Matching Items (89)
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- All Subjects: Machine Learning
- All Subjects: ios
- Creators: Computer Science and Engineering Program
Description
Robots are often used in long-duration scenarios, such as on the surface of Mars,where they may need to adapt to environmental changes. Typically, robots have been built specifically for single tasks, such as moving boxes in a warehouse or surveying construction sites. However, there is a modern trend away from human hand-engineering and toward robot learning. To this end, the ideal robot is not engineered,but automatically designed for a specific task. This thesis focuses on robots which learn path-planning algorithms for specific environments. Learning is accomplished via genetic programming. Path-planners are represented as Python code, which is optimized via Pareto evolution. These planners are encouraged to explore curiously and efficiently. This research asks the questions: “How can robots exhibit life-long learning where they adapt to changing environments in a robust way?”, and “How can robots learn to be curious?”.
ContributorsSaldyt, Lucas P (Author) / Ben Amor, Heni (Thesis director) / Pavlic, Theodore (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
This project explores how modern mobile technology can be used to provide support for domestic violence victims. The goal of the project is to create a proof-of-concept iOS mobile application that maintains a discreet safety front and provides domestic violence victims with resources and safety planning. The design and implementation are disguised as a hair salon app to maintain a low profile on the user’s phone. The HairHelp app features quick exit navigation, a secure database to store a user’s private and personal documents in case of emergency, and a checklist of safety planning measures. The steps taken in this project serve as the foundation for a larger project in the long term.
ContributorsShovkovy, Sophia (Author) / Balasooriya, Janaka (Thesis director) / Wilkey, Douglas (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Twitter, the microblogging platform, has grown in prominence to the point that the topics that trend on the network are often the subject of the news and other traditional media. By predicting trends on Twitter, it could be possible to predict the next major topic of interest to the public. With this motivation, this paper develops a model for trends leveraging previous work with k-nearest-neighbors and dynamic time warping. The development of this model provides insight into the length and features of trends, and successfully generalizes to identify 74.3% of trends in the time period of interest. The model developed in this work provides understanding into why par- ticular words trend on Twitter.
ContributorsMarshall, Grant A (Author) / Liu, Huan (Thesis director) / Morstatter, Fred (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2015-05
Description
Bots tamper with social media networks by artificially inflating the popularity of certain topics. In this paper, we define what a bot is, we detail different motivations for bots, we describe previous work in bot detection and observation, and then we perform bot detection of our own. For our bot detection, we are interested in bots on Twitter that tweet Arabic extremist-like phrases. A testing dataset is collected using the honeypot method, and five different heuristics are measured for their effectiveness in detecting bots. The model underperformed, but we have laid the ground-work for a vastly untapped focus on bot detection: extremist ideal diffusion through bots.
ContributorsKarlsrud, Mark C. (Author) / Liu, Huan (Thesis director) / Morstatter, Fred (Committee member) / Barrett, The Honors College (Contributor) / Computing and Informatics Program (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2015-05
Description
Food safety is vital to the well-being of society; therefore, it is important to inspect food products to ensure minimal health risks are present. A crucial phase of food inspection is the identification of foreign particles found in the sample, such as insect body parts. The presence of certain species of insects, especially storage beetles, is a reliable indicator of possible contamination during storage and food processing. However, the current approach to identifying species is visual examination by human analysts; this method is rather subjective and time-consuming. Furthermore, confident identification requires extensive experience and training. To aid this inspection process, we have developed in collaboration with FDA analysts some image analysis-based machine intelligence to achieve species identification with up to 90% accuracy. The current project is a continuation of this development effort. Here we present an image analysis environment that allows practical deployment of the machine intelligence on computers with limited processing power and memory. Using this environment, users can prepare input sets by selecting images for analysis, and inspect these images through the integrated pan, zoom, and color analysis capabilities. After species analysis, the results panel allows the user to compare the analyzed images with referenced images of the proposed species. Further additions to this environment should include a log of previously analyzed images, and eventually extend to interaction with a central cloud repository of images through a web-based interface. Additional issues to address include standardization of image layout, extension of the feature-extraction algorithm, and utilizing image classification to build a central search engine for widespread usage.
ContributorsMartin, Daniel Luis (Author) / Ahn, Gail-Joon (Thesis director) / Doupé, Adam (Committee member) / Xu, Joshua (Committee member) / Computer Science and Engineering Program (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
When planning a road trip today, there are solutions that let the user know what comes along their route, but the user is often presented with too much information, which can overwhelm the user. They are provided suggestions all along the route, not just at those times when they would be needed. RoutePlanner simply takes all that information and only presents that data to the user, that they would need at a particular time. Gas station suggestions would show when the gas tank range is going to be hit soon, and restaurant suggestions would only be shown around lunch time. The iOS app takes in the users origin and destination and provides the user the route as given by GoogleMaps, and then various stop suggestions at their given time. Each route that is obtained, is broken down into a number of steps, which are basically a connection of coordinate points. These coordinate point collections are used to point to a location at a certain distance or duration away from the origin. Given a coordinate, we query the APIs for places of interest and move to the next stop, until the end of the route.
ContributorsDamania, Harsh Abhay (Author) / Balasooriya, Janaka (Thesis director) / Faucon, Christophe (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2014-12
Description
As technology's influence pushes every industry to change, healthcare professionals must move to a more connected model. The nearly ubiquitous presence of smartphones presents a unique opportunity for physicians to collect and process data from their patients more frequently. The Mayo Clinic, in partnership with the Barrett Honors College, has designed and developed a prototype smartphone application targeting palliative care patients. The application collects symptom data from the patients and presents it to the doctors. This development project serves as a proof-of-concept for the application, and shows how such an application might look and function. Additionally, the project has revealed significant possibilities for the future of the application.
ContributorsGaney, David Howard (Author) / Balasooriya, Janaka (Thesis director) / Lipinski, Christopher (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor) / Computer Science and Engineering Program (Contributor)
Created2015-05
Description
Due to the popularity of the movie industry, a film's opening weekend box-office performance is of great interest not only to movie studios, but to the general public, as well. In hopes of maximizing a film's opening weekend revenue, movie studios invest heavily in pre-release advertisement. The most visible advertisement is the movie trailer, which, in no more than two minutes and thirty seconds, serves as many people's first introduction to a film. The question, however, is how can we be confident that a trailer will succeed in its promotional task, and bring about the audience a studio expects? In this thesis, we use machine learning classification techniques to determine the effectiveness of a movie trailer in the promotion of its namesake. We accomplish this by creating a predictive model that automatically analyzes the audio and visual characteristics of a movie trailer to determine whether or not a film's opening will be successful by earning at least 35% of a film's production budget during its first U.S. box office weekend. Our predictive model performed reasonably well, achieving an accuracy of 68.09% in a binary classification. Accuracy increased to 78.62% when including genre in our predictive model.
ContributorsWilliams, Terrance D'Mitri (Author) / Pon-Barry, Heather (Thesis director) / Zafarani, Reza (Committee member) / Maciejewski, Ross (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2014-05
Description
With the development of technology, there has been a dramatic increase in the number of machine learning programs. These complex programs make conclusions and can predict or perform actions based off of models from previous runs or input information. However, such programs require the storing of a very large amount of data. Queries allow users to extract only the information that helps for their investigation. The purpose of this thesis was to create a system with two important components, querying and visualization. Metadata was stored in Sedna as XML and time series data was stored in OpenTSDB as JSON. In order to connect the two databases, the time series ID was stored as a metric in the XML metadata. Queries should be simple, flexible, and return all data that fits the query parameters. The query language used was an extension of XQuery FLWOR that added time series parameters. Visualization should be easily understood and be organized in a way to easily find important information and details. Because of the possibility of a large amount of data being returned from a query, a multivariate heat map was used to visualize the time series results. The two programs that the system performed queries on was Energy Plus and Epidemic Simulation Data Management System. By creating such a system, it would be easier for people of the project's fields to find the relationship between metadata that leads to the desired results over time. Over the time of the thesis project, the overall software was completed, however the software must be optimized in order to take the enormous amount of data expected from the system.
ContributorsTse, Adam Yusof (Author) / Candan, Selcuk (Thesis director) / Chen, Xilun (Committee member) / Barrett, The Honors College (Contributor) / School of Music (Contributor) / Computer Science and Engineering Program (Contributor)
Created2015-05
Description
The face of computing is constantly changing. Wearable computers in the form of glasses or watches are becoming more and more common. These devices have very small screens (measured in millimeters), and users often interact with them through voice input and audio feedback. Weather is one of the most regularly checked app category on smart devices, but weather results on these devices are often limited to raw data, canned responses, or sentence templates with numbers plugged in. The goal for this project was to build a system that could generate weather forecast text, which could then be read to a user through text-to-speech. By using methods in language generation, the system can generate weather forecast text in millions of different ways. This is all computed locally, and it covers every possible weather case. In order to generate natural weather forecast texts, the system retrieved raw weather data from a weather API and created the text through six methods: content determination, document structuring, sentence aggregation, lexical choice, referring expression generation, and text realization. Content determination is the process of deciding on what information to include in a computer generated text. The document structuring phase deals with the order and structure of the information. Sentence aggregation is the merging of similar sentences to improve readability and to reduce redundancy. Lexical choice is the process of putting words to concepts. Referring expression generation is the process of identifying objects, regions, time periods, and locations within a text. Finally text realization involves creating sentences with proper syntax, morphology, and orthography. Through these six stages, a system was developed that could generate unique weather forecast text from raw data accurately and efficiently. It was built for iOS devices with Apple's new programming language, Swift, and it will be ported to the Apple Watch when the API is fully opened to developers.
ContributorsJorgensen, Jacob Paul (Author) / Baral, Chitta (Thesis director) / Faucon, Christophe (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2015-05