Physical Sciences and Mathematics Commons^™

Fdi : Attack Neural Code Generation Systems Through User Feedback Channel, Zhensu Sun, Xiaoning Du, Xiapu Luo, Fu Song, David Lo, Li Li

Research Collection School Of Computing and Information Systems

Neural code generation systems have recently attracted increasing attention to improve developer productivity and speed up software development. Typically, these systems maintain a pre-trained neural model and make it available to general users as a service (e.g., through remote APIs) and incorporate a feedback mechanism to extensively collect and utilize the users' reaction to the generated code, i.e., user feedback. However, the security implications of such feedback have not yet been explored. With a systematic study of current feedback mechanisms, we find that feedback makes these systems vulnerable to feedback data injection (FDI) attacks. We discuss the methodology of FDI …

Monocular Bev Perception Of Road Scenes Via Front-To-Top View Projection, Wenxi Liu, Qi Li, Weixiang Yang, Jiaxin Cai, Yuanhong Yu, Yuexin Ma, Shengfeng He, Jia Pan

Research Collection School Of Computing and Information Systems

HD map reconstruction is crucial for autonomous driving. LiDAR-based methods are limited due to expensive sensors and time-consuming computation. Camera-based methods usually need to perform road segmentation and view transformation separately, which often causes distortion and missing content. To push the limits of the technology, we present a novel framework that reconstructs a local map formed by road layout and vehicle occupancy in the bird's-eye view given a front-view monocular image only. We propose a front-to-top view projection (FTVP) module, which takes the constraint of cycle consistency between views into account and makes full use of their correlation to strengthen …

Putting Gpt-4o To The Sword: A Comprehensive Evaluation Of Language, Vision, Speech, And Multimodal Proficiency, Sakib Shahriar, Brady D. Lund, Nishith Reddy Mannuru, Muhammad Arbab Arshad, Kadhim Hayawi, Ravi Varma Kumar Bevara, Aashrith Mannuru, Laiba Batool

All Works

As large language models (LLMs) continue to advance, evaluating their comprehensive capabilities becomes significant for their application in various fields. This research study comprehensively evaluates the language, vision, speech, and multimodal capabilities of GPT-4o. The study employs standardized exam questions, reasoning tasks, and translation assessments to assess the model’s language capability. Additionally, GPT-4o’s vision and speech capabilities are tested through image classification and object-recognition tasks, as well as accent classification. The multimodal evaluation assesses the model’s performance in integrating visual and linguistic data. Our findings reveal that GPT-4o demonstrates high accuracy and efficiency across multiple domains in language and reasoning …

Paper-Recorded Ecg Digitization Method With Automatic Reference Voltage Selection For Telemonitoring And Diagnosis, Liang Hung Wang, Chao Xin Xie, Tao Yang, Hong Xin Tan, Ming Hui Fan, I. Chun Kuo, Zne Jung Lee, Tsung Yi Chen, Pao Cheng Huang, Shih Lun Chen, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

In electrocardiograms (ECGs), multiple forms of encryption and preservation formats create difficulties for data sharing and retrospective disease analysis. Additionally, photography and storage using mobile devices are convenient, but the images acquired contain different noise interferences. To address this problem, a suite of novel methodologies was proposed for converting paper-recorded ECGs into digital data. Firstly, this study ingeniously removed gridlines by utilizing the Hue Saturation Value (HSV) spatial properties of ECGs. Moreover, this study introduced an innovative adaptive local thresholding method with high robustness for foreground–background separation. Subsequently, an algorithm for the automatic recognition of calibration square waves was proposed …

Bridging Disciplines With Ai-Powered Coding: Empowering Non-Stem Students To Build Advanced Apis In The Humanities, Daniel Plate, James Hutson

Faculty Scholarship

The integration of AI-powered coding assistants, such as Cursor AI, GitHub Copilot, and Replit’s Ghostwriter AI, represents a transformative shift in programming education, particularly for non-STEM students. These tools democratize coding by enabling natural language code generation, intelligent error correction, and context-aware assistance within familiar coding environments. This article explores how these technologies empower educators across disciplines to introduce basic and advanced coding concepts to humanities students, a demographic traditionally underserved in programming education. By leveraging AI, instructors can teach non-STEM students the foundational principles of coding and guide them through the development of sophisticated projects, such as building APIs …

Technoculture And Language Models In Archaeology: Reconstructing And Preserving Cultural Narratives Through Digital Humanities, James Hutson

Faculty Scholarship

Technoculture, which examines the intersection of culture and technology, has increasingly permeated archaeological practice, transforming both scholarly research and public engagement [1-3]. The introduction of digital tools such as virtual reality (VR), geographic information systems (GIS), and large language models (LLMs) has democratized access to archaeological knowledge, enabling communities to engage more actively with their cultural heritage [4-6]. This short article explores the mutual influence of technocultural studies and AI technologies on archaeology, with a focus on the preservation and reconstruction of cultural narratives through digital means.

The first aspect of this intersection lies in how technocultural tools are creating …

Developing Empathetic Ai: Exploring The Potential Of Artificial Intelligence To Understand And Simulate Family Dynamics And Cultural Identity, Emily Barnes, James Hutson

Faculty Scholarship

The rapid advancement of Artificial Intelligence (AI) has significantly impacted various domains. Yet, the exploration of AI's potential to develop a deep understanding of family culture and identity remains underexplored. This study introduces the concept of "a love of grandma and apple pie" to symbolize the potential of various AI to internalize and appreciate familial relationships, cultural traditions, and personal identity. The proposed study would investigate how an advanced deep learning model, trained on diverse unstructured datasets—including multimedia data from 100 families-could learn and reflect human-like emotions, values, and cultural understanding. Utilizing Convolutional Neural Networks (CNNs) for visual data processing …

Testing Feed Is Vital For Feed Budgeting, Department Of Primary Industries And Regional Development, Western Australia

Animal production and livestock factsheets

A little knowledge of the nutritional value of your supplementary feeds can save you a lot of money and hassle.

In dry seasons, it is likely that farmers will need to start supplementary feeding earlier than usual. The higher requirement for supplementary feeds over dry seasons means accurate feed budgeting becomes increasingly important to minimise feed wastage, for weaner survival, and to maintain stock at the right condition score (CS) for joining.

Spray-Topping Pastures, Department Of Primary Industries And Regional Development, Western Australia

Grains and other field crops factsheets

Spray-topping or pasture topping is the application of a sub-lethal rate of herbicide when grasses are coming into head and flowering.

The aim is to reduce the production of viable seed and the seedling population in the following year. The grass is not killed by the herbicide application.

Spray-topping is normally carried out in preparation for the following season's cropping program.

Foliar Diseases And Their Management In Lupins, Department Of Primary Industries And Regional Development, Western Australia

Grains and other field crops factsheets

This factsheet describes symptoms and management of the major fungal and viral diseases affecting lupin foliage, stems, and pods. Several of these diseases have the capacity to cause significant losses if not managed effectively.

Inoculum can be carried in soil, seed, stubble, on green regrowth, or by insect vectors, depending on the disease. Therefore, an integrated approach to disease management is required using tactics such as crop rotation, stubble management, fungicide seed dressing, variety selection, and seed testing

Artificial Intelligence In Orthopaedic Education: A Comparative Analysis Of Chatgpt And Bing Ai’S Orthopaedic In-Training Examination Performance, Clark Chen, Vivek Biololikar, Duncan Vannest, James Raphael, Gene Shaffer

Einstein Health Papers

Background: This study evaluated the performance of generative artificial intelligence (AI) models on the Orthopaedic In-Training Examination (OITE), an annual exam administered to U.S. orthopaedic residency programs. Methods: ChatGPT 3.5 and Bing AI GPT 4.0 were evaluated on standardised sets of multiple-choice questions drawn from the American Academy of Orthopaedic Surgeons OITE online question bank spanning 5 years (2018–2022). A total of 1165 questions were posed to each AI system. The performance of both systems was standardised using the latest versions of ChatGPT 3.5 and Bing AI GPT 4.0. Historical data of resident scores taken from the annual OITE technical …

Exploring The Potential Of Neutrosophic Topological Spaces In Computer Science, A. A. Salama, Huda E. Khalid, Ahmed K. Essa, Ahmed G. Mabrouk

Neutrosophic Systems with Applications

Neutrosophic topological spaces (NTS) offer a novel framework for uncertainty modeling by incorporating degrees of truth, indeterminacy, and falsity. This paper investigates the potential applications of NTS in computer science. We provide background on neutrosophic sets and their extension to topological spaces. We then explore how NTS could be used for uncertainty modeling in data analysis (e.g., handling noisy data in sensor networks), pattern recognition (e.g., improving image classification with imprecise features), and information retrieval (e.g., enhancing search results by considering relevance uncertainty). We discuss the challenges associated with applying NTS and highlight promising areas for future research, such as …

Multi-Modal Alignment Via Hyperbolic Geometry, Suyu Liu

Dissertations and Theses Collection (Open Access)

Strong capabilities of generalization to unseen domains are vital for deep neural networks. While existing methods have shown promising results without source domain access, they mostly rely on models that are extensively pre-trained on source domains or overlook the intricate hierarchical structures inherent in visual and textual features. These limitations may have bad impacts on performances, especially on datasets with many classes. To overcome this, in this paper we propose a novel approach that projects the model onto hyperbolic geometry and employs geometric optimal transport to align cross-modal features in an unsupervised manner. Unlike Euclidean geometry, hyperbolic geometry is characterized …

Quality Assurance In Software Engineering: A Journey Towards Explainable Automated Solutions, Ratnadira Widyasari

Dissertations and Theses Collection (Open Access)

In today's digital era, the pervasive influence of software on daily life underscores the necessity for high-quality and reliable systems. Software failures can result in substantial harm and financial losses, highlighting the pivotal role of Software Quality Assurance (SQA). While automated SQA techniques have evolved to aid developers in ensuring software quality, the necessity for explainability in these automated solutions has become equally important. For example, in automated fault localization, only identifying suspicious locations is insufficient; it is essential to provide reasoning on why these locations are suspicious. This dissertation presents a series of interconnected studies aimed at developing explainable …

Draft Final Quarterly Operations And Maintenance Report Butte Treatment Lagoon System – Second Quarter 2024, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Representation Theory Arising From Groups In Physics, Jaxon Green

Master's Theses

A representation is a group homomorphism whose image is a group of invertible matrices. Representations and their associated matrices are analyzed through well-established techniques from linear algebra. We characterize representations by a unique decomposition into irreducible representations just as we characterize the decomposition of matrices into their eigenspaces. Through the study of these representations, we uncover mathematical relationships that underlie groups with physical applications. Due to physical symmetries, we study how the irreducible representations of groups that embody the actions of even the most basic rotations are utilized in the computation of irreducible representations groups that reflect more complicated mechanics, …

Neutrosophic Model For Measuring And Evaluating The Role Of Digital Transformation In Improving Sustainable Performance Using The Balanced Scorecard In Egyptian Universities, A. A. Salama, Osama Mohamed Mobarez, Mohamed Hamed Elfar, Rafif Alhabib

Neutrosophic Systems with Applications

This paper proposes a neutrosophic model for measuring and evaluating the role of digital transformation in improving sustainable performance using the balanced scorecard in Egyptian universities. The model takes into account uncertainty, ambiguity, and incompleteness in the data. The model first calculates the neutrosophic measures of digital transformation and sustainable performance for each university. Then, it uses neutrosophic logic to evaluate the causal relationship between digital transformation and sustainable performance. The results of the analysis can used to identify the digital transformation indicators that have the greatest impact on sustainable performance. This information can then be used to develop strategies …

Climate Change Prediction Model Using Mcdm Technique Based On Neutrosophic Soft Functions With Aggregate Operators, Kainat Muniba, Muhammad Naveed Jafar, Asma Riffat, Jawaria Mukhtar, Adeel Saleem

Neutrosophic Systems with Applications

The increasing impact of climate change necessitates innovative approaches in modeling and prediction to mitigate its adverse effects. This paper introduces a novel methodology integrating Neutrosophic Soft Functions (NSFs) into climate change prediction frameworks. NSFs, a hybrid of Neutrosophic Set Theory and Soft Set Theory, provide a flexible framework for handling uncertain and imprecise information inherent in climate data. This study explores the application of NSFs in capturing the complex interplay of various climatic variables, including temperature, precipitation, humidity, and atmospheric pressure, thereby enhancing the accuracy and reliability of climate change predictions. By incorporating NSFs into existing predictive models, such …

Tulane Virus Persistence And Microbial Stability In 3d Food Ink Under Various Storage Conditions: A Pre- And Post-Printing Analysis, Allyson N. Hamilton, Kristen E. Gibson

Food Science Faculty Publications and Presentations

3D food printers facilitate novel customization of the physicochemical properties of food. This study aimed to investigate the impact of storage conditions on the inactivation of the human norovirus surrogate, Tulane virus (TuV), within 3D printed foods. TuV-inoculated protein cookie food ink (∽ 4 log PFU/g) was distributed into 18 3D food printer capsules (50 g each); half immediately underwent extrusion. Storage of the capsules and printed food products at 20 °C (0, 6, 12, and 24 h), 4 °C (0, 1, 3, and 5d), and − 18 °C (0, 1, 3, and 5d) was completed before analysis for TuV …

Michael Mcgraw: Profile Of The Newest Chbc Faculty Member, Danna Jaimes-Fajardo, Jeremy J. Smith, Lutishoor Salisbury

University Libraries Faculty Publications and Presentations

Presents the research profile of Michael McGraw, Assistant Professor, Chemistry and Biochemistry department. It highlights his research interests, education, and impact of his research.

Investigation Of The Distribution Of Transuranic Radionuclides In Marine Sediment At The Montebello Islands, Western Australia, Madison Williams-Hoffman, Megan Cook, Jack K. Clegg, Ross Kleinschmidt, Pere Masqué, Mathew P. Johansen

Research outputs 2022 to 2026

Three nuclear weapons tests were conducted in the 1950s at the Montebello Islands, Western Australia. The detonations were of different yields and configurations (two tower tests, one ship test), and led to substantial radionuclide contamination within the surrounding terrestrial and marine ecosystems. The region possesses great ecological and recreational significance, particularly within the marine environment. However, studies conducted so far have largely neglected the marine ecosystem which makes up the majority of the Montebello Island Marine Park and in which most test fallout would have deposited. Here we investigated the distribution of the transuranic radionuclides 238Pu, 239,240Pu and 241Am in …

Detecting Anomalies In Blockchain Transactions Using Machine Learning Classifiers And Explainability Analysis, Mohammad Hasan, Mohammad Shahriar Rahman, Helge Janicke, Iqbal H. Sarker

Research outputs 2022 to 2026

As the use of blockchain for digital payments continues to rise, it becomes susceptible to various malicious attacks. Successfully detecting anomalies within blockchain transactions is essential for bolstering trust in digital payments. However, the task of anomaly detection in blockchain transaction data is challenging due to the infrequent occurrence of illicit transactions. Although several studies have been conducted in the field, a limitation persists: the lack of explanations for the model's predictions. This study seeks to overcome this limitation by integrating explainable artificial intelligence (XAI) techniques and anomaly rules into tree-based ensemble classifiers for detecting anomalous Bitcoin transactions. The shapley …

Solving Water Scarcity Challenges In Arid Regions: A Novel Approach Employing Human-Based Meta-Heuristics And Machine Learning Algorithm For Groundwater Potential Mapping, Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Farbod Farhangi, Mehdi Khiadani, Saied Pirasteh, Soo Mi Choi

Research outputs 2022 to 2026

Addressing water scarcity challenges in arid regions is a pressing concern and demands innovative solutions for accurate groundwater potential mapping (GPM). This study presents a comprehensive evaluation of advanced modeling techniques to enhance the precision of GPM. This study, conducted in the Zayandeh Rood watershed, Iran, employed a spatial database comprising 16 influential factors on groundwater potential and data from 175 wells. This study introduced an innovative approach to GPM by enhancing the Random Forest (RF) algorithm. This enhancement involved integrating three metaheuristic algorithms inspired by human behavior: ICA (Imperialist Competitive Algorithm), TLBO (Teaching-Learning-Based Optimization), and SBO (Student Psychology Based …

Toi–757 B: An Eccentric Transiting Mini–Neptune On A 17.5–D Orbit, A Alqasim, N Grieves, N M. Rosário, D Gandolfi, J H. Livingston, S Sousa, K A. Collins, J K. Teske, M Fridlund, Ryan J. Oelkers

Physics & Astronomy Faculty Publications

We report the spectroscopic confirmation and fundamental properties of TOI 757 b, a mini Neptune on a 17.5 d orbit transiting a bright star (⁠ mag) discovered by the TESS mission. We acquired high precision radial velocity measurements with the HARPS, ESPRESSO, and PFS spectrographs to confirm the planet detection and determine its mass. We also acquired space borne transit photometry with the CHEOPS space telescope to place stronger constraints on the planet radius, supported with ground based LCOGT photometry. WASP and KELT photometry were used to help constrain the stellar rotation period. We also determined the fundamental parameters of …

Spatial Planning Offshore Wind Energy Farms In California For Mediating Fisheries And Wildlife Conservation Impacts, Crow White, Yi-Hui Wang, Ryan K. Walter, Benjamin Ruttenberg, Danny Han, Eli Newman, Ethan R. Deyle, Sucharita Gopal, Les Kaufman

Physics

Achieving a blue economy will require reconciling the value of emerging ocean uses with their impacts on the seascape and sectors with historical access to marine resources and areas. To meet this challenge, we developed an analytical framework for conducting marine spatial planning through tradeoff analysis, and applied it to prospective offshore wind energy development in the ∼974 km2 Morro Bay, California, USA Wind Energy Area (WEA). We generated spatial data layers estimating MW power production and impacts on fisheries value and marine wildlife conservation (seabird and cetacean populations) from wind farm development. We then quantified each sector's response to …

Deciphering The Interdomain Coupling In A Gram-Negative Bacterial Membrane Insertase, Adithya Polasa, Shadi A. Badiee, Mahmoud Moradi

Chemistry & Biochemistry Faculty Publications and Presentations

YidC is a membrane protein that plays an important role in inserting newly generated proteins into lipid membranes. The Sec-dependent complex is responsible for inserting proteins into the lipid bilayer in bacteria. YidC facilitates the insertion and folding of membrane proteins, both in conjunction with the Sec complex and independently. Additionally, YidC acts as a chaperone during the folding of proteins. Multiple investigations have conclusively shown that Gram-positive bacterial YidC has Sec-independent insertion mechanisms. Through the use of microsecond-level all-atom molecular dynamics (MD) simulations, we have carried out an in-depth investigation of the YidC protein originating from Gram-negative bacteria. This …

Synthesis Of Amorphous And Various Phase-Pure Nanoparticles Of Nickel Phosphide With Uniform Sizes Via A Trioctylphosphine-Mediated Pathway, David Thompson, Adam S. Hoffman, Zachary R. Mansley, Sarah York, Feng Wang, Yimei Zhu, Simon R. Bare, Jingyi Chen

Chemistry & Biochemistry Faculty Publications and Presentations

Nickel phosphides are of particular interest because they are highly active and stable catalysts for petroleum/biorefinery and hydrogen production. Despite their significant catalytic potential, synthesizing various phase-pure nickel phosphide nanoparticles of uniform size remains a challenge. In this work, we develop a robust trioctylphosphine (TOP)-mediated route to make highly uniform phase-pure Ni₁₂P₅, Ni₂P, and Ni₅P₄ nanoparticles. The synthetic route forms amorphous Ni₇₀P₃₀ nanoparticle intermediates. The reactions can be stopped at the amorphous stage when amorphous particles are desired. The amount of P incorporation can be controlled by varying …

Shifting Baselines: Visualizing Climate Change In America’S National Parks, Lee Lines

Faculty Publications

No abstract provided.

La Vida: Towards A Motivated Goal Reasoning Agent, Ursula Addison

Dissertations, Theses, and Capstone Projects

An autonomous agent deployed to operate over extended horizons in uncertain environments will encounter situations for which it was not designed. A class of these situations involves an invalidation of agent goals and limited guidance in establishing a new set of goals to pursue. An agent will benefit from some mechanism that will allow it to pursue new goals under these circumstances such that the goals are broadly useful in its environment and take advantage of its existing skills while aligning with societal norms. We propose augmenting a goal reasoning agent, i.e., an agent that can deliberate on and self-select …

Improving Large Language Models For Clinical Named Entity Recognition Via Prompt Engineering, Vipina Kuttichi Keloth, Xu Zuo, Yujia Zhou, Zehan Li, Xiaoqian Jiang, Zhiyong Lu, Kirk Roberts, Hua Xu

Faculty, Staff and Student Publications

IMPORTANCE: The study highlights the potential of large language models, specifically GPT-3.5 and GPT-4, in processing complex clinical data and extracting meaningful information with minimal training data. By developing and refining prompt-based strategies, we can significantly enhance the models' performance, making them viable tools for clinical NER tasks and possibly reducing the reliance on extensive annotated datasets.

OBJECTIVES: This study quantifies the capabilities of GPT-3.5 and GPT-4 for clinical named entity recognition (NER) tasks and proposes task-specific prompts to improve their performance.

MATERIALS AND METHODS: We evaluated these models on 2 clinical NER tasks: (1) to extract medical problems, treatments, …