Matching Items (3)
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- All Subjects: Website
- Creators: Anderson, Derrick
Description
Visualizations are an integral component for communicating and evaluating modern networks. As data becomes more complex, info-graphics require a balance between visual noise and effective storytelling that is often restricted by layouts unsuitable for scalability. The challenge then rests upon researchers to effectively structure their information in a way that allows for flexible, transparent illustration. We propose network graphing as an operative alternative for demonstrating community behavior over traditional charts which are unable to look past numeric data. In this paper, we explore methods for manipulating, processing, cleaning, and aggregating data in Python; a programming language tailored for handling structured data, which can then be formatted for analysis and modeling of social network tendencies in Gephi. We implement this data by applying an algorithm known as the Fruchterman-Reingold force-directed layout to datasets of Arizona State University’s research and collaboration network. The result is a visualization that analyzes the university’s infrastructure by providing insight about community behaviors between colleges. Furthermore, we highlight how the flexibility of this visualization provides a foundation for specific use cases by demonstrating centrality measures to find important liaisons that connect distant communities.
ContributorsMcMichael, Jacob Andrew (Author) / LiKamWa, Robert (Thesis director) / Anderson, Derrick (Committee member) / Goshert, Maxwell (Committee member) / Arts, Media and Engineering Sch T (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
This paper introduces MisophoniAPP, a new website for managing misophonia. It will briefly discuss the nature of this chronic syndrome, which is the experience of reacting strongly to certain everyday sounds, or “triggers”. Various forms of Cognitive Behavioral Therapy and the Neural Repatterning Technique are currently used to treat misophonia, but they are not guaranteed to work for every patient. Few apps exist to help patients with their therapy, so this paper describes the design and creation of a new website that combines exposure therapy,
relaxation, and gamification to help patients alleviate their misophonic reflexes.
relaxation, and gamification to help patients alleviate their misophonic reflexes.
ContributorsNoziglia, Rachel Elisabeth (Author) / McDaniel, Troy (Thesis director) / Anderson, Derrick (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Although Spotify’s extensive library of songs are often seen broken up by “Top 100” and main lyrical genres, these categories are primarily based on popularity, artist and general mood alone. If a user wanted to create a playlist based on specific or situationally specific qualifiers from their own downloaded library, he/she would have to hand pick songs that fit the mold and create a new playlist. This is a time consuming process that may not produce the most efficient result due to human error. The objective of this project, therefore, was to develop an application to streamline this process, optimize efficiency, and fill this user need.
Song Sift is an application built using Angular that allows users to filter and sort their song library to create specific playlists using the Spotify Web API. Utilizing the audio feature data that Spotify attaches to every song in their library, users can filter their downloaded Spotify songs based on four main attributes: (1) energy (how energetic a song sounds), (2) danceability (how danceable a song is), (3) valence (how happy a song sounds), and (4) loudness (average volume of a song). Once the user has created a playlist that fits their desired genre, he/she can easily export it to their Spotify account with the click of a button.
Song Sift is an application built using Angular that allows users to filter and sort their song library to create specific playlists using the Spotify Web API. Utilizing the audio feature data that Spotify attaches to every song in their library, users can filter their downloaded Spotify songs based on four main attributes: (1) energy (how energetic a song sounds), (2) danceability (how danceable a song is), (3) valence (how happy a song sounds), and (4) loudness (average volume of a song). Once the user has created a playlist that fits their desired genre, he/she can easily export it to their Spotify account with the click of a button.
ContributorsDiMuro, Louis (Author) / Balasooriya, Janaka (Thesis director) / Chen, Yinong (Committee member) / Arts, Media and Engineering Sch T (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05