Physical Sciences and Mathematics Commons^™

Lilac: Log Parsing Using Llms With Adaptive Parsing Cache, Zhihan Jiang, Jinyang Liu, Zhuangbin Chen, Yichen Li, Junjie Huang, Yintong Huo, Pinjia He, Jiazhen Gu, R. Michael Lyu

Research Collection School Of Computing and Information Systems

Log parsing transforms log messages into structured formats, serving as the prerequisite step for various log analysis tasks. Although a variety of log parsing approaches have been proposed, their performance on complicated log data remains compromised due to the use of human-crafted rules or learning-based models with limited training data. The recent emergence of powerful large language models (LLMs) demonstrates their vast pre-trained knowledge related to code and logging, making it promising to apply LLMs for log parsing. However, their lack of specialized log parsing capabilities currently hinders their parsing accuracy. Moreover, the inherent inconsistent answers, as well as the …

Agchain: A Blockchain-Based Gateway For Trustworthy App Delegation From Mobile App Markets, Mengjie Chen, Xiao Yi, Daoyuan Wu, Jianliang Xu, Yingjiu Li, Debin Gao

Research Collection School Of Computing and Information Systems

The popularity of smartphones has led to the growth of mobile app markets, creating a need for enhanced transparency, global access, and secure downloading. This paper introduces AGChain, a blockchain-based gateway that enables trustworthy app delegation within existing markets. AGChain ensures that markets can continue providing services while users benefit from permanent, distributed, and secure app delegation. During its development, we address two key challenges: significantly reducing smart contract gas costs and enabling fully distributed IPFS-based file storage. Additionally, we tackle three system issues related to security and sustainability. We have implemented a prototype of AGChain on Ethereum and Polygon …

Automating Maritime Risk Data Collection And Identification Leveraging Large Language Models, Donghao Huang, Xiuju Fu, Xiaofeng Yin, Haibo Pen, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Maritime risk research is crucial yet challenging for improving safety, efficiency, and sustainability in maritime operations. This paper presents an innovative method for automating the collection and identification of risk data related to global maritime risks from news sources, addressing the limitations of traditional manual methods. To evaluate the proposed method, different learning-based models, including conventional machine learning approaches and advanced Large Language Models (LLMs) such as GPT-4 and LLaMA-3.1, are comprehensively studied for comparison. In addition, not only do we use popular evaluation metrics to assess the proposed method, but we also introduce a new evaluation metric, called the …

Editorial For The Special Issue Of The Metaverse, Fiona Fui-Hoon Nah, Gert-Jan De Vreede, Lakshmi Goel, Eric Lim, Shu Schiller, Chee-Wee Tan

Research Collection School Of Computing and Information Systems

The metaverse is laying the groundwork for more accessible and immersive experiences by blending the physical and virtual worlds into a unified space where people can interact, create, and connect in entirely new ways. It holds the potential to revolutionize how we work, socialize, and learn, which in turn gives rise to unprecedented opportunities for innovation. In this special issue, we present four articles that depict the current state of research in metaverse, the key themes and theoretical underpinnings within this space, as well as emerging directions for future work. This special issue delivers valuable insights for both researchers and …

Virtual Conferencing Fatigue: Look‑Alike Avatar And Facial Attractiveness, Yuxin Liu, Keng Siau, Xueqing Wang, Yang Yang

Research Collection School Of Computing and Information Systems

The rapid evolution of avatar-related technologies provides extensive opportunities for diverse avatar applications in various areas. This study aims to investigate the innovative use of avatars to mitigate virtual conferencing fatigue, which refers to the physical and mental exhaustion from the inappropriate use of virtual conferencing applications. Grounded in Self-Awareness Theory, the research compares the impact of using real faces and user-look-alike avatars on virtual conferencing fatigue, delving into its underlying factors. In addition, the study examines the role of facial attractiveness enhancement on virtual conferencing fatigue. Laboratory experiments with a 2-by-2 between-subject design are employed to test hypotheses. The …

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 …

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 …

Bounds On 𝑎hvp,Lo Using Hölder’S Inequalities And Finite-Energy Qcd Sum Rules, Siyuan Li, T. G. Steele, Jason N.E. Ho, R. Raza, K. Williams, R. T. Kleiv

Faculty Work Comprehensive List

This study establishes bounds on the leading-order (LO) hadronic vacuum polarization (HVP) contribution to the anomalous magnetic moment of the muon (𝑎HVP,LO 𝜇 ,𝑎𝜇 =(𝑔 −2)𝜇∕2) by using Hölder’s inequality and related inequalities in Finite-Energy QCD sum rules. Considering contributions from light quarks (𝑢, 𝑑, 𝑠) up to five-loop order in perturbation theory within the chiral limit, leading-order light-quark mass corrections, next-to-leading order for dimension-four QCD condensates, and leading-order for dimension-six QCD condensates, the study finds QCD lower and upper bounds as (657.0±34.8)×10−10 ≤𝑎HVP,LO 𝜇 ≤(788.4±41.8)×10−10.

Rare Variants Of Dna Ligase 1 Show Distinct Mechanisms Of Deficiency, Jenna H. Veenstra, Alexandria Chabez, Terrance J. Haanen Iii, Austin Keranen, Charlotte Cunningham-Rundles, Patrick J. O'Brien

Faculty Work Comprehensive List

Human DNA ligase 1 (LIG1) performs the final step in DNA repair and recombination pathways by sealing DNA breaks, and it functions as the main replicative ligase. Hypomorphic LIG1 variants R771W and R641L cause immune deficiencies in LIG1 Syndrome patients. In vitro these LIG1 variants have decreased catalytic efficiency and increased abortive ligation and it is not known if either biochemical defect is sufficient on its own to cause immune deficiency. We investigated the enzymatic activity of several new candidate LIG1 Syndrome variants chosen based on their structural proximity to known clinical variants, low minor allele frequency (MAF), high level …

Optimizing Mobility On Demand Systems: Multiagent Reinforcement Learning Approaches To Order Assignment And Vehicle Guidance, Jiyao Li

All Graduate Theses and Dissertations, Fall 2023 to Present

This dissertation explores ways to improve Mobility on Demand (MoD) systems, which are services like ride-sharing and autonomous taxi systems. The main goal is to make these services more efficient and reliable, benefiting both passengers and drivers by better matching the number of available vehicles with the number of people needing rides.

For ride-sharing services, a new method called T-Balance helps match riders with drivers and guides empty taxis to areas where more people need rides. This reduces wait times for passengers and increases earnings for drivers. Another method, called GRL-HM, looks at how riders and drivers behave to further …

Quantifying The Impact Of Rain-On-Snow Induced Flooding In The Western United States, Emma M. Watts

All Graduate Theses and Dissertations, Fall 2023 to Present

Serious flooding can happen when rain falls on snow, which we call a rain-on-snow (ROS) event. Increasing our understanding of the behavior of floods resulting from ROS events can help us design better systems to manage flood water and prevent it from causing damage. This thesis explores how ROS events affect streamflow in the Western United States by examining the weather conditions that precede a streamflow surge. We classify stream surges as ROS or non-ROS induced based on these weather conditions, which helps us separate floods caused by ROS events from those caused by other factors. By comparing these different …

Interpretable And Robust Deep Anomaly Detection, He Cheng

All Graduate Theses and Dissertations, Fall 2023 to Present

Anomaly detection is crucial in fields like cybersecurity, healthcare, and finance, as it helps identify unusual or potentially harmful events in data. With the rise of deep learning, advanced models have been developed for anomaly detection, but they often operate as "black boxes" that lack transparency and can be susceptible to malicious attacks. My research addresses these issues by creating methods that make deep learning-based anomaly detection more understandable and by investigating how such models can be compromised by backdoor attacks.

To improve transparency, I propose three methods that explain how these models detect anomalies. The first method, called Anomalous …

Mathematical And Statistical Methods To Harness Limited Data In Models For Ecological Space Use Under Global Change, Sarah C. Bogen

All Graduate Theses and Dissertations, Fall 2023 to Present

The dynamics of how plants and animals use space in their habitats has important implications for the fields of ecology and conservation. However, understanding and responding to these spatial and temporal dynamics is often limited by data availability, financial resources and biases. As average global temperatures increase, suitable habitats shift poleward and require local populations to move with suitable habitat, adapt to the changing environment, or risk extinction. Capacity to persist without movement may be estimated by considering changes to a combination of habitat characteristics. Capacity to track suitable habitat may be modeled through synthesizing information on species demographic mechanisms …

Explainable Ai In Medical Imaging: An Interdisciplinary Translational Approach, Caitlyn Chavez

Computational and Data Sciences (PhD) Dissertations

Advances in computer vision and image processing have made a clear impact on many fields, from healthcare diagnostics to autonomous driving. However, as these models become more complex, understanding their decision-making processes has grown increasingly challenging, making explainable AI (XAI) a crucial component of modern AI systems. The focus of this work is to integrate these new technologies alongside foundational methods of image processing to create tools that can be used by domain experts who are not programmers. Prior to delving into the projects which investigate these concepts, the methodologies, background, and the overall frameworks are discussed. In the first …

Temporal Metadata Analysis: A Learning Classifier System Approach, Michael C. Todd, Gilbert L. Peterson

Faculty Publications

Digital forensics is a complex field that requires expert knowledge (EK) and specialized tools to collect, analyze, and report on digital evidence. Temporal metadata analysis is particularly challenging, requiring expert knowledge to understand and interpret underlying traces and associate them with their source. This paper introduces Digital Trace Inspector (DTI), a Learning Classifier System (LCS)-based decision support tool for temporal metadata analysis. DTI leverages a binary Michigan-style LCS to locate and group corroborating temporal digital traces of targeted user activity. Rules are built from expert-created atomics encoded as feature vectors using patterns defined in a structured EK rule framework. The …

Dual-Channel Side Channel Attack: Improved Aes Key Decryption By Combining Power And Electromagnetic Side Channels With Convolutional Neural Networks, Sean P. O'Neill

Theses and Dissertations

This research introduces a novel DL approach for SCA that combines power consumption and EM signals to enhance encryption key deduction by leveraging a dual-channel CNN architecture. A new dataset, consisting of simultaneous power and EM signal collections during 128-bitAES encryption, was developed to train and evaluate the model’s effectiveness. The combined approach achieved an 88% reduction in traces needed, from 50 traces to 6, for encryption key classification, outperforming traditional methods such as random forest, DPA, DEMA,and individual side channel CNN models. These findings highlight the potential of integrating multiple side channels in SCA to improve performance without the …

Mitigating Code Reuse Attacks On Risc-V Binaries: Minimizing Gadget Availability Using The Compressed Extension, Heitor Vieira

Theses and Dissertations

Embedded systems are vital in civilian and military applications, requiring high performance and security. The open RISC-V Instruction Set Architecture (ISA) offers significant advantages, including security through community review and strategic independence in microchip supplies. Brazil’s recent partnership with RISC-V highlights its potential for national technological sovereignty. However, RISC-V is not inherently resistant to code reuse attacks (CRAs), highlighting the need to integrate security measures early in development. The RISC-V Compressed extension, while beneficial for optimizing performance and code flexibility, introduces security trade-offs. As RISC-V adoption grows, particularly in critical systems, addressing these security challenges from the start is crucial …

Environmental Dna Identifies Coastal Plant Community Shift 1,000 Years Ago In Torrens Island, South Australia, Nicole R. Foster, Alice R. Jones, Oscar Serrano, Anna Lafratta, Paul S. Lavery, Kor-Jent Van Dijk, Ed Biffin, Bronwyn M. Gillanders, Jennifer Young, Pere Masque, Patricia S. Gadd, Geraldine E. Jacobsen, Atun Zawadzki, Andria Greene, Michelle Waycott

Research outputs 2022 to 2026

Anthropogenic activities are causing detrimental changes to coastal plants– namely seagrass, mangrove, and tidal marshes. Looking beyond recent times to past vegetation dynamics is critical to assess the response and resilience of an environment to change. Here, we develop a high-resolution multi-proxy approach, providing a new evidence base to decipher long-term change in coastal plant communities. Combining targeted environmental DNA analysis with chemical analysis of soils, we reconstructed 4,000 years of change at a temperate wetland on Torrens Island South Australia and identified an ecosystem shift that occurred ~ 1000 years ago. What was once a subtidal seagrass system shifted …

A Nursery Study Of The Interplay Between Soil Texture, Rooting Space, And Nurse Plants On Seedling Growth And Root Development In Woody Species Of Tamaulipan Thornscrub, Lance I. Santos

Theses and Dissertations

Reforestation attempts in semi-arid regions are plagued with seedling mortality. Blanket greenhouse practices may not be optimal, in addition, commensalism interactions between nurse plants are considered. To better understand the effects of soil texture and nurse plant interactions on growth and drought tolerance, two experiments were developed. For soil texture, Border palo verde seedlings were grown in different-sized plant bands with three soil types. To observe the effects of nurse plants, an experiment using guayacan, was developed using Texas Ebony, as a leguminous nurse plant. Plants were also exposed to a drought experiment. The results suggest that some soil textures …

Investigation Of Magnesium-Potassium Phosphates As Potential Nuclear Waste Form For The Immobilization Of Minor Actinides, Hans Conrad Zur Loye, Petr Vecernik, Monika Kiselova, Vlastislav Kašpar, Hana Korenkova, Vlastimil Miller, Petr Bezdicka, Jan Šubrt, Natalija Murafa, Volodymyr Shkuropatenko, Sergey Sayenko

Faculty Publications

Several recent studies have evaluated technologies of spent nuclear fuel processing specifically for solidifying transuranic (TRU) waste as a by-product of fission. Of the TRU group, plutonium and the minor actinides will be responsible for the bulk of the radiotoxicity and heat generation of spent nuclear fuel in the long term (300 to 20,000 years). In this study, we investigated magnesium potassium phosphate (MKP)-based compounds as host waste forms for the encapsulation of inactive trivalent Nd and Sm as analogues of the minor trivalent actinides, Am and Cm. Waste forms were fabricated under ambient atmospheric conditions by adding 5 wt.% …

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. …

Analysis Of A Gravity Wave Horizontal Blocking Event Using Hodograph Techniques Applied To Sodium Lidar Data, Neal R. Criddle

All Graduate Theses and Dissertations, Fall 2023 to Present

Atmospheric gravity waves are ubiquitous and important events in Earth’s atmosphere with impacts on space weather and associated technological effects. The population of gravity waves is determined by their formation mechanisms, and their interactions with the background atmosphere. These gravity waves transport energy and momentum into the near Earth space environment, and are responsible for movement of chemical species. Predicting the occurrence of space weather effects, then, is in part a process requiring the characterization of gravity waves. Case studies of unique and interesting gravity wave events are an important part of this process. These allow for the investigation of …

The Temperature Trend In The Upper Mesosphere Between ∼84 Km And 98 Km Based On Diurnal Cycle Observations By A Na Lidar At Middle Latitudes Between 2002 And 2017, Melania Carolina Pena

All Graduate Theses and Dissertations, Fall 2023 to Present

Carbon dioxide is one of the main greenhouse gasses that contributes to maintaining Earth’s warm global temperature in its atmosphere. Recent studies have indicated that this cooling trend has been determined to vary between less than 1 K/decade or 2 K/decade. However, these results have been determined using data that have been obtained using nighttime lidar observations and potentially do not give the complete picture on how the cooling trend is behaving. The main goal of this research is to understand the behavior of the linear temperature trend between the daytime and nighttime profiles using a 16-year dataset (2002-2017), when …

Using Luminescence Dating To Investigate Geomorphic And Archaeologic Features At The Wiggins Fork Bison Jump Complex, Northwestern Wyoming, Emma T. Krolczyk

All Graduate Theses and Dissertations, Fall 2023 to Present

The Wiggins Fork Bison Jump Complex, located in northwestern Wyoming, is comprised of thousands of human-placed rock piles, known as cairns, that were placed within lines to form an extensive network of drivelines and funnels leading to cliffs or steep slopes. These archaeological features were used by Native Americans as a bison hunting method, where the animals were driven off cliffs for harvest. This research uses luminescence dating, which determines when sediment was last exposed to sunlight, to date the timing of driveline construction and the deposition ages of the underlying river deposits.

Research focused on Jump #4, which had …

Kid Tech Balance: Providing Children Self-Management Tools As An Alternative To Parental Controls, Michael Scott Wendell

Boise State University Theses and Dissertations

Technology integration into the household is ever expanding and so is the need for children's safety when it comes to accessing this technology. Parental controls exist as a way for parents to be able to control and protect their children from possible hazards of technology use. However, many controls provide only the ability to help parents lock or restrict their children from using technology. This research seeks to identify and create a control solution that helps develop moderation habits in children instead of restrictions, thereby helping both parents and children. I developed a new control application through this research, aptly …

Neural Network Architecture Search Enabled Wide-Deep Learning (Nas-Wd) For Spatially Heterogenous Property Awared Chicken Woody Breast Classification And Hardness Regression, Chaitanya Pallerla, Yihong Feng, Casey M. Owens, Ramesh Bahadur Bist, Siavash Mahmoudi, Pouya Sohrabipour, Amirreza Davar, Dongyi Wang

Poultry Science Faculty Publications and Presentations

Due to intensive genetic selection for rapid growth rates and high broiler yields in recent years, the global poultry industry has faced a challenging problem in the form of woody breast (WB) conditions. This condition has caused significant economic losses as high as $200 million annually, and the root cause of WB has yet to be identified. Human palpation is the most common method of distinguishing a WB from others. However, this method is time-consuming and subjective. Hyperspectral imaging (HSI) combined with machine learning algorithms can evaluate the WB conditions of fillets in a non-invasive, objective, and high-throughput manner. In …

Locally Varying Geostatistical Machine Learning For Spatial Prediction, Francky Fouedjio, Emet Arya

Research outputs 2022 to 2026

Machine learning methods dealing with the spatial auto-correlation of the response variable have garnered significant attention in the context of spatial prediction. Nonetheless, under these methods, the relationship between the response variable and explanatory variables is assumed to be homogeneous throughout the entire study area. This assumption, known as spatial stationarity, is very questionable in real-world situations due to the influence of contextual factors. Therefore, allowing the relationship between the target variable and predictor variables to vary spatially within the study region is more reasonable. However, existing machine learning techniques accounting for the spatially varying relationship between the dependent variable …

Lessons Learned From Establishing And Running A Mössbauer Laboratory In Industry, Paulo De Souza

Research outputs 2022 to 2026

Mössbauer spectroscopy offers unique capabilities for primary industry sectors such as mining, mineral processing, and steelmaking, as well as in environmental services and advanced manufacturing. However, there are three key limiting factors: the time required to record a single spectrum, the challenges of managing an additional radioactive source on company premises, and the lack of personnel skilled in analysing Mössbauer spectra and understanding its operation. More commonly used techniques such as XRD, SEM, or XRF have technical staff who can interpret data with support from standard libraries and user-friendly software packages. In contrast, Mössbauer spectroscopists, who often hold degrees in …

New Constraints On The Stratigraphic Architecture And Evolution Of Mississippi River Delta Lobe Activity: Barataria Basin, Southeastern La, Wilke Coleman

LSU New Orleans Theses and Dissertations

Barataria Basin is a shallow water, interdistributary estuary that has evolved in response to dynamic depositional regimes governed by distributary avulsion and reworking of sediments by coastal processes. In the Mississippi River delta plain, the migratory nature of the River has produced an evolving landscape constructed through episodes of regressive and transgressive sedimentation. These processes produce unique depositional signatures in the stratigraphic record, however the transgressive component of the delta cycle often results in obliteration of the regressive phase. This study uses a modern suite of geophysical and sediment core data to construct a revised geologic framework in Barataria Basin, …

Impact Of Weather Systems On Uav Parameters Using Computational Fluid Dynamics, Saif Aljuhaishi, Yaseen K. Al-Timimi, Basim I. Wahab

Karbala International Journal of Modern Science

Since drones cannot fly in any kind of weather, they are not safe for time-sensitive activities. The study examines how the passage of weather systems in Iraq leads to the ban on drone flights, and how these weather conditions impact the aerodynamic forces of the drone. Hourly climate data for the study area were obtained from ECMWF ERA5 and CAMS in NetCDF format for four climate stations (Erbil, Baghdad, Rutbah, and Basrah). A ScanEagle drone was chosen for this study. The Python programming language was used to perform mathematical operations to calculate the ban on drone flights. ArcGIS 10.8 was …