Current and past senior research
This page lists senior research students for which I was the advisor. To browse all Math/CS Department senior research over the years, visit the Math/CS Department website.
Current senior research (2016-2017)
Michael Clay: “Git-Advise: An Automated Git Workflow Adviser”
Proposal: We propose to build Git-Advise, a software tool that produces a sequence of Git commands that are able to transform a Git repository’s current state into a goal state described by the user in a domain-specific menu system. Existing planning technologies such as the Pyhop Planner and Hierarchical Task Network planning structure will be used to build the foundation of Git-Advise. This research project will be divided into two phases. First, we will build an HTN-Pyhop “domain file” that represents the various commands that Git supports for manipulating repositories. Second, we will build a graphical user interface that allows users to describe their goals. The inspiration for this project comes from the EAAI Model AI assignment “Git Planner.” We will expand on their ideas to build a much larger and more capable Git adviser tool.
Christian Decker: “Scene by Scene Script Generation for Live Action Hollywood Movies”
Proposal: Today there is no “Google for video” that actually searches the content of the video. Content-based video search can be achieved by indexing video scene transcriptions much like a film script. We propose to develop a tool that automatically processes video and produces a transcript representing its contents. This processing step may involve scene segmentation, object recognition, behavior recognition, face recognition, and speech transcription. We propose to address each one of these as time permits. In the first semester we expect to have scene segmentation accomplished by implementing existing research. During the second semester we will apply techniques for object recognition, face recognition, speech transcription, and develop techniques for scene transcription.
Jacob Hell: “Advaisor: A Rule Based Expert System for Academic Advising”
Proposal: Academic advising is an essential process for improving student retention and academic performance. When an advisor and student come together in an academic advising session, the student should leave with their questions answered and insight gained toward their academic future. However, there are some questions that a student might have that an advisor might not be prepared to answer. Such questions might relate to majors and minors in other disciplines with which the advisor is not familiar, or complex constraint satisfaction questions regarding graduation requirements and timelines. We believe that the advisor may be freed of much of the burden of delving into the Stetson Catalog and Degree Audit tools, and that advising time may be better spent interacting with the student than solving such queries. We propose a system that will meet this need by engaging in hypothetical reasoning to produce alternative pathways that meet students’ needs and interests. The system will internally represent facts and constraints from the Stetson Catalog and provide advisors and students with a user interface that supports complex queries about coursework, degree requirements, and elective courses based on student interests.
Richard Roe: “Denial of Service via Internet of Things Devices: Attack Methodologies and Mitigation Techniques”
Proposal: The purpose of this research is to compare the effectiveness of traditional Denial of Service (DoS) attack vectors to a new attack method that is specifically designed for use in devices that have limited resources, such as Internet of Things (IoT) devices. New mitigation techniques will also be explored to help prevent, or reduce the effectiveness of, these attacks. While classical DoS attacks generally require both a large source of computing power and a specially crafted payload to be able to efficiently render the target machine or service inoperable, this research will focus on utilizing an attack that uses a generalized payload that targets a wide variety of internet services, and uses as little resources as possible. We will port the attack to common DoS utilities, as well as to a powerful IoT worm, so that the original tools’ attack methods can be compared to the new attack’s effectiveness and resource consumption. Once done, they will again be compared, but when attacking new mitigation techniques specifically designed to thwart both these and other attacks of their class. The results of this research can be applied to helping defend internet-facing web services from attack in both the public and private sector, because a free and open local proxy is cheaper and easier to setup than an online, paid, cloud solution. We aim to study the effectiveness of different denial of service attacks, and to develop a mitigation solution that can help to prevent these attacks in a way that does not affect the performance of the target when under normal usage.
Past senior research
Melissa Abramson: “Signer-Independent Recognition of Static ASL Signs”
As the Deaf community continues to grow, so does the market for tools that aid communication between the Deaf and hearing communities. One such tool is software for automatically recognizing ASL signs that could assist in learning and communicating in ASL. We have investigated the development of such a system that recognizes 28 static ASL signs. We have also developed several tools for data collection, modification, and classification. We found several common machine learning techniques that are not ideal to use for ASL recognition. We also explored the use of Caffe, a deep learning framework using convolutional neural networks. Due to hardware limitations and time constraints, we were not able to complete the training using Caffe and thus are unable to confirm if this is the ideal approach.
Marisa Gomez: “A Mobile Fitness Application for Asthma Sufferers”
Asthma sufferers can experience shortness of breath, wheezing and coughing during moderate or high intensity workouts. We developed a mobile fitness application that enables asthma sufferers to safely achieve their fitness goals without having to consult a personal trainer. This mobile application will present exercises for the user to complete, and provide a self rating form for after completion of the exercise. This form will indicate whether the user experienced symptoms of an asthma attack, difficulty in breathing, ease of completing the exercise, and preference for repeating the exercise at a later date. Once the user has completed twentyfive exercises, the application will offer to suggest an exercise based on the learned user preferences and health status. The application will calculate the probability of the user experiencing symptoms for each available exercise, and present the exercise that is the most safe.
Joshua Letcher: “A Genetic Approach to the University Timetabling Problem”
I propose two approaches to solving the University Timetabling problem. In the first approach, an optimal solution will be shown using Mixed Integer Programming (MIP). The second approach uses a genetic algorithm to ease the computational cost associated with the MIP approach. While the solutions given by the genetic approach may not be optimal, they are near optimal and much faster to find.
Alex Ordonez: “Analysis of the Expected Payout of Bitcoin Mining on Common Consumer Hardware”
Bitcoin does not yet have a solid definition. Some call it a cryptocurrency, some a commodity, some an investment target. Bitcoins can be acquired through distributed computing. Bitcoin mining, however, has an interesting catch. Only one block is added to the Blockchain every ten minutes. Every ten minutes, a Proof of Work is submitted and accepted by the Network. This Proof of Work problem can be distributed among many machines, such as dedicated servers. In this research, we will analyze the implementation of passive, fault-tolerant, client-side distributed Bitcoin mining. Key points of interest include speed, energy efficiency, network latency, client reliability, and population size to optimize Bitcoins acquired per minute. One of the most anticipated outcomes of this research is the possibility to reduce the dependency on advertising revenue on websites and mobile applications. However, the optimizations for solving the Proof of Work involved in mining Bitcoins can be applied to other problems, such as protein folding.
Katie Porterfield: “Brain Drain: Using Brainwaves and Machine Learning to Detect Errors in Human Problem Solving”
The Muse Headband is a simple to use EEG machine that provides realtime measurements of brain waves. Using this headband, we built a model using big data and machine learning techniques to interpret brain wave patterns to create a real time feedback system that helps the user understand their cognitive thinking while solving a problem. Once further developed, this model could then be applied in an educational setting to help a student understand how they are progressing through a problem without the interactions of a teacher to support them.