Honours and Minor Thesis projects

Using relevant available data-sets, we compare appliance usage across households of different demographics.  We then use machine learning techniques to infer how different households use different appliances at different times, resulting in diverse energy consumption behaviours. 

Climate change will affect us all, and we have to do everything we can to minimize the magnitude of change. Investments in renewable generation help to reduce the impact of energy usage on the supply side, but that will not get us all the way there, especially in the near term. Consumers will also have to become much more efficient with their energy use.

Behavioural manifestations of epileptic seizures (ESs) and certain non-epileptic seizures (psychogenic non-epileptic seizures, or PNESs) have considerable overlap, and so discerning between these solely based on clinical criteria is difficult.  Video EEG (electroencephalogram) monitoring (VEM) has high resource demands and is also expensive.  We endeavour to classify seizures based on non-invasive measures.

The security threat by quantum computing to almost all currently used digital signatures was triggered by the discovery of Shor’s quantum algorithm, which efficiently breaks the two problems underlying the security of these schemes, namely integer factoring, and elliptic curve discrete logarithms (ECDLP). When quantum computers become widespread, all security for the current digital signatures that are widely used to secure a wide range of systems is lost.

This multidisciplinary project combines cutting-edge Natural Language Processing (NLP), Chinese Studies and Political Science. The project aims to develop a deeper understanding of how official discourse has developed throughout the history of the People’s Republic of China. The main focus will be on text in the People’s Daily, the largest newspaper in China and the official newspaper of the Chinese Communist Party.

Commonsense reasoning refers to the ability of capitalising on commonly used knowledge by most people, and making decisions accordingly. This process usually involves combining multiple commonsense facts and beliefs to draw a conclusion or judgement. While human trivially performs such reasoning, current Artificial Intelligence models fail, mostly due to challenges of acquiring relevant knowledge and forming logical connections between them. This project aims to develop and evaluate machine learning models for commonsense reasoning, with question answering as the key application. 

Developing quality AI tools for legal texts is the focus of enormous industry, government and
scholarly attention. The potential benefits include greater efficiency, transparency and access to justice.
Moving beyond the hype requires novel transdisciplinary effort to combine IT and Law expertise.
This project engages this challenge by developing a semi-structured knowledge base (KB) and
reasoners for statutes and cases. The project will also construct corresponding training and evaluation
datasets.

This project is within the scope of the project “Artificial Intelligence in carDiac arrEst” (AIDE), which was led by Ambulance Victoria (AV) in Australia, involving a team of researchers at Monash University. This AIDE project has developed an Artificial Intelligence (AI) tool to recognise potential Out-of-Hospital-Cardiac Arrest (OHCA) during the Triple Zero (000) call by using transcripts produced by Microsoft Automatic Speech Recognition service.

A lot of decision support systems have been developed to predict or suggest a diagnosis about the health conditions of patients with the aim to assist clinicians in their decisional process. One of the techniques that is proved to present an efficient tool for medical healthcare decision making is Bayesian networks (BNs). BNs are recognized as efficient graphical models that can be used to explain the relationships between variables.

Digital signatures are asymmetric cryptographic schemes used to validate the authenticity and integrity of digital messages or documents. The signer uses their private key to generate a signature on a message. Then, this signature can be validated by any verifier who knows the signer’s corresponding public key. Sometimes a digital message might require signatures from a group of signers. The naïve method to achieve this goal is collecting distinct signatures from all signers.…