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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.

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, 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.

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.

Automatic report-based labelling of clinical EEGs for classifier training (2022)
Conference Proceeding
Western, D., Weber, T., Kandasamy, R., May, F., Taylor, S., Zhu, Y., & Canham, L. (2022). Automatic report-based labelling of clinical EEGs for classifier training. In 2021 IEEE Signal Processing in Medicine and Biology Symposium (SPMB). https://doi.org/10.1109/SPMB52430.2021.9672295

Machine learning classifiers for detection of abnormal clinical electroencephalography (EEG) signals have advanced signficantly in recent years, largely supported by the carefully curated Temple University Hospital Abnormal EEG Corpus (TUAB). Further... Read More about Automatic report-based labelling of clinical EEGs for classifier training.

Inter-annotator agreement using the Conversation Analysis Modelling Schema, for dialogue (2022)
Journal Article
Duran, N., Battle, S., & Smith, J. (in press). Inter-annotator agreement using the Conversation Analysis Modelling Schema, for dialogue. Communication Methods and Measures, https://doi.org/10.1080/19312458.2021.2020229

We present the Conversation Analysis Modeling Schema (CAMS), a novel dialogue labeling schema that combines the Conversation Analysis concept of Adjacency Pairs, with Dialogue Acts. The aim is to capture both the semantic and syntactic structure of d... Read More about Inter-annotator agreement using the Conversation Analysis Modelling Schema, for dialogue.

Marimo actuated rover systems (2022)
Journal Article
Phillips, N., Draper, T. C., Mayne, R., Reynolds, D. M., & Adamatzky, A. (2022). Marimo actuated rover systems. Journal of Biological Engineering, 16(1), https://doi.org/10.1186/s13036-021-00279-0

Background: The potential to directly harness photosynthesis to make actuators, biosensors and bioprocessors has been previously demonstrated in the literature. Herein, this capability has been expanded to more advanced systems — Marimo Actuated Rove... Read More about Marimo actuated rover systems.

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.

Automatic for the people: Crowd-driven generative scores using Manhattan and machine vision (2021)
Conference Proceeding
Nash, C. (2021). Automatic for the people: Crowd-driven generative scores using Manhattan and machine vision

This paper details a workshop and optional public installation based on the development of situational scores that combine music notation, AI, and code to create dynamic interactive art driven by the realtime movements of objects and people in a live... Read More about Automatic for the people: Crowd-driven generative scores using Manhattan and machine vision.

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.

Precision fibre angle inspection for carbon fibre composite structures using polarisation vision (2021)
Journal Article
Atkinson, G., O'Hara Nash, S., & Smith, L. (2021). Precision fibre angle inspection for carbon fibre composite structures using polarisation vision. Electronics, 10(22), https://doi.org/10.3390/electronics10222765

This paper evaluates the precision of polarisation imaging technology for the inspection of carbon fibre composite components. Specifically, it assesses the feasibility of the technology for fibre orientation measurements based on the premise that li... Read More about Precision fibre angle inspection for carbon fibre composite structures using polarisation vision.

Rainfall Prediction: A Comparative Analysis of Modern Machine Learning Algorithms for Time-Series Forecasting (2021)
Journal Article
Barrera Animas, A., Oladayo Oyedele, L., Bilal, M., Dolapo Akinosho, T., Davila Delgado, J. M., & Adewale Akanbi, L. (2022). Rainfall Prediction: A Comparative Analysis of Modern Machine Learning Algorithms for Time-Series Forecasting. Machine Learning with Applications, 7, https://doi.org/10.1016/j.mlwa.2021.100204

Rainfall forecasting has gained utmost research relevance in recent times due to its complexities and persistent applications such as flood forecasting and monitoring of pollutant concentration levels, among others. Existing models use complex statis... Read More about Rainfall Prediction: A Comparative Analysis of Modern Machine Learning Algorithms for Time-Series Forecasting.

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.