Computer Engineering Commons^™

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 …

Machine Learning For Accessible Web Navigation, Tlamelo W. Makati

Conference papers

This research looks at the application of Machine Learning to Web Accessibility. It considers how Machine Learning (ML) can be used to help make the processes of Web Navigation more accessible in line with Web Content Accessibility Guideline (WCAG) 2.4 Navigable, which demands that ways be provided "to help users navigate, find content, and determine where they are." ML techniques such as reinforcement learning have been applied to website navigation in diverse ways. These include goal-directed search to answer questions and task-oriented problems such as booking a flight. Related work includes Web Automation and Testing. These techniques typically involve a …

Using Satellite Images Datasets For Road Intersection Detection In Route Planning, Fatmaelzahraa Eltaher, Susan Mckeever, Ayman Taha, Jane Courtney

Datasets

Understanding road networks plays an important role in navigation applications such as self-driving vehicles and route planning for individual journeys. Intersections of roads are essential components of road networks. Understanding the features of an intersection, from a simple T-junction to larger multi-road junctions is critical to decisions such as crossing roads or selecting safest routes. The identification and profiling of intersections from satellite images is a challenging task. While deep learning approaches offer state-of-the-art in image classification and detection, the availability of training datasets is a bottleneck in this approach. In this paper, a labelled satellite image dataset for the …

The Evolution Of The Internet And Social Media: A Literature Review, Charles Alves De Castro, Isobel O'Reilly Dr, Aiden Carthy

Articles

This article reviews and analyses factors impacting the evolution of the internet, the web, and social media channels, charting historic trends and highlight recent technological developments. The review comprised a deep search using electronic journal databases. Articles were chosen according to specific criteria with a group of 34 papers and books selected for complete reading and deep analysis. The 34 elements were analysed and processed using NVIVO 12 Pro, enabling the creation of dimensions and categories, codes and nodes, identifying the most frequent words, cluster analysis of the terms, and creating a word cloud based on each word's frequency. The …

Real-Time Bidding Campaigns Optimization Using User Profile Settings, Luis Miralles-Pechuán, Muhammad Atif Qureshi, Brian Mac Namee

Articles

Real-time bidding is nowadays one of the most promising systems in the online advertising ecosystem. In this study, the performance of RTB campaigns is improved by optimising the parameters of the users' profiles and the publishers' websites. Most studies concerning optimising RTB campaigns are focused on the bidding strategy, i.e., estimating the best value for each bid. However, this research focuses on optimising RTB campaigns by finding out configurations that maximise both the number of impressions and the average profitability of the visits. An online campaign configuration generally consists of a set of parameters along with their values such as …

Formulating Automated Responses To Cognitive Distortions For Cbt Interactions, Ignacio De Toledo, Giancarlo Salton, Robert J. Ross

Conference papers

One of the key ideas of Cognitive Behavioural Therapy (CBT) is the ability to convert negative or distorted thoughts into more realistic alternatives. Although modern machine learning techniques can be successfully applied to a variety of Natural Language Processing tasks, including Cognitive Behavioural Therapy, the lack of a publicly available dataset makes supervised training difficult for tasks such as reforming distorted thoughts. In this research, we constructed a small CBT dataset via crowd-sourcing, and leveraged state of the art pre-trained architectures to transform cognitive distortions, producing text that is relevant and more positive than the original negative thoughts. In particular, …

Perspectives On Computing Ethics: A Multi-Stakeholder Analysis, Damian Gordon, Ioannis Stavrakakis, Paul J. Gibson, Brendan Tierney, Anna Becevel, Andrea Curley, Michael Collins, William O'Mahony, Dympna O'Sullivan

Articles

Purpose:

Computing ethics represents a long established, yet rapidly evolving, discipline that grows in complexity and scope on a near-daily basis. Therefore, to help understand some of that scope it is essential to incorporate a range of perspectives, from a range of stakeholders, on current and emerging ethical challenges associated with computer technology. This study aims to achieve this by using, a three-pronged, stakeholder analysis of Computer Science academics, ICT industry professionals, and citizen groups was undertaken to explore what they consider to be crucial computing ethics concerns. The overlap between these stakeholder groups are explored, as well as whether …

Finding Bert’S Idiomatic Key, Vasudevan Nedumpozhimana, John Kelleher

Conference papers

Sentence embeddings encode information relating to the usage of idioms in a sentence. This paper reports a set of experiments that combine a probing methodology with input masking to analyse where in a sentence this idiomatic information is taken from, and what form it takes. Our results indicate that BERT’s idiomatic key is primarily found within an idiomatic expression, but also draws on information from the surrounding context. Also, BERT can distinguish between the disruption in a sentence caused by words missing and the incongruity caused by idiomatic usage.

Bayesian Adaptive Path Allocation Techniques For Intra-Datacenter Workloads, Ali Malik, Ruairí De Fréin, Chih-Heng Ke, Hasanen Alyasiri, Obinna Izima

Conference papers

Data center networks (DCNs) are the backbone of many cloud and Internet services. They are vulnerable to link failures, that occur on a daily basis, with a high frequency. Service disruption due to link failure may incur financial losses, compliance breaches and reputation damage. Performance metrics such as packet loss and routing flaps are negatively affected by these failure events. We propose a new Bayesian learning approach towards adaptive path allocation that aims to improve DCN performance by reducing both packet loss and routing flaps ratios. The proposed approach incorporates historical information about link failure and usage probabilities into its …

Demo: Codec-Aware Video Delivery Over Sdns, Obinna Izima, Ruairí De Fréin, Ali Malik

Other resources

To guarantee quality of delivery for video streaming over software defined networks, efficient predictors and adaptive routing frameworks are required. We demonstrate an agent that predicts video quality of delivery metrics in a scalable way using a bespoke codec-aware learning model. We also demonstrate the integration of this agent with an adaptive framework for centrally controlled software-defined networks that re-configures network operational paths in response to the learning agent, ensuring that good quality of delivery of video is maintained during periods of congestion. The demo scenario highlights the feasibility, scalability and accuracy of the framework.

A Systematic Review Of Urban Navigation Systems For Visually Impaired People, Fatma Eltaher, Ayman Taha, Jane Courtney, Susan Mckeever

Articles

Blind and Visually impaired people (BVIP) face a range of practical difficulties when undertaking outdoor journeys as pedestrians. Over the past decade, a variety of assistive devices have been researched and developed to help BVIP navigate more safely and independently. In~addition, research in overlapping domains are addressing the problem of automatic environment interpretation using computer vision and machine learning, particularly deep learning, approaches. Our aim in this article is to present a comprehensive review of research directly in, or relevant to, assistive outdoor navigation for BVIP. We breakdown the navigation area into a series of navigation phases and tasks. We …

Towards A Blockchain Assisted Patient Owned System For Electronic Health Records, Tomilayo Fatokun, Avishek Nag, Sachin Sharma

Articles

Security and privacy of patients’ data is a major concern in the healthcare industry. In this paper, we propose a system that activates robust security and privacy of patients’ medical records as well as enables interoperability and data exchange between the different healthcare providers. The work proposes the shift from patient’s electronic health records being managed and controlled by the healthcare industry to a patient-centric application where patients are in control of their data. The aim of this research is to build an Electronic Healthcare Record (EHR) system that is layered on the Ethereum blockchain platform and smart contract in …

A Comparison Of Risk Factors And Risk Models For Stroke By Age Group Using Tilda Data, Elizabeth Hunter, John D. Kelleher

Conference papers

Models to predict stroke risk with the aim of stroke prevention often use age as a factor in the model (Choudhury et al., 2015; Conroy et al., 2003; D’Agostino etal., 2008; Wolf et al., 1991). However, stroke risk scores often underestimate risk
for specific age groups, particularly younger age groups and the contribution of different risk factors to overall stroke risk changes over time (Boehme et al., 2017; Seshadri et al., 2006). Additionally, because age is a strong predictor of stroke, age can dominate the risk score (Leening et al., 2017). Longitudinal Studies such as the Irish Longitudinal study on …

Examining The Modelling Capabilities Of Defeasible Argumentation And Non-Monotonic Fuzzy Reasoning, Luca Longo, Lucas Rizzo, Pierpaolo Dondio

Articles

Knowledge-representation and reasoning methods have been extensively researched within Artificial Intelligence. Among these, argumentation has emerged as an ideal paradigm for inference under uncertainty with conflicting knowledge. Its value has been predominantly demonstrated via analyses of the topological structure of graphs of arguments and its formal properties. However, limited research exists on the examination and comparison of its inferential capacity in real-world modelling tasks and against other knowledge-representation and non-monotonic reasoning methods. This study is focused on a novel comparison between defeasible argumentation and non-monotonic fuzzy reasoning when applied to the representation of the ill-defined construct of human mental workload …

Developing An Open-Book Online Exam For Final Year Students, Keith Quille, Keith Nolan, Brett Becker, Sean Mchugh

Conference Papers

Like many others, our institution had to adapt our traditional proctored, written examinations to open-book online variants due to the COVID-19 pandemic. This paper describes the process applied to develop open-book online exams for final year (undergraduate) students studying Applied Machine Learning and Applied Artificial Intelligence and Deep Learning courses as part of a four-year BSc in Computer Science. We also present processes used to validate the examinations as well as plagiarism detection methods implemented. Findings from this study highlight positive effects of using open-book online exams, with 85% of students reporting that they either prefer online open-book examinations or …

Stellar Classification Of Folded Spectra Using The Mk Classification Scheme And Convolutional Neural Networks, John Magee

Dissertations

The year 1943 saw the introduction of the Morgan-Keenan (MK) classification scheme and this replaced the existing Harvard Classification scheme. Both stellar classification scheme are fundamentally grounded in the field of spectroscopy. The Harvard Classification scheme classified stars based on stellar surface temperature. The MK Classification scheme introduced the concept of a luminosity class that is intrinsically linked to the surface gravity of a star. Temperature and luminosity class values are estimated directly from the stellar spectrum.

Machine learning is a well-established technique in astronomy. Traditionally, a spectrum is treated as a one-dimensional sequence of data. Techniques such as artificial …

Exploiting Bert And Roberta To Improve Performance For Aspect Based Sentiment Analysis, Gagan Reddy Narayanaswamy

Dissertations

Sentiment Analysis also known as opinion mining is a type of text research that analyses people’s opinions expressed in written language. Sentiment analysis brings together various research areas such as Natural Language Processing (NLP), Data Mining, and Text Mining, and is fast becoming of major importance to companies and organizations as it is started to incorporate online commerce data for analysis. Often the data on which sentiment analysis is performed will be reviews. The data can range from reviews of a small product to a big multinational corporation. The goal of performing sentiment analysis is to extract information from those …

Feature Augmentation For Improved Topic Modeling Of Youtube Lecture Videos Using Latent Dirichlet Allocation, Nakul Srikumar

Dissertations

Application of Topic Models in text mining of educational data and more specifically, the text data obtained from lecture videos, is an area of research which is largely unexplored yet holds great potential. This work seeks to find empirical evidence for an improvement in Topic Modeling by pre- extracting bigram tokens and adding them as additional features in the Latent Dirichlet Allocation (LDA) algorithm, a widely-recognized topic modeling technique. The dataset considered for analysis is a collection of transcripts of video lectures on Machine Learning scraped from YouTube. Using the cosine similarity distance measure as a metric, the experiment showed …

Human Age And Gender Classification Using Convolutional Neural Networks, Eamon Kelliher

Dissertations

In a world relying ever more on human classification, this papers aims to improve on age and gender image classification through the use of Convolutional Neural Networks (CNN). Age and gender classification has become a popular area of study in the past number of years however there are still improvements to be made, particularly in the area of age classification. This research paper aims to test the currently accepted fact that CNN models are the superior model type for image classification by comparing CNN performance against Support Vector Machine performance on the same dataset. Using the Adience image classification dataset, …

Evaluating The Performance Of Transformer Architecture Over Attention Architecture On Image Captioning, Deepti Balasubramaniam

Dissertations

Over the last few decades computer vision and Natural Language processing has shown tremendous improvement in different tasks such as image captioning, video captioning, machine translation etc using deep learning models. However, there were not much researches related to image captioning based on transformers and how it outperforms other models that were implemented for image captioning. In this study will be designing a simple encoder-decoder model, attention model and transformer model for image captioning using Flickr8K dataset where will be discussing about the hyperparameters of the model, type of pre-trained model used and how long the model has been trained. …

Finetuning Bert And Xlnet For Sentiment Analysis Of Stock Market Tweets Using Mixout And Dropout Regularization, Shubham Jangir

Dissertations

Sentiment analysis is also known as Opinion mining or emotional mining which aims to identify the way in which sentiments are expressed in text and written data. Sentiment analysis combines different study areas such as Natural Language Processing (NLP), Data Mining, and Text Mining, and is quickly becoming a key concern for businesses and organizations, especially as online commerce data is being used for analysis. Twitter is also becoming a popular microblogging and social networking platform today for information among people as they contribute their opinions, thoughts, and attitudes on social media platforms over the years. Because of the large …

Covid-19 Prediction Using Lstm Algorithm: Gcc Case Study, Kareem Kamal A. Ghany, Hossam Zawbaa, Heba M. Sabri

Articles

Coronavirus-19 (COVID-19) is the black swan of 2020. Still, the human response to restrain the virus is also creating massive ripples through different systems, such as health, economy, education, and tourism. This paper focuses on research and applying Artificial Intelligence (AI) algorithms to predict COVID-19 propagation using the available time-series data and study the effect of the quality of life, the number of tests performed, and the awareness of citizens on the virus in the Gulf Cooperation Council (GCC) countries at the Gulf area. So we focused on cases in the Kingdom of Saudi Arabia (KSA), United Arab of Emirates …

Are We In The Digital Dark Times? How The Philosophy Of Hannah Arendt Can Illuminate Some Of The Ethical Dilemmas Posed By Modern Digital Technologies, Damian Gordon, Anna Becevel

Conference Papers

Philosophers are not generally credited with being clairvoyant, and yet because they recognise, record and reflect on trends in their society, their observations can often appear prescient. In the field of the ethics of technology, there is, perhaps, no philosopher whose perspective on these issues is worth examining in detail more than that of Hannah Arendt, who can offer real perspective on the challenges we are facing with technologies in the twenty-first century. Arendt, a thinker of Jewish-German origin, student of Martin Heidegger and Karl Jaspers, encountered her life turning point when she was forced into becoming a refugee as …

Case Study: Transition To A Vegan Diet In An Elite Male Gaelic Football Player, Daniel Davey, Shane Malone, Brendan Egan

Articles

Vegan diets are increasingly of interest to athletes, but require a well-planned approach in order to mitigate the risk of potential adverse effects on nutrient intakes, and consequently performance. This case study reports the process of an elite male Gaelic football player (age 25 years; height, 1.88 m; body mass, 87.8 kg; lean body mass, 73.26 kg; body fat, 11.3%) transitioning from an omnivorous diet to a vegan diet at the beginning of a competitive season. The report encompasses key considerations in the planning and provision of nutrition support in this context, in addition to iterations needed based on challenges …

Fedoram: A Federated Oblivious Ram Scheme, Alexandre Pujol, Liam Murphy, Christina Thorpe

Articles

Instant messaging (IM) applications, even with end-to-end encryption enabled, pose privacy issues due to metadata and pattern leakage. Our goal is to develop a model for a privacy preserving IM application, by designing an IM application that focuses on hiding metadata and discussion patterns. To solve the issue of privacy preservation through the obfuscation of metadata, cryptographic constructions like Oblivious Random Access Machines (ORAM) have been proposed in recent years. However, although they completely hide the user access patterns, they incur high computational costs, often resulting in excessively slow performance in practice. We propose a new federated model, FedORAM, which …

Disaster Analysis Using Satellite Image Data With Knowledge Transfer And Semi-Supervised Learning Techniques, Palavi Jain

Reports

With the increase in frequency of disasters and crisis situations like floods, earthquake and hurricanes, the requirement to handle the situation efficiently through disaster response and humanitarian relief has increased. Disasters are mostly unpredictable in nature with respect to their impact on people and property. Moreover, the dynamic and varied nature of disasters makes it difficult to predict their impact accurately for advanced preparation of responses [104]. It is also notable that the economical loss due to natural disasters has increased in recent years, and it, along with the pure humanitarian need, is one of the reasons to research innovative …

Codec-Aware Video Delivery Over Sdns, Obinna Izima, Ruairi Defrein, Ali Malik

Conference papers

To guarantee quality of delivery for video streaming over software defined networks, efficient predictors and adaptive routing frameworks are required. We demonstrate an agent that predicts video quality of delivery metrics in a scalable way using a bespoke codec-aware learning model. We also demonstrate the integration of this agent with an adaptive framework for centrally controlled software-defined networks that re-configures network operational paths in response to the learning agent, ensuring that good quality of delivery of video is maintained during periods of congestion. The demo scenario highlights the feasibility, scalability and accuracy of the framework

Adequately Generating Captions For An Image Using Adaptive And Global Attention Mechanisms., Shravan Kumar Talanki Venkatarathanaiahsetty

Dissertations

Generating description to images is a recent surge and with latest developments in the field of Artificial Intelligence, it can be one of the prominent applications to bridge the gap between Computer vision and Natural language processing fields. In terms of the learning curve, Deep learning has become the main backbone in driving many new applications. Image Captioning is one such application where the usage of Deep learning methods enhanced the performance of the captioning accuracy. The introduction of the Encoder-Decoder framework was a breakthrough in Image captioning. But as the sequences got longer the performance of captions was affected. …

Improving A Network Intrusion Detection System’S Efficiency Using Model-Based Data Augmentation, Vinicius Waterkemper Lodetti

Dissertations

A network intrusion detection system (NIDS) is one important element to mitigate cybersecurity risks, the NIDS allow for detecting anomalies in a network which may be a cyberattack to a corporate network environment. A NIDS can be seen as a classification problem where the ultimate goal is to distinguish between malicious traffic among a majority of benign traffic. Researches on NIDS are often performed using outdated datasets that don’t represent the actual cyberspace. Datasets such as the CICIDS2018 address this gap by being generated from attacks and an infrastructure that reflects an up-to-date scenario.

A problem may arise when machine …

An Evaluation On The Performance Of Code Generated With Webassembly Compilers, Raymond Phelan

Dissertations

WebAssembly is a new technology that is revolutionizing the web. Essentially it is a low-level binary instruction set that can be run on browsers, servers or stand-alone environments. Many programming languages either currently have, or are working on, compilers that will compile the language into WebAssembly. This means that applications written in languages like C++ or Rust can now be run on the web, directly in a browser or other environment. However, as we will highlight in this research, the quality of code generated by the different WebAssembly compilers varies and causes performance issues. This research paper aims to evaluate …