Computer Engineering Commons^™

Modeling Wealth Distribution In A Society, Dylan Berns, Peihsien Sun, Adrian V. Gheorghe

Engineering Management & Systems Engineering Faculty Publications

The interconnectedness of social mood, changing dynamics, income inequality, and wealth distribution underscores the complexity of understanding and addressing these issues. This complexity inspires researchers to develop models and conduct further research to gain insights into the mechanisms driving income inequality and wealth distribution. By studying these phenomena more comprehensively, one can aim to develop strategies and policies that promote a more equitable distribution of wealth and opportunities, thereby fostering social stability and economic prosperity. In the present paper, there was build a model on wealth distribution and income inequality to help people understand the complexities of wealth inequality and …

A Novel Approach To Detecting And Mitigating Keyloggers, Damilola Osedumbi Elelegwu

College of Graduate Studies: Theses & Dissertations

As the digital world gets increasingly ingrained in our daily lives, cyberattacks—especially those involving malware—are growing more complex and common, which calls for developing innovative safeguards. Keylogger spyware, which combines keylogging and spyware functionalities, is one of the most insidious types of cyberattacks. This malicious software stealthily monitors and records user keystrokes, amassing sensitive data, such as passwords and confidential personal information, which can then be exploited. This research work introduces a novel browser extension designed to thwart keylogger spyware attacks effectively. The extension is underpinned by a cutting-edge algorithm that meticulously analyzes input-related processes, promptly identifying and flagging any …

Energy-Aware Ai-Driven Framework For Edge-Computing-Based Iot Applications, Muhammad Zawish, Nouman Ashraf, Rafay Iqbal Ansari, Steven Davy

Conference papers

The significant growth in the number of Internet of Things (IoT) devices has given impetus to the idea of edge computing for several applications. In addition, energy harvestable or wireless-powered wearable devices are envisioned to empower the edge intelligence in IoT applications. However, the intermittent energy supply and network connectivity of such devices in scenarios including remote areas and hard-to-reach regions such as in-body applications can limit the performance of edge computing-based IoT applications. Hence, deploying state-of-the-art convolutional neural networks (CNNs) on such energy-constrained devices is not feasible due to their computational cost. Existing model compression methods, such as network …

Does Self-View Mode Generate Video Conferencing Fatigue? An Experiment Using Eeg Signals, Jin Xu, Eoin Whelan, Ann O'Brien, Denis O’Hora

Conference papers

The ability to see or hide one’s own image is a typical feature of video conferencing platforms. This study will conduct an EEG-based neurobiological experiment to determine if the self-view mode generates video conference fatigue and if this differs between males and females. 40 volunteers will participate in a simulated video conference meeting with the self-view mode on and off at different times. In addition, an EEG-based fatigue monitor will be proposed to demonstrate the level of human mental fatigue. The experimental insights will provide direct biological evidence of the impact of video conferencing features on the user experience and …

Medical Concept Mention Identification In Social Media Posts Using A Small Number Of Sample References, Vasudevan Nedumpozhimana, Sneha Rautmare, Meegan Gower, Maja Popovic, Nishtha Jain, Patricia Buffini, John Kelleher

Conference papers

Identification of mentions of medical concepts in social media text can provide useful information for caseload prediction of diseases like Covid-19 and Measles. We propose a simple model for the automatic identification of the medical concept mentions in the social media text. We validate the effectiveness of the proposed model on Twitter, Reddit, and News/Media datasets.

Co-Design Of An Interactive Wellness Park: Exploring Design Requirements For A Multimodal Outdoor Physical Web Installation With Older Adults, Fatima Badmos

Academic Posters Collection

The global demographic landscape is experiencing a notable shift, characterised by a growing proportion of adults over 60. According to projections, the proportion of individuals aged 60 and above is expected to reach one-sixth of the global population by 2030. Furthermore, by 2050, this demographic is projected to exceed a staggering two billion people. Amidst this shift, there is an urgent need to develop interactive and innovative solutions to address older adults' unique challenges, particularly in outdoor physical activity.

A co-design methodology involving older adults’ participation from the idea generation to the application development process will be adopted to address …

Automation, Ai, And Future Skills Needs: An Irish Perspective, Raimunda Bukartaite, Daire Hooper

Articles

This study explores insights from key stakeholders into the skills they believe will be necessary for the future of work as we become more reliant on artificial intelligence (AI) and technology. The study also seeks to understand what human resource policies and educational interventions are needed to support and take advantage of these changes.

Detection Of Truthful, Semi-Truthful, False And Other News With Arbitrary Topics Using Bert-Based Models, Elena Shushkevich, John Cardiff, Anna Boldyreva

Conference Papers

Easy and uncontrolled access to the Internet provokes the wide propagation of false information, which freely circulates in the Internet. Researchers usually solve the problem of fake news detection (FND) in the framework of a known topic and binary classification. In this paper we study possibilities of BERT-based models to detect fake news in news flow with unknown topics and four categories: true, semi-true, false and other. The object of consideration is the dataset CheckThat! Lab proposed for the conference CLEF-2022. The subjects of consideration are the models SBERT, RoBERTa, and mBERT. To improve the quality of classification we use …

Enhancing Zero‑Shot Action Recognition In Videos By Combining Gans With Text And Images, Kaiqiang Huang, Luis Miralles-Pechuán, Susan Mckeever

Articles

Zero-shot action recognition (ZSAR) tackles the problem of recognising actions that have not been seen by the model during the training phase. Various techniques have been used to achieve ZSAR in the field of human action recognition (HAR) in videos. Techniques based on generative adversarial networks (GANs) are the most promising in terms of performance. GANs are trained to generate representations of unseen videos conditioned on information related to the unseen classes, such as class label embeddings. In this paper, we present an approach based on combining information from two different GANs, both of which generate a visual representation of …

A Real-Time Machine Learning Framework For Smart Home-Based Yoga Teaching System, Jothika Sunney, Musfira Jilani, Pramod Pathak, Paul Stynes

Conference papers

Practicing yoga poses in a home-based environment has increased due to Covid19. Yoga poses without a trainer can be challenging, and incorrect yoga poses can cause muscle damage. Smart home-based yoga teaching systems may aid in performing accurate yoga poses. However, the challenge with such systems is the computational time required to detect yoga poses. This research proposes a real-time machine learning framework for teaching accurate yoga poses. It combines a pose estimation model, a pose classification model, and a real-time feedback mechanism. The dataset consists of five popular yoga poses namely the downdog pose, the tree pose, the goddess …

Show, Prefer And Tell: Incorporating User Preferences Into Image Captioning, Annika Lindh, Robert J. Ross, John Kelleher

Conference papers

Image Captioning (IC) is the task of generating natural language descriptions for images. Models encode the image using a convolutional neural network (CNN) and generate the caption via a recurrent model or a multi-modal transformer. Success is measured by the similarity between generated captions and human-written “ground-truth” captions, using the CIDEr [14], SPICE [1] and METEOR [2] metrics. While incremental gains have been made on these metrics, there is a lack of focus on end-user opinions on the amount of content in captions. Studies with blind and low-vision participants have found that lack of detail is a problem [6, 13, …

Dynamic Influence Diagram-Based Deep Reinforcement Learning Framework And Application For Decision Support For Operators In Control Rooms, Joseph Mietkiewicz, Ammar N. Abbas, Chidera Winifred Amazu, Anders L. Madsen, Gabriele Baldissone

Conference papers

In today’s complex industrial environment, operators are often faced with challenging situations that require quick and accurate decision-making. The human-machine interface (HMI) can display too much information, leading to information overload and potentially compromising the operator’s ability to respond effectively. To address this challenge, decision support models are needed to assist operators in identifying and responding to potential safety incidents. In this paper, we present an experiment to evaluate the effectiveness of a recommendation system in addressing the challenge of information overload. The case study focuses on a formaldehyde production simulator and examines the performance of an improved Human-Machine Interface …

Exploring The Impact Of Competition And Incentives On Game Jam Participation And Behaviour, John Healy, Niamh Germaine

Conference papers

Competitive elements are a common feature of many game jams. However, there has been little research to date on the impact of competition on participants and their behaviours. To better understand how incentives and competition may affect the motivations and behaviour of game jam participants, we surveyed 47 game jam participants and analysed data from 4,564 online game jams. We found that incentives and competition were neither strong deterrents nor significant motivators for game jam participation. However, a significant percentage of the participants surveyed indicated that incentives and competition would affect their behaviour during a game jam. Our findings suggest …

Actor-Centric Spatio-Temporal Feature Extraction For Action Recognition, Anil Kunchala, Bianca Schoen-Phelan, Mélanie Bouroche

Conference papers

Action understanding involves the recognition and detection of specific actions within videos. This crucial task in computer vision gained significant attention due to its multitude of applications across various domains. The current action detection models, inspired by 2D object detection methods, employ two-stage architectures. The first stage is to extract actor-centric video sub-clips, i.e. tubelets of individuals, and the second stage is to classify these tubelets using action recognition networks. The majority of these recognition models utilize a frame-level pre-trained 3D Convolutional Neural Networks (3D CNN) to extract spatio-temporal features of a given tubelet. This, however, results in suboptimal spatio-temporal …

How Online Discourse Networks Fields Of Practice: The Discursive Negotiation Of Autonomy On Art Organisation About Pages, Tommie Soro

Articles

This article examines how the online discourse of art organisations forges relationships between the artworld and the fields of politics and economy. Combining elements of Pierre Bourdieu’s field analysis and Norman Fairclough’s critical discourse analysis, the article analyses an elite art magazine, e-flux, and an elite art museum, IMMA, and the activities of discourses, genres, and utterances on their about pages. Its results suggest that the about pages of these organisations forge links between the artworld and the fields of politics and economy by mobilising discourse in these fields and by incorporating discourse practices from these fields. The ideological tension …

Music For Visual Media Of The 21st Century What Role Does Music Have In Visual Media, Sigrid Heugen

Doctoral

This research consists of a composition portfolio which examines new perspectives on musical experiences in virtual environments, through the exploration and development of immersive adaptive music for a visual medium. The research addresses the usage, adaptation, and creation of music in visual media such as Virtual Reality (VR) and other Extended Realities (XR).

Setransformer: A Transformer-Based Code Semantic Parser For Code Comment Generation, Zheng Li, Yonghao Wu, Bin Peng, Xiang Chen, Zeyu Sun, Yong Liu, Paul Doyle

Conference Papers

Automated code comment generation technologies can help developers understand code intent, which can significantly reduce the cost of software maintenance and revision. The latest studies in this field mainly depend on deep neural networks, such as convolutional neural networks and recurrent neural network. However, these methods may not generate high-quality and readable code comments due to the long-term dependence problem, which means that the code blocks used to summarize information are far from each other. Owing to the long-term dependence problem, these methods forget the previous input data’s feature information during the training process. In this article, to solve the …

Meme Sentiment Analysis Enhanced With Multimodal Spatial Encoding And Face Embedding, Muzhaffar Hazman, Susan Mckeever, Josephine Griffith

Conference papers

Internet memes are characterised by the interspersing of text amongst visual elements. State-of-the-art multimodal meme classifiers do not account for the relative positions of these elements across the two modalities, despite the latent meaning associated with where text and visual elements are placed. Against two meme sentiment classification datasets, we systematically show performance gains from incorporating the spatial position of visual objects, faces, and text clusters extracted from memes. In addition, we also present facial embedding as an impactful enhancement to image representation in a multimodal meme classifier. Finally, we show that incorporating this spatial information allows our fully automated …

Using Machine Learning To Identify Patterns In Learner-Submitted Code For The Purpose Of Assessment, Botond Tarcsay, Fernando Perez-Tellez, Jelena Vasic

Conference papers

Programming has become an important skill in today’s world and is taught widely both in traditional and online settings. Instructors need to grade increasing amounts of student work. Unit testing can contribute to the automation of the grading process but it cannot assess the structure or partial correctness of code, which is needed for finely differentiated grading. This paper builds on previous research that investigated machine learning models for determining the correctness of programs from token-based features of source code and found that some such models can be successful in classifying source code with respect to whether it passes unit …

A Tutoring Framework To Support Computer Science Programmes In Higher Education, Emer Thornbury, Frances Sheridan, Pramod Pathak, Cristina Hava Muntean, Paul Stynes

Conference papers

Computing Support is the provision of academic supports such as individual tutoring and support classes to students studying computing at third level. Students can struggle with computing as it requires practice involving trial and error. This work proposes a research informed tutoring framework to support computer science students at third level. The tutoring framework combines three pillars; staff and training, pedagogies and activities. Support is put in place to help students develop technical and programming skills. Essential tutoring is provided for those who might otherwise drop out of college. The framework was applied to first and second-year undergraduate programmes and …

Work In Progress: A Virtual Educational Robotics Coding Club Framework To Improve K-6 Students Emotional Engagement In Stem, Kate Carmody, Julie Booth, Jospehine Bleach, Pramod Pathak, Paul Styles

Conference papers

The growing popularity and deployment of Internet of Things (IoT) devices has led to serious security concerns. The integration of a security operations center (SOC) becomes increasingly important in this situation to ensure the security of IoT devices. In this article, we will present a summary of IoT device security issues, their vulnerabilities, a review of current challenges to keep these devices secure, and discuss the role that SOC can bring in protecting IoT devices while considering the challenges encountered and the directions to consider when implementing a reliable SOC for IoT monitoring.

Detecting Road Intersections From Satellite Images Using Convolutional Neural Networks, Fatmaelzahraa Eltaher Ph.D, Luis Miralles-Pechuán, Jane Courtney, Susan Mckeever

Conference papers

Automatic detection of road intersections is an important task in various domains such as navigation, route planning, traffic prediction, and road network extraction. Road intersections range from simple three-way T-junctions to complex large-scale junctions with many branches. The location of intersections is an important consideration for vulnerable road users such as People with Blindness or Visually Impairment (PBVI) or children. Route planning applications, however, do not give information about the location of intersections as this information is not available at scale. As a first step to solving this problem, a mechanism for automatically mapping road intersection locations is required, ideally …

Queer In Ai: A Case Study In Community-Led Participatory Ai, Anaelia Ovalle, Arjun Subramonian, Ashwiin Singh, Claas Voelcker, Danica Sutherland, Davide Locatelli, Eva Breznik, Felip Klubicka, Hang Yuan, Hetvi J, Huan Zhang, Jaidev Shriram, Kruno Lehman, Luca Soldaini, Maarten Sap, Marc Peter Deisenroth, Maria Leonor Pacheco, Maria Ryskina, Martin Mundt, Melind Agarwal, Nyx Mclean, Pan Xu, A. Pranav, Raj Korpan, Ruchira Ray, Sarah Mathew, Sarthak Arora, S.T. John, Tanvi Anand, Vishakha Agrawal, William Agnew, Yanan Long, Zijie J. Wang, Zeerak Talat, Avijit Ghosh, Nathaniel Dennler, Michael Noseworthy, Sharvani Jha, Emi Baylor, Aditya Joshi, Natalia Y. Bilenko, Andrew Mcnamara, Raphael Gontijo-Lopes, Alex Markham, Evyn Dong, Jackie Kay, Manu Saraswat, Nikhil Vytla, Luke Stark

Conference papers

Queerness and queer people face an uncertain future in the face of ever more widely deployed and invasive artificial intelligence (AI). These technologies have caused numerous harms to queer people, including privacy violations, censoring and downranking queer content, exposing queer people and spaces to harassment by making them hypervisible, deadnaming and outing queer people. More broadly, they have violated core tenets of queerness by classifying and controlling queer identities. In response to this, the queer community in AI has organized Queer in AI, a global, decentralized, volunteer-run grassroots organization that employs intersectional and community-led participatory design to build an inclusive …

Robustness Of Image-Based Malware Classification Models Trained With Generative Adversarial Networks, Ciaran Reilly, Stephen O Shaughnessy, Christina Thorpe

Conference papers

As malware continues to evolve, deep learning models are increasingly used for malware detection and classification, including image based classification. However, adversarial attacks can be used to perturb images so as to evade detection by these models. This study investigates the effectiveness of training deep learning models with Generative Adversarial Network-generated data to improve their robustness against such attacks. Two image conversion methods, byte plot and space-filling curves, were used to represent the malware samples, and a ResNet-50 architecture was used to train models on the image datasets. The models were then tested against a projected gradient descent attack. It …

Interpretable Input-Output Hidden Markov Model-Based Deep Reinforcement Learning For The Predictive Maintenance Of Turbofan Engines, Ammar N. Abbas, Georgios C. Chasparis, John Kelleher

Conference papers

An open research question in deep reinforcement learning is how to focus the policy learning of key decisions within a sparse domain. This paper emphasizes on combining the advantages of input-output hidden Markov models and reinforcement learning. We propose a novel hierarchical modeling methodology that, at a high level, detects and interprets the root cause of a failure as well as the health degradation of the turbofan engine, while at a low level, provides the optimal replacement policy. This approach outperforms baseline deep reinforcement learning (DRL) models and has performance comparable to that of a state-of-the-art reinforcement learning system while …

Graph-Based Mutations For Music Generation, Maziar Kanani, Sean O'Leary, James Mcdermott

Conference papers

Our study aims to compare the effects of direct mutation and graphbased mutation on representations of music domain. We focus on short tunes from the Irish folk tradition, represented as integer sequences, and use a graph-based representation based on Pathway Assembly (a directed acyclic graph) and the Sequitur algorithm. We define multiple mutation operators to work directly on the sequences or on the graphs, hypothesizing that graph-based mutations will tend to preserve the pattern used per tune, while direct mutation of sequences will tend to destroy patterns, resulting in new generated tunes that are more complex. We perform experiments on …

Bilstm−Bigru: A Fusion Deep Neural Network For Predicting Air Pollutant Concentration, Prasanjit Dey, Soumyabrata Dev, Bianca Schoen-Phelan

Conference papers

Predicting air pollutant concentrations is an efficient way to prevent incidents by providing early warnings of harmful air pollutants. A precise prediction of air pollutant concentrations is an important factor in controlling and preventing air pollution. In this paper, we develop a bidirectional long-short-term memory and a bidirectional gated recurrent unit (BiLSTM−BiGRU) to predict PM 2.5 concentrations in a target city for different lead times. The BiLSTM extracts preliminary features, and the BiGRU further extracts deep features from air pollutant and meteorological data. The fully connected (FC) layer receives the output and makes an accurate prediction of the PM 2.5 …

Combinedeepnet: A Deep Network For Multistep Prediction Of Near-Surface Pm2.5 Concentration, Prasanjit Dey, Soumyabrata Dev, Bianca Schoen-Phelan

Conference papers

PM2.5 is a type of air pollutant that can cause respiratory and cardiovascular problems. Precise PM2.5 ( μg/m3 ) concentration prediction may help reduce health concerns and provide early warnings. To better understand air pollution, a number of approaches have been presented for predicting PM2.5 concentrations. Previous research used deep learning models for hourly predictions of air pollutants due to their success in pattern recognition, however, these models were unsuitable for multisite, long-term predictions, particularly in regard to the correlation between pollutants and meteorological data. This article proposes the combine deep network (CombineDeepNet), which combines multiple deep networks, including a …

Investigating K-12 Computing Education In Four African Countries (Botswana, Kenya, Nigeria, And Uganda), Ethel Tshukudu, Sue Sentance, Oluwatoyin Adelakun-Adeyemo, Keith Quille, Ziling Zhong

Articles

As K-12 computing education becomes more established throughout the world, there is an increasing focus on accessibility for all, whether in a particular country or setting or in areas of the world that may not yet have computing established. This is primarily articulated as an equity issue. The recently developed capacity for, access to, participation in, and experience of computer science education (CAPE) Framework is one way of demonstrating stages and dependencies and understanding relative equity, taking into consideration the disparities between sub-populations. While there is existing research that covers the state of computing education and equity issues, it is …

Robustembed: Robust Sentence Embeddings Using Self-Supervised Contrastive Pre-Training, Javad Asl, Eduardo Blanco, Daniel Takabi

School of Cybersecurity Faculty Publications

Pre-trained language models (PLMs) have demonstrated their exceptional performance across a wide range of natural language processing tasks. The utilization of PLM-based sentence embeddings enables the generation of contextual representations that capture rich semantic information. However, despite their success with unseen samples, current PLM-based representations suffer from poor robustness in adversarial scenarios. In this paper, we propose RobustEmbed, a self-supervised sentence embedding framework that enhances both generalization and robustness in various text representation tasks and against diverse adversarial attacks. By generating high-risk adversarial perturbations to promote higher invariance in the embedding space and leveraging the perturbation within a novel contrastive …