Digital Commons Network^™

Enhancing Galaxy Surveys With Machine Learning, Steven Karst

Undergraduate Research Conference at Missouri S&T

Applications of machine learning (ML) or artificial intelligence (AI) to problems in astrophysics and cosmology have recently entered a golden era. In response, we have updated two of our recent ML/ AI efforts that contribute to galaxy surveys whose main scientific target is to reveal the nature of the Cosmic Acceleration or Dark Energy. We first revised our effort to infer cosmological information beyond the survey geometry using Graph Neural Networks (GNN) to take advantage of supercomputing resources on campus. We then updated our reinforcement learning methods for galaxy target selection in the Subaru Prime Focus Spectrograph (PFS) survey with …

Ta Theoretical Framework For Comparing Rlhf Method, Matthew Dominicis

Undergraduate Research Conference at Missouri S&T

Reinforcement Learning from Human Feedback (RLHF) can be used as a means to align Al agents and Large Language Models (LLM) to better represent human expectations. There is a myriad of RLHF methods that exist, however it is difficult to benchmark and compare such methods in terms of alignment, training cost, data collection cost, and other metrics. This project aims to create a robust classification for different RLHF methods from a theoretical point of view. Additionally, this project will attempt to propose bounds for the degree of influence on LLMs that stems from human feedback. Open source LLMs will be …

Effects Of Reproduction On Senescence In Local Species, Nathan Smith

Undergraduate Research Conference at Missouri S&T

Understanding the impact of reproduction on the aging process within species populations remains an ongoing challenge in ecological and evolutionary research. In this study, we aim to clarify the relationship between reproduction and senescence using agent-based modeling using wild species data sets. Our objectives include investigating how variations in reproductive rates influence the lifespan and aging trajectories of individuals within populations, as well as identifying potential mechanisms underlying these effects. We will employ agent-based modeling to simulate populations and explore the dynamics of reproduction and senescence using publicly available datasets of local wild species. By manipulating parameters related to reproductive …

Enhancing Galaxy Surveys With Machine Learning, Steven Karst

Undergraduate Research Conference at Missouri S&T

Applications of machine learning (ML) or artificial intelligence (Al) to problems in astrophysics and cosmology have recently entered a golden era. In response, we have updated two of our recent ML/Al efforts that contribute to galaxy surveys whose main scientific target is to reveal the nature of the Comsic Acceleration or Dark Energy. We first revised our effort to infer cosmological information beyond the survey geometry using Graph Neural Networks (GNNs) to take advantage of supercomputing resources on campus. We then updated our methods for galaxy target selection in the Subaru Prime Focus Spectrograph (PFS) survey with modern reinforcement learning …

Application Of T Gates For Anti-Concentration In Clifford Circuits, Matthew Dominicis, Mason Toombs, Gabriel Riddle, Parineeta Puja Saha, Himanth Bobba

Undergraduate Research Conference at Missouri S&T

Our study examines the integration of non-Clifford T gates into randomly generated Clifford circuits to enhance their universal unitary capacity. We investigate the impact of T gates on circuit output randomness, focusing on generating random Clifford circuits and analyzing the effects of T gates. Through simulations and analysis, we assess the effectiveness of this modification in achieving outputs consistent with Anti concentration properties while minimizing the required number of Clifford gates. Our findings provide valuable insights into quantum circuits, with implications for quantum computing applications.

A Fisher Information-Based Approach To Improve Labeling Efficiency Of Neural Network Models In Image Classification, Joshua Caruso

Undergraduate Research Conference at Missouri S&T

Active learning is a framework for training machine learning models where the goal is to reduce the number of labels used during training. Neural network models used for image classification require a large training dataset to achieve good accuracy. This project will use active learning to reduce the number of labels needed for training neural network models. We propose to use the Fisher information of the neural network parameters to actively select which images are labelled and included in the training data. A key challenge is the large number of parameters in commonly used neural network models, which significantly increases …

Neural Machine Translation Vs. Computer Assisted Technology: Machine Translation Post Editing Human Quality Assurance, Sarah Aland, Sarah Kane, Kenya Merida Millan

Student Research Symposium

What is neural machine Translation (NMT) and Computer Assisted Technology (CAT)?

Simulating Inter-Species Competition In C. Elegans, Kevin Lai

Undergraduate Research Conference at Missouri S&T

In biological research, understanding the life cycles of Caenorhabditis elegans (C. elegans) is pivotal for insights into developmental biology, genetics, and population dynamics. Our project builds on Worm-Pop, a Python-based multi-agent simulation of Caenorhabditis elegans (C. elegans) , to enhance its capabilities in simulating survival strategies, reproductive success, and genetic drift. The current model simulates a uniform population without inter-agent interactions. I plan to introduce multiple species of worms into the simulation to study competitive dynamics and determine which variants are most successful under various conditions. Pheromones significantly influence C. elegans behavior, affecting mating, foraging, and social interactions. To address …

A Smart Resume Builder Tool Using Generative Ai, Ivan A. Velo Castaneda, Anas Hourani, Magdalene Moy

SACAD: Scholarly Activities

Crafting a standout resume is crucial in today’s competitive job market. Not only does it create a strong first impression on employers but it also it opens the doors for endless job opportunities. Despite existing resume assistance for FHSU students on the Career Services page, there's a lack of tools for generating or streamlining the resume writing process. To address this issue, an efficient resume builder utilizing OpenAI’s GPT-3.5 model was developed specifically for FHSU students. Its key features include intuitive template selection, dynamic AI-generated content for tailored resumes, multi-format output supporting PDF and Word formats, and a user-friendly experience …

Image De‑Photobombing Benchmark, Vatsa S. Patel, Kunal Agrawal, Samah Baraheem, Amira Yousif, Tam Nguyen

Computer Science Faculty Publications

Removing photobombing elements from images is a challenging task that requires sophisticated image inpainting techniques. Despite the availability of various methods, their effectiveness depends on the complexity of the image and the nature of the distracting element. To address this issue, we conducted a benchmark study to evaluate 10 state-of-the-art photobombing removal methods on a dataset of over 300 images. Our study focused on identifying the most effective image inpainting techniques for removing unwanted regions from images. We annotated the photobombed regions that require removal and evaluated the performance of each method using peak signal-to-noise ratio (PSNR), structural similarity index …

Megordle: A Wdolre Usclnbaermr, Meg Arney

Undergraduate Research Conference

Our goal was to make a dynamic word unscrambler app based on the random generation of words from a database. The original database contains 4,320 of the most common words in the English language. This project was originally designed in Visual Studio Code in java and takes input from the terminal. The project was later translated into C# and transferred into Unity to provide a visual and dynamic interface for users.

No Generation Without Representation: Solving Ai Art Attribution With Sno-E, Sadie Campbell

Undergraduate Research Conference

Artificial intelligence has sparked a new-age debate about its ethical implications, specifically in the world of art. The Signature Neural Operative- Environment (SNO-E) is a multidisciplinary solution that draws on computer science, statistics, and art. It addresses the issue of proper attribution for Al-generated artworks through a Convolution Neural Network that adopts signature checks to cite artists whose works are sampled by Al. This novel approach ensures proper recognition and compensation for artists in the Al-generated era.

Software Based Approach To Realtime Sports Graphics, Honesty Beaton

SACAD: Scholarly Activities

My research presents a software-based approach to real-time sports graphics, leveraging Unity, C#, and OpenCV. We aimed to enhance viewer engagement by providing dynamic and interactive graphics during sports broadcasts. My method involves real-time analysis of video feeds to cut out players, place them onto a virtual court, and underlay immersive visuals, giving the appearance that virtual visuals physically exist beneath a player. Evaluation of this approach demonstrates the effectiveness of utilizing a software-based approach for real-time sports graphics, akin to traditional hardware-based solutions

Gender Detection In Facial Images: A Comprehensive Cnn Analysis, Jose N T Ambrosio, Anas Hourani, Magdalene Moy

SACAD: Scholarly Activities

This research investigates the construction of a robust gender detection system using facial features and Convolutional Neural Networks (CNNs), exploring the impact of different layer configurations on accuracy and computational efficiency. With a validation accuracy of 91%, findings illuminate the nuanced relationship between precision and computational resources, enriching discussions on facial recognition technologies.

Artificial Intelligence Could Probably Write This Essay Better Than Me, Claire Martino

Augustana Center for the Study of Ethics Essay Contest

No abstract provided.

An Exploration Of Companion Robots, Annelyse Lockhart

2024 Student Academic Showcase

Japan and the United States have a drastically different view towards artificial intelligence and smart machines. Within my project, I did an exploratory analysis of robotics within the United States and Japan, and posed the question as to why Japan has substantially more robotics within their day-to-day life. I took an in-depth look at Japanese robotics that do not exist within the United States, as well as explored the biases behind smart machines in both cultures. Judging Category: Exploratory

Ua12/2/1 College Heights Herald: Big Red By Ai, Wku Student Affairs

WKU Administration Documents

Magazine edition of the College Heights Herald for the period March 3 - April 8, 2024.

• Big Red, Is That You?
• Anderson, Alexandria. Letter from the Editor - Artificial Intelligence
• Phelps, Maggie. What is AI? - Artificial Intelligence
• AI’s Impact on Higher Education
• Reed, Bailey. Students Share Opinions on AI
• Abney, Shayla. WKU Faculty Discuss AI in Courses
• Hawkins, Kaylee. AI’s Impact on Blackboard Ultra
• Randolph, Eli. AI Recreates a WKU Tour
• Shaw, Cameron. WKU Department of Political Science Hosts Dead President’s Tour

Game 'Make 24', Seunghyeok Jang

SACAD: Scholarly Activities

• Basic numerical skills are a must-have in today’s world. However, children are not picking up the four basic numerical skills adequately.

• To improve their mathematical skills, they need a way to learn the numerical skills easily.

• "Make 24" is a game for young children who are having a difficult time with basic numerical operations. The game helps children improve their numerical skills by playing this game.

Comparing Cognitive Theories Of Learning Transfer To Advance Cybersecurity Instruction, Assessment, And Testing, Daniel T. Hickey Ph.D., Ronald J. Kantor

Journal of Cybersecurity Education, Research and Practice

The cybersecurity threat landscape evolves quickly, continually, and consequentially. This means that the transfer of cybersecurity learning is crucial. We compared how different recognized “cognitive” transfer theories might help explain and synergize three aspects of cybersecurity education. These include teaching and training in diverse settings, assessing learning formatively & summatively, and testing & measuring achievement, proficiency, & readiness. We excluded newer sociocultural theories and their implications for inclusion as we explore those theories elsewhere. We first summarized the history of cybersecurity education and proficiency standards considering transfer theories. We then explored each theory and reviewed the most relevant cybersecurity education …

Performing Distributed Quantum Calculations In A Multi-Cloud Architecture Secured By The Quantum Key Distribution Protocol, Jose Luis Lo Huang, Vincent C. Emeakaroha

Department of Computer Science Publications

Quantum computing (QC) is an emerging area that yearly improves and develops more advances in the number of qubits and the available infrastructure for public users. Nowadays, the main cloud service providers (CSP) are implementing different mechanisms to support access to their quantum computers, which can be used to perform small experiments, test hybrid algorithms and prove quantum theories. Recent research work have discussed the low capacity of using quantum computers in a single CSP to perform quantum computation that are needed to solve different experiments for real world problems. Thus, there are needs for computing powers in the form …

A Novel Approach On Energy Of Λj-Dominating Single-Valued Neutrosophic Graph Structure, S.N. Suber Bathusha, S. Angelin Kavitha Raj

Neutrosophic Systems with Applications

The concept of dominance is one of the most important ideas in graph theory for handling random events, and it has drawn the interest from many scholars. Research related to graph energy has garnered a lot focus recently. The application of single-valued neutrosophic graphs (SVNGs) for energy, Laplacian energy, and dominating energy has been recommended by previous studies. In this research, we apply the concepts of single-valued neutrosophic sets (SVNS) to graph structures (GSs) and investigate some intriguing features of single-valued neutrosophic graph structures (SVNGS). Moreover, the notions of λJ-dominating energy GS in an SVNGS environment are analyzed in this …

Bv2trs Appraiser Model: Enforcing Bharat Version2 In Tree Soft Modelling For Appraising E-Mobility Hurdles, Mona Mohamed, Florentin Smarandache, Michael Voskoglou

Neutrosophic Systems with Applications

Electric vehicles (EVs) are being introduced to lessen greenhouse gas (GHG) emissions, air pollution, and reliance on fossil fuels. As a result of the government's aggressive promotion of EVs and rising environmental consciousness, EVs are quickly rising to the top of the low-carbon transportation market. Several viewpoints suggested that shifting to electric vehicles has been seen as a potential way to achieve sustainable mobility. Nevertheless, many studies discussed the obstacles and hurdles that obstruct the embracing of various electric-mobility (E-mobility) as EVs and electric-scooters (E-scooters) as eco-friendly means. Herein, we discussed these hurdles and determined them through surveys for prior …

Discourse- And Lesion-Based Aphasia Quotient Estimation Using Machine Learning, Nicholas Riccardi, Satvik Nelakuditi, Dirk B. Den Ouden, Chris Rorden, Julius Fridriksson, Rutvik H. Desai

Communication Sciences and Disorders Faculty Articles and Research

Discourse is a fundamentally important aspect of communication, and discourse production provides a wealth of information about linguistic ability. Aphasia commonly affects, in multiple ways, the ability to produce discourse. Comprehensive aphasia assessments such as the Western Aphasia Battery-Revised (WAB-R) are time- and resource-intensive. We examined whether discourse measures can be used to estimate WAB-R Aphasia Quotient (AQ), and whether this can serve as an ecologically valid, less resource-intensive measure. We used features extracted from discourse tasks using three AphasiaBank prompts involving expositional (picture description), story narrative, and procedural discourse. These features were used to train a machine learning model …

Bv2trs Appraiser Model: Enforcing Bharat Version2 In Tree Soft Modelling For Appraising E-Mobility Hurdles, Mona Mohamed, Florentin Smarandache, Michael Voskoglou

Neutrosophic Systems with Applications

Electric vehicles (EVs) are being introduced to lessen greenhouse gas (GHG) emissions, air pollution, and reliance on fossil fuels. As a result of the government's aggressive promotion of EVs and rising environmental consciousness, EVs are quickly rising to the top of the low-carbon transportation market. Several viewpoints suggested that shifting to electric vehicles has been seen as a potential way to achieve sustainable mobility. Nevertheless, many studies discussed the obstacles and hurdles that obstruct the embracing of various electric-mobility (E-mobility) as EVs and electric-scooters (E-scooters) as eco-friendly means. Herein, we discussed these hurdles and determined them through surveys for prior …

A Novel Approach On Energy Of Λj-Dominating Single-Valued Neutrosophic Graph Structure, S.N. Suber Bathusha, S. Angelin Kavitha Raj

Neutrosophic Systems with Applications

The concept of dominance is one of the most important ideas in graph theory for handling random events, and it has drawn the interest from many scholars. Research related to graph energy has garnered a lot focus recently. The application of single-valued neutrosophic graphs (SVNGs) for energy, Laplacian energy, and dominating energy has been recommended by previous studies. In this research, we apply the concepts of single-valued neutrosophic sets (SVNS) to graph structures (GSs) and investigate some intriguing features of single-valued neutrosophic graph structures (SVNGS). Moreover, the notions of λJ-dominating energy GS in an SVNGS environment are analyzed in this …

Visual Analytics In Explaining Neural Networks With Neuron Clustering, Gulsum Alicioglu, Bo Sun

College of Science & Mathematics Departmental Research

Deep learning (DL) models have achieved state-of-the-art performance in many domains. The interpretation of their working mechanisms and decision-making process is essential because of their complex structure and black-box nature, especially for sensitive domains such as healthcare. Visual analytics (VA) combined with DL methods have been widely used to discover data insights, but they often encounter visual clutter (VC) issues. This study presents a compact neural network (NN) view design to reduce the visual clutter in explaining the DL model components for domain experts and end users. We utilized clustering algorithms to group hidden neurons based on their activation similarities. …

Unveiling The Origins Of Source Code Through Authorship Attribution: A Comparative Study Of Ai And Human Coding Patterns, Shamma Humaid Alalawi

Thesis/ Dissertation Defenses

In recent years, artificial intelligence (AI) techniques have been used for source code authorship attribution, which is the process of identifying the original author of a given piece of code. With the advancement of AI technologies like ChatGPT, which can generate code, there is a need to accurately identify whether a piece of code is written by a human or generated by a machine. This is crucial for intellectual property protection, cybersecurity, and software forensics. The main objective of this thesis is to review existing research on source code authorship attribution and conduct several experiments to determine the best AI …

Choosing Optimal Supply Radius Of Transformer Substations (Tss) In Iraq’S Cities Using Geometric Programming With Neutrosophic Coefficients, Ahmed K. Essa, Montifort Blessings Andrew Mitungwi, Tuweh Prince Gadama, Ahmed A. Salama

Neutrosophic Systems with Applications

Numerous uncertainties exist in various electricity power system problems due to the size, complexity, geographical distribution, and influence of unforeseen events in these systems, making it difficult for traditional mathematics tools based on crisp set theory to have an impact on and solve many power system problems. As a new branch of mathematical uncertainty techniques, the neutrosophic expert systems approach has therefore emerged with the development of electric power systems and has proven successful when correctly linked. The expert typically uses ambiguous or neutrosophic language to describe their empirical knowledge, such as "very likely," "quite likely," "if x is large, …

Enhancing Cybersecurity Awareness In The United Arab Emirates: An Assessment Of Current Practices And The Development Of An Ai-Enhanced Mobile Application, Meera Alalawi

Thesis/ Dissertation Defenses

In today's interconnected world, individuals, private corporations, public institutions, and governments face increasingly sophisticated cyber threats and attacks, highlighting the critical need for individuals and organizations to understand cybersecurity comprehensively. Cyberattacks have affected many countries and infrastructures in different sectors worldwide, including the United Arab Emirates (UAE), which has become a main target for cybercrime due to its booming economy and tourism. The UAE considers cybersecurity an increasingly critical issue in our digital world, and increasing cybersecurity awareness among residents is essential to protect themselves and their organizations from cyberattacks. The primary objectives of this study are to identify key …

Choosing Optimal Supply Radius Of Transformer Substations (Tss) In Iraq’S Cities Using Geometric Programming With Neutrosophic Coefficients, Ahmed K. Essa, Montifort Blessings Andrew Mitungwi, Tuweh Prince Gadama, Ahmed A. Salama

Neutrosophic Systems with Applications

Numerous uncertainties exist in various electricity power system problems due to the size, complexity, geographical distribution, and influence of unforeseen events in these systems, making it difficult for traditional mathematics tools based on crisp set theory to have an impact on and solve many power system problems. As a new branch of mathematical uncertainty techniques, the neutrosophic expert systems approach has therefore emerged with the development of electric power systems and has proven successful when correctly linked. The expert typically uses ambiguous or neutrosophic language to describe their empirical knowledge, such as "very likely," "quite likely," "if x is large, …