In this project, we aim to enable the head-mounted display based virtual museum (HMD-based VM) to prepare in advance natural responses by predicting the user’s visual attention using their current eye movement with respect to the exhibits in the VM.
In this project, we are exploring various visualisation techniques for both the process and the output of Machine Learning (ML) algorithms. The aim is to investigate how user trust can be affected, thus proposing mechanisms of increasing user trust.
In this project, we are developing and evaluating a gamified educational system and exploring the effects of different game elements and mechanisms on different learners, as well as implementing design strategies to adapt gamification to suit individual user’s needs.
In this project, we use computer vision to track and examine the trajectories of weightlifters in order to help them improve their form and technique, with the goal of improving how efficiently they can carry out lifts with heavier weights whilst also avoiding injury.
In this project, we aim to develop an aid tool for suffers from colour blindness which is integrated with their current workflow and existing technology. It should be intuitive and affordable, allowing sufferers to overcome barriers to education and technology access.
In this project, we aim to produce an iOS-based app that teaches people how to edit photos effectively and provide an intuitive interface to practice applying these edits. The app is developed in Swift and utilises the SwiftUI framework and CIKernel language.
In this project, we explored how game-design elements and game principles can support social interactions. We proposed a set of contextual gamification strategies, which apply flow and self-determination theory for increasing intrinsic motivation in social e-learning environments.
In this project, we analysed different architectures used in abstractive text summarisation in terms of their generalisation ability by studying their behaviour with unseen data distributions and underrepresented topics. Models were implemented using OpenNMT toolkit.
We proposed to embed a novel attention mechanism at the semantic level in the bi-directional GRU-CNN structure, which is more fine-grained than the existing token-level attention mechanism.
How can a small group of humans and machines collaborate in decision making? What mechanisms and frameworks are needed for humans and machines to coordinate their activities and efficiently make decisions? Is it possible to scale up these methods thus supporting massive activities? In this project, we will address these questions by exploring new forms of human-machine interaction and collaboration.
The ultimate goal of this research is to explore and implement emotional intervention mechanisms for ensuring affective and cognitive quality for distance learning that are gamified using game design strategies and game elements, by analysing and mapping relationships between key factors affecting students’ emotions and learning effectiveness, and adaptively generating and delivering personalised learning materials that can intervene students’ emotional state thus ultimately improving learning outcomes.
Haptic technologies can contribute to improving immersion, interaction and imagination in virtual environments. They offer tactile feedback and tactile illusions on interactive virtual systems. In this project, we aim to develop novel and affordable haptic devices, towards enhancing immersion by providing meaningful haptic information that can increase the user's emotional and physiological sensations.
Below are potential L3/L4 projects for Durham UG students.
Explainable AI (XAI) refers to methods and techniques in the application of Artificial Intelligence such that the results of the solution can be understood and trusted by humans. There is a strong need for quantifiable measurements used by actual humans to assess the interpretability of AI decisions. In this project, you will develop a novel, scalable system (mobile (iOS/Android) or web-based) that supports the quantifiable assessment of AI Explanations with humans.
Concept Mapping has been proven to have a promising impact on learning. Some studies suggest that effective feedback, hyperlinks, expert templates, comparative strategies, etc. may support better learning behaviour. In this project, you will be developing and evaluating a mobile (iOS/Android) or web-based application providing concept mapping facilities that can help students learn more efficiently and effectively.
At present, Machine Learning (ML) is among the most popular modules/courses in many undergraduate programmes in the globe. However, there are many challenges in learning ML. For example, (1) to start learning ML, it is important that a student has essential math knowledge including Equations, Functions, Graphs, Differentiation and Optimisation, Vectors and ics, Statistics and Probability and so on; (2) ML processes and many of its algorithms are very complex and abstract, thus in need of good visualisations and explanations; and (3) students normally come with diverse background (knowledge, skills, cognitive preferences) such that the “one-size-fits-all” approach does not really work. In this project, you will develop a digital game to help undergraduate students learn ML. It could be either web-based or mobile-based application. You will also evaluate the application with real users.
Swarm Robotics is an approach to coordinating many simple physical robots which have collective behaviour when interacting with each other and with the environment. In this project, you will develop a collection of small, simple and affordable robots. These robots will be able to work together to demonstrate the different states of matter (solid, liquid, gaseous), so that the students can, e.g. reproduce the behaviour of atoms using the small robots.
If your project is related to Machine Learning, Behavioural Analytics, Recommender Systems, User Modelling, Personalisation, Cognitive Computing, Affective Computing, Gamification, Serious Games, Participatory Design, Intelligent Tutoring Systems, etc., please let us know.