Physical Sciences and Mathematics Commons^™

Ungrading: Reflections Through A Feminist Pedagogical Lens, Erin M. Eggleston, Shelby Kimmel

Feminist Pedagogy

Ungrading is a pedagogical approach in which no grades are given on any assignments. Instead, students are provided with many opportunities to submit work and gain feedback. The goal is to shift student focus from achieving a grade to growth as a learner and a person. As instructors, our ungrading approach utilized personalized learning plans, checkpoint reflections, and student-professor learning conferences to put agency in the hands of our students. We employed this method in upper-level biology and computer science courses and provide critical reflections here regarding our experiences and the connections between this approach and feminist STEM pedagogy tenets. …

Predictive Machine Learning And Its Future In Professional Basketball, Zachary Harmon

Honors College Theses

Artificial Intelligence (AI) is an ever-evolving field, transforming various aspects of contemporary life. From language models to immersive gaming experiences, AI technologies have become integral to our daily existence. Among the most promising arenas for AI integration is the world of sports. This research delves into the application of machine learning models to predict NBA game outcomes, shedding light on the profound impact of machine learning in the realm of professional basketball. Beyond the scope of game prediction, this study explores the broader implications, such as optimizing the selection of televised games, assisting players in showcasing their skills, and much …

An Open Guide To Data Structures And Algorithms, Paul W. Bible, Lucas Moser

Computer Science Faculty publications

This textbook serves as a gentle introduction for undergraduates to theoretical concepts in data structures and algorithms in computer science while providing coverage of practical implementation (coding) issues. The field of computer science (CS) supports a multitude of essential technologies in science, engineering, and communication as a social medium. The varied and interconnected nature of computer technology permeates countless career paths making CS a popular and growing major program. Mastery of the science behind computer science relies on an understanding of the theory of algorithms and data structures. These concepts underlie the fundamental tradeoffs that dictate performance in terms of …

On Teaching Multi-Criteria Decision Making With A Robot Assistant, Chen Zhang, Hakan Saraoglu, David A. Louton

Information Systems and Analytics Department Faculty Conference Proceedings

We propose a system and method for a robot assistant for teaching multi-attribute decision making (MCDM). Through questions and answers in natural language, the robot assistant learns the user’s preferences on multiple criteria involving a selection decision and makes recommendations using data on each criterion and the learned user preferences. It will include a use-case demonstration where NAO the robot will assist a human in forming a simple portfolio of mutual funds. Presenters will illustrate the architecture of the robot assisted MCDM and describe a method that is extensively used to structure complex decision problems and has been applied to …

Data-Optimized Spatial Field Predictions For Robotic Adaptive Sampling: A Gaussian Process Approach, Zachary Nathan

Computer Science Senior Theses

We introduce a framework that combines Gaussian Process models, robotic sensor measurements, and sampling data to predict spatial fields. In this context, a spatial field refers to the distribution of a variable throughout a specific area, such as temperature or pH variations over the surface of a lake. Whereas existing methods tend to analyze only the particular field(s) of interest, our approach optimizes predictions through the effective use of all available data. We validated our framework on several datasets, showing that errors can decline by up to two-thirds through the inclusion of additional colocated measurements. In support of adaptive sampling, …

Algorithmic Bias: Causes And Effects On Marginalized Communities, Katrina M. Baha

Undergraduate Honors Theses

Individuals from marginalized backgrounds face different healthcare outcomes due to algorithmic bias in the technological healthcare industry. Algorithmic biases, which are the biases that arise from the set of steps used to solve or analyze a problem, are evident when people from marginalized communities use healthcare technology. For example, many pulse oximeters, which are the medical devices used to measure oxygen saturation in the blood, are not able to accurately read people who have darker skin tones. Thus, people with darker skin tones are not able to receive proper health care due to their pulse oximetry data being inaccurate. This …

The Process Of Using Unity To Create A 2d Video Game, Sean Tammelleo

Honors Program Theses and Projects

No abstract provided.

Music On Canvas: A Quest To Generate Art That Evokes The Feeling Of Music, My Linh (Lucy) Tran

Mathematics, Statistics, and Computer Science Honors Projects

Although the idea of connecting music and art dates back to ancient Greece, recent advancements in computing have made automating this feasible. This project represents a quest to transform music into art, using three methodologies where each is an improvement towards generating images that convey our feelings and imaginations during music listening. The three methods respectively involve:

1. An element-wise mapping of sound and colors
2. Using song tags
3. Tuning an Artificial Intelligence (AI) model to generate pictorial text captions.

To create artistic images, methods two and three utilize an existing text-to-image generative AI.

Universal Back-End Design, Jason Kalili

LMU/LLS Theses and Dissertations

Accessibility in back-end development is often overlooked, with the majority of discussions and efforts centered on front-end design. To make applications usable for a wider audience, developers must also prioritize incorporating accessibility from the back-end. Back-end web accessibility encompasses the design and development of web-based systems and applications that are accessible to all users, including those with disabilities. This involves optimizing the underlying code and infrastructure for accessibility and implementing features that enable users with disabilities to navigate and interact with the site or application. Ensuring back-end web accessibility is crucial for creating an inclusive online environment accessible to everyone, …

Quantum Multi-Solution Bernoulli Search With Applications To Bitcoin’S Post-Quantum Security, Alexandru Cojocaru, Juan Garay, Fang Song, Petros Wallden

Computer Science Faculty Publications and Presentations

A proof of work (PoW) is an important cryptographic construct which enables a party to convince other parties that they have invested some effort in solving a computational task. Arguably, its main impact has been in the setting of cryptocurrencies such as Bitcoin and its underlying blockchain protocol, which have received significant attention in recent years due to its potential for various applications as well as for solving fundamental distributed computing questions in novel threat models. PoWs enable the linking of blocks in the blockchain data structure, and thus the problem of interest is the feasibility of obtaining a sequence …

The Impact Of Virtual Reality On The Healthcare Industry, Peter Sullivan

Honors Projects in Information Systems and Analytics

Virtual reality (VR) took off in 2013 and has touched many public sectors, from gaming, to business, to healthcare. This study looks at virtual reality's impact has affected the healthcare system, with a focus on its use for medical training, patient recovery, patient pain management, and mental health care. A literature review was conducted on the current state of the industry addressing virtual reality's performance in the field, the perception of experts, and an estimation of financial undertakings. Looking at cost analyses brought a fuller approach to the research. Surveying researchers and workers within the realm of healthcare and VR …

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.

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

Open Educational Resources

No abstract provided.

Cp 6200 Java Programming 2 Syllabus (Oer), 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.

Cp6200 Javaprogramming2 Oer - Oop Course Project, Shoshana Marcus

Open Educational Resources

No abstract provided.

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 …

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 …

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 …

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 …

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 …

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 …