MASTER’S PRESENTATIONS FALL 2018 Thursday, December 13, 2018 9:00 am – 11:00 am Room KC 2204
SCHOOL OF CIS FALL 2018 MASTERS PRESENTATIONS Thursday, December 13, 2018 Schedule of Presentations KC 2204: 9:00 am - Three to Five Minute Lightning Rounds Daniel Lindeman – MS Project, Advisor: Dr. D. Robert Adams “Puzzle Level Generation with Answer Set Programming” Katherine Skocelas – MS Project, Advisor: Dr. D. Robert Adams & Dr. Bryon DeVries “ Systemic Lupus Erythematosus Symptom Severity Prediction Using a Recursive Neural Network ” Ryan Solnik – MS Project, Advisor: Dr. D. Robert Adams “Story Parsing and Adventure Generation with Python and Postgres” Dipana Sorathiya – MS Project, Advisor: Dr. D. Robert Adams “Graphical Log Analyser” Evelyn Edwards – MS Project, Advisor: Dr. Andrew Kalafut “The Insecurity of Things (IoT)” Achyutarama Ganti – MS Project, Advisor: Dr. Jared Moore “Stock Market Analysis Using Machine Learning Algorithms” Debaditya Gautam – MS Project, Advisor: Dr. Christian Trefftz “A Parallel Algorithm to Calculate an Approximation to the Order-K Voronoi Diagram” Brett VanderHaar – MS Project, Advisor: Dr. Jonathan Leidig “A Framework for Discovering Latent Insights in Clinical Data” Nicolás Arias González – MS Project, Advisor: Dr. Jonathan Engelsma “Web-Based, Deep Learning Assisted Medical Image Tagging Tool” David Dick – MS Project, Advisor: Dr. Jonathan Engelsma “Development of a Mobile Friendly Self-Service Experience at Grand Rapids Community College” Joseph McCartney – DSA Internship, Internship Supervisor: Mr. Aaron Kamphuis “Open Systems Technologies Data Analyst Internship: AWS Recommendation System” Samantha Milano – DSA Internship, Internship Supervisor: Mr. Josh Schwannecke “Amway Data Science Internship: An Analysis of the Sky Air Filtration System and Indoor Air Quality” Sixty-minute poster presentations to immediately follow.
Puzzle Level Generation with Answer Set Programming Masters Project Presented By: Daniel Lindeman Advisor: Dr. Robert Adams Abstract: Swappy is a puzzle game that requires different character tokens to cooperatively navigate a maze to reach their goals. Swappy characters are special in that whenever they are collinear with another character, they may swap places. In practice, generating levels manually may take upwards of 20 hours, and is error prone. By employing Answer Set Programming (ASP), it is possible to generate and constrain level creation such that levels are solvable, meet an aesthetic standard, and follow the rules of the game. Using the grounder/solver tool, Clingo, level creation can be done in a matter of seconds or minutes. The expressive power of rules and constraints allows the developer to more clearly see their game for the abstract ruleset that it is. In this project we explore the use of ASP Prolog to generate artifacts useful for level generation for the puzzle game Swappy - finding succinct and expressive ways to do so compared to traditional programming languages.
Systemic Lupus Erythematosus Symptom Severity Prediction Using a Recursive Neural Network Masters Project Presented By: Katherine G. Skocelas Advisors: Dr. Robert Adams & Dr. Byron DeVries Abstract: Systemic Lupus Erythematosus (SLE) is a chronic autoimmune disease that causes the immune system to attack the body’s own connective tissues and organs. Humans have difficulty predicting SLE symptom severity levels because of the complex interactions of disease trigger exposure levels over time. To address this issue, we constructed a novel machine learning solution that generates a model capable of predicting SLE symptom severity levels with 8.3-19.9% average error. It does so by inputting trigger exposure levels into a recursive neural network and training them with a unique method that continually turns training on and off based on the maximum error each day. This allows the RNN to learn SLE flare activity without overtraining remission activity, thus maintaining a greater degree of plasticity. Models trained in this fashion performed 3.5-5% better on average than those trained via the standard method. Future areas of work include replicating these results with a large patient training data set and evolving the model to predict disease trajectory.
Story Parsing and Adventure Generation with Python and Postgres Masters Project Presented By: Ryan Solnik Advisor: Dr. Robert Adams Abstract: Dungeons and Dragons is a tabletop roleplaying game that allows players to assume the roles of adventurers in medieval fantasy setting while one player is tasked as playing the role of the Dungeon Master (DM). This player facilitates the story and all other characters not played by the other players. Adventure Day is a toolset for Dungeons and Dragons 5th Edition that assists Dungeon Master in formatting their Story as well as gathering useful details for the challenges presented within that adventure. Adventure day aims to accomplish this by associating relevant monster data from postgres database while using the text input of a desired adventure parsed through a Python application to filter for highlighted terms and take action based on a defined dictionary within the same text file. Adventure day will be capable of building encounters with monsters specified by monster environment and/or abilities that they possess, provide inspiration for non-player characters that the DM will portray via personality traits, and finally help to scale monster difficulty depending on the power level of the player’s characters. Adventure day will work in tandem with a helper site that formats Markdown text into a format that mirrors that of published material.
Graphical Log Analyzer Masters Project Presented By: Dipana Sorathiya Advisor: Dr. Robert Adams Abstract: In an automotive embedded system Inter Process Communication (IPC) messages are exchanged between various software modules. These messages (or signals) are exchanged at high frequencies and recorded in a text-based log file. Unfortunately, this format is generally tedious to read and difficult to analyze. Graphical Log Analyzer is a tool, which parses the debug, logs (output) from an automotive embedded system and converts them into a graphical representation so they can become easily understandable for system debugging purpose. Graphical Log Analyzer parses the software modules names and message names, and identifies the direction of message transmission and the end point of reception. It then generates a “.diag” file that is processed by the seqdiag too to produce sequence-diagram. This visual representation of software module exchanges allows developers to more easily debug complex embedded software issues.
The Insecurity of Things (IoT) Masters Project Presented By: Evelyn Edwards Advisor: Dr. Andrew Kalafut Abstract: Convenience is important to everyone. In our fast-paced society, people are willing to pay for devices that can save them time, even if it is just a few minutes. Over the past few years, the Internet of Things (IoT), or smart devices, have become a popular way for people to leverage technology in order to save them time. These devices can be used in every area of a home, including the entryways, the kitchen, and the living room. While all of these devices make daily life more convenient, their lack of security makes hacker’s lives more convenient, too. The majority of IoT devices lack basic security features and most consumers install the devices in their homes with the default settings. This provides cyber criminals with the means to hack into a system with minimal time and effort. I focused on the security of a popular smart device, the smart light bulb. I compared the security features of two different smart light bulbs by running a series of penetration tests against them. The main aspects of the light bulbs that were tested include the phone application that controlled the light bulb and the Bluetooth protocol that the phone application used to communicate with the light bulb. These tests show the lack of security in common IoT devices is a serious problem that cyber criminals could take advantage of.
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