Optimizing Outpatient Parenteral Antibiotic Therapy To Support Hospital-in-the-home Program Across The Unique Environmental Conditions Of Australia
Funder
National Health and Medical Research Council
Funding Amount
$645,205.00
Summary
This project aims to solve problems associated with stability and dosing of antibiotic infusions given to patients in their homes, to support decision making by doctors and regulators. Currently, there is a lack of adequate data that complies with contemporary requirements. We will study infusion preparation both in laboratory settings and in ‘real-life’ conditions while being used by patients. We will enhance the stability of antibiotic preparations and optimize doses for effective treatment.
Broadening Choice and Increasing Diversity in Public Schools. Currently, most families are limited to the public school in their catchment area, meaning the area in which they can afford to live. This leads to socio-economically and ethnically homogenous schools and entrenches disadvantage, as well as denying students the crucial life lessons that flow from being part of a diverse student body. This project aims to investigate a model for allocating public school places that integrates catchment ....Broadening Choice and Increasing Diversity in Public Schools. Currently, most families are limited to the public school in their catchment area, meaning the area in which they can afford to live. This leads to socio-economically and ethnically homogenous schools and entrenches disadvantage, as well as denying students the crucial life lessons that flow from being part of a diverse student body. This project aims to investigate a model for allocating public school places that integrates catchment areas. The expected outcome would be a system that gives families a wider choice, enabling them to enrol in out-of-area schools, while ensuring that allocations remain fair, equitable and balanced, and also delivering benefits such as achieving a desired level of diversity in student populations within schoolsRead moreRead less
3D Diffusion Models for Generating and Understanding 3D Scenes. Diffusion models, such as DALL-E2 and Imagen, have achieved remarkable success in generating photorealistic images and hold promise to solve long-standing computer vision problems. However, 3D scene generation remains unexplored. This research project aims to bridge the gap by developing 3D diffusion models capable of generating complete 3D scenes. This will advance our theoretical understanding of diffusion in complex 3D environmen ....3D Diffusion Models for Generating and Understanding 3D Scenes. Diffusion models, such as DALL-E2 and Imagen, have achieved remarkable success in generating photorealistic images and hold promise to solve long-standing computer vision problems. However, 3D scene generation remains unexplored. This research project aims to bridge the gap by developing 3D diffusion models capable of generating complete 3D scenes. This will advance our theoretical understanding of diffusion in complex 3D environments and open up new possibilities for applications in fields such as virtual reality, architecture, and city planning. The proposed 3D diffusion models will also enhance the accuracy of computer vision tasks related to 3D scene understanding, such as object detection, tracking, and semantic segmentation.Read moreRead less
MemberGuard: Protecting Machine Learning Privacy from Membership Inference. Machine Learning has become a core part of many real-world applications. However, machine learning models are vulnerable to membership inference attacks. In these attacks, an adversary can infer if a given data record has been part of the model's training data. In this project, the team aims to develop new techniques that can be used to counter these attacks, such as 1) new analytical models for membership leakage, 2) ne ....MemberGuard: Protecting Machine Learning Privacy from Membership Inference. Machine Learning has become a core part of many real-world applications. However, machine learning models are vulnerable to membership inference attacks. In these attacks, an adversary can infer if a given data record has been part of the model's training data. In this project, the team aims to develop new techniques that can be used to counter these attacks, such as 1) new analytical models for membership leakage, 2) new methods for susceptibility diagnosis, 3) new defences that leverage privacy and utility. Data-oriented services are estimated to be valuable assets in the future. These techniques can help Australia gain cutting edge advantage in machine learning security and privacy and protect its intellectual property on these services.Read moreRead less
Scalable & Accountable Privacy-Preserving Blockchain with Enhanced Security. This project aims to address the scalability and accountability of privacy-preserving blockchain by advancing cryptographic techniques. This project expects to develop scalable protocols for privacy-preserving blockchain while also adding accountability for authority to trace cyber crime activities, which is a missing piece in any state-of-the-art public blockchain system. Expected outcomes of this project include not o ....Scalable & Accountable Privacy-Preserving Blockchain with Enhanced Security. This project aims to address the scalability and accountability of privacy-preserving blockchain by advancing cryptographic techniques. This project expects to develop scalable protocols for privacy-preserving blockchain while also adding accountability for authority to trace cyber crime activities, which is a missing piece in any state-of-the-art public blockchain system. Expected outcomes of this project include not only practical solutions for protecting sensitive data recorded in blockchain but also crucial techniques to make the blockchain accountable for practical applications with enhanced security. This project provides significant benefits, such as building a trusted environment for sensitive transactions in the digital economy.Read moreRead less
Preventing sensitive data exfiltration from insiders . Confidential data such as military secrets or intellectual property must never be disclosed outside the organisation; formally protecting data exfiltration from insider attacks is a major challenge. This project aims to develop a pattern matching based systematic methodology for data exfiltration in database systems. We will devise highly accurate detection tools and secure provenance techniques that can effectively protect against insider a ....Preventing sensitive data exfiltration from insiders . Confidential data such as military secrets or intellectual property must never be disclosed outside the organisation; formally protecting data exfiltration from insider attacks is a major challenge. This project aims to develop a pattern matching based systematic methodology for data exfiltration in database systems. We will devise highly accurate detection tools and secure provenance techniques that can effectively protect against insider attacks. The outcomes of the project will incorporate new security constraints and policies raised by emerging technologies to enable better protection of sensitive information. Read moreRead less
Testing strategy-proofness in matching markets: an experimental study. This project seeks to test and improve matching algorithms by investigating the effect of advice on strategy-proofness. Matching algorithms are used to solve allocation problems in designed markets (eg school or house allocation problems). Many of the algorithms employed are strategy-proof: participants never gain from strategising, that is, from lying about their preferences. Strategy-proofness had been seemingly validated b ....Testing strategy-proofness in matching markets: an experimental study. This project seeks to test and improve matching algorithms by investigating the effect of advice on strategy-proofness. Matching algorithms are used to solve allocation problems in designed markets (eg school or house allocation problems). Many of the algorithms employed are strategy-proof: participants never gain from strategising, that is, from lying about their preferences. Strategy-proofness had been seemingly validated by experimental research, but new evidence suggests that participants could be prone to follow wrong advice and therefore lie. In order to improve the performance of designed markets, the project proposes to further test strategy-proofness by investigating how advice can affect truth-telling in strategy-proof algorithms and whether learning can counteract or complement the effect of advice.Read moreRead less
Advancing Physiotherapy Management Of Chronic Pain By Improving Implementation Of Evidence-based Practice
Funder
National Health and Medical Research Council
Funding Amount
$115,883.00
Summary
Chronic musculoskeletal pain is major and concerning public health burden and physiotherapists provide a vital and influential role in managing this problem. High level research has demonstrated effective approaches to managing chronic pain yet there appears to be barriers to implementing such strategies clinically. This project aims to explore these barriers with physiotherapists, devise an intervention to overcome them, and test the feasibility and acceptability of the intervention.
Defending AI based FinTech Systems against Model Extraction Attacks. This project aims to develop new methods for defending artificial intelligence (AI) based FinTech systems from highly potent and insidious model extraction attacks whereby an adversary can steal the AI model from the system to cause intellectual property (IP) violation, business advantage disruption, and financial loss. This can be achieved by examining various attack models, creating active and utility-preserving defences, and ....Defending AI based FinTech Systems against Model Extraction Attacks. This project aims to develop new methods for defending artificial intelligence (AI) based FinTech systems from highly potent and insidious model extraction attacks whereby an adversary can steal the AI model from the system to cause intellectual property (IP) violation, business advantage disruption, and financial loss. This can be achieved by examining various attack models, creating active and utility-preserving defences, and inventing non-removable watermarks on AI models. The outcomes are new tools for securing AI-based FinTech systems before deployment and tools for IP violation forensics post-deployment. Such capabilities are beneficial by improving the security and safety of FinTech systems and other nationally critical AI systems.Read moreRead less
Mapping the Effectiveness of Automated Software Testing. This project aims to help software engineers build complex software systems in far more reliable and cost-effective ways. It takes an interdisciplinary approach by applying machine learning techniques to automatically test complex software systems. Expected outcomes include a novel methodology for assessing the strengths and weaknesses of test suites generated by automated software testing techniques and the approaches required for generat ....Mapping the Effectiveness of Automated Software Testing. This project aims to help software engineers build complex software systems in far more reliable and cost-effective ways. It takes an interdisciplinary approach by applying machine learning techniques to automatically test complex software systems. Expected outcomes include a novel methodology for assessing the strengths and weaknesses of test suites generated by automated software testing techniques and the approaches required for generating high-quality test cases. Such advances are urgently needed to avoid disasters when deploying software systems in the real world.Read moreRead less