Linkage Infrastructure, Equipment And Facilities - Grant ID: LE0347131
Funder
Australian Research Council
Funding Amount
$115,490.00
Summary
Intelligent Computer System to Access Information Directly from the Brain Using High Resolution Electroencephalography and Repetitive Transcranial Magnetic Stimulation. The ambitious aim of this project is to create a novel intelligent computer system which accesss information from the brain. To do this we use repetitive Transcranial Magnetic Stimulation and high resolution Electroencephalography.
Visual Analytics for Next Generation Sequencing. Next-generation sequencing technologies have brought a revolution in biology and healthcare, while taxing the ability of scientists and clinicians to identify and process relevant data, to make sense of it all and communicate it to others in a concise and meaningful way. This project aims to tackle this problem through fundamentally new approaches to data selection and visualisation at very large scale, actively encoding for insight into underlyin ....Visual Analytics for Next Generation Sequencing. Next-generation sequencing technologies have brought a revolution in biology and healthcare, while taxing the ability of scientists and clinicians to identify and process relevant data, to make sense of it all and communicate it to others in a concise and meaningful way. This project aims to tackle this problem through fundamentally new approaches to data selection and visualisation at very large scale, actively encoding for insight into underlying biological and biomedical processes, bringing sustainable discovery of new relationships and variations within the data. The project aims to support new approaches to medical diagnosis and treatment, and offer crucial lessons to address the broader challenge of understanding large, complex data sets.Read moreRead less
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
Discovery Early Career Researcher Award - Grant ID: DE170101081
Funder
Australian Research Council
Funding Amount
$360,000.00
Summary
Adaptive value-flow analysis to improve code reliability and security. This project aims to develop client-driven adaptive value-flow analysis to detect software bugs in system software written in the C/C++ programme language. Static analysis tools for automated code inspections can benefit software developers, but are imprecise, inefficient and not user-friendly for analysing real-world industrial-sized software. The project will investigate static, dynamic and user-guided value-flow analysis t ....Adaptive value-flow analysis to improve code reliability and security. This project aims to develop client-driven adaptive value-flow analysis to detect software bugs in system software written in the C/C++ programme language. Static analysis tools for automated code inspections can benefit software developers, but are imprecise, inefficient and not user-friendly for analysing real-world industrial-sized software. The project will investigate static, dynamic and user-guided value-flow analysis to efficiently and precisely analyse large-scale programs according to clients’ needs, thereby allowing compilers to generate safe, reliable and secure code. This project is expected to advance value-flow analysis for industrial-sized software, improve software reliability and security, and benefit Australian software systems and industries.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
Privacy-preserving data processing on the cloud. This project aims to address the current lack of privacy of user data processed by common cloud computing web servers, including email, business data, and confidential files. This project aims to develop new techniques in cryptography. The anticipated outcome is a suite of practical tools enabling common cloud computing processing operations such as search, statistical analysis, and multi-user access control, to be performed efficiently while pres ....Privacy-preserving data processing on the cloud. This project aims to address the current lack of privacy of user data processed by common cloud computing web servers, including email, business data, and confidential files. This project aims to develop new techniques in cryptography. The anticipated outcome is a suite of practical tools enabling common cloud computing processing operations such as search, statistical analysis, and multi-user access control, to be performed efficiently while preserving the data privacy. These tools should provide significant benefits to the privacy of cloud users, as well as financial and reputation benefits to the IT industry, by significantly reducing the likelihood of massive user data privacy breaches in the event of a cyber-hacking attack on the cloud server.Read moreRead less
Developing A Smart Farming Oriented Secure Data Infrastructure. Smart farming is the future of agriculture. However, recently the Federal Bureau of Investigation has issued a
warning that the lack of data privacy and cyber security mechanisms in the field runs a high risk of disaster. This
project aims to establish an innovative secure data infrastructure for smart farming including secure and automated smart farming supply-chain management. The deliverables of this project will include the cutt ....Developing A Smart Farming Oriented Secure Data Infrastructure. Smart farming is the future of agriculture. However, recently the Federal Bureau of Investigation has issued a
warning that the lack of data privacy and cyber security mechanisms in the field runs a high risk of disaster. This
project aims to establish an innovative secure data infrastructure for smart farming including secure and automated smart farming supply-chain management. The deliverables of this project will include the cutting-edge Blockchain based secure IoT data management and privacy-preserving smart contracts for smart farming supply-chain management. This data infrastructure will be the first of its kind which will lay a solid foundation for smart farming technology.Read moreRead less
Lightweight security solutions for wearable healthcare sensor devices. The aim of this project is to develop new methods to secure the data and context associated with body-wearable health monitoring devices. The novelty of the scheme is in making the methods work on resource-poor devices, by combining new security capabilities derived from the operating environment with conventional cryptographic techniques. This project aims to increase the trust that medical practitioners and insurance provid ....Lightweight security solutions for wearable healthcare sensor devices. The aim of this project is to develop new methods to secure the data and context associated with body-wearable health monitoring devices. The novelty of the scheme is in making the methods work on resource-poor devices, by combining new security capabilities derived from the operating environment with conventional cryptographic techniques. This project aims to increase the trust that medical practitioners and insurance providers can place on health data from wearable devices, and showcase Australian innovation in developing world-class security solutions. The outcome of this project is expected to be the development and demonstration of ultra-lightweight algorithms and mechanisms that execute in wearable devices to safeguard the integrity of the data.Read moreRead less
Verified concurrent memory management on modern processors. This project aims to formally verify automatic memory managers in the presence of concurrency and the weakly ordered memory of modern processors. A new framework for verifying memory managers, reusable for a wide range of managed programming languages, target hardware, policies, and algorithms will be developed. Expected technical outcomes include improved techniques to ensure trustworthiness of the foundations on which critical softwar ....Verified concurrent memory management on modern processors. This project aims to formally verify automatic memory managers in the presence of concurrency and the weakly ordered memory of modern processors. A new framework for verifying memory managers, reusable for a wide range of managed programming languages, target hardware, policies, and algorithms will be developed. Expected technical outcomes include improved techniques to ensure trustworthiness of the foundations on which critical software infrastructures are built. This will significantly enhance the security of public and private cyber assets, and deliver applications that are more robust and trustworthy, across a range of critical infrastructure such as transportation, communication, energy and defence.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