Learning the complexity of scientific knowledge about climate change with computer modelling and visualization technologies. This project provides benefits to the national priorities of a environmentally sustainable Australia; and frontier technologies for building and transforming Australian industries. The project helpins students in Australia more deeply understand the sciences that underlie environmental sustainability. Learning with modelling and visualization technologies will help student ....Learning the complexity of scientific knowledge about climate change with computer modelling and visualization technologies. This project provides benefits to the national priorities of a environmentally sustainable Australia; and frontier technologies for building and transforming Australian industries. The project helpins students in Australia more deeply understand the sciences that underlie environmental sustainability. Learning with modelling and visualization technologies will help students learn important scientific knowledge and prepare them for the use of frontier technologies that are becoming infused into the practices of scientists and professionals in many fields. This project also directly contributes to the national Digital Education Revolution initiative.Read moreRead less
Practice-based Systematized Nomenclature of Medicine (SNOMED) concept learning for drug-disease precaution early detection and refinement. The outcome of the Systematized Nomenclature of Medicine (SNOMED) concept learning system will help mitigate the impact of Adverse Drug Events hence directly contribute to the National Research Priority promoting and maintaining good health. It will tailor SNOMED knowledge to different clinical settings and provide evidence-based preventative health care. Th ....Practice-based Systematized Nomenclature of Medicine (SNOMED) concept learning for drug-disease precaution early detection and refinement. The outcome of the Systematized Nomenclature of Medicine (SNOMED) concept learning system will help mitigate the impact of Adverse Drug Events hence directly contribute to the National Research Priority promoting and maintaining good health. It will tailor SNOMED knowledge to different clinical settings and provide evidence-based preventative health care. The enabling methodology from this project for building computerised cognitive learning systems will be a frontier technology to enhance smart information use in clinical decision support. It will also contribute to the development of knowledge-based systems. A network version of the developed system will assist doctors working in rural and remote areas with their clinical decision making and prescribing practice.Read moreRead less
Developing a personalised Music Affect Recommender System. The project aims to develop a personalised music recommender system using perceived tone quality, affect and liking. Recommender systems using prior verbal annotations and ratings are common (Amazon) but inappropriate for less popular music by unfamiliar artists, which lacks social use data. The project intends to build on work into perception of musical affect and its relation to loudness and tone quality; and the automation of the orga ....Developing a personalised Music Affect Recommender System. The project aims to develop a personalised music recommender system using perceived tone quality, affect and liking. Recommender systems using prior verbal annotations and ratings are common (Amazon) but inappropriate for less popular music by unfamiliar artists, which lacks social use data. The project intends to build on work into perception of musical affect and its relation to loudness and tone quality; and the automation of the organisation of digital libraries both by labels and acoustic content. Developing this, the project seeks to create a model that gives recommendations which accounts for an individual's preferences based on acoustic content, affect and liking. The system will be designed to update rapidly and to encourage exploration of familiar and unfamiliar music.Read moreRead less
Design of adaptive learning visual sensor networks for crowd modelling in high-density and occluded scenarios. Partnering University of Melbourne researchers, with video surveillance experts SenSen, engineering consultants ARUP and the Melbourne Cricket Club, the project addresses research enabling a system-integrating, existing surveillance, infrastructure to model crowd behaviour and exit strategies, providing real-time analysis, prediction and response capabilities for venue managers and emer ....Design of adaptive learning visual sensor networks for crowd modelling in high-density and occluded scenarios. Partnering University of Melbourne researchers, with video surveillance experts SenSen, engineering consultants ARUP and the Melbourne Cricket Club, the project addresses research enabling a system-integrating, existing surveillance, infrastructure to model crowd behaviour and exit strategies, providing real-time analysis, prediction and response capabilities for venue managers and emergency services. This new capability enhances utilisation of security resources to prevent injury and fatalities in evacuation scenarios, applicable to existing venues and influencing the development of new facilities around the country. The project delivers researcher training, global clientele for local technology and a platform for local industry growth.Read moreRead less
Faster, cheaper, safer: how to accelerate rail driver training and avert the looming skills shortage. The Australian rail industry is growing rapidly and needs to double the number of drivers trained in order to meet demand. This project will bring together Australia's leading hi-tech simulator company and Australia's leading rail human factors research team to 'reinvent' driver training technologies and techniques for the 21st century.