Honours and Masters project

Combating cybercrime and maintaining national security is a global challenge. In light of this the Cybersecurity Capacity Maturity Model for Nations (CMM) has been deployed in over 80 countries; the objective of the CMM is to understand and evaluate cybersecurity capacity within these national contexts in order to support the “well-being, human rights and prosperity”. The outcome of each deployment is a comprehensive report.

This project involves enhancing traditional object detection methods by incorporating human pose estimation to identify weapons in various contexts, especially in surveillance and security applications. This approach leverages computer vision techniques that analyse the positions and movements of individuals, allowing systems to recognize not just the presence of weapons but also the intent and behaviour of the person carrying them.

Since the 1990s, researchers have known that commonly-used public-key cryptosystems (such as RSA and Diffie-Hellman systems) could be potentially broken using efficient algorithms running on a special type of computer based on the principles of quantum mechanics, known as a quantum computer. Due to significant recent advances in quantum computing technology, this threat may become a practical reality in the coming years. To mitigate against this threat, new `post-quantum’ (a.k.a.

Communication networks show interaction between people over time, and are key to the identification of criminal networks and criminal activity. This project will investigate how future events might be able to be predicted, based on dynamic graphs representing prior interpersonal communications. The project will consider (a) how Graph Neural Networks can best be used for this machine learning task; (b) how visualisation techniques can best depict both known-past and predicted-future events.

As a pregnancy approaches term (the point at which the foetus is considered fully developed), decisions are made about the timing of birth and the way babies are born. These decisions are incredibly challenging for clinicians and pregnant women. Digital health records, advances in big data, machine learning and artificial intelligence methodologies, and novel data visualisation capabilities have opened up opportunities for a dynamic, ‘Learning Health System’ – where data can be harnessed to inform real-time and personalised decision-making.

Is the user paying attention? Is the content engaging enough?

The degree of concentration, enthusiasm, optimism, and passion displayed by individual(s) while interacting with a machine is referred to as ‘user engagement’. Engagement is a positive psychological state characterized by active behavioral participation, positive emotional experiences, and intense cognitive focus. Being able to detect engagement and/or attention has wide applications in consumer commerce, smart cars, augmented reality etc. 

Is the user paying attention? Is the content engaging enough?

The degree of concentration, enthusiasm, optimism, and passion displayed by individual(s) while interacting with a machine is referred to as ‘user engagement’. Engagement is a positive psychological state characterized by active behavioral participation, positive emotional experiences, and intense cognitive focus. Being able to detect engagement and/or attention has wide applications in consumer commerce, smart cars, augmented reality etc. 

People who are blind need to touch surfaces and materials to get information. These surfaces can be a Braille paper that has Braille text, a swell paper that has embossed shapes, and a button that is used to turn on and off a device like a TV or to open a train carriage door. Due to Covid-19, hygienic practices will be more and more important, and it will be very restrictive for people who are blind to touch surfaces in public places.

Despite the rapid progress made recently, deep learning (DL) approaches are data-hungry. To achieve their optimum performance, a significantly large amount of labeled data is required. Very often, unlabelled data is abundant but acquiring their labels is costly and difficult. Many domains require a specialist to annotate the data samples, for instance, the medical domain. Data dependency has become one of the limiting factors to applying deep learning in many real-world scenarios.

Graph neural networks (GNNs) are widely used in many applications. Their training graph data and the model itself are considered sensitive and face growing privacy threats.