Linkage Infrastructure, Equipment And Facilities - Grant ID: LE240100131
Funder
Australian Research Council
Funding Amount
$539,000.00
Summary
Federated Omniverse Facilities for Smart Digital Futures. A world-first trans-disciplinary, -domain, and -institutional smart 3D omniverse R&D ecosystem AuVerse will be built in NSW, affiliated with Queensland, and accessible to academia and industry. AuVerse will support cloud-based, reality-virtuality-fused, immersive, interactive and secure future-oriented digital design, development, training and society. In the new era of digital innovation and paradigm shift, AuVerse will substantially boo ....Federated Omniverse Facilities for Smart Digital Futures. A world-first trans-disciplinary, -domain, and -institutional smart 3D omniverse R&D ecosystem AuVerse will be built in NSW, affiliated with Queensland, and accessible to academia and industry. AuVerse will support cloud-based, reality-virtuality-fused, immersive, interactive and secure future-oriented digital design, development, training and society. In the new era of digital innovation and paradigm shift, AuVerse will substantially boost Australia’s pivotal research leadership and business competitiveness in nurturing new-generation, collaborative and transformative digital R&D and talent pipeline. It will enable large-scale strategic business innovation and transformation including smart manufacturing and Industry 4.0.Read moreRead less
Towards Generalisable and Unbiased Dynamic Recommender Systems. This project aims to develop the foundations, including models, methodology, and algorithms for building generalisable and unbiased dynamic recommender systems to facilitate intelligent decision-making, prompt contextualised and personalised strategic plans, and support context-aware action recourse. To ensure that fundamental principles, such as fairness and transparency, are respected, a set of algorithms and techniques are propos ....Towards Generalisable and Unbiased Dynamic Recommender Systems. This project aims to develop the foundations, including models, methodology, and algorithms for building generalisable and unbiased dynamic recommender systems to facilitate intelligent decision-making, prompt contextualised and personalised strategic plans, and support context-aware action recourse. To ensure that fundamental principles, such as fairness and transparency, are respected, a set of algorithms and techniques are proposed to develop recommender systems in a more responsible manner. The result of this project will not only maintain Australia's leadership in this frontier research area, but also serve as an excellent vehicle for the education and training of Australia's next generation of scholars and engineers.Read moreRead less
A Data-Centric Mobile Edge Platform for Resilient Logistics & Supply Chain. This project aims to develop a secure mobile edge computing platform for resilient logistic and supply chain management. It consists of easy-used functions that help businesses realise low latency, high reliability, low cost, and high security in their logistics and supply chain system. To cope with the vast generated application data, we invent new data replication, placement, and deduplication techniques to optimise th ....A Data-Centric Mobile Edge Platform for Resilient Logistics & Supply Chain. This project aims to develop a secure mobile edge computing platform for resilient logistic and supply chain management. It consists of easy-used functions that help businesses realise low latency, high reliability, low cost, and high security in their logistics and supply chain system. To cope with the vast generated application data, we invent new data replication, placement, and deduplication techniques to optimise the mobile edge computing platform from the computation, storage, and network aspects. The invented mobile edge computing platform will enable more intelligent business applications for various industries, e.g., IT, manufacturing, and media, to appear, thus benefiting both the economy of Australia.Read moreRead less
Situated Anomaly Detection in an Open Environment. This project aims to investigate situated anomaly detection in an open environment. Existing anomaly detection techniques follow the setting of conventional machine learning and discover anomalies from a set of collected data. In contrast, this project proposes to develop the next-generation of anomaly detection algorithms by learning from interactions with an open environment, which enables the discovery of new anomalies and the early detection ....Situated Anomaly Detection in an Open Environment. This project aims to investigate situated anomaly detection in an open environment. Existing anomaly detection techniques follow the setting of conventional machine learning and discover anomalies from a set of collected data. In contrast, this project proposes to develop the next-generation of anomaly detection algorithms by learning from interactions with an open environment, which enables the discovery of new anomalies and the early detection of anomalies. The established theories and developed algorithms will advance frontier technologies in machine intelligence. The success of the project will contribute to a wide range of real applications in cybersecurity, defence and finance, bringing massive social and economic benefits. Read moreRead less
Mitigating the Influence of Social Bots in Heterogeneous Social Networks. This project aims to mitigate the influence of social bots in dynamic and constantly changing social networks. Social bots can spread misinformation, manipulate public opinion, and compromise privacy and security. This project will use advanced algorithms to detect and neutralize the impact of social bots, improving the integrity and accuracy of information on social media. The expected outcomes include the development of ....Mitigating the Influence of Social Bots in Heterogeneous Social Networks. This project aims to mitigate the influence of social bots in dynamic and constantly changing social networks. Social bots can spread misinformation, manipulate public opinion, and compromise privacy and security. This project will use advanced algorithms to detect and neutralize the impact of social bots, improving the integrity and accuracy of information on social media. The expected outcomes include the development of a robust system for identifying and mitigating social bot influence, and the reduction of harmful content and misinformation on social media. The benefits of this project include a more trustworthy and secure social media environment, protection of individuals and organizations from malicious activities.Read moreRead less
Trust-Oriented Data Analytics in Online Social Networks. Trust-oriented data analytics is essential in online social networks for reducing deceitful interactions and enhancing trust between users. This project aims to systematically devise innovative solutions by considering rich social contextual information as an important source of trust. The expected outcomes of this project include innovative solutions from a fundamental perspective to the challenges of context-aware trust propagation, trus ....Trust-Oriented Data Analytics in Online Social Networks. Trust-oriented data analytics is essential in online social networks for reducing deceitful interactions and enhancing trust between users. This project aims to systematically devise innovative solutions by considering rich social contextual information as an important source of trust. The expected outcomes of this project include innovative solutions from a fundamental perspective to the challenges of context-aware trust propagation, trust network searching/matching, and trustworthy/malicious user prediction in online social networks. This project is significant as it will advance the knowledge base for enabling a trustworthy social networking environment, benefiting billions of Australian and worldwide online social network users.Read moreRead less
Knowledge Graph-driven Software Vulnerability Risk Discovery and Assessment. This project aims to alleviate cyberattacks which are increasingly being crafted to attack software vulnerabilities and weaknesses by utilising advanced knowledge graphs and deep learning techniques. This project expects to construct an innovative software vulnerability knowledge graph and develop advanced graph-based algorithms and models. Expected outcomes of this project include the enhanced capacity to defend agains ....Knowledge Graph-driven Software Vulnerability Risk Discovery and Assessment. This project aims to alleviate cyberattacks which are increasingly being crafted to attack software vulnerabilities and weaknesses by utilising advanced knowledge graphs and deep learning techniques. This project expects to construct an innovative software vulnerability knowledge graph and develop advanced graph-based algorithms and models. Expected outcomes of this project include the enhanced capacity to defend against cyberattacks for both organisations and individuals in Australia and beyond, theory development in graph theory, refined graph neural network models and improved graph transfer learning algorithms.Read moreRead less
Information-theoretic secure communications via caching. This project aims to address the cybersecurity problem of securing telecommunication networks to prevent data leakage. Current widely-adopted data-encryption approaches to secure communications will be broken with large-scale quantum computers, and existing information-theoretic approaches rely on the channel quality of the network. To circumvent these risks, this project proposes a new information security approach using information cache ....Information-theoretic secure communications via caching. This project aims to address the cybersecurity problem of securing telecommunication networks to prevent data leakage. Current widely-adopted data-encryption approaches to secure communications will be broken with large-scale quantum computers, and existing information-theoretic approaches rely on the channel quality of the network. To circumvent these risks, this project proposes a new information security approach using information cached at devices to camouflage data. The project will future-proof secure communication systems against large-scale quantum computers, which threaten current encryption approaches. This should ensure that data transmitted over communication networks can never be revealed to interceptors or hackers, even in public WiFi.Read moreRead less
Big temporal graph processing in the Cloud. This project aims to develop efficient and scalable algorithms to process big temporal graphs in the Cloud. In particular, we will investigate three most representative types of queries over big temporal graphs including vertex-based queries, path-based queries, and subgraph-based queries. Expected outcomes of this project include theoretical foundations and scalable algorithms to process big temporal graphs as well as a system prototype for evaluation ....Big temporal graph processing in the Cloud. This project aims to develop efficient and scalable algorithms to process big temporal graphs in the Cloud. In particular, we will investigate three most representative types of queries over big temporal graphs including vertex-based queries, path-based queries, and subgraph-based queries. Expected outcomes of this project include theoretical foundations and scalable algorithms to process big temporal graphs as well as a system prototype for evaluation and to demonstrate the practical value. Success in this project should see significant benefits for many important applications such as cybersecurity, e-commerce, health and road networks.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240100668
Funder
Australian Research Council
Funding Amount
$435,000.00
Summary
Towards Processing of Big Streaming Temporal Graphs. This project aims to develop efficient and scalable algorithms to process big streaming temporal graphs, which is in high demand for many data-intensive applications such as cybersecurity, crime monitoring, and e-marketing. In particular, I will investigate three most representative types of queries including vertex-based queries, path-based queries, and subgraph-based queries. Expected outcomes of this project include theoretical foundations ....Towards Processing of Big Streaming Temporal Graphs. This project aims to develop efficient and scalable algorithms to process big streaming temporal graphs, which is in high demand for many data-intensive applications such as cybersecurity, crime monitoring, and e-marketing. In particular, I will investigate three most representative types of queries including vertex-based queries, path-based queries, and subgraph-based queries. Expected outcomes of this project include theoretical foundations and scalable algorithms to process big streaming temporal graphs as well as a system prototype for evaluation and to demonstrate the practical value. Success in this project should see significant benefits for many important applications such as cybersecurity, e-commerce, health and social analysis.Read moreRead less