Computer Sciences Commons^™

From Pong To Narrative: The Evolution Of Ai In Gaming, Muhammad Assaf

ART 108: Introduction to Games Studies

As video games evolve, the role of artificial intelligence also referred to as AI has been essential. Games have come a long way from being small and having rudimentary logic to being extremely complex and narrative-driven. This research paper will dive into the vast history of AI in gaming, following its path from the simple ball-paddle mechanics of Pong, to the intricate entanglements presented in modern games such as Fortnite, Call of Duty, League of Legends, and more. The interplay between game design and AI development doesn’t just show how far we have come in technological advancement, but rather it …

Ascot App, Milla Penelope Markovic

Honors Thesis

The Ascot App is a research tool for acquiring and analyzing data. The app comprises of both mobile and web platforms, each serving a unique purpose. The mobile side allows users to input data through the app’s form, which is uploaded to a database for further processing and analysis. The web app, which is still under development as of April of 2024, allows users to manage their research project and download data in the form of a parsed CSV. These components ensure a seamless process for research teams to record data with persistence and security while allowing for analysis.

Ascot …

Simulation-Based Adaptive Interface For Personalized Learning Of Ai Fundamentals In Secondary School, Sara Guerreiro-Santalla, Dalila Duraes, Helen Crompton, Paulo Novais, Francisco Bellas

STEMPS Faculty Publications

This paper presents the first results on the validation of a new Adaptive E-learning System, focused on providing personalized learning to secondary school students in the field of education about AI by means of an adaptive interface based on a 3D robotic simulator. The prototype tool presented here has been tested at schools in USA, Spain, and Portugal, obtaining very valuable insights regarding the high engagement level of students in programming tasks when dealing with the simulated interface. In addition, it has been shown the system reliability in terms of adjusting the students’ learning paths according to their skills and …

An Empirical Study Of Machine Learning Techniques For Accurate Stock Price Forecasting, Daniel Paliulis, Hari Patchigolla

Honors Scholar Theses

This paper presents a comprehensive approach to predicting future stock prices of companies using machine learning and time series analysis. The research problem is centered around addressing the complexity and emotion-driven nature of stock investment decisions. To create an objective determinant in stock decisions, we propose a machine learning model utilizing time series data from major companies, including Amazon, Apple, Google, Nvidia, Meta, Tesla, Salesforce, Intel, and Microsoft. We explore the use of Long Short-Term Memory (LSTM) neural networks, to capture the temporal dynamics of stock prices. These models are designed to process sequential data, maintaining short term and long …

A Randomised Non-Descent Method For Global Optimisation, Dmitry A. Pasechnyuk, Alexander Gornov

Machine Learning Faculty Publications

This paper proposes novel algorithm for non-convex multimodal constrained optimisation problems. It is based on sequential solving restrictions of problem to sections of feasible set by random subspaces (in general, manifolds) of low dimensionality. This approach varies in a way to draw subspaces, dimensionality of subspaces, and method to solve restricted problems. We provide empirical study of algorithm on convex, unimodal and multimodal optimisation problems and compare it with efficient algorithms intended for each class of problems.

Improved Image Recognition Via Synthetic Plants Using 3d Modelling With Stochastic Variations, Chris C. Napier, David M. Cook, Leisa Armstrong, Dean Diepeveen

Research outputs 2022 to 2026

This research extends previous plant modelling using L-systems by means of a novel arrangement comprising synthetic plants and a refined global wheat dataset in combination with a synthetic inference application. The study demonstrates an application with direct recognition of real plant stereotypes, and augmentation via a plant-wide stochastic growth variation structure. The study showed that the automatic annotation and counting of wheat heads using the Global Wheat dataset images provides a time and cost saving over traditional manual approaches and neural networks. This study introduces a novel synthetic inference application using a plant-wide stochastic variation system, resulting in improved structural …

Race: An Efficient Redundancy-Aware Accelerator For Dynamic Graph Neural Network, Hui Yu, Yu Zhang, Jin Zhao, Yujian Liao, Zhiying Huang, Donghao He, Lin Gu, Hai Jin, Xiaofei Liao, Haikun Liu, Bingsheng He, Jianhui Yue

Michigan Tech Publications

Dynamic Graph Neural Network (DGNN) has recently attracted a significant amount of research attention from various domains, because most real-world graphs are inherently dynamic. Despite many research efforts, for DGNN, existing hardware/software solutions still suffer significantly from redundant computation and memory access overhead, because they need to irregularly access and recompute all graph data of each graph snapshot. To address these issues, we propose an efficient redundancy-aware accelerator, RACE, which enables energy-efficient execution of DGNN models. Specifically, we propose a redundancy-aware incremental execution approach into the accelerator design for DGNN to instantly achieve the output features of the latest graph …

Movie Recommendation System Using Content Based Filtering, Sribhashyam Rakesh

Al-Bahir

The movie recommendation system plays a crucial role in assisting movie enthusiasts in finding movies that match their interests, saving them from the overwhelming task of sifting through countless options. In this paper, we present a content-grounded movie recommendation system that leverages an attribute-based approach to offer personalized movie suggestions to users. The proposed method focuses on attributes such as cast, keywords, crew, and genres of movies to predict users' preferences accurately. Through extensive evaluation, our content-grounded recommendation system demonstrated significant improvements in performance compared to conventional methods. The precision and recall scores increased by an average of 20% and …

Ai Empire: Unraveling The Interlocking Systems Of Oppression In Generative Ai's Global Order, Jasmina Tacheva, Srividya Ramasubramanian

Media Studies - All Scholarship

As artificial intelligence (AI) continues to captivate the collective imagination through the latest generation of generative AI models such as DALL-E and ChatGPT, the dehumanizing and harmful features of the technology industry that have plagued it since its inception only seem to deepen and intensify. Far from a “glitch” or unintentional error, these endemic issues are a function of the interlocking systems of oppression upon which AI is built. Using the analytical framework of “Empire,” this paper demonstrates that we live not simply in the “age of AI” but in the age of AI Empire. Specifically, we show that …

Deep Learning Uncertainty Quantification For Clinical Text Classification, Alina Peluso, Ioana Danciu, Hong-Jun Yoon, Jamaludin Mohd Yusof, Tanmoy Bhattacharya, Adam Spannaus, Noah Schaefferkoetter, Eric B. Durbin, Xiao-Cheng Wu, Antoinette Stroup, Jennifer Doherty, Stephen Schwartz, Charles Wiggins, Linda Coyle, Lynne Penberthy, Georgia D. Tourassi, Shang Gao

School of Public Health Faculty Publications

INTRODUCTION: Machine learning algorithms are expected to work side-by-side with humans in decision-making pipelines. Thus, the ability of classifiers to make reliable decisions is of paramount importance. Deep neural networks (DNNs) represent the state-of-the-art models to address real-world classification. Although the strength of activation in DNNs is often correlated with the network's confidence, in-depth analyses are needed to establish whether they are well calibrated. METHOD: In this paper, we demonstrate the use of DNN-based classification tools to benefit cancer registries by automating information extraction of disease at diagnosis and at surgery from electronic text pathology reports from the US National …

Probing And Enhancing The Reliance Of Transformer Models On Poetic Information, Almas Abdibayev

Dartmouth College Ph.D Dissertations

Transformer models have achieved remarkable success in the widest variety of domains, spanning not just a multitude of tasks within natural language processing, but also those in computer vision, speech, and reinforcement learning. The key to this success is largely attributed to the self-attention mechanism, particularly its ability to scale in performance as it grows in the number of parameters. Extensive effort has been underway to study the major linguistic properties learned by these models during the course of their pretraining. However, the role of certain finer linguistic phenomena present in language and their utilization by Transformers has not been …

E-Governance: The Implication Of Next Social Generation Welfare Information System, Yaya Mulyana Abdul Aziz, Andre Ariesmansyah

Smart City

Accelerating bureaucracy can be performed by e-governance present in order to improve the quality of government administration in the world. E-governance draft is closely related to the development of information and communication technology (ICT) globally. One form of embodiment of e-governance is the implementation of a smart city. smart cities are expected to be able to become a liaison between the demands of the community in appropriate, effective, and efficient services from the city government, by utilizing ICT. There are various related definitions of smart city in this world. One of them is as explained by Nam and Pardo who …

Deep Learning Image Analysis To Isolate And Characterize Different Stages Of S-Phase In Human Cells, Kevin A. Boyd, Rudranil Mitra, John Santerre, Christopher L. Sansam

SMU Data Science Review

Abstract. This research used deep learning for image analysis by isolating and characterizing distinct DNA replication patterns in human cells. By leveraging high-resolution microscopy images of multiple cells stained with 5-Ethynyl-2′-deoxyuridine (EdU), a replication marker, this analysis utilized Convolutional Neural Networks (CNNs) to perform image segmentation and to provide robust and reliable classification results. First multiple cells in a field of focus were identified using a pretrained CNN called Cellpose. After identifying the location of each cell in the image a python script was created to crop out each cell into individual .tif files. After careful annotation, a CNN was …

Turnstile File Transfer: A Unidirectional System For Medium-Security Isolated Clusters, Mark Monnin, Lori L. Sussman

Journal of Cybersecurity Education, Research and Practice

Data transfer between isolated clusters is imperative for cybersecurity education, research, and testing. Such techniques facilitate hands-on cybersecurity learning in isolated clusters, allow cybersecurity students to practice with various hacking tools, and develop professional cybersecurity technical skills. Educators often use these remote learning environments for research as well. Researchers and students use these isolated environments to test sophisticated hardware, software, and procedures using full-fledged operating systems, networks, and applications. Virus and malware researchers may wish to release suspected malicious software in a controlled environment to observe their behavior better or gain the information needed to assist their reverse engineering processes. …

Exploring The Nexus Of Cybersecurity Leadership, Human Factors, Emotional Intelligence, Innovative Work Behavior, And Critical Leadership Traits, Sharon L. Burton, Darrell Norman Burrell, Calvin Nobles, Laura A. Jones

Publications

Data shows that 12% of leaders are rated as 'very effective' at leadership. This research emphasizes the importance of understanding human behavior and its impact on leadership effectiveness, innovative work behavior (IWB), and the ability to respond to complex cyber threats, particularly in the realm of cybersecurity leadership. Emotional intelligence (EI), a key human factor, is highlighted as a crucial element that can stimulate cognitive absorption, leading to innovative work behavior and improved innovation efficiency (IE). This underscores the need for leaders to not only be technically proficient but also emotionally intelligent to effectively manage their teams and respond to …

Cta’S ‘L’ System Visualization And Animation, Julia Finegan

Honors Capstones

The Chicago Transit Authority (CTA) is a vital public transportation system for the city of Chicago and the surrounding suburbs, and all of its ‘L’ train data was recorded from March 2022 to February 2023 for this research. The main goal of this project was to create interactive/animated charts, graphs, and/or transit maps to present this raw data in a meaningful form that could help future researchers learn more about the CTA system, its patterns, and/or its unexplained inconsistencies/irregularities. A simple animation of the ‘L’ trains running within a specified time frame was created with the Python libraries Pandas, Shapely, …

Cm-Ii Meditation As An Intervention To Reduce Stress And Improve Attention: A Study Of Ml Detection, Spectral Analysis, And Hrv Metrics, Sreekanth Gopi

Master of Science in Computer Science Theses

Students frequently face heightened stress due to academic and social pressures, particularly in de- manding fields like computer science and engineering. These challenges are often associated with serious mental health issues, including ADHD (Attention Deficit Hyperactivity Disorder), depression, and an increased risk of suicide. The average student attention span has notably decreased from 21⁄2 minutes to just 47 seconds, and now it typically takes about 25 minutes to switch attention to a new task (Mark, 2023). Research findings suggest that over 95% of individuals who die by suicide have been diagnosed with depression (Shahtahmasebi, 2013), and almost 20% of students …

The Transformative Integration Of Artificial Intelligence With Cmmc And Nist 800-171 For Advanced Risk Management And Compliance, Mia Lunati

Cybersecurity Undergraduate Research Showcase

This paper explores the transformative potential of integrating Artificial Intelligence (AI) with established cybersecurity frameworks such as the Cybersecurity Maturity Model Certification (CMMC) and the National Institute of Standards and Technology (NIST) Special Publication 800-171. The thesis argues that the relationship between AI and these frameworks has the capacity to transform risk management in cybersecurity, where it could serve as a critical element in threat mitigation. In addition to addressing AI’s capabilities, this paper acknowledges the risks and limitations of these systems, highlighting the need for extensive research and monitoring when relying on AI. One must understand boundaries when integrating …

Superhyperfunction, Superhyperstructure, Neutrosophic Superhyperfunction And Neutrosophic Superhyperstructure: Current Understanding And Future Directions, Florentin Smarandache

Neutrosophic Systems with Applications

The n-th PowerSet of a Set {or Pn(S)} better describes our real world, because a system S (which may be a company, institution, association, country, society, set of objects/plants/animals/beings, set of concepts/ideas/propositions, etc.) is formed by sub-systems, which in their turn by sub-sub-systems, and so on. We prove that the SuperHyperFunction is a generalization of classical Function, SuperFunction, and HyperFunction. And the SuperHyperAlgebra, SuperHyperGraph are part of the SuperHyperStructure. Almost all structures in our real world are Neutrosophic SuperHyperStructures since they have indeterminate/incomplete/uncertain/conflicting data.

On B-Anti-Open Sets: A Formal Definition, Proofs, And Examples, Sudeep Dey, Priyanka Paul, Gautam Chandra Ray

Neutrosophic Systems with Applications

The concepts of open sets, closed sets, the interior of a set, and the exterior of a set are the most basic concepts in the study of topological spaces in any setting. When we turn our attention to the concept of anti-topological spaces, we encounter analogous fundamental concepts, such as the definition of anti-open sets, anti-closed sets, anti-interior, anti-exterior, etc. These concepts have already been introduced and studied by mathematicians worldwide. In this article, we introduce and study the concepts of b-anti-open set, b-anti-closed set, anti-b-interior, and anti-b-closure in the context of anti-topological spaces and investigate some of their basic …

Ncmpy: A Modelling Software For Neutrosophic Cognitive Maps Based On Python Package, Ilanthenral Kandasamy, Divakar Arumugam, Aryan Rathore, Ateeth Arun, Manan Jain, Vasantha .W.B, Florentin Smarandache

Neutrosophic Systems with Applications

Cognitive maps are a vital tool that can be used for knowledge representation and reasoning. Fuzzy Cognitive Maps (FCMs) are popular soft computing techniques used to model large and complex systems, and they can aid in explainable artificial intelligence (AI). FCMs, however, cannot model the indeterminacy that arises in a system due to various uncertainties. Neutrosophic Cognitive Maps (NCMs), upgraded FCMs that could model indeterminacy, were introduced to address this issue. NCMs are a generalization of FCMs, a field of cognitive science firmly based on neural networks. NCMs have been used to solve a wide range of problems. NCMs were …

Some Special Refined Neutrosophic Ideals In Refined Neutrosophic Rings: A Proof-Of-Concept Study, Murhaf Riad Alabdullah

Neutrosophic Systems with Applications

In this research, we created notions of a refined neutrosophic prime (completely prime, semiprime, and completely semiprime) ideal in a refined neutrosophic ring. If R(I1, I2) is a refined neutrosophic ring, then each ideal of R(I1, I2) has the form J+KI1+LI2 are ideals of the classical ring R. The objective of this work is to find the necessary and sufficient condition on classical ideals J, L and K that makes J+KI1+LI2 a prime (completely prime, semiprime, and completely semiprime) ideal in R(I1, I2). We studied some of the elementary properties of these concepts and the most important properties that link …

Scalable And Explainable Self-Supervised Motif Discovery In Temporal Data, Somayeh Bakhtiari Ramezani

Theses and Dissertations

The availability of a scalable and explainable rule extraction technique via motif discovery is crucial for identifying the health states of a system. Such a technique can enable the creation of a repository of normal and abnormal states of the system and identify the system’s state as we receive data. In complex systems such as ECG, each activity session can consist of a long sequence of motifs that form different global structures. As a result, applying machine learning algorithms without first identifying the local patterns is not feasible and would result in low performance. Thus, extracting unique local motifs and …

Privacy Within Autonomous Vehicle Cameras, Joshua Montgomery

Honors Theses

In recent years, cameras have become ubiquitous in daily life, constantly surveilling, and taking in information. This leads to a potential security risk of the invasion in one’s privacy without their knowledge or any ability to prevent the privacy threat. While cameras alone are an issue, they are often only in locations where a user has some expectation of a loss of privacy, such as public locations with security systems. However, systems that rely on cameras to operate correctly, including autonomous vehicles, are becoming a more prominently used technology while often appearing in places where an average person has some …

Superhyperfunction, Superhyperstructure, Neutrosophic Superhyperfunction And Neutrosophic Superhyperstructure: Current Understanding And Future Directions, Florentin Smarandache

Neutrosophic Systems with Applications

The n-th PowerSet of a Set {or Pn(S)} better describes our real world, because a system S (which may be a company, institution, association, country, society, set of objects/plants/animals/beings, set of concepts/ideas/propositions, etc.) is formed by sub-systems, which in their turn by sub-sub-systems, and so on. We prove that the SuperHyperFunction is a generalization of classical Function, SuperFunction, and HyperFunction. And the SuperHyperAlgebra, SuperHyperGraph are part of the SuperHyperStructure. Almost all structures in our real world are Neutrosophic SuperHyperStructures since they have indeterminate/incomplete/uncertain/conflicting data.

Some Special Refined Neutrosophic Ideals In Refined Neutrosophic Rings: A Proof-Of-Concept Study, Murhaf Riad Alabdullah

Neutrosophic Systems with Applications

In this research, we created notions of a refined neutrosophic prime (completely prime, semiprime, and completely semiprime) ideal in a refined neutrosophic ring. If R(I1, I2) is a refined neutrosophic ring, then each ideal of R(I1, I2) has the form J+KI1+LI2 are ideals of the classical ring R. The objective of this work is to find the necessary and sufficient condition on classical ideals J, L and K that makes J+KI1+LI2 a prime (completely prime, semiprime, and completely semiprime) ideal in R(I1, I2). We studied some of the elementary properties of these concepts and the most important properties that link …

Ncmpy: A Modelling Software For Neutrosophic Cognitive Maps Based On Python Package, Ilanthenral Kandasamy, Divakar Arumugam, Aryan Rathore, Ateeth Arun, Manan Jain, Vasantha .W.B, Florentin Smarandache

Neutrosophic Systems with Applications

Cognitive maps are a vital tool that can be used for knowledge representation and reasoning. Fuzzy Cognitive Maps (FCMs) are popular soft computing techniques used to model large and complex systems, and they can aid in explainable artificial intelligence (AI). FCMs, however, cannot model the indeterminacy that arises in a system due to various uncertainties. Neutrosophic Cognitive Maps (NCMs), upgraded FCMs that could model indeterminacy, were introduced to address this issue. NCMs are a generalization of FCMs, a field of cognitive science firmly based on neural networks. NCMs have been used to solve a wide range of problems. NCMs were …

Finding Diffs Of Pull Request Commits, Chalinee Karawek, John Businge

Undergraduate Research Symposium Posters

Github is a social network that allows developers' projects to be forked for various uses, such as developing the existing repository or use the code to steer development into a new direction. As more forks are made, the more bugs that can occur due to new pull requests not synchronizing with the upstream repository. PaReco is a clone detection tool by Ramkisoen et al. (Ramkisoen et al. 2022) that identifies the changes inside the files inside a pull request by performing diff on every file. However, in this study, we take a different approach to identifying the changes inside the …

On B-Anti-Open Sets: A Formal Definition, Proofs, And Examples, Sudeep Dey, Priyanka Paul, Gautam Chandra Ray

Neutrosophic Systems with Applications

The concepts of open sets, closed sets, the interior of a set, and the exterior of a set are the most basic concepts in the study of topological spaces in any setting. When we turn our attention to the concept of anti-topological spaces, we encounter analogous fundamental concepts, such as the definition of anti-open sets, anti-closed sets, anti-interior, anti-exterior, etc. These concepts have already been introduced and studied by mathematicians worldwide. In this article, we introduce and study the concepts of b-anti-open set, b-anti-closed set, anti-b-interior, and anti-b-closure in the context of anti-topological spaces and investigate some of their basic …

A Conceptual Decentralized Identity Solution For State Government, Martin Duclos

Theses and Dissertations

In recent years, state governments, exemplified by Mississippi, have significantly expanded their online service offerings to reduce costs and improve efficiency. However, this shift has led to challenges in managing digital identities effectively, with multiple fragmented solutions in use. This paper proposes a Self-Sovereign Identity (SSI) framework based on distributed ledger technology. SSI grants individuals control over their digital identities, enhancing privacy and security without relying on a centralized authority. The contributions of this research include increased efficiency, improved privacy and security, enhanced user satisfaction, and reduced costs in state government digital identity management. The paper provides background on digital …