Handling unreliable, uncertain and inadequate data for Intelligence led Investigation. Intelligence led investigation has been successful recently in drug and people smuggling, preparation or instigation of acts of terrorism, and can benefit profoundly from the techniques we will develop, in the timely management and inference from many sources and kinds of uncertain information. This work will assist in making Australia a safer and more secure country.
E.g., Australian Bureau of Statistics ....Handling unreliable, uncertain and inadequate data for Intelligence led Investigation. Intelligence led investigation has been successful recently in drug and people smuggling, preparation or instigation of acts of terrorism, and can benefit profoundly from the techniques we will develop, in the timely management and inference from many sources and kinds of uncertain information. This work will assist in making Australia a safer and more secure country.
E.g., Australian Bureau of Statistics figures show that for 2004, investigations of some 35% of murders, 63% of kidnappings, and 80% of robberies are incomplete at 30 days. Terrorism investigations are harder in that usually there is no initial crime trigger for an investigation. Any assistance our tools can provide in will be of significant benefit to Australia.Read moreRead less
Kernel and Margin Based Machine Learning Algorithms. Certain machine learning algorithms, such as support vector machines, utilizing
the ideas of large margins and kernels have attracted much attention lately
because of their impressive performance on real world problems such as optical
character recognition. We plan to refine and extend such algorithms to a wide
range of different machine learning problems such as gene sequence analysis,
image processing and text classification. Expected ....Kernel and Margin Based Machine Learning Algorithms. Certain machine learning algorithms, such as support vector machines, utilizing
the ideas of large margins and kernels have attracted much attention lately
because of their impressive performance on real world problems such as optical
character recognition. We plan to refine and extend such algorithms to a wide
range of different machine learning problems such as gene sequence analysis,
image processing and text classification. Expected outcomes include the
development of software that allows the solution of hitherto unsolved machine
learning problems, and the ability to solve problems larger than those solvable
by the current generation of machine learning tools.Read moreRead less
ARC Research Network on Intelligent Sensors, Sensor Networks and Information Processing. Sensor networks, a collection of diverse sensors interconnected via an ad-hoc communication network, are identified as one of the key technologies that over the next two decades will change the way we live. This research network brings together an interdisciplinary team of outstanding Australian researchers representing all the key disciplines required to successfully deploy sensor networks and links this te ....ARC Research Network on Intelligent Sensors, Sensor Networks and Information Processing. Sensor networks, a collection of diverse sensors interconnected via an ad-hoc communication network, are identified as one of the key technologies that over the next two decades will change the way we live. This research network brings together an interdisciplinary team of outstanding Australian researchers representing all the key disciplines required to successfully deploy sensor networks and links this team with the foremost international authorities and leading industry players in the area of sensor networks. This research network will guide collaborative research that will ensure Australia to play a world leading role in sensor network development and implementation.
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Accurate Performance Modelling and Prediction of Cluster Computers. The tools, methodologies and data produced by this project will assist
Australian academic and industrial organisations in choosing the most
cost-effective cluster configurations for their specific high
performance computing requirements. It will also help an Australian
company to compete with increasing strength against the major
multinationals. The project will also draw together and promote future
research links between ....Accurate Performance Modelling and Prediction of Cluster Computers. The tools, methodologies and data produced by this project will assist
Australian academic and industrial organisations in choosing the most
cost-effective cluster configurations for their specific high
performance computing requirements. It will also help an Australian
company to compete with increasing strength against the major
multinationals. The project will also draw together and promote future
research links between two major academic institutions in this field.
Finally, the project will provide high-level training in research,
with industrial grounding, in the high performance computing industry.
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Network Intrusion Detection via Machine Learning. Computer security is an increasingly important, yet complex task. It
takes significant skills to configure systems properly such that they
are safe from malicious attacks.
The proposed project aims at designing automatic systems which are
able to adapt to an existing network configuration and which detect
novel and unusual events. For this purpose we will use modern machine
learning techniques, mainly based on kernels. In particular, rec ....Network Intrusion Detection via Machine Learning. Computer security is an increasingly important, yet complex task. It
takes significant skills to configure systems properly such that they
are safe from malicious attacks.
The proposed project aims at designing automatic systems which are
able to adapt to an existing network configuration and which detect
novel and unusual events. For this purpose we will use modern machine
learning techniques, mainly based on kernels. In particular, recently
developed algorithms to estimate the support of a distribution and
detect rare events will be employed in this context.
The project is in cooperation with Dr. Ralf Herbrich (Microsoft
Research, Cambridge).
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