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Research projects in Information Technology

Displaying 41 - 50 of 196 projects.

Developing and evaluating educational chatbot to support self-regulated learning

The project involves design, implementation and evaluation of rule-based chatbot to support students when they study information from multiple texts, e.g., reading a few articles about global warming. The bot will support students' self-regulated learning skills which were theorised to promote learning achievements and boost motivation.

This research will unfold over the following 3 phases:

1. Reviewing the literature on self-regulated learning and creating a set of responses from the bot

2. Developing rule-based chatbot

Supervisor: Dr Mladen Rakovic

Context-aware physical activity recognition and monitoring

The project will focus on developing a context-aware physical activity recognition and monitoring. The project aims to incorporate context-awareness into the physical activity recognition. The contextual data will be collected from the user's mobile phone's sensors, external sensors and wearables (if available) and public web APIs. The outcomes could be used in a number of healthcare applications to assist patients with diabetes, low back pain, or other chronic diseases for self-management of chronic pain and providing them with personalised, context-aware recommendations.

Active Visual Navigation in an Unexplored Environment

In this project, the goal is to develop a new method (using computer vision and machine learning techniques) for robotic navigation in which goals can be specified at a much higher level of abstraction than has previously been possible. This will be achieved using deep learning to make informed predictions about a scene layout and navigating as an active observer in which the predictions inform actions.

Explainable Multi-Agent Path Finding (XMAPF)

The multi-agent path finding problem (MAPF) asks us to find a collision-free plan for a team of moving agents. Such problems appear in many application settings (including robotics, logistics, computer games) and a wide variety of solution methods have been proposed. Once a plan is computed, execution proceeds under the supervision of a human operator who is free to modify and adjust the plan, or even reject it entirely, because of changing operational requirements.

Supervisor: Dr Daniel Harabor

Values-oriented Defect Fixing for Mobile Software Applications

This project aims to define the human-centric features of mobile applications (apps) reflecting end-user human values and to model mobile app defects violating those values. The PhD candidate will compile a corpus of existing mobile apps and develop an automated “app feature values miner” to incrementally develop a new taxonomy and characterisation of human values associated with mobile app features that are to be validated with end-users and software engineers. In the next step, the PhD candidate will define a set of human values-based “anti-patterns” for mobile app features, i.e.

Supervisor: Dr Waqar Hussain

Generating human-centered explanation for a social robot capable of multimodal emotion recognition

Robots in Human-Robot Interaction (HRI) often contain complex components and advanced functions based on automated decision-making models. In particular, affective HRI systems aim at achieving intended outcomes, such as mental or physical health of the user, through understanding, responding to, and influencing the emotional states of the users.

Supervisor: Dr Mor Vered

Predicting short- and long-term outcomes of pregnancy to optimise maternal health care (PhD)

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.

Supervisor: Dr Lan Du

Understanding material failure by machine learning analysis of pattern strains

Metals are made of small crystals - i.e., atoms are arranged in a particular geometric arrangement, which are typically in the range of a few 10s of microns (0.01 mm). The arrangement of these crystals greatly affects the performance of the metal and hence the performance of components where metals are used - such as in aeroplanes, gas turbine engines, cars, etc. The manner in which such materials deform, crack and fail under a variety of conditions is an important area in terms of cost and safety.