Computer Sciences Commons^™

Using Llms To Establish Implicit User Sentiment Of Software Desirability, Sherri Weitl-Harms, John D. Hastings, Jonah Lum

Research & Publications

This study explores the use of LLMs for providing quantitative zero-shot sentiment analysis of implicit software desirability, addressing a critical challenge in product evaluation where traditional review scores, though convenient, fail to capture the richness of qualitative user feedback. Innovations include establishing a method that 1) works with qualitative user experience data without the need for explicit review scores, 2) focuses on implicit user satisfaction, and 3) provides scaled numerical sentiment analysis, offering a more nuanced understanding of user sentiment, instead of simply classifying sentiment as positive, neutral, or negative.

Data is collected using the Microsoft Product Desirability Toolkit (PDT), …

Q-Learning In Starclash, Hanani Pankaj

2024 Fall Honors Capstone Projects - Archive

Developers create video games using Artificial Intelligence (AI) agents to provide a challenging opponent in a single-player game. However, studies show that when Reinforcement Learning (RL) agents are used, they outperform the AI agents. This project sought to test how RL agents would perform in StarClash, a video game without RL agents, using Q-Learning. This was done by creating two Q-Learning agents: a Simple agent and an Advanced (more complex) agent. These two agents were tested against each other and a Random AI agent. As expected, the Advanced agent did better than the Simple agent but only performed slightly better, …

Pixel: Ai Chatbot For Clear And Effective Senior Design Assistance, Asmin Pothula

2024 Fall Honors Capstone Projects - Archive

This research explores the development of an AI-driven chatbot named Pixel, specifically designed to assist Computer Science and Engineering Senior Design students by providing immediate, clear, and accurate responses to project-related queries. While my Senior Design project focuses on developing a "Senior Design Project Management Tool," my honors capstone project centers on developing Pixel and integrating it into both the project management tool and the CSE Senior Design Knowledge Base. Pixel leverages this knowledge base to offer guidance on tasks such as using lab equipment, performing technical procedures, and troubleshooting common issues, ensuring that students have swift access to relevant …

A Machine Learning Approach To Multifactorial Modeling Of Episodic Memory Performance, Evan Fletcher, Marianne Chanti-Ketterl, Emily Hokett, Yi Lor, Umesh Venkatesan, Paola Gilsanz, Rachel Whitmer, Ruijia Chen, Kristen George, Zvinka Zlatar

Moss-Magee Rehabilitation Papers

BACKGROUND: Neurocognitive health is influenced by multiple modifiable and non-modifiable lifestyle factors. Machine learning tools offer a promising approach to better understand complex models of cognitive function. We used extreme gradient boosting (XG Boost), an algorithm of decision-tree modeling, to analyze the association between 15 late-life lifestyle and demographic factors with episodic memory performance. METHOD: Our dataset consisted of 2247 participants from the KHANDLE and STAR cohorts. Participants included 841 men and 1406 women, an ethnoracial diversity of 413 Asian, 987 Black, 349 Latinx, and 496 White adults with age range 54-90 (mean = 74). XG Boost models of continuous …

The Evolving Role Of Copyright Law In The Age Of Ai-Generated Works, James Hutson

Faculty Scholarship

Objective: to identify the prospects and directions of copyright law development associated with the increasing use of generative artificial intelligence.

Methods: the study is based on the formal-legal, comparative, historical methods, doctrinal analysis, legal forecasting and modeling.

Results:the article states that the emergence of generative artificial intelligence makes one rethink the processes occurring in the field of creative activity and the traditional copyright system, which becomes inadequate to modern realities. The author substantiates the necessity of legal reassessment of copyright and emphasizes the urgent need for updated means of copyright protection. Unlike previous digital tools, which expanded …

Learning To Represent Temporal Dynamics And Generative Factors For Intelligent Visual Navigation, Sahand Khoshdel

All Theses

Visual navigation systems are crucial in various applications, including autonomous driving, unmanned aerial systems (UAS), and industrial automation. For these systems to operate efficiently in dynamic environments, they must not only interpret complex surroundings but also anticipate changes over time. Temporal prediction—forecasting environmental changes like moving obstacles or shifting lighting conditions—enables navigation systems to act proactively, enhancing both safety and performance. This dissertation investigates representation learning methods both as a backbone feature extractor for RL agents as well as a proxy for systems oriented for Explainable AI (XAI). Two main projects are presented as case studies to achieve the aforementioned …

Intellectual Property Liability For Businesses In The Age Of Ai: What New Liabilities Businesses Using Ai Could Face And The Possible Methods Of Self-Protection, Elizabeth Anne Henderson

Michigan Business & Entrepreneurial Law Review

The invention of Artificial Intelligence (“AI”) has triggered a wave of copyright and trademark litigation that will likely shape the intellectual property laws governing AI for the foreseeable future. Lawsuits against AI giants like Meta and OpenAI stand to declare popular uses of AI as actionable infringement as well as possibly reshape how copyright and trademark law view concepts, such as fair use and derivative works in the age of technology. Meanwhile, businesses are pushing forward rapidly with adopting AI and implementing its use in everyday functions. For many of these businesses, AI is a highly desirable but poorly understood …

Reducing Token Redundancy In Video-Language Models Via Memory Consolidation Algorithm, Matt Couts

Electrical Engineering and Computer Science Undergraduate Honors Theses

Video Question Answering (VideoQA) focuses on developing mod- els capable of engaging in natural language conversations about video con- tent. Current state-of-the-art typically analyze videos frame-by-frame, a process that is both computationally and memory-intensive. Integrating the Atkinson-Shiffrin memory model with Video Language Models has demon- strated potential for enhancing video understanding capabilities. Reducing the number of frames processed by the model is a crucial operation in this approach, which is achieved by a memory consolidation algorithm. This al- gorithm condenses a video sequence into a small set of representative frames which capture the essence of the video content. However, due …

The Algorithm Of Fear: Unpacking Prejudice Against Ai And The Mistrust Of Technology, James Hutson, Daniel Plate

Faculty Scholarship

The mistrust of AI seen in the media, industry and education reflects deep-seated cultural anxieties, often comparable to societal prejudices like racism and sexism. Throughout history, literature and media have portrayed machines as antagonists, amplifying fears of technological obsolescence and identity loss. Despite the recent remarkable advancements in AI—particularly in creative and decision-making capacities—human resistance to its adoption persists, rooted in a combination of technophobia, algorithm aversion, and cultural narratives of dystopia. This review investigates the origins of this prejudice, focusing on the parallels between current attitudes toward AI and historical resistance to new technologies. Drawing on examples from popular …

From Concept To Creation: The Role Of Generative Artificial Intelligence In The New Age Of Digital Marketing, Andrew Smith, James Hutson

Faculty Scholarship

Artificial intelligence (AI) has been extensively used in digital marketing. Still, the recent advances in generative AI (GAI) have revolutionized social media marketing and content creation, lowering barriers that once restricted high-quality design to professionals well versed in expensive and complex software like Adobe Suite. GAI tools enable anyone, from students to marketers, to generate logos, branding, and multimedia content without extensive training. This shift has empowered more people to engage in creative expression, expanding the pool of ideas and creativity. However, the abundance of AI-generated content raises questions about the evolving definition of “art” and the emergence of a …

Algotric: Symmetric And Asymmetric Encryption Algorithms For Cryptography – A Comparative Analysis In Ai Era, Naresh Kshetri, Mir Mehedi Rahman, Md Masud Rana, Omar Faruq Osama, James Hutson

Faculty Scholarship

The increasing integration of artificial intelligence (AI) within cybersecurity has necessitated stronger encryption methods to ensure data security. This paper presents a comparative analysis of symmetric (SE) and asymmetric encryption (AE) algorithms, focusing on their role in securing sensitive information in AI-driven environments. Through an in-depth study of various encryption algorithms such as AES, RSA, and others, this research evaluates the efficiency, complexity, and security of these algorithms within modern cybersecurity frameworks. Utilizing both qualitative and quantitative analysis, this research explores the historical evolution of encryption algorithms and their growing relevance in AI applications. The comparison of SE and AE …

Robust Learning With Probabilistic Relaxation Using Hypothesis-Test-Based Sampling, Zilin Wang

Dissertations and Theses Collection (Open Access)

In recent years, deep learning has been a vital tool in various tasks. The performance of a neural network is usually evaluated by empirical risk minimization. However, robustness issues have gained great concern which can be fatal in safety-critical applications. Adversarial training can mitigate the issue by minimizing the loss of worst-case perturbations of data. It is effective in improving the robustness of the model, but it is too conservative, and the plain performance of the model can be unsatisfying. Probabilistic Robust Learning (PRL) empirically balances the average- and worst-case performance while the robustness of the model is not provable …

Enhancing Low-Resource Language Performance In Multilingual Large Language Models, Mingqi Li

All Dissertations

The large language models play an important role in many natural language tasks. However, training these models requires large amounts of data, which is not available for many languages. A noticeable performance gap exists between English and other languages, with low-resource languages showcasing this gap prominently. Therefore, it becomes imperative to improve large language models for low-resource languages. To address these challenges, we developed knowledge distillation and strategic prompt-learning, and attention alignment methods to improve the representation capabilities of large language models for low-resource language, and then enhanced their performance in downstream tasks.

In our first study, we developed a …

Video Game Development 3.0: Ai-Driven Collaborative Co-Creation, Jay Ratican, James Hutson

Faculty Scholarship

The evolution of game development has transitioned from manual coding (Software 1.0) to data-driven Artificial Intelligence (AI) (Software 2.0), and now to a more advanced stage—video game development 3.0. This phase is characterized by AI-driven processes leveraging large language models (LLMs), neural networks, and other AI techniques that autonomously generate code, content, and narratives. This paper explores the foundational technologies underpinning this paradigm shift, including customizable AI modules, dynamic asset creation, and intelligent non player characters (NPCs) that adapt to player interactions. It also highlights the integration of AI with emerging technologies like Virtual Reality (VR), Augmented Reality (AR), and …

Exploring The Cognitive Sense Of Self In Ai: Ethical Frameworks And Technological Advances For Enhanced Decision-Making, Emily Barnes, James Hutson

Faculty Scholarship

The burgeoning field of Artificial Intelligence (AI) increasingly focuses on developing systems capable of self-awareness, merging technological innovation with deep ethical and philosophical considerations. This article explores the cognitive sense of self within AI, examining mechanisms through which AI systems may mirror human-like consciousness and self-perception. Despite significant advances, substantial gaps remain in the understanding and practical implementation of self-aware characteristics in AI, particularly in applying theoretical models and ethical frameworks to real-world scenarios. There is a pressing need for comprehensive research to explore these theoretical underpinnings and translate them into operational systems capable of ethical and adaptable behaviors. This …

Basic Safety Message Generation Through A Video-Based Analytics For Potential Safety Application, Abyad Enan

All Theses

With the advancement of modern artificial intelligence techniques, computer vision can play a vital role in enhancing roadway safety by reducing the risk of imminent collisions. To do so, a vision-based safety application is required, where a roadside camera can monitor the roadway traffic and predict potential risks of crashes in real-time. If any risky situation or behavior is observed that may lead to a crash, then a safety application can send warnings to the vehicles at risk. For vision-based safety applications on a roadway section, it is important to accurately monitor each vehicle’s location, speed, acceleration, heading direction, etc. …

Unsupervised Moving Object Segmentation With Atmospheric Turbulence, Dehao Qin

All Theses

Moving object segmentation in the presence of atmospheric turbulence is a highly challenging task due to the irregular and time-varying distortions induced by the atmospheric turbulence. This thesis presents an unsupervised approach for segmenting moving objects in videos affected by such atmospheric turbulence. The proposed methodology is grounded in a detect-then-grow scheme: the algorithm begins by identifying a small set of moving object pixels (seed points) with high confidence and progressively expanding a foreground mask from these seed points to segment all moving objects. The proposed approach capitalizes on rigid geometric consistency across video frames to disentangle different types of …

Regulating Robo-Advisors In An Age Of Generative Artificial Intelligence, Daniel Schwarcz, Tom Baker

Law & Economics Working Papers

New generative Artificial Intelligence (AI) tools can increasingly engage in personalized, sustained and natural conversations with users. This technology has the capacity to reshape the financial services industry, making customized expert financial advice broadly available to consumers. However, AI’s ability to convincingly mimic human financial advisors also creates significant risks of large-scale financial misconduct. Which of these possibilities becomes reality will depend largely on the legal and regulatory rules governing “robo-advisors” that supply fully automated financial advice to consumers. This Article consequently critically examines this evolving regulatory landscape, arguing that current U.S. rules fail to adequately limit the risk that …

Deep Learning Approach For Accurate Segmentation Of Oil Spills In Marine Systems, Mohamed Elsheref

LSU New Orleans Theses and Dissertations

Oil spills present critical environmental hazards, threatening marine ecosystems and necessitating fast, accurate detection for effective mitigation. Synthetic Aperture Radar (SAR) imagery has been instrumental in detecting oil spills, but manual interpretation is often inefficient and prone to errors. This study addresses the limitations of manual methods by proposing a deep learning approach for automated oil spill detection and segmentation.

Utilizing a novel transfer learning-based semantic segmentation model, this research focuses on detecting oil slicks on the sea surface with higher accuracy and efficiency. The model leverages pre-trained networks and incorporates U-Net variants, including UNet++ and MultiResUNet, to optimize spatial …

Enhancing Password Security And Memorability Using Machine Learning And Linguistic Patterns, Jared Wise

LSU New Orleans Theses and Dissertations

In the digital age, text-based passwords remain a primary method for securing online accounts. Yet, users frequently face a dilemma between creating passwords that are easy to remember and sufficiently secure against cyberattacks. This research introduces an approach to password generation that bridges this gap by utilizing linguistic patterns, particularly song lyrics, to develop highly secure and naturally memorable passwords. Using large lyric datasets gained from web scrapes from popular song lyric websites (AZ Lyrics, Genius), features are extracted from a corpus of over 5 million lyrics using sentence structure and natural language processing in a novel way. In using …

Mechanically Cost-Effective Approach For Bipedal Walking In Robots Using Instantaneous Collision Angle, Smit R. Patel

UNLV Theses, Dissertations, Professional Papers, and Capstones

Humans, as bipedal locomotors, are effective at reducing the mechanical cost of transport (CoTmech) by adopting movement strategies and gaits that minimize energy expenditure for a given distance. By using different gaits at different speeds, leveraging their long spring-like tendons and muscle elasticity which store and release energy during movement, humans reduce the mechanical effort required for locomotion. Current locomotion solutions offered in bipedal robots, based on legacy walking and running gait models, are not great at energy efficiency unless walking at very low speeds. Additionally, the control system of robots, designed to ensure stability and adaptability, requires substantial resources, …

Llm Potentiality And Awareness: A Position Paper From The Perspective Of Trustworthy And Responsible Ai Modeling, Iqbal H. Sarker

Research outputs 2022 to 2026

Large language models (LLMs) are an exciting breakthrough in the rapidly growing field of artificial intelligence (AI), offering unparalleled potential in a variety of application domains such as finance, business, healthcare, cybersecurity, and so on. However, concerns regarding their trustworthiness and ethical implications have become increasingly prominent as these models are considered black-box and continue to progress. This position paper explores the potentiality of LLM from diverse perspectives as well as the associated risk factors with awareness. Towards this, we highlight not only the technical challenges but also the ethical implications and societal impacts associated with LLM deployment emphasizing fairness, …

Interpreting Neural Networks For Particle Tracing In Fluid Simulation Ensembles: An Interactive Visualization Framework, Maanav Choubey

All Graduate Theses and Dissertations, Fall 2023 to Present

Understanding the internal mechanisms of neural networks, particularly Multi-Layer Perceptrons (MLP), is essential for their effective application in a variety of scientific domains. In particular, in the scientific visualization domain their adoption has recently shown to be a promising tool to predict particle trajectories in fluid dynamics simulation and aid the interactive visualization of flows. This research addresses the critical challenge of interpretability of such models.

While interpretability has been extensively explored in fields like computer vision and natural language processing, its application to time series data, particularly for particle tracing (or prediction of trajectories), has not garnered sufficient attention. …

Enhancing Assessment And Feedback In Game Design Programs: Leveraging Generative Ai For Efficient And Meaningful Evaluation, James Hutson, Ben Fulcher, Jay Ratican

Faculty Scholarship

The integration of generative AI tools in game design education offers promising ways to streamline the grading, assessment, and feedback processes that are typically labor-intensive. In game design programs, faculty often deal with varied file formats, including 3D models, executable prototypes, videos, and complex game design documents. Traditional methods of assessment and feedback, primarily text-based, struggle to provide timely and actionable insights for students. Furthermore, only a small percentage of top students consistently review and apply feedback, leading to inefficiencies. This article explores how generative AI tools can augment these processes by automating aspects of grading, generating more personalized and …

Exploring Post-Covid-19 Health Effects And Features With Advanced Machine Learning Techniques, Muhammad N. Islam, Md S. Islam, Nahid H. Shourav, Iftiaqur Rahman, Faiz A. Faisal, Md M. Islam, Iqbal H. Sarker

Research outputs 2022 to 2026

COVID-19 is an infectious respiratory disease that has had a significant impact, resulting in a range of outcomes including recovery, continued health issues, and the loss of life. Among those who have recovered, many experience negative health effects, particularly influenced by demographic factors such as gender and age, as well as physiological and neurological factors like sleep patterns, emotional states, anxiety, and memory. This research aims to explore various health factors affecting different demographic profiles and establish significant correlations among physiological and neurological factors in the post-COVID-19 state. To achieve these objectives, we have identified the post-COVID-19 health factors and …

Phypo: Priority-Based Hybrid Task Partitioning And Offloading In Mobile Computing Using Automated Machine Learning, Shehr Bano, Ghulam Abbas, Muhammad Bilal, Ziaul Haq Abbas, Zaiwar Ali, Muhammad Waqas

Research outputs 2022 to 2026

With the increasing demand for mobile computing, the requirement for intelligent resource management has also increased. Cloud computing lessens the energy consumption of user equipment, but it increases the latency of the system. Whereas edge computing reduces the latency along with the energy consumption, it has limited resources and cannot process bigger tasks. To resolve these issues, a Priority-based Hybrid task Partitioning and Offloading (PHyPO) scheme is introduced in this paper, which prioritizes the tasks with high time sensitivity and offloads them intelligently. It also calculates the optimal number of partitions a task can be divided into. The utility of …

Universal Shape Replication Via Self-Assembly With Signal-Passing Tiles, Andrew Alseth, Daniel Hader, Matthew J. Patitz

Computer Science and Computer Engineering Faculty Publications and Presentations

In this paper, we investigate shape-assembling power of a tile-based model of self-assembly called the Signal-Passing Tile Assembly Model (STAM). In this model, the glues that bind tiles together can be turned on and off by the binding actions of other glues via “signals”. Specifically, the problem we investigate is “shape replication” wherein, given a set of input assemblies of arbitrary shape, a system must construct an arbitrary number of assemblies with the same shapes and, with the exception of size-bounded junk assemblies that result from the process, no others. We provide the first fully universal shape replication result, namely …

Efficient Anomaly Detection Driven By Different Machine Learning Architectures And Models, William Marfo

Open Access Theses & Dissertations

The rapid growth and ubiquitous adoption of the internet and cyber-physical systems (CPS) have fundamentally transformed modern communication, work, and human-system interactions. While networks now form the backbone of critical digital ecosystems, enabling seamless data transmission across diverse, interconnected systems, this increased connectivity also expands the attack surface, making real-time detection of network intrusions and anomalies a pressing challenge. Detecting unusual activities within network infrastructure requires advanced data traffic analysis to differentiate between legitimate and malicious interactions. Traditional approaches to network anomaly detectionâ??such as rule-based and signature-based systemsâ??often depend on predefined patterns to identify known anomalies, limiting their effectiveness against …

Addressing Ethical Issues In Healthcare Artificial Intelligence Using A Lifecycle-Informed Process, Benjamin X Collins, Jean-Christophe Bélisle-Pipon, Barbara J Evans, Kadija Ferryman, Xiaoqian Jiang, Camille Nebeker, Laurie Novak, Kirk Roberts, Martin Were, Zhijun Yin, Vardit Ravitsky, Joseph Coco, Rachele Hendricks-Sturrup, Ishan Williams, Ellen W Clayton, Bradley A Malin, Bridge2ai Ethics And Trustworthy Ai Working Group

Faculty, Staff and Student Publications

OBJECTIVES: Artificial intelligence (AI) proceeds through an iterative and evaluative process of development, use, and refinement which may be characterized as a lifecycle. Within this context, stakeholders can vary in their interests and perceptions of the ethical issues associated with this rapidly evolving technology in ways that can fail to identify and avert adverse outcomes. Identifying issues throughout the AI lifecycle in a systematic manner can facilitate better-informed ethical deliberation.

MATERIALS AND METHODS: We analyzed existing lifecycles from within the current literature for ethical issues of AI in healthcare to identify themes, which we relied upon to create a lifecycle …

Reference Dependence In Queue Design And Pricing Strategies, Jian Liu, Yongpin Zhou, Jian Chen, Peng Li

Electrical and Computer Engineering Faculty Research & Creative Works

This research investigates the effect of reference dependence on waiting times in service systems which formerly used a first-in-first-out (FIFO) service but have introduced a priority line with a fee. Our model combines reference-dependent gain-loss utility with standard customer utility, and we posit that customers are pleased with shorter-than-expected waiting times, whereas longer-than-expected times lead to dissatisfaction and an increased likelihood of balking. The study explores two scenarios: a captive customer system (CCS) and a noncaptive customer system (NCCS), with a focus on optimal pricing and segmentation strategies for revenue and social welfare maximization. The results reveal that, in a …