Physical Sciences and Mathematics Commons^™

A Formal Approach To Circle Formation In Multi-Agent Systems, Rui Yang

Computer Science Graduate Research Workshop

No abstract provided.

Conflict Resolution Using Α-Shapes For Distributed Robotic Sampling Of Ambient Phenomena In Initially Unknown Environments, Brad Woosley

Computer Science Graduate Research Workshop

No abstract provided.

Dynamic Lazy Grounding In Answer Set Programming, Brian Hodges

Computer Science Graduate Research Workshop

No abstract provided.

Automatic Program Rewriting For Non-Ground Answer Set Programs, Nicholas Hippen

Computer Science Graduate Research Workshop

No abstract provided.

Automated Tool Support For Security Bug Repair In Mobile Applications, Larry Singleton

Computer Science Graduate Research Workshop

No abstract provided.

Tool Support For Recurring Code Change Inspection With Deep Learning, Krishna Teja Ayinala

Computer Science Graduate Research Workshop

No abstract provided.

Analysis Of Subgoal Data In Computer Science Principles - Data Cleaning, Hari Ramilison

Computer Science Graduate Research Workshop

No abstract provided.

Co-Designing An Academic Focus Mobile Application, Mohammad Farhad Afzali

Computer Science Graduate Research Workshop

No abstract provided.

Processing Narratives By Means Of Action Languages, Craig Olson

Computer Science Graduate Research Workshop

No abstract provided.

Intelligent And Human-Aware Decision Making For Semi-Autonomous Human Rehabilitation Assistance Using Modular Robots, Anoop Mishra

Computer Science Graduate Research Workshop

No abstract provided.

Machine Learning Techniques For Predicting Mobility-Related Perception Errors In Astronauts, Steven Belcher

Computer Science Graduate Research Workshop

No abstract provided.

A Technique For Improving Classification Accuracy Of Highly Imbalanced And Sparse Datasets, Sindhura Bonthu

Computer Science Graduate Research Workshop

No abstract provided.

Learning From Imbalanced Datasets: Evaluating The Predictive Accuracy Of Minority Classes, Adithi Deborah Chakravarthy

Computer Science Graduate Research Workshop

No abstract provided.

Cognitive Understanding Of Reverse Engineering Assistant, Nathan Elliot Harris, Bertrand L. Lachance, Jeremy William Tiberg, Faith Trautmann

The Research and Scholarship Symposium (2013-2019)

The Cognitive Understanding of Reverse Engineering Assistant, or C.U.R.E. Assistant for short, is an independently developed program with the purpose of introducing students of the software reverse-engineering world to the art of disassembly. Reverse Engineering, or R.E. for short, is the process of deducing the source instructions or mechanisms of a device. This can be done to software to figure out how it works and how it can be exploited. While hackers employ this method for breaking into software systems, this is very useful for security researchers to determine security vulnerabilities in internet browsers, operating systems, apps, and more, so …

Gradel, Brennon Gee, Malachi Marshall, Nathan Barlow, James Osborne

The Research and Scholarship Symposium (2013-2019)

Grading homework is a major time-consuming process for professors. This is especially true of computer science professors who each teach multiple sections and classes. Our Senior design project, Gradel is a web application created to help professors with this task. It does this in two ways, firstly by allowing the professors to create customized assignments, problems, test cases and correct solutions. After a class is created our application compares the student's submission with the professor’s solution to automatically grade the submissions. This is also very helpful for students, by giving them instant feedback it allows them to make corrections or …

Low Cost Vehicular Autonomy Using Radar And Gps, Nathan Jessurun, Ryan Gordon, Danielle Fredette

The Research and Scholarship Symposium (2013-2019)

This presentation describes a subset of the systems devised for this year's autonomous golf cart senior design project. Our goal is to explore the possibilities of low cost autonomy using only radar and GPS for environmental sensing and navigation. Although autonomous and semi-autonomous ground vehicles are a relatively new reality, prototypes have been a subject of engineering research for decades, often utilizing an array of sensors and sensor fusion techniques. State of the art autonomous ground vehicle prototypes typically use a combination of LIDAR and other distance sensors (such as radar or sonar) as well as cameras and GPS, sometimes …

Quantifying Iron Overload Using Mri, Active Contours, And Convolutional Neural Networks, Andrea Sajewski, Stacey Levine

Undergraduate Research and Scholarship Symposium

Iron overload, a complication of repeated blood transfusions, can cause tissue damage and organ failure. The body has no regulatory mechanism to excrete excess iron, so iron overload must be closely monitored to guide therapy and measure treatment response. The concentration of iron in the liver is a reliable marker for total body iron content and is now measured noninvasively with magnetic resonance imaging (MRI). MRI produces a diagnostic image by measuring the signals emitted from the body in the presence of a constant magnetic field and radiofrequency pulses. At each pixel, the signal decay constant, T2*, can be calculated, …

Lightweight Formal Methods For Improving Software Security, Andrew Berns, James Curbow, Joshua Hilliard, Sheriff Jorkeh, Miho Sanders

Research in the Capitol

This research examines how software specifications could be used to build more-secure software. For this project, we analyzed known vulnerabilities for open source projects to identify the corrective actions required to patch the vulnerability. For each vulnerability, we then augmented the program with formal assertions in an attempt to allow a static analysis tool to find the vulnerability. Using the information gathered from these assertions, we hope to determine which assertions are most effective at finding vulnerabilities with today's tools and evaluate new assertions that could be added to the static analysis tool to help uncover more vulnerabilities. My work …

A Storytelling, Social-Belonging Intervention In An Introductory Computer Science Course, Shanon Reckinger, Chris Gregg

ASEE IL-IN Section Conference

A brief social-belonging intervention was tested in two introductory computer science (CS) courses. This intervention used storytelling to help improve a sense of belonging and establish the importance of persistence in the classroom. In previous experiments using this one-time intervention, there were significant results (Walton & Brady, 2017). Recent CS graduates were interviewed about their own struggles and failures in their computer science courses. These interviews were videotaped and edited to follow the storytelling pattern of a struggle, followed by an attribution, and concluding with redemption. Interviewees were selected to represent a diverse group of students including both dominant majority …

Teaching Young Learners Computational Thinking, Tingxuan Li, Shengwei An, Xuan Wang, Hengrong Du, Guanhong Tao

Engagement & Service-Learning Summit

No abstract provided.

Evaluating An Electronic Protocol In A Pediatric Intensive Care Unit, Jeanette Rose

UNO Student Research and Creative Activity Fair

A team of clinicians at Children’s Hospital and Medical Center (CHMC) developed a standardized protocol in 2018 for the care of patients needing sedation. This protocol is ordered through the EPIC electronic health record system for patients in the pediatric intensive care unit (PICU). When used, electronic protocols reduce the variation in clinical decision making which can ultimately improve patient outcomes. The goal of this project is to evaluate this technology, how the protocol is being used, and how it may be improved. Actual users of the EPIC sedation protocol were the subjects of this study, including PICU physicians, physician …

Forensics Analysis For Bone Pair Matching Using Bipartite Graphs In Commingled Remains, Ryan Ernst

UNO Student Research and Creative Activity Fair

Identification of missing prisoners of war is a complex and time consuming task. There are many missing soldiers whose remains have yet to be returned to their families and loved ones. This nation has a solemn obligation to its soldiers and their families who have made the ultimate sacrifice for their country. There are currently over 82,000 unidentified prisoners of war which are identified at a rate of 100+ per year. At this rate it would take 300+ years to complete the identification process. Previously, anthropologists used excel spreadsheets to sort through skeletal data. This project aims to streamline the …

Use Of Exploratory Data Analysis And Visualization In Identification Of Commingled Human Remains, Vivek Bhat Hosmat

UNO Student Research and Creative Activity Fair

The field of Exploratory data analysis and Visualization is revolutionizing the way we perceive things. At times, Visualization is seen as a subset of Exploratory data analysis and then there are branches in Visualization, types, technologies, tools which makes one feel that Visualization is a standalone pillar. Either way, they both work well together to extract meaningful information from almost any kind of data. These fields can very well be the stepping stone for in-depth or conclusive research. They can direct research based on facts and observations rather than one’s intuition or even a brute force technique. This project was …

International Nuclear Cyber Security Threat Landscape, Shannon Eggers, Charles Nickerson

Purdue Workshop on Nonproliferation: Technology and Approaches

The Idaho National Laboratory (INL) Nuclear Cyber Team (NCT) is a recognized leader in international nuclear cyber program assessments as well as nuclear cyber training development and delivery. This paper discusses how the NCT is implementing their vision to protect global nuclear infrastructure from cyber threats. Also outlined is the team’s perspectives on the global nuclear cyber threat landscape and the top ten approachable cyber security measures that can be applied to immediately improve a country’s nuclear cyber security posture.

Astria Ontology: Open, Standards-Based, Data-Aggregated Representation Of Space Objects, Jennie Wolfgang, Kathleen Krysher, Michael Slovenski, Unmil P. Karadkar, Shiva Iyer, Moriba K. Jah

Space Traffic Management Conference

The necessity for standards-based ontologies for long-term sustainability of space operations and safety of increasing space flights has been well-established [6, 7]. Current ontologies, such as DARPA’s OrbitOutlook [5], are not publicly available, complicating efforts for their broad adoption. Most sensor data is siloed in proprietary databases [2] and provided only to authorized users, further complicating efforts to create a holistic view of resident space objects (RSOs) in order to enhance space situational awareness (SSA).

The ASTRIA project is developing an open data model with the goal of aggregating data about RSOs, parts, space weather, and governing policies in order …

Session: 4 Multilinear Subspace Learning And Its Applications To Machine Learning, Randy Hoover, Kyle Caudle Dr., Karen Braman Dr.

SDSU Data Science Symposium

Multi-dimensional data analysis has seen increased interest in recent years. With more and more data arriving as 2-dimensional arrays (images) as opposed to 1-dimensioanl arrays (signals), new methods for dimensionality reduction, data analysis, and machine learning have been pursued. Most notably have been the Canonical Decompositions/Parallel Factors (commonly referred to as CP) and Tucker decompositions (commonly regarded as a high order SVD: HOSVD). In the current research we present an alternate method for computing singular value and eigenvalue decompositions on multi-way data through an algebra of circulants and illustrate their application to two well-known machine learning methods: Multi-Linear Principal Component …

Analyzing Neuronal Dendritic Trees With Convolutional Neural Networks, Olivier Trottier, Jonathon Howard

Yale Day of Data

In the biological sciences, image analysis software are used to detect, segment or classify a variety of features encountered in living matter. However, the algorithms that accomplish these tasks are often designed for a specific dataset, making them hardly portable to accomplish the same tasks on images of different biological structures. Recently, convolutional neural networks have been used to perform complex image analysis on a multitude of datasets. While applications of these networks abound in the technology industry and computer science, use cases are not as common in the academic sciences. Motivated by the generalizability of neural networks, we aim …