A Theory of Innovation Systems. The goal of the project is to develop and validate a new theory for how information systems can be designed to assist organisations in becoming innovative. Technological innovation is designed to increase productivity and economic growth, but knowledge is lacking about how information systems can meaningfully support organisations in becoming innovative. The goal of this project is to develop and test a theory of ‘innovation systems’ that would describe design pri ....A Theory of Innovation Systems. The goal of the project is to develop and validate a new theory for how information systems can be designed to assist organisations in becoming innovative. Technological innovation is designed to increase productivity and economic growth, but knowledge is lacking about how information systems can meaningfully support organisations in becoming innovative. The goal of this project is to develop and test a theory of ‘innovation systems’ that would describe design principles for information systems that provide effective and efficient support to organisational innovation processes. The expected project outcomes would assist the development of new systems to support organisational innovations, the management of innovation initiatives to increase productivity and growth, and the effective assessment of technologies to support innovation.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE210101091
Funder
Australian Research Council
Funding Amount
$402,160.00
Summary
Data-Driven Code Reviews for Cost-Effective Software Quality Assurance. This DECRA project aims to create advanced techniques that will enable software engineers to effectively assure the highest quality of software systems with minimal cost through data-driven recommendations. The current standard practices in software quality assurance involve the manual and tedious process of code review, which can lead to high costs and cause severe delays in software development. The expected outcomes of th ....Data-Driven Code Reviews for Cost-Effective Software Quality Assurance. This DECRA project aims to create advanced techniques that will enable software engineers to effectively assure the highest quality of software systems with minimal cost through data-driven recommendations. The current standard practices in software quality assurance involve the manual and tedious process of code review, which can lead to high costs and cause severe delays in software development. The expected outcomes of this project include new theories, techniques, and an automated system that provides insightful feedback, suitable reviewer recommendations, and fine-grained effort prioritisation. Significant benefits are expected to improve the production of Australia's software and the quality of safety-critical software systems.Read moreRead less
Risk-aware business process management. Risk-aware business process management will revolutionise the identification and treatment of risks in business processes by integrating the latest technologies for risk management and process management. It will provide organisations with a range of new tools and techniques for designing, deploying and monitoring risk-aware business processes.
Cost-aware business process management. The project aims to inform business process management (BPM) with the latest insights from the field of management accounting in order to make BPM systems cost-aware. By incorporating the cost dimension, organisations can obtain an accurate and immediate overview of the true cost of their processes and make cost-informed decisions.
Discovery Early Career Researcher Award - Grant ID: DE210100160
Funder
Australian Research Council
Funding Amount
$423,000.00
Summary
Information Extraction from Large-scale Low-quality Data. Information extraction which identifies entities and relations from data is a key technology that lays the foundation for understanding the semantics of data. This project aims to investigate the problem of information extraction by innovatively exploring the informality and temporal evolution of data. It expects to develop novel techniques for reliable, efficient, and scalable information discovery from large-scale low-quality data. Expe ....Information Extraction from Large-scale Low-quality Data. Information extraction which identifies entities and relations from data is a key technology that lays the foundation for understanding the semantics of data. This project aims to investigate the problem of information extraction by innovatively exploring the informality and temporal evolution of data. It expects to develop novel techniques for reliable, efficient, and scalable information discovery from large-scale low-quality data. Expected outcomes include a set of collective, contextualised, and temporal-aware algorithms for information extraction and integration, built on top of effective indexing and in-parallel processing. This project is anticipated to benefit a considerable number of data-driven intelligence-based applications.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE140100215
Funder
Australian Research Council
Funding Amount
$394,752.00
Summary
Searching Activity Trajectories for Intention Oriented Recommendations. The ubiquitous fusion of social network services and Global Positioning System-enabled mobile devices has generated large-scale activity trajectory data representing the footprint of people's daily activities. It presents an unprecedented opportunity to build highly intelligent recommendation systems. Existing approaches that merely focus on the location aspect of trajectories are limited in their ability to understand genui ....Searching Activity Trajectories for Intention Oriented Recommendations. The ubiquitous fusion of social network services and Global Positioning System-enabled mobile devices has generated large-scale activity trajectory data representing the footprint of people's daily activities. It presents an unprecedented opportunity to build highly intelligent recommendation systems. Existing approaches that merely focus on the location aspect of trajectories are limited in their ability to understand genuine preferences from travel histories, due to lack of consideration for activity information as well as the associated semantics and context. This project aims to address these issues and provide effective recommendations by considering both users’ intention and collective behavioural knowledge inferred from activity trajectories.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
Developing Adversary-Aware Classifiers for Android Malware Detection. Smartphones have become increasingly ubiquitous in people’s everyday life. However, it was reported that one in every five Android applications were actually malware, considering that Android has taken 88% market share of mobile phones. As an effective technique, machine learning has been widely adopted to detect Android malware. However, recent work suggests that deliberately-crafted malware makes machine learning ineffective ....Developing Adversary-Aware Classifiers for Android Malware Detection. Smartphones have become increasingly ubiquitous in people’s everyday life. However, it was reported that one in every five Android applications were actually malware, considering that Android has taken 88% market share of mobile phones. As an effective technique, machine learning has been widely adopted to detect Android malware. However, recent work suggests that deliberately-crafted malware makes machine learning ineffective. In this project, we propose to develop a series of new techniques, such as 1) Android contextual analysis, 2) wrapper-based hill climbing algorithm, and 3) ensemble learning, to solve this problem. The outcomes will help Australia gain cutting edge technologies in adversarial machine learning and mobile security.Read moreRead less
Privacy-Preserving Classification for Big-Data Driven Network Traffic. Protecting sensitive information in large network traffic flows while ensuring data usability for classification emerges as a critical problem of increasing significance. Existing techniques do not work on highly heterogeneous traffic from big-data applications for both privacy protection and classification (such as port-based and load- based methods). This project investigates new theories, methods and techniques for solving ....Privacy-Preserving Classification for Big-Data Driven Network Traffic. Protecting sensitive information in large network traffic flows while ensuring data usability for classification emerges as a critical problem of increasing significance. Existing techniques do not work on highly heterogeneous traffic from big-data applications for both privacy protection and classification (such as port-based and load- based methods). This project investigates new theories, methods and techniques for solving this problem. It proposes to develop a set of effective methods for privacy-preserving data publication through combining randomisation with anonymisation, and for classifying the published data through uncertainty leveraging by probabilistic reasoning and accuracy lifting by inter-flow correlation analysis and active learning.Read moreRead less
Reputation-based trust management in crowdsourcing environments. This project aims to address the critical need for enabling trustworthy crowd sourcing environments. Expected outcomes include innovative solutions to evaluate the reputation and expertise portfolio of workers and identify malicious workers, with the ultimate goal of making personalised recommendations of trustworthy workers with expertise to the requesters who have published tasks. This project is expected to provide key solutions ....Reputation-based trust management in crowdsourcing environments. This project aims to address the critical need for enabling trustworthy crowd sourcing environments. Expected outcomes include innovative solutions to evaluate the reputation and expertise portfolio of workers and identify malicious workers, with the ultimate goal of making personalised recommendations of trustworthy workers with expertise to the requesters who have published tasks. This project is expected to provide key solutions to globally leading crowd sourcing platforms originating in Australia and benefit Australian and worldwide Internet users.Read moreRead less