Engineering Commons^™

Multi-Perspective Analysis For Derivative Financial Product Prediction With Stacked Recurrent Neural Networks, Natural Language Processing And Large Language Model, Ethan Lo

Dissertations, Theses, and Capstone Projects

This study developed a multi-perspective, AI-powered model for predicting E-Mini S&P 500 Index Futures prices, tackling the challenging market dynamics of these derivative financial instruments. Leveraging FinBERT for analysis of Wall Street Journal data alongside technical indicators, trader positioning, and economic factors, my stacked recurrent neural network built with LSTMs and GRUs achieves significantly improved accuracy compared to single sub-models. Furthermore, ChatGPT generation of human-readable analysis reports demonstrates the feasibility of using large language models in financial analysis. This research pioneers the use of stacked RNNs and LLMs for multi-perspective financial analysis, offering a novel blueprint for automated prediction and …

Stand-Up Comedy Visualized, Berna Yenidogan

Dissertations, Theses, and Capstone Projects

Stand-up comedy has become an increasingly popular form of comedy in the recent years and comedians reach audiences beyond the halls they are performing through streaming services, podcasts and social media. While comedic performances are typically judged by how 'funny' they are, which could be proxied by the frequency and intensity of laughs through the performance, comedians also explore untapped social issues and provoke conversation, especially in this age where interaction with artists goes beyond their act. It is easy to see commonalities in the topics addressed in comedians’ work such as relationships, race and politics.This project provides an interactive …

Deception Detection Across Domains, Languages And Modalities, Subhadarshi Panda

Dissertations, Theses, and Capstone Projects

With the increase of deception and misinformation especially in social media, it has become crucial to develop machine learning methods to automatically identify deception. In this dissertation, we identify key challenges underlying text-based deception detection in a cross-domain setting, where we do not have training data in the target domain. We analyze the differences between domains and as a result develop methods to improve cross-domain deception detection. We additionally develop approaches that take advantage of cross-lingual properties to support deception detection across languages. This involves the usage of either multilingual NLP models or translation models. Finally, to better understand multi-modal …

Happiness And Policy Implications: A Sociological View, Sarah M. Kahl

Dissertations, Theses, and Capstone Projects

The World Happiness Report is released every year, ranking each country by who is “happier” and explaining the variables and data they have used. This project attempts to build from that base and create a machine learning algorithm that can predict if a country will be in a “happy” or “could be happier” category. Findings show that taking a broader scope of variables can better help predict happiness. Policy implications are discussed in using both big data and considering social indicators to make better and lasting policies.

A Machine Learning Approach To Predicting The Onset Of Type Ii Diabetes In A Sample Of Pima Indian Women, Meriem Benarbia

Dissertations, Theses, and Capstone Projects

Type II diabetes is a disease that affects how the body regulates and uses sugar (glucose) as a fuel. This chronic disease results in too much sugar circulating in the bloodstream. High blood sugar levels can lead to circulatory, nervous, and immune systems disorders. Machine learning (ML) techniques have proven their strength in diabetes diagnosis. In this paper, we aimed to contribute to the literature on the use of ML methods by examining the value of a number of supervised machine learning algorithms such as logistic regression, decision tree classifiers, random forest classifiers, and support vector classifiers to identify factors …

Efficient Protocols For Multi-Party Computation, Tahereh Jafarikhah

Dissertations, Theses, and Capstone Projects

Secure Multi-Party Computation (MPC) allows a group of parties to compute a join function on their inputs without revealing any information beyond the result of the computation. We demonstrate secure function evaluation protocols for branching programs, where the communication complexity is linear in the size of the inputs, and polynomial in the security parameter. Our result is based on the circular security of the Paillier's encryption scheme. Our work followed the breakthrough results by Boyle et al. [9; 11]. They presented a Homomorphic Secret Sharing scheme which allows the non-interactive computation of Branching Programs over shares of the secret inputs. …

Two Techniques For Automated Logging Statement Evolution, Allan R. Spektor

Theses and Dissertations

This thesis presents and explores two techniques for automated logging statement evolution. The first technique reinvigorates logging statement levels to reduce information overload using degree of interest obtained via software repository mining. The second technique converts legacy method calls to deferred execution to achieve performance gains, eliminating unnecessary evaluation overhead.

Quorum Blockchain Stress Evaluation In Different Environments, Daniel P. Mera

Student Theses

In today’s world, the Blockchain technology is used for different purposes has brought an increment in the development of different Blockchain platforms, services, and utilities for storing data securely and efficiently. Quorum Blockchain, an Ethereum fork created by JPMorgan Chase, has placed itself in one of the widely used, efficient and trustful Blockchain platforms available today. Because of the importance which Quorum is contributing to the world, it is important to test and measure different aspects of the platform, not only to prove how efficient the software can be but as well as to have a clear view on what …

A Purely Defeasible Argumentation Framework, Zimi Li

Dissertations, Theses, and Capstone Projects

Argumentation theory is concerned with the way that intelligent agents discuss whether some statement holds. It is a claim-based theory that is widely used in many areas, such as law, linguistics and computer science. In the past few years, formal argumentation frameworks have been heavily studied and applications have been proposed in fields such as natural language processing, the semantic web and multi-agent systems. Studying argumentation provides results which help in developing tools and applications in these areas. Argumentation is interesting as a logic-based approach to deal with inconsistent information. Arguments are constructed using a process like logical inference, with …

Integrating Multi-Source Weather Data For Deep Learning, Haidar A. Alanbari Mr

Dissertations and Theses

Big Data has been playing a major role in the domain of Deep Learning applications as many companies and institutions continue to find solutions and extract certain trends in fields of climate change, weather forecasting and meteorology. This project extracts weather events data from multiple data sources that are supported by National Centers for Environmental information (NCEI) [1] and Amazon Web Services (AWS) [2]. Data sources include Next-Generation NEXRAD [3] Doppler radar reflectivity, GOES-16 [4] multi-channel satellite imagery and NCEI [1] storm events. Then, it integrates and refines data in proper formats to be fed to the open-source Detectron [5] …

In Search Of Homo Sociologicus, Yunqi Xue

Dissertations, Theses, and Capstone Projects

The subject of this dissertation is to build an epistemic logic system that is able to show the spreading of knowledge and beliefs in a social network that contains multiple subgroups. Epistemic logic is the study of logical systems that express mathematical properties of knowledge and belief. In recent years, there have been increasing number of new epistemic logic systems that are focused on community properties such as knowledge and belief adoption among friends.

We are interested in revisable and actionable social knowledge/belief that leads to a large group action. Instead of centralized coordination, bottom-up approach is our focus. We …

Cyber Security Risks In Public High Schools, Ion Goran

Student Theses

Today, just like other organizations, schools are vulnerable to cyber-attacks. This vulnerability has vividly revealed itself in recent years, with the number of attacks on public schools increasing and taking ever-changing forms. Today, the student’s grades, disciplinary notes, learning diagnoses, phone numbers, addresses, and another identifying information is all at risk of being exposed. Moreover, poor network security poses a dire threat to parents of school children whose personal records contain sensitive or dangerous information. The practical implications of these attacks require intervention or remedy to increase cyber security. Cyberattacks may take place when storage facilities or infected devices are …

Brief Study Of Classification Algorithms In Machine Learning, Ramesh Sankara Subbu

Dissertations and Theses

The purpose of this study is to briefly learn the theory and implementation of three most commonly used Machine Learning algorithms: k-Nearest Neighbors (kNN), Decision Trees and Naïve Bayes. All these algorithms fall under the Classification algorithm category of Unsupervised Machine Learning. This paper is constructed structurally in explaining the working theory behind each algorithm and an implementation of a Machine Learning problem solved by each algorithm. KNN algorithm is designed using Euclidean distance measurement and Decision Trees make use of ID3 algorithm as a basis. We conclude the study by providing an overall picture of its strengths and weaknesses …

Scale Up Bayesian Network Learning, Xiannian Fan

Dissertations, Theses, and Capstone Projects

Bayesian networks are widely used graphical models which represent uncertain relations between the random variables in a domain compactly and intuitively. The first step of applying Bayesian networks to real-word problems is typically building the network structure. Optimal structure learning via score-and-search has become an active research topic in recent years. In this context, a scoring function is used to measure the goodness of fit of a structure to given data, and the goal is to find the structure which optimizes the scoring function. The problem has been viewed as a shortest path problem, and has been shown to be …