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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

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
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Description

When examining the average college campus, it becomes obvious that students feel rushed from one place to another as they try to participate in class, clubs, and extracurricular activities. One way that students can feel more comfortable and relaxed around campus is to introduce the aspect of gaming. Studies show

When examining the average college campus, it becomes obvious that students feel rushed from one place to another as they try to participate in class, clubs, and extracurricular activities. One way that students can feel more comfortable and relaxed around campus is to introduce the aspect of gaming. Studies show that “Moderate videogame play has been found to contribute to emotional stability” (Jones, 2014). This demonstrates that the stress of college can be mitigated by introducing the ability to interact with video games. This same concept has been applied in the workplace, where studies have shown that “Gaming principles such as challenges, competition, rewards and personalization keep employees engaged and learning” (Clark, 2020). This means that if we manage to gamify the college experience, students will be more engaged which will increase and stabilize the retention rate of colleges which utilize this type of experience. Gaming allows students to connect with their peers in a casual environment while also allowing them to find resources around campus and find new places to eat and relax. We plan to gamify the college experience by introducing augmented reality in the form of an app. Augmented reality is “. . . a technology that combines virtual information with the real world” (Chen, 2019). College students will be able to utilize the resources and amenities available to them on campus while completing quests that help them within the application. This demonstrates the ability for video games to engage students using artificial tasks but real actions and experiences which help them feel more connected to campus. Our Founders Lab team has developed and tested an AR application that can be used to connect students with their campus and the resources available to them.

ContributorsRangarajan, Padmapriya (Co-author) / Klein, Jonathan (Co-author) / Li, Shimei (Co-author) / Byrne, Jared (Thesis director) / Pierce, John (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

Stress for college students is nothing new and as more kids go to college the number of cases are on the rise. This issue is apparent at colleges across the nation including Arizona State University. StreetWise aims to help students prevent or appropriately deal with stress through interactive lessons teaching

Stress for college students is nothing new and as more kids go to college the number of cases are on the rise. This issue is apparent at colleges across the nation including Arizona State University. StreetWise aims to help students prevent or appropriately deal with stress through interactive lessons teaching students life skills, social skills, and emotional intelligence.<br/>In order to prove the value of our service, StreetWise conducted a survey that asked students about their habits, thoughts on stress, and their future. Students from Arizona State University were surveyed with questions on respondent background, employment, number one stressor, preferred learning method, and topics that students were interested in learning. We found that students’ number one stressor was school but was interested in learning skills that would prepare them for their future after graduation. We used the results to make final decisions so that StreetWise could offer lessons that students would get the most value out of. This led to us conducting a second survey which included mock ups of the website, examples of interactive lesson plans, and an overview of the app. Students from the first survey were surveyed in addition to new respondents. This survey was intended for us to ensure that our service would maintain its value to students with the aesthetic and interface that we envisioned.

ContributorsWard, William Henry (Co-author) / Ahir, Hiral (Co-author) / Compton, Katherine (Co-author) / Byrne, Jared (Thesis director) / Hall, Rick (Committee member) / Materials Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description

Stress for college students is nothing new and as more kids go to college the number of cases are on the rise. This issue is apparent at colleges across the nation including Arizona State University. StreetWise aims to help students prevent or appropriately deal with stress through interactive lessons teaching

Stress for college students is nothing new and as more kids go to college the number of cases are on the rise. This issue is apparent at colleges across the nation including Arizona State University. StreetWise aims to help students prevent or appropriately deal with stress through interactive lessons teaching students life skills, social skills, and emotional intelligence.<br/>In order to prove the value of our service, StreetWise conducted a survey that asked students about their habits, thoughts on stress, and their future. Students from Arizona State University were surveyed with questions on respondent background, employment, number one stressor, preferred learning method, and topics that students were interested in learning. We found that students’ number one stressor was school but was interested in learning skills that would prepare them for their future after graduation. We used the results to make final decisions so that StreetWise could offer lessons that students would get the most value out of. This led to us conducting a second survey which included mock ups of the website, examples of interactive lesson plans, and an overview of the app. Students from the first survey were surveyed in addition to new respondents. This survey was intended for us to ensure that our service would maintain its value to students with the aesthetic and interface that we envisioned.

ContributorsAhir, Hiral V (Co-author) / Compton, Katherine (Co-author) / Ward, William (Co-author) / Byrne, Jared (Thesis director) / Hall, Rick (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Description
The OMFIT (One Modeling Framework for Integrated Tasks) modeling environment and the BRAINFUSE module have been deployed on the PPPL (Princeton Plasma Physics Laboratory) computing cluster with modifications that have rendered the application of artificial neural networks (NNs) to the TRANSP databases for the JET (Joint European Torus), TFTR (Tokamak

The OMFIT (One Modeling Framework for Integrated Tasks) modeling environment and the BRAINFUSE module have been deployed on the PPPL (Princeton Plasma Physics Laboratory) computing cluster with modifications that have rendered the application of artificial neural networks (NNs) to the TRANSP databases for the JET (Joint European Torus), TFTR (Tokamak Fusion Test Reactor), and NSTX (National Spherical Torus Experiment) devices possible through their use. This development has facilitated the investigation of NNs for predicting heat transport profiles in JET, TFTR, and NSTX, and has promoted additional investigations to discover how else NNs may be of use to scientists at PPPL. In applying NNs to the aforementioned devices for predicting heat transport, the primary goal of this endeavor is to reproduce the success shown in Meneghini et al. in using NNs for heat transport prediction in DIII-D. Being able to reproduce the results from is important because this in turn would provide scientists at PPPL with a quick and efficient toolset for reliably predicting heat transport profiles much faster than any existing computational methods allow; the progress towards this goal is outlined in this report, and potential additional applications of the NN framework are presented.
ContributorsLuna, Christopher Joseph (Author) / Tang, Wenbo (Thesis director) / Treacy, Michael (Committee member) / Orso, Meneghini (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Department of Physics (Contributor)
Created2015-05
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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.

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
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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

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
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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

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
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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

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
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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

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