Author: caropark

“If a picture is worth a thousand words, an interactive 3D simulation is worth tens of thousands,” says Diane Favro, UCLA professor and director of the Experiential Technologies Center. Favro explains in her essay, “Meaning in Motion” the development of digital modeling and reconstruction of historical environments here in the Graduate School of Architecture and Urban Planning back in the late 1990s. The initiative for this research on digital technologies was definitely new and unknown territory for scholars and researchers like Favro which coincided with a generally undefined and unregulated environment to work within. The Cultural Virtual Reality Lab, established by Favro and Professor Frischer, attempted to establish guidelines with their five principles for creating these high-performance computing, historical simulations. Their five principles definitely adheres to the same principles and attributes of digital humanities, echoing key words like metadata, knowledge representations, and generally combining scientific methods with studies in humanities.  Today, especially with the launch of Google Earth, web-based geographic computing systems are readily accessible and increasingly user friendly. The standard for historical representations are only getting higher and higher and a polysensorial experience for users are not only immersive, but also becoming expected.

A colleague of mine, Nikita Arefkia worked on this project, “Stellarscope” alongside her bandmate, Lionel Williams. The creative duo collaborated to incorporate an interactive music video/game to pair as a visual for their band’s track, “Stellarscope”. Designed as a generative process, players are invited to essentially explore the interactive environment and create their own visuals while listening to the song. Sounds are incorporated into the environment that can only be accessed through close proximity. The represented environment takes inspiration from historic temples and outer space, but is virtually imagined from the duo’s minds. The simulations of Favro’s historic environments immediately reminded me of “Stellarscope” despite the obvious differences. For one, “Stellarscope” is made for purely artistic value while Favro focuses her work on academic and scholarly progression. However, I appreciated that Favro emphasizes the importance of forming a relationship between motion and meaning. Acknowledging the Roman importance of combining physical movement and mental thought, Favro strives to create the ultimate kinetic experience that stimulates a user’s multiple senses.

With “Stellarscope”, the interactive environment incorporates this same crucial concept, by allowing users to essentially form their own personal memories and experiences each time they explore the game. Although the game was made with no scholarly objective nor did it face the challenge of documenting historic data, it does act as an example of interactive reconstructions that aims to give a polysensorial experience for the user.

As a loyal user of Adobe programs for years now, I have become fairly adequate in using programs like InDesign, Illustrator, and Photoshop. Adobe offers more useful programs like AfterEffects, Lightroom, Dreamweaver, and Audition, but I find that their top three essential programs for a graphic designer would be the first three mentioned earlier. While going through the short and sweet list of Ben Shneiderman’s eight golden rules of interface design, each rule seemed to bring back flashes of the programs. The majority, if not all, of these golden rules are applicable to the construction of Adobe programs. The popularity of Adobe programs usage definitely is based on the merit of their well designed user interface. Since I am most familiar and comfortable with InDesign, I decided to run through Shneiderman’s rules as a sort of check-list to see if the program fulfilled his guidelines. First, the consistency of the language and terminology used within InDesign is definitely consistent not only within itself, but also carries over into Illustrator and Photoshop. However, users of Photoshop will find that although all three programs share terminologies, the process of executing an action is varied. We can compare an example like if we were to drop in an image into InDesign and Photoshop. In InDesign we can easily crop and maneuver the image to whichever size we would like without damaging its quality and original image size. Photoshop makes this process a bit more hidden to the user meaning that the user will have to actually change the image size, calling for a longer process. Although between the programs the language and actions may not be consistent, I think that each individual program is well-designed in terms of the user interface. The challenge for Adobe would be to better connect the consistencies between these programs so that the user can easily transition into each program masterfully. Shortcuts are readily available within Adobe programs and are clearly labeled next to each tool for future reference. These shortcuts are definite help for advanced users and lessens the amount work time. The informative feedback is fairly consistent and frequent, but I think the responses can be more simplified and helpful. The messages are usually a bit vague and uses terminology that would not be familiar to a beginner user. The sense of closure in Adobe is acceptable, but it would definitely help if the program utilized more sounds as a part of their feedback. Usually it only responds with a error sound if I do something wrong, but it might be worthwhile to implement some sounds when you do something right. Error handling is fairly simple in InDesign and users can easily utilize layers or keyboard shortcuts to undo a certain action. Adobe is definitely given less credit than it deserves as an interface for highly critical designers. I always hear designers complaining and even cussing out the programs while using them, but in the end they make great work through Adobe. I think that Adobe has done a fantastic job in managing to brand their identity so strongly, while still keeping the user interface design objective as possible. When I open up InDesign, I only see a blank canvas surrounded by tools. The aesthetic of its design does not inhibit me from creating my ideas. I think this is an important factor when it comes to user interface design, especially for an interactive system developed for users to create and design projects in.

In his blog post, Scott Weingart offers his explanation of how a network works. He gets down to the fundamental basics of what makes a network and simplifies them into terms even people with little-to-no background in math can understand. He acknowledges the flexibility of the network as a tool, which can be applicable to any data studied within network analysis. However he warns that the network tool should not be abused and should be selectively used. He also warns that the appropriation of data, especially when it comes to that of humanities scholars who are, “often dealing with the interactions of many types of things, and so the algorithms developed for traditional network studies are insufficient for the networks we often have.”

Weingart breaks down the components of any network to ultimately be simply “stuff and relationships.” These components are interdependent and their connection cannot exist without each other. He then performs a run-through of a very simple network formation starting with books as nodes before connecting their variety of attributes to form relationships, or edges.

After reading Weingart’s post, I came across an impressive, preliminary study on Twitter’s billion-scale network conducted by Masaru Watanabe and Toyotaro Suzumura of Tokyo Institute of Technology. While reading through their process and concluding results, I was delightfully surprised at how I was at least able to identify key terms in their comprehensive study after reading Weingart’s explanation. This made it a lot easier to approach Watanabe and Suzumura’s study which I found to also be fairly straightforward in general. The process of their study however is far from straightforward and seems highly complex considering the amount of data they collected among 469 million users between the months of July to October 2012. They categorized the data, which included follower-friend information, into two formats, XML and CSV and also utilized the analysis tool, Apache Hadoop and later HyperANF to compute the degree of separation.

Inspired by a Facebook network analysis study conducted by Lars Backstrom who managed to compute a degree of separation using graph analysis tool, HyperANF and resulted in a surprisingly low number of 4.74. The network structure of Facebook is more friend-based that resembles the way human relationships work in the real world, while a social graph like Twitter is based more on interests and differs from Facebook due to its directed graph. Directed graphs allow everyone to follow someone freely while an undirected graph like Facebook requires approval. Considering that Facebook and Twitter have a different network structure, Watanabe and Suzumura analyzed Twitter’s network in an attempt to compute a degree of separation and diameter. Interestingly, both degree of separation  and diameter are used to measure networks in terms of scale and graph. Degree of separation is found by the average value of the shortest-path length of all pairs of users while the diameter is given through the maximum value. For the data collected within July to October 2012, the analysts concluded in a degree of separation of 4.59 in the Twitter network. After reading a simplified explanation of what makes a network and its analysis, it was interesting to see this applied to a large-scale network like Twitter.

http://www2013.wwwconference.org/companion/p531.pdf

This week’s reading features a web-based data visualization that accompanies the project, Invisible Australians. A research collaboration instigated between Dr. Kate Bagnall and Dr. Tim Sherratt, Invisible Australians was created to identify and reveal the true face of the so-called White Australia during the early 20th century. During this time in White Australia, thousands of non-Europeans residing in the country faced discriminatory laws and policies that denied their rights as Australians. Although shunned and marginalized as a Australian minorities, these non-Europeans, including Chinese, Indians, Japanese, Syrians, and Malyans, were ironically well-documented through government records. Bagnall and Sherratt have taken advantage of these extensive records in order to develop a database intended to commemorate and identify the thousands of non-white Australians who made up the true face of “White Australia”.

While browsing The Real Face of White Australia, I also kept in mind Johanna Drucker’s distinction between capta and data. She emphasizes the need for humanists to utilize conceptual tools like capta and stray away from tools and methods developed from fundamentally epistemological disciplines. This distinction is definitely a core concept to understand when approaching any kind of archived information. When data is presented in a way to prove a certain opinion or thesis, this is essentially converting this hard, cold data into capta. Capta, in Drucker’s definition, carries a constructed interpretation of the data it came from. The Real Face of White Australia is a great example of humanizing data and presenting it in a way to prove a point. The simple composition of “the real face of white australia” at the header of the browser accompanied by a grid layout of all the collected images of these discriminated Australians is for the most part a self-explanatory. A visitor first sees the header and is immediately gratified with Bagnall and Sherratt’s findings. With the documented identities that Bagnall and Sheratt have found, they are ultimately imposing the idea that the true face of Australia was formed by the non-white residents of that time.

Drucker’s distinction between capta and data is a definite step in the right direction for how people typically analyze so-called data visualizations. I feel that we have come to a point where we give the term data too much authority. Too many times have we trusted a visualization that claims to be based on a found set of data, only to later find that certain kinds of data was omitted, duplicated, etc. We need to realize that data is extremely vulnerable to be skewed and constructed to fit the data miner’s own opinionated agenda or perspective.

One project that came to mind was photographer Giles Revell and graphic designer Matt Wiley’s collaborative project called “Photofit: Self-Portraits”. Using a now outdated and disregarded Penry Facial photofit kit from the 1970s which were used for constructing police sketches of suspects, the creative duo called upon a number of test subjects to use the kit to compose their own indetikit image purely by memory. These participants  put together the tactile components of the kit which include paper strips of various facial features. The result of these participant interviews and Photofit assemblies reveal the complexity of these participants’ relationship with their self-identity suggesting that their distorted compositions of themselves show more about the subject’s personality than a straight-forward photograph would.

Here the photograph could be considered as the straight-forward data and the test subjects’ photofit compositions of themselves are the capta. Each participants’ own personal perspective about their appearances converts the data of their facial topography into the capta of what they view themselves as.

http://gilesrevell.com/files/photofit2.pdf

http://www.nytimes.com/2008/11/23/magazine/23Netflix-t.html?pagewanted=all&_r=0

http://genresofnetflix.tumblr.com

http://www.netflixprize.com

As just one of the millions of Netflix subscribers and self-diagnosed binger, I have definitely spent many long nights getting familiarized with the altgenre system implemented into the streaming media site. I’ve been avidly using Netflix since 2011, but I’ve only really started taking notice of some of its extremely specific genres until just this year. With thousands of titles to sort through on Netflix, their personalized genres are definitely useful, maybe a bit absurd, but still useful. My personal favorites are “hidden gems” or “visually-striking movies” where in these categories I can usually find many independent and quirky films that are difficult to describe.

It is apparent that there is a growing trend of implementing these personalizing algorithms into more and more media sources including the likes of Spotify, Amazon, and Soundcloud. What I find most intriguing and even slightly disturbing about Netflix’s system is the crossover of both human and machine intelligence. It has come to the point where you can probably learn a lot about a person’s interests by simply looking through their Netflix account. In order to achieve this, Netflix engineers definitely had a strong input in creating micro tags for these films based on the Netflix Quantum Theory which makes me question how objective the algorithm system remains to be. There seems to be an ideology similar to the whole “I know it when I see it” expression that is crossing into Netflix’s system.

Netflix has evolved their past system that was based more heavily on numerical values and user ratings to a more human method of introspection. Todd Yellin, VP of product innovation at Netflix had this to say about their new approach:

“Predicting something is 3.2 stars is kind of fun if you have an engineering sensibility, but it would be more useful to talk about dysfunctional families and viral plagues. We wanted to put in more language,”

I think it was a very progressive approach for Netflix that also reveals some very interesting quirks about the relationship between categorizing systems and human nature. Atlantic’s article on Netflix’s genre algorithm system mentions the $1 million prize that the company had offered back in 2006 which reminded me of the Napoleon Dynamite problem. As a film, Napoleon Dynamite seems to be the most difficult movie to pinpoint and recommend to Netflix users. The quirky film remains to be the most stubbornly unpredictable movie as it is attracts many users to rate the film while still being hard to predict. This imbalance, while probably a headache for Netflix developers and engineers, is to me a very humorous quirk in the system that shows how difficult it is to categorize human interests and behaviors.