Skip to main content

Research Repository

Advanced Search

All Outputs (10947)

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.

Library catalogue’s search interface: Making the most of subject metadata (2024)
Journal Article
Gnoli, C., Golub, K., Haynes, D., Salaba, A., Shiri, A., & Slavic, A. (2024). Library catalogue’s search interface: Making the most of subject metadata. Knowledge Organization, 51, 169-186. https://doi.org/10.5771/0943-7444-2024-3-169

This article addresses the underutilization of knowledge organization systems (KOS) elements in online library catalogues, hindering effective subject-based search and discovery. It highlights the International Society for Knowledge Organization's in... Read More about Library catalogue’s search interface: Making the most of subject metadata.

Digital Twins: Towards an Overarching Framework for the Built Environment (2024)
Journal Article
Bagireanu, A., Bros-Williamson, J., Duncheva, M., & Currie, J. (2024). Digital Twins: Towards an Overarching Framework for the Built Environment. Architectural and Environmental Engineering, 18(1), 1-11

Digital Twins (DTs) have entered the built environment from more established industries like aviation and manufacturing, although there has never been a common goal for utilising DTs at scale. Their assimilation into the built environment lacked its... Read More about Digital Twins: Towards an Overarching Framework for the Built Environment.

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.

Eco-friendly 3D Printing Mortar with Low Cement Content: Investigation on Printability and Mechanical Properties (2024)
Journal Article
Sukontasukkul, P., Komkham, S., Jamnam, S., Zhang, H., Fujikake, K., Puttiwongrak, A., & Hansapinyo, C. (2024). Eco-friendly 3D Printing Mortar with Low Cement Content: Investigation on Printability and Mechanical Properties. Civil Engineering Journal, 10(3), https://doi.org/10.28991/CEJ-2024-010-03-010

The conventional approach to achieving optimal printability and buildability in 3D printing mortar relies heavily on cement, which is both costly and environmentally detrimental due to substantial carbon emissions from its production. This study aims... Read More about Eco-friendly 3D Printing Mortar with Low Cement Content: Investigation on Printability and Mechanical Properties.

ABCNN-IDS: Attention-Based Convolutional Neural Network for Intrusion Detection in IoT Networks (2024)
Journal Article
Momand, A., Jan, S. U., & Ramzan, N. (in press). ABCNN-IDS: Attention-Based Convolutional Neural Network for Intrusion Detection in IoT Networks. Wireless Personal Communications, 136(4), 1981-2003. https://doi.org/10.1007/s11277-024-11260-7

This paper proposes an attention-based convolutional neural network (ABCNN) for intrusion detection in the Internet of Things (IoT). The proposed ABCNN employs an attention mechanism that aids in the learning process for low-instance classes. On the... Read More about ABCNN-IDS: Attention-Based Convolutional Neural Network for Intrusion Detection in IoT Networks.

Merging Fact & Fiction in War Comics: Diversity, Identity and Social Injustice (2024)
Book Chapter
Donald, I., Austin, H., & Pittner, F. Merging Fact & Fiction in War Comics: Diversity, Identity and Social Injustice. In Battle Lines Drawn: War Comics since 1914

This abstract discusses how war comics portray the historical record through a theoretical and conceptual textual model - the 3A Framework (3AF) which considers the representation of historical accuracy, authenticity and account (Donald & Reid, 2023)... Read More about Merging Fact & Fiction in War Comics: Diversity, Identity and Social Injustice.

‘It’s NOT in the Game’ – Commemoration and Commerce in EA Sports FIFA Franchise (2024)
Book Chapter
Donald, I. ‘It’s NOT in the Game’ – Commemoration and Commerce in EA Sports FIFA Franchise. In The Interactive Past Re-Imagined: New Horizons of Video Games, History, and Archaeology. Sidestone Press

In 2023 witnessed a seismic shift in the world of virtual football as the licensing agreement between EA Sports and FIFA (the governing body of football) that had existed since 1993 came to an end and it was announced that going forward FIFA (the gam... Read More about ‘It’s NOT in the Game’ – Commemoration and Commerce in EA Sports FIFA Franchise.

Assessing the Performance of Ethereum and Hyperledger Fabric Under DDoS Attacks for Cyber-Physical Systems (2024)
Presentation / Conference Contribution
Jayadev, V., Moradpoor, N., & Petrovski, A. (2024, July). Assessing the Performance of Ethereum and Hyperledger Fabric Under DDoS Attacks for Cyber-Physical Systems. Paper presented at 19th International Conference on Availability, Reliability and Security (ARES 2024), Vienna, Austria

Blockchain technology offers a decentralized and secure platform for addressing various challenges in smart cities and cyber-physical systems, including identity management, trust and transparency, and supply chain management. However, blockchains ar... Read More about Assessing the Performance of Ethereum and Hyperledger Fabric Under DDoS Attacks for Cyber-Physical Systems.

A Blockchain-based Multi-Factor Honeytoken Dynamic Authentication Mechanism (2024)
Presentation / Conference Contribution
Papaspirou, V., Kantzavelou, I., Yigit, Y., Maglaras, L., & Katsikas, S. (2024, July). A Blockchain-based Multi-Factor Honeytoken Dynamic Authentication Mechanism. Presented at ARES 2024: The 19th International Conference on Availability, Reliability and Security, Vienna, Austria

The evolution of authentication mechanisms in ensuring secure access to systems has been crucial for mitigating vulnerabilities and enhancing system security. However, despite advancements in two-factor authentication (2FA) and multi-factor authentic... Read More about A Blockchain-based Multi-Factor Honeytoken Dynamic Authentication Mechanism.

The ‘Skills Gap’ in the Animation/VFX industry in Scotland (2024)
Journal Article
Mortimer, J., Richards, K., & Pilcher, N. (in press). The ‘Skills Gap’ in the Animation/VFX industry in Scotland. Animation,

Integral to the Animation and visual effects (VFX) industry are graduates with industry focused skills. Yet, the industry is rapid and ever-changing, almost defying attempts to define it. How then should Animation/VFX, and indeed any subject of a sim... Read More about The ‘Skills Gap’ in the Animation/VFX industry in Scotland.

Neurosymbolic Learning intheXAI Framework forEnhanced Cyberattack Detection withExpert Knowledge Integration (2024)
Presentation / Conference Contribution
Kalutharage, C. S., Liu, X., Chrysoulas, C., & Bamgboye, O. (2024, June). Neurosymbolic Learning intheXAI Framework forEnhanced Cyberattack Detection withExpert 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 intheXAI Framework forEnhanced Cyberattack Detection withExpert Knowledge Integration.

Transforming EU Governance: The Digital Integration Through EBSI and GLASS (2024)
Presentation / Conference Contribution
Kasimatis, D., Buchanan, W. J., Abubakar, M., Lo, O., Chrysoulas, C., Pitropakis, N., Papadopoulos, P., Sayeed, S., & Sel, M. (2024, June). Transforming EU Governance: The Digital Integration Through EBSI and GLASS. Presented at 39th IFIP International Conference, Edinburgh, UK

Traditionally, government systems managed citizen identities through disconnected data systems, using simple identifiers and paper-based processes, limiting digital trust and requiring citizens to request identity verification documents. The digital... Read More about Transforming EU Governance: The Digital Integration Through EBSI and GLASS.

Multi-Objective Evolutionary Algorithm for Automatic Generation of Adversarial Metamorphic Malware (2024)
Presentation / Conference Contribution
Babaagba, K., Wylie, J., Ayodele, M., & Tan, Z. (2024, September). Multi-Objective Evolutionary Algorithm for Automatic Generation of Adversarial Metamorphic Malware. Presented at 29th European Symposium on Research in Computer Security - SECAI, Bydgoszcz, Poland

The rise of metamorphic malware, a dangerous type of malware, has sparked growing research interest due to its increasing attacks on information assets and computer networks. Sophos’ recent threat report reveals that 94% of malware targeting organiza... Read More about Multi-Objective Evolutionary Algorithm for Automatic Generation of Adversarial Metamorphic Malware.

Towards a Cyberbullying Detection Approach: Fine-Tuned Contrastive Self- Supervised Learning for Data Augmentation (2024)
Journal Article
Alharigy, L., Alnuaim, H., Moradpoor, N., & Tan, T. (online). Towards a Cyberbullying Detection Approach: Fine-Tuned Contrastive Self- Supervised Learning for Data Augmentation. International Journal of Data Science and Analytics, https://doi.org/10.1007/s41060-024-00607-9

Cyberbullying on social media platforms is pervasive and challenging to detect due to linguistic subtleties and the need for extensive data annotation. We introduce a Deep Contrastive Self-Supervised Learning (DCSSL) model that integrates a Natural L... Read More about Towards a Cyberbullying Detection Approach: Fine-Tuned Contrastive Self- Supervised Learning for Data Augmentation.

Optimizing DMF Utilization for Improved MXene Dispersions in Epoxy Nanocomposites (2024)
Preprint / Working Paper
Ali Janjua, A., Younas, M., Ahmad Ilyas, R., Shyha, I., Haque Faisal, N., Inam, F., & Shahneel Saharudin, M. Optimizing DMF Utilization for Improved MXene Dispersions in Epoxy Nanocomposites

Dimethylformamide (DMF), a polar solvent, is commonly used for preparing graphene/epoxy nanocomposites. While commonly previous research has predominantly highlighted the improvement in physio-mechanical properties of these nanocomposites, the effect... Read More about Optimizing DMF Utilization for Improved MXene Dispersions in Epoxy Nanocomposites.

An Open Intent Discovery Evaluation Framework (2024)
Presentation / Conference Contribution
Anderson, G., Hart, E., Gkatzia, D., & Beaver, I. (2024, September). An Open Intent Discovery Evaluation Framework. Presented at SIGDIAL 2024, Kyoto, Japan

Improving Algorithm-Selection and Performance-Prediction via Learning Discriminating Training Samples (2024)
Presentation / Conference Contribution
Renau, Q., & Hart, E. (2024, July). Improving Algorithm-Selection and Performance-Prediction via Learning Discriminating Training Samples. Presented at GECCO 2024, Melbourne, Australia

The choice of input-data used to train algorithm-selection models is recognised as being a critical part of the model success. Recently, feature-free methods for algorithm-selection that use short trajec-tories obtained from running a solver as input... Read More about Improving Algorithm-Selection and Performance-Prediction via Learning Discriminating Training Samples.