The red belly blockchain: a scalable blockchain for internet of things. This project aims to offer a blockchain that scales with the number of participants. There have been major investments in blockchain technologies during the last year as blockchains promise to disrupt industries like supply chains. Unfortunately, blockchains cannot solve this problem in their current form, because they cannot scale. They require resources that grow with the number of participants and yet fail at providing in ....The red belly blockchain: a scalable blockchain for internet of things. This project aims to offer a blockchain that scales with the number of participants. There have been major investments in blockchain technologies during the last year as blockchains promise to disrupt industries like supply chains. Unfortunately, blockchains cannot solve this problem in their current form, because they cannot scale. They require resources that grow with the number of participants and yet fail at providing increasing performance. The project will leverage many devices of limited resources to offer higher performance and will impact the distributed computing field by establishing a new connection between energy efficient systems and highly scalable distributed algorithms.Read moreRead less
Provably Secure Cryptography Techniques: Effective, Elegant, and Economic. This project aims to contribute to advanced knowledge and techniques to remove relaxed proof factors from provable security. Cryptography nowadays can be proven secure and must be provably secure before being adopted for data protection. Until today, most cryptography schemes are still using some relaxed proof factors to prove security, but using these relaxed factors was risky. The expected outcomes are proof methodolog ....Provably Secure Cryptography Techniques: Effective, Elegant, and Economic. This project aims to contribute to advanced knowledge and techniques to remove relaxed proof factors from provable security. Cryptography nowadays can be proven secure and must be provably secure before being adopted for data protection. Until today, most cryptography schemes are still using some relaxed proof factors to prove security, but using these relaxed factors was risky. The expected outcomes are proof methodologies for researchers to prove security in an easy way (effective), cryptography techniques for proving security without any relaxed proof factors for cryptography schemes (elegant), and more practical cryptography schemes with elegant proofs to enable Australians to receive benefit from secure data protection (economic).
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Computational Mechanisms of Online Attention Markets. The internet has operated as an major exchange of information and attention for the past few decades, yet surprisingly little is known about how individual choices and collective attention interact, let alone about how different parties can influence or control it. This project aims to uncover the mathematical underpinnings between individual actions and collective trends in online attention market, design computational methods for estimating ....Computational Mechanisms of Online Attention Markets. The internet has operated as an major exchange of information and attention for the past few decades, yet surprisingly little is known about how individual choices and collective attention interact, let alone about how different parties can influence or control it. This project aims to uncover the mathematical underpinnings between individual actions and collective trends in online attention market, design computational methods for estimating and influencing attention allocation, and enable applications where content consumers, producers, hosting platforms and regulatory bodies are each empowered with their share of influence in the attention market.Read moreRead less
Deep Adder Networks on Edge Devices. This project aims to empower edge devices with intelligence by developing advanced deep neural networks that address the conflict between the high resource requirements of deep learning and the generally inadequate performance of the edge. Multiplication has been the dominant type of operation in deep learning, though the addition is known to be much cheaper. This project expects to yield theories and algorithms that allow deep neural networks consisting of n ....Deep Adder Networks on Edge Devices. This project aims to empower edge devices with intelligence by developing advanced deep neural networks that address the conflict between the high resource requirements of deep learning and the generally inadequate performance of the edge. Multiplication has been the dominant type of operation in deep learning, though the addition is known to be much cheaper. This project expects to yield theories and algorithms that allow deep neural networks consisting of nearly pure additions to fulfil the requisites of accuracy, robustness, calibration and generalisation in real-world computer vision tasks. The success of this project will benefit deep learning-based products on smartphones or robots in health and cybersecurity.Read moreRead less
Performing cold microwave measurements with warm diamonds. Detecting weak microwave signals at room temperature is an exceptionally difficult task, due to the excessive thermal microwave noise that exists all around us. At present, the best microwave receivers must be cooled to cryogenic temperatures, restricting their widespread use. This project aims to apply diamond-based quantum technologies to achieve unprecedented microwave signal detection sensitivities with a room-temperature setup, prov ....Performing cold microwave measurements with warm diamonds. Detecting weak microwave signals at room temperature is an exceptionally difficult task, due to the excessive thermal microwave noise that exists all around us. At present, the best microwave receivers must be cooled to cryogenic temperatures, restricting their widespread use. This project aims to apply diamond-based quantum technologies to achieve unprecedented microwave signal detection sensitivities with a room-temperature setup, providing more accessible ultra-low noise detectors. The ability to measure weak microwave signals is crucial for a range of sectors and the results of this project are expected to have applications in defence (radar), space exploration (satellite communication), and fundamental research (spectroscopy).Read moreRead less
Parameter estimation for genetic time-series data: Theory and methods. This project aims to develop a novel computational framework for solving parameter estimation problems in evolutionary modelling by leveraging genetic time-series data measured by Next-Generation Sequencing technologies. It will foster international collaboration, cutting across disciplines. By introducing new techniques from signal processing and tools from random matrix theory commonly employed for mobile wireless communica ....Parameter estimation for genetic time-series data: Theory and methods. This project aims to develop a novel computational framework for solving parameter estimation problems in evolutionary modelling by leveraging genetic time-series data measured by Next-Generation Sequencing technologies. It will foster international collaboration, cutting across disciplines. By introducing new techniques from signal processing and tools from random matrix theory commonly employed for mobile wireless communications, it seeks to design scalable inference methods for resolving mutational fitness effects from genetic time-series measurements of complex evolving populations. This would enable new understanding of complex adaptive systems, such as pathogen evolution, host-immune dynamics, and acquisition of drug resistance. Read moreRead less
Decentralised Collaborative Predictive Analytics on Personal Smart Devices. This project tackles the challenging problem of personalised predictive analytics with resource-constrained personal devices and massive-scale data. The knowledge to be generated concerns privacy, fairness, and resource efficiency in the era of Internet of Things. The expected outcomes include a collaborative learning paradigm for building personalised models on personal smart devices in open and fully decentralised sett ....Decentralised Collaborative Predictive Analytics on Personal Smart Devices. This project tackles the challenging problem of personalised predictive analytics with resource-constrained personal devices and massive-scale data. The knowledge to be generated concerns privacy, fairness, and resource efficiency in the era of Internet of Things. The expected outcomes include a collaborative learning paradigm for building personalised models on personal smart devices in open and fully decentralised settings. Privacy and model fairness are core tenets of the paradigm. Personalised predictive analytics is frontier research that will position Australia at the forefront of AI and give business the tools needed to deploy innovative business systems for market exploitation with a secure, equitable and competitive advantage.Read moreRead less
Declarative Networks: Towards Robust and Explainable Deep Learning. The aim of this project is to develop declarative machine learning techniques that exploit inherent structure and models of the world. Deep learning has become the dominant approach for machine learning with many products and promises built on this technology. But deep learning is expensive, opaque, brittle and relies solely on human labelled data. This project intends to make deep learning more reliable by establishing theory a ....Declarative Networks: Towards Robust and Explainable Deep Learning. The aim of this project is to develop declarative machine learning techniques that exploit inherent structure and models of the world. Deep learning has become the dominant approach for machine learning with many products and promises built on this technology. But deep learning is expensive, opaque, brittle and relies solely on human labelled data. This project intends to make deep learning more reliable by establishing theory and algorithms that allow physical and mathematical models to be embedded within a deep learning framework, providing performance guarantees and interpretability. This would likely benefit machine learning based products that can understand the world and interact with humans naturally through vision and language.Read moreRead less
Adapting Deep Learning for Real-world Medical Image Datasets. The project aims to investigate new deep learning modelling approaches to leverage real-world large-scale image data sets that contain noisy and incomplete labels and imbalanced class prevalence – to enable the use of these data sets for modelling deep learning classifiers. Expected outcomes include an innovative method for modelling deep learning classifiers. The research will involve new inter-disciplinary and international collabor ....Adapting Deep Learning for Real-world Medical Image Datasets. The project aims to investigate new deep learning modelling approaches to leverage real-world large-scale image data sets that contain noisy and incomplete labels and imbalanced class prevalence – to enable the use of these data sets for modelling deep learning classifiers. Expected outcomes include an innovative method for modelling deep learning classifiers. The research will involve new inter-disciplinary and international collaborations with machine learning and medical image analysis research institutions. This should provide significant benefits, such as better understanding of deep learning theory, new deep learning applications that can use previously unexplored data sets, and training for the future Australian workforce.Read moreRead less
The role of strong duality in computer vision. This project aims to undertake research in the fields of computer vision and optimization that will have a significant impact on the design of numerical algorithms for solving a wide range of problems in Computer Vision, Virtual Reality and Robotic Navigation. This project will advance understanding of a broad class of problems related to how computers interpret images. An expected outcome is the generation of novel mathematical theory and numerical ....The role of strong duality in computer vision. This project aims to undertake research in the fields of computer vision and optimization that will have a significant impact on the design of numerical algorithms for solving a wide range of problems in Computer Vision, Virtual Reality and Robotic Navigation. This project will advance understanding of a broad class of problems related to how computers interpret images. An expected outcome is the generation of novel mathematical theory and numerical algorithms capable of fundamentally changing the way problems relevant to a wide range of vision-related applications are solved. This should offer Australia a strong competitive advantage as a leader in scientific innovation in the areas of Computer Vision, Virtual Reality and Robotics and Autonomous Systems.Read moreRead less