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Outputs (86)

Generalized Early Stopping in Evolutionary Direct Policy Search (2024)
Journal Article
Arza, E., Le Goff, L. K., & Hart, E. (2024). Generalized Early Stopping in Evolutionary Direct Policy Search. ACM Transactions on Evolutionary Learning and Optimization, 4(3), Article 14. https://doi.org/10.1145/3653024

Lengthy evaluation times are common in many optimization problems such as direct policy search tasks, especially when they involve conducting evaluations in the physical world, e.g. in robotics applications. Often when evaluating solution over a fixe... Read More about Generalized Early Stopping in Evolutionary Direct Policy Search.

Design and Evaluation of Memory Efficient Data Structure Scheme for Energy Drainage Attacks in Wireless Sensor Networks (2024)
Journal Article
Bhatti, D. S., Saleem, S., Ali, Z., Park, T., Suh, B., Kamran, A., Buchanan, W. J., & Kim, K. (2024). Design and Evaluation of Memory Efficient Data Structure Scheme for Energy Drainage Attacks in Wireless Sensor Networks. IEEE Access, 12, 41499-41516. https://doi.org/10.1109/access.2024.3377144

Wireless Sensor Networks (WSN) are deployed on a large scale and require protection from malicious energy drainage attacks, particularly those directed at the routing layer. The complexity increases during critical operations like cluster head select... Read More about Design and Evaluation of Memory Efficient Data Structure Scheme for Energy Drainage Attacks in Wireless Sensor Networks.

A Novel RFID Tag's Antenna for Mounting on Metallic Objects (2024)
Presentation / Conference Contribution
Gharbia, I., Aldelemy, A., Elmegri, F., Ismail, . A. S., Darwish, M., See, C. H., & Abd-Alhameed, R. A. (2024, May). A Novel RFID Tag's Antenna for Mounting on Metallic Objects. Presented at 14th International Conference on Electrical Engineering (ICEENG), Cairo, Egypt

Cluster-based oversampling with area extraction from representative points for class imbalance learning (2024)
Journal Article
Farou, Z., Wang, Y., & Horváth, T. (2024). Cluster-based oversampling with area extraction from representative points for class imbalance learning. Intelligent Systems with Applications, 22, Article 200357. https://doi.org/10.1016/j.iswa.2024.200357

Class imbalance learning is challenging in various domains where training datasets exhibit disproportionate samples in a specific class. Resampling methods have been used to adjust the class distribution, but they often have limitations for small dis... Read More about Cluster-based oversampling with area extraction from representative points for class imbalance learning.

PHASER: Perceptual Hashing Algorithms Evaluation and Results -an Open Source Forensic Framework (2024)
Presentation / Conference Contribution
Mckeown, S., Aaby, P., & Steyven, A. PHASER: Perceptual Hashing Algorithms Evaluation and Results -an Open Source Forensic Framework. Presented at DFRWS EU 2024, Zaragoza, Spain

The automated comparison of visual content is a contemporary solution to scale the detection of illegal media and extremist material, both for detection on individual devices and in the cloud. However, the problem is difficult, and perceptual similar... Read More about PHASER: Perceptual Hashing Algorithms Evaluation and Results -an Open Source Forensic Framework.

A spatio-temporal graph convolutional approach to real-time load forecasting in an edge-enabled distributed Internet of Smart Grids energy system (2024)
Journal Article
Liu, Q., Pan, L., Cao, X., Gan, J., Huang, X., & Liu, X. (2024). A spatio-temporal graph convolutional approach to real-time load forecasting in an edge-enabled distributed Internet of Smart Grids energy system. Concurrency and Computation: Practice and Experience, 36(13), Article e8060. https://doi.org/10.1002/cpe.8060

As the edge nodes of the Internet of Smart Grids (IoSG), smart sockets enable all kinds of power load data to be analyzed at the edge, which create conditions for edge calculation and real-time (RT) load forecasting. In this article, an edge-cloud co... Read More about A spatio-temporal graph convolutional approach to real-time load forecasting in an edge-enabled distributed Internet of Smart Grids energy system.

A Probability Mapping-Based Privacy Preservation Method for Social Networks (2024)
Presentation / Conference Contribution
Li, Q., Wang, Y., Wang, F., Tan, Z., & Wang, C. (2023, November). A Probability Mapping-Based Privacy Preservation Method for Social Networks. Presented at The 3rd International Conference on Ubiquitous Security 2023 (UbiSec-2023), Exeter

The mining and analysis of social networks can bring significant economic and social benefits. However, it also poses a risk of privacy leakages. Differential privacy is a de facto standard to prevent such leaks, but it suffers from the high sensitiv... Read More about A Probability Mapping-Based Privacy Preservation Method for Social Networks.

Utilizing the Ensemble Learning and XAI for Performance Improvements in IoT Network Attack Detection (2024)
Presentation / Conference Contribution
Kalutharage, C. S., Liu, X., Chrysoulas, C., & Bamgboye, O. (2023, September). Utilizing the Ensemble Learning and XAI for Performance Improvements in IoT Network Attack Detection. Presented at The 4th International Workshop on Cyber-Physical Security for Critical Infrastructures Protection (CPS4CIP 2023) - in conjunction with ESORICS 2023, The Hague, Netherlands

Expressive Talking Avatars (2024)
Journal Article
Pan, Y., Tan, S., Cheng, S., Lin, Q., Zeng, Z., & Mitchell, K. (2024). Expressive Talking Avatars. IEEE Transactions on Visualization and Computer Graphics, 30(5), 2538-2548. https://doi.org/10.1109/TVCG.2024.3372047

Stylized avatars are common virtual representations used in VR to support interaction and communication between remote collaborators. However, explicit expressions are notoriously difficult to create, mainly because most current methods rely on geome... Read More about Expressive Talking Avatars.

Digital Deviance/Digital Compliance: Criminology, Social Interaction and the Videogame (2024)
Report
Henry, A., & Horgan, S. (2024). Digital Deviance/Digital Compliance: Criminology, Social Interaction and the Videogame. Scottish Centre for Crime and Justice Research

This project sought to begin a process of scoping out and developing criminological perspectives on videogames and the social worlds of videogames and gamers through two interactive workshops. Its starting orientation was social interactionism and th... Read More about Digital Deviance/Digital Compliance: Criminology, Social Interaction and the Videogame.

SkipGateNet: A Lightweight CNN-LSTM Hybrid Model with Learnable Skip Connections for Efficient Botnet Attack Detection in IoT (2024)
Journal Article
Alshehri, M. S., Ahmad, J., Almakdi, S., Qathrady, M. A., Ghadi, Y. Y., & Buchanan, W. J. (2024). SkipGateNet: A Lightweight CNN-LSTM Hybrid Model with Learnable Skip Connections for Efficient Botnet Attack Detection in IoT. IEEE Access, 12, https://doi.org/10.1109/access.2024.3371992

The rise of Internet of Things (IoT) has led to increased security risks, particularly from botnet attacks that exploit IoT device vulnerabilities. This situation necessitates effective Intrusion Detection Systems (IDS), that are accurate, lightweigh... Read More about SkipGateNet: A Lightweight CNN-LSTM Hybrid Model with Learnable Skip Connections for Efficient Botnet Attack Detection in IoT.

Hearing Abilities Assessment (2024)
Patent
McGregor, I. (2024). Hearing Abilities Assessment. W O 2024/041821 A1. World Intellectual Property Organization International Bureau

A method for assessing hearing abilities of a human person having two ears, the method comprising: - providing two audio signals to two respective ear speakers at a person's corresponding ears; - measuring at least one value of at least one parameter... Read More about Hearing Abilities Assessment.

STIDNet: Identity-Aware Face Forgery Detection with Spatiotemporal Knowledge Distillation (2024)
Journal Article
Fang, M., Yu, L., Xie, H., Tan, Q., Tan, Z., Hussain, A., Wang, Z., Li, J., & Tian, Z. (online). STIDNet: Identity-Aware Face Forgery Detection with Spatiotemporal Knowledge Distillation. IEEE Transactions on Computational Social Systems, https://doi.org/10.1109/tcss.2024.3356549

The impressive development of facial manipulation techniques has raised severe public concerns. Identity-aware methods, especially suitable for protecting celebrities, are seen as one of promising face forgery detection approaches with additional ref... Read More about STIDNet: Identity-Aware Face Forgery Detection with Spatiotemporal Knowledge Distillation.

Influence Government, Platform Power And The Patchwork Profile: Exploring The Appropriation Of Targeted Advertising Infrastructures For Government Behaviour Change Campaigns (2024)
Journal Article
Collier, B., Stewart, J., Horgan, S., Thomas, D. R., & Wilson, L. (2024). Influence Government, Platform Power And The Patchwork Profile: Exploring The Appropriation Of Targeted Advertising Infrastructures For Government Behaviour Change Campaigns. First Monday, 29(1), https://doi.org/10.5210/fm.v29i2.13579

The targeted digital advertising infrastructures on which the business models of the social media platform economy rest have been the subject of significant academic and political interest. In this paper, we explore and theorise the appropriation of... Read More about Influence Government, Platform Power And The Patchwork Profile: Exploring The Appropriation Of Targeted Advertising Infrastructures For Government Behaviour Change Campaigns.

An Entity Ontology-Based Knowledge Graph Embedding Approach to News Credibility Assessment (2024)
Journal Article
Liu, Q., Jin, Y., Cao, X., Liu, X., Zhou, X., Zhang, Y., Xu, X., & Qi, L. (online). An Entity Ontology-Based Knowledge Graph Embedding Approach to News Credibility Assessment. IEEE Transactions on Computational Social Systems, https://doi.org/10.1109/TCSS.2023.3342873

Fake news is a prevalent issue in modern society, leading to misinformation and societal harm. News credibility assessment is a crucial approach for evaluating the accuracy and authenticity of news. It plays a significant role in enhancing public awa... Read More about An Entity Ontology-Based Knowledge Graph Embedding Approach to News Credibility Assessment.

DenMerD: a feature enhanced approach to radar beam blockage correction with edge-cloud computing (2024)
Journal Article
Liu, Q., Sun, J., Zhang, Y., & Liu, X. (2024). DenMerD: a feature enhanced approach to radar beam blockage correction with edge-cloud computing. Journal of cloud computing: advances, systems and applications, 13, Article 32. https://doi.org/10.1186/s13677-024-00607-x

In the field of meteorology, the global radar network is indispensable for detecting weather phenomena and offering early warning services. Nevertheless, radar data frequently exhibit anomalies, including gaps and clutter, arising from atmospheric re... Read More about DenMerD: a feature enhanced approach to radar beam blockage correction with edge-cloud computing.

AI’s Journey In Schools: From Chalkboards To Chatbots (2024)
Digital Artefact
Illingworth, S. (in press). AI’s Journey In Schools: From Chalkboards To Chatbots. [Blog]

Looking back at the explosive growth of generative AI since 2022, Dr Sam Illingworth advises school leaders on what the future might bring.

Image Forgery Detection using Cryptography and Deep Learning (2024)
Presentation / Conference Contribution
Oke, A., & Babaagba, K. O. (2024). Image Forgery Detection using Cryptography and Deep Learning. In Big Data Technologies and Applications. BDTA 2023 (62-78). https://doi.org/10.1007/978-3-031-52265-9_5

The advancement of technology has undoubtedly exposed everyone to a remarkable array of visual imagery. Nowadays, digital technology is eating away the trust and historical confidence people have in the integrity of imagery. Deep learning is often us... Read More about Image Forgery Detection using Cryptography and Deep Learning.

Can Federated Models Be Rectified Through Learning Negative Gradients? (2024)
Presentation / Conference Contribution
Tahir, A., Tan, Z., & Babaagba, K. O. Can Federated Models Be Rectified Through Learning Negative Gradients?. Presented at 13th EAI International Conference, BDTA 2023, Edinburgh

Federated Learning (FL) is a method to train machine learning (ML) models in a decentralised manner, while preserving the privacy of data from multiple clients. However, FL is vulnerable to malicious attacks, such as poisoning attacks, and is challen... Read More about Can Federated Models Be Rectified Through Learning Negative Gradients?.