Digital twins in industry 4.0 cyber security (2024)
Conference Proceeding
Lo, C., Win, T. Y., Rezaeifar, Z., Khan, Z., & Legg, P. (2024). Digital twins in industry 4.0 cyber security. In Proceedings of the IEEE Smart World Congress 2023. https://doi.org/10.1109/swc57546.2023.10449147

The increased adoption of sophisticated Cyber Physical Systems (CPS) in critical infrastructure and various aspects of Industry 4.0 has exposed vulnerabilities stemming from legacy CPS and Industrial Internet of Things (IIoT) devices. The interconnec... Read More about Digital twins in industry 4.0 cyber security.

Hear here: Sonification as a design strategy for robot teleoperation using virtual reality (2023)
Conference Proceeding
Simmons, J., Bown, A., Bremner, P., McIntosh, V., & Mitchell, T. J. (2023). Hear here: Sonification as a design strategy for robot teleoperation using virtual reality.

This paper introduces a novel methodology for the sonification of data, and shares the results of a usability study, putting the method- ology into practice within an industrial use case. Working with partners at Sellafield nuclear facility, we explo... Read More about Hear here: Sonification as a design strategy for robot teleoperation using virtual reality.

Disruptive learning and optimal flow: Game jams in heterotopic affinity space (2022)
Thesis
King, A. Disruptive learning and optimal flow: Game jams in heterotopic affinity space. (Thesis). University of the West of England. Retrieved from https://uwe-repository.worktribe.com/output/9303643

Game jams are intensive, time-bound videogame-development events where students, professionals, and hobbyists form teams to create games against the clock. Jams are popular, offering accessible, informal learning environments which promote teamwork,... Read More about Disruptive learning and optimal flow: Game jams in heterotopic affinity space.

Defending against adversarial machine learning attacks using hierarchical learning: A case study on network traffic attack classification (2022)
Journal Article
McCarthy, A., Ghadafi, E., Andriotis, P., & Legg, P. (2023). Defending against adversarial machine learning attacks using hierarchical learning: A case study on network traffic attack classification. Journal of Information Security and Applications, 72, Article 103398. https://doi.org/10.1016/j.jisa.2022.103398

Machine learning is key for automated detection of malicious network activity to ensure that computer networks and organizations are protected against cyber security attacks. Recently, there has been growing interest in the domain of adversarial mach... Read More about Defending against adversarial machine learning attacks using hierarchical learning: A case study on network traffic attack classification.

Development of communicating stream x-machine tool for modeling and generating test cases for automated teller machine (2022)
Presentation / Conference
Adewale Sanusi, B., Ogunshile, E., Aydin, M., Olatunde Olabiyisi, S., & Oyedepo Oyediran, M. (2022, August). Development of communicating stream x-machine tool for modeling and generating test cases for automated teller machine. Paper presented at 9th International Conference on Computer Science and Information Technology (CSIT 2022), Chennai, India

The improvement of this paper takes advantage of the existing formal method called Stream X-Machine by optimizing the theory and applying it to practice in a large-scale system. This optimized formal approach called Communicating Stream X-Machine (CS... Read More about Development of communicating stream x-machine tool for modeling and generating test cases for automated teller machine.

Disclosure risks in odds ratios and logistic regression (2022)
Presentation / Conference
Derrick, B., Green, E., Ritchie, F., & White, P. (2022, April). Disclosure risks in odds ratios and logistic regression. Paper presented at Scottish Economic Society Annual Conference 2022: Special session 'Protecting confidentiality in social science research outputs', Glasgow

When publishing statistics from confidential data, there exists a risk that the statistic might inadvertently reveal confidential information. Statistical disclosure control (SDC) aims to reduce that risk to an acceptable level. Most SDC theory is co... Read More about Disclosure risks in odds ratios and logistic regression.

Chlorella sensors in liquid marbles and droplets (2022)
Journal Article
Phillips, N., Mayne, R., & Adamatzky, A. (2022). Chlorella sensors in liquid marbles and droplets. Sensing and Bio-Sensing Research, 36, Article 100491. https://doi.org/10.1016/j.sbsr.2022.100491

The use of live organisms in electrically-coupled sensing devices has been suggested as an alternative low-cost, low-environmental footprint and robust technology for continuous monitoring and sensing applications. The utility of Chlorella vulgaris a... Read More about Chlorella sensors in liquid marbles and droplets.

Variational restricted Boltzmann machines to automated anomaly detection (2022)
Journal Article
Demertzis, K., Iliadis, L., Pimenidis, E., & Kikiras, P. (2022). Variational restricted Boltzmann machines to automated anomaly detection. Neural Computing and Applications, 34, 15207–15220. https://doi.org/10.1007/s00521-022-07060-4

Data-driven methods are implemented using particularly complex scenarios that reflect in-depth perennial knowledge and research. Hence, the available intelligent algorithms are completely dependent on the quality of the available data. This is not po... Read More about Variational restricted Boltzmann machines to automated anomaly detection.

Blockchain and artificial intelligence – Managing a secure and sustainable supply chain (2021)
Book Chapter
Pimenidis, E., Patsavellas, J., & Tonkin, M. (2021). Blockchain and artificial intelligence – Managing a secure and sustainable supply chain. In H. Jahankhani, A. Jamal, & S. Lawson (Eds.), Cybersecurity, Privacy and Freedom Protection in the Connected World. (1). Springer. https://doi.org/10.1007/978-3-030-68534-8

Supply chain management is often the most challenging part of any business that manufactures, sells goods, or provides services. Regardless of whether the operations are mostly physical or online, managing supply chains largely relies on being able t... Read More about Blockchain and artificial intelligence – Managing a secure and sustainable supply chain.

Exploring a web-based application to convert Tamil and Vietnamese speech to text without the effect of code- switching and code-mixing (2021)
Journal Article
Phung, K., Ramachandran, R., & Ogunshile, E. (2021). Exploring a web-based application to convert Tamil and Vietnamese speech to text without the effect of code- switching and code-mixing. Programming and Computer Software, 47(8), 757-764. https://doi.org/10.1134/S036176882108020X

This paper attempts to develop an application that converts Tamil and Vietnamese speech to text, with a view to encourage usage and indirectly ensure linguistic preservation of a classical language. The application converts spoken Tamil and Vietnames... Read More about Exploring a web-based application to convert Tamil and Vietnamese speech to text without the effect of code- switching and code-mixing.

A novel software fault prediction approach to predict error-type proneness in the Java programs using Stream X-Machine and machine learning (2021)
Conference Proceeding
Phung, K., Ogunshile, E., & Aydin, M. (2021). A novel software fault prediction approach to predict error-type proneness in the Java programs using Stream X-Machine and machine learning. In 2021 9th International Conference in Software Engineering Research and Innovation (CONISOFT) (168-179). https://doi.org/10.1109/CONISOFT52520.2021.00032

Software fault prediction makes software quality assurance process more efficient and economic. Most of the works related to software fault prediction have mainly focused on classifying software modules as faulty or not, which does not produce suffic... Read More about A novel software fault prediction approach to predict error-type proneness in the Java programs using Stream X-Machine and machine learning.

Modeling diseases with Stream X Machine (2021)
Conference Proceeding
Jayatilake, S., Ogunshile, E., Aydin, M., & Phung, K. (2021). Modeling diseases with Stream X Machine. In 2021 9th International Conference in Software Engineering Research and Innovation (CONISOFT) (61-68). https://doi.org/10.1109/CONISOFT52520.2021.00020

At present the world is moving towards alternative medicine and behavioural alteration for treating, managing, and preventing chronical diseases. With the individuality of the human beings has added more complexity in a domain where very high accurac... Read More about Modeling diseases with Stream X Machine.

A Stream X-Machine tool for modelling and generating test cases for chronic diseases based on state-counting approach (2021)
Journal Article
Phung, K., Jayatilake, D., Ogunshile, E., & Aydin, M. (2021). A Stream X-Machine tool for modelling and generating test cases for chronic diseases based on state-counting approach. Programming and Computer Software, 47(8), 765-777. https://doi.org/10.1134/S0361768821080211

In the biomedical domain, diagrammatical models have been extensively used to describe and understand the behaviour of biological organisms (biological agents) for decades. Although these models are simple and comprehensive, they can only offer a sta... Read More about A Stream X-Machine tool for modelling and generating test cases for chronic diseases based on state-counting approach.

Fungal architectures (2021)
Exhibition / Performance
Nikolaidou, A., Adamatzky, A., Phillips, N., Roberts, N., & Petrova, I. Fungal architectures. [Installations, Prints]. 13 December 2021 - 19 December 2021. (Unpublished)

"Fungal architectures" arts exhibition presents works inspired by protocognition of fungi and slime moulds and fungal materials. Fungal Architectures is a new cross-disciplinary research project that seeks to develop a fully integrated structural and... Read More about Fungal architectures.

Statistical disclosure controls for machine learning models (2021)
Conference Proceeding
Krueger, S., Mansouri-Benssassi, E., Ritchie, F., & Smith, J. (2021). Statistical disclosure controls for machine learning models

Artificial Intelligence (AI) models are trained on large datasets. Where the training data is sensitive, the data holders need to consider risks posed by access to the training data and risks posed by the models that are released. The first problem c... Read More about Statistical disclosure controls for machine learning models.

Estimation of the two-group pilot sample size with a cautionary note on Browne’s formula (2021)
Journal Article
Obodo, S., Toher, D., & White, P. (2021). Estimation of the two-group pilot sample size with a cautionary note on Browne’s formula. Journal of Applied Quantitative Methods, 16(3),

Using data obtained from a pilot study, Browne (1995) proposed a procedure for estimating the sample size needed for a definitive two-arm randomised controlled trial when the minimal important difference is specified. Simulations confirm these findi... Read More about Estimation of the two-group pilot sample size with a cautionary note on Browne’s formula.

Ordinal Logistic Regression as an alternative analysis strategy for the comparison of two independent samples (2021)
Journal Article
Bilski, B., Derrick, B., Toher, D., & White, P. (2021). Ordinal Logistic Regression as an alternative analysis strategy for the comparison of two independent samples. Journal of Applied Quantitative Methods, 16(3),

The two group between subjects design is pervasive with analyses often performed using the Mann Whitney Rank Sum test or using the Welch variant of the t-test. Using simulation it is shown that a dummy variable ordinal logistic regression (OLR) mode... Read More about Ordinal Logistic Regression as an alternative analysis strategy for the comparison of two independent samples.

"Hacking an IoT Home": New opportunities for cyber security education combining remote learning with cyber-physical systems (2021)
Conference Proceeding
Legg, P., Higgs, T., Spruhan, P., White, J., & Johnson, I. (2021). "Hacking an IoT Home": New opportunities for cyber security education combining remote learning with cyber-physical systems. In 2021 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA). https://doi.org/10.1109/CyberSA52016.2021.9478251

In March 2020, the COVID-19 pandemic led to a dramatic shift in educational practice, whereby home-schooling and remote working became the norm. Many typical schools outreach projects to encourage uptake of learning cyber security skills therefore we... Read More about "Hacking an IoT Home": New opportunities for cyber security education combining remote learning with cyber-physical systems.

Blockchained Αdaptive Federated Auto Meta Learning Big Data and DevOps CyberSecurity Architecture in Industry 4.0 (2021)
Conference Proceeding
Kikiras, P., Koziri, M., Tziritas, N., Pimenidis, E., Iliadis, L., & Demertzis, K. (2021). Blockchained Αdaptive Federated Auto Meta Learning Big Data and DevOps CyberSecurity Architecture in Industry 4.0. In Proceedings of the 22nd Engineering Applications of Neural Networks Conference (345-363). https://doi.org/10.1007/978-3-030-80568-5_29

Maximizing the production process in modern industry, as proposed by Industry 4.0, requires extensive use of Cyber-Physical Systems (CbPS). Artificial intelligence technologies, through CbPS, allow monitoring of natural processes , making autonomous,... Read More about Blockchained Αdaptive Federated Auto Meta Learning Big Data and DevOps CyberSecurity Architecture in Industry 4.0.

Recommender systems algorithm selection using machine learning (2021)
Conference Proceeding
Pimenidis, E., Kapetanakis, S., & Polatidis, N. (2021). Recommender systems algorithm selection using machine learning. In Proceedings of the 22nd Engineering Applications of Neural Networks Conference (477-487). https://doi.org/10.1007/978-3-030-80568-5_39

This article delivers a methodology for recommender system algorithm selection using a machine learning classifier. Initially, statistical data from real collaborative filtering recommender systems have been collected to form the basis for a syntheti... Read More about Recommender systems algorithm selection using machine learning.