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Studying how digital luthiers choose their tools (2022)
Conference Proceeding
Renney, N., Renney, H., Mitchell, T. J., & Gaster, B. R. (2022). Studying how digital luthiers choose their tools. . https://doi.org/10.1145/3491102.3517656

Digital lutherie is a sub-domain of digital craft focused on creating digital musical instruments: high-performance devices for musical expression. It represents a nuanced and challenging area of human-computer interaction that is well established an... Read More about Studying how digital luthiers choose their tools.

Supporting patient nutrition in critical care units (2022)
Conference Proceeding
Soomro, K., Pimenidis, E., & Mcwilliams, C. (in press). Supporting patient nutrition in critical care units

Critical Care Unit (CCU) patients often benefit from being referred to dietitians for various reasons. This can help improve recovery time, resulting in more effective utilisation of valuable resources within the NHS (National Health System) in the U... Read More about Supporting patient nutrition in critical care units.

HyperModels - A framework for GPU accelerated physical modelling sound synthesis (2022)
Conference Proceeding
Renney, H., Willemsen, S., Gaster, B. R., & Mitchell, T. J. (in press). HyperModels - A framework for GPU accelerated physical modelling sound synthesis

Physical modelling sound synthesis methods generate vast and intricate sound spaces that are navigated using meaningful parameters. Numerical based physical modelling synthesis methods provide authentic representations of the physics they model. Unfo... Read More about HyperModels - A framework for GPU accelerated physical modelling sound synthesis.

Fast and accurate evaluation of collaborative filtering recommendation algorithms (2022)
Conference Proceeding
Polatidis, N., Kapetanakis, S., Pimenidis, E., & ManolopouLos, Y. (in press). Fast and accurate evaluation of collaborative filtering recommendation algorithms

Collaborative filtering are recommender systems algorithms that provide personalized recommendations to users in various online environments such as movies, music, books, jokes and others. There are many such recommendation algorithms and, regarding... Read More about Fast and accurate evaluation of collaborative filtering recommendation algorithms.

Variational restricted Boltzmann machines to automated anomaly detection (2022)
Journal Article
Demertzis, K., Iliadis, L., Pimenidis, E., & Kikiras, P. (in press). Variational restricted Boltzmann machines to automated anomaly detection. Neural Computing and Applications, 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.

Memory-constrained context-aware reasoning (2022)
Conference Proceeding
Uddin, I., Rakib, A., Ali, M., & Vinh, P. C. (2022). Memory-constrained context-aware reasoning. In P. Cong Vinh, & A. Rakib (Eds.), Context-Aware Systems and Applications (133-146). https://doi.org/10.1007/978-3-030-93179-7_11

The context-aware computing paradigm introduces environments, known as smart spaces, which can unobtrusively and proactively assist their users. These systems are currently mostly implemented on mobile platforms considering various techniques, includ... Read More about Memory-constrained context-aware reasoning.

Bespoke anywhere (2021)
Conference Proceeding
Gaster, B., & Challinor, R. (2021). Bespoke anywhere

This paper reports on a project aimed to break away from the portability concerns of native DSP code between different platforms, thus freeing the instrument designer from the burden of porting new Digital Musical Instruments (DMIs) to different arch... Read More about Bespoke anywhere.

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.

A Stream X-Machine tool for modelling and generating test cases for chronic diseases based on state-counting approach (2021)
Journal Article
Ogunshile, E., Phung, K., Jayatilake, D., & 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.

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
Ogunshile, E., Phung, K., & Ramachandran, R. (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.

Users’ experiences of enhancing underwater images: An empirical study (2021)
Journal Article
Emberton, S., & Simons, C. (2021). Users’ experiences of enhancing underwater images: An empirical study. Quality and User Experience, 7, https://doi.org/10.1007/s41233-021-00048-3

Within the worldwide diving community, underwater photography is becoming increasingly popular. However, the marine environment presents certain challenges for image capture, with resulting imagery often suffering from colour distortions, low contras... Read More about Users’ experiences of enhancing underwater images: An empirical study.

Sentence encoding for Dialogue Act classification (2021)
Journal Article
Duran, N., Battle, S., & Smith, J. (in press). Sentence encoding for Dialogue Act classification. Natural Language Engineering, https://doi.org/10.1017/S1351324921000310

In this study, we investigate the process of generating single-sentence representations for the purpose of Dialogue Act (DA) classification, including several aspects of text pre-processing and input representation which are often overlooked or under... Read More about Sentence encoding for Dialogue Act classification.

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.

Establishing the informational requirements for modelling open domain dialogue and prototyping a retrieval open domain dialogue system (2021)
Conference Proceeding
Meier, T., & Pimenidis, E. (2021). Establishing the informational requirements for modelling open domain dialogue and prototyping a retrieval open domain dialogue system. In N. Nguyen, L. Iliadis, I. Maglogiannis, & B. Trawiński (Eds.), Computational Collective Intelligence (655-667). https://doi.org/10.1007/978-3-030-88081-1_49

Open domain dialogue systems aim to coherently respond to users over long conversations through multiple conversational turns. Modelling open domain dialogue is challenging as both the syntactic and semantic features of language play a role in respon... Read More about Establishing the informational requirements for modelling open domain dialogue and prototyping a retrieval open domain dialogue system.

Unsupervised one-class learning for anomaly detection on home IoT network devices (2021)
Conference Proceeding
White, J., & Legg, P. (2021). Unsupervised one-class learning for anomaly detection on home IoT network devices. In 2021 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA)https://doi.org/10.1109/CyberSA52016.2021.9478248

In this paper we study anomaly detection methods for home IoT devices. Specifically, we address unsupervised one-class learning methods due to their ability to learn deviations from a single normal class. In a home IoT environment, this consideration... Read More about Unsupervised one-class learning for anomaly detection on home IoT network devices.

"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.