Digital Commons Network^™

Ct Image Segmentation Using Optimization Techniques Under Neutrosophic Domain, Doaa El-Shahat, Nourhan Talal, Jun Ye, Wen-Hua Cui

Neutrosophic Systems with Applications

In this paper, we introduce a hybrid technique between optimization algorithms and neutrosophic theory. This new hybridization can deal with uncertainties in brain computed tomography (CT) images in three different memberships very effectively. To prove the real-time application of this theory, a new segmentation method for brain CT medical images is presented. The grayscale medical image suffers from uncertainties and inconsistencies in the gray levels due to their bad luminance. The proposed technique addressed this problem by performing neutrosophic operations on gray levels based on the S membership function.

How Difficult Is It To Comprehend A Program That Has Significant Repetitions: Fuzzy-Related Explanations Of Empirical Results, Christian Servin, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In teaching computing and in gauging the programmers' productivity, it is important to property estimate how much time it will take to comprehend a program. There are techniques for estimating this time, but these techniques do not take into account that some program segments are similar, and this similarity decreases the time needed to comprehend the second segment. Recently, experiments were performed to describe this decrease. These experiments found an empirical formula for the corresponding decrease. In this paper, we use fuzzy-related ideas to provide commonsense-based theoretical explanation for this empirical formula.

Enhancing Rotating Machinery Fault Diagnosis: A Dual-Head Attention Mechanism In Deep Learning Neural Networks, Qing Snyder

Dissertations

Rotating machinery is crucial to production efficiency and safety in manufacturing industries for an extended time. Ensuring machinery reliability necessitates effective diagnostic systems, particularly for rotating bearings, the key components of such equipment. Fault diagnosis in rotating machinery is essential to prevent failures and minimize downtime, thereby playing an important role in industrial operations. The application of advanced neural network techniques in industry has risen recently. Among these, attention-based neural networks, especially the Transformer models, are originally noteworthy for their sequential data handling capability. This research delves into attention-based algorithms for rotating machinery fault diagnosis, signifying a substantial advancement in …

Why Bernstein Polynomials: Yet Another Explanation, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In many computational situations -- in particular, in computations under interval or fuzzy uncertainty -- it is convenient to approximate a function by a polynomial. Usually, a polynomial is represented by coefficients at its monomials. However, in many cases, it turns out more efficient to represent a general polynomial by using a different basis -- of so-called Bernstein polynomials. In this paper, we provide a new explanation for the computational efficiency of this basis.

How To Make A Decision Under Interval Uncertainty If We Do Not Know The Utility Function, Jeffrey Escamilla, Vladik Kreinovich

Departmental Technical Reports (CS)

Decision theory describes how to make decisions, in particular, how to make decisions under interval uncertainty. However, this theory's recommendations assume that we know the utility function -- a function that describes the decision maker's preferences. Sometimes, we can make a recommendation even when we do not know the utility function. In this paper, we provide a complete description of all such cases.

Shall We Place More Advanced Students In A Separate Class?, Shahnaz Shahbazova, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In every class, we have students who are more advanced and students who are more behind. From this viewpoint, it seems reasonable to place more advanced students in a separate class. This should help advanced students progress faster, and it should help other students as well, since the teachers in the remaining class can better attend to their needs. However, empirically, this does not work: when we form a separate class, the overall amount of gained knowledge decreases. In this paper, we provide a possible explanation for this seemingly counterintuitive phenomenon.

Towards An Optimal Design: What Can We Recommend To Elon Musk?, Martine Ceberio, Olga Kosheleva, Vladik Kreinovich, Hung T. Nguyen

Departmental Technical Reports (CS)

Elon Musk's successful "move fast and break things" strategy is based on the fact that in many cases, we do not need to satisfy all usual constraints to be successful. By sequentially trying smaller number of constraints, he finds the smallest number of constraints that are still needed to succeed -- and using this smaller number of constrains leads to a much cheaper (and thus, more practical) design. In this strategy, Musk relies on his intuition -- which, as all intuitions, sometimes works and sometimes doesn't. To replace this intuition, we propose an algorithm that minimizes the worst-case cost of …

Exploring Tokenization Techniques To Optimize Patch-Based Time-Series Transformers, Gabriel L. Asher

Computer Science Senior Theses

Transformer architectures have revolutionized deep learning, impacting natural language processing and computer vision. Recently, PatchTST has advanced long-term time-series forecasting by embedding patches of time-steps to use as tokens for transformers. This study examines and seeks to enhance PatchTST's embedding techniques. Using eight benchmark datasets, we explore explore novel token embedding techniques. To this end, we introduce several PatchTST variants, which alter the embedding methods of the original paper. These variants consist of the following architectural changes: using CNNs to embed inputs to tokens, embedding an aggregate measure like the mean, max, or sum of a patch, adding the exponential …

Engr 691: Trustworthy Machine Learning, University Of Mississippi. School Of Engineering

GMAS Course Syllabi

No abstract provided.

Visualizing Routes With Ai-Discovered Street-View Patterns, Tsung Heng Wu, Md Amiruzzaman, Ye Zhao, Deepshikha Bhati, Jing Yang

Computer Science Faculty Publications

Street-level visual appearances play an important role in studying social systems, such as understanding the built environment, driving routes, and associated social and economic factors. It has not been integrated into a typical geographical visualization interface (e.g., map services) for planning driving routes. In this article, we study this new visualization task with several new contributions. First, we experiment with a set of AI techniques and propose a solution of using semantic latent vectors for quantifying visual appearance features. Second, we calculate image similarities among a large set of street-view images and then discover spatial imagery patterns. Third, we integrate …

Increased Perceived Confidence In Professional Role Skills Among Undergraduate Dietetic Students Following Simulation-Based Learning Experiences, Makenzie Barr-Porter, Elizabeth Combs, Lauren Batey, Dawn Brewer, Aaron Kyle Schwartz, Tammy Stephenson

UK CARES Faculty Publications

Simulation-based learning experiences (SBLEs) are effective for teaching healthcare students clinical and communication skills. The current study assessed self-perceived clinical and communication confidence among dietetics students completing a series of four SBLEs (3 group, 1 individual) across nine months. Dietetics students were recruited in February 2023 prior to their first SBLE. Simultaneously through the academic year, students completed clinical and communication courses. Students were invited to complete an online, anonymous self-reported survey regarding confidence with nutrition care and communication prior to their first SBLE (Time 1), prior to their third SBLE (Time 2), and following their final SBLE (Time 3). …

Lyraquist: Language Learning Via Music App, Vivian D'Souza, Siri Avula, Mahi Patel, Tanvi Singh, Ashley Bickham

Senior Theses

Lyraquist is a new language learning mobile app that encourages the practice of a foreign language through music. Language learners can connect their Spotify Premium account to Lyraquist to listen to music in their target languages and utilize tools such as translation and vocabulary lists to facilitate language practice. Through integration with Spotify, users can import and create Spotify playlists and search the service’s entire catalog. By combining daily listening habits with several tasks associated with language learning in one place, Lyraquist hopes to be a useful language learning tool.

Unearthing The Past: A Comprehensive Study Of Natural And Anthropogenic Changes At An Archaeological Site Through Hydrogeologic Connectivity Utilizing Gis, Mehlich Ii Phosphorus Extractant, And Ph, Dana L. F. Herren

Theses

This thesis aims to thoroughly analyze the Mehlich II Phosphorus Extractant and pH levels at the Bains Gap Village Site in Anniston, AL., while examining the impact of various environmental factors and human activities on them. Phosphorus is often used in archaeology as an indicator of human activity. Soil core samples were collected to analyze anomalies in phosphorus levels.

To establish any relationships, phosphorus and pH levels from soil cores were correlated with findings from past excavation units and features. The potential effects of hydrogeologic connectivity on soil phosphorus and pH levels were investigated. Geospatial technologies were used to manage …

Predicting Biomolecular Properties And Interactions Using Numerical, Statistical And Machine Learning Methods, Elyssa Sliheet

Mathematics Theses and Dissertations

We investigate machine learning and electrostatic methods to predict biophysical properties of proteins, such as solvation energy and protein ligand binding affinity, for the purpose of drug discovery/development. We focus on the Poisson-Boltzmann model and various high performance computing considerations such as parallelization schemes.

Revolutionizing Access To Justice: The Role Of Ai-Powered Chatbots And Retrieval-Augmented Generation In Legal Self-Help, Ayyoub Ajmi

Faculty Works

Advancements in artificial intelligence (AI) present numerous opportunities to routinize and make the law more accessible to self-represented litigants, notably through AI chatbots employing natural language processing for conversational interactions. These chatbots exhibit legal reasoning abilities without explicit training on legal-specific datasets. However, they face challenges processing less common and more specific knowledge from their training data. Additionally, once trained, their static status makes them susceptible to knowledge obsolescence over time. This article explores the application of retrieval-augmented generation (RAG) to enhance chatbot accuracy, drawing insights from a real-world implementation developed for a court system to support self-help litigants.

A New Canvas Of Learning: Enhancing Formal Analysis Skills In Ap Art History Through Ai-Generated Islamic Art, Krista Carpino, James Hutson

Faculty Scholarship

This study explores the use of AI art generators to enhance formal analysis skills in AP Art History students, with a focus on Islamic Art and Architecture. Students, often entering the course with high academic achievements, find the unique challenge of articulating detailed visual descriptions of artworks. The study’s approach involves using AI image-generation websites, like wepik.com, where students create AI images resembling Islamic artworks studied in class. This method aims to refine their descriptive skills, focusing on visual evidence rather than relying on identifying details. The choice of Islamic Art, markedly different from other historical periods covered in the …

Finding Identities: Identities In Video Games From A Gender, Race, And Identity Representation, Osayame Erinmwingbovo

ART 108: Introduction to Games Studies

In this paper I will bring light to the exploration of gender, race, and identity in video games. While also having a focus on how the representation crosses with social and cultural contexts. I will be researching different games from many different genres, which will show light to the way video games reflect and shape societal attitudes towards gender, race, and identity. When using close textual analysis and theoretical framework from topics that include critical race theory, media, and feminist theory. This research will help to seek to explore the nuances and complexities of representation in gaming which implicates video …

Artificial General Intelligence And The Mind-Body Problem: Exploring The Computability Of Simulated Human Intelligence In Light Of The Immaterial Mind, Caleb Parks

Senior Honors Theses

In this thesis I explore whether achieving artificial general intelligence (AGI) through simulating the human brain is theoretically possible. Because of the scientific community’s predominantly physicalist outlook on the mind-body problem, AGI research may be limited by erroneous foundational presuppositions. Arguments from linguistics and mathematics demonstrate that the human intellect is partially immaterial, opening the door for novel analysis of the mind’s simulability. I categorize mind-body problem philosophies in a manner relevant to computer science based upon state transitions, and determine their ramifications on mind-simulation. Finally, I demonstrate how classical architectures cannot resolve so-called Gödel statements, discuss why this inability …

Modeling, Characterization, And Machine Learning Algorithm For Rectangular Choke Horn Antennas, Ibrahim N. Alquaydheb, Saleh A. Alfawaz, Amirreza Ghadimi Avval, Sara Ghayouraneh, Samir M. El-Ghazaly

Electrical Engineering and Computer Science Faculty Publications and Presentations

In this work, we present the design and modeling of a new type of choke horn antenna. It incorporates a rectangular waveguide and a rectangular choke acting as a parasitic element. The four-sided geometry of the antenna is applicable to systems that utilize rectangular waveguides. Also, it can overcome the need for rectangular-to-circular transition of transmission line or mode conversion. The main objective of this paper is to develop a model that calculates the far field radiation characteristics of the proposed antenna (analytical part) and to incorporate a finite element method (FEM) solver that adds to the theoretical solution (empirical …

Improving Ethics Surrounding Collegiate-Level Hacking Education: Comprehensive Implementation Plan And Affiliation With Peer-Led Initiatives, Shannon O. Morgan

ALL - Honors Theses

The purpose of this research study is to analyze the ethical ramifications of hacking education on the collegiate level in relation to cybersecurity majors and minors. Educators and university officials must take action to prevent the misuse of the information, skills, and knowledge gained from being a student in a cyber-related course. As the dependence on technology rises, it is crucial that future technology professionals are effectively trained and taught the ins and outs of the field to better protect users from becoming a victim of a cyberattack. The outcome of this study is to compile a comprehensive list of …

The Social Pot: A Social Media Application, Reid Long

Honors Projects

The Social Pot is a web application that allows a user to post to Instagram and X simultaneously from one place. The user creates a Social Pot Account and from there can set their Instagram username and password within the home page. Once the user attempts to post, it will redirect them to login to X which once successful will make the tweet. Used the API 'instagram-private-api'. User needed to give access to my X Project which in turn gave an Auth token (via X redirect URL). The auth token was then sent to my endpoint in order to get …

A Deep Dive On The Groundbreak Role Of Role Playing Games, Lawton Fong

ART 108: Introduction to Games Studies

Role-playing games (RPGs) have revolutionized the gaming industry, fundamentally altering the way we engage with and perceive games. RPGs are defined by their emphasis on narrative-driven gameplay, character development, and immersive worlds. Originating from tabletop games like Dungeons & Dragons (D&D), RPGs have evolved into complex digital experiences that allow players to interact with richly detailed virtual environments and storylines. This genre's growth has been propelled by advances in technology, expanding from simple text-based adventures to fully realized 3D worlds. Overall, role-playing games have significantly influenced the gaming landscape and transformed our perceptions of games as interactive experiences by advancing …

Towards Low-Resource Rumor Detection: Unified Contrastive Transfer With Propagation Structure, Hongzhan Lin, Jing Ma, Ruichao Yang, Zhiwei Yang, Mingfei Cheng

Research Collection School Of Computing and Information Systems

The truth is significantly hampered by massive rumors that spread along with breaking news or popular topics. Since there is sufficient corpus gathered from the same domain for model training, existing rumor detection algorithms show promising performance on yesterday's news. However, due to a lack of substantial training data and prior expert knowledge, they are poor at spotting rumors concerning unforeseen events, especially those propagated in different languages (i.e., low-resource regimes). In this paper, we propose a simple yet effective framework with unified contrastive transfer learning, to detect rumors by adapting the features learned from well-resourced rumor data to that …

Continual Normalization: Rethinking Batch Normalization For Online Continual Learning, Quang Pham, Chenghao Liu, Steven Hoi

Research Collection School Of Computing and Information Systems

Existing continual learning methods use Batch Normalization (BN) to facilitate training and improve generalization across tasks. However, the non-i.i.d and non-stationary nature of continual learning data, especially in the online setting, amplify the discrepancy between training and testing in BN and hinder the performance of older tasks. In this work, we study the cross-task normalization effect of BN in online continual learning where BN normalizes the testing data using moments biased towards the current task, resulting in higher catastrophic forgetting. This limitation motivates us to propose a simple yet effective method that we call Continual Normalization (CN) to facilitate training …

Environmental, Social, And Governance (Esg) And Artificial Intelligence In Finance: State-Of-The-Art And Research Takeaways, Tristan Lim

Research Collection School Of Computing and Information Systems

The rapidly growing research landscape in finance, encompassing environmental, social, and governance (ESG) topics and associated Artificial Intelligence (AI) applications, presents challenges for both new researchers and seasoned practitioners. This study aims to systematically map the research area, identify knowledge gaps, and examine potential research areas for researchers and practitioners. The investigation focuses on three primary research questions: the main research themes concerning ESG and AI in finance, the evolution of research intensity and interest in these areas, and the application and evolution of AI techniques specifically in research studies within the ESG and AI in finance domain. Eight archetypical …

Redriver: Runtime Enforcement For Autonomous Vehicles, Yang Sun, Christopher M. Poskitt, Xiaodong Zhang, Jun Sun

Research Collection School Of Computing and Information Systems

Autonomous driving systems (ADSs) integrate sensing, perception, drive control, and several other critical tasks in autonomous vehicles, motivating research into techniques for assessing their safety. While there are several approaches for testing and analysing them in high-fidelity simulators, ADSs may still encounter additional critical scenarios beyond those covered once they are deployed on real roads. An additional level of confidence can be established by monitoring and enforcing critical properties when the ADS is running. Existing work, however, is only able to monitor simple safety properties (e.g., avoidance of collisions) and is limited to blunt enforcement mechanisms such as hitting the …

Flgan: Gan-Based Unbiased Federated Learning Under Non-Iid Settings, Zhuoran Ma, Yang Liu, Yinbin Miao, Guowen Xu, Ximeng Liu, Jianfeng Ma, Robert H. Deng

Research Collection School Of Computing and Information Systems

Federated Learning (FL) suffers from low convergence and significant accuracy loss due to local biases caused by non-Independent and Identically Distributed (non-IID) data. To enhance the non-IID FL performance, a straightforward idea is to leverage the Generative Adversarial Network (GAN) to mitigate local biases using synthesized samples. Unfortunately, existing GAN-based solutions have inherent limitations, which do not support non-IID data and even compromise user privacy. To tackle the above issues, we propose a GAN-based unbiased FL scheme, called FlGan, to mitigate local biases using synthesized samples generated by GAN while preserving user-level privacy in the FL setting. Specifically, FlGan first …

Unleashing The Power Of Clippy In Real-World Rust Projects, Chunmiao Li, Yijun Yu, Haitao Wu, Luca Carlig, Shijie Nie, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

The error messages generated by the Rust compiler (rustc) are useful for developers to identify and diagnose suspicious code segments. Complementing the compiler, linters can also play an important role in promoting the adherence to certain coding style conventions and best practices. Prominent linters utilized in the Rust ecosystem include Clippy [1] and Rustfmt [2]. Among them, the Rust community particularly emphasizes on the importance of heeding the warnings provided by Clippy to mitigate common errors and promote the adoption of idiomatic conventions. Clippy provides a set of more than 600 lints in addition to the built-in rustc lints. These …

My Github Sponsors Profile Is Live!": Investigating The Impact Of Twitter/X Mentions On Github Sponsors, Youmei Fan, Tao Xiao, Hideaki Hata, Christoph Treude, Kenichi Matsumoto

Research Collection School Of Computing and Information Systems

GitHub Sponsors was launched in 2019, enabling donations to opensource software developers to provide financial support, as per GitHub’s slogan: “Invest in the projects you depend on”. However, a 2022 study on GitHub Sponsors found that only two-fifths of developers who were seeking sponsorship received a donation. The study found that, other than internal actions (such as offering perks to sponsors), developers had advertised their GitHub Sponsors profiles on social media, such as Twitter (also known as X). Therefore, in this work, we investigate the impact of tweets that contain links to GitHub Sponsors profiles on sponsorship, as well as …

Concretely Mapped Symbolic Memory Locations For Memory Error Detection, Haoxin Tu, Lingxiao Jiang, Jiaqi Hong, Xuhua Ding, He Jiang

Research Collection School Of Computing and Information Systems

Memory allocation is a fundamental operation for managing memory objects in many programming languages. Misusing allocated memory objects (e.g., buffer overflow and use-after-free) can lead to catastrophic consequences. Symbolic execution-based approaches are often used to detect such memory errors, leveraging their capabilities in automatic path exploration and test case generation. However, existing symbolic execution engines face significant limitations in modeling dynamic memory layouts. These engines either represent memory object locations as concrete addresses, limiting analyses to specific address layouts and missing errors that occur at special addresses, or represent locations as simple symbolic variables without sufficient constraints, resulting in memory …