PROJECT: In exploring Traceroute, I choose to look at news/media sites and everyday websites that I frequent from 3 different locations (school, work and home). My main intention was to plot the hops on a map in Illustrator or Processing, but I ended up plotting the points on Google Maps in order to see the paths on a more detailed map that you can zoom in and out of to analyze and see patterns.
NEWS SITES: The New York Times, The Washington Post, The Guardian
The New York Times = Blue; The Washington Post = Yellow/Orange; The Guardian = Green
EVERYDAY SITES: Chase, Amazon.com
Traceroute from: Blue = School; Orange = Home; Yellow = Work
As I was gathering the data for this exercise, I was trying to purchase tickets to shows that sold out in second after tickets were released (on Ticketfly) or the site broke down (on Eventbrite), so I did a traceroute on the sites out of curiosity.
SHOWS & TICKETING SITES: Eventbrite, Ticketfly
Eventbrite = Blue; Ticketfly = Yellow/Orange
NETWORK LOCATION TOOLS USED:
http://www.ip2location.com/ (*NOTE: this site was able to give me more information when the the yougetsignal.com couldn’t find info on an IP address)
Journey #1: Start from Form
Attending networking events or parties by myself and talking to strangers causes me and lots of other people social anxiety. I wanted to create a journey in the form of a game on a mobile phone to simulate a party, a conference, or networking scenario to help a viewer/participant mentally prepare for the uncomfortable situation. Instead of playing a meditation or mindfulness app to help bring the mind to a calmer state, I’m hoping that a game to play before an event can help the user figure out conversation starters and maybe alleviate their anxiety with repetition of a scenario that could possibly rewire the brain as to not be so anxious.
It’s Open, Come In…
The participant goes through the doors and is given choices:
A. Start Mingling
B. Head for the beverage and snack table
C. Run to the Bathroom
D. Look at phone
If the player starts mingling with the guests, the more conversations he or she has, the more points the player earns. And with each guest that the player converses with, the more personalities (whether outgoing, shy, into science, pop culture, politics, religion, history, etc…) the player is exposed to for conversation starters. Also through these interactions, there will be a ongoing conversation throughout the party about finding the mystery guest who has all the inside knowledge, like which waiter has the good bottle of wine. If the player can find the mystery guest and the waiter with the good wine (recommended by the mystery guest), the player wins the game.
TITLE: Found Object
DESCRIPTION: Working with a negative sheet as our data set, our story is a live performance of two detectives’ search into the story behind these captured images.
1. We were tasked to create a non-fiction narrative about 24 film negatives that were found on the street.
2. After getting them all printed. We took some time to see if we could come up with the story from the negatives.
3. From looking at the photos, we saw that the they were taken on one day at a gathering, possibly a wedding. With this in mind, We decided to tell this one woman’s story since it seems like the camera could have been hers, we call her Andrea:
4. We decided to do a live performance/skit where two detectives are tasked with finding the missing person’s case of Andrea Leandro. She was last scene at this wedding and in the skit we go through the images and slides, and through her life to try to determine what happened to her. We developed her relationships with the individuals in the photos and added some more background information to portray the complete picture of Andrea.
Here are our analysis of the photos and the stories we developed around them.
Here is the script of the detective and how they portray Andrea’s life by trying to figure out what happened to her.
For an interactive component, we handed out this sheet for the viewers to be an active participants in the search for our missing person.
FINAL PROJECT: Mechanical Reproductions
Week 4: Putting the pieces together for the final presentation
1. Painting with matte & metallic acrylic paints
2. Laser cutting last little details
3. Improvising after the laser cutter that you booked for 2 hours is broken and the one working machine is booked for everyone else’s finals and thesis projects. Went to Blick and bought a bunch of wooden dowels and a cutter. Luckily, I had some wood scraps from my mid-term project and some test printed gears, so I was able to scrape together the final details.
4. The moment that it came together, along with an illustration for the inspiration and model.
Figuring out how to make the gears actually work together to create more movement and integrating it with the pipe line.
TITLE: Case by Case
DESCRIPTION: Case by Case, my Nature of Code final project came from the idea to distinguish lowercase letters from uppercase letters for little kids to use. This was inspired by a parent-teacher meeting for my 3-year old. The teacher told me that my child was really good at identifying uppercase letters, but not lower case letters. When we read, we are deciphering many strings of lowercase letters, so I wanted to figure out a fun exercise to help him learn in preparation to learning how to read.
MAIN GOAL: To explore and understand the initial steps of letter and number recognition in a machine learning system using Shiffman’s Neural Network with p5 example of handwritten numbers and applying letters to his sketch.
Continued from last week’s Final Project: Step 1….
1) To distinguish between upper and lowercase letters, I needed to creating the handwritten letter dataset to add to Shiffman’s Neural Network.
NEXT STEPS: To keep exploring this method and eventually build this kid’s app that could not only create testing data from what the child writes, but could also be a fun way for kid’s to practice writing their letters and identifying the letters case by case.
PROCESSING PDF OUTPUT
DESCRIPTION: Data visualization of free wi-fi spots versus telephone booth locations in New York City, Brooklyn, Queens and the Bronx. Data sources came from NYC Open Data.
CLOSEUP OF PROCESSING PDF OUTPUT
BACKGROUND MAPS: ORIGINAL & UPDATED COLORS
PROCESSING & ILLUSTRATOR COMPOSITE
1. Bought Materials: wood, tubing, metallic paints, gesso, 1/2″ diameter copper plumbing pipes, pipe cutter (the best cutting tool ever), marbles
2. Building 12″ x 24″ frame, laser-cutting pieces and figuring out the design to make a marble move through the pipes and system.
1. Finalizing Design.
2. Adding an interactive component to it, like a motion or touch sensor to trigger sounds.
3. Painting and piecing it together.
THE PIECE…so far with 1 week left to finish
TITLE: Pep Talk
DESCRIPTION: Non-linear storytelling using video and Eko Studio.
TITLE: Case by Case
DESCRIPTION: Nature of Code final project that distinguishes lowercase letters from uppercase letters for little kids to use. The idea was inspired by a children’s app called Endless Wordplay and from a parent-teacher meeting for my 3-year old. His teacher informed me that he was really good at identifying uppercase letters, but not lower case letters. In preparation towards the next steps of reading, deciphering lowercase letters is something that we needed to work on since most of reading is strings of lowercase letters. And for myself, in effort to grasp the idea of neural networks and machine learning, I decided to work with Shiffman’s neural network of handwritten numbers using the MNIST database and apply letters to his sketch. Eventually, I would like to apply that model to identify letters and numbers in graphic illustrations, photos and different typefaces.
1) In building upon Daniel Shiffman’s Neural Network example from Nature of Code, which was also based on Tariq Rashid’s Make Your Own Neural Network, I want to use a training set of handwritten letters to distinguish between upper and lowercase letters as an initial step. I eventually want to take photos or illustrations of letters and numbers, similar to pieces in 36 Days of Type below, and teach the neural network to identify the letter or number.
2) Converting a test image of a number illustration into a bitmap file to add to the training set. Converted the test image into a 28 x 28 pixel greyscale image in Photoshop and then used Python to extract the pixel values from the photo illustration.
from PIL import Image
im = Image.open('um_000000.png')
pixels = list(im.getdata())
3) Creating the upper and lowercase training set, adding that to the data folder and build…
PROJECT: Mechanical Reproductions
DESCRIPTION: Using Francis Picabia’s collage, Very Rare Picture on the Earth (1915) as a blueprint and building a 3D version using the laser cutters, the CNC router and 3D printers. Also planning on adding a moving mechanical feature to it using gears and marbles or ball bearings.
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STEP #1: The Sketch
I sketched Picabia’s painting in Illustrator keeping in mind that this 2d vector drawing would be laser-cutted and possibly 3D printed for some parts. I wanted to build a drawing of a structure from scratch that symbolizes a working system (ex: filtration of data or the human body), but I found it helpful to replicate and work off Picabia’s design. I will eventually adjust things to make the gears and tubes more functional to create movement of a ball or marble inside.
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STEP #2: Materials
- Base/ Frame: To create the base and frame by layering wood on top of another and cutting it on a laser printer or using the CNC machine.
- Cylindrical Pieces: Using the CNC machine to make the cylindrical pieces.
- Gears and Flat Pieces: Laser cutting them from wood and adding metallic colors of silver and copper to make them more metal-like.
- Metallic Finishes: Going to look into gold leaf or patinas, but bought some metallic paints to also test on the wood.