Displaying 131 - 140 of 220 projects.
Machine learning has recently made significant progress for medical imaging applications including image segmentation, enhancement, and reconstruction.
Funded as an Australian Research Council Discovery Project, this research aims to develop highly novel physics-informed deep learning methods for Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) and applications in image reconstruction and data analysis.
On-device machine learning (ML) is rapidly gaining popularity on mobile devices. Mobile developers can use on-device ML to enable ML features at users’ mobile devices, such as face recognition, augmented virtual reality, voice assistance, and medical diagnosis. This new paradigm is further accelerated by AI chips and ASICs embedded on mobile devices, e.g., Apple’s Bionic neural engine. Compared to cloud-based machine learning services, on-device ML is privacy-friendly, of low latency, and can work offline. User data will remain at the mobile device for ML inference.
This research project is part of a DECRA fellowship funded by the Australian Research Council for a project titled, Everyday Insurtech: Impacts of Emerging Technology for Insurance. The fellowship will study the development, adoption, and implications of digital technology and insurance—such as tools for capturing individualised data about behavioural risk factors and automating enforcement of policy conditions.
People with disabilities are excluded from the assistive technology creation process because the methods and tools that are used are inaccessible. This leads to missed opportunities to create more accessible technologies for everyone including assistive technologies. This project will engage people with disabilities in the technology creation process at many levels, from engagement activities, input into designs and creation of technology and the facilitation of independent making of assistive technologies.
With success stories ranging from speech recognition to self-driving cars, machine learning (ML) has been one of the most impactful areas of computer science. ML’s versatility stems from the wealth of techniques it offers, making ML seem an excellent tool for any task that involves building a model from data. Nevertheless, ML makes an implicit overarching assumption that severely limits its applicability to a broad class of critical domains: the data owner is willing to disclose the data to the model builder/holder.
The existing deep learning based time series classification (TSC) algorithms have some success in multivariate time series, their accuracy is not high when we apply them on brain EEG time series (65-70%). This is because there is a large variation between EEG data of different subjects, so a TSC model cannot generalise on unseen subjects well. In this research project we investigate self-supervised contrastive learning to encode the EEG data. This way we can better model the distribution of our EEG data before classifying it to different mental status. See a recent work here [1].
Identifying vulnerabilities in real-world applications is challenging. Currently, static analysis tools are concerned with false positives; runtime detection tools are free of false positives but inefficient to achieve a full spectrum examination. A generic, scalable and effective vulnerability detection platform, taking advantage of both static and dynamic techniques, is desirable. To further overcome the shortcomings of these techniques, deep learning is more and more involved in static vulnerability localization and improving fuzzing efficiency.
Over the past decades, we have witnessed the emergence and rapid development of deep learning. DL has been successfully deployed in many real-life applications, including face recognition, automatic speech recognition, and autonomous driving, etc. However, due to the intrinsic vulnerability and the lack of rigorous verification, DL systems suffer from quality and security issues, such as the Alexa/Siri manipulation and the autonomous car accidents. Developing secure and trustworthy DL systems is challenging, especially given the limited time budget.
The project involves design, implementation and evaluation of rule-based chatbot to support students when they study information from multiple texts, e.g., reading a few articles about global warming. The bot will support students' self-regulated learning skills which were theorised to promote learning achievements and boost motivation.
This research will unfold over the following 3 phases:
1. Reviewing the literature on self-regulated learning and creating a set of responses from the bot
2. Developing rule-based chatbot
The multi-agent path finding problem (MAPF) asks us to find a collision-free plan for a team of moving agents. Such problems appear in many application settings (including robotics, logistics, computer games) and a wide variety of solution methods have been proposed. Once a plan is computed, execution proceeds under the supervision of a human operator who is free to modify and adjust the plan, or even reject it entirely, because of changing operational requirements.