Computer Sciences Commons^™

Natural Language Processing For Digital Advertising, Yiping Jin

Chulalongkorn University Theses and Dissertations (Chula ETD)

Advertising is not only a marketing or sales activity but a particular form of two-way communication. In this thesis, we propose to apply the two main subtasks of natural language processing (NLP), namely natural language understanding (NLU) and natural language generation (NLG), to digital advertising to enhance the effectiveness of advertising. We apply weakly-supervised text classification to rapidly build text classifiers for contextual advertising (Jin et al. 2022). The method requires a handful of labeled keywords instead of a large corpus of labeled documents and can be easily transferred to new domains. We further evaluate the weakly-supervised models using unsupervised …

Constructing And Validating Feature Models Using Relational, Document, And Graph Databases, Hazim Shatnawi

Electronic Theses and Dissertations

Building a software product line (SPL) is a systematic strategy for reusing software within a family of related systems from some application domain. To define an SPL, a domain analyst must identify the common and variable aspects of a family of systems and capture them for later use in construction of specific products. To do so, Feature-Oriented Domain Analysis (FODA) introduced the feature model as an abstraction to represent the common and variable aspects, using a feature diagram to depict the model visually. However, this abstraction is often difficult for developers to use because most tools rely on specialized theories, …

A Hybrid Decision Tree - Neural Network (Dt-Nn) Model For Predictive Maintenance Applications In Aircraft, Jarrod Carson

Honors Theses

As the Age of Information has evolved over the last several decades, the demand for technology which stores, analyzes, and utilizes data has increased substantially. For countless industries such as the medical, retail, and aircraft industries, such technology is crucial to their operation. This project proposes a hybrid machine learning model consisting of Decision Trees and Neural Networks which is able to classify data of varying volume and variety effectively and efficiently. The model’s structure consists of a decision tree with each node of the tree containing a neural network trained to classify a specific category of the output using …

Impromptune: Symbolic Music Generation With Relative Attention Mechanisms, Connor J. Lennox

Honors Theses and Capstones

By combining attention-based mechanisms that have proved beneficial in the field of natural language processing with domain-specific knowledge about the structure of music, better predictions about piece continuations can be made. The goal of this work is to adapt current natural language processing techniques to a musical domain, and to generate new music by predicting continuations on a sequence of notes. An adaptation of traditional attention mechanisms to create a single prediction from sequential input is used to extend musical pieces by appending new elements repeatedly.

Cross-Model Parameter Estimation In Epidemiology, Julia R. Fitzgibbons

Honors Theses and Capstones

No abstract provided.

Lstm-Based Model For Human Brain Decisions Using Eeg Signals Analysis, Lorela Bano

College of Graduate Studies: Theses & Dissertations

As machine learning models become more sophisticated, and biometric data becomes more readily available through new non-invasive technologies, it becomes increasingly possible to gain access to interesting biometric data that could revolutionize Human Computer Interaction. In this research, we propose a framework to assess and quantify human preference (like or dislike) on presenting various external visual stimuli. Our framework relies on an Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) based model and on electroencephalogram (EEG) signals analysis to predict Like or Dislike preference of human subjects when presented with various marketing images.

Arnold Transformations As Applied To Data Encryption, Haley N. Anderson

College of Graduate Studies: Theses & Dissertations

As our world becomes increasingly digital, data security becomes key. Data must be encrypted such that it can be easily encrypted only by the intended recipient. Arnold Transformations are a useful tool in this because of its unpredictable periodicity. Our goal is to outline a method for choosing an Arnold Transformation that is both secure and easy to implement. We find the necessary and sufficient condition that a key matrix has periodicity. The chosen key matrix has a random structure, and it has a periodicity that is sufficiently high. We apply this method to several image and data string examples …

Computational Intelligent Impact Force Modeling And Monitoring In Hislo Conditions For Maximizing Surface Mining Efficiency, Safety, And Health, Danish Ali

Doctoral Dissertations

"Shovel-truck systems are the most widely employed excavation and material handling systems for surface mining operations. During this process, a high-impact shovel loading operation (HISLO) produces large forces that cause extreme whole body vibrations (WBV) that can severely affect the safety and health of haul truck operators. Previously developed solutions have failed to produce satisfactory results as the vibrations at the truck operator seat still exceed the “Extremely Uncomfortable Limits”. This study was a novel effort in developing deep learning-based solution to the HISLO problem.

This research study developed a rigorous mathematical model and a 3D virtual simulation model to …

Exposure Assessment Of Emerging Contaminants: Rapid Screening And Modeling Of Plant Uptake, Majid Bagheri

Doctoral Dissertations

"With the advent of new chemicals and their increasing uses in every aspect of our life, considerable number of emerging contaminants are introduced to environment yearly. Emerging contaminants in forms of pharmaceuticals, detergents, biosolids, and reclaimed wastewater can cross plant roots and translocate to various parts of the plants. Long-term human exposure to emerging contaminants through food consumption is assumed to be a pathway of interest. Thus, uptake and translocation of emerging contaminants in plants are important for the assessment of health risks associated with human exposure to emerging contaminants. To have a better understanding over fate of emerging contaminants …

Neural Network Supervised And Reinforcement Learning For Neurological, Diagnostic, And Modeling Problems, Donald Wunsch Iii

Masters Theses

“As the medical world becomes increasingly intertwined with the tech sphere, machine learning on medical datasets and mathematical models becomes an attractive application. This research looks at the predictive capabilities of neural networks and other machine learning algorithms, and assesses the validity of several feature selection strategies to reduce the negative effects of high dataset dimensionality. Our results indicate that several feature selection methods can maintain high validation and test accuracy on classification tasks, with neural networks performing best, for both single class and multi-class classification applications. This research also evaluates a proof-of-concept application of a deep-Q-learning network (DQN) to …

Mobile Technology Use By Rural Farmers And Herders, Mohamed Ali

Walden Dissertations and Doctoral Studies

Mobile technology business leaders from many mobile technology companies practice digital apartheid when improving rural farmers' communication and technology infrastructure in developing countries. Mobile technology business leaders who do not effectively plan mobile technology infrastructure deployment to rural farmers and herders are at a high risk of failure. Grounded in adaptive structuration theory of technology, the purpose of this qualitative multiple case study was to explore strategies mobile technology business leaders used to improve the mobile technology infrastructure for rural farmers and herders in the United Republic of Tanzania. The participants comprised three mobile technology business leaders from three different …

Understanding Artificial Intelligence Adoption, Implementation, And Use In Small And Medium Enterprises In India, Dipak Sadashiv Jadhav

Walden Dissertations and Doctoral Studies

This quantitative cross-sectional correlational study involves understanding the impact of various factors on Artificial Intelligence (AI) adoption, implementation, and use in the small and medium enterprises (SME) sector in India. Increased AI use across industry sectors including SMEs makes it essential to analyze decisions involving AI adoption. The main research question and secondary research questions were used to help understand correlations between diffusion of innovation (DOI), the technology, organization, and environment (TOE) framework, and technology adoption model (TAM) and decisions involving AI adoption. I used prevalidated survey instruments and online surveys via the Survey Monkey platform as part of data …

A Gpu Parallelized Application To Study Artificial Spin Systems Emulating The Random Bond Ising Model, Joseph Latessa

Wayne State University Theses

In a collaboration between researchers in the physics and computer science departments at Wayne State University, we have developed and implemented a GPU-accelerated application to study artificial spin systems emulating the Random Bond Ising model. To emulate the Random Bond Ising Model, we generate quadrupolar lattices with both ferroquadrupolar and antiferroquadrupolar ordered phases. We also introduce structural disorder into the system by randomly removing a percentage of individual spins. The presence of structural disorder gives rise to phase transitions that can be observed and mapped using our application. Our algorithm implements a Monte Carlo simulation based on the Metropolis model …

From The Editors, Michael E. Whitman, Herbert J. Mattord, Hossain Shahriar

Journal of Cybersecurity Education, Research and Practice

A commentary from the editors, with an overview of the articles contained in this issue of the Journal.

Sentiment Analysis Of Long-Term Social Data During The Covid-19 Pandemic, Sophanna Ek, Marco Curci, Xiaokun Yang, Beiyu Lin, Pinchao Liu, Hailu Xu

Computer Science Faculty Publications

The COVID-19 pandemic has bringing the “infodemic” in the social media worlds. Various social platforms play a significant role in instantly acquiring the latest updates of the pandemic. Social media such as Twitter and Facebook produce vast amounts of posts related to the virus, vaccines, economics, and politics. In order to figure out how public opinion and sentiments are expressed during the pandemic, this work analyzes the long-term social posts from social media and conducts sentiment analysis on tweets within 12 months. Our findings show the trend topics of long-term social communities during the pandemic and express people’s attitudes towards …

Question Answering By Bert, Suman Karanjit

Student Academic Conference

No abstract provided.

The Introduction Of Big Data In Cloud Computing, Austin Gruenberg

Student Academic Conference

One of the fastest-growing technologies that many people are unaware of is the world of cloud computing. Having started in 2006, it is a relatively new technological advancement in the computer industry. The major branch of cloud computing that I decided to focus on was big data. I decided to research this topic to better understand what its current uses are, to see what the future holds for Big Data and cloud computing and because it is a growing, significant piece of technology being used in our society today. Big data and cloud computing are very important industries and have …

Integrating The Bullet Physics Engine Into Minecraft, Ethan Johnson

Student Academic Conference

During the past fall semester, I started a programming project called Rayon which is designed to be a realistic physics engine implementation that runs alongside the videogame Minecraft. It is a library which Minecraft mod developers can use to implement realistic entity movement into their own mods. Rayon, being entirely written in the Java programming language, currently uses a port of the Bullet physics engine called JBullet which is very outdated and no longer being maintained. To find a more performant solution, I have set out to replace JBullet with an alternative library called LibBulletJME which is designed to interface …

Automatic Subtyping Of Individuals With Primary Progressive Aphasia, Charalambos Themistocleous, Bronte Ficek, Kimberly Webster, Dirk B. Den Ouden, Argye Hillis, Kyrana Tsapkini

Communication Sciences and Disorders Faculty Articles and Research

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Applied Machine Learning In Extrusion-Based Bioprinting, Shuyu Tian

Theses and Dissertations

Optimization of extrusion-based bioprinting (EBB) parameters have been systematically conducted through experimentation. However, the process is time and resource-intensive and not easily translatable across different laboratories. A machine learning (ML) approach to EBB parameter optimization can accelerate this process for laboratories across the field through training using data collected from published literature. In this work, regression-based and classification-based ML models were investigated for their abilities to predict printing outcomes of cell viability and filament diameter for cell-containing alginate and gelatin composite hydrogels. Regression-based models were investigated for their ability to predict suitable extrusion pressure given desired cell viability when keeping …

Xtreme-Noc: Extreme Gradient Boosting Based Latency Model For Network-On-Chip Architectures, Ilma Sheriff

All Graduate Theses, Dissertations, and Other Capstone Projects

Multiprocessor System-on-Chip (MPSoC) integrating heterogeneous processing elements (CPU, GPU, Accelerators, memory, I/O modules ,etc.) are the de-facto design choice to meet the ever-increasing performance/Watt requirements from modern computing machines. Although at consumer level the number of processing elements (PE) are limited to 8-16, for high end servers, the number of PEs can scale up to hundreds. A Network-on-Chip (NoC) is a microscale network that facilitates the packetized communication among the PEs in such complex computational systems. Due to the heterogeneous integration of the cores, execution of diverse (serial and parallel) applications on the PEs, application mapping strategies, and many other …

Reviving Mozart With Intelligence Duplication, Jacob E. Galajda

Honors Undergraduate Theses

Deep learning has been applied to many problems that are too complex to solve through an algorithm. Most of these problems have not required the specific expertise of a certain individual or group; most applied networks learn information that is shared across humans intuitively. Deep learning has encountered very few problems that would require the expertise of a certain individual or group to solve, and there has yet to be a defined class of networks capable of achieving this. Such networks could duplicate the intelligence of a person relative to a specific task, such as their writing style or music …

Examining Everyday Literacies: An Autoethnographic Analysis Of Mundane Textualities, Kyle J. Mauter

Honors Undergraduate Theses

As a way of extending perspectives of writing and learning, this thesis explores everyday literacy activities and their role in function in shaping people's activities. Taking up an autoethnographic approach to studying the mundane literacies of everyday life, this thesis offers a fine-grained analysis of the processes and practices involved in two specific literate activities I have engaged in over the two years: creating a mixtape for a friend and streaming my participation in online video games. As key findings, the analysis of these everyday literate activities suggests that the interactions between people and social contexts figure prominently in the …

Recapture: A Virtual Reality Interactive Narrative Experience Concerning Perspectives And Self-Reflection, Indira Avendano

Honors Undergraduate Theses

This project presents a virtual reality (VR) Interactive Narrative aiming to leave users reflecting on the perspectives one chooses to view life through. The narrative is driven by interactions designed using the concept of procedural rhetoric, which explores how rules and mechanics in games can persuade people about an idea, and Shin's cognitive model, which presents a dynamic view of immersion in VR. The persuasive nature of procedural rhetoric in combination with immersion techniques such as tangible interfaces and first-person elements of VR can effectively work together to immerse users into a compelling narrative experience with an intended emotional response …

A Deep Learning Approach For Learning Human Gait Signature, Alexander Matasa

Electronic Theses and Dissertations, 2020-2023

With advancements in biometric securities, focus has increased on utilizing gait as a means of recognition. Gait describes the unique walking pattern present in humans and has shown promising results in person re-identification tasks. Unlike other biometric features, gait is unique in that it is a subconscious behavior minimizing the risk of purposeful obfuscation. In this research, we first cover supervised approaches showing that current methods fail to learn a unique signature that describes the motion of a subject. Rather they extract frame-based feature information which is then aggregated. While these methods have shown to be effective, they do not …

Secure And Trustworthy Hardware And Machine Learning Systems For Internet Of Things, Shayan Taheri

Electronic Theses and Dissertations, 2020-2023

The advancements on the Internet have enabled connecting more devices into this technology every day. This great connectivity has led to the introduction of the internet of things (IoTs) that is a great bed for engagement of all new technologies for computing devices and systems. Nowadays, the IoT devices and systems have applications in many sensitive areas including military systems. These challenges target hardware and software elements of IoT devices and systems. Integration of hardware and software elements leads to hardware systems and software systems in the IoT platforms, respectively. A recent trend for the hardware systems is making them …

Towards Improving The Robustness Of Neural Abstractive Summarization, Kaiqiang Song

Electronic Theses and Dissertations, 2020-2023

Recent deep learning and sequence-to-sequence learning technology have produced impressive results on automatic summarization. However, the models have limited insights on the underlying language and it remains challenging for system-generated summaries to be truthful to the original input or cover the most important information. This is especially the case for generating abstractive summaries using neural models. My work aims for a flexible and controllable summarization system that can be adapted to cater to different scenarios. It is designed to incorporate linguistic structure information into deep neural networks, have the capability to produce abstracts by re-using a varying amount of source …

Network Function Virtualization Technology Adoption Strategies, Abdlrazaq Ayodeji Adeyi Shittu

Walden Dissertations and Doctoral Studies

Network function virtualization (NFV) is a novel system adopted by service providers and organizations, which has become a critical organizational success factor. Chief information officers (CIOs) aim to adopt NFV to consolidate and optimize network processes unavailable in conventional methods. Grounded in the diffusion of innovation theory (DOI), the purpose of this multiple case research study was to explore strategies chief information officers utilized to adopt NFV technology. Participants include two CIOs, one chief security information officer (CSIO), one chief technical officer (CTO), and two senior information technology (IT) executives. Data were collected through semi-structured telephone interviews and eight organizational …

An Acceptable Cloud Computing Model For Public Sectors, Eswar Kumar Devarakonda

Walden Dissertations and Doctoral Studies

Cloud computing enables information technology (IT) leaders to shift from passive business support to active value creators. However, social economic-communication barriers inhibit individual users from strategic use of the cloud. Grounded in the theory of technology acceptance, the purpose of this multiple case study was to explore strategies IT leaders in public sector organizations implement to utilize cloud computing. The participants included nine IT leaders from public sector organizations in Texas, USA. Data were collected using semi-structured interviews, field notes, and publicly available artifacts documents. Data were analyzed using thematic analysis: five themes emerged (a) user-centric and data-driven cloud model; …

User Awareness And Knowledge Of Cybersecurity And The Impact Of Training In The Commonwealth Of Dominica, Jermaine Jewel Jean-Pierre

Walden Dissertations and Doctoral Studies

The frequency of cyberattacks against governments has increased at an alarming rate and the lack of user awareness and knowledge of cybersecurity has been considered a contributing factor to the increase in cyberattacks and cyberthreats. The purpose of this quantitative experimental study was to explore the role and effectiveness of employee training focused on user awareness of cyberattacks and cybersecurity, with the intent to close the gap in understanding about the level of awareness of cybersecurity within the public sector of the Commonwealth of Dominica. The theoretical framework was Bandura’s social cognitive theory, following the idea that learning occurs in …