Discovery Early Career Researcher Award - Grant ID: DE230100049
Funder
Australian Research Council
Funding Amount
$459,030.00
Summary
Towards automated Australian Sign Language translation. This project aims to address the computational modelling of Auslan. The project expects to generate knowledge by creating the largest Auslan dataset, enabling further advancements in this research area. The dataset will also play an essential role in other research fields, e.g., sign linguistics. Expected outcomes include the invention of the first Auslan recogniser and generator capable of distinguishing and synthesising 1000+ signs, repre ....Towards automated Australian Sign Language translation. This project aims to address the computational modelling of Auslan. The project expects to generate knowledge by creating the largest Auslan dataset, enabling further advancements in this research area. The dataset will also play an essential role in other research fields, e.g., sign linguistics. Expected outcomes include the invention of the first Auslan recogniser and generator capable of distinguishing and synthesising 1000+ signs, representing a substantial advancement towards fully automated Auslan translation. This should provide significant benefits for the Australian Deaf community, such as high-quality digital systems for education and communication, resulting in increased quality of life and inclusion in the Australian society.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE230101281
Funder
Australian Research Council
Funding Amount
$329,278.00
Summary
An Efficient Computational Solver for Complex Engineering Problems. This project aims to address significant gaps in the existing knowledge about solving complex engineering problems that involve conflicting objectives and unquantifiable features. In these problems, the decision-maker is interested in knowing high-quality and dissimilar solutions that determine the trade-off between the problem objectives. The intended outcomes of this project include a novel robust computational solver that can ....An Efficient Computational Solver for Complex Engineering Problems. This project aims to address significant gaps in the existing knowledge about solving complex engineering problems that involve conflicting objectives and unquantifiable features. In these problems, the decision-maker is interested in knowing high-quality and dissimilar solutions that determine the trade-off between the problem objectives. The intended outcomes of this project include a novel robust computational solver that can automatically find such solutions. The decision-makers can then choose the final solution based on their expertise and preferences. This expects to offer significant benefits to diverse engineering disciplines by finding superior and more practical solutions to their complex multidisciplinary problems.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200101439
Funder
Australian Research Council
Funding Amount
$418,998.00
Summary
Towards a Reliable and Explainable Health Monitoring and Caring System. This project aims to unleash the power of deep learning on health monitoring and caring domain through a safe, reliable and explainable way. Its innovations lie on 1) developing a set of robust and explainable deep learning models that are guaranteed to be safe to complex environmental uncertainty; 2) designing an intelligent health monitoring and caring platform, powered by robust deep learning models, to better support the ....Towards a Reliable and Explainable Health Monitoring and Caring System. This project aims to unleash the power of deep learning on health monitoring and caring domain through a safe, reliable and explainable way. Its innovations lie on 1) developing a set of robust and explainable deep learning models that are guaranteed to be safe to complex environmental uncertainty; 2) designing an intelligent health monitoring and caring platform, powered by robust deep learning models, to better support the home-based health monitoring and caring for the elderly. The result will enable end-users to trust the decisions of deep learning models in safety-critical systems and significantly contribute to Australian aging society and national healthcare economy.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE140101628
Funder
Australian Research Council
Funding Amount
$301,970.00
Summary
Non-Intrusive Resource Sharing for Cloud Data Centre Efficiency. Resource sharing using hardware virtualisation has become increasingly common for cloud data centre efficiency. Such virtualisation allows multiple workloads to share a common set of resources in a single physical machine. In practice, however, these co-located workloads often compete for resources, leading to their resource usage being non-isolable and intrusive. This intrusive resource sharing is a major source of cloud data cent ....Non-Intrusive Resource Sharing for Cloud Data Centre Efficiency. Resource sharing using hardware virtualisation has become increasingly common for cloud data centre efficiency. Such virtualisation allows multiple workloads to share a common set of resources in a single physical machine. In practice, however, these co-located workloads often compete for resources, leading to their resource usage being non-isolable and intrusive. This intrusive resource sharing is a major source of cloud data centre inefficiency. This project will develop non-intrusive resource allocation and scheduling solutions that enable co-located workloads to organically use resources. These solutions exploit the heterogeneity and dynamicity of cloud data centres that are often perceived as the main hurdles of resource management.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE210100263
Funder
Australian Research Council
Funding Amount
$425,775.00
Summary
Adaptive Resource Management for Sustainable Edge Computing Systems. This project aims to develop adaptive resource management solutions in edge computing systems for efficient management of the use of limited computing resources and varying renewable energy resources without compromising the stringent needs of emerging Internet of Things applications. These resources will be jointly managed on the diverse, dispersed, often independently owned and operated edge devices with a set of prediction, ....Adaptive Resource Management for Sustainable Edge Computing Systems. This project aims to develop adaptive resource management solutions in edge computing systems for efficient management of the use of limited computing resources and varying renewable energy resources without compromising the stringent needs of emerging Internet of Things applications. These resources will be jointly managed on the diverse, dispersed, often independently owned and operated edge devices with a set of prediction, scheduling and energy saving techniques. The expected outcome is to realise a sustainable edge computing system to reduce both operational cost and negative environmental impact of the system. This project will elevate Australia to be a dominant player in sustainable computing and lead future development trends.
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Discovery Early Career Researcher Award - Grant ID: DE190101118
Funder
Australian Research Council
Funding Amount
$339,000.00
Summary
High performance density-based clustering in parallel environments. This project aims to conduct a comprehensive study on density-based clustering to improve data management in parallel computing environments. Clustering, a fundamental task in data management, is to group a set of objects such that objects in the same group (called a cluster) are more similar to each other than those in other groups in order to simplify retrieval of similar information. Clustering is widely used in many fields i ....High performance density-based clustering in parallel environments. This project aims to conduct a comprehensive study on density-based clustering to improve data management in parallel computing environments. Clustering, a fundamental task in data management, is to group a set of objects such that objects in the same group (called a cluster) are more similar to each other than those in other groups in order to simplify retrieval of similar information. Clustering is widely used in many fields including machine learning, pattern recognition, information retrieval, bioinformatics and image analysis. It is expected that the developed clustering techniques will provide significant performance improvements in industry sectors where decisions are made based on clustering data analytics, such as the sectors of finance, renewable energy and artificial intelligence.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE180100315
Funder
Australian Research Council
Funding Amount
$368,446.00
Summary
Social attentive user interfaces for the age of interruption. This proposal aims to enable the development of social attentive user interfaces—those that employ sensors such as eye trackers and thermal cameras to monitor the locus and level of users' attention and adapt their behaviour accordingly. The project lies in the field of Human-Computer Interaction, drawing from machine learning methods to design novel user experiences. Expected outcomes include insights into how people manage their att ....Social attentive user interfaces for the age of interruption. This proposal aims to enable the development of social attentive user interfaces—those that employ sensors such as eye trackers and thermal cameras to monitor the locus and level of users' attention and adapt their behaviour accordingly. The project lies in the field of Human-Computer Interaction, drawing from machine learning methods to design novel user experiences. Expected outcomes include insights into how people manage their attention, new methods for attention estimation and classification, and novel systems for e-learning and work productivity that demonstrate these new capabilities. As a result, this project will provide the benefit of enabling system to no longer be blind to users’ attentional, social, and cognitive contexts.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE180101416
Funder
Australian Research Council
Funding Amount
$338,446.00
Summary
Broadening horizons: using curiosity to diversify behaviour. This project aims to explore how interactive systems can encourage their users to try new things. This is made possible by recent developments in artificial intelligence that can estimate what will make users curious. This project expects to generate new knowledge about how interactive technology can encourage diverse behaviour by stimulating curiosity. Expected outcomes include a framework for how to design interactive systems that en ....Broadening horizons: using curiosity to diversify behaviour. This project aims to explore how interactive systems can encourage their users to try new things. This is made possible by recent developments in artificial intelligence that can estimate what will make users curious. This project expects to generate new knowledge about how interactive technology can encourage diverse behaviour by stimulating curiosity. Expected outcomes include a framework for how to design interactive systems that encourage users to try new things, and a greater theoretical understanding of how to diversify user behaviour.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE220101597
Funder
Australian Research Council
Funding Amount
$360,264.00
Summary
Empowering Users to Protect their Personal Privacy on Social Media. This Information Systems project aims to take a bold approach to finally overcome the paradoxical inertia of people who care about their privacy but do not protect it. This project integrates different psychological theories proposing a paradigm shift expecting to generate new knowledge in privacy research, which can currently neither explain nor provide means to overcome the vexing issue. Expected outcomes of the project includ ....Empowering Users to Protect their Personal Privacy on Social Media. This Information Systems project aims to take a bold approach to finally overcome the paradoxical inertia of people who care about their privacy but do not protect it. This project integrates different psychological theories proposing a paradigm shift expecting to generate new knowledge in privacy research, which can currently neither explain nor provide means to overcome the vexing issue. Expected outcomes of the project include a privacy behaviour model (PIM), privacy training program and system design solutions. This should offer substantial benefits as it integrates privacy research and guides behavioural models beyond Information Systems, provide means to solve the paradox, guide legislation and the privacy consent mechanism design.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