Physical Sciences and Mathematics Commons^™

Brain-Inspired Spatio-Temporal Learning With Application To Robotics, Thiago André Ferreira Medeiros

USF Tampa Graduate Theses and Dissertations

The human brain still has many mysteries and one of them is how it encodes information. The following study intends to unravel at least one such mechanism. For this it will be demonstrated how a set of specialized neurons may use spatial and temporal information to encode information. These neurons, called Place Cells, become active when the animal enters a place in the environment, allowing it to build a cognitive map of the environment. In a recent paper by Scleidorovich et al. in 2022, it was demonstrated that it was possible to differentiate between two sequences of activations of a …

All Quiet On The Digital Front: The Unseen Psychological Impacts On Cybersecurity First Responders, Tammie R. Hollis

USF Tampa Graduate Theses and Dissertations

Driven by the increasing frequency of cyberattacks and the existing talent gap between industry needs and skilled professionals, this research study focused on the crucial human element in the domain of cybersecurity incident response. The objective of this dissertation was to offer a meaningful exploration of the lived experiences encountered by cybersecurity incident responders and an assessment of the subsequent impacts on their well-being. Additionally, this study sought to draw comparisons between the experiences of cybersecurity incident responders and their counterparts in traditional emergency response roles. Semi-structured interviews were conducted with a cohort of 22 individuals with first-hand experience working …

Refining The Machine Learning Pipeline For Us-Based Public Transit Systems, Jennifer Adorno

USF Tampa Graduate Theses and Dissertations

According to the Population Division of the United Nations, in the United States, almost 90% of the population will live in urban areas by the year 2050. As the population in a given area increases, higher traffic congestion follows due to an increase of vehicles in the road. A possible way to alleviate congestion could be with widespread use of public transit. However, according to the US Census Bureau, the percentage of individuals commuting through public transportation has been decreasing steadily over time, and the American Community Survey reports that during 2019, only around five percent of the US population …

Human Vs Machine: Hyper-Realistic Avatars And Their Efficacy As A Communication Channel, Jill S. Schiefelbein

USF Tampa Graduate Theses and Dissertations

Hyper-realistic avatars (HRAs), a form of synthetic media, are custom-created digital embodiments of a human, created by capturing and combining that person’s video and vocal likeness. This is the first known study of the efficacy of videos delivered by hyper-realistic avatars as a communication channel in comparison to videos delivered by their human counterparts. An experiment testing how information retention, engagement, and trust vary between viewers of videos delivered by a real human, videos delivered by the HRA representing that same human, and videos delivered by the HRA that discloses to viewers that it is a hyper-realistic avatar is presented. …

Deciphering Trends And Tactics: Data-Driven Techniques For Forecasting Information Spread And Detecting Coordinated Campaigns In Social Media, Kin Wai Ng Lugo

USF Tampa Graduate Theses and Dissertations

The main objective of this dissertation is to develop models that predict and investigate the spread of information in social media over time. In this context, we consider topics of discussions as the information that spreads. Thus, we are interested in forecasting the number of messages per day in a future interval of time. We take a data-driven approach, in which we compare our results with real datasets from a multitude of socio-political contexts and from multiple social media platforms, specifically, Twitter and YouTube.

We identified a number of challenges related to forecasting social media time series per topic. First, …

Deep Learning-Based Automatic Stereology For High- And Low-Magnification Images, Hunter Morera

USF Tampa Graduate Theses and Dissertations

Quantification of the true number of stained cells in specific brain regions is an important metric in many fields of biomedical research involving cell degeneration, cytotoxicology, cellular inflammation, and drug development for a wide range of neurological disorders and mental illnesses. Unbiased stereology is the current state-of-the-art method for collecting the cell count data from tissue sections. These studies require trained experts to manually focus through a z-stack of microscopy images and count (click) on a hundred or more cells per case, making this approach time consuming (~1 hour per case) and prone to human error (i.e., inter-rater variability). Thus, …

Evaluating Methods For Improving Dnn Robustness Against Adversarial Attacks, Laureano Griffin

USF Tampa Graduate Theses and Dissertations

Deep learning has become more widespread as advances in the field continue. As aresult, making sure deep learning is safe has become a priority. A seemingly normal image with intentional pixel changes can cause a well-trained model to misclassify the image with high confidence. Those kinds of images are called adversarial attacks. Adversarial training has been developed to defend against adversarial attacks. This thesis evaluates different adversarial training methods against a variety of adversarial attacks. The key metrics for evaluation are classification accuracy and training time. This thesis also experiments with an improvement on an existing adversarial training method, the …

A Psychometric Analysis Of Natural Language Inference Using Transformer Language Models, Antonio Laverghetta Jr.

USF Tampa Graduate Theses and Dissertations

Large language models (LLMs) are poised to transform both academia and industry. But the excitement around these generative AIs has also been met with concern for the true extent of their capabilities. This dissertation helps to address these questions by examining the capabilities of LLMs using the tools of psychometrics. We focus on analyzing the capabilities of LLMs on the task of natural language inference (NLI), a foundational benchmark often used to evaluate new models. We demonstrate that LLMs can reliably predict the psychometric properties of NLI items were those items administered to humans. Through a series of experiments, we …

Deep Learning Enhancement And Privacy-Preserving Deep Learning: A Data-Centric Approach, Hung S. Nguyen

USF Tampa Graduate Theses and Dissertations

Deep Learning and its applications have become attractive to a lot of research recentlybecause of its capability to capture important information from large amounts of data. While most of the work focuses on finding the best model parameters, improving machine learning performance from data perspective still needs more attention. In this work, we propose techniques to enhance the robustness of deep learning classification by tackling data issue. Specifically, our data processing proposals aim to alleviate the impacts of class-imbalanced data and non- IID data in deep learning classification and federated learning scenarios. In addition, data pre-processing strategies such that dimensionality …

Insect Classification And Explainability From Image Data Via Deep Learning Techniques, Tanvir Hossain Bhuiyan

USF Tampa Graduate Theses and Dissertations

Since the dawn of the Industrial Revolution, humanity has always tried to make labor more efficient and automated, and this trend is only continuing in the modern digital age. With the advent of artificial intelligence (AI) techniques in the latter part of the 20th century, the speed and scale with which AI has been leveraged to automate tasks defy human imagination. Many people deeply entrenched in the technology field are genuinely intrigued and concerned about how AI may change many of the ways in which humans have been living for millennia. Only time will provide the answers. This dissertation is …

Automated Approaches To Enable Innovative Civic Applications From Citizen Generated Imagery, Hye Seon Yi

USF Tampa Graduate Theses and Dissertations

Smart governance is an area, that is increasingly becoming important, not only in advanced countries, but all across the globe. Thanks to global scale network connectivity, permeance of smart-devices of various form-factors, and overall improvement in digital literary, we are now seeing "smartness" everywhere, or if not, the general public is expecting the same. Ultimately, the goal of smart governance is to facilitate state-of-the-art technologies to improve citizens’ lives. With the ubiquity of smart phone technologies today, citizens more readily participate in collaboration with public officials for improved quality of life and their communities. By utilizing optimal tools, public officials …

What Senior U.S. Leaders Say We Should Know About Cyber, Dr. Joseph H. Schafer

Military Cyber Affairs

On April 6, 2023, the Atlantic Council’s Cyber Statecraft Initiative hosted a panel discussion on the new National Cybersecurity Strategy. The panel featured four senior officials from the Office of the National Cyber Director (ONCD), the Department of State (DoS), the Department of Justice (DoJ), and the Department of Homeland Security (DHS). The author attended and asked each official to identify the most important elements that policymakers and strategists must understand about cyber. This article highlights historical and recent struggles to express cyber policy, the responses from these officials, and the author’s ongoing research to improve national security cyber policy.

Combining Frameworks To Improve Military Health System Quality And Cybersecurity, Dr. Maureen L. Schafer, Dr. Joseph H. Schafer

Military Cyber Affairs

Existing conceptual frameworks and commercially available technology could be considered to rapidly operationalize the use of Quality Measures (QM) within military health systems (Costantino et al. 2020). Purchased healthcare as well as digital healthcare services have paved the way for data collection from multiple information systems thus offering stakeholders actionable intelligence to both guide and measure healthcare outcomes. However, the collection of data secondary to Smart Devices, disparate information systems, cloud services, and the Internet of Medical Things (IOMT) is a complication for security experts that also affect clients, stakeholders, organizations, and businesses delivering patient care. We have combined three …

Deep Reinforcement Learning Based Optimization Techniques For Energy And Socioeconomic Systems, Salman Sadiq Shuvo

USF Tampa Graduate Theses and Dissertations

Optimization, which refers to making the best or most out of a system, is critical for an organization's strategic planning. Optimization theories and techniques aim to find the optimal solution that maximizes/minimizes the values of an objective function within a set of constraints. Deep Reinforcement Learning (DRL) is a popular Machine Learning technique for optimization and resource allocation tasks. Unlike the supervised ML that trains on labeled data, DRL techniques require a simulated environment to capture the stochasticity of real-world complex systems. This uncertainty in future transitions makes the planning authorities doubt real-world implementation success. Furthermore, the DRL methods have …

Graph Analysis On Social Networks, Shen Lu

USF Tampa Graduate Theses and Dissertations

With the development of transportation network, social network, and communication network, there are many applications in streaming data. For example, traffic congestion happens between the origin and destination of daily trips. Traffic analysis can help plan the trips so that traffic congestion can be avoided. Social network and communication network represent the behaviors of the entire population. People build connections based on their hobbies, daily activities, photos, videos, simple messages, and even anonymous web surfing. All of these can be turned into commercial use, such as product marketing, business network building, and technology trending. Data science is about how to …

Artificial Intelligence, Basic Skills, And Quantitative Literacy, Gizem Karaali

Numeracy

The introduction in November 2022 of ChatGPT, a freely available language-based artificial intelligence, has led to concerns among some educators about the feasibility and benefits of teaching basic writing and critical thinking skills to students in the context of easily accessed, AI-based cheating mechanisms. As of now, ChatGPT can write pretty convincing student-level prose, but it is still not very good at answering quantitatively rich questions. Therefore, for the time being, the preceding concerns may not be shared by a large portion of the numeracy education community. However, as Google and WolframAlpha are definitely capable of answering standard and some …

A Multiple Input Multiple Output Framework For The Automatic Optical Fractionator-Based Cell Counting In Z-Stacks Using Deep Learning, Palak Dave

USF Tampa Graduate Theses and Dissertations

Quantifying cells in a defined region of biological tissue is critical for many clinical and preclinical studies, especially in pathology, toxicology, cancer, and behavior. Unbiased stereology is the state-of-art method for quantification of the total number and other morphometric parameters of stained objects in a defined region of biological tissue. As part of a program to develop accurate, precise, and more efficient automatic approaches for quantifying morphometric changes in biological tissue, our group has shown that both deep learning-based and hand-crafted algorithms can estimate the total number of histologically stained cells at their maximal profile of focus in extended depth …

Farmer Adoption Of Advanced Technology In Agribusiness, Justin W. Belcher

USF Tampa Graduate Theses and Dissertations

Normally, family-owned farms are slow to adopt advanced technologies though these technologies can provide several benefits to the farm and have the potential to increase farm production volumes to help meet future population growth. The goal of this study was to document the factors that influence the adoption decision of advanced technologies by family-owned farms and what strategies can be used to motivate adoption. Case study research was conducted to gather data in a more structured way from family-owned farms typically excluded from past research for the purpose of comparing similarities across similar and dissimilar farms. For generalizing similarities, a …

Designing A Messaging Strategy To Improve Information Security Policy Compliance, Federico Giovannetti

USF Tampa Graduate Theses and Dissertations

Lack of employee compliance with information security policies is a key factor driving security incidents. Information security practitioners struggle to enforce policy compliance while employees try to curtail safeguards in favor of expediency and other perceived business goals. Several studies have shown individual and organizational factors influencing this type of employee behavior. However, few have recommended management-level interventions that can be used as a solution framework by information security practitioners.

This research utilized the Design Science Research (DSR) methodology to develop a management-level intervention based on a messaging strategy that aims to help information security practitioners improve the information security …

Development Of An Automated Platform For Sensing And Differentiating Vapor-Phase Btex Constituents, Jonathan Samuelson

USF Tampa Graduate Theses and Dissertations

Light aromatic hydrocarbons are an inevitable byproduct of fossil fuel extraction, refinement, distribution, and use. The four lightest and most prevalent of these are benzene, toluene, ethylbenzene, and xylene, which are known collectively as BTEX. In spite of their chemical similarity these species have markedly different effects on human health and substantially different concentrations are permitted by OSHA in workplaces and by the EPA in ambient air and groundwater. Real-time detection, identification, and quantification of these species is therefore of great importance wherever they see industrial use.This work represents the continuation and advancement of a line of research in which …

Task Progress Assessment And Monitoring Using Self-Supervised Learning, Sainath Reddy Bobbala

USF Tampa Graduate Theses and Dissertations

Robotic manipulation for cooking requires a thorough understanding of the cooking environment. The robot must understand the cooking objects and their states at each intermediate level as the process continues. To understand these states, we need frame-level annotations. To overcome this frame-level dependency, we introduce a self-supervised learning method to obtain the frame-level state representation with ”temporal video alignment” and ”contrastive learning.”In this work, we use self-supervised learning to train a model using multiple videos of the same action being performed in various settings. This model can extract frame-level embedding space and align videos via simple distance-based matching. We show …

Generative Spatio-Temporal And Multimodal Analysis Of Neonatal Pain, Md Sirajus Salekin

USF Tampa Graduate Theses and Dissertations

Neonates can not express their pain like an adult person. Due to the lacking of proper muscle growth and inability to express non-verbally, it is difficult to understand their emotional status. In addition, if the neonates are under any treatment or left monitored after any major surgeries (post-operative), it is more difficult to understand their pain due to the side effect of medications and the caring system (i.e. intubated, masked face, covered body with blanket, etc.). In a clinical environment, usually, bedside nurses routinely observe the neonate and measure the pain status following any standard clinical pain scale. But current …

A Scientometric Review Of Artificial Intelligence In Tourism (2000-2021), Rujun Wang, Yu Mu, Ying Huang

University of South Florida (USF) M3 Publishing

With the increase in the combination of artificial intelligence and the service industry, many applications of artificial intelligence in tourism have been gradually spawned. However, most of the existing research focuses on the algorithms and models of artificial intelligence, and few scholars have systematically reviewed the intersection of tourism and artificial intelligence, this study is based on scientometric, reviewing and sorting out 2689 relevant literature published in 2000-2021, and achieving the three purposes of status carding, hot spot snooping and trend prediction. First, through the participating locations, institutions and authors of collaborative networks, the main sources of AI-related research in …

Preventing Variadic Function Attacks Through Argument Width Counting, Brennan Ward

USF Tampa Graduate Theses and Dissertations

Format String attacks, first noted in June 2000 [1], are a type of attack in which anadversary has control of the string argument (the format string) passed to a string format function (such as printf). Such control allows the attacker to read and write arbitrary program memory. To prevent these attacks, various methodologies have been proposed, each with their own costs and benefits. I present a novel solution to this problem through argument width counting, ensuring that such format functions cannot access stack memory beyond the space where arguments were placed. Additionally, I show how this approach can be expanded …

Social Media Time Series Forecasting And User-Level Activity Prediction With Gradient Boosting, Deep Learning, And Data Augmentation, Fred Mubang

USF Tampa Graduate Theses and Dissertations

In the overall history of technological innovations, social media has only existed for a brief time, however its influence is undeniable. Researchers have found that it can be used to influence elections, spread health misinformation, and aid with financial pump-and-dump schemes. Keeping all this in mind, it is clear that more research is needed to predict the spread of information on social media in order to combat its malicious use.

To that end, in this dissertation, we explore the use of Machine Learning algorithms to perform time series forecasting and user-level activity prediction in social media. We address the different …

Adaptive Multi-Scale Place Cell Representations And Replay For Spatial Navigation And Learning In Autonomous Robots, Pablo Scleidorovich

USF Tampa Graduate Theses and Dissertations

Place cells are one of the most widely studied neurons thought to play a vital role in spatial cognition. Extensive studies show that their activity in the rodent hippocampus is highly correlated with the animal’s spatial location, forming “place fields” of smaller sizes near the dorsal pole and larger sizes near the ventral pole. Despite advances, it is yet unclear how this multi-scale representation enables navigation in complex environments.

In this dissertation, we analyze the place cell representation from a computational point of view, evaluating how multi-scale place fields impact navigation in large and cluttered environments. The objectives are to …

Towards High Performing And Reliable Deep Convolutional Neural Network Models For Typically Limited Medical Imaging Datasets, Kaoutar Ben Ahmed

USF Tampa Graduate Theses and Dissertations

Artificial Intelligence (AI) is “The science and engineering of making intelligent machines, especially intelligent computer programs”. Artificial Intelligence has been applied in a wide range of fields including automobiles, space, robotics, and healthcare.

According to recent reports, AI will have a huge impact on increasing the world economy by 2030 and it's expected that the greatest impact will be in the field of healthcare. The global market size of AI in healthcare was estimated at USD 10.4 billion in 2021 and is expected to grow at a high rate from 2022 to 2030 (CAGR of 38.4%). Applications of AI in …

Pandemic Time And Tourism In Oecd Countries: Artificial Intelligence And Digital Platforms, Alfonso Marino, Paolo Pariso, Michele Picariello

University of South Florida (USF) M3 Publishing

Introduction underline the three phases related to sector crisis, Background, starting from literature highlight the importance of what are the main actions implemented in 38 Member States. Methodology, with SPAD, elaborates a qualitative and quantitative set of policy responses that are displayed in Results. Discussions highlight the different approaches within the OECD area, but also the absence of a common strategy to exit to the sector crisis. The conclusion emphasizes that crisis response policies still need to be built and developed in the OECD area, even though initial responses showed strong responses in individual Member States that did not address …

An Enterprise Risk Management Framework To Design Pro-Ethical Ai Solutions, Quintin P. Mcgrath

USF Tampa Graduate Theses and Dissertations

The effective use of Artificial Intelligence (AI) has immediate business benefits for an organization and its stakeholders through efficiency and quality gains, and the potential to explore and implement new business models. However, there are risks of unintended ethical consequences. Enterprise Risk Management (ERM) focuses on managing risk while maximizing business value from exploiting opportunities. Using applied ethics as a basis and the perspective that ethics includes both enabling human flourishing and not violating accepted norms, I argue that greater business value is achieved when an organization simultaneously targets the maximization of benefits and the minimization of harms for the …

Interdisciplinary Communication By Plausible Analogies: The Case Of Buddhism And Artificial Intelligence, Michael Cooper

USF Tampa Graduate Theses and Dissertations

Communicating interdisciplinary information is difficult, even when two fields are ostensibly discussing the same topic. In this work, I’ll discuss the capacity for analogical reasoning to provide a framework for developing novel judgments utilizing similarities in separate domains. I argue that analogies are best modeled after Paul Bartha’s By Parallel Reasoning, and that they can be used to create a Toulmin-style warrant that expresses a generalization. I argue that these comparisons provide insights into interdisciplinary research. In order to demonstrate this concept, I will demonstrate that fruitful comparisons can be made between Buddhism and Artificial Intelligence research.