NeFT-Net: N-window extended frequency transformer for rhythmic motion prediction
Journal Article
Ademola, A., Sinclair, D., Koniaris, B., Hannah, S., & Mitchell, K. (2025)
NeFT-Net: N-window extended frequency transformer for rhythmic motion prediction. Computers and Graphics, 129, Article 104244. https://doi.org/10.1016/j.cag.2025.104244
Advancements in prediction of human motion sequences are critical for enabling online virtual reality (VR) users to dance and move in ways that accurately mirror real-world ac...
MMST-LSTM: Leveraging Radar Echo Prediction for Emerging Consumer Applications in Edge Computing
Journal Article
Wu, M., Xiao, B., Yang, Z., Sun, J., Liu, Q., Zhang, Y., & Liu, X. (online)
MMST-LSTM: Leveraging Radar Echo Prediction for Emerging Consumer Applications in Edge Computing. IEEE Transactions on Consumer Electronics, https://doi.org/10.1109/TCE.2025.3566725
With the increasing frequency of extreme weather events, there is a growing demand from the public for rapid and accurate short-term heavy precipitation forecasts. This study ...
XAI for Algorithm Configuration and Selection
Book Chapter
Thomson, S. L., Hart, E., & Renau, Q. (2025)
XAI for Algorithm Configuration and Selection. In N. van Stein, & A. V. Kononova (Eds.), Explainable AI for Evolutionary Computation. Springer. https://doi.org/10.1007/978-981-96-2540-6_6
In this chapter, we consider, formalise, and demonstrate the ways in which XAI can assist or inform algorithm selection and configuration. Reviewing the literature, we notice ...
Algorithm Selection with Probing Trajectories: Benchmarking the Choice of Classifier Model
Presentation / Conference Contribution
Renau, Q., & Hart, E. (2025, April)
Algorithm Selection with Probing Trajectories: Benchmarking the Choice of Classifier Model. Presented at EvoSTAR 2025, Trieste, Italy
Recent approaches to training algorithm selectors in the black-box optimisation domain have advocated for the use of training data that is 'algorithm-centric' in order to enca...
Beyond the Hype: Benchmarking LLM-Evolved Heuristics for Bin Packing
Presentation / Conference Contribution
Sim, K., Hart, E., & Renau, Q. (2025, April)
Beyond the Hype: Benchmarking LLM-Evolved Heuristics for Bin Packing. Presented at EvoSTAR 2025, Trieste, Italy
Coupling Large Language Models (LLMs) with Evolutionary Algorithms has recently shown significant promise as a technique to design new heuristics that outperform existing meth...
Into the Black Box: Mining Variable Importance with XAI
Presentation / Conference Contribution
Hunter, K., Thomson, S. L., & Hart, E. (2025, April)
Into the Black Box: Mining Variable Importance with XAI. Presented at Evostar 2025, Trieste, Italy
Recent works have shown that the idea of mining search spaces to train machine learning models can facilitate increasing understanding of variable importance in optimisation p...
Stalling in Space: Attractor Analysis for any Algorithm
Presentation / Conference Contribution
Thomson, S. L., Renau, Q., Vermetten, D., Hart, E., van Stein, N., & Kononova, A. V. (2025, April)
Stalling in Space: Attractor Analysis for any Algorithm. Paper presented at EvoStar 2025, Trieste, Italy
Network-based representations of fitness landscapes have grown in popularity in the past decade; this is probably because of growing interest in explainability for optimisatio...
Evaluating the Application and Performance of Regression Models in Predicting Cycling Power Output
Presentation / Conference Contribution
Walker, E., Bamgboye, O., Thomson, S. L., & Liu, X. (2025, July)
Evaluating the Application and Performance of Regression Models in Predicting Cycling Power Output. Paper presented at COMPSAC 2025: IEEE Computers, Software, and Applications Conference, Toronto, Canada
HoloJig: Interactive Spoken Prompt Specified Generative AI Environments
Journal Article
Casas, L., Hannah, S., & Mitchell, K. (online)
HoloJig: Interactive Spoken Prompt Specified Generative AI Environments. IEEE Computer Graphics and Applications, https://doi.org/10.1109/mcg.2025.3553780
HoloJig offers an interactive, speech-to-VR, virtual reality experience that generates diverse environments in real-time based on live spoken descriptions. Unlike traditional ...
GSFL: A Privacy-Preserving Grouping-Split Federated Learning Approach in Resource-Constrained Edge Computing Scenarios
Journal Article
Liu, Q., Wang, Z., Zhou, X., Zhang, Y., Liu, X., & Lin, H. (online)
GSFL: A Privacy-Preserving Grouping-Split Federated Learning Approach in Resource-Constrained Edge Computing Scenarios. ACM transactions on autonomous and adaptive systems, https://doi.org/10.1145/3725221
The advancement of mobile multimedia communications, 5G, and Internet of Things (IoT) has led to the widespread use of edge devices, including sensors, smartphones, and wearab...