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Family Dining: Food And Drink In The Sopranos – A Gastrocritical Approach, Lisa Davies
Family Dining: Food And Drink In The Sopranos – A Gastrocritical Approach, Lisa Davies
Dissertations
This thesis uses gastrocriticism to explore food and drink in the 86 episodes of the long-form narrative HBO television series The Sopranos. Gastrocriticism is an emerging branch of literary criticism that draws on scholarship from a range of disciplines such as sociology, anthropology and cultural studies. A deeper understanding of the series was gained by using a structured framework of enquiry to explore narrative, setting, characterisation and genre through the lens of food and foodways. The Sopranos is a story of an Italian-American crime family and food is abundant in the series and bound up with the identity of …
Exploring The Fifth Quarter: An Enquiry Into Offal Eating In Contemporary Irish Food Culture, Its History, And Its Future, Niall Toner
Dissertations
Animal offal and organ meats seem to have all but disappeared from domestic cuisine in Ireland, despite the recent renaissance in the country’s food culture. This thesis has examined the extent and nature of the consumption of these comestibles in contemporary Irish food culture, and the perceived decline in offal’s popularity in Ireland in the past fifty years. It also sought to discover whether offal and organ meats might have a place in the future of our cuisine, and whether the consumption of more offal and organ meats in Ireland might contribute towards a more sustainable food production system, and …
A Computational Model Of Trust Based On Dynamic Interaction In The Stack Overflow Community, Patrick O’Neill
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 …
Development Of A Hospital Discharge Planning System Augmented With A Neural Clinical Decision Support Engine, David Mulqueen
Development Of A Hospital Discharge Planning System Augmented With A Neural Clinical Decision Support Engine, David Mulqueen
Dissertations
The process of discharging patients from a tertiary care hospital, is one of the key activities to ensure the efficient and effective operation of a hospital. However, the decision to discharge a patient from a hospital is complex, as it requires multiple interactions with nurses, family, consultants, health information records and doctors, which can be very time consuming and prone to error. This thesis descries how a neural network based Clinical Decision Support system can be developed, to help in the decision making process and dramatically reduce the time and effort in running the discharge process in a hospital. A …
Exploring Gender Bias In Semantic Representations For Occupational Classification In Nlp: Techniques And Mitigation Strategies, Joseph Michael O'Carroll
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 …
The Effects Of Disinformation Upon National Attitudes Towards The Eu And Its Institutions, Alex Murphy
The Effects Of Disinformation Upon National Attitudes Towards The Eu And Its Institutions, Alex Murphy
Dissertations
This work explores the effects of misinformation and disinformation upon national attitudes towards the EU. Several nations, in particular the Russian Federation, have been working for decades to spread narratives that debase the political processes of healthy democracies around the world. There is strong evidence to show that extensive efforts have been made to disrupt the inner workings and overall membership of the EU, to support disruptive policies in the United States such that political deadlock is maintained indefinitely. These efforts are largely based on the spreading of misinformation and disinformation across social networks that have done very little to …
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 …
Explaining Deep Q-Learning Experience Replay With Shapley Additive Explanations, Robert S. Sullivan
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
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 …
The Vulnerability Through Cyberattacks Related To Technological Differences Between Centralized, Server-Based And Ethereum Based Smart Home Systems, Friederike Johanna Haberl
The Vulnerability Through Cyberattacks Related To Technological Differences Between Centralized, Server-Based And Ethereum Based Smart Home Systems, Friederike Johanna Haberl
Dissertations
Many people use the digital support of smart home systems to improve their homes' comfort, security, and efficiency (Komninos et al., 2011). However, with the growing number of users comes an increasing number of security challenges that must be addressed (Albany et al., 2022). To mitigate these concerns, the smart home industry is exploring new technologies beyond the traditional centralized server model. One promising technology is the Ethereum blockchain, a distributed database technology. However, it is unclear which concrete security advantages the use of this technology offers in the smart home area. Therefore, the purpose of this research is to …
Application Of Shallow Neural Networks To Retail Intermittent Demand Time Series, Urko Allende
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 …
La Vraie Bouillabaisse: An Investigation Into The History And Current Practice Of The Provencal Dish Bouillabaisse, And Its Significance As A Traditional Dish, Mathieu Belledent
La Vraie Bouillabaisse: An Investigation Into The History And Current Practice Of The Provencal Dish Bouillabaisse, And Its Significance As A Traditional Dish, Mathieu Belledent
Dissertations
This thesis examines the history and the current practices (popularity, service styles, and recipes) of the Provencal dish bouillabaisse. It aims to establish the evolution and the traditional characteristics of the dish. It also explores the historical and contemporary popularity as well as the everyday role that bouillabaisse plays in the regional identity of Provencal cooking. Finally, the research questions if bouillabaisse would benefit from a European Union quality schemes protection or official recognition by UNESCO. This research uses an exploratory sequential mixed methods model combining qualitative and quantitative data collection which are analysed in a sequence of phases. …
An Investigative Analysis On Female Presence And Highly Ranked Positions In Professional Kitchens In Ireland, Roann Byrne
An Investigative Analysis On Female Presence And Highly Ranked Positions In Professional Kitchens In Ireland, Roann Byrne
Dissertations
This study aims to gain an understanding of the state of the cheffing industry currently, to analyse whether there is a lack of women within the industry particularly in positions of high power. This research intends to understand the causes for the lack and showcase possible solutions and recommendations for this. It exists as a role of advocacy; hoping to inspire more people into the career of cheffing, and to retain women within it. It aspires to challenge and thus forth change the narratives that have pushed many people, particularly women, out of this work for so long. This research …
Market Segmentation Of Wine In Ireland: Are We Fostering A Desirable Consumption Culture?, Enea Bent
Market Segmentation Of Wine In Ireland: Are We Fostering A Desirable Consumption Culture?, Enea Bent
Dissertations
The aim of this research is to evaluate the wine sector in Ireland and its impact on the wine consumption culture that is being promoted here as a result. With supermarkets leading in terms of sales, this study evaluates the product offering of the various types of retailers and the attainability of the same to different demographics of consumer. A high level of government intervention in the industry is highlighted throughout the study, the intention and subsequent successes and failures are examined. A comparison to the rest of Europe and the United Kingdom is carried out to understand Ireland’s position …
Dark Patterns: Effect On Overall User Experience And Site Revisitation, Deon Soul Calawen
Dark Patterns: Effect On Overall User Experience And Site Revisitation, Deon Soul Calawen
Dissertations
Dark patterns are user interfaces purposefully designed to manipulate users into doing something they might not otherwise do for the benefit of an online service. This study investigates the impact of dark patterns on overall user experience and site revisitation in the context of airline websites. In order to assess potential dark pattern effects, two versions of the same airline website were compared: a dark version containing dark pattern elements and a bright version free of manipulative interfaces. User experience for both websites were assessed quantitatively through a survey containing a User Experience Questionnaire (UEQ) and a System Usability Scale …
Ensemble Approach To The Semantic Segmentation Of Satellite Images, Brendan Kent
Ensemble Approach To The Semantic Segmentation Of Satellite Images, Brendan Kent
Dissertations
Automatic classification and segmentation of land use land cover(LULC) is extremely important for understanding the relationship between humans and nature. Human pressures on the environment have drastically accelerated in the last decades, risking biodiversity and ecosystem services. Remote sensing via satellite imagery is an excellent tool to study LULC. Research has shown that deep learning encoder-decoder architectures have achieved worthy results in the area of LULC, however the application of an ensemble approach has not been well quantified. Studies have shown it to be useful in the area of medical imaging. Ensembling by pooling together predictions to produce better predictions …
Hybridization Of Biologically Inspired Algorithms For Discrete Optimisation Problems, Elihu Essian-Thompson
Hybridization Of Biologically Inspired Algorithms For Discrete Optimisation Problems, Elihu Essian-Thompson
Dissertations
In the field of Optimization Algorithms, despite the popularity of hybrid designs, not enough consideration has been given to hybridization strategies. This paper aims to raise awareness of the benefits that such a study can bring. It does this by conducting a systematic review of popular algorithms used for optimization, within the context of Combinatorial Optimization Problems. Then, a comparative analysis is performed between Hybrid and Base versions of the algorithms to demonstrate an increase in optimization performance when hybridization is employed.
Development Of An Explainability Scale To Evaluate Explainable Artificial Intelligence (Xai) Methods, Stephen Mccarthy
Development Of An Explainability Scale To Evaluate Explainable Artificial Intelligence (Xai) Methods, Stephen Mccarthy
Dissertations
Explainable Artificial Intelligence (XAI) is an area of research that develops methods and techniques to make the results of artificial intelligence understood by humans. In recent years, there has been an increased demand for XAI methods to be developed due to model architectures getting more complicated and government regulations requiring transparency in machine learning models. With this increased demand has come an increased need for instruments to evaluate XAI methods. However, there are few, if none, valid and reliable instruments that take into account human opinion and cover all aspects of explainability. Therefore, this study developed an objective, human-centred questionnaire …
An Investigation Of The Relationship Between Subjective Mental Workload And Objective Indicators Of User Activity, Greg Byrne
Dissertations
Whilst the concept of physical workload is intuitively understood and readily applicable in system design, the same cannot be said of mental workload (MWL), despite its importance in our increasingly technological society. Despite its origin in the mid 20th century, the very concept of ”mental workload” is still a topic of debate in the literature, although it can be loosely defined as “the amount of mental work necessary for a person to complete a task” (Miller, 1956; Longo, 2014). Several methods have been utilized to measure of MWL, including physiological methods such as neuro-imagery, performance-based metrics, and subjective measures via …
Improving Dysarthric Speech Recognition By Enriching Training Datasets, Sophie Cullen
Improving Dysarthric Speech Recognition By Enriching Training Datasets, Sophie Cullen
Dissertations
Dysarthria is a motor speech disorder that results from disruptions in the neuro-motor interface and is characterised by poor articulation of phonemes and hyper-nasality and is characteristically different from normal speech. Many modern automatic speech recognition systems focus on a narrow range of speech diversity therefore as a consequence of this they exclude a groups of speakers who deviate in aspects of gender, race, age and speech impairment when building training datasets. This study attempts to develop an automatic speech recognition system that deals with dysarthric speech with limited dysarthric speech data. Speech utterances collected from the TORGO database are …
An Analysis On Network Flow-Based Iot Botnet Detection Using Weka, Cian Porteous
An Analysis On Network Flow-Based Iot Botnet Detection Using Weka, Cian Porteous
Dissertations
Botnets pose a significant and growing risk to modern networks. Detection of botnets remains an important area of open research in order to prevent the proliferation of botnets and to mitigate the damage that can be caused by botnets that have already been established. Botnet detection can be broadly categorised into two main categories: signature-based detection and anomaly-based detection. This paper sets out to measure the accuracy, false-positive rate, and false-negative rate of four algorithms that are available in Weka for anomaly-based detection of a dataset of HTTP and IRC botnet data. The algorithms that were selected to detect botnets …
Evaluating The Performance Of Vision Transformer Architecture For Deepfake Image Classification, Devesan Govindasamy
Evaluating The Performance Of Vision Transformer Architecture For Deepfake Image Classification, Devesan Govindasamy
Dissertations
Deepfake classification has seen some impressive results lately, with the experimentation of various deep learning methodologies, researchers were able to design some state-of-the art techniques. This study attempts to use an existing technology “Transformers” in the field of Natural Language Processing (NLP) which has been a de-facto standard in text processing for the purposes of Computer Vision. Transformers use a mechanism called “self-attention”, which is different from CNN and LSTM. This study uses a novel technique that considers images as 16x16 words (Dosovitskiy et al., 2021) to train a deep neural network with “self-attention” blocks to detect deepfakes. It creates …
Measuring And Comparing Social Bias In Static And Contextual Word Embeddings, Alan Cueva Mora
Measuring And Comparing Social Bias In Static And Contextual Word Embeddings, Alan Cueva Mora
Dissertations
Word embeddings have been considered one of the biggest breakthroughs of deep learning for natural language processing. They are learned numerical vector representations of words where similar words have similar representations. Contextual word embeddings are the promising second-generation of word embeddings assigning a representation to a word based on its context. This can result in different representations for the same word depending on the context (e.g. river bank and commercial bank). There is evidence of social bias (human-like implicit biases based on gender, race, and other social constructs) in word embeddings. While detecting bias in static (classical or non-contextual) word …
Development Of An Explainability Scale To Evaluate Explainable Artificial Intelligence (Xai) Methods, Stephen Mccarthy
Development Of An Explainability Scale To Evaluate Explainable Artificial Intelligence (Xai) Methods, Stephen Mccarthy
Dissertations
Explainable Artificial Intelligence (XAI) is an area of research that develops methods and techniques to make the results of artificial intelligence understood by humans. In recent years, there has been an increased demand for XAI methods to be developed due to model architectures getting more complicated and government regulations requiring transparency in machine learning models. With this increased demand has come an increased need for instruments to evaluate XAI methods. However, there are few, if none, valid and reliable instruments that take into account human opinion and cover all aspects of explainability. Therefore, this study developed an objective, human-centred questionnaire …
An Investigation Of The Relationship Between Subjective Mental Workload And Objective Indicators Of User Activity, Greg Byrne
Dissertations
Whilst the concept of physical workload is intuitively understood and readily applicable in system design, the same cannot be said of mental workload (MWL), despite its importance in our increasingly technological society. Despite its origin in the mid 20th century, the very concept of ”mental workload” is still a topic of debate in the literature, although it can be loosely defined as “the amount of mental work necessary for a person to complete a task” (Miller, 1956; Longo, 2014). Several methods have been utilized to measure of MWL, including physiological methods such as neuro-imagery, performance-based metrics, and subjective measures via …
Kg-Cnn: Augmenting Convolutional Neural Networks With Knowledge Graphs For Multi-Class Image Classification, Aidan O'Neill
Kg-Cnn: Augmenting Convolutional Neural Networks With Knowledge Graphs For Multi-Class Image Classification, Aidan O'Neill
Dissertations
Computer vision is slowly becoming more and more prevalent in daily life. Tesla has recently announced that it plans to scale up the manufacturing of their Robotaxis by 2024, with this increase in self-driving vehicles being just one example, the importance of computer vision is growing year by year. Vision can be easy to take for granted, as most humans grow up using vision as their primary way of absorbing environmental information. The way humans process and classify visual information differs significantly from how current computer vision systems process and organise visual information. The human brain can use its past …
The Impact Of Emotion Focused Features On Svm And Mlr Models For Depression Detection, Alexandria Mulligan
The Impact Of Emotion Focused Features On Svm And Mlr Models For Depression Detection, Alexandria Mulligan
Dissertations
Major depressive disorder (MDD) is a common mental health diagnosis with estimates upwards of 25% of the United States population remain undiagnosed. Psychomotor symptoms of MDD impacts speed of control of the vocal tract, glottal source features and the rhythm of speech. Speech enables people to perceive the emotion of the speaker and MDD decreases the mood magnitudes expressed by an individual. This study asks the questions: “if high level features deigned to combine acoustic features related to emotion detection are added to glottal source features and mean response time in support vector machines and multivariate logistic regression models, would …
Evaluating The Performance Impact Of Fine-Tuning Optimization Strategies On Pre-Trained Distilbert Models Towards Hate Speech Detection In Social Media, Aidan Mcgovern
Dissertations
Hate speech can be defined as forms of expression that incite hatred or encourage violence towards a person or group based on race, religion, gender, or sexual orientation. Hate speech has gravitated towards social media as its primary platform, and its propagation represents profound risks to both the mental well-being and physical safety of targeted groups. Countermeasures to moderate hate speech face challenges due to the volumes of data generated in social media, leading companies, and the research community to evaluate methods to automate its detection. The emergence of BERT and other pre-trained transformer-based models for transfer learning in the …