Programming Languages and Compilers Commons^™

Addressing Equity Issues In Elementary Computer Science Education: Knowns, Unknowns, And Implications For Future Work, Mike Karlin, Yin-Chan Janet Liao, Swati Mehta, Afreen Iqbal, Mahya Minaiy, Minhye Son, Jessica Pandya

Journal of Computer Science Integration

In 2016, a national coalition of stakeholders released the K-12 computer science (CS) framework. In the time since, there has been an increased push at the local, state, and national level to integrate CS knowledge and skills into K-12 education. Despite this push, significant equity issues exist within the field. While growing research has been done on CS equity issues at the high school level, we know these equity gaps often begin to emerge in elementary school where less is known. Therefore, we conducted a systematic literature review to better understand and explore the elementary CS equity research landscape from …

Birdie: Natural Language-Driven Table Discovery Using Differentiable Search Index, Yuxiang Guo, Zhonghao Hu, Yuren Mao, Baihua Zheng, Yunjun Gao, Mingwei Zhou

Research Collection School Of Computing and Information Systems

Natural language (NL)-driven table discovery identifies relevant tables from large table repositories based on NL queries. While current deep-learning-based methods using the traditional dense vector search pipeline, i.e., representation-index-search, achieve remarkable accuracy, they face several limitations that impede further performance improvements: (i) the errors accumulated during the table representation and indexing phases affect the subsequent search accuracy; and (ii) insufficient query-table interaction hinders effective semantic alignment, impeding accuracy improvements. In this paper, we propose a novel framework Birdie, using a differentiate search index. It unifies the indexing and search into a single encoder-decoder language model, thus getting rid of error …

Attackg+: Boosting Attack Graph Construction With Large Language Models, Yongheng Zhang, Tingwen Du, Yunshan Ma, Xiang Wang, Yi Xie, Guozheng Yang, Yuliang Lu, Ee‑Chien Chang

Research Collection School Of Computing and Information Systems

Attack graph construction seeks to convert textual cyber threat intelligence (CTI) reports into structuredrepresentations, portraying the evolutionary traces of cyber attacks. Even though previous research hasproposed various methods to construct attack graphs, they generally suffer from limited generalizationcapability to diverse knowledge types as well as requirement of expertise in model design and tuning.Addressing these limitations, we seek to utilize Large Language Models (LLMs), which have achieved enormoussuccess in a broad range of tasks given exceptional capabilities in both language understanding and zeroshot task fulfillment. Thus, we propose a fully automatic LLM-based framework to construct attack graphsnamed: AttacKG+. Our framework consists …

Adversarial Attacks And Defense Methods In Robotic Systems, Thanh D. Le

Shelby Hall Graduate Research Forum Presentations

Presentation slides for a presentation given at the 1st annual Shelby Hall Graduate Research Forum at the University of South Alabama.

Choosing Robust Leadership: Encompassing The Best-Of-N Model And Swarm Intelligence Optimization For Heterogeneous Multiple Autonomous Unmanned Aerial Vehicle Systems, Kudamuhandiramlage Harith Kolitha Warnakulasooriya

Shelby Hall Graduate Research Forum Presentations

Presentation slides for a presentation given at the 1st annual Shelby Hall Graduate Research Forum at the University of South Alabama.

Turbulence Prediction Using Non-Linear Phase Space Analysis, Jeremy Quijano

Shelby Hall Graduate Research Forum Posters

Our research presents a novel approach for turbulence prediction in computational fluid dynamics (CFD) simulations using a non-linear phase space analysis (NLPSA) and threshold algorithm. NLPSA has been utilized in medical applications to predict seizures, as well as in cybersecurity to detect malicious control and utilization of computing systems. NLPSA uses time-series data to learn the normal operating state of the system, then sets a threshold to predict when the system becomes abnormal. Turbulence prediction is similar, such that a fluid system changes from normal to abnormal. Turbulence prediction methods currently utilize machine learning tools, such as convolutional neural networks …

Establishing A Framework For Evaluating Machine Learning Performance And Security Across Computational Ecosystems, Krista Stacey, Todd R. Andel

Shelby Hall Graduate Research Forum Posters

The rapid evolution of computational ecosystems—ranging from embedded systems and cloud platforms to hybrid and quantum architectures—has introduced new challenges in deploying machine learning (ML) applications. While cloud computing offers scalability, it comes with increased latency and security risks, whereas edge computing, such as FPGA-based systems, provides real-time processing with constrained resources. Hybrid and quantum ecosystems further complicate decision-making, requiring careful trade-offs between performance and security. This research seeks to establish a framework for evaluating ML performance and security risks across these ecosystems, forming the foundation of the Computational Performance And Security System (COMPASS) decision-support tool. The study will systematically …

Topical Text Segmentation For Stream Of Consciousness Writing, Yuwei Lu, Ryan Benton

Shelby Hall Graduate Research Forum Posters

Stream of consciousness writing has a long history, including novelists James Joyce and Virginia Woolf. However, there has been little work done in automated and semi-automated analysis of such writing, which is the focus of this work. We plan to divide real streams of consciousness writing into distinct topical units and then capture different momentary meaningful topics from these units. By doing this, researchers and readers could gain a more nuanced understanding of the narrative structure and thematic elements. In addition, it would also support applications in fields like psychology and linguistics, where understanding thought processes and narrative structures is …

Using Machine Learning Models To Improve The Cyber Physical Security Of Drones, Sean Lee, Aviv Segev

Shelby Hall Graduate Research Forum Posters

This research proposes a new manner of implementing machine learning models such that, when applied on a drone, it will be able to accurately identify and maintain the authenticity of the entity sending the control data to the drone. To begin with, the drone will, for a pre-determined amount of signals received per unit time, determine the average signal strength (RSSI) of them and use that average to determine the approximate distance between the drone and the source of those signals. This single data point will be fed into a custom implementation of the SCluStream algorithm (a real-time clustering machine …

Tla+ For All: Model Checking In A Python Notebook, Konstantin Laufer, George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

TLA+ is widely recognized for its effectiveness in specifying and verifying concurrent and distributed systems. However, for educators and practitioners, barriers to adoption include installation complexity and tooling setup. In the proposed presentation, we demonstrate a lightweight, easily shareable, and fully reproducible approach to running TLA+ in a Python notebook hosted on Google Colab without requiring new tools or custom Jupyter kernel development. By creating an environment where users can experiment with TLA+ models instantly, we lower these barriers and demonstrate the suitability for education and outreach.

A Causality-Aware Paradigm For Evaluating Creativity Of Multimodal Large Language Models, Zhongzhan Huang, Shanshan Zhong, Pan Zhou, Shanghua Gao, Marink Zitnik, Liang Lin

Research Collection School Of Computing and Information Systems

Recently, numerous benchmarks have been developed to evaluate the logical reasoning abilities of large language models (LLMs). However, assessing the equally important creative capabilities of LLMs is challenging due to the subjective, diverse, and data-scarce nature of creativity, especially in multimodal scenarios. In this paper, we consider the comprehensive pipeline for evaluating the creativity of multimodal LLMs, with a focus on suitable evaluation platforms and methodologies. First, we find the Oogiri game—a creativity-driven task requiring humor, associative thinking, and the ability to produce unexpected responses to text, images, or both. This game aligns well with the input-output structure of modern …

Computing In The Commonwealth: Specialized Education In Computer Science And Information Technology For High School Students In Virginia – An Environmental Scan, Amy Corning, Jonathan D. Becker, Jon Graham, James Carrigan, Keisha Tennessee

ICRE Publications

Over the past two decades, Virginia has invested substantially in STEM education, in part through specialized programs focused on computer science and information technology (CS/IT). This study represents the first effort to identify Virginia’s specialized secondary CS/IT programs and examine them collectively. Findings from the statewide environmental scan indicate that the programs are delivered through a wide variety of institutional structures, including Governor’s STEM Academies, Governor’s Schools, specialty centers, and academies, but most often through Career and Technical Education (CTE) centers. Programs tend to be concentrated in metropolitan areas, and some rural divisions may not be served. The programs provide …

Relational Database Schema To Support Research Profiling Studies, Natural Language Processing, And Bibliometric Analysis, Darnelle Melvin

Library Faculty Research

In this paper, a relational database schema is introduced that supports rapid prototyping, data preprocessing, and warehousing tasks associated with research profiling studies, natural language processing, and bibliometric analysis. Python scripts are leveraged for the seamless retrieval and processing of data from Semantic Scholar. This schema is tailored to efficiently analyze entities such as authors, their scientific papers, referenced papers, and cited papers. Adhering to the relational model, this schema offers a standardized approach to data storage and detailed information retrieval for scientific papers. Enhancing knowledge discovery in scientific databases, this schema provides researchers with a powerful platform for robust …

Towards Smart Farming: Image-Based Crop Health Assessment And Disease Diagnosis Using Deep Learning Techniques, Kristina Botova

Master's Theses or Doctor of Nursing Practice

Accurate crop monitoring is essential for optimizing agricultural productivity and ensuring food security. This study presents a comprehensive deep learning framework for image crop type recognition, health status prediction, and disease detection using multiple Convolutional Neural Network (CNN) models. The proposed approach uses open-source datasets consisting of five crop types (apple, corn, grape, potato, tomato), varying health conditions, and common diseases. By deploying specialized CNN architecture focused on each task, the system achieves a high accuracy of 99.25% in classifying crop types, identifying health status, and detecting specific diseases. Compared to a single CNN model, the use of the proposed …

Finding Antipatterns Across Languages With Abstract Syntax Trees, Daniel T. Masker

Dissertations, Master's Theses and Master's Reports

Finding antipatterns in student code is a difficult task that is useful for helping beginner programmers. Antipatterns are common mistakes that students make while writing code. Code critiquers are tools that find antipatterns and provide rich, immediate feedback to students, even when professors aren’t available. WebTA is a code critiquer that finds antipatterns using regular expressions (regex), error messages, and language-specific abstract syntax trees (ASTs). Each of these tools has obstacles to antipattern searching that are difficult to overcome. Regex is without context, limiting the patterns it can recognize. Additionally, even experienced users have difficulty reading and debugging regex. Error …

Digital Platform Transitions In The Finance Industry: Three Essays, Cheryll-Ann Wilson

2025

This three-paper dissertation is motivated by an emerging dichotomy in the financial sector: an increasing use of an open-source digital platform—the Python platform—in an industry that historically has been wedded to proprietary systems.

Chapter 1 is a qualitative pilot study to ascertain which factors are likely to motivate investment professionals to select Python versus other tools and/or technologies. I find that efficiency and access to industry-specific libraries—notably Pandas and NumPy—are significant motivators in their selection of Python over Excel. Chapters 2 and 3 examine the issues through a sequential, exploratory mixed methods approach.

Chapter 2—the qualitative field study—investigates how and …

Ultrasonic Sensor-Based Sound Synthesis Using Raspberry Pi Pico W, Niraj Jaishwal

Williams Honors College, Honors Research Projects

At the intersection of Human Computer Interaction and digital art, this project transforms simple motion into musical expression. It explores an interactive real-time sound synthesis system using ultrasonic sensors to generate continuous audio. The objective is to design a system that maps physical distances into musical parameters such as pitch and amplitude, which will create a responsive audio environment. Two ultrasonic sensors are used in combination with the Raspberry Pi Pico W microcontroller running CircuitPython and Adafruit Audio Hat for real-time sound output. One sensor controls the pitch of the generated tone, while the other controls volume. This enables expressive …

Bytes, Banter, And The Bible: An Interdisciplinary Account Of Objective Meaning, Cameron Bonin

Senior Honors Theses

The claim that the Bible has objective meaning is contested in a postmodern world. This claim can be more persuasively defended when it is addressed by insights from multiple disciplines. In particular, the field of computer science is apt to illuminate the concept of meaning through its reflection on the nature of languages and its concern with the accurate transmission of information. By synthesizing insights from the field of computer science, such as that of Claude Shannon, with Nicholas Wolterstorff’s use of speech-act theory, the concept of meaning can be understood more clearly. Consequently, this synthesis assists in answering questions …

Individual And Collective Properties Of Tunable Photochemical Belousov-Zhabotinsky Micro-Reactors, Kudakwashe Benedict Shumba

Graduate Theses, Dissertations, and Problem Reports (ETD)

Cell-like model chemical systems are powerful tools that can be used to explore the role of intercellular coupling on population level behaviors in communities of biological cells. Firstly, we present a new method for fabricating such micro-reactors using the photosensitive Belousov–Zhabotinsky (BZ) reaction system employed in silica microparticles. These BZ micro-reactors have a tunable response to photochemical coupling, varying from a fully excitatory response to a fully inhibitory response. Their response can be tuned through variations in either the reactive mixture or, on an individual micro-reactor level, by changes in the synthesis temperature used during the fabrication of the silica …

Financial Named Entity Recognition: How Far Can Llm Go?, Yi-Te Lu, Yintong Huo

Research Collection School Of Computing and Information Systems

The surge of large language models (LLMs) has revolutionized the extraction and analysis of crucial information from a growing volume of financial statements, announcements, and business news. Recognition for named entities to construct structured data poses a significant challenge in analyzing financial documents and is a foundational task for intelligent financial analytics. However, how effective are these generic LLMs and their performance under various prompts are yet need a better understanding. To fill in the blank, we present a systematic evaluation of state-of-the-art LLMs and prompting methods in the financial Named Entity Recognition (NER) problem. Specifically, our experimental results highlight …