Digital Commons Network^™

A Strong Scattering Solution With Applications To Remote Sensing Systems Modelling, Jonathan Blackledge

Conference papers

Although not always fully appreciated as such, the linear stationary convolution model for a signal and/or image, generated by the interaction of an incident wave field with an inhomogeneous medium, is based on the ‘weak scattering condition’, commonly referred to as the ‘Born approximation’. This approximation forms the basis for signal processing and analysis associated with applications over a broad range of frequencies. The Born approximation assumes that a scattering interaction is based on single scattering events alone and that the scattered field is therefore a weak field in comparison to the incident field. The physical limitations of such a …

Printed-Dipole End-Fire Array For Mm-Wave Applications, Neeraj Kumar Maurya, Max Ammann

Conference papers

A novel curved printed-dipole element in an end-fire array for mmWave communication is presented. The curved dipole element is printed on both sides of a RO5880 substrate of 0.5mm thickness. The proposed antenna operates in the frequency bands n257, n258, and n261 (22.79 GHz - 29.7 GHz) and the 4-element array has a peak gain of 8.85 dBi.

Fully 3d-Printed Hemispherical Dielectric Resonator Antenna For C-Band Applications, Jakub Przepiorowski, Irina Munina, Max Ammann, Daniel Trimble

Conference papers

This paper investigates the 3D printing of a hemispherical dielectric resonator antenna (DRA) on a ground plane made from a 3D printed conductive material. The DRA is designed to operate in the C-band (3700 – 4200 MHz) and is intended for satellite communication (SATCOM) applications. The proposed antenna prototype achieved a -10 dB bandwidth of 12.2% with an average and peak gain of 4.69 dBi and peak gain of 5.39 dBi respectively.

H2020 Auto-Dan Project: Enhance The Participation Of The Community To Demand Response By Providing The State-Of-The-Art Technological And Policy Solution, Rene Peeren, Dharmesh Dabhi, John Dalton

Conference papers

The growing demand for electricity in Europe has increased the need for a more flexible and sustainable power system. In recent years, Demand Response (DR) has emerged as a promising solution to meet this need, by providing an opportunity for residential and smaller commercial consumers to actively participate in the electricity market. This research paper investigates the potential for DR among the residential community and small commercial electricity consumers in Europe and identifies the technological barriers and drivers that impact consumer engagement with DR programs in Europe. The different DR opportunities are identified and validated at the six different demo …

Quantum Classifiers For Video Quality Delivery, Tautvydas Lisas, Ruairí De Fréin

Conference papers

Classical classifiers such as the Support Vector Classifier (SVC) struggle to accurately classify video Quality of Delivery (QoD) time-series due to the challenge in constructing suitable decision boundaries using small amounts of training data. We develop a technique that takes advantage of a quantum-classical hybrid infrastructure called Quantum-Enhanced Codecs (QEC). We evaluate a (1) purely classical, (2) hybrid kernel, and (3) purely quantum classifier for video QoD congestion classification, where congestion is either low, medium or high, using QoD measurements from a real networking test-bed. Findings show that the SVC performs the classification task 4% better in the low congestion …

Optimising Electric Vehicle Charging Infrastructure In Dublin Using Geecharge, Alexander Mutua Mutiso, Ruairí De Fréin, Ali Malik, Eliel Kibanza, Marco Sahbane, Maxime Pantel

Conference papers

Range anxiety poses a hurdle to the adoption of Electric Vehicles (EVs), as drivers worry about running out of charge without timely access to a Charging Point (CP). We present novel methods for optimising the distribution of CPs, namely, EV portacharge and GEECharge. These solutions distribute CPs in Dublin, in this paper, by considering the population density and Points Of Interest (POIs) or road traffic. The object of this paper is to (1) develop and evaluate methods to distribute CPs in Dublin city; (2) optimise CP allocation; (3) visualise paths in the graph network to show the most used roads …

Informal Learning As Opportunity For Competency Development And Broadened Engagement In Engineering, Madeline Polmear, Shannon Chance, Roger Hadgraft, Corrinne Shaw

Books/Book chapters

Informal learning is increasingly being recognized as a way to complement the formal curriculum within engineering and provide additional opportunities for competency development while engaging diverse students. Learning about engineering occurs throughout life, via experiential and spontaneous opportunities that inform our understandings of the world. Learning is not confined to the engineering curriculum and class time but, rather, continues informally and implicitly throughout the daily lives and activities of university students. Often framed in contrast to formal learning, informal learning is more as it represents a significant portion of students’ time and effort and contributes to their persistence, competence development, …

Extraction, Quantification, Characterization, And Application In Food Packaging Of Chitin And Chitosan From Mushrooms: A Review, Buliyaminu A. Alimi, Shivani Pathania, Jude Wilson, Brendan Duffy, Jesus Maria Frias

Articles

The application of chitin in food systems is limited by its insolubility in some common solvents and poor degradability. Hence, it is deacetylated to obtain chitosan, an industrially important derivative with excellent biological properties. Fungal-sourced chitosan is gaining prominence and industrial attraction because of its superior functional and biological properties, and vegan appeal. Further, the absence of such compounds as tropomyosin, myosin light chain, and arginine kinase, which are known to trigger allergic reactions, gives it an edge over marine-sourced chitosan in food and pharmaceutical applications. Mushrooms are macro-fungi with a significant content of chitin, with many authors reporting the …

Understanding And Predicting Cognitive Improvement Of Young Adults In Ischemic Stroke Rehabilitation Therapy, Helard Becerra Martinez, Katryna Cisek, Alejandro Garcia-Rudolph, John Kelleher, Andrew Hines

Articles

Accurate early predictions of a patient's likely cognitive improvement as a result of a stroke rehabilitation programme can assist clinicians in assembling more effective therapeutic programs. In addition, sufficient levels of explainability, which can justify these predictions, are a crucial requirement, as reported by clinicians. This article presents a machine learning (ML) prediction model targeting cognitive improvement after therapy for stroke surviving patients. The prediction model relies on electronic health records from 201 ischemic stroke surviving patients containing demographic information, cognitive assessments at admission from 24 different standardized neuropsychology tests (e.g., TMT, WAIS-III, Stroop, RAVLT, etc.), and therapy information collected …

Discovering Child Sexual Abuse Material Creators’ Behaviors And Preferences On The Dark Web, Vuong Ngo, Rahul Gajula, Christina Thorpe, Susan Mckeever

Articles

Background: Producing, distributing or discussing child sexual abuse materials (CSAM) is often committed through the dark web in order to remain hidden from search engines and regular users. Additionally, on the dark web, the CSAM creators employ various techniques to avoid detection and conceal their activities. The large volume of CSAM on the dark web presents a global social problem and poses a significant challenge for helplines, hotlines and law enforcement agencies.

Objective: Identifying CSAM discussions on the dark web and uncovering associated metadata insights into characteristics, behaviours and motivation of CSAM creators.

Participants and Setting: We have conducted an …

Biogeography Based Optimization With Salp Swarm Optimizer Inspired Operator For Solving Non-Linear Continuous Optimization Problems, Vanita Garg, Kusum Deep, Khalid Abdulaziz Alnowibet, Hossam Zawbaa, Ali Wagdy Mohamed

Articles

In this paper, a novel attempt is made to incorporate the two effective algorithm strategies, where BBO has a strong exploration and Salp Swarm Algorithm (SSA) is used for exploitation of the search space. The proposed algorithm is tested on IEEE CEC 2014 and statistical, convergence graphs are given. The proposed algorithm is also applied to 10 real life problems and compared with its counterpart algorithm. Results obtained by above experiments have demonstrated the outperformance of the hybrid version of BBO over other algorithms.

Learnings From A National Cyberattack Digital Disaster During The Sars-Cov-2 Pandemic In A Pediatric Emergency Medicine Department, Fiona Leonard, Hugh O'Reilly, Carol Blackburn, Laura Melody, Dani Hall, Eleanor Ryan, Kate Bruton, Pamela Doyle, Bridget Conway, Michael Barrett

Articles

Objective: The primary objective was to analyze the impact of the national cyberattack in May 2021 on patient flow and data quality in the Paediatric Emergency Department (ED), amid the SARS-CoV-2 (COVID-19) pandemic. Methods: A single site retrospective time series analysis was conducted of three 6-week periods: before, during, and after the cyberattack outage. Initial emergent workflows are described. Analysis includes diagnoses, demographic context, key performance indicators, and the gradual return of information technology capability on ED performance. Data quality was compared using 10 data quality dimensions. Results: Patient visits totaled 13 390. During the system outage, patient experience times …

New Fxlmat-Based Algorithms For Active Control Of Impulsive Noise, Alina Mirza, Farkhanda Afzal, Ayesha Zeb, Abdul Wakeel, Waqar Shahid Qureshi, Ali Akgul

Articles

In the presence of non-Gaussian impulsive noise (IN) with a heavy tail, active noise control (ANC) algorithms often encounter stability problems. While adaptive filters based on the higher-order error power principle have shown improved filtering capability compared to the least mean square family algorithms for IN, however, the performance of the filtered-x least mean absolute third (FxLMAT) algorithm tends to degrade under high impulses. To address this issue, this paper proposes three modifications to enhance the performance of the FxLMAT algorithm for IN. To improve stability, the first alteration i.e. variable step size FxLMAT (VSSFxLMAT)algorithm is suggested that incorporates the …

A Comparison Of Feature Selection Methodologies And Learning Algorithms In The Development Of A Dna Methylation-Based Telomere Length Estimator, Trevor Doherty, Emma Dempster, Eilis Hannon, Jonathan Mill, Richie Poulton, David Corcoran, Karen Sugden, Ben Williams, Avshalom Caspi, Terrie E. Moffitt, Sarah Jane Delany, Therese Murphy Dr

Articles

The field of epigenomics holds great promise in understanding and treating disease with advances in machine learning (ML) and artificial intelligence being vitally important in this pursuit. Increasingly, research now utilises DNA methylation measures at cytosine–guanine dinucleotides (CpG) to detect disease and estimate biological traits such as aging. Given the challenge of high dimensionality of DNA methylation data, feature-selection techniques are commonly employed to reduce dimensionality and identify the most important subset of features. In this study, our aim was to test and compare a range of feature-selection methods and ML algorithms in the development of a novel DNA methylation-based …

Argframe: A Multi-Layer, Web, Argument-Based Framework For Quantitative Reasoning, Lucas Rizzo

Articles

Multiple systems have been proposed to perform computational argumentation activities, but there is a lack of options for dealing with quantitative inferences. This multi-layer, web, argument-based framework has been proposed as a tool to perform automated reasoning with numerical data. It is able to use boolean logic for the creation of if-then rules and attacking rules. In turn, these rules/arguments can be activated or not by some input data, have their attacks solved (following some Dung or rank-based semantics), and finally aggregated in different fashions in order to produce a prediction (a number). The framework is implemented in PHP for …

Enhancing The Prediction For Shunt‑Dependent Hydrocephalus After Aneurysmal Subarachnoid Hemorrhage Using A Machine Learning Approach, Dietmar Frey, Adam Hilbert, Anton Früh, Vince Istvan Madai, Tabea Kossen, Julia Kiewitz, Jenny Sommerfeld, Peter Vajkoczy, Meike Unteroberdörster, Esra Zihni, Sophie Charlotte Brune, Stefan Wolf, Nora Franziska Dengler

Articles

Early and reliable prediction of shunt-dependent hydrocephalus (SDHC) after aneurysmal subarachnoid haemorhage (a SAH) may decrease the duration of in-hospital stay and reduce the risk of catheter-associated meningitis. Machine learning (ML) may improve predictions of SDHC in comparison to traditional non-ML methods. ML models were trained for CHESS and SDASH and two combined individual feature sets with clinical, radiographic, and laboratory variables. Seven different algorithms were used including three types of generalized linear models (GLM) as well as a tree boosting (Cat Boost) algorithm, a Nave Bayes (NB) classifier, and a multilayer perceptron (MLP) artificial neural net. The discrimination of …

Techmate: A Research-Driven Toolkit To Enhance Gender Balance In Computing Education, Alina Berry, Sarah Jane Delany

Academic Posters Collection

This poster presents a toolkit of practical initiatives and guidance on how to enhance gender balance in computing higher education. The suggested initiatives are designed in the way that could be adapted for a use in a local context, especially in universities in the UK or in Ireland. The initiatives are categorised under four main areas: Policy, Pedagogy, Influence & Support and Promotion & Engagement. Additionally, guidance is given on mechanisms to evaluate the impact of these initiatives. This work will be of interest to champions looking to enhance gender balance in their computing courses.

Investigating The Effects Of Network Dynamics On Quality Of Delivery Prediction And Monitoring For Video Delivery Networks, Obinna C. Izima

Doctoral

Video streaming over the Internet requires an optimized delivery system given the advances in network architecture, for example, Software Defined Networks. Machine Learning (ML) models have been deployed in an attempt to predict the quality of the video streams. Some of these efforts have considered the prediction of Quality of Delivery (QoD) metrics of the video stream in an effort to measure the quality of the video stream from the network perspective. In most cases, these models have either treated the ML algorithms as black-boxes or failed to capture the network dynamics of the associated video streams.

This PhD investigates …

Understanding The Interplay Between Immigrant Nascent Entrepreneurship And Cross-Cultural Adaptation, Lucia Walsh, Thomas M. Cooney

Articles

Purpose – All entrepreneurs face challenges during their venture start-up process, but immigrant entrepreneurs face additional and distinctive challenges due to their contextual newness. This paper focuses on understanding the intertwined journeys of nascent entrepreneurship and cross-cultural adaptation of immigrants in a small Western European country where immigrant entrepreneurship is still a relatively new phenomenon.

Design/methodology/approach – The induction-driven, 18-month longitudinal empirical inquiry focused on six early-stage nascent entrepreneurs. Qualitative methods included participant observation during an enterprise program, qualitative interviews and ongoing informal communication.

Findings – The data uncovered the interplay between the nascent immigrant entrepreneurship and crosscultural adaptation. This …

Biomolecules From Macroalgae-Nutritional Profile And Bioactives For Novel Food Product Development, Laura E. Healy, Xianglu Zhu, Milica Pojić, Carl Sullivan, Uma Tiwari, James Curtin, Brijesh K Tiwari

Articles

Seaweed is in the spotlight as a promising source of nutrition for humans as the search for sustainable food production systems continues. Seaweed has a well-documented rich nutritional profile containing compounds such as polyphenols, carotenoids and polysaccharides as well as proteins, fatty acids and minerals. Seaweed processing for the extraction of functional ingredients such as alginate, agar, and carrageenan is well-established. Novel pretreatments such as ultrasound assisted extraction or high-pressure processing can be incorporated to more efficiently extract these targeted ingredients. The scope of products that can be created using seaweed are wide ranging: from bread and noodles to yoghurt …

Portable Hepa Filtration Successfully Augments Natural-Ventilation-Mediated Airborne Particle Clearance In A Legacy Design Hospital Ward, Mehael Fennelly, S. Hellebust, J. Wenger, D. O'Connor, G.W. Griffith, B.J. Plant, A.P.C. Microbiome Institute

Articles

As the severe acute respiratory syndrome coronavirus-2 pandemic has proceeded, ventilation has been recognized increasingly as an important tool in infection control. Many hospitals in Ireland and the UK do not have mechanical ventilation and depend on natural ventilation. The effectiveness of natural ventilation varies with atmospheric conditions and building design. In a challenge test of a legacy design ward, this study showed that portable air filtration significantly increased the clearance of pollutant aerosols of respirable size compared with natural ventilation, and reduced spatial variation in particle persistence. A combination of natural ventilation and portable air filtration is significantly more …

A Computational Model Of Trust Based On Dynamic Interaction In The Stack Overflow Community, Patrick O’Neill

Dissertations

A member’s reputation in an online community is a quantified representation of their trustworthiness within the community. Reputation is calculated using rules-based algorithms which are primarily tied to the upvotes or downvotes a member receives on posts. The main drawback of this form of reputation calculation is the inability to consider dynamic factors such as a member’s activity (or inactivity) within the community. The research involves the construction of dynamic mathematical models to calculate reputation and then determine to what extent these results compare with rules-based models. This research begins with exploratory research of the existing corpus of knowledge. Constructive …

Exploring Gender Bias In Semantic Representations For Occupational Classification In Nlp: Techniques And Mitigation Strategies, Joseph Michael O'Carroll

Dissertations

Gender bias in Natural Language Processing (NLP) models is a non-trivial problem that can perpetuate and amplify existing societal biases. This thesis investigates gender bias in occupation classification and explores the effectiveness of different debiasing methods for language models to reduce the impact of bias in the model’s representations. The study employs a data-driven empirical methodology focusing heavily on experimentation and result investigation. The study uses five distinct semantic representations and models with varying levels of complexity to classify the occupation of individuals based on their biographies.

Evaluating The Performance Of Vulkan Glsl Compute Shaders In Real-Time Ray-Traced Audio Propagation Through 3d Virtual Environments, James Buggy

Dissertations

Real time ray tracing is a growing area of interest with applications in audio processing. However, real time audio processing comes with strict performance requirements, which parallel computing is often used to overcome. As graphics processing units (GPUs) have become more powerful and programmable, general-purpose computing on graphics processing units (GPGPU) has allowed GPUs to become extremely powerful parallel processors, leading them to become more prevalent in the domain of audio processing through platforms such as CUDA. The aim of this research was to investigate the potential of GLSL compute shaders in the domain of real time audio processing. Specifically …

Evaluation Of Text Transformers For Classifying Sentiment Of Reviews By Using Tf-Idf, Bert (Word Embedding), Sbert (Sentence Embedding) With Support Vector Machine Evaluation, Mina Jamshidian

Dissertations

As the online world evolves and new media emerge, consumers are sharing their reviews and opinions online. This has been studied in various academic fields, including marketing and computer science. Sentiment analysis, a technique used to identify the sentiment of a piece of text, has been researched in different domains such as movie reviews and mobile app ratings. However, the video game industry has received relatively little research on experiential products. The purpose of this study is to apply sentiment analysis to user reviews of games on Steam, a popular gaming platform, in order to produce actionable results. The video …

Application Of Shallow Neural Networks To Retail Intermittent Demand Time Series, Urko Allende

Dissertations

Accurate sales predictions are essential for businesses in the fast-moving consumer goods (FMCG) industry. However, their demand forecasts are often unreliable, leading to imprecisions that affect downstream decisions. This dissertation proposes using an artificial neural network to improve intermittent demand forecasting in the retail sector. The research investigates the validity of using unprocessed historical information, eluding hand-crafted features, to learn patterns in intermittent demand data. The experiment tests a selection of shallow neural network architectures that can expedite the time-to-market in comparison to conventional demand forecasting methods. The results demonstrate that organisations that still rely on manual and direct forecasting …

Explaining Deep Q-Learning Experience Replay With Shapley Additive Explanations, Robert S. Sullivan

Dissertations

Reinforcement Learning (RL) has shown promise in optimizing complex control and decision-making processes but Deep Reinforcement Learning (DRL) lacks interpretability, limiting its adoption in regulated sectors like manufacturing, finance, and healthcare. Difficulties arise from DRL’s opaque decision-making, hindering efficiency and resource use, this issue is amplified with every advancement. While many seek to move from Experience Replay to A3C, the latter demands more resources. Despite efforts to improve Experience Replay selection strategies, there is a tendency to keep capacity high. This dissertation investigates training a Deep Convolutional Q-learning agent across 20 Atari games, in solving a control task, physics task, …

The Use Of Data Balancing Algorithms To Correct For The Under-Representation Of Female Patients In A Cardiovascular Dataset, Sian Miller

Dissertations

Given that women are under-represented in medical datasets, and that machine learning classification algorithms are known to exhibit bias towards the majority class, the growing application of machine learning in the medical field risks resulting in worse medical outcomes for female patients. The Heart Failure Prediction (HFP) dataset is a historical dataset used for the training of models for the prediction of heart disease. This dataset contains significantly fewer female patients than male patients, and as such it is expected that models trained using this data will inherit a gender bias to favour male patients. This dissertation explores the use …

Probability Expressions In Ai Decision Support: Impacts On Human+Ai Team Performance, Elias Spinn

Dissertations

AI decision support systems aim to assist people in highly complex and consequential domains to make efficient, effective, and high-quality decisions. AI alone cannot be guaranteed to be correct in these complex decision tasks, and a human is often needed to ensure decision accuracy. The ambition is for these human+ AI teams to perform better together than either would individually. To realise this, decision makers must trust their AI partners appropriately, knowing when to rely on their recommendations and when to be sceptical. However, research has shown that decision makers often either mistrust and underutilise these systems, or trust them …

Modelling And Mitigating Interlocutor Confusion In Situated Human-Avatar And Human-Robot Interaction, Na Li

Dissertations

Human-Robot Interaction (HRI) is an important but challenging field focused on improving the interaction between humans and robots, to make the interaction more intelligent and effective. However, building a natural conversational HRI is an interdisciplinary challenge for scholars, engineers, and designers. Achieving successful conversational interaction with a social robot necessitates not only observing a user’s active participation in the interaction but also being aware of their emotional and attitudinal states as the interaction progresses. On the topic of attitudinal states, one field that has received little attention to date is monitoring the user for possible confusion states. Confusion is a …