Computer Sciences Commons^™

Making Music Social: Creating A Spotify-Based Social Media Platform, Dalton J. Craven

Senior Theses

DKMS is a new type of social media platform for music lovers and groups of friends. It integrates tightly with Spotify, one of the largest music streaming services in the world. Users of DKMS can see what their friends are listening to, receive recommendations of new songs to listen to, and analyze their several key numerical metrics (happiness, danceability, loudness, and energy) of their top songs.

DKMS was built as part of the year-long Capstone senior design course at the University of South Carolina. A deployed app is visible at https://dkms.vercel.app, and the open-source code is visible at https://github.com/SCCapstone/DKMS.

Computer Science 521 Intensive Introduction To Programming, Beth Allen

Open Educational Resources (OER)

This is a comprehensive, intensive introduction to computers, programming, data structures, abstraction, software engineering processes, and problem-solving fundamentals for learners preparing to take graduate-level courses in computer science.

The primary language used in this course is C++, with an introduction to other currently widely used languages, such as Java and Python.

Geo-Distributed Multi-Tier Workload Migration Over Multi-Timescale Electricity Markets, Sourav Kanti Addya, Anurag Satpathy, Bishakh Chandra Ghosh, Sandip Chakraborty, Soumya K. Ghosh, Sajal K. Das

Computer Science Faculty Research & Creative Works

Virtual machine (VM) migration enables cloud service providers (CSPs) to balance workload, perform zero-downtime maintenance, and reduce applications' power consumption and response time. Migrating a VM consumes energy at the source, destination, and backbone networks, i.e., intermediate routers and switches, especially in a Geo-distributed setting. In this context, we propose a VM migration model called Low Energy Application Workload Migration (LEAWM) aimed at reducing the per-bit migration cost in migrating VMs over Geo-distributed clouds. With a Geo-distributed cloud connected through multiple Internet Service Providers (ISPs), we develop an approach to find out the migration path across ISPs leading to the …

Cp6200 Javaprogramming2 Oer - Oop Course Project, Shoshana Marcus

Open Educational Resources

No abstract provided.

Review Java Basics In 2 Weeks (Slides), Shoshana Marcus

Open Educational Resources

No abstract provided.

Cp6200 Javaprogramming2 Oer - Oop Assignment - Item And Shopping Cart Classes, Shoshana Marcus

Open Educational Resources

No abstract provided.

Cp 6200 Java Programming 2 Syllabus (Oer), Shoshana Marcus

Open Educational Resources

No abstract provided.

Development Of Sensing And Programming Activities For Engineering Technology Pathways Using A Virtual Arduino Simulation Platform, Murat Kuzlu, Vukica Jovanovic, Otilia Popescu, Salih Sarp

Engineering Technology Faculty Publications

The Arduino platform has long been an efficient tool in teaching electrical engineering technology, electrical engineering, and computer science concepts in schools and universities and introducing new learners to programming and microcontrollers. Numerous Arduino projects are widely available through the open-source community, and they can help students to have hands-on experience in building circuits and programming electronics with a wide variety of topics that can make learning electrical prototyping fun. The educational fields of electrical engineering and electrical engineering technology need continuous updating to keep up with the continuous evolution of the computer system. Although the traditional Arduino platform has …

Making Sense Of Big (Kinematic) Data: A Comprehensive Analysis Of Movement Parameters In A Diverse Population, Naomi Wilma Nunis

University of the Pacific Theses and Dissertations

OBJECTIVE

The purpose of this study was to determine how kinematic, big data can be evaluated using computational, comprehensive analysis of movement parameters in a diverse population.

METHODS

Retrospective data was collected, cleaned, and reviewed for further analysis of biomechanical movement in an active population using 3D collinear resistance loads. The active sample of the population involved in the study ranged from age 7 to 82 years old and respectively identified as active in 13 different sports. Moreover, a series of exercises were conducted by each participant across multiple sessions. Exercises were measured and recorded based on 6 distinct biometric …

An Explainable Artificial Intelligence Framework For The Predictive Analysis Of Hypo And Hyper Thyroidism Using Machine Learning Algorithms, Md. Bipul Hossain, Anika Shama, Apurba Adhikary, Avi Deb Raha, K. M. Aslam Uddin, Mohammad Amzad Hossain, Imtia Islam, Saydul Akbar Murad, Md. Shirajum Munir, Anupam Kumur Bairagi

Electrical & Computer Engineering Faculty Publications

The thyroid gland is the crucial organ in the human body, secreting two hormones that help to regulate the human body's metabolism. Thyroid disease is a severe medical complaint that could be developed by high Thyroid Stimulating Hormone (TSH) levels or an infection in the thyroid tissues. Hypothyroidism and hyperthyroidism are two critical conditions caused by insufficient thyroid hormone production and excessive thyroid hormone production, respectively. Machine learning models can be used to precisely process the data generated from different medical sectors and to build a model to predict several diseases. In this paper, we use different machine-learning algorithms to …

Deeppatent2: A Large-Scale Benchmarking Corpus For Technical Drawing Understanding, Kehinde Ajayi, Xin Wei, Martin Gryder, Winston Shields, Jian Wu, Shawn M. Jones, Michal Kucer, Diane Oyen

Computer Science Faculty Publications

Recent advances in computer vision (CV) and natural language processing have been driven by exploiting big data on practical applications. However, these research fields are still limited by the sheer volume, versatility, and diversity of the available datasets. CV tasks, such as image captioning, which has primarily been carried out on natural images, still struggle to produce accurate and meaningful captions on sketched images often included in scientific and technical documents. The advancement of other tasks such as 3D reconstruction from 2D images requires larger datasets with multiple viewpoints. We introduce DeepPatent2, a large-scale dataset, providing more than 2.7 million …

Comparison Of Physics-Based Deformable Registration Methods For Image-Guided Neurosurgery, Nikos Chrisochoides, Yixun Liu, Fotis Drakopoulos, Andriy Kot, Panos Foteinos, Christos Tsolakis, Emmanuel Billias, Olivier Clatz, Nicholas Ayache, Andrey Fedorov, Alex Golby, Peter Black, Ron Kikinis

Computer Science Faculty Publications

This paper compares three finite element-based methods used in a physics-based non-rigid registration approach and reports on the progress made over the last 15 years. Large brain shifts caused by brain tumor removal affect registration accuracy by creating point and element outliers. A combination of approximation- and geometry-based point and element outlier rejection improves the rigid registration error by 2.5 mm and meets the real-time constraints (4 min). In addition, the paper raises several questions and presents two open problems for the robust estimation and improvement of registration error in the presence of outliers due to sparse, noisy, and incomplete …

Charged Track Reconstruction With Artificial Intelligence For Clas12, Gagik Gavalian, Polykarpos Thomadakis, Angelos Angelopoulos, Nikos Chrisochoides

Computer Science Faculty Publications

In this paper, we present the results of charged particle track reconstruction in CLAS12 using artificial intelligence. In our approach, we use neural networks working together to identify tracks based on the raw signals in the Drift Chambers. A Convolutional Auto-Encoder is used to de-noise raw data by removing the hits that do not satisfy the patterns for tracks, and second Multi-Layer Perceptron is used to identify tracks from combinations of clusters in the drift chambers. Our method increases the tracking efficiency by 50% for multi-particle final states already conducted experiments. The de-noising results indicate that future experiments can run …

A Structure-Aware Generative Adversarial Network For Bilingual Lexicon Induction, Bocheng Han, Qian Tao, Lusi Li, Zhihao Xiong

Computer Science Faculty Publications

Bilingual lexicon induction (BLI) is the task of inducing word translations with a learned mapping function that aligns monolingual word embedding spaces in two different languages. However, most previous methods treat word embeddings as isolated entities and fail to jointly consider both the intra-space and inter-space topological relations between words. This limitation makes it challenging to align words from embedding spaces with distinct topological structures, especially when the assumption of isomorphism may not hold. To this end, we propose a novel approach called the Structure-Aware Generative Adversarial Network (SA-GAN) model to explicitly capture multiple topological structure information to achieve accurate …

Obstacles In Learning Algorithm Run-Time Complexity Analysis, Bailey Licht

Theses/Capstones/Creative Projects

Algorithm run-time complexity analysis is an important topic in data structures and algorithms courses, but it is also a topic that many students struggle with. Commonly cited difficulties include the necessary mathematical background knowledge, the abstract nature of the topic, and the presentation style of the material. Analyzing the subject of algorithm analysis using multiple learning theories shows that course materials often leave out key steps in the learning process and neglect certain learning styles. Students can be more successful at learning algorithm run-time complexity analysis if these missing stages and learning styles are addressed.

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

Each year, about 14,000 Chicago Public Schools (CPS) students graduate with one year of high school computer science (CS) in fulfillment of the district’s CS graduation requirement. This accomplishment was the culmination of a decade of work by the Chicago Alliance for Equity in Computer Science (CAFÉCS), which includes CPS teachers and administrators, university CS faculty, and educational researchers. CAFÉCS research indicates that CPS significantly increased the capacity of schools to offer the Exploring Computer Science (ECS) introductory course, resulting in a rapid, equitable increase in students’ participation in CS. Making CS mandatory did not negatively impact performance in ECS. …

The Minority In The Minority, Black Women In Computer Science Fields: A Phenomenological Study, Blanche' D. Anderson

Doctoral Dissertations and Projects

The purpose of this transcendental phenomenological study was to describe the lived experiences of Black women with a bachelor’s, master’s, or doctoral degree in computer science, currently employed in the United States. The theory guiding this study was Krumboltz’s social learning theory of career decision-making, as it provides a foundation for understanding how a combination of factors leads to an individual’s educational and occupational preferences and skills. This qualitative study answered the following central research question: What are the lived experiences of Black women with a bachelor’s, master’s, or doctoral degree in computer science, currently employed in the United States? …

Generalization In Quantum Machine Learning From Few Training Data, Matthias C Caro, Hsin-Yuan Huang, M Cerezo, Kunal Sharma, Andrew Sornborger, Lukasz Cincio, Patrick J Coles

Faculty, Staff and Student Publications

Modern quantum machine learning (QML) methods involve variationally optimizing a parameterized quantum circuit on a training data set, and subsequently making predictions on a testing data set (i.e., generalizing). In this work, we provide a comprehensive study of generalization performance in QML after training on a limited number N of training data points. We show that the generalization error of a quantum machine learning model with T trainable gates scales at worst as [Formula: see text]. When only K ≪ T gates have undergone substantial change in the optimization process, we prove that the generalization error improves to [Formula: see …

Ontology-Based Formal Approach For Safety And Security Verification Of Industrial Control Systems, Ramesh Neupane

Boise State University Theses and Dissertations

Control logics, as part of the Industrial Control Systems (ICS), are used to control the physical processes of the critical infrastructures such as power plants, water, and gas distribution, etc. Most commonly, the Programmable Logic Controller (PLC) manages these processes through actuators based on information received from sensor readings. Any safety issues or cyberattacks on these systems may have catastrophic consequences on human lives and the environment. In an effort to improve the resilience and security of control logics, this thesis provides algorithms and tools to formally define the safety and security requirements w.r.t. the physical processes, and the industrial …

"Design For Co-Design" In A Computer Science Curriculum Research-Practice Partnership, Victor R. Lee, Jody Clarke-Midura, Jessica F. Shumway, Mimi Recker

Publications

This paper reports on a study of the dynamics of a Research-Practice Partnership (RPP) oriented around design, specifically the co-design model. The RPP is focused on supporting elementary school computer science (CS) instruction by involving paraprofessional educators and teachers in curricular co-design. A problem of practice addressed is that few elementary educators have backgrounds in teaching CS and have limited available instructional time and budget for CS. The co-design strategy entailed highlighting CS concepts in the mathematics curriculum during classroom instruction and designing computer lab lessons that explored related ideas through programming. Analyses focused on tensions within RPP interaction dynamics …

A Self-Regulating System For Assessing Scientific Predictive Power, Ted C. Rogers

Physics Faculty Publications

I propose a method for tracking and assessing scientific progress using a prediction consensus algorithm designed for the purpose. The protocol obviates the need for centralized referees to generate scientific questions, gather predictions, and assess the accuracy or success of those predictions. It relies instead on crowd wisdom and a system of checks and balances for all tasks. It is intended to take the form of a web-based, searchable database. I describe a prototype implementation that I call Ex Quaerum. The main purpose of the present document is to motivate the project, to explain it's underlying philosophy, to explain the …

Simulating Polistes Dominulus Nest-Building Heuristics With Deterministic And Markovian Properties, Benjamin Pottinger

Undergraduate Honors Theses

European Paper Wasps (Polistes dominula) are social insects that build round, symmetrical nests. Current models indicate that these wasps develop colonies by following simple heuristics based on nest stimuli. Computer simulations can model wasp behavior to imitate natural nest building. This research investigated various building heuristics through a novel Markov-based simulation. The simulation used a hexagonal grid to build cells based on the building rule supplied to the agent. Nest data was compared with natural data and through visual inspection. Larger nests were found to be less compact for the rules simulated.

Understanding User Perceptions Of Voice Personal Assistants, Ha Young Kim

All Theses

As the artificial intelligence (AI) technique improves, voice assistant (smart speaker) such as Amazon Alexa and Google Assistant are quickly, surely permeating into people's daily lives. With its powerful and convenient benefits and the circumstances that people started to stay at their home longer due to the pandemic, reliance on smart speakers has increased rapidly. But at the same time, concerns of security on smart speakers have increased.

In this thesis, we conducted an online user survey of smart speaker users with five different perspectives – 1) Users’ engagement with privacy policy; 2) Awareness of different policy requirements defined by …

Einstein-Roscoe Regression For The Slag Viscosity Prediction Problem In Steelmaking, Hiroto Saigo, Dukka Kc, Noritaka Saito

Michigan Tech Publications, Part 1

In classical machine learning, regressors are trained without attempting to gain insight into the mechanism connecting inputs and outputs. Natural sciences, however, are interested in finding a robust interpretable function for the target phenomenon, that can return predictions even outside of the training domains. This paper focuses on viscosity prediction problem in steelmaking, and proposes Einstein-Roscoe regression (ERR), which learns the coefficients of the Einstein-Roscoe equation, and is able to extrapolate to unseen domains. Besides, it is often the case in the natural sciences that some measurements are unavailable or expensive than the others due to physical constraints. To this …

Cook-It!: A Web Application For Easy Meal Planning, Carol Juneau

Senior Theses

Cook-it! is a web application for meal planning based on the Django framework and deployed on the Heroku platform. This application has an intuitive interface to make it easy to use. The project has been developed over two semesters, roughly separated into a planning phase and an implementation phase. Cook-it! incorporates a robust feature set and an attractive design. Its core purpose is to make it easy for users to plan meals, interact with other users, and keep track of user information such as a grocery list.

Optimal Eavesdropping In Quantum Cryptography, Atanu Acharyya Dr.

Doctoral Theses

Quantum key distribution (QKD) has raised some promise for more secured communication than its classical counterpart. It allows the legitimate parties to detect eavesdropping which introduces error in the channel. If disturbed, there are ways to distill a secure key within some threshold error-rate. The amount of information gained by an attacker is generally quantified by (Shannon) mutual information. Knowing the maximum amount of information that an intruder can gain is important for post-processing purposes, and we mainly focus on that side in the thesis. Renyi information is also useful especially when post-processing is considered. The scope of this thesis …

Factors Affecting Student Educational Choices Regarding Oer Material In Computer Science, Anastasia Angelopoulou, Rania Hodhod, Alfredo J. Perez

Computer Science Faculty Publications

The use of Open Educational Resources (OER) in course settings provides a solution to reduce the textbook barrier. Several published studies have concluded that high textbook costs may influence students' educational choices. However, there are other student characteristics that may be relevant to OER. In this work, we study various factors that may influence students' educational choices regarding OER and their impact on a student’s perspectives on OER use and quality. More specifically, we investigate whether there are significant differences in the frequency of use and perceived quality of the OER textbook based on gender, prior academic achievements, income, seniority, …

Computer-Based Scaffolding In Computer Science Education, Rebecca Trinh, Simone Levy

Summer REU Program

No abstract provided.

Utilization Of Virtual Reality For General Education Purposes, Amit Lal

University of the Pacific Theses and Dissertations

The use of Virtual Reality (VR) in a variety of professional, military, governmental, and educational fields has continued to expand over the past several decades, and the recent Covid-19 pandemic has brought attention to this field. This study surveys 154 college students over 23 questions that include various demographics that can be used to look for discriminators, multiple-choice VR-related questions, as well as a few free-form questions about use of VR in learning environments. The students’ experience with, interest in, and thoughts on how to best use VR vary considerably. The Covid-19 pandemic is found to have limited impact thus …

Harnessing The Power Of Interdisciplinary Research With Psychology-Informed Cyberbullying Detection Models, Deborah Hall, Yasin N. Silva, Brittany Wheeler, Lu Cheng, Katie Baumel

Computer Science: Faculty Publications and Other Works

Cyberbullying has become increasingly prevalent, particularly on social media. There has also been a steady rise in cyberbullying research across a range of disciplines. Much of the empirical work from computer science has focused on developing machine learning models for cyberbullying detection. Whereas machine learning cyberbullying detection models can be improved by drawing on psychological theories and perspectives, there is also tremendous potential for machine learning models to contribute to a better understanding of psychological aspects of cyberbullying. In this paper, we discuss how machine learning models can yield novel insights about the nature and defining characteristics of cyberbullying and …