Computer Sciences Commons^™

Fast Flow Reconstruction Via Robust Invertible N X N Convolution, Thanh-Dat Truong, Chi Nhan Duong, Minh-Triet Tran, Ngan Le, Khoa Luu

Computer Science and Computer Engineering Faculty Publications and Presentations

Flow-based generative models have recently become one of the most efficient approaches to model data generation. Indeed, they are constructed with a sequence of invertible and tractable transformations. Glow first introduced a simple type of generative flow using an invertible 1x1 convolution. However, the 1x1 convolution suffers from limited flexibility compared to the standard convolutions. In this paper, we propose a novel invertible n x n convolution approach that overcomes the limitations of the invertible 1x1 convolution. In addition, our proposed network is not only tractable and invertible but also uses fewer parameters than standard convolutions. The experiments on CIFAR-10, …

A Comparison Of Word Embedding Techniques For Similarity Analysis, Tyler Gerth

Computer Science and Computer Engineering Undergraduate Honors Theses

There have been a multitude of word embedding techniques developed that allow a computer to process natural language and compare the relationships between different words programmatically. In this paper, similarity analysis, or the testing of words for synonymic relations, is used to compare several of these techniques to see which performs the best. The techniques being compared all utilize the method of creating word vectors, reducing words down into a single vector of numerical values that denote how the word relates to other words that appear around it. In order to get a holistic comparison, multiple analyses were made, with …

Data Forgery Detection In Automatic Generation Control: Exploration Of Automated Parameter Generation And Low-Rate Attacks, Yatish R. Dubasi

Computer Science and Computer Engineering Undergraduate Honors Theses

Automatic Generation Control (AGC) is a key control system utilized in electric power systems. AGC uses frequency and tie-line power flow measurements to determine the Area Control Error (ACE). ACE is then used by the AGC to adjust power generation and maintain an acceptable power system frequency. Attackers might inject false frequency and/or tie-line power flow measurements to mislead AGC into falsely adjusting power generation, which can harm power system operations. Various data forgery detection models are studied in this thesis. First, to make the use of predictive detection models easier for users, we propose a method for automated generation …

Improving Bayesian Graph Convolutional Networks Using Markov Chain Monte Carlo Graph Sampling, Aneesh Komanduri

Computer Science and Computer Engineering Undergraduate Honors Theses

In the modern age of social media and networks, graph representations of real-world phenomena have become incredibly crucial. Often, we are interested in understanding how entities in a graph are interconnected. Graph Neural Networks (GNNs) have proven to be a very useful tool in a variety of graph learning tasks including node classification, link prediction, and edge classification. However, in most of these tasks, the graph data we are working with may be noisy and may contain spurious edges. That is, there is a lot of uncertainty associated with the underlying graph structure. Recent approaches to modeling uncertainty have been …

Applying Emotional Analysis For Automated Content Moderation, John Shelnutt

Computer Science and Computer Engineering Undergraduate Honors Theses

The purpose of this project is to explore the effectiveness of emotional analysis as a means to automatically moderate content or flag content for manual moderation in order to reduce the workload of human moderators in moderating toxic content online. In this context, toxic content is defined as content that features excessive negativity, rudeness, or malice. This often features offensive language or slurs. The work involved in this project included creating a simple website that imitates a social media or forum with a feed of user submitted text posts, implementing an emotional analysis algorithm from a word emotions dataset, designing …

Trunctrimmer: A First Step Towards Automating Standard Bioinformatic Analysis, Z. Gunner Lawless, Dana Dittoe, Dale R. Thompson, Steven C. Ricke

Computer Science and Computer Engineering Undergraduate Honors Theses

Bioinformatic analysis is a time-consuming process for labs performing research on various microbiomes. Researchers use tools like Qiime2 to help standardize the bioinformatic analysis methods, but even large, extensible platforms like Qiime2 have drawbacks due to the attention required by researchers. In this project, we propose to automate additional standard lab bioinformatic procedures by eliminating the existing manual process of determining the trim and truncate locations for paired end 2 sequences. We introduce a new Qiime2 plugin called TruncTrimmer to automate the process that usually requires the researcher to make a decision on where to trim and truncate manually after …

Why Too Much Interaction Between Different Parts Of The Brain Leads To Unhappiness, Ricardo Alvarez, Yamel Hernandez, Vladik Kreinovich

Departmental Technical Reports (CS)

Reasonably recent experiments show that unhappiness is strongly correlated with the excessive interaction between two parts of the brain -- amygdala and hippocampus. At first glance, in situations when outside signals are positive, additional interaction between two parts of the brain that get signals from different sensors should only reinforce the positive feeling. In this paper, we provide a simple explanation of why, instead of the expected reinforcement, we observe unhappiness.

The Generalized Riemann Hypothesis And Applications To Primality Testing, Peter Hall

University Scholar Projects

The Riemann Hypothesis, posed in 1859 by Bernhard Riemann, is about zeros
of the Riemann zeta-function in the complex plane. The zeta-function can be repre-
sented as a sum over positive integers n of terms 1/ns when s is a complex number
with real part greater than 1. It may also be represented in this region as a prod-
uct over the primes called an Euler product. These definitions of the zeta-function
allow us to find other representations that are valid in more of the complex plane,
including a product representation over its zeros. The Riemann Hypothesis says that
all …

Sports Data Analysis – Application Of Sports Data In Athletics, Zhong Zhuang

School of Professional Studies

No abstract provided.

An Analysis Of The Applications Of Technology To Health Care Within The Caribbean And Latin America, Chineme Ezema

School of Professional Studies

This project aims to investigate and document the evolution of health technology applications and uses within the regions of Latin America and the Caribbean. Originally inspired by the Cayman Islands’ thus far successful handling of the Coronavirus pandemic, I was eager to explore how the applications of technology to the field of healthcare have existed within the region currently and over time. With this foundation, I then explored the regions’ trajectory in terms of technological growth and what the future may hold for these countries and communities.

Title Ix: Perceptions And Utilization On U.S. College Campuses, Emma Narkewicz

School of Professional Studies

The research study investigated student perceptions and utilization of Title IX services on U.S. university and college campuses, testing the hypothesis that if students hold negative perceptions of Title IX offices, then they will not report campus sexual violence they experience to Title IX offices. There are currently high rates of sexual violence on college campuses but very low rates of reporting. Current or former U.S. college students aged 18-30 (N = 47) completed a mixed methods anonymous survey composed of Likert scale and open response questions. Participants were asked about prior interactions with Title IX offices and their perceptions …

Broadband In Rhode Island, Deborah Ruggiero

School of Professional Studies

Broadband is the 21st century fiber-optic highway in our technology and information economy. The need for high-speed fiber-optic connectivity is critical for economic development, education, business, e-learning, and telehealth

Happening In Plain Sight: An Evaluation Of Sexual Harassment In Municipal Government Through A Case Study Of Newark, New Jersey, Hoween R. A. Flexer, Caitlin R. Louie

School of Professional Studies

In the city of Newark, New Jersey, Sebrevious Scott, a participant in the New Jersey Reentry program was hired as part-time office assistant in the city's re-entry office. After being transferred to the city’s Parks and Grounds Departments, she started being sexually harassed, inappropriately touched and propositioned by her supervisor, Richard Kirkland. Scott made repeated attempts to report these actions through the appropriate channels. She was met with dismissal, resistance, and later retaliation. While working in this hostile environment she was also pursuing a full-time employment opportunity with the city upon the completion of the reentry program. Unfortunately, this never …

Using Large Pre-Trained Language Models To Track Emotions Of Cancer Patients On Twitter, Will Baker

Computer Science and Computer Engineering Undergraduate Honors Theses

Twitter is a microblogging website where any user can publicly release a message, called a tweet, expressing their feelings about current events or their own lives. This candid, unfiltered feedback is valuable in the spaces of healthcare and public health communications, where it may be difficult for cancer patients to divulge personal information to healthcare teams, and randomly selected patients may decline participation in surveys about their experiences. In this thesis, BERTweet, a state-of-the-art natural language processing (NLP) model, was used to predict sentiment and emotion labels for cancer-related tweets collected in 2019 and 2020. In longitudinal plots, trends in …

Using Deep Learning To Analyze Materials In Medical Images, Carson Molder

Computer Science and Computer Engineering Undergraduate Honors Theses

Modern deep learning architectures have become increasingly popular in medicine, especially for analyzing medical images. In some medical applications, deep learning image analysis models have been more accurate at predicting medical conditions than experts. Deep learning has also been effective for material analysis on photographs. We aim to leverage deep learning to perform material analysis on medical images. Because material datasets for medicine are scarce, we first introduce a texture dataset generation algorithm that automatically samples desired textures from annotated or unannotated medical images. Second, we use a novel Siamese neural network called D-CNN to predict patch similarity and build …

Employee Retention Rate, Lila Sorenson

School of Professional Studies

This paper addresses the following problem regarding low employee retention rates in non-profit mental health organizations, specifically at NFI. The problem at hand is that majority of staff at NFI have an average length of stay of one year or less. The purpose of this paper is to address the current problem and hypothesize factors that could impact the low retention rates. Additionally, the purpose of the paper is to brainstorm potential solutions regarding the low retention rates and suggestions of how to implement the solutions. The documents contained in this paper include data analysis of employee average length of …

Automatically Selecting Follow-Up Questions For Deficient Bug Reports, Mia Mohammad Imran, Agnieszka Ciborowska, Kostadin Damevski

Computer Science Faculty Research & Creative Works

The availability of quality information in bug reports that are created daily by software users is key to rapidly fixing software faults. Improving incomplete or deficient bug reports, which are numerous in many popular and actively developed open-source software projects, can make software maintenance more effective and improve software quality. In this paper, we propose a system that addresses the problem of bug report incompleteness by automatically posing follow-up questions, intended to elicit answers that add value and provide missing information to a bug report. Our system is based on selecting follow-up questions from a large corpus of already posted …

Fuzzy Logic Beyond Traditional "And"-Operations, Vladik Kreinovich, Olga Kosheleva

Departmental Technical Reports (CS)

In the traditional fuzzy logic, we can use "and"-operations (also known as t-norms) to estimate the expert's degree of confidence in a composite statement A&B based on his/her degrees of confidence d(A) and d(B) in the corresponding basic statements A and B. But what if we want to estimate the degree of confidence in A&B&C in situations when, in addition to the degrees of estimate d(A), d(B), and d(C) of the basic statements, we also know the expert's degrees of confidence in the pairs d(A&B), d(A&C), and d(B&C)? Traditional ``and''-operations can provide such an estimate -- but only by ignoring …

Randomized Tax Deadlines Can Help Economy, Julio C. Urenda, Olga Kosheleva

Departmental Technical Reports (CS)

Purpose: While the main purpose of reporting -- e.g., reporting for taxes -- is to gauge the economic state of a company, the fact that reporting is done at pre-determined dates distorts the reporting results. For example, to create a larger impression of their productivity, companies fire temporary workers before the reporting date and re-hire then right away. The purpose of this study is to decide how to avoid such distortion.

Design/methodology/approach: We want to make our solution applicable for all possible reasonable optimality criteria. Thus, we use a general formalism for describing and analyzing all such criteria.

Findings: We …

Why Chomsky Normal Form: A Pedagogical Note, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

To simplify the design of compilers, Noam Chomsky proposed to first transform a description of a programming language -- which is usually given in the form of a context-free grammar -- into a simplified "normal" form. A natural question is: why this specific normal form? In this paper, we provide an answer to this question.

Godel's Proof Of Existence Of God Revisited, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In his unpublished paper, the famous logician Kurt Godel provided arguments in favor of the existence of God. These arguments are presented in a very formal way, which makes them difficult to understand to many interested readers. In this paper, we describe a simplifying modification of Godel's proof which will hopefully make it easier to understand. We also describe, in clear terms, why Godel's arguments are just that -- arguments -- and not a convincing proof.

Five Revolutionary Ideas In The 1950s-70s Science: 90th Birthday Of Revolt Pimenov, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

This year, Revolt Pimenov, a philosophical thinker whose main ideas were in geometry of space-time, would have turned 90. In this essay, we explain how in the 1950s-70s, when he was most productive, his were two of the five natural and important revolutionary scientific ideas -- along with fuzzy logic, constructive mathematics, and scalar-tensor theory of gravitation, ideas that, in our opinions, still have potential to change the world.

How To Teach Advanced Highly Motivated Students: Teaching Strategy Of Iosif Yakovlevich Verebeichik, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

The paper describes and explains the teaching strategy of Iosif Yakovlevich Verebeichik, a successful mathematics teacher at special mathematical high schools -- schools for students interested in and skilled in mathematics. The resulting strategy seems counterintuitive and contrary to all the pedagogical advice. Our explanation is not complete: it worked well for this teacher, but others who tried to follow seemingly the same strategy did not succeed. How he made it work, how can others make it work -- this is still not clear. In the words of Verebeichik himself, while mathematics itself is a science, teaching mathematics is an …

Low-Dose Ct Image Denoising Using Deep Learning Methods, Zeheng Li

Computer Science and Engineering Theses - Archive

Low-dose computed tomography (LDCT) has raised highly attention since the counterpart, full-dose computed tomography (FDCT), brings potential ionizing radiation influence to patients. However, LDCT still suffers from several issues such as relatively higher noise level, which limits its uses in practical applications. To improve LDCT image quality, conventional denoising methods, such as KSVD and BM3D, are first introduced to suppress noise in low-dose images. These methods, however, works under assumptions that are not robust to various data. In this paper, we conduct an extensive research on deep learning based denoising method in LDCT images. We mainly base on Generative-Adversarial Network …

Machine Learning With Graphs, Jianjin Deng

Computer Science and Engineering Dissertations - Archive

In recent years, graph-based machine learning methods have attracted great attention because of their effectiveness and efficiency. Inspired by this trend, this thesis summarizes my research topics on machine learning techniques for the purpose of handling various kinds of problems on large graph data. Generally, this thesis contains two parts. The first part is devoted to graph embedding, which aims to encode graph structure into dense vectors (or embeddings). In particular, we will consider a low rank-matrix factorization based approach to learn embeddings of attributed graphs. By jointly preserving graph structure and attribute-level similarity, our approach can generate embeddings, whose …

End-User Framework For Robot Control, Kaustubh Kedar Rajpathak

Computer Science and Engineering Theses - Archive

This thesis describes in detail a developed end-user framework for a humanrobot collaborative system for common tasks, such as pick and place. The system is designed for semi-automated pick and place tasks as well as manual operation making it flexible for multiple use-case scenarios. The goal of the system is to make the robotic system multi-functional, easy to use with a graphical user interface and should perform common tasks with the help of a human teammate. Integration with object recognition neural networks (YOLOv3) and an RGB-Depth camera help automate pick and place tasks with a wide variety of objects.

Using Sentiment And Emotion Analysis Of News Articles To Analyze The Effects Of Leader’S Statements On Covid-19 Spread, Poojitha Thota

Computer Science and Engineering Theses - Archive

Leaders generally include government officials, politicians, etc. Their statements can highly affect people’s decisions in many ways. Currently, in the pandemic situation, many statements were being passed every hour and day, which showed an impact on the spread of corona virus cases at certain location. So, this paper proposes a supervised model to analyze the variations of COVID-19 data based upon the leader’s statements passed at certain time and location. The proposed methodology consists of sentiment and emotion analysis for the leader’s statements to determine the true intentions of the leader. The leader’s statements are a collection of data obtained …

Learning Hierarchical Traversability Representation For Efficient Multi-Resolution Path Planning, Reza Etemadi Idgahi

Computer Science and Engineering Theses - Archive

Path finding on grid-based obstacle maps is an important and much studied problem with applications in robotics and autonomy. Traditionally, in the AI community, heuristic search methods (e.g. based on Dijkstra and A*, or based on random trees) are used to solve this problem. This search, however incurs significant computational cost that grows with the size and resolution of the obstacle grid and has to be mitigated with effective heuristics in order to allow path finding in real time. In this work we introduce a learning framework using deep neural networks with a stackable convolution kernel to establish a hierarchy …

Structure Aware Human Pose Estimation Using Adversarial Learning, Suryam Sharma

Computer Science and Engineering Theses - Archive

Pose estimation using Deep Neural Networks (DNNs) has shown outstanding performance in recent years, due to the availability of powerful GPUs and larger training datasets. However, there are still many challenges due to the large variability of human body appearances, lighting conditions, complex background, occlusions and postures. Among all these peculiarities, partial occlusions, and overlapping body poses often result in deviated pose predictions. These circumstances can result in wrong and sometimes unrealistic results. The human mind can predict such poses because of the underlying structural awareness of the geometry, of a human body. In this thesis, we discuss an efficient …

Glaze Epochs: Externalizing Material Knowledge Through Tangible Data Records In A Ceramics Studio, Hedieh Moradi

Computer Science and Engineering Theses - Archive

The "material turn" in HCI has placed a renewed focus on informing design from the relationships found in material-based interactions. While several ethnographic works provide insight into how practitioners converse with materials, it is less understood how these conversations transform into a skilled practitioner's mental model. I examine the material practice of glazing that gives ceramics its decorative and functional characteristics and involves fusing mixtures of silica, alumina, and flux onto a clay body through kiln firing. This practice evolves over decades, developing from multiple trajectories, including theoretical foundations, systematic experimentation, and happy accidents. This work describes virtual site visits …