While living in Seoul, South Korea as an English teacher back in 2004-2009, I came across an article about Second Life – a virtual world with its own scripting language.   After downloading the program and logging in, I instantly saw its potentials for teaching.  Suddenly I was connected, in Avatar form, to multitudes of people around the World! Could it be used for education? – I asked myself.  After a few minutes of exploration, I soon discovered that, not only could I upload my own textures to this platform, but it even had its own scripting language! Wait whaaaat?  I was excited.

Fast forward 1 year later, and on the side – I became a well known Avatar – Fire Centaur – creating free educational gaming tools to educators around the World.

Below is a video exploration of a Game I created called “Puzzle Fun”. Please let me know what you think.

All the scripts for the game can be found on github here https://github.com/firecentaur/lsl-scripts

Your avatar in Secondlife can get the game for FREE here: https://marketplace.secondlife.com/p/Single-Multi-Player-Puzzle-Fun-14-great-for-events/2766235

Another cool project I worked on recently at Globify.com was a subtitle search engine for searching through subtitles by parts of speech! Normally, search engines only allow the user to specify the search term they are interested in. In our use case however, we wanted the ability for our editors to be able to specify the parts of speech as well, so that they could find appropriate educational content quicker.

In the picture below, you will see a screenshot of the Parts of Speech search engine I created.  In the top bar is a query field, then a combo-box that allows the user to specify Noun, Verb, Adverb, or Adjective.  Next, we have a search button.  below that is the results of the search.  On the right, are a list of videos where the search term was found. On the left is the current video selected, along with a list of its subtitles. For each subtitle, I then list the Stanford Taggers Parts of Speech conversion.  The search query is highlighted in Yellow.  The user can mouse over any term to see the converted parts of speech.

How I did it:

Converting Each word into its constituent parts of speech
In order to convert each word of our subtitle database into its constituent parts of speech, I needed to dive into the world of Natural Language Processing.  For this, Google sent me over to The Stanford Natural language Processing Group.  The great folks at the Stanford NLP have graciously created a very cool  opensource POS tagger called: Stanford Log-linear Part-of-Speech Tagger. This software however, was written in JAVA, so in order to integrate it with our Laravel Backend I would need a PHP Wrapper class.  Fortunately, I was able to utilize an already existing PHP-Wrapper written by of Patrick Schur.  Once integrated into Laravel, I created a quick test – and wham-mo! I saw beautiful verbs, adjectives, and noun classifications scrolling across my screen.  With a functioning prototype in place, I then created code to start converting our entire subtitle database… but that’s when I ran into a speed roadblock. In order to process several millions of words, I would need to dive into the world of Big Data.

Big Data and Parallel Processing on Heroku with Laravel / Amazon SQS
Fortunately, the hosting platform Heroku allows developers to easily spin up armies of worker servers with a few key presses (and some configuration of course 😉

But wait a second, its not that easy! Heroku doesn’t provide the software to coordinate process delegation! For that, I utilized Laravel’s built in Queuing system, and connected it to Amazon’s Simple Queue Service (SQS).  With these tools wired up and ready, I then launched an army of servers on our data to churn out the parts of speech word by word.

Delivering fast Search Results with AWS Elastic Search and Kibana
In order to search through millions of database records for the search query, a good indexing solution was needed.  For this, I turned to Amazon Elastic search service.  I had used AWS Elastic Search before, so it was just a mater of getting the search query right.  To aid the development of the search query, I used Amazons Built in Query visualizer called Kibana.  This too took a bit of configuration, but once set up, I crafted the correct query, and then created an indexing Job in Laravel, which I again through at my army of Heroku servers for processing…. and voila — a subtitle search engine filtered by parts of speech!

This project was quite fun to put together.  I am amazed at the amount of power a developer can harness when utilizing Laravel Queues, Amazon SQS and Heroku!

For those interested, here is a list of some of the software used for this project:

Technologies Used

While working for Globify.com as their Chief Technology Officer, I was tasked to create a feature for our software that would allow editors to easily translate subtitles which they had created into 28 different languages.To achieve this, I utilized the the following technologies:

• Google Translate API
• Elastic Search
• AngularJS

The first order of business was to properly install Google’s PHP SDK into Yii. I then created an endpoint in our VideoController to receive the request from AngularJs. Results output were then received and rendered by our AngularJs User interface.

While working on this task, we quickly found out that the literal translations produce by Google Translate weren’t very useful to us for our educational audience. So we decided to create an interface which would allow our editing team the ability to rapidly tweak the translations produced by Google.

So, we came up with the idea of a “Simple Subtitle”. Rather than sending the original form of the subtitle to Google Translate, we created an additional field on our subtitles table whereupon editors could enter a simpler version of the actual subtitle, which would be used to get a better translated result from Google.

With this approach, editors could quickly create a version of the subtitle that produced satisfactory translations in their native language. Once the editor was happy with the translation achieved, we then used the interface to send another request to Google translate to process the remaining 27 languages using the Simple Subtitle. This improved our translated results immensely.

Below is a video I created for our team after the feature was delivered: