-sensitive biological complexes. Thanks to superconducting technology, the next generation of x-ray lasers will be able to produce x-ray pulses at greater rates than ever before. But because of the sheer number of possible molecular configurations, these molecular movies will have only a small amount of data per frame, posing an enormous challenge for current imaging methods. I aim to meet this challenge by developing an innovative multi-conformational image reconstruction algorithm. This will provide a new window into the molecular dynamics of biological systems, the building blocks of life, and enable rational drug design.Read moreRead less