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Outputs (135)

Scalable Machine Learning Architectures for IPA-Driven Maintenance Task Allocation in Large-Scale Building Portfolios (2025)
Presentation / Conference Contribution
Huang, Z., Liu, X., Romdhani, I., & Shih, C.-S. (2024, August). Scalable Machine Learning Architectures for IPA-Driven Maintenance Task Allocation in Large-Scale Building Portfolios. Presented at The 7th International Conference on Information Science and Systems (ICISS 2024), Edinburgh, UK

This research presents a groundbreaking approach to Building Maintenance Management (BMM) by introducing an Intelligent Process Automation (IPA)-Driven Building Maintenance Management (IBMM) model. This innovative model harnesses the synergies betwee... Read More about Scalable Machine Learning Architectures for IPA-Driven Maintenance Task Allocation in Large-Scale Building Portfolios.

Enhancing Automotive Intrusion Detection Systems with Capability Hardware Enhanced RISC Instructions-Based Memory Protection (2025)
Journal Article
Kalutharage, C. S., Mohan, S., Liu, X., & Chrysoulas, C. (2025). Enhancing Automotive Intrusion Detection Systems with Capability Hardware Enhanced RISC Instructions-Based Memory Protection. Electronics, 14(3), 474. https://doi.org/10.3390/electronics14030474

The rapid integration of connected technologies in modern vehicles has introduced significant cybersecurity challenges, particularly in securing critical systems against advanced threats such as IP spoofing and rule manipulation. This study investiga... Read More about Enhancing Automotive Intrusion Detection Systems with Capability Hardware Enhanced RISC Instructions-Based Memory Protection.

Neurosymbolic learning and domain knowledge-driven explainable AI for enhanced IoT network attack detection and response (2025)
Journal Article
Kalutharage, C. S., Liu, X., & Chrysoulas, C. (2025). Neurosymbolic learning and domain knowledge-driven explainable AI for enhanced IoT network attack detection and response. Computers and Security, 151, Article 104318. https://doi.org/10.1016/j.cose.2025.104318

In the dynamic landscape of network security, where cyberattacks continuously evolve, robust and adaptive detection mechanisms are essential, particularly for safeguarding Internet of Things (IoT) networks. This paper introduces an advanced anomaly d... Read More about Neurosymbolic learning and domain knowledge-driven explainable AI for enhanced IoT network attack detection and response.

Beyond the Hype: Benchmarking LLM-Evolved Heuristics for Bin Packing (2025)
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 methods, particularly in the field of combinatorial optimisation. An escalating... Read More about Beyond the Hype: Benchmarking LLM-Evolved Heuristics for Bin Packing.

Into the Black Box: Mining Variable Importance with XAI (2025)
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 problems. However , so far, the problems studied have typically either been... Read More about Into the Black Box: Mining Variable Importance with XAI.

Stalling in Space: Attractor Analysis for any Algorithm (2025)
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 optimisation algorithms. Local optima networks (LONs) have been especially dominant in... Read More about Stalling in Space: Attractor Analysis for any Algorithm.

Algorithm Selection with Probing Trajectories: Benchmarking the Choice of Classifier Model (2025)
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 encapsulate information about how an algorithm performs on an instance, rather... Read More about Algorithm Selection with Probing Trajectories: Benchmarking the Choice of Classifier Model.

A Multi-Tier Offloading Optimization Strategy for Consumer Electronics in Vehicular Edge Computing (2025)
Journal Article
Lin, H., Xiao, B., Zhou, X., Zhang, Y., & Liu, X. (2025). A Multi-Tier Offloading Optimization Strategy for Consumer Electronics in Vehicular Edge Computing. IEEE Transactions on Consumer Electronics, https://doi.org/10.1109/tce.2025.3527043

In the domain of consumer electronics, vehicular edge computing (VEC) technology is emerging as a novel data processing paradigm within vehicular networks. By sending tasks related to vehicular applications to the edge, this model makes it easier for... Read More about A Multi-Tier Offloading Optimization Strategy for Consumer Electronics in Vehicular Edge Computing.

An Evaluation of Domain-agnostic Representations to Enable Multi-task Learning in Combinatorial Optimisation (2025)
Presentation / Conference Contribution
Stone, C., Renau, Q., Miguel, I., & Hart, E. (2024, June). An Evaluation of Domain-agnostic Representations to Enable Multi-task Learning in Combinatorial Optimisation. Presented at 18th Learning and Intelligent Optimization Conference, Ischia, Italy

We address the question of multi-task algorithm selection in combinatorial optimisation domains. This is motivated by a desire to simplify the algorithm-selection pipeline by developing a more general classifier that does not require specialised info... Read More about An Evaluation of Domain-agnostic Representations to Enable Multi-task Learning in Combinatorial Optimisation.

How Much do Robots Understand Rudeness? Challenges in Human-Robot Interaction (2024)
Presentation / Conference Contribution
Orme, M., Yu, Y., & Tan, Z. (2024, May). How Much do Robots Understand Rudeness? Challenges in Human-Robot Interaction. Presented at The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), Torino, Italy

This paper concerns the pressing need to understand and manage inappropriate language within the evolving human-robot interaction (HRI) landscape. As intelligent systems and robots transition from controlled laboratory settings to everyday households... Read More about How Much do Robots Understand Rudeness? Challenges in Human-Robot Interaction.

The Performance of Frequency Fitness Assignment on JSSP for Different Problem Instance Sizes (2024)
Presentation / Conference Contribution
Pijning, I., Koppenhol, L., Dijkzeul, D., Brouwer, N., Thomson, S. L., & van den Berg, D. (2024, November). The Performance of Frequency Fitness Assignment on JSSP for Different Problem Instance Sizes. Presented at ECTA 2024: 16th International Conference on Evolutionary Computation Theory and Applications, Porto, Portugal

The Frequency Fitness Assignment (FFA) method steers evolutionary algorithms by objective rareness instead of objective goodness. Does this mean the size of the combinatorial search space influences its performance when compared to more traditional e... Read More about The Performance of Frequency Fitness Assignment on JSSP for Different Problem Instance Sizes.

Frequency Fitness Assignment: Optimization without Bias for Good Solution outperforms Randomized Local Search on the Quadratic Assignment Problem (2024)
Presentation / Conference Contribution
Chen, J., Wu, Z., Thomson, S. L., & Weise, T. (2024, November). Frequency Fitness Assignment: Optimization without Bias for Good Solution outperforms Randomized Local Search on the Quadratic Assignment Problem. Presented at ECTA 2024: 16th International Conference on Evolutionary Computation Theory and Applications, Porto, Portugal

The Quadratic Assignment Problem (QAP) is one of the classical N P-hard tasks from operations research with a history of more than 65 years. It is often approached with heuristic algorithms and over the years, a multitude of such methods has been app... Read More about Frequency Fitness Assignment: Optimization without Bias for Good Solution outperforms Randomized Local Search on the Quadratic Assignment Problem.

A New Improved Method of Recurrent Memory Perception for Radar Echo Extrapolation (2024)
Presentation / Conference Contribution
Ji, R., Liu, Q., Zhang, Y., & Liu, X. (2024, July). A New Improved Method of Recurrent Memory Perception for Radar Echo Extrapolation. Presented at 2024 IEEE 24th International Conference on Software Quality, Reliability, and Security Companion (QRS-C), Cambridge, United Kingdom

Precipitation forecasting has long been a prominent topic in meteorology, as accurate predictions of impending rainfall are crucial for daily life and travel planning. Currently, radar echo extrapolation serves as the primary method for precipitation... Read More about A New Improved Method of Recurrent Memory Perception for Radar Echo Extrapolation.

High Intensity Radar Echo Extrapolation Based on Stacked Generative Structure (2024)
Presentation / Conference Contribution
Tao, S., Liu, Q., Zhang, Y., & Liu, X. (2024, July). High Intensity Radar Echo Extrapolation Based on Stacked Generative Structure. Presented at 2024 IEEE 24th International Conference on Software Quality, Reliability, and Security Companion (QRS-C), Cambridge, UK

In recent years, the phenomenon of severe convective disaster weather has increased significantly in the world. Due to the characteristics of small spatial scale, short occurrence period, great destructiveness and drastic changes, severe convective d... Read More about High Intensity Radar Echo Extrapolation Based on Stacked Generative Structure.

FedCST: Federated Learning on Heterogeneous Resource-constrained Devices Using Clustering and Split Training (2024)
Presentation / Conference Contribution
Wang, Z., Lin, H., Liu, Q., Zhang, Y., & Liu, X. (2024, July). FedCST: Federated Learning on Heterogeneous Resource-constrained Devices Using Clustering and Split Training. Presented at The 24th International Conference on Software Quality, Reliability, and Security Companion (QRS-C), Cambridge, UK

With the rapid development of 5G and Internet of Things (IoT) technologies, edge devices such as sensors, smartphones, and wearable devices have become increasingly prevalent. The massive amount of distributed data generated by these devices offers u... Read More about FedCST: Federated Learning on Heterogeneous Resource-constrained Devices Using Clustering and Split Training.

Automated Human-Readable Label Generation in Open Intent Discovery (2024)
Presentation / Conference Contribution
Anderson, G., Hart, E., Gkatzia, D., & Beaver, I. (2024, September). Automated Human-Readable Label Generation in Open Intent Discovery. Presented at Interspeech 2024, Kos, Greece

The correct determination of user intent is key in dialog systems. However, an intent classifier often requires a large, labelled training dataset to identify a set of known intents. The creation of such a dataset is a complex and time-consuming task... Read More about Automated Human-Readable Label Generation in Open Intent Discovery.

DeFT-Net: Dual-Window Extended Frequency Transformer for Rhythmic Motion Prediction (2024)
Presentation / Conference Contribution
Ademola, A., Sinclair, D., Koniaris, B., Hannah, S., & Mitchell, K. (2024, September). DeFT-Net: Dual-Window Extended Frequency Transformer for Rhythmic Motion Prediction. Presented at EG UK Computer Graphics & Visual Computing (2024), London, UK

Enabling online virtual reality (VR) users to dance and move in a way that mirrors the real-world necessitates improvements in the accuracy of predicting human motion sequences paving way for an immersive and connected experience. However, the drawba... Read More about DeFT-Net: Dual-Window Extended Frequency Transformer for Rhythmic Motion Prediction.

A Deep Dive into Effects of Structural Bias on CMA-ES Performance along Affine Trajectories (2024)
Presentation / Conference Contribution
van Stein, N., Thomson, S. L., & Kononova, A. V. (2024, September). A Deep Dive into Effects of Structural Bias on CMA-ES Performance along Affine Trajectories. Presented at Parallel Problem Solving from Nature (PPSN) 2024, Hagenberg, Austria

To guide the design of better iterative optimisation heuristics, it is imperative to understand how inherent structural biases within algorithm components affect the performance on a wide variety of search landscapes. This study explores the impact o... Read More about A Deep Dive into Effects of Structural Bias on CMA-ES Performance along Affine Trajectories.

Evaluating the Robustness of Deep-Learning Algorithm-Selection Models by Evolving Adversarial Instances (2024)
Presentation / Conference Contribution
Hart, E., Renau, Q., Sim, K., & Alissa, M. (2024, September). Evaluating the Robustness of Deep-Learning Algorithm-Selection Models by Evolving Adversarial Instances. Presented at 18th International Conference on Parallel Problem Solving From Nature PPSN 2024, Hagenburg, Austria

Deep neural networks (DNN) are increasingly being used to perform algorithm-selection in combinatorial optimisation domains, particularly as they accommodate input representations which avoid designing and calculating features. Mounting evidence fro... Read More about Evaluating the Robustness of Deep-Learning Algorithm-Selection Models by Evolving Adversarial Instances.