Computer Sciences Commons^™

Artificial Intelligence In Surgical Coding: Evaluating Large Language Models For Current Procedural Terminology Accuracy In Hand Surgery, Emily Isch, Jamie Lee, D. Mitchell Self, Abhijeet Sambangi, Theodore E. Habarth-Morales, John R. Vaile, E. J. Caterson

Department of Surgery Faculty Papers

PURPOSE: The advent of large language models (LLMs) like ChatGPT has introduced notable advancements in various surgical disciplines. These developments have led to an increased interest in the use of LLMs for Current Procedural Terminology (CPT) coding in surgery. With CPT coding being a complex and time-consuming process, often exacerbated by the scarcity of professional coders, there is a pressing need for innovative solutions to enhance coding efficiency and accuracy.

METHODS: This observational study evaluated the effectiveness of five publicly available large language models-Perplexity.AI, Bard, BingAI, ChatGPT 3.5, and ChatGPT 4.0-in accurately identifying CPT codes for hand surgery procedures. A …

Reimagining Education: Keeping The Human In The Loop, Pradeep Varakantham, Sidney Tio

Asian Management Insights

How educators can work with generative artificial intelligence models to improve learning. Artificial intelligence (AI) helps break the mould of one-size-fits-all education by creating personalised learning paths that adapt to each student’s pace and style. By combining human expertise with AI capabilities, educators can create learning experiences that are both structured and flexible, thus getting the best of both worlds. While promising, AI used in educational contexts must carefully navigate privacy concerns, ensure fairness across all student groups, and support appropriate learning progression.

A Contrastive Framework With User, Item And Review Alignment For Recommendation, Viet Hoang Dong, Yuan Fang, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Learning effective latent representations for users and items is the cornerstone of recommender systems. Traditional approaches rely on user-item interaction data to map users and items into a shared latent space, but the sparsity of interactions often poses challenges. While leveraging user reviews could mitigate this sparsity, existing review-aware recommendation models often exhibit two key limitations. First, they typically rely on reviews as additional features, but reviews are not universal, with many users and items lacking them. Second, such approaches do not integrate reviews into the useritem space, leading to potential divergence or inconsistency among user, item, and review representations. …

Hand1000: Generating Realistic Hands From Text With Only 1,000 Images, Haozhuo Zhang, Bin Zhu, Yu Cao, Yanbin Hao

Research Collection School Of Computing and Information Systems

Text-to-image generation models have achieved remarkable advancements in recent years, aiming to produce realistic images from textual descriptions. However, these models often struggle with generating anatomically accurate representations of human hands. The resulting images frequently exhibit issues such as incorrect numbers of fingers, unnatural twisting or interlacing of fingers, or blurred and indistinct hands. These issues stem from the inherent complexity of hand structures and the difficulty in aligning textual descriptions with precise visual depictions of hands. To address these challenges, we propose a novel approach named Hand1000 that enables the generation of realistic hand images with target gesture using …

Generalization Analysis For Deep Contrastive Representation Learning, Minh Hieu Nong, Antoine Ledent, Yunwen Lei, Cheng Yeaw Ku

Research Collection School Of Computing and Information Systems

In this paper, we present generalization bounds for the unsupervised risk in the Deep Contrastive Representation Learning framework, which employs deep neural networks as representation functions. We approach this problem from two angles. On the one hand, we derive a parameter-counting bound that scales with the overall size of the neural networks. On the other hand, we provide a norm-based bound that scales with the norms of neural networks’ weight matrices. Ignoring logarithmic factors, the bounds are independent of k, the size of the tuples provided for contrastive learning. To the best of our knowledge, this property is only shared …

How To Securely Delegate And Revoke Partial Authorization Credentials, Meng Sun, Junzuo Lai, Wei Wu, Ye Yang, Cheng-Kang Chu, Robert H. Deng

Research Collection School Of Computing and Information Systems

An attribute-based credential (ABC) system allows a user, obtaining a credential on a set of attributes from an issuer, to anonymously prove a subset of attributes to a service provider. Nowadays, delegation is an important requirement of ABC, which allows a user to delegate his credentials to other users. However, traditional delegatable ABC systems only support delegating a credential with all attributes. In many scenarios, an appropriate delegation is a user can delegate his credential on parts of attributes to others. Another requirement is revocation of credentials in case of unexpected events. In this article, we propose a delegatable and …

Exploring Intelligent Manufacturing: How Artificial Intelligence Affects Productivity And Labor Demand At The Enterprise Level, Jie Gu

Dissertations and Theses Collection (Open Access)

Manufacturing is a cornerstone of national economic health and social stability, yet it faces challenges such as declining profits, rising labor costs, and an aging workforce. In China, the manufacturing sector is undergoing a critical transformation, driven by technological advancements like artificial intelligence (AI) and the push for intelligent manufacturing. This study explores how AI revitalizes the manufacturing sector by enhancing enterprise productivity and reshaping labor demand, with a focus on quality inspection processes. Using a leading bearing factory as a case study, the research employs econometric models, A/B testing, and interviews to quantify AI’s impact on production efficiency, costs, …

Patient Consent And The Right To Notice And Explanation Of Ai Systems Used In Health Care, Meghan E Hurley, Benjamin H Lang, Kristin Marie Kostick-Quenet, Jared N Smith, Jennifer Blumenthal-Barby

Center for Medical Ethics and Health Policy Staff Publications

Given the need for enforceable guardrails for artificial intelligence (AI) that protect the public and allow for innovation, the U.S. Government recently issued a Blueprint for an AI Bill of Rights which outlines five principles of safe AI design, use, and implementation. One in particular, the right to notice and explanation, requires accurately informing the public about the use of AI that impacts them in ways that are easy to understand. Yet, in the healthcare setting, it is unclear what goal the right to notice and explanation serves, and the moral importance of patient-level disclosure. We propose three normative functions …

Improving Smartphone Gnss Jammer Localization With Cloud-Based Environmental Occlusion Modeling, Glenn H. Jones

Theses and Dissertations

The advancement of Global Navigation Satellite System (GNSS) technology in modern smartphones has made these devices pervasive in both civilian and military applications. Although smartphone GNSS chipsets are more susceptible to jamming and spoofing than military grade hardware, smartphone networks offer an underutilized opportunity to detect and mitigate threats to position, navigation, and timing (PNT) services essential to the Department of Defense (DoD) and civilian first responders. Traditional methods for geolocating ground-based jamming sources using smartphone GNSS often fail in environments with dense vegetation or significant occlusions, resulting in substantial localization errors.

A Machine Learning/Deep Learning Investigation On Remote Manufacturing Machine State Classification, Ajeet S. Parmar

Theses and Dissertations

Determining the extent of manufacturing capabilities with respect to adversarial or hostile nations is a topic of significant importance to the Department of Defense. Manufacturing capabilities can serve as indications of a nation's industrial power and its economy of force in warfare. Remotely detecting machine operations via electromagnetic sensors may be possible via Deep Learning (DL) and Machine Learning (ML) algorithms. To predict machine states, sensor data is collected externally from a machine shop on a college campus to monitor the operating states of lathes and mills in individual and concurrent operation. Furthermore, several sensors are placed in various positions, …

Jamming-Tolerant Low-Rate Wireless Personal Area Network For Detection Sensor Networks, Michael A. Eddy

Theses and Dissertations

This research evaluates the impact of electronic warfare, particularly jamming, on an audio-based drone detection wireless sensor network (WSN) using Monte Carlo simulations. A six-node IEEE 802.15.4 network, with five edge nodes and a central sink, is tested against jamming probabilities ranging from 0-100% in 5% increments across 30 iterations per configuration. Results show that packet delivery ratio (PDR) degrades linearly at approximately 20% per jammed node, while detection performance often exceeds PDR. Even at 80% jamming, detection success rates remain above 57%, highlighting resilience despite network degradation. The study reveals that jamming effectiveness depends on node placement relative to …

Machine Learning Techniques To Predict Solar Particle Events And Radiation Of Aircrew, Haley Traub

Theses and Dissertations

Solar Particle Events (SPEs) are high-energy phenomena from the Sun that pose risks to technology, human health, and Air Force operations. Accurate prediction of SPEs exceeding 100 MeV is crucial for mitigating these risks. This thesis explores using Bayesian statistical models to predict such events, integrating prior knowledge from solar physics with the ability to update predictions based on new data. The research uses a dataset spanning three solar cycles (21–23) and incorporates attributes like flare fluence, peak flux, latitude, longitude, and class. Four Bayesian models (PyMC, Bnlearn, and two Dredge models) were compared to machine learning models. The Bayesian …

A Heterogeneous Graph-Based Multi-Task Learning For Fault Event Diagnosis In Smart Grid, Dibaloke Chanda, Nasim Yahyasoltani

Computer Science Faculty Research and Publications

Precise and timely fault diagnosis is a prerequisite for a distribution system to ensure minimum downtime and maintain reliable operation. This necessitates access to a comprehensive procedure that can provide the grid operators with insightful information in the case of a fault event. In this paper, we propose a heterogeneous multi-task learning graph neural network (MTL-GNN) capable of detecting, locating and classifying faults in addition to providing an estimate of the fault resistance and current. Using a graph neural network (GNN) allows for learning the topological representation of the distribution system as well as feature learning through a message-passing scheme. …

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.

Private Reachability Queries On Structured Encrypted Temporal Bipartite Graphs, Yulin Wu, Lanxiang Chen, Gaolin Chen, Yi Mu, Robert H. Deng

Research Collection School Of Computing and Information Systems

A temporal bipartite graph is a graph model that incorporates time-related information into its edges, making it suitable for modeling real-world phenomena like disease outbreaks. However, this temporal information is often sensitive. To protect the privacy of graph data, researchers have explored various approaches to preserve privacy in graph queries, with reachability queries being popular and fundamental as they determine the possibility of reaching one node from others in a graph. While privacy-preserving reachability queries have been extensively studied, existing efforts often overlook the valuable attribute information present in both edges and nodes of the graphs. Moreover, reachability queries on …

Learning To Identify Seen, Unseen And Unknown In The Open World: A Practical Setting For Zero-Shot Learning, Sethupathy Parameswaran, Yuan Fang, Chandan Gautam, Savitha Ramasamy, Xiaoli Li

Research Collection School Of Computing and Information Systems

As vision-language models advance, addressing the Zero-Shot Learning (ZSL) problem in the open world becomes increasingly crucial. Specifically, a robust model must handle three types of samples during inference: seen classes with visual and semantic information provided in training, unseen classes with only the semantic information in training, and unknown samples with no prior information from training. Existing methods either handle seen and unseen classes together (ZSL) or seen and unknown classes (known as Open-Set Recognition, OSR). However, none addresses the simultaneous handling of all three, which we term Open-Set Zero-Shot Learning (OZSL). To address this problem, we propose a …

Divide-And-Conquer: Confluent Triple-Flow Network For Rgb-T Salient Object Detection, Hao Tang, Zechao Li, Dong Zhang, Shengfeng He, Jinhui Tang

Research Collection School Of Computing and Information Systems

RGB-Thermal Salient Object Detection (RGB-T SOD) aims to pinpoint prominent objects within aligned pairs of visible and thermal infrared images. A key challenge lies in bridging the inherent disparities between RGB and Thermal modalities for effective saliency map prediction. Traditional encoder-decoder architectures, while designed for cross-modality feature interactions, may not have adequately considered the robustness against noise originating from defective modalities, thereby leading to suboptimal performance in complex scenarios. Inspired by hierarchical human visual systems, we propose the ConTriNet, a robust Confluent Triple-Flow Network employing a "Divide-and-Conquer"strategy. This framework utilizes a unified encoder with specialized decoders, each addressing different subtasks …

Selecting Comparative Sets Of Reviews Across Multiple Items, Trung Hoang Le, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

While choosing among several products, users may look up reviews from each product they are considering. Due to the large number of reviews of products, selecting representative reviews from one product alone is already a challenging problem. In this work, we further aim to conduct review selection for multiple products simultaneously for comparative purposes. We formulate objective functions that synchronize the review selection and design efficient algorithms to optimize for the objective functions. To narrow down the potentially long list of comparison items into a shorter list of more similar items, we construct a graph representing items’ similarity and design …

Abolition By Algorithm, Peter N. Salib

Michigan Law Review

In one sense, America’s newest abolitionist movement—advocating the elimination of policing and prison—has been a success. Following the 2020 Black Lives Matter protests, a small group of self-described radicals convinced a wide swath of ordinary liberals to accept a sweeping claim: Mere reforms cannot meaningfully reduce prison and policing’s serious harms. Only elimination can. On the other hand, abolitionists have failed to secure lasting policy change. The difficulty is crime. In 2021, following a nationwide uptick in homicides, liberal support for abolitionist proposals collapsed. Despite being newly “abolition curious,” left-leaning voters consistently rejected concrete abolitionist policies. Faced with the difficult …

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 …

Out-Of-Band Anomaly Detection For Real Time Operating Systems, Jeff K. Holifield

Shelby Hall Graduate Research Forum Posters

Real Time Operating Systems (RTOS) are increasing present throughout the industrial, business, defense, and healthcare spaces. These lightweight and efficient operating systems are designed to run on embedded, resource constrained devices, often within cyber-physical systems (CFS). A defining characteristic of RTOSs is that they are deterministic. Tasks are scheduled to run on fixed timelines within guaranteed execution windows. To accomplish tasks on time, real time software must conform to worst case execution times (WCETs) as design parameters. WCET is the maximum time a particular task can take to complete. Exceeding the WCET could cause system failure and lead to damage, …

A Framework For Design Recovery, Eric Diep

Shelby Hall Graduate Research Forum Posters

Due to the increase in diverse chip production over the past decade, reverse engineering has become a difficult and daunting task. This research will create a guideline for methods to achieve design recovery of microchip logic. To accomplish this, we plan on using hardware tools such as laser delayering and microscopy imaging. We will be focusing on the DA14580 Dialog semiconductor, commonly implemented in tile trackers, to extract information for design recovery. This implementation technique is novel due to tools that have not been performed with this type of microchip. The purpose for this research is to create a framework …

Analysis Of Forensic Techniques For Additive Manufacturing Devices, Daniel B. Miller, Brad Glisson, Mark Yampolskiy, J Todd Mcdonald

Shelby Hall Graduate Research Forum Posters

Additive Manufacturing (AM) is a set of newer computer-dependent production technologies that is seeing rapid adoption across a wide variety of industries, including defense, aerospace, automotive, and healthcare. With increased adoption comes an increased opportunity for misuse and abuse of such systems, which will lead to an increased need for Digital Forensic investigations into these platforms. This research forensically analyzes a number of AM devices to explore the options available for data acquisition as well as the impacts of hardware and software design choices on the analysis and investigation results. Hardware is investigated using Open-Source Intelligence (OSINT) sources to determine …

Polyglot File Detection For Forensics, Chase Stevens, Michael Black

Shelby Hall Graduate Research Forum Posters

Polyglot files are problematic as payloads can be hidden inside them while simultaneously evading discovery by current forensic tools. Autopsy, one of the most used forensic tools in investigations, uses known signatures of file types (e.g., headers, trailers) to identify and recover files. Polyglot files are a unique case in which files are intentionally combined with other file types to make them appear benign while still containing malicious payloads. Polyglots are possible due to combinations being considered valid in both formats. Polyglot files inherently evade detection from signature-based forensic tools and malware scanners because the tools and malware scanners are …

Using Image-Based Representation For Network Intrusion Detection, Chakriya Suon, J. Todd Mcdonald

Shelby Hall Graduate Research Forum Posters

The primary focus of our research is to evaluate the effectiveness of converting network traffic data, PCAPs, into image-based representations for anomaly-based network intrusion detection. We aim to analyze PCAPs to detect malware, or malicious software in hopes of creating a useful approach for anomaly detection against cyber threats including Advanced Persistent Threats (APTs). With the rise of cyber threats, cybersecurity continues to play a critical role in the ever-changing landscape of technology, by protecting and defending against threat agents. Our research will apply novel machine learning (ML) techniques to detect potential malware transmitted over a network effectively. The overall …

Learning Without Labels: A Self-Supervised Learning Approach For Anomaly Detection In Control Systmes, Barbara Gladney

Shelby Hall Graduate Research Forum Posters

Oil pipelines, water plant systems, and other critical infrastructure are managed and operated by industrial control systems (ICS). These systems safeguard the operations of critical infrastructures, requiring minimal disruption from cyberattacks or malfunctions. The use of anomaly detection methods in control systems (ICS) can reduce system interruptions. However, anomaly detection methods often require annotated data, which may not be available for the control system. Additionally, the datasets used for the control systems do not include sensor outputs and environmental data, resulting in a restricted view of the system. This research investigates how SSL models can be applied to different control …

Directing Sophisticated Cyberattacks On Public Water Infrastructure, Ayrton C. Purdy, Jordan Shropshire

Shelby Hall Graduate Research Forum Posters

In recent years there has been an increasing number of cyberattacks on public water generation and distribution systems. Advanced persistent attackers could usurp sensor and control systems to contaminate public drinking water. In order to conceal their malicious activity, they can manipulate sensor data flows to give the appearance of normal activity. The compromised sensors would report normal chemical levels even though unsafe water is entering the distribution system. In response, this research proposes a multi-sensor, cross-validation approach to anomaly detection. The proposed approach is designed to detect sophisticated cyberattacks which are not easily detectable using traditional cyber tools. The …

Security Vulnerabilities Of A Field Programmable Gate Array, Kylie Arnett

Shelby Hall Graduate Research Forum Posters

The primary goal of this research is to understand and exploit the security vulnerabilities of a Field Programmable Gate Array (FPGA), at the bitstream level. This paper is working to successfully show that an FPGA can be altered via the bitstream file, and a Trojan can be inserted into the device. Once a Trojan is successfully inserted into the FPGA and activated through a certain input value, a Siamese Neural Network (SNN) will be used to test the effectiveness of Trojan detection. Followed by recording the successful flag rate to detect Trojans, which will be averaged to determine the accuracy …

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 …