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projects:hack_ucsc_2015 [2015-01-18 23:44]
jbergamini@jeff.cis.cabrillo.edu
projects:hack_ucsc_2015 [2015-01-18 23:45]
jbergamini@jeff.cis.cabrillo.edu
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 {{:projects:clearskin.png?direct&400|Clear Skin screenshot}} {{:projects:clearskin.png?direct&400|Clear Skin screenshot}}
  
-We are building a database for skincare products that allows the user to search for products by ingredients. We utilized Kimono Lab's chrome extension to build custom API's used to scrape websites for ingredients. We used SQLite for the database itself, PHP on the server, and AJAX. +We are building a database for skincare products that allows the user to search for products by ingredients. We utilized Kimono Lab's chrome extension to build custom API's used to scrape websites for ingredients. We used SQLite for the database itself, PHP on the server, and AJAX. 
  
 Team members: Christopher Chen, Bruno Hernandez, Nikolas Payne, Francisco Piva Team members: Christopher Chen, Bruno Hernandez, Nikolas Payne, Francisco Piva
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 {{:projects:whatsmissing.png?direct&400|What's Missing screenshot}} {{:projects:whatsmissing.png?direct&400|What's Missing screenshot}}
  
-We made a media-analysis tool that takes a primary source and an article and shows you exactly how much of the primary source the article's author failed to include.+We made a media-analysis tool that takes a primary source and an article and shows you exactly how much of the primary source the article's author failed to include.
  
-It finds the greatest common subsequences between the texts by creating a hash table with the location of all the words in the primary source. It uses this hash to guess where all the possible sub-sequences might start and then it iterates forward and compares how much the sequences have in common with each-other using a scoring mechanism that takes into account how long the words are and how different (we used something called the Levenshtein distance to give each word a rating.) It has a few warts and looks like something we came up with in a weekend, but it runs fast and works exactly like we thought it would. We even caught some biased reporting - look at our screenshot!+It finds the greatest common subsequences between the texts by creating a hash table with the location of all the words in the primary source. It uses this hash to guess where all the possible sub-sequences might start and then it iterates forward and compares how much the sequences have in common with each-other using a scoring mechanism that takes into account how long the words are and how different (we used something called the Levenshtein distance to give each word a rating.) It has a few warts and looks like something we came up with in a weekend, but it runs fast and works exactly like we thought it would. We even caught some biased reporting - look at our screenshot!
  
 Team members: Bradley Lacombe, Will Mosher, Julya Wacha Team members: Bradley Lacombe, Will Mosher, Julya Wacha
 
projects/hack_ucsc_2015.txt · Last modified: 2015-01-30 12:23 by jbergamini@jeff.cis.cabrillo.edu · []
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