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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
Rethinking Topological Persistence. This project aims to address the lack of transferability and uncertainty-awareness in AI models. Despite their success, AI models are met with bias and uncertainty when deployed in the real world. As a result, they are rarely used in high-risk industries like cybersecurity or transport. This project expects to build uncertainty-awareness into models by teaching them to return UNKNOWN when they encounter a previously unseen thing, instead of misclassifying it. ....Rethinking Topological Persistence. This project aims to address the lack of transferability and uncertainty-awareness in AI models. Despite their success, AI models are met with bias and uncertainty when deployed in the real world. As a result, they are rarely used in high-risk industries like cybersecurity or transport. This project expects to build uncertainty-awareness into models by teaching them to return UNKNOWN when they encounter a previously unseen thing, instead of misclassifying it. Further, the evaluation methods to be developed will not rely on access to test data, allowing cost-effective, private, and safe AI for high-stakes decision support. The outcomes will benefit Australia by accelerating economic investment and fostering greater social acceptance of AI.Read moreRead less
Readying Wireless Networks for Future Communications Systems: From Ubiquitous Computing to the Internet of Things. This project aims to prepare wireless networks for future communications systems, by improving the data transmission rates of wireless networks, through developing new coding schemes based on the synergy of noisy-channel coding and index coding. This will allow wireless networks, used in conjunction with the fibre-optic National Broadband Network, to support future high-data-rate an ....Readying Wireless Networks for Future Communications Systems: From Ubiquitous Computing to the Internet of Things. This project aims to prepare wireless networks for future communications systems, by improving the data transmission rates of wireless networks, through developing new coding schemes based on the synergy of noisy-channel coding and index coding. This will allow wireless networks, used in conjunction with the fibre-optic National Broadband Network, to support future high-data-rate and ubiquitous communication services. This project aims to produce new theoretical results in the field of communication theory, and efficient practical coding schemes for wireless communications.Read moreRead less
Homomorphic cryptography: computing on encrypted data. This project is driven by the groundbreaking applications of a new cryptographic technology that allows analysis of encrypted (scrambled) data without needing to decrypt (unscramble) it first. The results of this project can be used to enable secure remote data storage, electronic auctions and voting, and protecting medical records.
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
A novel and theoretically consistent method for correcting systematic errors in earth observation data and earth system model results. For a correct interpretation of satellite-based earth observation data and/or Earth system model results, it is very important that these data are free of systematic errors, commonly referred to as bias. It is well known that both these data sources are prone to a significant bias, which is currently neglected in many environmental impact and prediction studies. ....A novel and theoretically consistent method for correcting systematic errors in earth observation data and earth system model results. For a correct interpretation of satellite-based earth observation data and/or Earth system model results, it is very important that these data are free of systematic errors, commonly referred to as bias. It is well known that both these data sources are prone to a significant bias, which is currently neglected in many environmental impact and prediction studies. This project will present a method to develop models for these biases. A state update technique, the Ensemble Kalman Filter, will be adapted to correctly take into account bias in the merging of the two data sources. The project outcomes will be of high importance for long-term environmental studies, since these strongly rely on physically-based models and remote sensing data.Read moreRead less
New high-performance iterative error correction codes. This project develops new error correction codes to underpin the success of next-generation communications technologies. The nature of the project presents significant potential for project outcomes to be beneficial to the Australian telecommunications industry in a wide range of application areas from optical communication to digital broadcasting.
Next-generation techniques for analysing massive data sets. To process enormous amounts of data, leading computing companies are turning to modern computing frameworks, for which little theory of efficient computational techniques has been developed. This project will resolve key theoretical questions and provide fast techniques for poorly understood pattern recognition and bioinformatics problems.
Ultimately Trusted Security through Human-Powered Cryptography. Cryptography offers wonderful tools for unbreakable data security, but only between computer nodes, leaving their human owners helpless. Encrypted tunnels terminate not at humans but at mobile phones and personal computers, exposing users' secrets to spyware from search-engine keyloggers to full-bore malware planted by crooks, hackers, and foreign spy agencies. This project aims to create a simple and strong cryptography, so that hu ....Ultimately Trusted Security through Human-Powered Cryptography. Cryptography offers wonderful tools for unbreakable data security, but only between computer nodes, leaving their human owners helpless. Encrypted tunnels terminate not at humans but at mobile phones and personal computers, exposing users' secrets to spyware from search-engine keyloggers to full-bore malware planted by crooks, hackers, and foreign spy agencies. This project aims to create a simple and strong cryptography, so that humans can, for the first time, take front seat in real security protocols. The technical challenge is to build public-key ciphers, operable manually from a mental key in seconds, and from there remake human-powered versions of many useful information security protocols.Read moreRead less
Automatic video annotation by learning from web data. This project aims to study next-generation video annotation technologies to automatically tag raw videos using a huge set of semantic concepts. The project will study new domain adaptation schemes and frameworks in order to substantially improve video annotation performance. The resulting prototype system can be directly used by ordinary users worldwide to search their personal videos using textual queries. The system is also applicable to vi ....Automatic video annotation by learning from web data. This project aims to study next-generation video annotation technologies to automatically tag raw videos using a huge set of semantic concepts. The project will study new domain adaptation schemes and frameworks in order to substantially improve video annotation performance. The resulting prototype system can be directly used by ordinary users worldwide to search their personal videos using textual queries. The system is also applicable to video surveillance applications, which can enhance Australia’s homeland security.Read moreRead less