Physical Sciences and Mathematics Commons^™

Introduction To Data Science Lti 110, Joanna Burkhardt

Library Impact Statements

No abstract provided.

Teaching Applications And Implications Of Blockchain Via Project-Based Learning: A Case Study, Kevin Mentzer, Mark Frydenberg, David J. Yates

Information Systems and Analytics Department Faculty Journal Articles

This paper presents student projects analyzing or using blockchain technologies, created by students enrolled in courses dedicated to teaching blockchain, at two different universities during the 2018-2019 academic year. Students explored perceptions related to storing private healthcare information on a blockchain, managing the security of Internet of Things devices, maintaining public governmental records, and creating smart contracts. The course designs, which were centered around project-based learning, include self-regulated learning and peer feedback as ways to improve student learning. Students either wrote a research paper or worked in teams on a programming project to build and deploy a blockchain-based application using …

Chicago Alliance For Equity In Computer Science, Steven Mcgee, Lucia Dettori, Ronald I. Greenberg, Andrew M. Rasmussen, Dale F. Reed, Don Yanek

Computer Science: Faculty Publications and Other Works

In 2016, CPS enacted a high school computer science graduation requirement as a means to broaden participation in computer science. ECS is the primary course that supports enactment of this policy. With limited numbers of certified computer science teachers, CPS relied on teachers from a variety of disciplines to teach ECS. The ECS professional development program is designed to prepare teachers from all backgrounds to support student success in ECS. This study examines how the profile of ECS teachers changed and the impact of that change on teachers' experiences with ECS professional development.

New Methods For Deep Learning Based Real-Valued Inter-Residue Distance Prediction, Jacob Barger

Theses

Background: Much of the recent success in protein structure prediction has been a result of accurate protein contact prediction--a binary classification problem. Dozens of methods, built from various types of machine learning and deep learning algorithms, have been published over the last two decades for predicting contacts. Recently, many groups, including Google DeepMind, have demonstrated that reformulating the problem as a multi-class classification problem is a more promising direction to pursue. As an alternative approach, we recently proposed real-valued distance predictions, formulating the problem as a regression problem. The nuances of protein 3D structures make this formulation appropriate, allowing predictions …

Analyzing Yankees And Red Sox Sentiment Over The Course Of A Season, Connor Koch

Honors Projects in Data Science

This paper investigates data collected on twitter which references the Yankees or Red Sox during the 2020 Major League Baseball (MLB) season. The objective is to analyze the sentiment of tweets referencing the Yankees and Red Sox over the course of the season. In addition, an investigation of the networks within the data and the topics that were prevalent will be conducted. The 2020 MLB season was started late because of the COVID-19 pandemic and was a season like no other. The expectation of a dataset revolving around baseball is that the topics discussed would be about baseball. The findings …

Provable Security Of Symmetric-Key Cryptographic Schemes., Ashwin Jha Dr.

Doctoral Theses

In this thesis, we provide quantitative and/or qualitative improvements in the provable security of several symmetric-key schemes, encompassing major information security goals, viz. data authentication, encryption, and authenticated encryption.AUTHENTICATION AND INTEGRITY: Among authentication schemes, we analyze the CBC-MAC family and counter-based MACs (XMACC, XMACR, PCS, LightMAC etc.), referred as the XMAC family. First, we revisit the security proofs for CBC-MAC and EMAC, and identify a critical flaw in the state-of-the-art results. We revise the security proofs and obtain significantly better bounds in case of EMAC, ECBC and FCBC. Second, we study the security of CBC-MAC family, when the underlying primitive …

Research In Data Science Dsp 599, Harrison Dekker

Library Impact Statements

No abstract provided.

Data Science Internship Dsp 477, Harrison Dekker

Library Impact Statements

No abstract provided.

Asymptotically-Optimal Topological Nearest-Neighbor Filtering, Read Sandström, Jory Denny, Nancy M. Amato

Department of Math & Statistics Faculty Publications

Nearest-neighbor finding is a major bottleneck for sampling-based motion planning algorithms. The cost of finding nearest neighbors grows with the size of the roadmap, leading to a significant computational bottleneck for problems which require many configurations to find a solution. In this work, we develop a method of mapping configurations of a jointed robot to neighborhoods in the workspace that supports fast search for configurations in nearby neighborhoods. This expedites nearest-neighbor search by locating a small set of the most likely candidates for connecting to the query with a local plan. We show that this filtering technique can preserve asymptotically-optimal …

Semantic-Driven Unsupervised Image-To-Image Translation For Distinct Image Domains, Wesley Ackerman

Theses and Dissertations

We expand the scope of image-to-image translation to include more distinct image domains, where the image sets have analogous structures, but may not share object types between them. Semantic-Driven Unsupervised Image-to-Image Translation for Distinct Image Domains (SUNIT) is built to more successfully translate images in this setting, where content from one domain is not found in the other. Our method trains an image translation model by learning encodings for semantic segmentations of images. These segmentations are translated between image domains to learn meaningful mappings between the structures in the two domains. The translated segmentations are then used as the basis …

Computer Science Teacher Survey, Josh B. Mcgee, Sarah C. Mckenzie

Arkansas Education Reports

In April/May of 2020, the University of Arkansas’ Office for Education Policy (OEP), in partnership with Arkansas Governor Asa Hutchinson's Computer Science and Cyber Security Task Force, fielded a survey with the 400+ Arkansas educators who at that time held a computer science endorsement (528), computer science approval code (5016), or computer science technical permit (5014) on their educator’s license. The survey received 153 responses, a nearly 40 percent response rate.

Find Me If You Can: Aligning Users In Different Social Networks, Priyanka Kasbekar, Katerina Potika, Chris Pollett

Faculty Publications, Computer Science

Online Social Networks allow users to share experiences with friends and relatives, make announcements, find news and jobs, and more. Several have user bases that number in the hundred of millions and even billions. Very often many users belong to multiple social networks at the same time under possibly different user names. Identifying a user from one social network on another social network gives information about a user's behavior on each platform, which in turn can help companies perform graph mining tasks, such as community detection and link prediction. The process of identifying or aligning users in multiple networks is …

Data Visualization And Infographics Design Art404g/Dsp Xxx, Harrison Dekker

Library Impact Statements

No abstract provided.

Improving Convolutional Neural Network Robustness To Adversarial Images Through Image Filtering, Natalie E. Bogda

Masters Theses

The field of computer vision and deep learning is known for its ability to recognize images with extremely high accuracy. Convolutional neural networks exist that can correctly classify 96\% of 1.2 million images of complex scenes. However, with just a few carefully positioned imperceptible changes to the pixels of an input image, an otherwise accurate network will misclassify this almost identical image with high confidence. These perturbed images are known as \textit{adversarial examples} and expose that convolutional neural networks do not necessarily "see" the world in the way that humans do. This work focuses on increasing the robustness of classifiers …

An Analysis Of Syntax Exercises On The Performance Of Cs1 Students, Shelsey B. Sullivan

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Students in introductory programming classes (CS1) generally have a difficult time learning the rules of programming. Although the general concepts of programming are relatively easy to learn, it can be difficult to learn what exactly can be typed in what order, which is known as syntax. To attempt to help students overcome this barrier, a study was conducted that introduced exercises into a CS1 class which taught the programming syntax in simple steps. The results of this study were obtained by analyzing the keys the students pressed, the errors of their code, their midterm exam scores, and their responses to …

Image Dehazing From The Perspective Of Environmental Illumination., Sanchayan Santra Dr.

Doctoral Theses

Haze and fog are atmospheric phenomena where the particles suspended in the air obscure visibility by scattering the light propagating through the atmosphere. As a result only a part of the reflected light reaches the observer. So, the apparent intensity of the objects get reduced. Apart from that, the in-scatter of the atmospheric light creates a translucent veil, which is a common sight during haze. Image dehazing methods try to recover a haze-free version of a given image by removing the effects of haze.Although attempts have been made to accurately estimate the scene transmittance, the estimation of environmental illumination has …

A Computer Science Academic Vocabulary List, David Roesler

Dissertations and Theses

This thesis documents the development of the Computer Science Academic Vocabulary List (CSAVL), a pedagogical tool intended for use by English-for-specific-purpose educators and material developers. A 3.5-million-word corpus of academic computer science textbooks and journal articles was developed in order to produce the CSAVL. This study draws on the improved methodologies used in the creation of recent lemma-based word lists such as the Academic Vocabulary List (AVL) and the Medical Academic Vocabulary List (MAVL), which take into account the discipline-specific meanings of academic vocabulary. The CSAVL provides specific information for each entry, including part of speech and CS-specific meanings in …

Being Human In Stem Csc 213x, Harrison Dekker

Library Impact Statements

No abstract provided.

Exploring Covid-19 Data Csc 292, Harrison Dekker

Library Impact Statements

No abstract provided.

Online Graduate Certificate In Gis, Joanna Burkhardt

Library Impact Statements

No abstract provided.

Engagement And Computational Thinking Through Creative Coding, Dana Hoppe

Honors Theses

Rising enrollments in Computer Science pose an opportunity to engage students from diverse backgrounds and interests; and a challenge to deliver on positive learning outcomes. While student engagement is the driving factor for increased learning performance and retention, it has been declining to new lows for Computer Science students in recent years. In order to further explore the potential of contextualized computing as a tool for increasing engagement in computing and developing Computational Thinking aptitude in students, we have developed an introductory computing course contextualized with Art and Design with modules centered around guiding pedagogical principles and aimed at middle …

Devops: Architecting Your Infrastructure (Syllabus), Jeremy Andrews, Nyc Tech-In-Residence Corps

Open Educational Resources

Syllabus for the "DevOps" course delivered at the City College of New York in Spring 2020 by Jeremy Andrews as part of the Tech-in-Residence Corps program.

Devops: Lecture 1 - "Overview", Jeremy Andrews, Nyc Tech-In-Residence Corps

Open Educational Resources

Overview lecture for the "DevOps" course delivered at the City College of New York in Spring 2020 by Jeremy Andrews as part of the Tech-in-Residence Corps program.

Some Advice For Psychologists Who Want To Work With Computer Scientists On Big Data, Cornelius J. König, Andrew M. Demetriou, Philipp Glock, Annemarie M. F. Hiemstra, Dragos Iliescu, Camelia Ionescu, Markus Langer, Cynthia C. S. Liem, Anja Linnenbürger, Rudolf Siegel, Ilias Vartholomaios

Personnel Assessment and Decisions

This article is based on conversations from the project “Big Data in Psychological Assessment” (BDPA) funded by the European Union, which was initiated because of the advances in data science and artificial intelligence that offer tremendous opportunities for personnel assessment practice in handling and interpreting this kind of data. We argue that psychologists and computer scientists can benefit from interdisciplinary collaboration. This article aims to inform psychologists who are interested in working with computer scientists about the potentials of interdisciplinary collaboration, as well as the challenges such as differing terminologies, foci of interest, data quality standards, approaches to data analyses, …

A Multi-Branch Separable Convolution Neural Network For Pedestrian Attribute Recognition, Imran N. Junejo, Naveed Ahmed

All Works

© 2020 The Authors Computer science; Computer Vision; Image processing; Deep learning; Pedestrian attribute recognition

Experiential Learning In The Technology Disciplines February 2020, Patricia Sendall, Christopher S. Stuetzle, Zachary A. Kissel, Tahir Hameed

Computer Science Faculty Publications

Learning-by-doing has long been a tradition in the technology disciplines. It is the "hands-on" work, combined with student reflection, feedback and assessment, that reinforces theory into practice. Over the past 40 years, experiential learning (EL) in higher education has grown beyond in-class assignments to include internships, cooperative education, team-based learning, project-based learning, community engagement, service learning, international and study-away experiences, capstone projects and research opportunities. This paper provides an overview of experiential education theory and practice in the undergraduate technology disciplines, and presents examples of how experiential learning practices have evolved over time at a medium-sized institution in the Northeast …

Ms In Quantum Computing, Joanna M. Burkhardt

Library Impact Statements

No abstract provided.

Identifying Knowledge Gaps Using A Graph-Based Knowledge Representation, Daniel P. Schmidt

Browse all Theses and Dissertations

Knowledge integration and knowledge bases are becoming more and more prevalent in the systems we use every day. When developing these knowledge bases, it is important to ensure the correctness of the information upon entry, as well as allow queries of all sorts; for this, understanding where the gaps in knowledge can arise is critical. This thesis proposes a descriptive taxonomy of knowledge gaps, along with a framework for automated detection and resolution of some of those gaps. Additionally, the effectiveness of this framework is evaluated in terms of successful responses to queries on a knowledge base constructed from a …

Ethics, Privacy And Data Collection: A Complex Intersection, Matthew S. Brown

Honors Theses

The technology around us enables incredible abilities such as high-resolution video calls and the ability to stay connected with everyone we care about through social media. This technology also comes with a hidden cost in the form of data collection.

This work explores what privacy means and how users understand what data social media companies collect and monetize. This thesis also proposes a more ethical business model that addresses privacy concerns from an individual perspective.

Prediction Of Sudden Cardiac Death Using Ensemble Classifiers, Ayman Momtaz El-Geneidy

CCE Theses and Dissertations

Sudden Cardiac Death (SCD) is a medical problem that is responsible for over 300,000 deaths per year in the United States and millions worldwide. SCD is defined as death occurring from within one hour of the onset of acute symptoms, an unwitnessed death in the absence of pre-existing progressive circulatory failures or other causes of deaths, or death during attempted resuscitation. Sudden death due to cardiac reasons is a leading cause of death among Congestive Heart Failure (CHF) patients. The use of Electronic Medical Records (EMR) systems has made a wealth of medical data available for research and analysis. Supervised …