Digital Commons Network^™

Star-Based Reachability Analysis Of Binary Neural Networks On Continuous Input, Mykhailo Ivashchenko

School of Computing: Dissertations, Theses, and Student Research

Deep Neural Networks (DNNs) have become a popular instrument for solving various real-world problems. DNNs’ sophisticated structure allows them to learn complex representations and features. However, architecture specifics and floating-point number usage result in increased computational operations complexity. For this reason, a more lightweight type of neural networks is widely used when it comes to edge devices, such as microcomputers or microcontrollers – Binary Neural Networks (BNNs). Like other DNNs, BNNs are vulnerable to adversarial attacks; even a small perturbation to the input set may lead to an errant output. Unfortunately, only a few approaches have been proposed for verifying …

Emotional Experiences Of Black U.S. Computer Science And Engineering Students Amid Faculty-Student Interactions, Kyle Shanachilubwa

Honors Theses

Black students encounter unique challenges in computer science and engineering education. Chief among these is a disconnect with faculty due to a lack of mentorship and difficulties in interactions with faculty. Despite these challenges, Black students bring many engineering and computer science assets. This study aims to understand the emotional experiences of Black students in computer science and engineering education. We present an interpretative phenomenological analysis (IPA) to answer the questions: (1) How do Black students experience faculty-student interactions in computer science and engineering education? (2) How do Black students experience professional shame in these interactions? To answer these research …

Investigating Causes Of Disputes Resulting In Litigation In Airport Development Projects In The United States Using Graph-Based Techniques, Ramy Khalef, Islam H. El-Adaway

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Airports represent a crucial a sector of the US infrastructure, but they were documented to have scored a D+. To mitigate this, governmental authorities have issued initiatives to update the airport infrastructure. With an expected critical junction for a major transformation in the airport sector, it is anticipated that airport projects will increase. This shall result in an escalation in the number of disputes. Previous research analyzed dispute concerns within the construction sector, but none tackled the perspective of airport projects. Accordingly, the goal of this study is to investigate the leading causes of disputes in litigation within US airport …

Attention Guided Data Augmentation For Improving The Classification Performance Of Vision Transformers., Nada Baili

Electronic Theses and Dissertations

For over a decade, Deep Neural Networks (DNNs) have been rapidly progressing and achieving great success, forming a robust foundation of state of the art machine learning algorithms that impacted various domains. The advances in data acquisition and processing have undeniably played a major role in these breakthroughs. Data is a crucial component in building successful DNNs, as it enables machine learning models to optimize complex architectures, necessary to perform certain difficult tasks. However, acquiring large-scale data sets is not enough to learn robust models with generalizable features. Instead, an ideal training set should be diverse enough and contain enough …

Magdm Model Using Single-Valued Neutrosophic Credibility Matrix Energy And Its Decision-Making Application, Jun Ye, Rui Yong, Wanlu Du

Neutrosophic Systems with Applications

This paper aims to develop a MAGDM model using single-valued neutrosophic credibility matrix (SVNCM) energy in a SVNCM scenario. To do it, first, SVNCM energy and its score function are presented as a conceptual extension of existing single-valued neutrosophic matrix (SVNM) energy. Then, a MAGDM model is developed in terms of SVNCM energy and its score function in a SVNCM scenario and also its decision algorithm is provided to solve MAGDM problems with SVNCMs. Finally, the developed MAGDM model is applied in the school site selection problem as an actual example, then the comparative investigation of the decision results in …

Data Analysis Of Twitter’S Nasdaq100 Sentiments And Topics As Indicators For News Articles Retrieval: Fine-Tuning Roberta And Rag, Kagan Timur

ICT

This study explores the combination of sentiment analysis with vader-lexicon and semantic analysis with latent dirichlet allocation to identify real-life events, particularly in the context of Twitter datasets. While sentiment analysis alone may not provide accurate guidance, the inclusion of semantic analysis enhances the research process by helping to identify relevant news articles and comprehend brand perception on social media. Furthermore, the study fine-tunes the RoBERTa model specifically for question-answering tasks

The Importance Of Text Representation For Neural Networks Through Natural Language Processing Techniques, William Parsley

Data Science Undergraduate Honors Theses

Text representation is a fundamental aspect of natural language processing (NLP) when it comes to the performance of neural networks. Free-form text fields are being utilized in more and more industries. Anything from a description of an item on a web store to tracking service events to military-grade aircraft is being collected in free-form text. The goal of the thesis is to highlight best practices and discuss trends in data to prepare text for a neural network. It will demonstrate various techniques for representing free-form text in the context of neural networks, focusing on data preparation decisions, embedding techniques, and …

Representation Learning For Generative Models With Applications To Healthcare, Astronautics, And Aviation, Van Minh Nguyen

Theses and Dissertations

This dissertation explores applications of representation learning and generative models to challenges in healthcare, astronautics, and aviation.

The first part investigates the use of Generative Adversarial Networks (GANs) to synthesize realistic electronic health record (EHR) data. An initial attempt at training a GAN on the MIMIC-IV dataset encountered stability and convergence issues, motivating a deeper study of 1-Lipschitz regularization techniques for Auxiliary Classifier GANs (AC-GANs). An extensive ablation study on the CIFAR-10 dataset found that Spectral Normalization is key for AC-GAN stability and performance, while Weight Clipping fails to converge without Spectral Normalization. Analysis of the training dynamics provided further …

Automatic Speech Recognition For Air Traffic Control Using Convolutional Lstm, Sakshi Nakashe

Electronic Theses, Projects, and Dissertations

The need for automatic speech recognition in air traffic control is critical as it enhances the interaction between the computer and human. Speech recognition helps to automatically transcribe the communication between the pilots and the air traffic controllers, which reduces the time taken for administrative tasks. This project aims to provide improvement to the Automatic Speech Recognition (ASR) system for air traffic control by investigating the impact of convolution LSTM model on ASR as suggested by previous studies. The research questions are: (Q1) Comparing the performance of ConvLSTM with other conventional models, how does ConvLSTM perform with respect to recognizing …

Methodologies For Optimization And Surface Heat Flux Estimation For Hybrid Thermal Protection Systems, Syed Arafun Nabi

Theses and Dissertations

Thermal protection system (TPS) plays a pivotal role in safeguarding space vehicles from extreme aerothermal heating during atmospheric entry. To ensure effective thermal management, Hybrid Thermal Protection seamlessly integrating both active and passive TPS is an innovative solution. The passive TPS consists of an insulative layer on vehicle skin to counteract the aerothermal heat. Although a thicker layer enhances insulation, a trade-off arises between the thickness of the passive layer and the spacecraft’s weight. Beneath the passive TPS layer lies the active TPS functioning as a heat exchanger where the vehicle fuel liquid is considered as coolant. The effectiveness of …

Cultural Awareness Application, Bharat Gupta

Electronic Theses, Projects, and Dissertations

In an increasingly interconnected global landscape, cultural awareness and competency have become indispensable skills for individuals and organizations alike. This paper introduces a pioneering cultural awareness application, grounded in the Cultural Orientation Model—a comprehensive framework devised by Dr. Walker [8]to guide individuals in understanding, appreciating, and effectively engaging with diverse cultures. The application encompasses ten primary dimensions, each representing fundamental aspects of social life shared by members of any socio-cultural environment. Through a combination of cultural education, interactive learning, guidance on cultural etiquette, and integration of cultural events, the application aims to foster empathy, tolerance, and effective cross-cultural communication skills. …

Supporting Text And Data Analysis Across Campus From The Academic Library, Amy Kirchhoff, Hejin Shin Phd

Digital Initiatives Symposium

The ability to comprehend and communicate with text-based data is essential to future success in academics and employment, as evidenced in a recent survey from Bloomberg Research Services which shows that nearly 97% of survey respondents now use data analytics in their companies and 58% consider data and text mining a business analytics tool (https://www.sas.com/content/dam/SAS/bp_de/doc/studie/ba-st-the-current-state-of-business-analytics-2317022.pdf). This has fueled a substantial growth in text analysis research (involving the use of technology to analyze un- and semi-structured text data for valuable insights, trends, and patterns) across disciplines and a corresponding demand on academic libraries to support text analysis pedagogy and text analysis …

Transferable User-Friendly Machine Learning For Normal Boiling Point Prediction, Frank Tafadzwa Mtetwa

Theses and Dissertations

The estimation of thermophysical properties of chemical compounds holds considerable importance across a multitude of fields, ranging from material science to process design. Among these properties, the normal boiling point (NBP) stands out as a pivotal parameter in scientific and engineering contexts, as it elucidates the state of a substance under standard conditions commonly encountered in nature. Additionally, NBP is extensively documented in various reference materials and databases. Given its paramount importance and widespread availability, prediction methodologies for other properties such as critical temperature, liquid density, vapor pressure, surface tension, liquid viscosity, liquid thermal conductivity, and flash point often rely …

Modeling The Human Learning Process Using An Industrial Steam Boiler Analogy To Design A Psychophysiological-Based Hypermedia Adaptive Automation System, Liliana María Villavicencio López

USF Tampa Graduate Theses and Dissertations

This dissertation aims to address the existing gap in the integration of various dimensions within the student learning system, encompassing cognitive, emotional, and physical variables. The primary objective is to construct a Personalized Learning Adaptive Automation model using Electroencephalography (EEG) technology.

To provide deeper insight into the intricate nature of the Human Learning Process, this study introduces a novel analogy with an Industrial Steam Boiler. This analogy serves as a distinctive contribution to research in the field.

The research methodology involved the collection of brainwaves data from engineering students while they undertook educational tasks of varying levels of difficulty, categorized …

Development Of Deep Neural Architecture For Continuous Sign Language Video Generation, Natarajan B

Theses and Dissertations

This dissertation presents a deep neural network based sign language video generation framework for translating the multilingual sentences into sign videos. This thesis addresses the challenges persist with the sign language video generation such as (i) Handling longer sequences of input sentences and new words (ii) Pose estimation with higher accuracy (iii) High quality photo realistic sign gesture video generation (iv) Improving realism in sign video generation. Hence, the thesis focuses four contributions to address the above issues.

The first contribution of this thesis automates the translation of multilingual sentences into sign glosses without manual intervention by incorporating Hybrid Neural …

Sustainability In Civil And Construction Engineering, Connotations, Paradigms, And Perceptions, Xiaomei Wang

Theses and Dissertations

Over the last four decades, the concept of sustainability has evolved significantly, gaining prominence since the Brundtland report in 1987. Operating at the intersection of built and natural environments, Civil and Construction Engineering (CCE) projects and designs inherently impact the natural surroundings. In this context, the concept of sustainability serves not only as a guiding principle to mitigate depletion but also as a pathway to achieving balance among diverse societal needs so every creature can survive and thrive. Despite considerable efforts in this domain, the definition of sustainability within the CCE discipline remains elusive. Paradoxically, the popularity and vagueness of …

Excellence Summit 2024, Office Of Research And Sponsored Projects Operations

Excellence Summit (Research Week) Archived Proceedings

In it's 17th year, the University of the Incarnate Word, welcome all to read and review the proceedings of the annual research week. The 2024 Excellence Summit highlighted academic excellence across all schools featuring topics from nutrition studies to soil amendments, artificial intelligence, medical case studies, and music composition studies. The rich mixture of humanities, health sciences, physical sciences, education, and business can be found in this booklet. We thank all that contributed to these works and hope the community enjoys learning from today's and tomorrow's leaders in research, inquiry, and scholarship.

A Biocompatible Hybrid Approach For The Selection Of Gas Drainage Methods In Coal Mines Based On The Best-Worst Method And Analytic Network Process, Amir Jafarpour, Mehdi Najafi, Ali Dabagh

Journal of Sustainable Mining

Methane drainage is one of the main steps in underground coal mining projects. The operation of methane extraction from coal mines/seams is important from the safety, economic, and technical points of view. Selecting the appropriate method for methane drainage is very difficult, and if the method is not chosen correctly, it will lead to environmental problems. The relationship between different influencing factors, parameters, and methods (and how they affect) is significant in choosing the method of gas drainage. In the current study, a hybrid algorithm is presented based on an Analytic Network Process (ANP) and a method based on the …

Immersive Japanese Language Learning Web Application Using Spaced Repetition, Active Recall, And An Artificial Intelligent Conversational Chat Agent Both In Voice And In Text, Marc Butler

MS in Computer Science Project Reports

In the last two decades various human language learning applications, spaced repetition software, online dictionaries, and artificial intelligent chat agents have been developed. However, there is no solution to cohesively combine these technologies into a comprehensive language learning application including skills such as speaking, typing, listening, and reading. Our contribution is to provide an immersive language learning web application to the end user which combines spaced repetition, a study technique used to review information at systematic intervals, and active recall, the process of purposely retrieving information from memory during a review session, with an artificial intelligent conversational chat agent both …

Partnership And Coalition Efforts For Community-Based Research: Towards A Liveable Urban And Rural Settlement In Kwazulu-Natal, Sibongile A. Ngcobo, Nene E. Khalema, Tawanda Makusha

CSID Journal of Infrastructure Development

This paper examines the role of community partnerships and coalitions in the intricate and constantly evolving dynamics of communities in KwaZulu-Natal, South Africa. Various aspects of the community context, such as socioeconomic conditions, geography, environmental factors, demographics, community politics, and cultural norms, often influence these community partnerships and coalitions. This study adopted the Community Coalition Action Theory as an organisational framework to examine the efforts of coalitions and partnerships in community-based research. The research applied a mixed-methods approach. This included a survey and individual interviews within the research organisation responsible for multiple community-based projects in rural and urban areas of …

Choosing Optimal Supply Radius Of Transformer Substations (Tss) In Iraq’S Cities Using Geometric Programming With Neutrosophic Coefficients, Ahmed K. Essa, Montifort Blessings Andrew Mitungwi, Tuweh Prince Gadama, Ahmed A. Salama

Neutrosophic Systems with Applications

Numerous uncertainties exist in various electricity power system problems due to the size, complexity, geographical distribution, and influence of unforeseen events in these systems, making it difficult for traditional mathematics tools based on crisp set theory to have an impact on and solve many power system problems. As a new branch of mathematical uncertainty techniques, the neutrosophic expert systems approach has therefore emerged with the development of electric power systems and has proven successful when correctly linked. The expert typically uses ambiguous or neutrosophic language to describe their empirical knowledge, such as "very likely," "quite likely," "if x is large, …

Use Of Mathematical Skills For Technical Condition Assessment Of Power Autotransformers, Dilafruz Rustamjon Qizi Abdullabekova, Nurali Berdiyorovich Pirmatov

Technical science and innovation

In this paper, we review innovative approaches to assess the technical condition of autotransformers using mathematical methods. The main purpose of these methods is to increase the efficiency of diagnostics and prevent possible failures in power systems. The developed mathematical expression is based on complex data analysis, application of statistical methods and machine learning technologies. This makes it possible to achieve more accurate and earlier detection of potential problems. As a result of these innovative approaches, the possibilities for early detection and prevention of faults are significantly increased. This is important for ensuring the smooth operation of power systems and …

2024- The Twenty-Eighth Annual Symposium Of Student Scholars

Symposium of Student Scholars Program Books

The full program book from the 28th Annual Symposium of Student Scholars, held on April 17-19, 2024. Includes abstracts from the presentations and posters.

Transformer-Based Deep Learning Model For Sign Language Recognition, Ganzorig Batnasan

Theses

Sign language recognition research aims to develop systems and tools that can interpret and translate sign language into text or spoken language. During the past two decades, the challenges faced in this domain are multifaceted. The first and foremost challenge is the complexity of sign language, which includes intricate hand gestures, facial expressions, and body movements. Recognizing and interpreting these components accurately is challenging. The second challenge is variability among different regions and communities, leading to variations in signs and gestures. This variability poses a challenge for developing universal recognition systems.
Limited data is another challenge which makes it difficult …

Performing Information Extraction For Mission Engineering Applications, Samuel R. Koski

Engineering Management & Systems Engineering Theses & Dissertations

The process of extracting structured data from unstructured and semi-structured text is manual, time consuming and error prone. Current natural language processing approaches for automating this process are difficult to verify for non-trivial and context-sensitive corpora. Large Language Models (LLMs) like ChatGPT have become a subject of considerable interest, opening a promising avenue of exploration. However, there is limited evidence on the performance of LLMs for information extraction.

In this dissertation, an approach is proposed to evaluate the accuracy of Stanford OpenIE and OpenAI's ChatGPT for this purpose. This includes comparing Resource Description Framework (RDF) triples extracted by each of …

Predicting The Rate Of Degradation Related To Oxygen Electrode Delamination In Solid Oxide-Ion Electrolyzers, Yeting Wen, Kevin Huang

Faculty Publications

Language comprehension is often affected in individuals with post-stroke aphasia. However, deficits in auditory comprehension are not fully correlated with deficits in reading comprehension and the mechanisms underlying this dissociation remain unclear. This distinction is important for understanding language mechanisms, predicting long-term impairments and future development of treatment interventions. Using comprehensive auditory and reading measures from a large cohort of individuals with aphasia, we evaluated the relationship between aphasia type and reading comprehension impairments, the relationship between auditory versus reading comprehension deficits and the crucial neuroanatomy supporting the dissociation between post-stroke reading and auditory deficits. Scores from the Western Aphasia …

Assessment Of Educational Facilities In Osogobo Metropolis Nigeria, Dele S. Ogundahunsi, Martins B. Gasu, Samuel B. Adedotun, Olayiwola Olayode, Akintayo O. Ojo, Christiana Olayode

CSID Journal of Infrastructure Development

This study assessed educational facilities in the Osogbo metropolis, Nigeria, aiming to evaluate the available educational facilities, examine student and staff satisfaction levels, and analyze their availability across schools in the area. The sample consisted of 476 schools, from which 73 were randomly selected. A multistage sampling procedure was employed to sample 1,863 pupils/students and 87 teachers for questionnaire administration, complemented by observation. Data were analyzed using Mean Weighted Average, Summation of Weighted Value, and Analysis of Variance. Findings showed that 63.4% of primary schools had the listed facilities, while 75% of secondary schools reported 81% availability. Public primary school …

Western Science And Eastern Zen To Seek The Origin Of Truth: Philosophical Background Of Scale Modeling, Kozo Saito

Progress in Scale Modeling, an International Journal

This article was written to introduce philosophical background of scale modeling, where Zen philosophy was applied to overcome the limitation of logical thinking and hypotheses-driven deductive science. Three specific reasons are as follows. The first is related to the law approach in scale modeling; it uses the kufu principle, originated in Zen Buddhism, together with the other three scientific methods: experimental, theoretical, and computational. The second reason is because scale modeling seeks relativistic understanding by attempting to realize similarity; the concept is closer to Eastern philosophy rather than absolute understanding cultivated by deductive science. The third is in the educational …

How Can Generative Ai (Genai) Enhance Or Hinder Qualitative Studies? A Critical Appraisal From South Asia, Nepal, Niroj Dahal

The Qualitative Report

Qualitative researchers can benefit from using generative artificial intelligence (GenAI), such as different versions of ChatGPT—GPT-3.5 or GPT-4, Google Bard—now renamed as a Gemini, and Bing Chat—now renamed as a Copilot, in their studies. The scientific community has used artificial intelligence (AI) tools in various ways. However, using GenAI has generated concerns regarding potential research unreliability, bias, and unethical outcomes in GenAI-generated research results. Considering these concerns, the purpose of this commentary is to review the current use of GenAI in qualitative research, including its strengths, limitations, and ethical dilemmas from the perspective of critical appraisal from South Asia, Nepal. …

Can Neural Networks Reach Human Vision Levels On Object Recognition Tasks?, Luke D. Baumel, Mikayla Cutler, Matt Hyatt, Joseph Tocco, William Friebel, Nicholas Baker Dr., George K. Thiruvathukal Dr.

Psychology: Faculty Publications and Other Works

Object recognition is a crucial function of biological vision; it allows us to draw conclusions about a visual scene that transcends the image formed by the retina. However, the task of object recognition quickly becomes a challenge when hindrances such as viewing angle, object distance from observer, illuminant qualities, and potential occlusions become active variables. Additionally, the diversity of visual features within the same category of object, coupled with the numerous contexts in which an object may be observed is demonstrative of the formidable task that is object recognition. Previous research showed a significant texture bias in Convolutional Neural Networks’ …