Engineering Commons^™

Micro-Credentialing With Fuzzy Content Matching: An Educational Data-Mining Approach, Paul Amoruso

Electronic Theses and Dissertations, 2020-2023

There is a growing need to assess and issue micro-credentials within STEM curricula. Although one approach is to insert a free-standing academic activity into the students learning and degree path, herein the development and mechanism of an alternative approach rooted in leveraging responses on digitized quiz-based assessments is developed. An online assessment and remediation protocol with accompanying Python-based toolset was developed to engage undergraduate tutors who identify and fill knowledge gaps of at-risk learners. Digitized assessments, personalized tutoring, and automated micro-credentialing scripts for Canvas LMS are used to issue skill-specific badges which motivate the learner incrementally, while increasing self-efficacy. This …

Fundraiser, Yasmeen Begum

All Capstone Projects

We are thinking of creating a website that allows individuals to raise money for a variety of occasions, including life milestones like graduations and celebrations as well as difficult situations like accidents and diseases. Users must create an account in order to request money from website subscribers since many of them will assist those in need. The site has to be pushed more on social media platforms such Advertisement Instagram, YouTube, and Facebook Pages so that our website may reach more people online. Admin will handle the users and campaigns and receive a fee of 10% for each transaction.

To …

Plagiarism Checker, Shanmuka Gopala Krishna Chikkam

All Capstone Projects

The Turnitin Plagiarism Checker App is a program that helps users to check their written content for plagiarism. This project is important because plagiarism is a major issue in academia and other fields, where it can lead to academic misconduct, professional repercussions, and legal issues. This application is designed to solve the problem of plagiarism by making it easier for users to check their work for originality before submitting it. The app uses Selenium and Beautiful Soup to automate the process of uploading files and retrieving plagiarism reports from the Turnitin website. The application is an enhancement of an existing …

Version Control Software And Its Possible Impact On Students Academic Integrity, David Mcquaid

HECA Research Conference

Ensuring that students treat their work with integrity has become increasingly difficult in recent years. The advent of Generative AI, Essay Mills, coupled with old fashioned plagiarism and a shift to “online” learning has created a huge shift in the domain of education. Unfortunately, this has manifested as Academic misconduct in many cases and indeed, it could be speculated, that many cases of misconduct are not recognised or discovered. This presentation discusses how version control can be used as a tool to avoid plagiarism.

Revised Avenues Of Assessment In Higher Education In The Presence Of Ai Generative Contents, Muhammad Iqbal

HECA Research Conference

This study explores the impact of Generative Artificial Intelligence (AI) tools on academic assessments, focusing on their efficacy in generating unique content across various domains. Dr. Muhammad Iqbal from CCT College Dublin emphasizes the increasing prevalence of AI generative tools and their potential influence on learning quality in academia. The study addresses concerns related to assessment standards in higher education and proposes the evaluation of AI-generated content reliability.

The Need For International Ai Activities Monitoring, Parviz Partow-Navid, Ludwig Slusky

Journal of International Technology and Information Management

This paper focuses primarily on the need to monitor the risks arising from the dual-use of Artificial Intelligence (AI). Dual-use AI technology capability makes it applicable for defense systems and consequently may pose significant security risks, both intentional and unintentional, with the national and international scope of effects. While domestic use of AI remains the prerogative of individual countries, the unregulated and nonmonitored use of AI with international implications presents a specific concern. An international organization tasked with monitoring potential threats of AI activities could help defuse AI-associated risks and promote global cooperation in developing and deploying AI technology. The …

Determinants Of Continuance Intention To Use Mobile Wallets Technology In The Post Pandemic Era: Moderating Role Of Perceived Trust, Shailja Tripathi

Journal of International Technology and Information Management

The Covid-19 pandemic amplified the volume and importance of mobile payments using digital wallets and placed a basis for their continued adoption. The objective of the study is to formulate and test a comprehensive model by integration of the technology acceptance model (TAM) and expectation confirmation model (ECM) with the addition of three constructs, namely perceived trust, perceived risk, and subjective norm, to identify the determinants of continuance intention to use mobile wallets. Questionnaire-based survey method was used to gather the data from 550 users having experience using mobile wallets for more than six months. The data were analyzed using …

An Optimized Deep Learning-Based Framework For Predicting Diabetes Mellitus Using Ffnn, Norhan S. Elmongy, Sally M. Elghamrawy, Amr M. T. Ali-Eldin, Ali I. Eldesouky

Mansoura Engineering Journal

Diabetes mellitus (DM) is a major public health problem in Egypt, and the illness is regarded as a contemporary epidemic across the world. Diabetes is becoming more common, which is a cause for serious concern. As a result, precise and timely identification of the illness is critical. Health and research institutions have also recently expressed a serious interest in developing and implementing cutting-edge healthcare systems. Therefore, it is necessary to accurately and quickly identify the condition. To solve this issue, scientific research has been carried out, but the outcomes have fallen short. Four layers make up the proposed Diabetes mellitus …

Depression Classification On Privacy Protected Facial Features Data, Yanisa Mahayossanunt

Chulalongkorn University Theses and Dissertations (Chula ETD)

This thesis presents depression classification on privacy protected facial features data. Fast depression classification to help patients receive proper treatment is a method that can prevent the damage of depression. However, fast and effective depression classification is difficult because medical personnel are adequate and the time to analyze depression is long per patient. Applied artificial intelligence in the medical field can help reduce the workload of medical personnel. It is also difficult because of privacy protection. Therefore, we utilize extracted facial features from facial expressions in clinical interview videos to develop a machine learning model. The model utilizes LSTM, attention …

Virtual Plc Platform For Security And Forensics Of Industrial Control Systems, Syed Ali Qasim

Theses and Dissertations

Industrial Control Systems (ICS) are vital in managing critical infrastructures, including nuclear power plants and electric grids. With the advent of the Industrial Internet of Things (IIoT), these systems have been integrated into broader networks, enhancing efficiency but also becoming targets for cyberattacks. Central to ICS are Programmable Logic Controllers (PLCs), which bridge the physical and cyber worlds and are often exploited by attackers. There's a critical need for tools to analyze cyberattacks on PLCs, uncover vulnerabilities, and improve ICS security. Existing tools are hindered by the proprietary nature of PLC software, limiting scalability and efficiency.

To overcome these challenges, …

Implicit Racial Bias: A Human Computer Interaction Study Using Eye Tracker, Wenfan Zhang

Master's Projects

Contemporary Human-Computer Interaction (HCI) research has an increasing emphasis on reducing ethnicity bias. The study presents a new method to explore and reduce biases using detailed experiments. The experimental procedure involves presenting participants with images of ethnically diverse characters across three conditions. The study's results significantly illuminate ethnicity bias in character selection dynamics. Participants exposed to targeted training interventions displayed a significant shift in preferences for characters engaged in intellectual activities. Notably, this shift was influenced by the ethnicity of the characters involved. Interestingly, the eye-tracking data unveiled distinct patterns of cognitive load, characterized by slower response times and greater …

Enhancing Driver Distraction Detection Through The Synergy Of Deep And Traditional Machine Learning, Gowtham Chandrasekaran

Master's Projects

Distracted driving is a major contributor to motor vehicle accidents, causing injury and loss of life. It is one of the major factors that affect the overall driving behavior of a person. Insurance companies take into consideration factors like gender, age, etc. to set insurance premiums for their customers. Today, machine learning and artificial intelligence can eradicate this bias. A machine learning model can analyze driving behavior, such as the frequency and severity of accidents, the speed at which they drive, and their habits such as distracted driving. Based on this information, the model can then determine the risk of …

Spartandark: Anonymity Model Integration With A Blockchain Network Using Spartangold, Nishanth Uchil

Master's Projects

Demand for blockchain ecosystems has seen exponential growth in recent times due to its decentralized nature and trustless verification process for the transactions involved. However, transaction data needs to be leveraged for verification, which coupled with the transparent nature of the blockchain ledger, provides sufficient data for malicious entities to reveal identities and even financial history of users. Data masking techniques have been employed over the years to make blockchain transactions anonymous, making them resistant to identity analysis, a key set of methods being zero-knowledge proof (zk-proof) protocols that guarantee zero data leak. In this research, we develop SpartanDark, a …

Enhancing The Queueing Process For Yioop's Scheduler, Gargi Sheguri

Master's Projects

Indexing in search engines is the process of storing information related to crawled pages to facilitate searches. A crucial determinant of the success of a search engine is the efficiency of the indexing process utilized, which greatly affects both the speed and relevancy of search results. Yioop is an open-source web search engine that employs an inverted index strategy, wherein each term is mapped to a list of the documents it appeared in while crawling.

The primary aim of this project is to better the indexing system used by Yioop, and thus improve the quality of the Search Engine Results …

Temporal Dilation In Video Resnet For Sign Language Translation, Xiaoqian Yang

Master's Projects

Sign languages, vital for communication among the deaf and hard-of-hearing (DHH) people, face a significant linguistic diversity challenge with over 200 distinct sign languages worldwide. Bridging this communication gap is a priority. Traditional tools like interpreters and costly translation devices have limitations. This project aims to use deep learning techniques to develop a model capable of recognizing sign language from short videos. Our model not only recognizes the sign from a single video clip, but is also capable of making prediction of consecutive pairs of signs. To achieve zero-short gesture sequence recognition, we propose a novel temporal dilation strategy, converting …

Graphical User Interface For Evidential Reasoning Models, Rohin Gopalakrishnan

Master's Projects

The Capri system is an evidential reasoning system based on the belief function calculus to support automated reasoning and decision making in uncertain environments. Example domains of application include, medical diagnosis, as well as identifying biological biomarkers. The purpose of this project is to build a Python web-based and app-based Graphical User Interface (GUI), called PyGrapher, that facilitates building graphical evidential reasoning models. The graphical models built using PyGrapher will then be converted to a form that is suitable for input to the Capri system. The PyGrapher system provides an intuitive means to build and manipulate evidential reasoning models as …

Nuancenet: Comparative Analysis Of Ai In Complex Language Interpretation For Disaster Detection, Pavan Koushik Kommuri

Master's Projects

Disaster Detection using Twitter content is critical for emergency response, but accurately identifying relevant tweets remains challenging due to nuances, informal language, and emotional expressions. This paper presents a comparative analysis between traditional Machine Learning models, Deep Learning models and Large Language Models (LLM) for classifying disaster vs. non-disaster tweets. While existing works have applied pattern recognition and dataset-specific learning, LLMs with their deeper understanding of linguistics and semantics can potentially handle the complexities of tweets more effectively. This study leverages LLMs including Llama2, Mistral, and Falcon, Open AI GPT 3.5, hypothesizing their superior contextual comprehension will excel in tweets …

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 …

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 …

Cslinc - Development Of A National Outreach Vle, Keith Nolan, Keith Quille

Conference Papers

Over the last year an online learning platform has been developed and piloted to the Irish second level education system allowing both students and teachers to participate in introductory computing modules. This poster will outline the development of the registration process of a system that is capable of managing potentially 728 schools, 1000+ classrooms and one million students (the entire Irish second level school system). CSLINC is an online student virtual learning environment for computing consisting of several modules built by academics and industry leaders and disseminated to schools through Moodle, our selected virtual learning environment. While Moodle has a …

An Aggregation-Based Algebraic Multigrid Method With Deflation Techniques And Modified Generic Factored Approximate Sparse Inverses, Anastasia Natsiou, George A. Gravvanis, Christos K. Filelis-Papadopoulos, Konstantinos M. Giannoutakis

Articles

In this paper, we examine deflation-based algebraic multigrid methods for solving large systems of linear equations. Aggregation of the unknown terms is applied for coarsening, while deflation techniques are proposed for improving the rate of convergence. More specifically, the V-cycle strategy is adopted, in which, at each iteration, the solution is computed by initially decomposing it utilizing two complementary subspaces. The approximate solution is formed by combining the solution obtained using multigrids and deflation. In order to improve performance and convergence behavior, the proposed scheme was coupled with the Modified Generic Factored Approximate Sparse Inverse preconditioner. Furthermore, a parallel version …

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 …

A New Approach To Linear Displacement Measurements Based On Hall Effect Sensors, İsmai̇l Yari̇çi̇, Yavuz Öztürk

Turkish Journal of Electrical Engineering and Computer Sciences

Since displacement is a vital variable to be considered in many industrial applications, displacement sensing devices have been extensively studied both theoretically and experimentally. There have been also many studies on Hall effect-based displacement measurement, but for many systems linearity still remains a problem. This paper discusses different approaches to calculate the magnetic field due to a cylindrical permanent magnet and proposes a new setup geometry with 2-Hall effect sensors and a permanent magnet between them to overcome the linearity problems. Furthermore, theoretical and experimental studies of the discussed displacement sensor were presented by focusing on the linear range and …

Ai Usage In Development, Security, And Operations, Maurice Ayidiya

Walden Dissertations and Doctoral Studies

Artificial intelligence (AI) has become a growing field in information technology (IT). Cybersecurity managers are concerned that the lack of strategies to incorporate AI technologies in developing secure software for IT operations may inhibit the effectiveness of security risk mitigation. Grounded in the technology acceptance model, the purpose of this qualitative exploratory multiple case study was to explore strategies cybersecurity professionals use to incorporate AI technologies in developing secure software for IT operations. The participants were 10 IT professionals in the United States with at least 5 years of professional experience working in DevSecOps and managing teams of at least …

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 …

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, …

Adversarial Training Of Deep Neural Networks, Anabetsy Termini

CCAC Theses and Dissertations

Deep neural networks used for image classification are highly susceptible to adversarial attacks. The de facto method to increase adversarial robustness is to train neural networks with a mixture of adversarial images and unperturbed images. However, this method leads to robust overfitting, where the network primarily learns to recognize one specific type of attack used to generate the images while remaining vulnerable to others after training. In this dissertation, we performed a rigorous study to understand whether combinations of state of the art data augmentation methods with Stochastic Weight Averaging improve adversarial robustness and diminish adversarial overfitting across a wide …

Improving The Performance, Energy Efficiency And Security Of Gpus, Xin Wang

Theses and Dissertations

The work in this dissertation achieves to enhance the performance, energy-efficiency, and security of the GPUs. We noticed that, as the demand of hardware resources keeps rising in GPUs, the energy consumption becomes unaffordable and places barriers for further performance boost. To resolve this issue, we have proposed several novel GPU micro-architectures that are able to assist the GPUs to execute in an energy-efficient manner. They also provide the potential for further performance enhancement in GPUs. Firstly, we proposed a GPU register packing scheme that stores multiple narrow-width operands to a single register to save register file resources. The unoccupied …

Improving The Flexibility And Robustness Of Machine Tending Mobile Robots, Richard Ethan Hollingsworth

Theses and Dissertations

While traditional manufacturing production cells consist of a fixed base robot repetitively performing tasks, the Industry 5.0 flexible manufacturing cell (FMC) aims to bring Autonomous Industrial Mobile Manipulators (AIMMs) to the factory floor. Composed of a wheeled base and a robot arm, these collaborative robots (cobots) operate alongside people while autonomously performing tasks at different workstations. AIMMs have been tested in real production systems, but the development of the control algorithms necessary for automating a robot that is a combination of two cobots remains an open challenge before the large scale adoption of this technology occurs in industry. Currently popular …

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 …