Honours and Masters project

Verifiable Dynamic Searchable Symmetric Encryption (VDSSE) enables users to securely outsource databases (document sets) to cloud servers and perform searches and updates. The verifiability property prevents users from accepting incorrect search results returned by a malicious server. However, the community currently only focuses on preventing malicious behavior from the server but ignores incorrect updates from the client, which are very likely to happen in multi-user settings. Indeed most existing VDSSE schemes are not sufficient to tolerate incorrect updates from users. For instance,…

The spectral density of a time series (a series of time ordered data points -- for example, daily rainfall in the Amazon or the monthly stocks of fish in the Pacific) gives substantial information about the periodic patterns hidden in the data. Learning a good model of the spectral density is usually done through parametric methods like autoregressive moving average processes [1] because non-parametric methods struggle to deal with the interesting “non-smooth” nature of spectral densities. This project aims to apply a powerful and new non-parametric smoothing technique to this problem.

Learning appropriate prior distributions from replications of experiments is a important problem in the space of hierarchical and empirical Bayes. In this problem, we exploit the fact that we have multiple repeats of similar experiments and pool these to learn an appropriate prior distribution for the unknown parameters of this set of problems. Standard solutions to this type of problem tend to be of mixed Bayesian and non-Bayesian form, and are somewhat ad-hoc in nature.

Current studies on diabetes recommender systems and apps mainly focus on the performance and personalisation of AI models and techniques, including machine learning and deep learning models that are trained on user data. These works often use a one-size-fits-all approach for presenting information to users. Yet, research shows that humans process information in different ways, and their attitudes towards an action depend on their attitude-function styles.

Current studies on diabetes recommender systems and apps mainly focus on the performance and personalisation of AI models and techniques, including machine learning and deep learning models that are trained on user data. These works often use a one-size-fits-all approach for presenting information to users. Yet, research shows that humans process information in different ways, and their attitudes towards an action depend on their attitude-function styles.

Collecting and analysing social media content (e.g., Reddit), along with using Google Trends, presents a great opportunity to develop social media epidemic intelligence. This approach can enhance the understanding of chronic conditions such as arthritis, back pain, and knee pain, as well as track associated areas such as treatments and risk factors, including obesity, diet, physical activity, and exercise. This approach can be also used to understand the community attitudes about these conditions, and see if there are changes over time as there as new public campaigns.

Mental health challenges disproportionately affect vulnerable populations, often due to limited access to traditional healthcare services. The rise of Generative AI offers a groundbreaking opportunity to bridge this gap by providing personalized, scalable, and accessible mental health support. This project, led out of Action Lab, aims to harness the potential of Generative AI to develop innovative technologies tailored for mental health interventions.

Project description

This research project aims to explore how researchers are investigating various epistemic emotions that arise within educational contexts. Through a combination of literature review, citation and bibliometric analysis, this project will uncover trends in the field and may guide future directions. The outcome of this research is expected to be a peer-reviewed publication that may provide a valuable addition to your CV.

Project Description: The project is focused on developing game-based learning environments where the users’ trace or interaction data could be collected. The game-based environment needs to be designed to allow the users to navigate and explore at their own pace. Using the environment, the participants can practice their technical/professional skills from various options.

Deepfakes detection deals with machine learning methods, which detect if an image/video/audio sample is manipulated with a generative AI software. In recent years, deepfakes have been increasingly used for malicious purposes, including financial fraud, misinformation campaigns, identity theft, and cyber harassment. The ability to generate highly realistic synthetic content poses a serious threat to digital security, privacy, and trust in media. This project will develop methods for detecting deepfakes.