Computer Engineering Commons^™

How Can Personalised Feedback In Assignments Help Address Gender Balance In Computing Education?, Alina Berry

Academic Posters Collection

Personalised feedback is frequently used in computing assessments in higher education. Research has shown that personalised feedback positively influences persistence in computer science. Computing and related disciplines are known to show relatively low retention rates. This includes female students, who are strongly underrepresented in computing disciplines, so they can be considered as a particularly important group for retention-driven initiatives. Female science students are more likely to act upon feedback, and personalised feedback has increased intentions to persist among female top performing students in computing. Hence, providing personalised feedback can be considered as a promising gender initiative that has a potential …

Human Centred Xai Taxonomy, Helen Sheridan, Emma Murphy, Dympna O'Sullivan

Academic Posters Collection

No abstract provided.

Design Strategies For Explainable Ai: 12 Guiding Design Principles For Human-Centred Xai, Helen Sheridan, Dympna O'Sullivan, Emma Murphy

Academic Posters Collection

With the growing demand for transparent Al systems, the EU's Al Act emphasizes the need for accessible explanations to foster user trust and ethical Al use [1]. Our research explores the "gulf of explanation" in XAl, evaluating how well current systems align with users' mental models and engaging experts to improve human-centred XAl design [2]. This work particularly considers the application of a new and novel HCl framework, a set of 12 key design principles for human-centred XAI.

Personalised Feedback On Assessments In Computing Modules - Gender Equality Action In Context, Alina Berry

Academic Posters Collection

Personalised feedback in computing higher education has been known to positively influence retention of women. The issue of gender inequality in computing field is well known and one of the efforts to address it is the development of a gender equality toolkit (TechMate), with one initiative (action) in the toolkit being personalised feedback. While all actions in the toolkit are research-driven, the aim of this work was to evaluate the action on personalised feedback in a local context.

The study comprised of 10 semi-structured interviews with computing lecturers at TU Dublin who provide personalised feedback, a student survey with 68 …

Emotional Intelligence Coaching For Higher Education Lecturers: Their Experience And Its Impact On Trait Emotional Intelligence, Stress, And Teaching Self-Efficacy, Eoghan Guiry

Doctoral

Previous research has established that emotional intelligence (EI) can be developed through targeted intervention; however, this was previously untested with lecturers. Guided by the job demands-resources model, this study explored the impact of an EI coaching intervention on lecturers’ trait EI, perceived stress, work-related stress, and teaching self-efficacy at Technological University Dublin. Additionally, the study explored the lecturers’ experiences of partaking in the intervention. Employing a pragmatic approach, mixed methods were used in a randomised controlled trial.

Applications Of Predictive And Generative Ai Algorithms: Regression Modeling, Customized Large Language Models, And Text-To-Image Generative Diffusion Models, Suhaima Jamal

College of Graduate Studies: Theses & Dissertations

The integration of Machine Learning (ML) and Artificial Intelligence (AI) algorithms has radically changed predictive modeling and classification tasks, enhancing a multitude of domains with unprecedented analytical capabilities. Predictive modeling leverages ML and AI to forecast future trends or behaviors based on historical data, while classification tasks categorize data into distinct classes, from email filtering to medical diagnosis. Concurrently, text-to-image generation has emerged as a transformative potential, allowing visual content creation directly from textual descriptions. These advancements are pivotal in design, art, entertainment, and visual communication, as well as enhancing creativity and productivity. This work explores three significant studies in …

Railroad Condition Monitoring Using Distributed Acoustic Sensing And Deep Learning Techniques, Md Arifur Rahman

College of Graduate Studies: Theses & Dissertations

Proper condition monitoring has been a major issue among railroad administrations since it might cause catastrophic dilemmas that lead to fatalities or damage to the infrastructure. Although various aspects of train safety have been conducted by scholars, in-motion monitoring detection of defect occurrence, cause, and severity is still a big concern. Hence extensive studies are still required to enhance the accuracy of inspection methods for railroad condition monitoring (CM). Distributed acoustic sensing (DAS) has been recognized as a promising method because of its sensing capabilities over long distances and for massive structures. As DAS produces large datasets, algorithms for precise …

Self-Supervised Contrastive Learning Using Eeg Signals For Mental Stress Assessment, Ugochukwu Uraechu

College of Graduate Studies: Theses & Dissertations

A study is presented to investigate self-supervised contrastive learning (SSCL) models using physiological data obtained from non-invasive wearable sensors for mental stress assessment. The present work involved acquisition of electroencephalography (EEG) signals using wearable sensors, signal preprocessing, data augmentation, and investigation of self-supervised contrastive learning (SSCL) algorithms for multi-class mental stress assessment. Seven volunteers participated in this study executing various mental tasks while wearing an OpenBCI head cap to acquire EEG signals. The acquired EEG signals were preprocessed and utilized for data augmentation in time and frequency domains with different SSCL models. Optimal data augmentation combinations and SSCL models were …

Multimodal Fusion For Audio-Image And Video Action Recognition, Muhammad B. Shaikh, Douglas Chai, Syed M. S. Islam, Naveed Akhtar

Research outputs 2022 to 2026

Multimodal Human Action Recognition (MHAR) is an important research topic in computer vision and event recognition fields. In this work, we address the problem of MHAR by developing a novel audio-image and video fusion-based deep learning framework that we call Multimodal Audio-Image and Video Action Recognizer (MAiVAR). We extract temporal information using image representations of audio signals and spatial information from video modality with the help of Convolutional Neutral Networks (CNN)-based feature extractors and fuse these features to recognize respective action classes. We apply a high-level weights assignment algorithm for improving audio-visual interaction and convergence. This proposed fusion-based framework utilizes …

The Integration Of Neuromorphic Computing In Autonomous Robotic Systems, Md Abu Bakr Siddique

Dissertations, Master's Theses and Master's Reports

Deep Neural Networks (DNNs) have come a long way in many cognitive tasks by training on large, labeled datasets. However, this method has problems in places with limited data and energy, like when planetary robots are used or when edge computing is used [1]. In contrast to this data-heavy approach, animals demonstrate an innate ability to learn by communicating with their environment and forming associative memories among events and entities, a process known as associative learning [2-4]. For instance, rats in a T-maze learn to associate different stimuli with outcomes through exploration without needing labeled data [5]. This learning paradigm …

Performance Characterization And Optimization Of A Point-Cloud-Based Path Planner In Off-Road Terrains, Casey D. Majhor

Dissertations, Master's Theses and Master's Reports

In this dissertation, I present a multifaceted study on the generation of artificial terrains using a multifractal method, and their use in autonomous ground vehicle (AGV) planning and navigation performance characterization in high-fidelity simulations, and automated parameter optimization.

My first contribution is a multifractal artificial terrain generation method leveraging the 3D Weierstrass-Mandelbrot function to control terrain roughness. We generate 60 unique off-road terrains while varying the fractal dimension and test the impact on vehicle traversal difficulty. Results show that increasing the fractal dimension decreases low-roughness areas, increases semi-rough and high-roughness areas, and decreases vehicle success rates while increasing vertical accelerations, …

V2i-Based Adaptive Collision Avoidance For Safety And Traffic Efficiency, Vaishnavi Balambeed

Dissertations, Master's Theses and Master's Reports

To leverage the growing communication and connectivity among modern vehicles, Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) systems are increasingly being used to implement active safety applications. However, current research often overlooks the impact of algorithms such as collision avoidance on traffic flow efficiency. This work investigates the adaptation of a collision avoidance algorithm implemented in V2I to incorporate a variable time headway and spacing control strategy. The proposed approach aims to maintain higher average speeds among vehicles, lower individual vehicle’s waiting, and travel times in the vicinity of the infrastructural unit while simultaneously avoiding collisions; thereby enhancing both safety and traffic …

Enhancing Privacy While Revealing Vulnerabilities: Strategies For Adaptation, Optimization, And Model Extraction, Madhureeta Das

Dissertations, Master's Theses and Master's Reports

In the evolving landscape of machine learning and artificial intelligence, this dissertation presents a series of innovative contributions spanning several critical areas: embracing semi-supervised domain adaptation for secure knowledge transfer, enhancing the model performance of tiny models, and executing model stealing attacks via diversified prompts. The overarching goal is to enhance the performance, scalability, and security of AI models across various applications.

The first research focus is on semi-supervised domain adaptation within federated learning frameworks. By leveraging semi-supervised learning techniques, this work addresses the challenge of adapting models trained on a source domain to perform effectively on a target domain …

Specification And Implementation Of Arm Isa Using Adl, Jonathan A. Rabideau

Dissertations, Master's Theses and Master's Reports

In this project, we implement a specification of ARM ISA using the ADL system. Current progress includes 79% of planned instructions, 79% of which are complete for our purposes, and also includes components such as a proxy kernel and a temporary preprocessor. Simple programs compiled directly from gcc can already be properly assembled and simulated using the tools generated by ADL. Once the specification is complete, we can integrate it with other components for testing ideas and theories. This report covers our progress and future plans regarding this project, including some background material and problem solutions.

Implementing Associative Learning Using Neuromorphic Robot, Vinay Kumar Pillalamarri

Dissertations, Master's Theses and Master's Reports

Associative learning, a key cognitive process seen across the animal kingdom, enables organisms to form connections between stimuli and adapt their behaviors based on past experiences. A particularly powerful example is fear conditioning, where animals learn to associate a neutral stimulus with an aversive one, allowing them to predict and avoid potential threats. Inspired by this mechanism, this project implements associative learning on an unmanned ground vehicle (UGV) to develop adaptive behavior through neuromorphic principles. Utilizing Nengo for neural modeling, the UGV learns to associate visual (red color) and tactile (vibration) stimuli through Hebbian learning, a biologically inspired synaptic adaptation …

Leveraging Large Language Models For Enhancing Well-Being In The Digital Age, Xiaobo Guo

Dartmouth College Ph.D Dissertations

The 21st century has seen dramatic shifts in human interactions with information, peers, and the environment, primarily driven by the proliferation of online platforms and social media. These advancements offer more access to information and global connectivity, but also present challenges such as information overload, misinformation, online harms, and biased reporting that can negatively impact user well-being. This thesis examines the role of Large Language Models (LLMs) — advanced forms of artificial intelligence that understand and generate human-like text — in enhancing well-being in the digital age. The study begins by exploring the potential of LLMs to detect early signs …

An Fpga-Based Eit System For Deep Space Medical Imaging, Kendall R. Farnham

Dartmouth College Ph.D Dissertations

Dangers associated with high radiation and microgravity exposure in space are critical challenges inhibiting us from exploring deep space and pursuing long-duration missions, as current medical systems are unable to monitor, diagnose, or treat tissue injury within physical spacecraft constraints and communication limits. Ultrasound (US) is the current imaging system used on the International Space Station, but this technology relies on telemedical support (or onboard artificial intelligence/autonomous capabilities) for both operation and diagnosis, posing challenges for crews isolated in deep space. Electrical impedance tomography (EIT) is a non-invasive, non-ionizing technology that produces images of the electrical properties of tissues and …

Smart Dog Door, Andrew Shetler, William Boissoneault, Jacob Stump, Benjamin Charlson

Williams Honors College, Honors Research Projects

With modern life getting busier and more complex, many have turned to autonomous assistance for taking care of pets. The objective of this project is to design and prototype a device and application that will allow only certain dogs to enter and exit through a dog door while making the owners aware of their dog's activity. The dog door will utilize wireless communication from the dog to the door to control the status of the door’s lock and update the database that will send updates to the application. The application will keep track of when the dog enters and exits …

Mobile Robot Adhesion Methodology And Development Of An Automatous Robot Module, Lauren Baird, Zachariah Stone, Madison Lemons, Jonathon Moody

Williams Honors College, Honors Research Projects

Due to the increasing availability of space travel as not only a scientific exploration but a commercial exploration, there is a need for an onsite repair station that can be deployed in the event of aircraft maintenance, damage, or failure. We have been tasked with researching and creating a prototype of an automatous robot that can be attached to a spacecraft body, move along the surface while avoiding obstacles, scan for damage, 3D print a repair piece, and then make the repair, all without the need of direct human input. Our team, as will be discussed throughout, was tasked with …

Improved Portable Back Pain Relief Device With User Interface, Zachary Bobango, Samuel J. Dauterman, Benjamin Bowman

Williams Honors College, Honors Research Projects

The objective of this project is to design and create a massage system that is user interactive, portable, safe, efficient, and comfortable. The system should allow for user feedback from an outside peripheral such as a phone to be able to modify the system. Some challenges facing the implementation of such a system include: ensuring the product can withstand substantial force without breaking or malfunctioning while simultaneously being light enough for a consumer to carry without difficulty, engineering the massage heads to be able to move in multiple different motion types, creating the software that can control the device, and …