A Winter’s Tale: An Interactive Book in a Pop Up Window Display
For the Automating Video Project Final, I experimented with Videogrep to group videos clips by words and continued with paper collaging to creates clips for a video bot.
Below is a very experimental video combining some techniques that I’ve been playing around with.
Videos made with Videogrep pulling words like COLOR, ELECTRONIC, TELEVISION using The Story of Television – 1956 RCA Educational Documentary
Magazine Collage with Servo & Microscope Camera
REST API: PART III
Group Project: Kenzo Nakamura and Anne-Michelle Gallero
Description: For this portion of the REST project, we were given another group’s project to create the hardware for. Below is types of requests and web interface for Koji and Patrick’s “Basics Guide to Blockchain.”
REST API HARDWARE
Video: Displaying the ‘BLOCKCHAIN’ hardware that includes 3 buttons to display the PRICE, BLOCKS, and MINING TIME on a LCD display and a servo using a Arduino MKR1000 with WiFi connectivity.
SETUP: Using 2 servos with python and a microscope camera to create my own automated footage. I created a scene of magazine collages to experiment with the servo movements, as well as playing with the magnification of the pixelated CMYK magazine imagery with the microscope camera.
Experiment 2: Closer Magnification of Material
For the first Automating Video homework assignment, I used found YouTube clips for the videos. Since I needed to do some research for another ITP project on spray painting and techniques for painting trees, I used “how to” tutorials as source materials for the re-mixed videos.
My first attempt was stacking and concatenating the clips of 6 different YouTube videos:
Then I tried to randomizing the clips to see what kind of happy accidents could arise. I tried to add multiple video clips, but I was having problems playing back the output–it wasn’t converting properly to a file that was readable in both Quicktime and VLC. In the video below, I liked how the random clips with the audio created a nice beat.
And finally, here’s a new interpretation of watching a painting tutorial:
Map to an IVF Baby is a place to start a conversation about the process of In Vitro Fertilization. The map above provides a visual terrain of what it’s like for a person to go through the IVF steps of trying to conceive a baby if they are unable to do it naturally. It’s a roller coaster ride of emotions and physical discomfort and I’m hoping to create an experience for the user going through the IVF process to help them cope with the anxiety and disappointment that comes with the procedure. These journeys can also help the friends and families of a person undergoing the IVF process to understand what goes into the IVF struggle and help them empathize to provide the right kind of support. My intention is to model a visual terrain of the map in a program like Unity and attach videos or interactive scenes to describe pieces of the process, which will be triggered as the user moves through the landscape. Below is a video of one part of the IVF journey:
This video was taken from 2 instructional videos on YouTube on how to mix the powder and liquids with the syringes and how to give yourself the one of many hormone injections.
Reflection: There are lot more steps to building this, but I also want to take my time and plan the journey in a more thoughtful way. I also plan on taking the feedback from the in-class presentation to help shape this. As a tool for people going through the IVF process and using my teacher’s constructive comments, the scenes can be more interactive by providing a sort of ritual or practice that the user can do in each mini journey, and by layering testimonials in combination to the video storytelling to further describe that particular moment. For myself, this practice of taking the discomforts that I’ve experienced in my own IVF failures and designing an experience around that created an outlet for myself to numb the sadness that comes along with IVF. I’m hoping to create that outlet for people experiencing the disappointments to do the same.
PACKET ANALYSIS: For this homework assignment, I used both Herbivore and Wireshark to learn about packet analysis to capture and analyze traffic on my home network. This particular capture had 8 devices on the network router one morning while getting myself and the kid ready for school.
NAVIGATING HERBIVORE: When I started playing with the packet analysis programs, Herbivore was the easiest to navigate initially with it’s simple and visual interface. The first thing that I did with Herbivore was to figure out which devices belonged to what IP address on the network. I had 8 devices on the network and since it was the morning, the last IP address that I was trying to match was my partner, who was the last to awake that morning. Once he started using his cell phone to check his email and read his top news articles, I was pretty surprised by all the sites that were popping up on Herbivore. At first I was fixated on a very suspicious web address, so I looked up it’s IP address and checked the message boards to find any info on the site and learned that it was probably some sort of Malware that is blocked on Chrome. Then, I started scrolling more through his feed and saw lots of activity on vogue.com and wondered why on earth was he was reading that when he asked me to cancel my subscription. We figured out that he was reading a Conde Nast article and not only were there tons of random ads, but also links on the web page to other Conde Nast publications like Teen Vogue, Vanity Fair, W Magazine, The New Yorker and Wired, which can also be seen in the packet sniffing. I found that Herbivore was really good at getting detailed capture of what sites you can see at that very moment and I later took all the IP addresses and website data from this capture to start mapping out a list of ad and marketing companies associated with specific content sites and media companies.
DATA COLLECTING FOR POINTS OF REFERENCE: In order to read and analyze the relationships faster, I started collecting IP addresses that were popping up in my packets to create my own personal ‘yellow pages.’ By doing this, I could read the IP address faster and concentrate more on understanding the conversations and actual activity. Below is a snapshot of the companies that I see often.
The packet capture that I saw on Herbivore gave me a quick glance at some of the ads and marketing companies associated with a website or media company. By getting those IP addresses and weblinks, I was able to define some of the major and minor companies for Content Delivery, Data Centers, Internet Providers, Commercial Advertising, Marketing and Technology companies in the digital space. For me knowing the players gives me a better overall view of how everything is related to one another. Through this exercise, I also found some questionable IP addresses that didn’t seem to be reputable companies and by keeping some sort of history or list, I could keep track of or flag certain IP addresses or companies that show up on my network.
NAVIGATING WIRESHARK: When I first tried to use Wireshark, I wasn’t getting any packet captures, so I watched a couple of YouTube videos and referenced the Wireshark website to understand the interface and utilize the program. Figuring out what everything means and the amount of data captured for 8 devices is very overwhelming. I found myself trying to define all the the different protocols and ports to better my understanding of it’s makeup. Below is a little cheat sheet of PROTOCOLS with it’s varying LENGTHS that I came across in this particular capture as my reference points for analyzing the packets. For PORTS, this wikipedia page has a reference chart for port numbers and a description of the system processes.
What I like about Wireshark in comparison to Herbivore, is the flexibility in filtering and sorting the data to focus on a particular PROTOCOL or conversations between specific IP addresses. To analyze and dissect the protocols and conversations further, I exported this particular capture as a CSV file and opened it in excel so I could sort and group each protocol into individual tabs more freely. By separating the packets by protocols in each tab, I can then sort by length or info to see how a certain characteristics is linked to a certain function or activity. For instance in the snapshot below, I’ve isolated a UDP protocol at the 215 length and see that it’s mostly associated to a IP broadcast address of 255.255.255.255, which I suspect might be the web streaming to the TV. I’m still in the process of exploring and understanding the data, and my next steps are to focus on the activity that goes on in each protocol and understanding payloads and varying lengths of the packets, as well as learning to find the vulnerable spots within a network. With all the references and rulers that I’ve made for myself, I’m finding it easier to read the packets and to better understand my home network to help me zoom in on questionable activity.