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Honours and Minor Thesis projects

Displaying 1 - 10 of 230 honours projects.

Primary supervisor: Wai Peng Wong

This project aims to analyse the comments of Twitter  on non-communicable diseases.  Students are expected to carry out Aspects Detection to identify the specific aspects discussed in the tweets e.g., causes, transmission and symptoms. Subsequently,  students are expected to conduct sentiment analysis utilizing tools like TextBlob or VADER, while also taking into account the importance of considering emojis to enhance classification accuracy.

Primary supervisor: Ehsan Shareghi

Large Language Models (LLMs) have revolutionized natural language processing (NLP). These models have shown an unprecedented level of knowledge and reasoning, pushing the boundaries of what is achievable in NLP. However, the use of LLMs in the real world still presents numerous difficult challenges and application of LLMs beyond simple API/Prompt calls is very under-explored.

Primary supervisor: Anuradha Madugalla

Recently, Aged Care has been in the news with the release of Royal Commissions report in to Aged Care and COVID-19. Both these situations highlighted the need of a better understanding of the aged care workforce. This project focuses on understanding the aged care workforce and their diversity in order to present data analytics information effectively.  The project will use Personas to model diverse user needs, and will develop UIs that automatically/semi-automatically adapt to fit these Personas.

Primary supervisor: Xingliang Yuan

The increasing integration of Large Language Models (LLMs) into various sectors has recently brought to light the pressing need to align these models with human preferences and implement safeguards against the generation of inappropriate content. This challenge stems from both ethical considerations and practical demands for responsible AI usage. Ethically, there is a growing recognition that the outputs of LLMs must align with laws, societal values, and norms.

Primary supervisor: Buser Say

SCIPPlan is a mathematical optimisation based automated planner for domains with i) mixed (i.e., real and/or discrete valued) state and action spaces, ii) nonlinear state transitions that are functions of time, and iii) general reward functions. SCIPPlan iteratively i) finds violated constraints (i.e., zero-crossings) by simulating the state transitions, and ii) adds the violated constraints back to its underlying optimization model, until a valid plan is found. The purpose of this project is to improve the performance of SCIPPlan.

Primary supervisor: Teresa Wang

Note: this project is filled

Primary supervisor: Maria Teresa Llano
An automatically generated blurred photo of a landscape by Dalle-2.

To make artwork more accessible to people who are blind or have low vision, museums often offer audio guides or tours. While these options improve accessibility, they do not always provide a complete aesthetic experience.

Primary supervisor: Raphaël C.-W. Phan


Cybersecurity researchers are contemplating how to best use the currently trending AI techniques to aid cybersecurity, beyond just for classification. 

The aim of this Honours project is to get the student to work with the supervisors on the latest AI techniques to adapt them over for cybersecurity, building first on baseline approaches for which code is available.

Primary supervisor: Yi-Shan Tsai

Feedback is crucial to learning success; yet, higher education continues to struggle with effective feedback processes. It is important to recognise that feedback as a process requires both teachers and students to take active roles and work as ‘partners’. This project seeks to enhance effective feedback processes by 1) exploring the alignment between current feedback practice with student-centred feedback principles and 2) investigating into student experience with feedback. The overall project will adopt mixed methods explained as follows:

Primary supervisor: Qiuhong Ke

Human action recognition plays an important role in various applications. Existing works assume that the training and testing share a common pre-defined list of action categories. Given a new unseen action during testing, the existing model will simply assign a wrong action category from the pre-defined list. This greatly limits the applicability of existing methods for practical model deployment.