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Full-Text Articles in Computer Sciences

Simulating Salience: Developing A Model Of Choice In The Visual Coordination Game, Adib Sedig Aug 2022

Simulating Salience: Developing A Model Of Choice In The Visual Coordination Game, Adib Sedig

Undergraduate Student Research Internships Conference

This project is primarily inspired by three papers: Colin Camerer and Xiaomin Li’s (2019 working paper)—Using Visual Salience in Empirical Game Theory, Ryan Oprea’s (2020)—What Makes a Rule Complex?, and Caplin et. al.’s (2011)—Search and Satisficing. Over the summer, I worked towards constructing a model of choice for the visual coordination game that can model player behavior more accurately than traditional game theoretic predictions. It attempts to do so by incorporating a degree of bias towards salience into a cellular automaton search algorithm and utilizing it alongside a sequential search mechanism of satisficing. This …


Phishing For Fun, Madeline Moran Apr 2022

Phishing For Fun, Madeline Moran

Computer Science Research Seminars and Symposia

The process of a phishing experiment that will be used to investigate a possible correlation between a person thinking style and their susceptibility to phishing scams.


Rotoscoping Image Processing, David J. Blackstone Apr 2022

Rotoscoping Image Processing, David J. Blackstone

Student Scholar Showcase

I wrote a program to perform rotoscoping image processing. Rotoscoping is an animation technique that is used to turn real images into cartoon images. This is a lengthy process to do by hand, taking hundreds of hours. To make this job easier, I have written a program to do automated rotoscoping. This program uses many algorithms and techniques such as edge detection, blurring, directional detection, and edge tracking. What once would take hours by hand, takes seconds!


Seizure Prediction In Epilepsy Patients, Gary Dean Cravens Feb 2022

Seizure Prediction In Epilepsy Patients, Gary Dean Cravens

NSU REACH and IPE Day

Purpose/Objective: Characterize rigorously the preictal period in epilepsy patients to improve the development of seizure prediction techniques. Background/Rationale: 30% of epilepsy patients are not well-controlled on medications and would benefit immensely from reliable seizure prediction. Methods/Methodology: Computational model consisting of in-silico Hodgkin-Huxley neurons arranged in a small-world topology using the Watts-Strogatz algorithm is used to generate synthetic electrocorticographic (ECoG) signals. ECoG data from 18 epilepsy patients is used to validate the model. Unsupervised machine learning is used with both patient and synthetic data to identify potential electrophysiologic biomarkers of the preictal period. Results/Findings: The model has shown states corresponding to …