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Honours and Minor Thesis projects

Displaying 181 - 190 of 224 honours projects.


Primary supervisor: Lan Du

The performance of deep neural models rely on large amounts of labeled data, however, most data remain unlabeled in the real world scenario. While annotating data is expensive and time consuming, active learning seeks to choose the most appropriate and worthwhile data for human annotation. It is noticed that humans give labels to some specific data with some labeling reasons or rationales,  which are often existing in the data.  The goal of this research is to develop effective deep active learning techniques with rationales.

Primary supervisor: Humphrey Obie

Mission-critical systems have to comply to various formal standards – e.g. DO-178C and ISO26262 - about their operation, usually heavily relying on formal specification languages such as TLA+. This presents many challenges to developers in terms of how to write, read and communicate the target system’s formal specifications. In most cases, having the right formal methods experts to write specifications does not solve the problem as the wider development team needs to be able to deeply understand the formal specifications.

Primary supervisor: Humphrey Obie

User reviews on app distribution platforms such as Google Play store and Apple App store are a valuable source of information, ideas, and requests from users. They reflect the needs and challenges users encounter including bugs, feature requests, and design. Recent research has shown that reviews can also serve as a proxy for understanding the values of the users and how users perceive that their values have been violated by the mobile app/mobile app developers. However, there are limited studies that show whether mobile app updates fix violations of the user's values and to what extent.…

Primary supervisor: Geoff Webb

This project will develop new technologies for supervised machine learning from time series building upon our world-leading and award winning research in the area. See my time series research for details of the research program on which this research will build.

Primary supervisor: Geoff Webb

The world is dynamic, in constant flux. However, machine learning typically learns static models from historical data. As the world changes, these models decline in performance, sometimes catastrophically so. This PhD will develop technologies for addressing this serious problem, building upon our groundbreaking research into the problem.

Primary supervisor: Bioinformatics

Bacteria can live in almost all possible environments on earth. In general, they contribute to the stability and health of ecosystems and are very beneficial. However, some bacteria when in contact with humans can cause diseases. Despite the efforts to control them using antimicrobial agents, some of these bacteria have developed resistance and impose a threat to public health. The ability to resist antimicrobial agents lies on the genetic content of these bacteria, in their genes.

Primary supervisor: Bioinformatics

Proteomics data generated by cutting-edge mass spectrometers play a crucial part in early disease diagnosis, prognosis and drug development in the biomedical sector. It can be used to understand the expression, structure, function, interactions and modifications of virtually any protein in any cell, tissue or organ. Moreover, proteomics can be used in conjunction with other “omics” technologies such as genomics, transcriptomics or metabolomics to further unravel the complexity of signalling pathways and other subcellular systems.

Primary supervisor: Bioinformatics

Lipids such as cholesterol or triglycerides are involved in a plethora of medical disorders and diseases ranging from cardiovascular diseases (including obesity and artherosclerosis) to neurodegenerative disorders such as Parkinson’s disease. An in-depth analysis of individual lipid classes and species is often indispensable to unravel the mechanisms underlying disease onset and progression.

Primary supervisor: Bioinformatics

DeepLabCut™ is an efficient method for 3D markerless pose estimation based on transfer learning with deep neural networks that achieves excellent results across a broad collection of behaviours. This project will utilise the DeepLabCut package to analyse the behaviour of rats and mice as they are trained and tested on reward-based learning tasks designed to examine aspects of attention, memory and impulsive behaviour.

Primary supervisor: Bioinformatics

Activity and movement are fundamental diagnostic parameters of animal behaviour. However, measuring long-term individual movement within groups was not possible until recently. Our ActivityMonitor provides accurate individual movement data in a fully automated way. This is a unique solution for the 24/7 long-term tracking of individual animals living in groups, which utilises an array of RFID readers positioned under the home cage of rats and mice that are implanted with RFID transponders.