The next generation speaker recognition system. The next generation of speaker recognition technologies developed through this project will enable secure person authentication by voice in financial transactions and benefit the community through the elimination of identity fraud. This project will safeguard Australia by identifying criminal suspects using their voice and combat terrorism by using voice to locate and track terrorists.
Towards interpretable deep learning with limited examples. Existing visual concept detection systems are incapable of detecting ever-evolving concepts in daily life. This project aims to extract patterns that describe the semantics of visual concepts and to develop or adapt knowledge transfer learning technologies for new concepts with limited examples. The expected outcomes will provide major technological breakthroughs for building efficient and interpretable learning systems for visual analys ....Towards interpretable deep learning with limited examples. Existing visual concept detection systems are incapable of detecting ever-evolving concepts in daily life. This project aims to extract patterns that describe the semantics of visual concepts and to develop or adapt knowledge transfer learning technologies for new concepts with limited examples. The expected outcomes will provide major technological breakthroughs for building efficient and interpretable learning systems for visual analysis and will open an entirely new research direction: interpretable deep learning with communication mechanism. This new field and its technologies will help us to recognise misuse of home patient medical devices and unauthorised activity, and enable us to devise effective responses to prevent cyberattacks.Read moreRead less
Data driven decision making for complex problems. This project aims to formulate methods for using constraint solving and data mining in a complementary and holistic manner. Complex health, educational and social issues require complex decisions supported by automated analysis techniques using rich data sources and human knowledge. Constraint solving and data mining make decisions easier, but are mostly deployed independently, limiting the effectiveness of decisions. This project’s methods shoul ....Data driven decision making for complex problems. This project aims to formulate methods for using constraint solving and data mining in a complementary and holistic manner. Complex health, educational and social issues require complex decisions supported by automated analysis techniques using rich data sources and human knowledge. Constraint solving and data mining make decisions easier, but are mostly deployed independently, limiting the effectiveness of decisions. This project’s methods should lead to effective and flexible data driven decision making tools for tackling challenging multi-component problems.Read moreRead less
Using data mining methods to remove uncertainties in sensor data streams. This project will develop key techniques for removing uncertainties in sensor data streams and thus improve the monitoring quality of sensor networks. The expected outcomes will benefit Australia by enabling improved, lower-cost monitoring of natural resources and management of stock raising.
Deep Data Mining for Anomaly Prediction from Sensor Data Streams. Sensor data streams are crucial for anomaly predictions in real-life monitoring. However, balancing efficiency and accuracy in predicting anomalies with sensor streams is a great challenge; it requires new techniques that go beyond detecting anomalies and predicting trends. This project will develop a deep mining method for anomaly prediction from sensor streams; it will comprise mining algorithms at various levels - from compress ....Deep Data Mining for Anomaly Prediction from Sensor Data Streams. Sensor data streams are crucial for anomaly predictions in real-life monitoring. However, balancing efficiency and accuracy in predicting anomalies with sensor streams is a great challenge; it requires new techniques that go beyond detecting anomalies and predicting trends. This project will develop a deep mining method for anomaly prediction from sensor streams; it will comprise mining algorithms at various levels - from compressing massive raw data, to recognition of abnormal waveforms preceding anomalies, and to retrieving and summarising similar past anomalies for creating descriptions of future anomalies. The project will demonstrate our method in health/environment monitoring applications, and its adoption will save resources, money and lives.Read moreRead less
Deep analytics of non-occurring but important behaviours. This project aims to build a systematic theory for the deep analytics of complex and important occurring and non-occurring behaviours. Behaviours that should occur but do not take place, called non-occurring behaviours (NOB), are widely evident but easily overlooked, such as missed important medical treatments. While often occurring behaviours are focused, such NOB may be associated with significant effects such as a threat to health. Thi ....Deep analytics of non-occurring but important behaviours. This project aims to build a systematic theory for the deep analytics of complex and important occurring and non-occurring behaviours. Behaviours that should occur but do not take place, called non-occurring behaviours (NOB), are widely evident but easily overlooked, such as missed important medical treatments. While often occurring behaviours are focused, such NOB may be associated with significant effects such as a threat to health. This project expects to fill the knowledge gaps in representing, analysing and evaluating NOB complexities and impact, with significant benefits for the evidence-based detection, prediction and risk management of covert NOB applications and their important effects.Read moreRead less
Deep Interaction Learning in Unlabelled Big Data and Complex Systems. This project aims to effectively model intricate interactions deeply embedded in unlabelled big data and complex systems, which are often hierarchical, heterogeneous, contextual, dynamic or even contrastive. Learning such interactions is the keystone of robust data science and for realizing the value of big data but it poses significant challenges and knowledge gaps to existing data analytics and learning systems. The expected ....Deep Interaction Learning in Unlabelled Big Data and Complex Systems. This project aims to effectively model intricate interactions deeply embedded in unlabelled big data and complex systems, which are often hierarchical, heterogeneous, contextual, dynamic or even contrastive. Learning such interactions is the keystone of robust data science and for realizing the value of big data but it poses significant challenges and knowledge gaps to existing data analytics and learning systems. The expected outcomes include new-generation theories and methods for the unsupervised learning of complex interactions in real-life big data, which are anticipated to enable the intrinsic processing of big data complexities and substantially enhance Australia’s leadership in frontier data science research and applications. Read moreRead less
Knowledge discovery from data in the context of prior beliefs. This project will invent user-centric technologies for discovering knowledge from data that are distinguished by taking account of the user's beliefs, enabling more useful discoveries to be made. This project will also invent methods that identify the relative potential value of those discoveries, helping the user derive greater value from their data assets.
Time Series Classification for Complex Dynamic Global Problems. This project aims to increase understanding of complex dynamic processes by creating new ways of analysing large quantities of data collected over time. These new approaches will be specifically designed to greatly improve the understanding obtained from time varying data for trillions of global earth observation data points in an application-agnostic way that is applicable to many tasks. The outcomes are expected to advance the the ....Time Series Classification for Complex Dynamic Global Problems. This project aims to increase understanding of complex dynamic processes by creating new ways of analysing large quantities of data collected over time. These new approaches will be specifically designed to greatly improve the understanding obtained from time varying data for trillions of global earth observation data points in an application-agnostic way that is applicable to many tasks. The outcomes are expected to advance the theory and practice of time-series data analysis and transform the analysis of complex dynamics. This should support innovation in industry, commerce, government and research and magnify benefit from many data investments including the $1 billion Australian governments invest annually in satellite imaging.Read moreRead less
Combining generative and discriminative strategies to facilitate efficient and effective learning from big data. Effective extraction of information from massive data stores is increasingly problematic as data quantities continue to grow rapidly. Quite simply, effective techniques for learning from small data do not scale. However, the problem is even worse than this. Big data contain more information than the small data in which context most state-of-the-art learning algorithms have been develo ....Combining generative and discriminative strategies to facilitate efficient and effective learning from big data. Effective extraction of information from massive data stores is increasingly problematic as data quantities continue to grow rapidly. Quite simply, effective techniques for learning from small data do not scale. However, the problem is even worse than this. Big data contain more information than the small data in which context most state-of-the-art learning algorithms have been developed. For small data overly detailed classifiers will overfit the data and so should be avoided. In contrast, big data provide fine detail and hence will benefit new types of learner that can capture it. This project will deliver learners that are not only capable of capturing this detail, but do so with the efficiency required to process terabytes of data.Read moreRead less