Engineering Commons^™

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 …

Classifying Sidewalk Materials Using Multi-Modal Data, Jiawei Liu

Dissertations and Theses

Navigating safely and independently presents considerable challenges for people who are blind or have low vision (BLV), as it requires a comprehensive understanding of their neighborhood environment. Our user study reveals that materials and objects on sidewalks play a crucial role in navigation tasks. Unfortunately, current methods for assessing sidewalk materials are suboptimal, often relying on labor-intensive and expensive manual assessments that fail to capture the full range of sidewalk features critical to individuals with BLV.

In response to this problem, this master’s thesis investigates deep learning approaches specifically designed for the classification of multi-modal sidewalk materials. The proposed framework …

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.

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 …

Proportional Voting Based Semi-Unsupervised Machine Learning Intrusion Detection System, Yang G. Kim, Ohbong Kwon, John Yoon

Publications and Research

Feature selection of NSL-KDD data set is usually done by finding co-relationships among features, irrespective of target prediction. We aim to determine the relationship between features and target goals to facilitate different target detection goals regardless of the correlated feature selection. The unbalanced data structure in NSL-KDD data can be relaxed by Proportional Representation (PR). However, adopting PR would deny the notion of winner-take-all by attracting a majority of the vote and also provide a fairly proportional share for any grouping of like-minded data. Furthermore, minorities and majorities would get a fair share of power and representation in data structure …

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 …