At the end of last semester I (Anna Katros), was struggling to choose a project for the next semester (the one I am currently at the end of). I spoke with NuVu fellow Anjali and we came up with a wearable oxygen bar powered by Bioluminescence. Anjali then decided because we both expressed an interest in my previous project (Aquaponics), that Isabella Bogdahn should join me in this project. Our final project ending up being the Oxygen Wearable in which we dropped the bioluminescent algae for chlorella algae which is best for oxygen production. The Oxygen Wearable is a wearable skirt that links clear, algae filtered air to the wearer via tubing and pumps. 

This past semester, instead of Isabella and I belonging to a specifically themed studio, we were in Open Innovation in which we presented our idea to a NuVu fellow, and they decided to accept or decline our proposal. This new concept is one of the many ways NuVu can help to prepare us for the future. I spoke a lot with Ryan (a NuVu fellow) who is currently going through the process of gaining his PhD. This conversation as well as a trip to NuVu studios in Cambridge where I met the CEO of NuVu's advisor helped me understand the importance of a thesis. Ryan compared his PhD proposal, thesis, and dissertation to the way that NuVu is formatted. In Cambridge, I learned about the CEO, Saeed Arida's dissertation topic. The topic was based on original and extensive research just like our ideas for NuVu projects are. The amount of research Isabella and I found on growing algae for oxygen production was slim to none. But regardless of the amount of information, we still had to propose our idea and well in order for it to get accepted. This is when I realized that Ryan was right. NuVu is very similar to applying for your doctorate so therefore it can undoubtedly help prepare students for the future. What I also realized was how hard it was going to be.

Isabella and I researched vigorously to find out what kind of algae to buy, how to grow it, and much more. We found that the majority of research on algae was either its health benefits, or the use of it for biofuel. While biofuel seemed quite neat and a lot more sustainable than oil, we realized that we could not use this research to help us with our project. Nor could we use the health benefits of adding algae supplements to smoothies, unfortunately.  What we did use to get our project going was research on how to grow algae. Once we ordered Chlorella, we filled up a tank with water, nutrients (iron),  salt, and of course, the algae cultures and thus, we became algae farmers. 

We spent quite some time trying to wrap our heads around the concept we were trying to create. Eventually we created our first prototype, which was a geometric skirt in which we would put algae inside each panel. There would be tubes that connected to a tank (reservoir) that would pump algae throughout the skirt, agitating it. This was the same principle as our final prototype, except the change of shape and use of acrylic. We had a difficult time sealing the skirt and the placement of the holes for our tubing could be improved in future iterations. Overall, our project was a successful prototype and we have a clear path for future iterations.

Our project is a computer program that uses Markov chains to generate a statistical model of  the style and content of an input text, and generates text based on that style. 

The purpose of this project is to explore what characterizes the style of a particular author, and to try and use that stylistic information to generate text. This type of information can be used both for text generation as well as identifying text. Identifying the style of a text has a variety of applications beyond text generation, including forensics (for example, matching up the writing of a criminal to text posted on social media)and history (for example, dating a document, or identifying whether a new document is a forgery). 

This project was inspired by several different existing projects that are looking at the intersection between Computer Science and art. One is Experiments in Musical Intelligence, or EMI, which is a computer program that can imitate the style of classical composers. This program can actually fool some into thinking that its compositions were composed by classical composers, like Bach. (http://www.nytimes.com/1997/11/11/science/undiscovered-bach-no-a-computer-wrote-it.html). Another precedent for this project is Google’s DeepDream project, which uses Deep Learning techniques to imitate the style of a painter and applying it to a given picture. This project isn’t designed to perfectly mimic the style of a painter, but it does learn and mimic noticeable aspects of the style of a given painter (https://github.com/google/deepdream). Some final precedents for our project are the computer programs which write simple news articles, created by companies like Automated Insights or Narrative Science, which write news articles based on preset templates, and neural networks which have learned how to write Shakespeare (https://www.wired.com/2015/10/this-news-writing-bot-is-now-free-for-everyone/, http://karpathy.github.io/2015/05/21/rnn-effectiveness/).  

The way the program works it that is uses a several different Markov chains, trained on different aspects of the text, to understand the style and content. We choose to use Markov chains, which are stochastic models that transition from state to state based on learned probabilities, because a lot of current research in this area uses machine learning. We wanted to see what the best model we could make with a simpler, Markov-chain based approach — setting it apart from many current research projects. However, unlike the spam bots or news writing programs, which utilize Markov chains alongside remade templates, our project learns and generates templates based on the input text. These templates can then be filled with words, either from the source text or from a different source (to apply the style to a different topic)

From a technical standpoint, our program works by separating the training and generation steps into three parts. The first part trains a Markov chain on transitions from punctuation mark to punctation mark, and generates a list of punctation marks to base the generated text on. For instance, it might add a transition from a period to a comma 25% of the time, from a period to a period 25% of the time, and from a period to a dash 50% of the time. It learns these transitions from the input text, and generates text uses a weight random number generator. The second part trains a Markov chain on transitions between periods and number of spaces. For instance, a period might transition to a series of 5 words 10% of the time, 2 words 5%, etc, etc. These transitions are also learned from the input text. The third part learns transitions from one word to the next. This part of the program is the most similar to previous Markov-chain based programs.

New Stuff

 - you can now search items in the queue by name, time speed, power, material (literally anything in the table)

 - removed option for engraving temporarily as there is no know sheet for that

- all data is sent to SQL database, time-stamp is also now generated by a PHP method not through the database itself, allowing for easier display 

- the SQL column for cut/eng has been removed, replaced with speed and power

- you can no longer upload an empty field to the database (e.g. no file, no material, no name) it will push you to an error page

- no longer kicks to generic landing page, now dumps you back at the index

Bugs

- when you search the table header disappears 

- still no standardized info file with all connection data

- archive page is useless and displays a message as such. 

- not nuvu colors because god knows what the color pallet is for this site

- still heavily reliant on bootstrap

Here is a video of Ryan, Mit and I shooting our sprinkle shotgun. 

Our project is to manage the laser cutter system. Currently it is vastly inefficient and could be greatly improved.


I have re-made the web interface to be efficient and good looking. Dominick has worked on a physical aspect with a shelf and a color coded system. Now when students use the laser queue it should be more efficient than ever. You can see the progression of my progress by watching the various demos I have made on my profile.


 

https://github.com/real-laser-hours/the-real-queue

XP compatibility status: questionable at best.

All the b̶u̶g̶s alternative features known:

• Error reporting is set to 0, no matter what happens, the FTP server will return no file uploaded, but the file will upload

• Security flaws out the wazoo -- not gonna sugar coat this -- all the connections are handled in the same file as the script. So…

The ‘it works, but’:

• To access /old/ it goes straight into the apache file interface, causing the user to need to hit the back button twice

• You can’t submit the same filename twice, more into that later

• It’s total spaghetti

Ways and means:

Naming syntax

$name-$filename

SQL & FTP syntax

INSERT INTO current (UUID, NAME, CUT, MATERIAL) VALUES ('$UUID', '$name', '$rad', '$material')

• Appending will come later

• It is sanitized, so there should be no way to inject. But they could literally inspect element and get the db password(fixing this later).
• The FTP server should always be set to: ftp_pasv($conn_id, true);
• Passive mode (ftp_pasv) fixes the need for forwarding rules in most instances

Renaming syntax

$destination_file = "/ftp". $_FILES["file"]["name"];

Dumps the uploaded file into the /ftp/ folder

$source_file = $_FILES["file"]["tmp_name"];

$fileInfo = pathinfo($_FILES["file"]["name"]);

Nabs the path info of the uploaded file (sorta redundant)

move_uploaded_file($_FILES["file"]["tmp_name"],

           "ftp/" . $_REQUEST['name'] . '-' . $_FILES["file"]["name"]);

Renames the file by moving it to the same location (linux anyone?)

Printing out the speed and power

This is just a long if else statement

if($material==”X or Y”){ etc.