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Human Intracranial EEG Biometric Identification (2025)
Presentation / Conference Contribution
Belay, B., & Katsigiannis, S. (2025, July). Human Intracranial EEG Biometric Identification. Presented at International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE EMBC), Copenhagen, Denmark

Semi-supervised Object-Wise Anomaly Detection for Firearm and Firearm Component Detection in X-ray Security Imagery (2025)
Presentation / Conference Contribution
Gaus, Y. F. A., Isaac-Medina, B. K. S., Bhowmik, N., Lam, Y. T., & Breckon, T. P. (2025, June). Semi-supervised Object-Wise Anomaly Detection for Firearm and Firearm Component Detection in X-ray Security Imagery. Presented at 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Nashville, Tennessee, USA

FineCausal: A Causal-Based Framework for Interpretable Fine-Grained Action Quality Assessment (2025)
Presentation / Conference Contribution
Han, R., Zhou, K., Atapour-Abarghouei, A., Liang, X., & Shum, H. P. H. (2025, June). FineCausal: A Causal-Based Framework for Interpretable Fine-Grained Action Quality Assessment. Presented at Proceedings of the 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2025, Music City Center, Nashville TN

Action quality assessment (AQA) is critical for evaluating athletic performance, informing training strategies, and ensuring safety in competitive sports. However, existing deep learning approaches often operate as black boxes and are vulnerable to s... Read More about FineCausal: A Causal-Based Framework for Interpretable Fine-Grained Action Quality Assessment.

Synergistic Reinforcement Learning Models for Pedestrian-Friendly Traffic Signal Control (2025)
Presentation / Conference Contribution
Chen, D., Hu, J., Zhang, H., & Chen, B. (2025, June). Synergistic Reinforcement Learning Models for Pedestrian-Friendly Traffic Signal Control. Presented at 2025 European Control Conference (ECC), Thessaloniki, Greece

Traffic signal control is essential for managing urban traffic, reducing congestion, and minimizing environmental impact by optimizing both vehicular and pedestrian flow. This paper investigates the application of Reinforcement Learning (RL) in traff... Read More about Synergistic Reinforcement Learning Models for Pedestrian-Friendly Traffic Signal Control.

Dynamic Calibration of Trust and Trustworthiness in AI-Enabled Systems (2025)
Journal Article
Liebherr, M., Enkel, E., Law, E. L.-C., Mousavi, M. R., Sammartino, M., & Sieberg, P. (in press). Dynamic Calibration of Trust and Trustworthiness in AI-Enabled Systems. International Journal on Software Tools for Technology Transfer,

Trust is a multi-faceted phenomenon traditionally studied in human relations and more recently in human-machine interactions. In the context of AI-enabled systems, trust is about the belief of the user that in a given scenario the system is going to... Read More about Dynamic Calibration of Trust and Trustworthiness in AI-Enabled Systems.

SKDU at De-Factify 4.0: Natural language features for AI-Generated Text-Detection (2025)
Presentation / Conference Contribution
Maviya, S., Arnau-González, P., Arevalillo-Herráez, M., & Katsigiannis, S. (2025, February). SKDU at De-Factify 4.0: Natural language features for AI-Generated Text-Detection. Presented at De-factify 4.0 Workshop at 39th Annual AAAI Conference on Artificial Intelligence, Philadelphia, PA, USA

T-BLAST: Token-Based Leveraging of Autonomous Spectrum Trading (2025)
Presentation / Conference Contribution
Singh, M., Bjorndahl, W., Aujla, G., & Camp, J. (2025, May). T-BLAST: Token-Based Leveraging of Autonomous Spectrum Trading. Presented at IEEE International Symposium on Dynamic Spectrum Access Networks, London

On the Locality of the Lovász Local Lemma (2025)
Presentation / Conference Contribution
Davies-Peck, P. (2025, June). On the Locality of the Lovász Local Lemma. Presented at 57th Annual ACM Symposium on Theory of Computing (STOC '25), Prague

The Lovász Local Lemma is a versatile result in probability theory, characterizing circumstances in which a collection of n ‘bad events’, each occurring with probability at most p and dependent on a set
of underlying random variables, can be avoided... Read More about On the Locality of the Lovász Local Lemma.

Deep Learning-Enhanced Visual Monitoring in Hazardous Underwater Environments with a Swarm of Micro-Robots (2025)
Presentation / Conference Contribution
Chen, S., He, Y., Lennox, B., Arvin, F., & Atapour-Abarghouei, A. (2025, May). Deep Learning-Enhanced Visual Monitoring in Hazardous Underwater Environments with a Swarm of Micro-Robots. Presented at IEEE International Conference on Robotics & Automation, Atlanta, USA

Long-term monitoring and exploration of extreme environments, such as underwater storage facilities, is costly, labor-intensive, and hazardous. Automating this process with low-cost, collaborative robots can greatly improve efficiency. These robots c... Read More about Deep Learning-Enhanced Visual Monitoring in Hazardous Underwater Environments with a Swarm of Micro-Robots.

COPS: Controller Placement in Next-Generation Software Defined Edge-Cloud Networks (2025)
Presentation / Conference Contribution
Singh Aujla, G., Jindal, A., Kaur, K., Garg, S., Chaudhary, R., Sun, H., & Kumar, N. (2025, June). COPS: Controller Placement in Next-Generation Software Defined Edge-Cloud Networks. Presented at 2025 IEEE International Conference on Communications (ICC), Montreal, Canada

To mitigate various challenges in the edge-cloud ecosystem, such as global monitoring, flow control, and policy modification of legacy networking paradigms, software-defined networks (SDN) have evolved as a major technology. However, the dependency o... Read More about COPS: Controller Placement in Next-Generation Software Defined Edge-Cloud Networks.

Surprise! Surprise! Learn and Adapt (2024)
Presentation / Conference Contribution
Samin, H., Walton, D., & Bencomo, N. (2025, May). Surprise! Surprise! Learn and Adapt. Presented at 24th International Conference on Autonomous Agents and Multiagent Systems, Detroit, Michigan, USA

Self-adaptive systems (SAS) adjust their behavior at runtime in response to environmental changes, which are often unpredictable at design time. SAS must make decisions under uncertainty, balancing trade-offs between quality attributes (e.g., cost mi... Read More about Surprise! Surprise! Learn and Adapt.

Beyond Syntax: How Do LLMs Understand Code? (2024)
Presentation / Conference Contribution
North, M., Atapour-Abarghouei, A., & Bencomo, N. (2025, April). Beyond Syntax: How Do LLMs Understand Code?. Presented at 2025 IEEE/ACM International Conference on Software Engineering ICSE, Ottawa , Canada

Within software engineering research, Large Language Models (LLMs) are often treated as 'black boxes', with only their inputs and outputs being considered. In this paper, we take a machine interpretability approach to examine how LLMs internally repr... Read More about Beyond Syntax: How Do LLMs Understand Code?.