Computer Sciences Commons^™

Optimizing Llm X86 Assembly Code Comprehension Through Fine-Tuning, Darrin Michael Lea

LSU Master's Theses

Reverse engineering is a cybersecurity process that focuses on understanding the underlying functionality of software or malware. This is an arduous process that demands large amounts of time and effort from cybersecurity practitioners. Large Language Models (LLMs) offer a potential solution to this problem. LLMs have worked their way into various fields of cybersecurity in recent years, including incident response and malware classification. However, LLMs have historically struggled with low-level code comprehension: a necessary part of reverse engineering. While LLMs can generate code and explain its function on the surface level, they struggle to grasp the wider context. In this …

Replicating And Testing The First Point-Contact Transistor, Lucas Ethington, Lana Herkenhoff, Braden Stillmaker, Punit Turlapati

Miners Solving for Tomorrow Research Conference

No abstract provided.

Reframing Information Seeking In The Age Of Generative Ai: A Critical And Humanistic Approach, Joseph Kevin Sebastian

Library Faculty Research

Information-seeking has long been the subject of theoretical modeling, often drawing from cognitive, behavioral, computational, and even evolutionary perspectives to explain how individuals navigate, filter, and utilize information. Several dominant frameworks—Carol Kuhlthau’s Information Search Process, Marcia Bates’ Berrypicking Model, Peter Pirolli & Stuart Card’s Information Foraging Theory, Kiyohiko Nakamura’s Information Criteria framework, and Ian Ruthven’s Information Shaping Theory —have provided structured ways of understanding how people interact with information environments. However, while these frameworks offer valuable insights, they often operate within mechanistic or efficiency-driven paradigms, which risk overlooking the complex, embodied, and socioculturally situated nature of human information behaviors. These …

Development And Application Of Self-Supervised Machine Learning For Smoke Plume And Active Fire Identification From The Fire Influence On Regional To Global Environments And Air Quality Datasets, Nicholas Lahaye, Anastasija Easley, Kyongsik Yun, Hugo Lee, Erik Linstead, Michael J. Garay, Olga V. Kalashnikova

Engineering Faculty Articles and Research

Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) was a field campaign aimed at better understanding the impact of wildfires and agricultural fires on air quality and climate. The FIREX-AQ campaign took place in August 2019 and involved two aircraft and multiple coordinated satellite observations. This study applied and evaluated a self-supervised machine learning (ML) method for the active fire and smoke plume identification and tracking in the satellite and sub-orbital remote sensing datasets collected during the campaign. Our unique methodology combines remote sensing observations with different spatial and spectral resolutions. With as much as a 10% …

7 Plus Minus 2 Law Revisited: Alternative Geometric Explanation, Mayan Arithmetic, And Using 9- And 18-Based Numbers In Jewish Tradition, Julio C. Urenda, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

A recent paper showed that to make sure that the movements in the crowd are not chaotic, the directions of all the motions should deviate from some fixed direction by no more than 13 degrees. We show that this results provides a new geometric explanation for the seven plus minus two law in psychology, according to which we can keep in mind no more than 7 plus minus 2 items. We also show that all this is related to the somewhat mysterious appearance of 9- and 18-based number systems in Jewish and Mayan traditions.

Why Um And U*Log(U) Are The Most Effective Nonlinear Functions In Fuzzy Clustering: Theoretical Explanation Of The Empirical Fact, Olga Kosheleva, Vladik Kreinovich, Yuchi Kanzawa

Departmental Technical Reports (CS)

In fuzzy clustering, we need to have non-linear functions of the membership degrees. Different nonlinear functions have been tried. Empirical evidence shows that for fuzzy clustering, the most effective nonlinear functions are um and u*log(u). In this paper, we provide a theoretical explanation for this empirical fact.

Egyptian Triangle And Geometry Of Airplane Wings: A Simplified Explanation, Julio C. Urenda, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In historically first planes, wings were orthogonal to the fuselage. However, later it turned out that from the aerodynamic viewpoint, it is most efficient to place the wings at about 37 degrees from this orthogonal direction -- and this is where wings are placed in most modern planes. There exist theoretical explanations for this optimality -- explanations based on solving the equations of aerodynamics. In such situations when only a complex not-very-intuitive explanation exists, it is desirable to come up with a simpler more intuitive explanation. For the wing angles, such an explanation is provided in this paper. Namely, we …

"At Least K Out Of N" Under Fuzzy Uncertainty: Efficient Algorithm For General "And"-Operations, Olga Kosheleva, Vladik Kreinovich, Klaus-Peter Adlassnig

Departmental Technical Reports (CS)

In medicine, many diagnoses are made when, for some value k, at least k of n possible symptoms are present. Many of such symptoms -- such as fever -- are, in reality, fuzzy. For example, it makes no sense that say that 38.0 is fever while 37.9 is not a fever, both are fever to some degree. Once such degrees are given, we need to use them to estimate the degree to which the patient has the corresponding disease. For this problem, the usual fuzzy techniques require exponentially many computational steps -- so it is desirable to have a more …

Why Interval-Valued (And Type-2) Fuzzy Methods Are Often More Effective, Christian Servin, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Interval-valued and type-2 fuzzy techniques were designed to provide a more adequate representation of expert knowledge than the traditional (type-1) fuzzy techniques. Somewhat unexpectedly, they also often turn out to be more effective even when there is no expert knowledge at all -- when we are simply using fuzzy rules to fit experimental data. In precise terms, for the same number of parameters, interval-valued and type-2 systems often provide a better fit for the data and/or better quality control than traditional (type-1) fuzzy techniques. In this paper, we provide a theoretical explanation for this surprising phenomenon.

Why Convex Combinations Of Interval Endpoints: Related Explanations For Cases Of Data Processing And Decision Making, Christian Servin, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

There are two cases in which it has been empirically shown that a convex combination of the interval's endpoints works better than any other combination: processing interval data and dealing with situations in which we know both approximate probability and possibility and we need to make a decision. In this paper, we provide an explanation of both phenomena.

Ceker: A Generalizable Llm Framework For Literature Analysis With A Case Study In Unikernel Security, Alex Wollman, John Hastings

Research & Publications

Literature reviews are a critical component of formulating and justifying new research, but are a manual and often time-consuming process. This research introduces a novel, generalizable approach to literature analysis called CEKER which uses a three-step process to streamline the collection of literature, the extraction of key insights, and the summarized analysis of key trends and gaps. Leveraging Large Language Models (LLMs), this methodology represents a significant shift from traditional manual literature reviews, offering a scalable, flexible, and repeatable approach that can be applied across diverse research domains. A case study on unikernel security illustrates CEKER's ability to generate novel …

Grounding Ai Use In Learning Science: A Conversation With Steven Miller, Steven Miller, Lieven Demeester

CASTLe: Collection of Articles on Scholarship for Teaching and Learning

In this insightful interview, SMU Associate Provost (Teaching and Learning Innovation) Lieven Demeester and Professor Emeritus of Information Systems Steven Miller discuss the integration of artificial intelligence (AI) in teaching and learning, emphasising the importance of grounding AI use in the fundamentals of learning science. They explore the evolving role of education in the context of AI advancements, highlighting the need for educators to focus on the cognitive aspects of learning, such as goal-directed practice and feedback. They also address the potential of AI as a collaborative agent in group projects and the importance of maintaining accountability and quality control …

Impact Of Data Snooping On Deep Learning Models For Locating Vulnerabilities In Lifted Code, Gary Mccully, John Hastings, Shengjie Xu

Research & Publications

This study examines the impact of data snooping on neural networks used to detect vulnerabilities in lifted code, and builds on previous research that used word2vec and unidirectional and bidirectional transformer-based embeddings. The research specifically focuses on how model performance is affected when embedding models are trained with datasets, which include samples used for neural network training and validation. The results show that introducing data snooping did not significantly alter model performance, suggesting that data snooping had a minimal impact or that samples randomly dropped as part of the methodology contained hidden features critical to achieving optimal performance. In addition, …

Coding An Assignment Calculator Exclusively With Chatgpt, Andy Tincknell, Heather P. Vandyne, Lisa K. Bell

SACAD: Scholarly Activities

Large Language Models like ChatGPT are influencing higher education and society in broader ways, including the coding and programming of applications and websites (Silva et al., 2024). This poster will profile how Forsyth Library, with no coders on staff, used ChatGPT to program an Assignment Calculator LibGuide without human coding. It details the process, challenges, and outcomes while highlighting AI’s potential to enhance resources for academic success and considers its efficacy and ethical implications.

Network-Based Crypto Asset Analysis, Ling Cheng

Dissertations and Theses Collection (Open Access)

The rise of cryptocurrency, particularly Bitcoin (BTC), has revolutionized the financial landscape, enabling decentralized, peer-to-peer transactions without the need for intermediaries such as banks or financial institutions. Since its inception in 2009, Bitcoin has grown exponentially, not only in terms of market value but also in its impact on global finance. However, together with this popularity comes a wide range of cybercrimes including hacking, Ponzi schemes, wash trading, extortion, and money laundering. As noted in recent research, the volume of illicit cryptocurrency activities has grown significantly, with billions of dollars in crypto assets being stolen or used for illegal purposes …

Ethical Work Cultures & Ai, Andrew Brei

Presentations - 2025

With the help of moral theories, several case studies, and insights from the world of behavioral ethics, my project aims to provide engineering professionals with the means to deal properly with moral issues that commonly arise in their chosen fields.

An Argument Against Gradual Type Systems In Programming Language Semantics, Natalie Lau

LMU Theses and Dissertations

In 2006, Jeremy Siek and Walid Taha formalized the concept of gradual type systems, which integrates static and dynamic typing in a single programming language. This allows the programmer to statically or dynamically type portions of their code at will, which offers more flexibility than languages that require all code to be statically typed. Despite the added convenience, gradual typing comes with its own set of tradeoffs, and researchers have been debating whether the drawbacks of integrating static and dynamic types outweigh its benefits. This thesis builds off of previous research to investigate the advantages and disadvantages of using a …

From Data To Decisions: Safeguarding Athletes In The Age Of Ai, Nathan Elmer

SLU Law Journal Online

Artificial intelligence (AI) and data analytics are transforming professional sports by enhancing player performance, injury prevention, and scouting. However, the rapid adoption of AI raises significant concerns about data privacy, ownership, and decision-making biases that affect athletes. While collective bargaining agreements in major sports leagues provide some protections, they fail to address the complexities of AI-driven data collection and processing. The United States should adopt a regulatory framework similar to the European Union’s General Data Protection Regulation (GDPR) to safeguard athletes’ personal data. Implementing explicit consent requirements, addressing power imbalances, and ensuring transparency in AI decision-making would protect athletes while …

Relationship Between Academic Influence And Institutional Cooperation In Specific Fields：Evidence From The Computer Science Domain, Yukai Yang, Yi Zhao, Chengzhi Zhang

Journal of Scientific Information Research

[Purpose/ significance]In scientific collaboration, institutions are the primary driving units of scientific research. Compared to intra-institutional collaboration, inter-institutional collaboration often has the potential to produce high-impact papers. Therefore, studying fine-grained collaboration at the institutional level holds significant importance.[Method/process]To explore the relationship between different types of institutional cooperation and academic influence, this paper classifies institutions and defines various types of cooperation. Using network analysis methods, it investigates the relationship between network indicators of different types of institutional cooperation and academic influence. [Result/conclusion]Taking the computer science domain as an example, the analysis of the relationship between network indicators of different types of …

Towards Connecting Requirements With Developer Artifacts In A Local Context: Supplemental Material, Sonora Halili, Karenna Kung, Paola Spoletini, Alicia M. Grubb

Computer Science: Faculty Publications

Supplemental material for the paper: "Towards Connecting Requirements with Developer Artifacts in a Local Context"

Development And Evaluation Of The Da Vinci Ai Tutor: Enhancing Accessibility And Personalized Learning In Art History Education, James Hutson, Tiffani Barner

Faculty Scholarship

This study examines the implementation of the Da Vinci AI Tutor, an innovative artificial intelligence (AI)-based tutoring platform designed specifically for enhancing personalized and accessible learning in art history within higher education. Launched in Fall 2024 at a private liberal arts institution in the Midwest, the system integrates a conversational AI avatar modeled after Leonardo da Vinci, incorporating immersive virtual reality environments and multimodal interaction capabilities to engage students across undergraduate survey courses, advanced Renaissance classes, and graduate comprehensive exam preparations. Addressing significant gaps in existing humanities education research, the current study explores two primary research questions: (i) How AI-driven …

Towards Testing, Detecting, And Debloating Insecure Components In Android Applications, Zicheng Zhang

Dissertations and Theses Collection (Open Access)

The Android ecosystem’s openness and extensibility have fueled its dominance in the mobile market, but they also broaden the attack surface of applications by introducing insecure or redundant methods. Vulnerabilities arise from various sources, including insecure API usage, code cloning, and feature bloat, especially from unneeded components introduced during development. To address these challenges, this dissertation presents a systematic, three-phase pipeline that transitions seamlessly from vulnerability discovery to clone-based detection and, ultimately, to dynamic mitigation through runtime debloating. Each phase builds upon the insights and limitations of the previous, collectively forming a practical approach to improving Android app security.

In …

A Data-Driven Recommendation System For Selecting The Appropriate Mode Of Learning And Instructional Tools Based On Course Characteristics, Ayisha Manzoor

Theses

The rapid transformation of educational delivery methods during the COVID-19 pandemic required institutions to transition between online, hybrid, and offline learning approaches, creating both challenges and opportunities for educators and students. While online and hybrid learning modes ensured continuity, their effectiveness across different course types remained uncertain. This thesis addresses this gap by developing a datadriven recommendation framework that predicts Course Learning Outcome (CLO) achievement scores using regression, and recommends the most appropriate learning mode (online, hybrid, or offline) along with instructional tools based on course characteristics. This study analyzed 100 undergraduate and postgraduate courses from the College of Information …

Ivyapc: Auditable Generalized Payment Channels, Ming Li, Yuxian Li, Jian Weng, Yingjiu Li, Jiasi Weng, Junzuo Lai, Robert H. Deng

Research Collection School Of Computing and Information Systems

Payment channels are a cornerstone of a scalable blockchain infrastructure that enables transacting parties to lock assets on the blockchain and perform rapid off-chain updates with minimal latency and overhead. These protocols dramatically reduce on-chain interaction and improve throughput, with blockchain consensus only invoked in the event of disputes or final closure. While widely adopted in single-chain settings—such as in the Lightning Network for Bitcoin—existing constructions have several limitations, in particular they suffer from at least one of the following limitations: 1. No cross-chain. They do not enable fast trading of assets that reside on multiple isolated blockchains. 2. Non-optimal …

How Developers Interact With Ai: A Taxonomy Of Human-Ai Collaboration In Software Engineering, Christoph Treude, Marco A. Gerosa

Research Collection School Of Computing and Information Systems

Artificial intelligence (AI), including large language models and generative AI, is emerging as a significant force in software development, offering developers powerful tools that span the entire development lifecycle. Although software engineering research has extensively studied AI tools in software development, the specific types of interactions between developers and these AI-powered tools have only recently begun to receive attention. Understanding and improving these interactions has the potential to enhance productivity, trust, and efficiency in AI-driven workflows. In this paper, we propose a taxonomy of interaction types between developers and AI tools, identifying eleven distinct interaction types, such as auto-complete code …

Bigcodebench: Benchmarking Code Generation With Diverse Function Calls And Complex Instructions, T.Y. Zhuo, M.C. Vu, J. Chim, ..., David Lo

Research Collection School Of Computing and Information Systems

Task automation has been greatly empowered by the recent advances in Large Language Models (LLMs) via Python code, where the tasks ranging from software engineering development to general-purpose reasoning. While current benchmarks have shown that LLMs can solve tasks using programs like human developers, the majority of their evaluations are limited to short and self-contained algorithmic tasks or standalone function calls. Solving challenging and practical tasks requires the capability of utilizing diverse function calls as tools to efficiently implement functionalities like data analysis and web development. In addition, using multiple tools to solve a task needs compositional reasoning by accurately …

Collaborative Network Traffic Management Strategies Using Distributed Reinforcement Learning And Large Language Models, Saeed Rashed Alkuwaiti

Theses

The focus of this research is to explore collaborative network traffic management strategies using the Distributed Reinforcement Learning (DRL) and Large Language Models (LLMs) approaches. It emphasizes exploring a new tool for addressing network traffic by utilizing Distributed Reinforcement Learning (DRL) and Large Language Models (LLMs). This is achieved by utilizing self-organizing and self-directing techniques to optimize the network performance. Using the NF-TON-IOT dataset, various classifiers such as Random Forest, AdaBoost, C4. 5, Multi-Layer Perceptron (MLP), and SVM with an RBF kernel were tested for traffic classification and intrusion detection. Research recommends that DRL optimizes the complexity of the network …

Analytical Dispatch Strategies For Pumped Storage Hydro: A Conditional Dynamic Programming Approach To Discontinuous Multi-Period Optimization Problems, Jian Liu, Jianwen Zhang, Zaiwu Gong, Donald C. Wunsch, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

The increasing integration of renewable energy sources like wind and solar poses significant challenges to secure and stable grid operation. Energy storage systems, particularly pumped storage hydro (PSH), play a crucial role in balancing power supply and demand. Traditional analytical studies of PSH economic dispatch problems often assume zero lower bounds for generating and pumping rates to simplify analysis and derive analytical solutions for multi-period optimization problems. However, the inherent mechanical design constraints of PSH require non-zero minimum flow rates for efficient operation. We analyze two scenarios, merchants having PSH only and merchants having both PSH and wind farms. In …

Exploring Transfer Learning For Deep Learning Polyp Detection In Colonoscopy Images Using Yolov8, Fabian Vazquez Jr., Jose Angel Nuñez, Xiaoyan Fu, Pengfei Gu, Bin Fu

Computer Science Faculty Publications

Deep learning methods have demonstrated strong performance in object detection tasks; however, their ability to learn domain-specific applications with limited training data remains a significant challenge. Transfer learning techniques address this issue by leveraging knowledge from pre-training on related datasets, enabling faster and more efficient learning for new tasks. Finding the right dataset for pre-training can play a critical role in determining the success of transfer learning and overall model performance. In this paper, we investigate the impact of pre-training a YOLOv8n model on seven distinct datasets, evaluating their effectiveness when transferred to the task of polyp detection. We compare …

Weapons Of Mass Disruption: How Small States Use Cyber To Resist Larger Powers, Russell Alexander Korb

Graduate Program in International Studies Theses & Dissertations

This paper examines the ways in which small states can engage larger actors using cyber- attacks. Since the end of both World Wars, small states have increased in both numbers and relevance, with strong international institutions and norms against military aggression allowing small states to gain legitimacy by the very act of participating in the international system. However, although small states can now do more than simply choose a larger, stronger benefactor to ward off their enemies, they still cannot defy larger powers outright due to the still- dramatic difference in capabilities between them. Those small states interested in confronting …