Digital Commons Network^™

Cyberbullying Detection On Twitter Data Using Machine Learning Classifiers, Pradip Dhakal

Data Science and Data Mining

This study compares some of the popular machine learning techniques like Logistic Regression, Multinomial Naive Bayes, K-Nearest Neighbor, and Extreme Gradient Boosting to classify the tweets into three different categories: cyberbullying based on religion, cyberbullying based on ethnicity, or no cyberbullying. First, various data-cleaning approaches are used to clean the tweet data. After the data is clean and ready, the word embedding techniques, such as a bag of words and term frequency-Inverse document frequency, are used to convert the words into mathematical vectors. Finally, the model will be fitted using the combination of the above-mentioned word embedding techniques and machine …

Artificial Sociality, Simone Natale, Iliana Depounti

Human-Machine Communication

This article proposes the notion of Artificial Sociality to describe communicative AI technologies that create the impression of social behavior. Existing tools that activate Artificial Sociality include, among others, Large Language Models (LLMs) such as ChatGPT, voice assistants, virtual influencers, socialbots and companion chatbots such as Replika. The article highlights three key issues that are likely to shape present and future debates about these technologies, as well as design practices and regulation efforts: the modelling of human sociality that foregrounds it, the problem of deception and the issue of control from the part of the users. Ethical, social and cultural …

Embedding Information Literacy Practices In An Upper Division Chemistry Lab Class At A University In The United States Of America, Christopher Randles, Matilynn Lamm, Katy A. Miller

Faculty Scholarship and Creative Works

This poster discusses the implementation of information literacy into an upper division inorganic chemistry lab and the strategies employed to develop students’ information literacy in the laboratory course.

Predicting Superconducting Critical Temperature Using Regression Analysis, Roland Fiagbe

Data Science and Data Mining

This project estimates a regression model to predict the superconducting critical temperature based on variables extracted from the superconductor’s chemical formula. The regression model along with the stepwise variable selection gives a reasonable and good predictive model with a lower prediction error (MSE). Variables extracted based on atomic radius, valence, atomic mass and thermal conductivity appeared to have the most contribution to the predictive model.

Advancing Cancer Classifcation Through Machine Learning Analysis Of Rna-Seq Gene Expression Data, Emil Agbemade, Amina Issoufou Anaroua, Dimitri Bamba

Data Science and Data Mining

This study delves into the classifcation of various cancer types using the RNA-Seq (HiSeq) PANCAN dataset from the UCI Machine Learning Repository, which encompasses a rich collection of gene expression data across multiple tumor samples. To improve cancer diagnosis and treatment, our methodology confronts the challenges inherent in high-dimensional datasets, such as the Hughes Effect and the Curse of Dimensionality, through innovative feature selection methods and machine learning approaches. A key component of our strategy includes the use of tree-based algorithms, particularly Random Forest, to refine the dataset to seventy genes of utmost relevance for tumor classifcation, and the application …

Bootstrap Regression For Investigating Macroeconomics Factors Affecting Usa Home Prices, Benedict Kongyir, Emil Agbemade

Data Science and Data Mining

This study investigates the impact of macroeconomic indicators on US home prices, underscoring the importance of understanding these dynamics due to their signifcant socioeconomic consequences. Utilizing a dataset from Kaggle, originally collected by FRED, the research examines variables like the Consumer Price Index, Population, Unemployment, GDP, Stock Prices, Income, and Mortgage Rate to discern their efect on housing market fuctuations. The analysis identifes multicollinearity among predictors, necessitating a shift from traditional multiple linear regression to a more robust bootstrap regression method due to violations of parametric assumptions. Key fndings reveal that Real Disposable Income is a signifcant predictor of home …

Xgboost Hyperberd Model Using Steam Platform, Yuh-Haur Chen

Data Science and Data Mining

This project investigates game pricing strategies in the Steam market using an XGBoost model, drawing motivation from Professor Xie's lecture, and presenting findings through a density plot that delineates two primary pricing strategies. A free-to-play approach, indicated by a significant hot spot, is adopted by developers focusing on post-purchase revenues through DLC, aesthetic purchases, and in-game transactions. This sailing strategy includes community-centric developers aiming to distribute their games for player engagement rather than profit.

The project illustrates the effectiveness of advanced modeling techniques in handling complex datasets, with significant predictive accuracy reflected by a reduced MSE from 0.3472 to 0.1397. …

Combating Cyberbullying On Social Media: A Machine Learning Approach With Text Analysis On Twitter, Amir Alipour Yengejeh

Data Science and Data Mining

The popularity of the electronic mobile devices along with social media as well as networking websites have been tremendously increased in the recent year. Most people around the world daily engage in the variety of cyberspace additives. Even though the users can take most advantages of these system such as exchange the idea and information, being sociable, and enjoyments, they might be faced with such adverse behaviors such as toxicity, bullying, extremism, and cruelty. The recent statistics reports that such mentioned behaviors has been noticeably grown on the cyberspace such that can threaten the individuals and even any community. Thus, …

Machine Learning Approaches For Cyberbullying Detection, Roland Fiagbe

Data Science and Data Mining

Cyberbullying refers to the act of bullying using electronic means and the internet. In recent years, this act has been identifed to be a major problem among young people and even adults. It can negatively impact one’s emotions and lead to adverse outcomes like depression, anxiety, harassment, and suicide, among others. This has led to the need to employ machine learning techniques to automatically detect cyberbullying and prevent them on various social media platforms. In this study, we want to analyze the combination of some Natural Language Processing (NLP) algorithms (such as Bag-of-Words and TFIDF) with some popular machine learning …

Diagnostic In Neuroimaging: A Comparative Study Of Deep Learning And Traditional Approaches, Amina Issoufou Anaroua

Data Science and Data Mining

In the realm of medical diagnostics, precise classification of brain tumors is pivotal. This study conducts a comprehensive comparative analysis of a Convolutional Neural Network (CNN) against traditional machine learning models, Logistic Regression (LR) and Support Vector Machines (SVM) on a dataset of MRI scans for multi-class brain tumor classification. The CNN, tailored for image recognition, is evaluated alongside LR and SVM, which have established benchmarks in classification tasks. The investigation reveals that the traditional models hold their ground in terms of precision and interpretability, with the SVM, in particular, achieving remarkable accuracy. However, the CNN distinguishes itself by demonstrating …

Optimizing Ai With Advanced Data Structuring: A Comparative Analysis Of K-Means And Gmm Clustering Techniques, Amir Alipour Yengejeh

Data Science and Data Mining

This study presents a detailed comparison of Kmeans and Gaussian Mixture Model (GMM) clustering algorithms, illustrating their unique capabilities and limitations across various synthetic datasets. By utilizing metrics such as the Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI), the research provides nuanced insights into how these algorithms handle datasets with varying structures and complexities. For instance, while both K-means and GMM show robust performance on well-separated clusters, GMM demonstrates a distinct advantage in scenarios with overlapping clusters or unbalanced data distributions. Conversely, K-means excels in identifying clear, distinct groupings, highlighting its utility in simpler clustering contexts. This study …

Florida's Vanishing Heritage: Climate Risk And Adaptation At Florida Heritage Sites, Levi Watson

Electronic Theses and Dissertations, 2020-

This thesis examines history and preservation at coastal cultural heritage sites threatened by climate change and explores climate adaptation strategies at two sites on Florida's Atlantic coast. Current climate change models indicate the planet may see as much as 1.1 meters, or four feet, of global average sea level rise by the year 2100, requiring site managers to intervene by using adaptation techniques to improve resilience and guard against the loss of cultural heritage monuments. Understanding the history and importance of these sites to the surrounding communities and their numerous stakeholders is the first step to ensuring these sites remain …

Theoretical Framework Of Exchange Coupled Tripartite Spin Systems With Magnetic Anisotropy And Predictions Of Spin And Electronic Transport Properties For Their Use In Quantum Architectures, Eric Switzer

Electronic Theses and Dissertations, 2020-

There has been significant interest in spin systems involving two or more coupled spins as a single logical qubit, particularly for scalable quantum computing architectures. Recent realizations include the so-called singlet-triplet qubits and coupled magnetic molecules. An important class of coupled-spin systems, the three-spin paradigm for spin greater than 1/2, has not yet been fully realized in scalable qubit architectures. In this thesis, I develop the theoretical framework to investigate a class of tripartite spin models for realistic systems. First, I model a spin 1/2 particle (e.g., an electron) and two spin 1 particles (in a dimer arrangement) coupled with …

Deep Video Understanding With Model Efficiency And Sparse Active Labeling, Aayush Jung Bahadur Rana

Electronic Theses and Dissertations, 2020-

Videos capture the inherently sequential nature of the real world, making automatic video understanding an essential need for automatic understanding of the real world. Due to major advancements in camera, communication, and storage hardware, videos have become a widely used data format for crucial applications such as home automation, security, analysis, robotics, and autonomous driving. Existing methods for video understanding require heavy computation and large training data for good performance, this limits how quick the videos can be processed and how much data can be labeled for training. Real-world video understanding requires analyzing dense scenes and sequential information, which increases …

Topological Data Analysis Using The Mapper Algorithm, Jessica Girard

Electronic Theses and Dissertations, 2020-

Topological data analysis is an expanding field that attempts to obtain qualitative information from a data set using topological ideas. There are two common methods of topological data analysis: persistent homology and the Mapper algorithm; the focus of this thesis is on the latter. In this thesis, we will be discussing the key ideas behind the Mapper algorithm, following the flow from Morse Theory to Reeb graphs to the topological version of the algorithm and finally to the statistical version. Lastly, we will present an application of Mapper to the USAIR97 data set using the RTDAmapper package.

Theoretical Analysis Of Charge Conduction And Rectification In Self-Assembled-Monolayers In Molecular Junctions, Francis Adoah

Electronic Theses and Dissertations, 2020-

As electrical devices shrink to the atomic scale, it is expected that Moore's law will soon be obsolete for semiconductor devices. In 1974, Avriam and Ratner predicted that organic devices could replace semiconductor technology, leading to extensive research on molecular-based organic devices. This dissertation delves into the theoretical frameworks used to examine the transport in molecular junctions and aims to enhance our comprehension of charge transport and conduction properties. The studies presented in this thesis illustrates that a molecule's alteration by just a single atom can change it from an insulator to a conductor, and also that, by fine-tuning the …

Annotation Efficient Visual Recognition: From Semi-Supervised To Few-Shot Learning, Mamshad Nayeem Rizve

Electronic Theses and Dissertations, 2020-

In recent years, supervised deep learning has achieved remarkable success in solving a wide range of visual recognition problems. Large-scale labeled datasets have been crucial for this success and the progress has primarily been limited to controlled environments. In this dissertation, we present methods to improve the annotation efficiency of deep visual recognition models and also propose methods to improve the performance of annotation-efficient models in unconstrained open-world settings. To address the annotation bottleneck in supervised learning, we introduce a pseudo-labeling framework for semi-supervised learning. While consistency regularization methods dominate the field, they heavily rely on domain-specific data augmentations, limiting …

Towards Efficient And Effective Representation Learning For Image And Video Understanding, Taojiannan Yang

Electronic Theses and Dissertations, 2020-

Deep learning has achieved tremendous success on various computer vision tasks. However, deep learning methods and models are usually computationally expensive, making it hard to train and deploy, especially on resource-constrained devices. In this dissertation, we explore how to improve the efficiency and effectiveness of deep learning methods from various perspectives. We first propose a new learning method to learn computationally adaptive representations. Traditional neural networks are static. However, our method trains adaptive neural networks that can adjust their computational cost during runtime, avoiding the need to train and deploy multiple networks for dynamic resource budgets. Next, we extend our …

Due Tomorrow, Do Tomorrow: Measuring And Reducing Procrastination Behavior Among Introductory Physics Students In An Online Environment, Zachary Felker

Electronic Theses and Dissertations, 2020-

This work is focused on the measurement and prevention of procrastination behavior among college level introductory physics students completing online assignments in the form of mastery-based online learning modules. The research is conducted in two studies. The first study evaluates the effectiveness of offering students the opportunity to earn a small amount of extra credit for completing portions of their homework early. Unsupervised machine learning is used to identify an optimum cutoff duration which differentiates taking a short break during a continuous study session from a long break between two different study sessions. Using this cutoff, the study shows that …

Detecting Team Conflict From Multiparty Dialogue, Ayesha Enayet

Electronic Theses and Dissertations, 2020-

The emergence of online collaboration platforms has dramatically changed the dynamics of human teamwork, creating a veritable army of virtual teams composed of workers in different physical locations. The global world requires a tremendous amount of collaborative problem solving, primarily virtual, making it an excellent domain for computer scientists and team cognition researchers who seek to understand the dynamics involved in collaborative tasks to provide a solution that can support effective collaboration. Mining and analyzing data from collaborative dialogues can yield insights into virtual teams' thought processes and help develop virtual agents to support collaboration. Good communication is indubitably the …

A Study On Robustness And Semantic Understanding Of Visual Models, Madeline Chantry

Electronic Theses and Dissertations, 2020-

Vision models have improved in popularity and performance on many tasks since the emergence of large-scale datasets, improved access to computational resources, and new model architectures like the transformer. However, it is still not well understood if these models can be deployed in the real world. Because these models are "blackbox" architectures, we do not fully understand what these models are truly learning. An understanding of what models learn "underneath the hood" would result in better improvements for real-world scenarios. Motivated by this, we benchmark these impressive visual models using newly proposed datasets and tasks on their robustness and their …

Human Recognition Theory And Facial Recognition Technology: A Topic Modeling Approach To Understanding The Ethical Implication Of A Developing Algorithmic Technologies Landscape On How We View Ourselves And Are Viewed By Others, Hajer Albalawi

Electronic Theses and Dissertations, 2020-

The emergence of algorithmic-driven technology has significantly impacted human life in the current century. Algorithms, as versatile constructs, hold different meanings across various disciplines, including computer science, mathematics, social science, and human-artificial intelligence studies. This study defines algorithms from an ethical perspective as the foundation of an information society and focuses on their implications in the context of human recognition. Facial recognition technology, driven by algorithms, has gained widespread use, raising important ethical questions regarding privacy, bias, and accuracy. This dissertation aims to explore the impact of algorithms on machine perception of human individuals and how humans perceive one another …

Efficient Convolutional Neural Networks For Image Classification And Regression, Muhammad Tayyab

Electronic Theses and Dissertations, 2020-

Neural networks have been a topic of research since 1970s and the Convolutional Neural Networks (CNNs) were first shown to work well for hand written digits recognition in 1998. These early networks were however still shallow and contained only a few layers. Moreover these networks were mostly trained on a small amount of data in contrast to the modern CNNs which contain hundreds of convolution layers and are trained on millions of images. However, this recent shift in machine learning comes at a cost. Modern neural networks have extremely large number of parameters and require huge amount of computations for …

Studying Memes During Covid Lockdown As A Lens Through Which To Understand Video-Mediated Communication Interactions, Tatyana Claytor

Electronic Theses and Dissertations, 2020-

The purpose of this study is to analyze image macros about video-mediated communication (VMC) created during the time frame of 2020-2021 when people all over the world started using Zoom and VMC for work and school. It is a unique opportunity to study how users' interactions with themselves and with others were affected at a time when a lot of people started using the technology at the same time. Because the focus is on interactions, I narrowed it down to three topics to analyze the memes: presence, self, and space and place to analyze the memes. I chose memes relating …

Design Of Stormwater Bmps For Surface And Groundwater Protection Based On Site-Scale Soil Properties: Phase I, Kelly Kibler, Lisa Chambers, Melanie Beazley

Florida DOT

Much of Earth’s nutrient cycling takes place in soils. Characteristics of soils control physical, chemical, and biological processes that determine rates of nutrient fluxes, storage, or transformation. As remediation of excess nutrients in stormwater runoff is one function of stormwater Best Management Practices (BMPs), the soil profile constitutes one of the most important factors of BMP design. Variation observed in BMP effectiveness (e.g., why one BMP design works effectively in one place and not another) can often be explained by variations in the soil profile, either through direct means or by a soil’s influence on hydraulics of stormwater flow through …

An Interactional Account Of Empathy In Human-Machine Communication, Shauna Concannon, Ian Roberts, Marcus Tomalin

Human-Machine Communication

Efforts to develop empathetic agents, or systems capable of responding appropriately to emotional content, have increased as the deployment of such systems in socially complex scenarios becomes more commonplace. In the context of human-machine communication (HMC), the ability to create the perception of empathy is achieved in large part through linguistic behavior. However, studies of how language is used to display and respond to emotion in ways deemed empathetic are limited. This article aims to address this gap, demonstrating how an interactional linguistics informed methodological approach can be applied to the study of empathy in HMC. We present an analysis …

Theme Park Visitors Prefer Human-Like Robots In Customer Service Interactions, Ady Milman, Asli D.A. Tasci

Rosen Research Review

Service robots are becoming increasingly popular in many industries and social settings, including education, childcare, elderly therapy centers, and even theme parks. Tourism and hospitality industries are adopting robots enthusiastically and are being closely studied to observe guest engagement and reaction to robotic services. Service robots are becoming increasingly popular in many industries and social settings, including education, childcare, elderly therapy centers, and even theme parks. Tourism and hospitality industries are adopting robots enthusiastically and are being closely studied to observe guest engagement and reaction to robotic services. UCF Rosen College of Hospitality Management researchers, Dr. Ady Milman and Dr. …

Linear Regression With Regularization On The Genetic Architecture Of Maize Flowering Time, Roland Fiagbe

Data Science and Data Mining

Over a century, the maize crop has been one of the most important crop species that is targeted for genetic investigations and experiments. One of the major experiments that have been a topic of interest is crossing inbred lines to produce better offspring through a process called heterosis. Crossing the inbred lines create numerous SNP markers that determine the time to male flowering. This project seeks to explore the SNP markers to select the most relevant ones for predicting time to male flowering using linear regression with regularization methods due to the fact that p > n in our dataset. Various …

A Recommender System For Movie Ratings With Matrix Factorization Algorithm, Amir Alipour Yengejeh

Data Science and Data Mining

Nowadays, a Recommender System is a technology
that aims to predict preferences based on the user’s selections.
These systems are applied in numerous fields, such as movies,
music, news, books, research articles, search queries, social tags,
and various products. In this study, we use this potential tool to
predict the ratings of users’ preferences in MovieLens datasets. To
do so, we applied the matrix factorization algorithm and calculate
the RMSE as our evaluation metric. The results represent that
RMSE estimated for the train and test set are 0.83 and 0.93 that
are very close one another. This results indicates that …

Genome-Wide Association Study Of The Maize Crop By The Lasso Regression Analysis, Amir Alipour Yengejeh

Data Science and Data Mining

The accurate estimation of the male flowering period in Maize crops is key for the prediction crop fertility. The recent scientific investigations has shown that the genetic single nucleotic polymorphism (SNP) can contribute in this regard. The genomewide association study (GWAS) is employed to generate these attributes (SNP). But it caused a high-dimensional data in which 4,981 observations with 7,389 SNP attributes. Hence, in this study, we used the penalized regression approach with the least absolute shrinkage and selection operator (Lasso) to reduce the dataset. In this regard, we set the regularization parameter to 0.21. It resulted in a set …