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 …

Resource Management In Mobile Edge Computing For Compute-Intensive Application, Xiaojie Zhang

Dissertations, Theses, and Capstone Projects

With current and future mobile applications (e.g., healthcare, connected vehicles, and smart grids) becoming increasingly compute-intensive for many mission-critical use cases, the energy and computing capacities of embedded mobile devices are proving to be insufficient to handle all in-device computation. To address the energy and computing shortages of mobile devices, mobile edge computing (MEC) has emerged as a major distributed computing paradigm. Compared to traditional cloud-based computing, MEC integrates network control, distributed computing, and storage to customizable, fast, reliable, and secure edge services that are closer to the user and data sites. However, the diversity of applications and a variety …

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 …

Coded Matrix Multiplication, Xiaodi Fan

Dissertations, Theses, and Capstone Projects

Matrix multiplication is a fundamental building block in many machine learning models. As the input matrices may be too large to be multiplied on a single server, it is common to split input matrices into multiple sub-matrices and execute the multiplications on different servers. However, in a distributed infrastructure, it is common to observe stragglers whose performance is significantly lower than other servers at some time. Compared to replicating each task on multiple servers, coded matrix multiplication, i.e., a combination of coding theoretic techniques and distributed matrix multiplication, can tolerate the same number of stragglers with much fewer servers. The …

Bitrdf: Extending Rdf For Bitemporal Data, Di Wu

Dissertations, Theses, and Capstone Projects

The Internet is not only a platform for communication, transactions, and cloud storage, but it is also a large knowledge store where people as well as machines can create, manipulate, infer, and make use of data and knowledge. The Semantic Web was developed for this purpose. It aims to help machines understand the meaning of data and knowledge so that machines can use the data and knowledge in decision making. The Resource Description Framework (RDF) forms the foundation of the Semantic Web which is organized as the Semantic Web Layer Cake. RDF is limited and can only express a binary …

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 …

Representation Learning For Chemical Activity Predictions, Mohamed S. Ayed

Dissertations, Theses, and Capstone Projects

Computational prediction of a phenotypic response upon the chemical perturbation on a biological system plays an important role in drug discovery and many other applications. Chemical fingerprints derived from chemical structures are a widely used feature to build machine learning models. However, the fingerprints ignore the biological context, thus, they suffer from several problems such as the activity cliff and curse of dimensionality. Fundamentally, the chemical modulation of biological activities is a multi-scale process. It is the genome-wide chemical-target interactions that modulate chemical phenotypic responses. Thus, the genome-scale chemical-target interaction profile will more directly correlate with in vitro and in …

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. …

Applying Deep Learning On Financial Sentiment Analysis, Cuiyuan Wang

Dissertations, Theses, and Capstone Projects

Portfolio Investment has always been appealing to investors and researchers. In the past, people tend to use historical trading information of the securities to predict the return or manage the portfolio. Nowadays, the literature has been proved that the market sentiment could predict asset prices. Specifically, it has been shown that the stock market movement is related to financial news and social media events. Thus, it becomes necessary to extract the sentiment of the financial news. We explicitly introduce the application of dictionary methods, traditional machine learning models and deep learning models on text classification. The experiment results show that …

Efficient Private Information Retrieval, Konstantinos Nikolopoulos

Dissertations, Theses, and Capstone Projects

A vast amount of today's Internet users' on line activities consists of queries to various types of databases. From traditional search engines to modern cloud based services, a person's everyday queries over a period of time on various data sources, will leave a trail visible to the query processor, which can reveal significant and possibly sensitive information about her. Private Information Retrieval (PIR) algorithms can be leveraged for providing perfect privacy to users' queries, though at a restrictive computational cost. In this work, we consider today's highly distributed computing environments, as well as certain secure-hardware devices, for optimizing existing PIR …

Fashion Merchandising: An Augmented Reality, Naeha A. Sayed

Dissertations, Theses, and Capstone Projects

There is a continuous and constant transformation in the field of Augmented Reality (AR), both in the Retail, and the Manufacturing sector. It has started to influence everything from fashion runway shows to online shopping. Consumers dynamics have shifted in the fashion industry, the ways becoming more dominant than the old observant ones- the simple buying experience no longer satisfies them. Due to the emergence of the new digital platforms and technological enhancements, the consumers are looking for more- be it a more exciting buying experience or more user interaction or more enhanced products. This technological change starts with the …

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 …

Enhancing 3d Visual Odometry With Single-Camera Stereo Omnidirectional Systems, Carlos A. Jaramillo

Dissertations, Theses, and Capstone Projects

We explore low-cost solutions for efficiently improving the 3D pose estimation problem of a single camera moving in an unfamiliar environment. The visual odometry (VO) task -- as it is called when using computer vision to estimate egomotion -- is of particular interest to mobile robots as well as humans with visual impairments. The payload capacity of small robots like micro-aerial vehicles (drones) requires the use of portable perception equipment, which is constrained by size, weight, energy consumption, and processing power. Using a single camera as the passive sensor for the VO task satisfies these requirements, and it motivates the …

Service Quality Assessment For Cloud-Based Distributed Data Services, Arun Adiththan

Dissertations, Theses, and Capstone Projects

The issue of less-than-100% reliability and trust-worthiness of third-party controlled cloud components (e.g., IaaS and SaaS components from different vendors) may lead to laxity in the QoS guarantees offered by a service-support system S to various applications. An example of S is a replicated data service to handle customer queries with fault-tolerance and performance goals. QoS laxity (i.e., SLA violations) may be inadvertent: say, due to the inability of system designers to model the impact of sub-system behaviors onto a deliverable QoS. Sometimes, QoS laxity may even be intentional: say, to reap revenue-oriented benefits by cheating on resource allocations and/or …

Vision-Based Assistive Indoor Localization, Feng Hu

Dissertations, Theses, and Capstone Projects

An indoor localization system is of significant importance to the visually impaired in their daily lives by helping them localize themselves and further navigate an indoor environment. In this thesis, a vision-based indoor localization solution is proposed and studied with algorithms and their implementations by maximizing the usage of the visual information surrounding the users for an optimal localization from multiple stages. The contributions of the work include the following: (1) Novel combinations of a daily-used smart phone with a low-cost lens (GoPano) are used to provide an economic, portable, and robust indoor localization service for visually impaired people. (2) …

Collaborative Appearance-Based Place Recognition And Improving Place Recognition Using Detection Of Dynamic Objects, Juan Pablo Munoz

Dissertations, Theses, and Capstone Projects

This dissertation makes contributions to the problem of Long-Term Appearance-Based Place Recognition. We present a framework for place recognition in a collaborative scheme and a method to reduce the impact of dynamic objects on place representations. We demonstrate our findings using a state-of-the-art place recognition approach.

We begin in Part I by describing the general problem of place recognition and its importance in applications where accurate localization is crucial. We discuss feature detection and description and also explain the functioning of several place recognition frameworks.

In Part II, we present a novel framework for collaboration between agents from a pure …

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 …

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 …

3d Scene Reconstruction With Micro-Aerial Vehicles And Mobile Devices, Ivan Dryanovski

Dissertations, Theses, and Capstone Projects

Scene reconstruction is the process of building an accurate geometric model of one's environment from sensor data. We explore the problem of real-time, large-scale 3D scene reconstruction in indoor environments using small laser range-finders and low-cost RGB-D (color plus depth) cameras. We focus on computationally-constrained platforms such as micro-aerial vehicles (MAVs) and mobile devices. These platforms present a set of fundamental challenges - estimating the state and trajectory of the device as it moves within its environment and utilizing lightweight, dynamic data structures to hold the representation of the reconstructed scene. The system needs to be computationally and memory-efficient, so …