Multiview Complete Space Learning for Sparse Camera Network Research. Data analytics in video surveillance and social computing is a problem because data are represented by multiple heterogeneous features. This project will develop a multiview complete space learning framework to exploit heterogeneous properties to represent images obtained from sparse camera networks. It will integrate multiple features to identify people and understand behaviour, to build a database of activities occurring in ....Multiview Complete Space Learning for Sparse Camera Network Research. Data analytics in video surveillance and social computing is a problem because data are represented by multiple heterogeneous features. This project will develop a multiview complete space learning framework to exploit heterogeneous properties to represent images obtained from sparse camera networks. It will integrate multiple features to identify people and understand behaviour, to build a database of activities occurring in a wide area of surveillance. It will expand frontier technologies and safeguard Australia by providing warnings for hazardous (for example, overcrowding, trespassing), criminal, and terrorist situations. Results will be applicable internationally and enhance Australia’s role in machine learning and computer vision communities.Read moreRead less
Towards High-Order Structure Search on Large-Scale Graphs. High-order structure search over large-scale graphs has many applications including cybersecurity, crime detection, social media, marketing recommendation, and public health. The project aims to lay the scientific foundations and develop novel computing techniques for efficiently conducting structure search. The outcomes include novel computing paradigms, algorithms, indexing, incremental computation, and distributed solutions. The succe ....Towards High-Order Structure Search on Large-Scale Graphs. High-order structure search over large-scale graphs has many applications including cybersecurity, crime detection, social media, marketing recommendation, and public health. The project aims to lay the scientific foundations and develop novel computing techniques for efficiently conducting structure search. The outcomes include novel computing paradigms, algorithms, indexing, incremental computation, and distributed solutions. The success of the project will directly contribute to the scientific foundation of Big Data computation. It will also contribute to the development of local industry involving cybersecurity, social media based recommendation, network management, and E-business.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: De120102900
Funder
Australian Research Council
Summary
WikiLinks: web-scale linking and fact extraction with Wikipedia. Wikipedia is the most popular web site for finding facts, but articles about local or specialist topics are often missing or unreliable. WikiLinks will use artificial intelligence to link names in text to corresponding Wikipedia articles, allowing us to automatically create and augment Wikipedia content by summarising existing material on the web.
Modernism and the British secret state. The purpose of the project is to explore interactions between modernist culture and intelligence agencies such as Military Intelligence, Section 5. It opens an exciting new field for modernist scholarship, and the resulting book will make an important contribution to the broader understanding of the process of government surveillance and its impact upon literature and culture.
Artificial intelligence meets wireless sensor networks: filling the gaps between sensors using spatial reasoning. Monitoring potential disaster regions and integrating available information with expert knowledge can prevent disasters and save many lives. The outcome of our project is one of the key components for intelligent systems that can autonomously monitor the environment, make the correct inferences and issue appropriate warnings and recommendations.
Development of globally optimal solutions to simultaneous localisation and mapping for robot navigation. Building robots that can operate on their own is one of the potentially transformational technologies of this century. This project will develop algorithms that are well understood and robust to allow the deployment of robots in environments populated with people and in search and rescue operations where global positioning system is not available.
Discovery Early Career Researcher Award - Grant ID: DE120102900
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
WikiLinks: web-scale linking and fact extraction with Wikipedia. Wikipedia is the most popular web site for finding facts, but articles about local or specialist topics are often missing or unreliable. WikiLinks will use artificial intelligence to link names in text to corresponding Wikipedia articles, allowing us to automatically create and augment Wikipedia content by summarising existing material on the web.
Semantic change detection through large-scale learning. This project aims to develop technologies which understand the content of images before higher-level analysis is performed. This approach is intended to allow more accurate and reliable decisions to be made using automated image analysis than has previously been possible. The project will particularly investigate the detection of change in the contents of an image.
Computer vision from a multi-structural analysis framework. Computer vision has applications in a wide variety of areas: security (video surveillance), entertainment (special effects), health care (medical imaging), and economy (improved automation and consumer products). This project will improve the accuracy and reliability of such applications. Advances will also lead to new products and industries.
Interacting with visualisations of extremely large graph structures on large displays. The latest technological progressions have delivered very large data sets that can be modelled as graphs or networks. Examples include: social networks, biological data, and software structures. This project will develop techniques to allow users to visualise the graphs in the entirety and directly interact with data.