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Film Buff Reviews Stuff: A Film Review Blog

Description

This creative project centers on creating evaluative writing about film, in the form of a film review blog. Preliminary writing was done, in which the distinction was made between critical film writing and movie reviewing, as well as an analysis

This creative project centers on creating evaluative writing about film, in the form of a film review blog. Preliminary writing was done, in which the distinction was made between critical film writing and movie reviewing, as well as an analysis of how film critics have honed in their criticism and what makes their content effective for their audience. The rest of the writing for this project consists of a total of 15 reviews for 15 different movies released in 2017 and 2018. In these reviews, there is a brief introduction of the plot and context in which the film is made, followed by an evaluative analysis of what made the film effective or ineffective in achieving its artistic goals. The reviews involve an amalgamation of the content and topics taught in the Film and Media Studies program at Arizona State University, from screenwriting to cinematography. This process of writing reviews and being edited by the Director and Second Reader allows for the opportunity to find a unique writing voice and create content that is accessible for the wide audience that would be reading the work. All of the writing completed for this project (except for the "My Favorite Film Critics" piece) is compiled together in a WordPress blog, in an easily readable and accessible format. The blog itself serves as a way to reach the desired audience, as well as entice them to engage with the writing and the films being written about. This includes providing images and trailers for each respective film, to add a visual component to the writing. The final product is a unique way to engage with the content taught in the Film and Media Studies program, while simultaneously building a portfolio of writing that will be expanded upon and continued in the future.

Contributors

Agent

Created

Date Created
2018-05

The Art of Accessibility: A Portfolio of Science Communication

Description

This creative project is a portfolio of accessible science communication. It consists of three multimedia texts, each one written and designed for a different audience about a different topic. The first project is an article/report about the recent launch delays

This creative project is a portfolio of accessible science communication. It consists of three multimedia texts, each one written and designed for a different audience about a different topic. The first project is an article/report about the recent launch delays and cost increases for the James Webb Space Telescope, written for adults in their 40s-50s. The second project is a children’s picture book about Einstein’s theory of general relativity, written for homeschoolers in 6th grade. The third project is an educational animated video about the difference between gravity waves and gravitational waves, written for students in 7th grade.

Contributors

Agent

Created

Date Created
2019-05

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Utilizing Machine Learning Methods to Model Cryptocurrency

Description

Cryptocurrencies have become one of the most fascinating forms of currency and economics due to their fluctuating values and lack of centralization. This project attempts to use machine learning methods to effectively model in-sample data for Bitcoin and Ethereum using

Cryptocurrencies have become one of the most fascinating forms of currency and economics due to their fluctuating values and lack of centralization. This project attempts to use machine learning methods to effectively model in-sample data for Bitcoin and Ethereum using rule induction methods. The dataset is cleaned by removing entries with missing data. The new column is created to measure price difference to create a more accurate analysis on the change in price. Eight relevant variables are selected using cross validation: the total number of bitcoins, the total size of the blockchains, the hash rate, mining difficulty, revenue from mining, transaction fees, the cost of transactions and the estimated transaction volume. The in-sample data is modeled using a simple tree fit, first with one variable and then with eight. Using all eight variables, the in-sample model and data have a correlation of 0.6822657. The in-sample model is improved by first applying bootstrap aggregation (also known as bagging) to fit 400 decision trees to the in-sample data using one variable. Then the random forests technique is applied to the data using all eight variables. This results in a correlation between the model and data of 9.9443413. The random forests technique is then applied to an Ethereum dataset, resulting in a correlation of 9.6904798. Finally, an out-of-sample model is created for Bitcoin and Ethereum using random forests, with a benchmark correlation of 0.03 for financial data. The correlation between the training model and the testing data for Bitcoin was 0.06957639, while for Ethereum the correlation was -0.171125. In conclusion, it is confirmed that cryptocurrencies can have accurate in-sample models by applying the random forests method to a dataset. However, out-of-sample modeling is more difficult, but in some cases better than typical forms of financial data. It should also be noted that cryptocurrency data has similar properties to other related financial datasets, realizing future potential for system modeling for cryptocurrency within the financial world.

Contributors

Agent

Created

Date Created
2018-05

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Machine Learning Enabled Analytics for Health-Related Demographics: a Case Study Identifying Important Factors in Cardiac Disease

Description

Machine learning for analytics has exponentially increased in the past few years due to its ability to identify hidden insights in data. It also has a plethora of applications in healthcare ranging from improving image recognition in CT scans to

Machine learning for analytics has exponentially increased in the past few years due to its ability to identify hidden insights in data. It also has a plethora of applications in healthcare ranging from improving image recognition in CT scans to extracting semantic meaning from thousands of medical form PDFs. Currently in the BioElectrical Systems and Technology Lab, there is a biosensor in development that retrieves and analyzes data manually. In a proof of concept, this project uses the neural network architecture to automatically parse and classify a cardiac disease data set as well as explore health related factors impacting cardiac disease in patients of all ages.

Contributors

Created

Date Created
2018-05

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FastStat: Online Statistics Calculator

Description

FastStat is a responsive website designed to work on any handheld, laptop, or desktop device. It serves as a first step into statistical calculations, educating the user on the basics of statistical analysis, and guiding them as they perform analyses

FastStat is a responsive website designed to work on any handheld, laptop, or desktop device. It serves as a first step into statistical calculations, educating the user on the basics of statistical analysis, and guiding them as they perform analyses of their own using built-in calculators. The calculators available can perform z tests, t tests, chi square tests, and analysis of variance tests to determine significant characteristics of the user's data. Outputted data includes means, standard deviations, significance levels, applicable statistics, and worded results indicating the outcome of the performed test. With its clean design, FastStat directs the user in an intuitive manner to fill in the information needed, giving clear indications of what types of values are needed where and flagging descriptive error messages if any inputted values are incorrect. FastStat also implements a halt to calculations if any errors are found, which saves time by avoiding impossible calculations. Once complete, FastStat outputs a variety of information of use to the user in a clearly labeled manner. The calculators are designed in such a way that the user will know what information they will get out of the calculator before performing any calculations at all. Aside from the calculators, FastStat includes introductory pages designed to get users familiar with common statistical terms and the associated tests, solidifying its purpose as an introductory tool. All tests are described by their typical uses, necessary inputs, calculated outputs, and extra notes of importance. Many terms are defined for the purpose of statistics, complete with examples to help educate the user on the concepts. With the information available, even the newest statistician can learn and begin performing tests almost immediately.

Contributors

Agent

Created

Date Created
2018-12

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Prediction of heat transport in multiple tokamak devices with neural networks

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

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.

Contributors

Agent

Created

Date Created
2015-05

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Using Facebook to Examine Smoking Behavior through ""Quit Smoking"" Support Groups

Description

Background: As the growth of social media platforms continues, the use of the constantly increasing amount of freely available, user-generated data they receive becomes of great importance. One apparent use of this content is public health surveillance; such as for

Background: As the growth of social media platforms continues, the use of the constantly increasing amount of freely available, user-generated data they receive becomes of great importance. One apparent use of this content is public health surveillance; such as for increasing understanding of substance abuse. In this study, Facebook was used to monitor nicotine addiction through the public support groups users can join to aid their quitting process. Objective: The main objective of this project was to gain a better understanding of the mechanisms of nicotine addiction online and provide content analysis of Facebook posts obtained from "quit smoking" support groups. Methods: Using the Facebook Application Programming Interface (API) for Python, a sample of 9,970 posts were collected in October 2015. Information regarding the user's name and the number of likes and comments they received on their post were also included. The posts crawled were then manually classified by one annotator into one of three categories: positive, negative, and neutral. Where positive posts are those that describe current quits, negative posts are those that discuss relapsing, and neutral posts are those that were not be used to train the classifiers, which include posts where users have yet to attempt a quit, ads, random questions, etc. For this project, the performance of two machine learning algorithms on a corpus of manually labeled Facebook posts were compared. The classification goal was to test the plausibility of creating a natural language processing machine learning classifier which could be used to distinguish between relapse (labeled negative) and quitting success (labeled positive) posts from a set of smoking related posts. Results: From the corpus of 9,970 posts that were manually labeled: 6,254 (62.7%) were labeled positive, 1,249 (12.5%) were labeled negative, and 2467 (24.8%) were labeled neutral. Since the posts labeled neutral are those which are irrelevant to the classification task, 7,503 posts were used to train the classifiers: 83.4% positive and 16.6% negative. The SVM classifier was 84.1% accurate and 84.1% precise, had a recall of 1, and an F-score of 0.914. The MNB classifier was 82.8% accurate and 82.8% precise, had a recall of 1, and an F-score of 0.906. Conclusions: From the Facebook surveillance results, a small peak is given into the behavior of those looking to quit smoking. Ultimately, what makes Facebook a great tool for public health surveillance is that it has an extremely large and diverse user base with information that is easily obtainable. This, and the fact that so many people are actually willing to use Facebook support groups to aid their quitting processes demonstrates that it can be used to learn a lot about quitting and smoking behavior.

Contributors

Agent

Created

Date Created
2016-05

Computer-Aided Space-Time-Energy Budgets for Round-Trip Relativistic Excursions

Description

Since the acceptance of Einstein's special theory of relativity by the scientific community, authors of science fiction have used the concept of time dilation to permit seemingly impossible feats. Simple spacecraft acceleration schemes involving time dilation have been considered by

Since the acceptance of Einstein's special theory of relativity by the scientific community, authors of science fiction have used the concept of time dilation to permit seemingly impossible feats. Simple spacecraft acceleration schemes involving time dilation have been considered by scientists and fiction writers alike. Using an original Java program based upon the differential equations for special relativistic kinematics, several scenarios for round trip excursions at relativistic speeds are calculated and compared, with particular attention to energy budget and relativistic time passage in all relevant frames.

Contributors

Agent

Created

Date Created
2015-05

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stillHUMAN: An Educational Empowerment Program

Description

The purpose of this creative project was to establish the foundation of an educational program that teaches financial literacy to the local homeless population. The name of this program is stillHUMAN. The project consisted of two parts, a needs analysis

The purpose of this creative project was to establish the foundation of an educational program that teaches financial literacy to the local homeless population. The name of this program is stillHUMAN. The project consisted of two parts, a needs analysis and a prototyping phase. The needs analysis was conducted at the Phoenix Rescue Mission Center, a faith-based homeless shelter that caters to male "clients", through written surveys and one-on-one interviews. Before interviewing the clients, the team acquired IRB approval as well as consent from the Center to carry out this study. These needs were then organized into a House of Quality. We concluded from Part 1 that we would need to create 3 - 7-minute-long video modules that would be available on an online platform and covered topics including professional development, budgeting, credit, and Internet literacy. In order to commence Part 2, each team member recorded a video module. These three videos collectively conveyed instruction regarding how to write a resume, use the Internet and fill out an application online, and how to budget money. These videos were uploaded to YouTube and shown to clients at Phoenix Rescue Mission, who were each asked to fill out a feedback survey afterwards. The team plans to use these responses to improve the quality of future video modules and ultimately create a holistic lesson plan that covers all financial literacy topics the clients desire. A website was also made to store future videos. The team plans to continue with this project post-graduation. Future tasks include creating and testing the a complete lesson plan, establishing a student organization at Arizona State University and recruiting volunteers from different disciplines, and creating an on-site tutoring program so clients may receive individualized attention. Once the lesson plan is demonstrated to be effective at Phoenix Rescue Mission, we plan to administer this lesson plan at other local homeless shelters and assess its efficacy in a non-faithbased and non-male environment. After a successful financial literacy program has been created, we aim to create lesson plans for other topics, including health literacy, human rights, and basic education. Ultimately stillHUMAN will become a sustainable program that unites the efforts of students and professionals to improve the quality of life of the homeless population.

Contributors

Agent

Created

Date Created
2016-05

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Predicting Trends on Twitter with Time Series Analysis

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

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.

Contributors

Created

Date Created
2015-05