Computer Engineering Commons^™

Improving Expressive Capacity Of Deep Neural Networks, Clayton Harper

Computer Science and Engineering Theses and Dissertations

Deep learning has had remarkable success in a variety of fields. However, architectures often rely on hyperparameter searches and heuristics for improved model performance. Performing hyperparameter searches is an arduous task--often time-consuming and potentially expensive to run on accelerated hardware. As a result, practitioners often rely on heuristics which may lead to sub-optimal results. In the context of deep learning, hyperparameters are set by the user prior to the training process and remain fixed. Deep learning uses gradient descent to learn complex feature representations from data, limiting human intervention. While the weights of the architecture can learn directly through data …

Welcome To Cheney App, Timothy Nelson, Nolan Posey, Tanner Stephenson, Daniel Palmer, Matthew Matriciano

2024 Symposium

Welcome to Cheney is a non-profit organization committed to fostering communication, connection, and action within the city of Cheney. Their primary purpose is to provide timely and accurate information to the residents of Cheney. Welcome to Cheney has tried utilizing other forms of social media such as Facebook and Instagram to share information, but their presence is being overshadowed amidst the noise on those platforms. Therefore, the intention of this project is to develop a mobile app with the sole purpose of being a reliable means of sharing important information with the residents of Cheney.

The information being shared on …

Problem Solving / Javascript Programming, Sarah Zelikovitz, Orit D. Gruber

Open Educational Resources

This Lab Experiment focuses on JavaScript Programming. Upon completing the lab, you will be able to understand the following:

· The definition of Algorithmic Problem Solving.

· The role of JavaScript in web pages.

· The concept of Iteration in computer programming.

Bridging Design And Perception: Novel Tools And Technologies For Creating Effective Human-Robot Interactions, Benjamin Dossett

Electronic Theses and Dissertations

This thesis explores human perception of robots through the use of novel tools and technologies. First, the impact of Augmented Reality (AR) data presentation on human perception of robots is investigated. A study conducted with the AR human-robot teaming system found that robot performance significantly influenced participants’ perceptions, overshadowing the impact of matching or mismatching robot confidence feedback. Second, the DU Want to Build-A-Bot platform is presented, which enables participatory robot design and opens the door for novel research of how robot design affects human perception. The Build-A-Bot platform enables the collection of diverse robot designs, facilitating machine learning analysis …

Beyond The Horizon: Exploring Anomaly Detection Potentials With Federated Learning And Hybrid Transformers In Spacecraft Telemetry, Juan Rodriguez

Computer Science and Engineering Theses and Dissertations

Telemetry sensors play a crucial role in spacecraft operations, providing essential data on efficiency, sustainability, and safety. However, identifying irregularities in telemetry data can be a time-consuming process that risks the success of missions. With the rise of CubeSats and smallsats, telemetry data has become more abundant, but concerns about privacy and scalability have resulted in untapped data potential. To address these issues, we propose a new approach to anomaly detection that utilizes machine learning models at data sources. These models solely transmit weights to a centralized server for aggregation, resulting in improved dataset performance with a single global model. …

Neuro-Symbolic Commonsense Reasoning With Resistance To Data Poisoning: A First-Order Logic And Sub-Symbolic Embeddings Framework, Bryce Shurts, King-Ip Lin

Computer Science and Engineering Theses and Dissertations

Commonsense reasoning has long presented a hurdle between conversational agents and their ability to naturally engage with humans in conversation, as the infinitely dimensional nature of a dialogue’s topics presents a significant reasoning challenge in the study of Natural Language Understanding (NLU). Such a system must conceivably be able to act as a generalizable system for evaluating and reasoning about commonsense statements, problems, and queries: in this way, the agent can attempt to quantify the reasonability of a given input. We attempt to address this through the integration of an explainable neuro-symbolic system that leverages Logical Tensor Networks (LTNs) and …

Analyzing Information Cascades Through Machine Learning And Data Analytics, Betul Agirman

Honors Scholar Theses

In today's digital age, social media platforms have become pivotal in influencing public opinion and behavior, with information spreading being both beneficial and detrimental. This rapid spread is typically called an information cascade, and they are important in further understanding social influence, managing misinformation, and even predicting potential trends of public responses. With social media, people are connected so easily to one another like a network, wherein it becomes possible for them to influence each other’s behavior and decisions. Utilizing a dataset from Weibo that spans critical periods of the COVID-19 outbreak, this study integrates machine learning and data analytics …

Data Analysis Of Twitter’S Nasdaq100 Sentiments And Topics As Indicators For News Articles Retrieval: Fine-Tuning Roberta And Rag, Kagan Timur

ICT

This study explores the combination of sentiment analysis with vader-lexicon and semantic analysis with latent dirichlet allocation to identify real-life events, particularly in the context of Twitter datasets. While sentiment analysis alone may not provide accurate guidance, the inclusion of semantic analysis enhances the research process by helping to identify relevant news articles and comprehend brand perception on social media. Furthermore, the study fine-tunes the RoBERTa model specifically for question-answering tasks

The Game Of Traffic Lights (Tgotl), Faith Chapman

Posters - 2024

“Computer Science can be applied to nearly ANY field.” In college, and high school especially, the phrase is just that—early comp. sci (CS) students don’t have realworld examples of how/where else they can use CS knowledge outside of CS focused jobs. For high school students, this a missed opportunity to plan for a career outside the obvious. One such career is in traffic lights.

U.S. traffic can be better. Engineering has a subfield dedicated to improving traffic, and part of that entails studying ways to improve traffic lights’ efficacy. Someone with CS knowledge can program a traffic simulator for data …

Yieldnet: Intelligent Fruit Yield Estimation For Selected Orchards Using Deep Learning Based Semantic Segmentation, Maheswari P

Theses and Dissertations

Agriculture contributes more resources for developing sustainable economic growth of the nation. Precision agriculture employs advanced techniques (machine learning and deep learning) for developing the intelligent systems of various agricultural applications. Among various agricultural tasks, yield estimation of crops plays a vital role in decision-making such as harvesting, marketing, cultivation practices, etc. Traditionally yield estimation is performed manually which has major drawbacks i.e., needs experts opinion, time-consuming and it is a challenging task for big orchards. To overcome these issues, an intelligent yield estimation model using neural network-based systems is required.

Some of the literature works have been explored for …

Understanding Impact Of Graph Structure On Knowledge Graph Embedding, Brandon Dave

Browse all Theses and Dissertations

The effectiveness of a deployed knowledge graph is commonly evaluated with defined use-cases from domain experts. This poses challenges during the development cycle in determining how to represent data. Developers of a knowledge graph can optionally include semantics into a knowledge graph by abstracting the data representation in such a way that mirrors information as it exists in the real world. Consequently, the abstraction is represented by additional layers, resulting in performant differences in knowledge graph embedding; such as, the embedded model's ability to infer facts between entities through link predictions. This thesis presents a comprehensive analysis of the performance …

Semantics-Aware Text-Guided Aerial Image Synthesis By Feature Augmented Diffusion Models, Douglas J. Townsell

Browse all Theses and Dissertations

Aerial imagery provides crucial insights for various fields, including remote monitoring, environmental assessment, and autonomous navigation. However, the availability of aerial image datasets is limited due to privacy concerns and imbalanced data distribution, impeding the development of robust deep learning models. While recent text-guided generative models have shown promise in synthesizing high-quality images, they fall short in handling the unique challenges of aerial imagery, including densely packed objects, intricate spatial relationships, and the absence of paired text-aerial image datasets. To tackle these limitations, we propose STARS, a groundbreaking framework for Semantic-aware Text-guided Aerial image Refinement and Synthesis. STARS introduces a …

Graph-Centric Bot Detection: Addressing Extreme Data Imbalances, Heterophily, And Scarcity, Bradley T. Ashmore

Browse all Theses and Dissertations

The digital landscape is ever-evolving. In recent years the amount of bot traffic, traffic generated by autonomous applications over the internet has increased significantly. Many bots perform useful and needed functions, however, malicious bots are known sources of both common and emerging security threats. Denial-of-Services (DoS), information theft, and credential stuffing have all been conducted by malicious software running on unknowingly infected machines. The dichotomy of useful bots operating in the same networks as malicious bots combined with novel bot attacks and an ever-increasing number of personal devices connecting to the Internet drives the need for continued advancement of malicious …

A Trusted Adversarial Ml Countermeasure Approach For Secure And Resilient Ai-Driven Hardware Trojan Detection, Mohammed G M Alkurdi

Browse all Theses and Dissertations

Semiconductor microelectronics Integrated Circuits (ICs) are increasingly integrated into critical life applications including medical, aerospace, and Internet of things. Their increasing importance as a technology gave rise to critical concerns regarding their security. This has led to the focus of the research community on hardware Trojans, which are malicious modifications to the ICs with undesirable outcomes. Their detection is becoming increasingly critical, with many researchers proposing methods to do so such as reverse engineering, logic testing, and side-channel analysis. Many of these proposals utilize machine learning methods to detect these malicious modifications with high accuracy and confidence. However, machine learning …

Cybersecurity In Critical Infrastructure Systems: Emulated Protection Relay, Mitchell Bylak

Computer Science and Computer Engineering Undergraduate Honors Theses

Cyber-attacks on Critical Systems Infrastructure have been steadily increasing across the world as the capabilities of and reliance on technology have grown throughout the 21st century, and despite the influx of new cybersecurity practices and technologies, the industry faces challenges in its cooperation between the government that regulates law practices and the private sector that owns and operates critical infrastructure and security, which has directly led to an absence of eas- ily accessible information and learning resources on cybersecurity for use in public environments and educational settings. This honors research thesis addresses these challenges by submitting the development of an …

End To End Routing Algorithms In Arbitrary Networks, Nada Alzaben

Computer Science and Engineering Theses and Dissertations

The End to End Routing Problem in computer communication networks is the problem of scheduling fixed routes through the network over time. All the packets in a single communication flow follow an assigned route and all flows are routed such that some function of the flows is optimized. A flow is a sequence of packets that are released by some source node and sent over the communication network to a destination node. Multiple objective functions may be used to evaluate the performance of the network. Common objective functions include minimize the mean wait time, minimize the total wait time, minimize …

Terrain And Adversary-Aware Autonomous Robot Navigation, Aniekan Ufot Inyang

Electronic Theses and Dissertations

In autonomous robot navigation, the robot is able to understand the environment around it for intelligent navigation. From its world model of this environment, it generates a global plan for navigation from a position to a goal based on different factors. This research aims to implement autonomous robot navigation by learning terrain affordances: traversability (moving quickly) and concealment (staying hidden from an adversary) using the Preference-based Inverse Reward Learning (PbIRL) methodology. The PbIRL methodology reduces the barrier of generating initial demonstration data to learn the terrain affordances by using a human expert’s preferences to learn individual weights over the terrain …

The First Annual Teaching And Research Showcase Poster Tu Dublin – The Proof Is In The Pudding – Using Perceived Stress To Measure Short-Term Impact In Initiatives To Enhance Gender Balance In Computing Education, Alina Berry, Sarah Jane Delany

Other resources

The problem of gender imbalance in computing higher education has forced academics and professionals to implement a wide range of initiatives. Many initiatives use recruitment or retention numbers as their most obvious evidence of impact. This type of evidence of impact is, however, more resource heavy to obtain, as well as often requires a longitudinal approach. There are many shorter term initiatives that use other ways to measure their success.

First, this poster presents with a review of existing evaluation measures in interventions to recruit and retain women in computing education across the board. Three main groups of evaluation come …

Modern Practices For Responsive Web Design And Web Accessibility, Keyaun Washington

Honors Program: Senior Projects (Public)

Responsive web design and web accessibility play crucial roles in ensuring an optimal user experience on the web. By designing websites with responsiveness and accessibility in mind, more opportunities are opened up for a wider audience to access and interact with our content. Through modern practices, responsive web design allows websites to reach several different devices ranging from compact smartwatches to expansive television screens. Designing for accessibility provides accommodations for individuals with impairments while also providing benefits for individuals without impairments. However, designing for responsiveness and accessibility can present challenges; a poor attempt at providing accessibility features can worsen a …

Grammatical Triples Extraction For The Distant Reading Of Textual Corpora, Stephanie Buongiorno, Stephanie Buongiorno

Multidisciplinary Studies Theses and Dissertations

Grammatical triples extraction has become increasingly important for the analysis of large, textual corpora. By providing insight into the sentence-level linguistic features of a corpus, extracted triples have supported interpretations of some of the most relevant problems of our time. The growing importance of triples extraction for analyzing large corpora has put the quality of extracted triples under new scrutiny, however. Triples outputs are known to have large amounts of erroneous triples. The extraction of erroneous triples poses a risk for understanding a textual corpus because erroneous triples can be nonfactual and even analogous to misinformation. Disciplines such as the …

Culture In Computing: The Importance Of Developing Gender-Inclusive Software, Creighton France

Computer Science and Computer Engineering Undergraduate Honors Theses

The field of computing as we know it today exists because of the contributions of numerous female mathematicians, computer scientists, and programmers. While working with hardware was viewed as “a man’s job” during the mid-20th century, computing and programming was viewed as a noble and high-paying field for women to occupy. However, as time has progressed, the U.S. has seen a decrease in the number of women pursuing computer science. The idea that computing is a masculine discipline is common in the U.S. today for reasons such as male-centered marketing of electronics and gadgets, an inaccurate representation of what it …

Design, Determination, And Evaluation Of Gender-Based Bias Mitigation Techniques For Music Recommender Systems, Sunny Shrestha

Electronic Theses and Dissertations

The majority of smartphone users engage with a recommender system on a daily basis. Many rely on these recommendations to make their next purchase, download the next game, listen to the new music or find the next healthcare provider. Although there are plenty of evidence backed research that demonstrates presence of gender bias in Machine Learning (ML) models like recommender systems, the issue is viewed as a frivolous cause that doesn’t merit much action. However, gender bias poses to effect more than half of the population as by default ML systems are designed to cater to a cisgender man. This …

Completeness Of Nominal Props, Samuel Balco, Alexander Kurz

Engineering Faculty Articles and Research

We introduce nominal string diagrams as string diagrams internal in the category of nominal sets. This leads us to define nominal PROPs and nominal monoidal theories. We show that the categories of ordinary PROPs and nominal PROPs are equivalent. This equivalence is then extended to symmetric monoidal theories and nominal monoidal theories, which allows us to transfer completeness results between ordinary and nominal calculi for string diagrams.

Encryption And Compression Classification Of Internet Of Things Traffic, Mariam Najdat M Saleh

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The Internet of Things (IoT) is used in many fields that generate sensitive data, such as healthcare and surveillance. Increased reliance on IoT raised serious information security concerns. This dissertation presents three systems for analyzing and classifying IoT traffic using Deep Learning (DL) models, and a large dataset is built for systems training and evaluation. The first system studies the effect of combining raw data and engineered features to optimize the classification of encrypted and compressed IoT traffic using Engineered Features Classification (EFC), Raw Data Classification (RDC), and combined Raw Data and Engineered Features Classification (RDEFC) approaches. Our results demonstrate …

Efficient Cloud-Based Ml-Approach For Safe Smart Cities, Niveshitha Niveshitha

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Smart cities have emerged to tackle many critical problems that can thwart the overwhelming urbanization process, such as traffic jams, environmental pollution, expensive health care, and increasing energy demand. This Master thesis proposes efficient and high-quality cloud-based machine-learning solutions for efficient and sustainable smart cities environment. Different supervised machine-learning models for air quality predication (AQP) in efficient and sustainable smart cities environment is developed. For that, ML-based techniques are implemented using cloud-based solutions. For example, regression and classification methods are implemented using distributed cloud computing to forecast air execution time and accuracy of the implemented ML solution. These models are …

Solidity Compiler Version Identification On Smart Contract Bytecode, Lakshmi Prasanna Katyayani Devasani

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Identifying the version of the Solidity compiler used to create an Ethereum contract is a challenging task, especially when the contract bytecode is obfuscated and lacks explicit metadata. Ethereum bytecode is highly complex, as it is generated by the Solidity compiler, which translates high-level programming constructs into low-level, stack-based code. Additionally, the Solidity compiler undergoes frequent updates and modifications, resulting in continuous evolution of bytecode patterns. To address this challenge, we propose using deep learning models to analyze Ethereum bytecodes and infer the compiler version that produced them. A large number of Ethereum contracts and the corresponding compiler versions is …

The Open Charge Point Protocol (Ocpp) Version 1.6 Cyber Range A Training And Testing Platform, David Elmo Ii

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The widespread expansion of Electric Vehicles (EV) throughout the world creates a requirement for charging stations. While Cybersecurity research is rapidly expanding in the field of Electric Vehicle Infrastructure, efforts are impacted by the availability of testing platforms. This paper presents a solution called the “Open Charge Point Protocol (OCPP) Cyber Range.” Its purpose is to conduct Cybersecurity research against vulnerabilities in the OCPP v1.6 protocol. The OCPP Cyber Range can be used to enable current or future research and to train operators and system managers of Electric Charge Vehicle Supply Equipment (EVSE). This paper demonstrates this solution using three …

A Secure And Efficient Iiot Anomaly Detection Approach Using A Hybrid Deep Learning Technique, Bharath Reedy Konatham

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The Industrial Internet of Things (IIoT) refers to a set of smart devices, i.e., actuators, detectors, smart sensors, and autonomous systems connected throughout the Internet to help achieve the purpose of various industrial applications. Unfortunately, IIoT applications are increasingly integrated into insecure physical environments leading to greater exposure to new cyber and physical system attacks. In the current IIoT security realm, effective anomaly detection is crucial for ensuring the integrity and reliability of critical infrastructure. Traditional security solutions may not apply to IIoT due to new dimensions, including extreme energy constraints in IIoT devices. Deep learning (DL) techniques like Convolutional …

Data-Driven Strategies For Pain Management In Patients With Sickle Cell Disease, Swati Padhee

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This research explores data-driven AI techniques to extract insights from relevant medical data for pain management in patients with Sickle Cell Disease (SCD). SCD is an inherited red blood cell disorder that can cause a multitude of complications throughout an individual’s life. Most patients with SCD experience repeated, unpredictable episodes of severe pain. Arguably, the most challenging aspect of treating pain episodes in SCD is assessing and interpreting the patient’s pain intensity level due to the subjective nature of pain. In this study, we leverage multiple data-driven AI techniques to improve pain management in patients with SCD. The proposed approaches …

Reinforcement Learning For Stock Option Trading, James Garza

ICT

Reinforcement learning has recently seen an increase in popularity due to its ability to learn from past experience and its capability of adapting quickly and effectively to new market conditions. This research will focus on reinforcement learning and its importance in trading stock options. Option traders can trade options with one of two option expirations: American or European style. This research will base the analysis on the American expiration style, considered more challenging in trading than the European expiration style. This could lead to the possibility of improving the current trading techniques. In addition, this research aims to understand the …