Physical Sciences and Mathematics Commons^™

Querai – A Smart Quiz Generator, Elton Da Silva, Fernando Aires Da Silva, Kim Jang Womg, Tai Teei Ho

ICT

QUERAI is a website powered by an Artificial Intelligence Question & Answer quiz generator model aiming to enhance students' learning experience and improve teachers' qualitative work by giving them more time to deal with other activities such as assignment correction, general grading, and class preparation.

Problem Solving For Industry, Jozimar Basilio Ferreira, Nicholas Chibuike-Eruba, José Fernando González Anavia, Jolomi (Oritsejolomi) Sillo

ICT

This project seeks to use reinforcement learning to develop AI agents used to controlled NPCs in video game worlds that are capable of mastering decision tasks in their video game environments. Our job will be to develop algorithms and methods that can effectively train the AI agents using Reinforcement learning, which can be used in various gaming environments and scenarios such as racing games and first-person shooters. We then market these agents to video game developers for use in their game worlds. The developer can use our agents as-is in their game without modifications or they can train them further, …

The Use Of Deep Learning Solutions To Develop A Practice Tool To Support Lámh Language For Communication Partners, Gabriel Bueno Pimentel Borges

ICT

This study has proposed an alternative to promote the learning and enhancement of Lámh language for communication partners that support current users by creating a real time detection tool to recognise 20 chosen Lámh signs based on existing studies in the field. This implementation was carried out by generating primary data composed by MediaPipe landmark numpy arrays of 40 frames and 45 repetitions per sign. The Neural Networks were built using the Python library Keras and the applied SVM models were built with the library sklearn. The real time detection was carried out by integrating the mentioned elements with the …

Defining Financial Risks And Market Trends Through Predictive Data Analysis, Marcelle Louise, Luciana Teixeira, Muhammad Shahbaz, Giovanni Andrade

ICT

This project focuses on Dublin short term rental market opportunities, by developing pricing and rate occupancy prediction models based on machine learning approaches to identify patterns that may impact or aid users in making smarter and cost-effective decisions. The concept of this research is to show the financial feasibility of data services, as well as how data science can improve business and operational efficiency.

Blockchain Storage – Drive Configurations And Performance Analysis, Jesse Garner, Aditya A. Syal, Ronald C. Jones

Other Student Works

This project will analyze the results of trials implementing various storage methods on Geth nodes to synchronize and maintain a full-archive state of the Ethereum blockchain. The purpose of these trials is to gain deeper insight to the process of lowering cost and increasing efficiency of blockchain storage using available technologies, analyzing results of various storage drives under similar conditions. It provides performance analysis and describes performance of each trial in relation to the others.

Human Trafficking And Machine Learning: A Data Pipeline From Law Agencies To Research Groups, Nathaniel Hites

Computer Science and Engineering Theses and Dissertations

Human trafficking is a form of modern-day slavery that, while highly illegal, is more dangerous with the advancements of modern technology (such as the Internet), which allows such a practice to spread more easily and quickly all over the world. While the number of victims of human trafficking is large (according to non-profit organization Safe House, there are estimated to be about 20.5 million human trafficking victims, worldwide (“Human Trafficking Statistics & Facts.” Safe Horizon)- co-erced or manipulated by traffickers into either forced labor, or sexual exploitation and encounters), the number of heard cases is proportionally low- several thousand successful …

Increasing Perceived Realism Of Objects In A Mixed Reality Environment Using 'Diminished Virtual Reality', Logan Scott Parker

Honors Theses

With the recent explosion of popularity of virtual and mixed reality, an important question has arisen: “Is there a way to create a better blend of real and virtual worlds in a mixed reality experience?” This research attempts to determine whether a visual filter can be created and applied to virtual objects to better convince the brain into interpreting a composite of virtual and real views as one seamless view. The method devised in this thesis is being called 'Diminished Virtual Reality'. The results found in this study show that when presented with a scene composed of a combination of …

Improved Sensor-Based Human Activity Recognition Via Hybrid Convolutional And Recurrent Neural Networks, Sonia Perez-Gamboa

Electronic Theses, Projects, and Dissertations

Non-intrusive sensor-based human activity recognition is utilized in a spectrum of applications including fitness tracking devices, gaming, health care monitoring, and smartphone applications. Deep learning models such as convolutional neural networks (CNNs) and long short-term memory (LSTMs) recurrent neural networks provide a way to achieve human activity recognition accurately and effectively. This project designed and explored a variety of multi-layer hybrid deep learning architectures which aimed to improve human activity recognition performance by integrating local features and was scale invariant with dependencies of activities. We achieved a 94.7% activity recognition rate on the University of California, Irvine public domain dataset …

Establishing A Machine Learning Framework For Discovering Novel Phononic Crystal Designs, Drew Feltner

Browse all Theses and Dissertations

A phonon is a discrete unit of vibrational motion that occurs in a crystal lattice. Phonons and the frequency at which they propagate play a significant role in the thermal, optical, and electronic properties of a material. A phononic material/device is similar to a photonic material/device, except that it is fabricated to manipulate certain bands of acoustic waves instead of electromagnetic waves. Phononic materials and devices have been studied much less than their photonic analogues and as such current materials exhibit control over a smaller range of frequencies. This study aims to test the viability of machine learning, specifically neural …

Multi-Agent Pathfinding In Mixed Discrete-Continuous Time And Space, Thayne T. Walker

Electronic Theses and Dissertations

In the multi-agent pathfinding (MAPF) problem, agents must move from their current locations to their individual destinations while avoiding collisions. Ideally, agents move to their destinations as quickly and efficiently as possible. MAPF has many real-world applications such as navigation, warehouse automation, package delivery and games. Coordination of agents is necessary in order to avoid conflicts, however, it can be very computationally expensive to find mutually conflict-free paths for multiple agents – especially as the number of agents is increased. Existing state-ofthe- art algorithms have been focused on simplified problems on grids where agents have no shape or volume, and …

Model-Based Testing Of Smart Home Systems Using Efsm, Cefsm, And Fsmapp, Afnan Mohammed Albahli

Electronic Theses and Dissertations

Smart Home Systems (SHS) are some of the most popular Internet of Things (IoT) applications. In 2021, there were 52.22 million smart homes in the United States and they are expected to grow to 77.1 million in 2025 [71]. According to MediaPost [74], 69 percent of American households have at least one smart home device. The number of smart home systems poses a challenge for software testers to find the right approach to test these systems. This dissertation employs Extended Finite State Machines (EFSMs) [6, 24, 105], Communicating Extended Finite State Machines (EFSMs) [68] and FSMApp [10] to generate reusable …

Frequency Analysis Of Trabecular Bone Structure, Daniel Parada San Martin

Electronic Theses and Dissertations

Medical data is hard to obtain due to privacy laws making research difficult. Many databases of medical data have been compiled over the years and are available to the scientific community. These databases are not comprehensive and lack many clinical conditions. Certain type of medical conditions are rare, making them harder to obtain, or are not present at all in the aforementioned databases. Due to the sparsity or complete lack of data regarding certain conditions, research has stifled. Recent developments in machine learning and generative neural networks have made it possible to generate realistic data that can overcome the lack …

Humanizing Computational Literature Analysis Through Art-Based Visualizations, Alexandria Leto

Electronic Theses and Dissertations

Inequalities in gender representation and characterization in fictional works are issues that have long been discussed by social scientists. This work addresses these inequalities with two interrelated components. First, it contributes a sentiment and word frequency analysis task focused on gender-specific nouns and pronouns in 15,000 fictional works taken from the online library, Project Gutenberg. This analysis allows for both quantifying and offering further insight on the nature of this disparity in gender representation. Then, the outcomes of the analysis are harnessed to explore novel data visualization formats using computational and studio art techniques. Our results call attention to the …

Data Analytics And Visualization For Virtual Simulation, Sri Lekha Koppaka

Browse all Theses and Dissertations

Healthcare organizations attract a diversity of caregivers and patients by providing essential care. While interacting with people of various races, ethnicity, and economical background, caregivers need to be empathetic and compassionate. Proper training and exposure are needed to understand the patient’s background and handle different situations and provide the best care for the patient. With social determinants of health (SDOH) as the basis, the thesis focuses on providing exposure through “Wright LIFE (Lifelike Immersion for Equity) - A simulation-based training tool” to two such scenarios covering patients from the LGBTQIA+ community & autism spectrum disorder (ASD). This interactive tool helps …

Computer Enabled Interventions To Communication And Behavioral Problems In Collaborative Work Environments, Ashutosh Shivakumar

Browse all Theses and Dissertations

Task success in co-located and distributed collaborative work settings is characterized by clear and efficient communication between participating members. Communication issues like 1) Unwanted interruptions and 2) Delayed feedback in collaborative work based distributed scenarios have the potential to impede task coordination and significantly decrease the probability of accomplishing task objective. Research shows that 1) Interrupting tasks at random moments can cause users to take up to 30% longer to resume tasks, commit up to twice the errors, and experience up to twice the negative effect than when interrupted at boundaries 2) Skill retention in collaborative learning tasks improves with …

Topological Hierarchies And Decomposition: From Clustering To Persistence, Kyle A. Brown

Browse all Theses and Dissertations

Hierarchical clustering is a class of algorithms commonly used in exploratory data analysis (EDA) and supervised learning. However, they suffer from some drawbacks, including the difficulty of interpreting the resulting dendrogram, arbitrariness in the choice of cut to obtain a flat clustering, and the lack of an obvious way of comparing individual clusters. In this dissertation, we develop the notion of a topological hierarchy on recursively-defined subsets of a metric space. We look to the field of topological data analysis (TDA) for the mathematical background to associate topological structures such as simplicial complexes and maps of covers to clusters in …

Virtual Reality-Based Serious Role-Playing Games As Digital Experiential Learning Tools To Deliver Healthcare Skills Through Mobile Devices, Dixit Bharatkumar Patel

Browse all Theses and Dissertations

Inadequate professional training and practices related to health care may result in severe complications to care experiences and outcomes. Moreover, healthcare professionals are as susceptible to the possibility of implicit biases as any other group. Importantly, the health care training is critical and challenging as minor prejudicial beliefs have an adverse influence or serious consequences on patients' health outcomes. Thus, facilitating serious role-playing virtual care practices along with raising awareness of healthcare professionals about the enduring impact of implicit/explicit biases and Social Determinants of Health (SDH) on health outcomes assist to advance the patient-provider relation, care experiences (e.g., healthcare experience …

Validating Software States Using Reverse Execution, Nathaniel Christian Boland

Browse all Theses and Dissertations

A key feature of software analysis is determining whether it is possible for a program to reach a certain state. Various methods have been devised to accomplish this including directed fuzzing and dynamic execution. In this thesis we present a reverse execution engine to validate states, the Complex Emulator. The Complex Emulator seeks to validate a program state by emulating it in reverse to discover if a contradiction exists. When unknown variables are found during execution, the emulator is designed to use constraint solving to compute their values. The Complex Emulator has been tested on small assembly programs and is …

Synthetic Aperture Ladar Automatic Target Recognizer Design And Performance Prediction Via Geometric Properties Of Targets, Jacob W. Ross

Browse all Theses and Dissertations

Synthetic Aperture LADAR (SAL) has several phenomenology differences from Synthetic Aperture RADAR (SAR) making it a promising candidate for automatic target recognition (ATR) purposes. The diffuse nature of SAL results in more pixels on target. Optical wavelengths offers centimeter class resolution with an aperture baseline that is 10,000 times smaller than an SAR baseline. While diffuse scattering and optical wavelengths have several advantages, there are also a number of challenges. The diffuse nature of SAL leads to a more pronounced speckle effect than in the SAR case. Optical wavelengths are more susceptible to atmospheric noise, leading to distortions in formed …

A Cloud Computing-Based Dashboard For The Visualization Of Motivational Interviewing Metrics, E Jinq Heng

Browse all Theses and Dissertations

Motivational Interviewing (MI) is an evidence-based brief interventional technique that has been demonstrated to be effective in triggering behavior change in patients. To facilitate behavior change, healthcare practitioners adopt a nonconfrontational, empathetic dialogic style, a core component of MI. Despite its advantages, MI has been severely underutilized mainly due to the cognitive overload on the part of the MI dialogue evaluator, who has to assess MI dialogue in real-time and calculate MI characteristic metrics (number of open-ended questions, close-ended questions, reflection, and scale-based sentences) for immediate post-session evaluation both in MI training and clinical settings. To automate dialogue assessment and …

Stroke Clustering And Fitting In Vector Art, Khandokar Shakib

Senior Independent Study Theses

Vectorization of art involves turning free-hand drawings into vector graphics that can be further scaled and manipulated. In this paper, we explore the concept of vectorization of line drawings and study multiple approaches that attempt to achieve this in the most accurate way possible. We utilize a software called StrokeStrip to discuss the different mathematics behind the parameterization and fitting involved in the drawings.

Novel Natural Language Processing Models For Medical Terms And Symptoms Detection In Twitter, Farahnaz Golrooy Motlagh

Browse all Theses and Dissertations

This dissertation focuses on disambiguation of language use on Twitter about drug use, consumption types of drugs, drug legalization, ontology-enhanced approaches, and prediction analysis of data-driven by developing novel NLP models. Three technical aims comprise this work: (a) leveraging pattern recognition techniques to improve the quality and quantity of crawled Twitter posts related to drug abuse; (b) using an expert-curated, domain-specific DsOn ontology model that improve knowledge extraction in the form of drug-to-symptom and drug-to-side effect relations; and (c) modeling the prediction of public perception of the drug’s legalization and the sentiment analysis of drug consumption on Twitter. We collected …

A Solder-Defined Computer Architecture For Backdoor And Malware Resistance, Marc W. Abel

Browse all Theses and Dissertations

This research is about securing control of those devices we most depend on for integrity and confidentiality. An emerging concern is that complex integrated circuits may be subject to exploitable defects or backdoors, and measures for inspection and audit of these chips are neither supported nor scalable. One approach for providing a “supply chain firewall” may be to forgo such components, and instead to build central processing units (CPUs) and other complex logic from simple, generic parts. This work investigates the capability and speed ceiling when open-source hardware methodologies are fused with maker-scale assembly tools and visible-scale final inspection.

The …

Few-Shot Malware Detection Using A Novel Adversarial Reprogramming Model, Ekula Praveen Kumar

Browse all Theses and Dissertations

The increasing sophistication of malware has made detecting and defending against new strains a major challenge for cybersecurity. One promising approach to this problem is using machine learning techniques that extract representative features and train classification models to detect malware in an early stage. However, training such machine learning-based malware detection models represents a significant challenge that requires a large number of high-quality labeled data samples while it is very costly to obtain them in real-world scenarios. In other words, training machine learning models for malware detection requires the capability to learn from only a few labeled examples. To address …

Evaluating Similarity Of Cross-Architecture Basic Blocks, Elijah L. Meyer

Browse all Theses and Dissertations

Vulnerabilities in source code can be compiled for multiple processor architectures and make their way into several different devices. Security researchers frequently have no way to obtain this source code to analyze for vulnerabilities. Therefore, the ability to effectively analyze binary code is essential. Similarity detection is one facet of binary code analysis. Because source code can be compiled for different architectures, the need can arise for detecting code similarity across architectures. This need is especially apparent when analyzing firmware from embedded computing environments such as Internet of Things devices, where the processor architecture is dependent on the product and …

Locality Analysis Of Patched Php Vulnerabilities, Luke N. Holt

Browse all Theses and Dissertations

The size and complexity of modern software programs is constantly growing making it increasingly difficult to diligently find and diagnose security exploits. The ability to quickly and effectively release patches to prevent existing vulnerabilities significantly limits the exploitation of users and/or the company itself. Due to this it has become crucial to provide the capability of not only releasing a patched version, but also to do so quickly to mitigate the potential damage. In this thesis, we propose metrics for evaluating the locality between exploitable code and its corresponding sanitation API such that we can statistically determine the proximity of …

Building An Understanding Of Human Activities In First Person Video Using Fuzzy Inference, Bradley A. Schneider

Browse all Theses and Dissertations

Activities of Daily Living (ADL’s) are the activities that people perform every day in their home as part of their typical routine. The in-home, automated monitoring of ADL’s has broad utility for intelligent systems that enable independent living for the elderly and mentally or physically disabled individuals. With rising interest in electronic health (e-Health) and mobile health (m-Health) technology, opportunities abound for the integration of activity monitoring systems into these newer forms of healthcare. In this dissertation we propose a novel system for describing ’s based on video collected from a wearable camera. Most in-home activities are naturally defined by …

Secure Authenticated Key Exchange For Enhancing The Security Of Routing Protocol For Low-Power And Lossy Networks, Sarah Mohammed Alzahrani

Browse all Theses and Dissertations

The current Routing Protocol for Low Power and Lossy Networks (RPL) standard provides three security modes Unsecured Mode (UM), Preinstalled Secure Mode (PSM), and Authenticated Secure Mode (ASM). The PSM and ASM are designed to prevent external routing attacks and specific replay attacks through an optional replay protection mechanism. RPL's PSM mode does not support key replacement when a malicious party obtains the key via differential cryptanalysis since it considers the key to be provided to nodes during the configuration of the network. This thesis presents an approach to implementing a secure authenticated key exchange mechanism for RPL, which ensures …

Realistic Virtual Human Character Design Strategy And Experience For Supporting Serious Role-Playing Simulations On Mobile Devices, Sindhu Kumari

Browse all Theses and Dissertations

Promoting awareness of social determinants of health (SDoH) among healthcare providers is important to improve the patient care experience and outcome as it helps providers understand their patients in a better way which can facilitate more efficient and effective communication about health conditions. Healthcare professionals are typically educated about SDoH through lectures, questionaries, or role-play-based approaches; but in today’s world, it is becoming increasingly possible to leverage modern technology to create more impactful and accessible tools for SDoH education. Wright LIFE (Lifelike Immersion for Equity) is a simulation-based training tool especially created for this purpose. It is a mobile app …

Design, Analysis, And Optimization Of Traffic Engineering For Software Defined Networks, Mohammed Ibrahim Salman

Browse all Theses and Dissertations

Network traffic has been growing exponentially due to the rapid development of applications and communications technologies. Conventional routing protocols, such as Open-Shortest Path First (OSPF), do not provide optimal routing and result in weak network resources. Optimal traffic engineering (TE) is not applicable in practice due to operational constraints such as limited memory on the forwarding devices and routes oscillation. Recently, a new way of centralized management of networks enabled by Software-Defined Networking (SDN) made it easy to apply most traffic engineering ideas in practice. \par Toward creating an applicable traffic engineering system, we created a TE simulator for experimenting …