Computer Sciences Commons^™

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 …

Predict Behavioural Scores In Sleep Apnea Patients From Resting State Near-Infrared Spectroscopy (Fnirs), Amnah Abdelrahman

Computer Science and Engineering Theses - Archive

Sleep disorders are common among adults and children; it has serious consequences on their heath, cognitive development and quality of life. However, some sleep disorders are challenging to diagnose and more challenging to treat. Practitioners often rely on AIH for OSA patients’ classification task, where considering one measurement could raise a risk of oversimplification. Studies show the correlation between sleep disorders, specifically OSA, and mental health. On the other side there are an increasing number of studies suggested evidence of a relationship between the dynamic properties of functional brain structure with the behaviors and cognition attributes. This novel work objective …

Continuous American Sign Language Translation With English Speech Synthesis Using Encoder-Decoder Approach, Preetham Ganesh

Computer Science and Engineering Theses - Archive

Interaction between human beings brings about improvements in science and technology. However, the interaction is limited for people who are deaf or hard-of-hearing, as they can only communicate with others who also know their sign language. With the help of recent technologies, such as Deep Learning, the gap can be bridged by converting Sentence-based Sign Language videos into English language speech. The methods discussed in this thesis are taking a step closer to solve that problem. There are four steps involved in converting ASL (American Sign Language) videos to English language speech. Step 1 is to recognize the phrases performed …

Extend The Sensing Boundary Of Mobile Systems: Security And New Applications, Wenqiang Jin

Computer Science and Engineering Dissertations - Archive

The exploding growth of mobile devices like smartphones and wearables has envisioned various applications, which are developed to collect a wide spectrum of data using on-board device sensors and process them to serve peoples' life in all kinds of scenarios. Noticing the sensing capabilities of current mobile device are limited to its on-board sensors' default functionalities. We study the mechanism designs that extend the mobile device's sensing capabilities to perform new sensing tasks other than its defaults. In this thesis, we investigate the mobile systems' security issues and develop new applications by exploring the device's sensing capabilities. Our contributions are …

Advanced Algorithms For Combinatorial And Sequential Test Generation With Constraints, Feng Duan

Computer Science and Engineering Dissertations - Archive

Combinatorial and sequential testing are software testing strategies that have attracted significant interests from both academic and industrial communities. This dissertation addresses the problem of how to efficiently generate tests for both combinatorial and sequential testing. The dissertation makes two major contributions. For combinatorial testing, we present several optimizations on an existing t-way test generation algorithm called IPOG. These optimizations are designed to reduce the number of tests generated by IPOG. For sequential testing, we develop a notion for expressing commonly used sequencing constraints and present a t-way test sequence generation algorithm that support constraints expressed using notation. We demonstrate …

Deep Learning Methods For Image Restoration And Reconstruction, Zahra Anvari

Computer Science and Engineering Dissertations - Archive

The problem of image reconstruction and restoration refers to recovering the clean images from corrupted ones. Corruption or degradation can occur due to atmospheric conditions such as rain, fog, mist, snow, dust, and air pollution or technical drawbacks of imaging devices such as motion blurriness, compression noise, low-resolution, etc. Image reconstruction algorithms aim at reducing these artifacts and degradation and generate clear images. Scenes captured under bad weather conditions such as rain, fog, mist, and haze suffer from visibility issues thus introduce obstacles for computer vision applications, e.g. object detection, recognition, tracking, and segmentation. In this dissertation, we focus on …

An Intelligent Framework To Assess Embodied Cognition From Physical Activities In Children, Ashwin Ramesh Babu

Computer Science and Engineering Dissertations - Archive

Cognition refers to "The mental actions or process of acquiring knowledge and understanding through thought, experience, and the senses". It encompasses many aspects of intellectual functions and processes such as attention, working memory, response inhibition, motor functions, and more. Humans start to develop these cognitive skills right from their childhood and become fully developed through their adulthood. Impairments in these cognitive functions, specifically in Executive Functions (Higher-order cognitive functions), disrupt their everyday life leading to a troubled childhood and lifelong difficulties in family, employment, and community functioning leading to socio-economic repercussions. Identifying such impairments at the right age (early childhood) …

Towards Efficient Testing And Debugging Of Emerging Software Applications, Huadong Feng

Computer Science and Engineering Dissertations - Archive

Big Data and Smart Contract are among the top emerging technologies tipped to revolutionize the way businesses and organizations are run. Testing and debugging are the most important tasks during the development of any software application. Big data and smart contract applications possess unique characteristics. There is an urgent need to develop efficient techniques for testing and debugging these applications. The first part of the dissertation addresses the problem of how to debug big data applications. When a failure occurs in big data applications, debugging at the system-level can be expensive due to the large amount of data being processed. …

Analysis Of Theoretical And Applied Machine Learning Models For Network Intrusion Detection, Jonah Baron

Masters Theses & Doctoral Dissertations

Network Intrusion Detection System (IDS) devices play a crucial role in the realm of network security. These systems generate alerts for security analysts by performing signature-based and anomaly-based detection on malicious network traffic. However, there are several challenges when configuring and fine-tuning these IDS devices for high accuracy and precision. Machine learning utilizes a variety of algorithms and unique dataset input to generate models for effective classification. These machine learning techniques can be applied to IDS devices to classify and filter anomalous network traffic. This combination of machine learning and network security provides improved automated network defense by developing highly-optimized …