Outputs (1532)

The intersection of datafication and data justice in higher education (2025)
Presentation / Conference Contribution
Fabian, K. (2025, October). The intersection of datafication and data justice in higher education. Presented at Connecting Space, Data and Society: Interdisciplinary Pathways, London

There has been an increasing uptake of technology in the classroom, either for teaching or for classroom management purposes. Along with this uptake is the increasing datafication of education. Datafication is the process of rendering social and natu... Read More about The intersection of datafication and data justice in higher education.

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.

Privacy-Aware Single-Nucleotide Polymorphisms (SNPs) Using Bilinear Group Accumulators in Batch Mode (2024)
Presentation / Conference Contribution
Buchanan, W., Grierson, S., & Uribe, D. (2024, February). Privacy-Aware Single-Nucleotide Polymorphisms (SNPs) Using Bilinear Group Accumulators in Batch Mode. Presented at 10th International Conference on Information Systems Security and Privacy, Rome, Italy

Biometric data is often highly sensitive, and a leak of this data can lead to serious privacy breaches. Some of the most sensitive of this type of data relates to the usage of DNA data on individuals. A leak of this type of data without consent could... Read More about Privacy-Aware Single-Nucleotide Polymorphisms (SNPs) Using Bilinear Group Accumulators in Batch Mode.

Convex neural network synthesis for robustness in the 1-norm (2024)
Presentation / Conference Contribution
Drummond, R., Guiver, C., & Turner, M. C. (2024, July). Convex neural network synthesis for robustness in the 1-norm. Presented at 6th Annual Learning for Dynamics & Control Conference, Oxford, England

With neural networks being used to control safety-critical systems, they increasingly have to be both accurate (in the sense of matching inputs to outputs) and robust. However, these two properties are often at odds with each other and a trade-off ha... Read More about Convex neural network synthesis for robustness in the 1-norm.

Exploring Dataset Diversity for GenAI Image Tampering Localisation in Digital Forensics (2024)
Presentation / Conference Contribution
Thomson, M., McKeown, S., Macfarlane, R., & Leimich, P. (2025, April). Exploring Dataset Diversity for GenAI Image Tampering Localisation in Digital Forensics. Presented at The Digital Forensics Research Conference Europe (DFRWS EU 2025) Digital Forensics Doctoral Symposium (DFDS), Brno, Czech Republic

Generative Artificial Intelligence (GenAI) has significantly increased the sophistication and ease of image tampering techniques, posing challenges for digital forensics in identifying manipulated images. A lack of dataset standardisation hinders the... Read More about Exploring Dataset Diversity for GenAI Image Tampering Localisation in Digital Forensics.

Beyond Hamming Distance: Exploring Spatial Encoding in Perceptual Hashes (2024)
Presentation / Conference Contribution
Mckeown, S. (2025, April). Beyond Hamming Distance: Exploring Spatial Encoding in Perceptual Hashes. Presented at DFRWS EU 2025, Brno, Czech Republic

Forensic analysts are often tasked with analysing large volumes of data in modern investigations, and frequently make use of hashing technologies to identify previously encountered images. Perceptual hashes, which seek to model the semantic (visual)... Read More about Beyond Hamming Distance: Exploring Spatial Encoding in Perceptual Hashes.

Applying the Right UX based on Users' Needs and Future Trends of UX (2024)
Presentation / Conference Contribution
Cameron, J. (2024, October). Applying the Right UX based on Users' Needs and Future Trends of UX. Presented at BINUS Business School International Lecture Week 2024, Jakarta, Indonesia and online

This lecture by Dr Jackie Cameron, an experienced digital marketing lecturer at Edinburgh Napier University, provides a comprehensive overview of user experience (UX) principles and their application in digital marketing contexts. Covering key defini... Read More about Applying the Right UX based on Users' Needs and Future Trends of UX.

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.

Unregulated Futures: Scenario Planning for the Unknown (2024)
Presentation / Conference Contribution
Crawford, M., Roubelat, F., & Marchais-Roubelat, A. (2024, September). Unregulated Futures: Scenario Planning for the Unknown. Presented at Anticipation 2024, Lancaster, UK

Building from years of piloting novel methods for future visioning through group engagement, this curated session will take attendees through a fast-tracked, yet fully- immersive scenario planning workshop. The prompt is, “What does 2034 look like if... Read More about Unregulated Futures: Scenario Planning for the Unknown.

A method for constrained energy-maximising control of heaving wave-energy converters via a nonlinear frequency response (2024)
Presentation / Conference Contribution
Guiver, C. (2024, August). A method for constrained energy-maximising control of heaving wave-energy converters via a nonlinear frequency response. Presented at The 8th IEEE Conference on Control Technology and Applications (CCTA) 2024, Newcastle Upon Tyne

A theoretical grounding is presented for justifying how frequency domain methods may be applied in the determination of constrained extracted-energy maximising controls in wave-energy conversion applications subject to nonlinear models. A computation... Read More about A method for constrained energy-maximising control of heaving wave-energy converters via a nonlinear frequency response.

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.

Graph Injection Attack based on Node Similarity and Non-linear Feature Injection Strategy (2024)
Presentation / Conference Contribution
Li, Q., Gao, Y., Wang, F., Wang, C., Babaagba, K. O., & Tan, Z. (2024, October). Graph Injection Attack based on Node Similarity and Non-linear Feature Injection Strategy. Presented at 20th EAI International Conference on Security and Privacy in Communication Networks, Dubai, United Arab Emirates

Graph Neural Networks (GNNs) exhibit promise in the domains of network analysis and recommendation systems. Notwithstanding , these networks are susceptible to node injection attacks. To mitigate this vulnerability, the academic community has put for... Read More about Graph Injection Attack based on Node Similarity and Non-linear Feature Injection Strategy.

VisCrypt: Image Encryption Featuring Novel Chaotic Key Generation and Block Permutation Techniques with Visual Cryptography (2024)
Presentation / Conference Contribution
Khan, M. S., Ahmad, J., Ali, M., Al Dubai, A., Pitropakis, N., & Buchanan, W. J. (2024, July). VisCrypt: Image Encryption Featuring Novel Chaotic Key Generation and Block Permutation Techniques with Visual Cryptography. Presented at 2024 IEEE 7th International Conference on Advanced Technologies, Signal and Image Processing (ATSIP), Sousse, Tunisia

In this digital era, ensuring the security of data transmission is critically important. Digital data, especially image data, needs to be secured against unauthorized access. In this regards, this paper presents a robust image encryption scheme named... Read More about VisCrypt: Image Encryption Featuring Novel Chaotic Key Generation and Block Permutation Techniques with Visual Cryptography.

Improving Efficiency of Evolving Robot Designs via Self-Adaptive Learning Cycles and an Asynchronous Architecture (2024)
Presentation / Conference Contribution
Le Goff, L., & Hart, E. (2024, July). Improving Efficiency of Evolving Robot Designs via Self-Adaptive Learning Cycles and an Asynchronous Architecture. Presented at GECCO 2024 Embodied and Evolved Artificial Intelligence Workshop, Melbourne, Australia

Algorithmic frameworks for the joint optimisation of a robot's design and controller often utilise a learning loop nested within an evolutionary algorithm to refine the controller associated with a newly generated robot design. Intuitively, it is rea... Read More about Improving Efficiency of Evolving Robot Designs via Self-Adaptive Learning Cycles and an Asynchronous Architecture.

Neurosymbolic Learning in the XAI Framework for Enhanced Cyberattack Detection with Expert Knowledge Integration (2024)
Presentation / Conference Contribution
Kalutharage, C. S., Liu, X., Chrysoulas, C., & Bamgboye, O. (2024, June). Neurosymbolic Learning in the XAI Framework for Enhanced Cyberattack Detection with Expert Knowledge Integration. Presented at The 39th International Conference on ICT Systems Security and Privacy Protection (SEC 2024), Edinburgh

The perpetual evolution of cyberattacks, especially in the realm of Internet of Things (IoT) networks, necessitates advanced, adaptive, and intelligent defence mechanisms. The integration of expert knowledge can drastically enhance the efficacy of Io... Read More about Neurosymbolic Learning in the XAI Framework for Enhanced Cyberattack Detection with Expert Knowledge Integration.

Investigating Markers and Drivers of Gender Bias in Machine Translations (2024)
Presentation / Conference Contribution
Barclay, P., & Sami, A. (2024, March). Investigating Markers and Drivers of Gender Bias in Machine Translations. Presented at IEEE International Conference on Software Analysis, Evolution and Reengineering, Rovaniemi, Finland

Implicit gender bias in Large Language Models (LLMs) is a well-documented problem, and implications of gender introduced into automatic translations can perpetuate real-world biases. However, some LLMs use heuristics or post-processing to mask such b... Read More about Investigating Markers and Drivers of Gender Bias in Machine Translations.

Learning Descriptors for Novelty-Search Based Instance Generation via Meta-evolution (2024)
Presentation / Conference Contribution
Marrero, A., Segredo, E., León, C., & Hart, E. (2024, July). Learning Descriptors for Novelty-Search Based Instance Generation via Meta-evolution. Presented at GECCO '24: Genetic and Evolutionary Computation Conference, Melbourne, Australia

The ability to generate example instances from a domain is important in order to benchmark algorithms and to generate data that covers an instance-space in order to train machine-learning models for algorithm selection. Quality-Diversity (QD) algorit... Read More about Learning Descriptors for Novelty-Search Based Instance Generation via Meta-evolution.

Virtual Rehabilitation: XR Design for Senior Users in Immersive Exergame Environments (2024)
Presentation / Conference Contribution
Charisis, V., Khan, S., AlTarteer, S., & Lagoo, R. (2024, June). Virtual Rehabilitation: XR Design for Senior Users in Immersive Exergame Environments. Presented at 2024 IEEE Gaming, Entertainment, and Media Conference (GEM), Turin, Italy

The global ageing population presents significant challenges, with healthcare systems strained to meet the needs of an increasingly elderly demographic. Societies face issues related to healthcare costs, caregiving, and maintaining quality of life fo... Read More about Virtual Rehabilitation: XR Design for Senior Users in Immersive Exergame Environments.

Participatory Design with Domain Experts: A Delphi Study for a Career Support Chatbot (2024)
Presentation / Conference Contribution
Wilson, M., Brazier, D., Gkatzia, D., & Robertson, P. (2024, July). Participatory Design with Domain Experts: A Delphi Study for a Career Support Chatbot. Presented at ACM Conversational User Interfaces 2024 (CUI ’24), Luxembourg, Luxembourg

We present a study of collaboration with expert participants for the purpose of the responsible design of a conversational agent. The Delphi study was used to identify and develop design and evaluation criteria for an automated career support interve... Read More about Participatory Design with Domain Experts: A Delphi Study for a Career Support Chatbot.