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This is one of the spaces I'll be taking notes for the Digital Humanities High-performance Computer Collaboratory.
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DHHPC 5: Wrapping Up

lransom2003's comment, June 26, 2012 10:24 AM
Thanks, Shelley, for posting these notes! Very useful to have.
Rochelle (Shelley) Rodrigo's comment, June 26, 2012 11:44 AM
My pleasure (and also selfishly useful!)
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Day 5. Thur. June 14, 2012; 9:00am-


Marshall Scott Poole, Director of ICHASS

GroupScope: A Multimodal System for the Study of Large Group Dynamics


Interested in collective action, tracking emergence service providers (live world tracking)...want to track large groups (hundreds...)


GroupScope: an envionment that will develop technologies to understand social interactions with a fidelity that enables breakthrough social research. 


Claims that he hopes the computer will do the drudgery and leave him to do the fun stuff; however, as the group of us work on coding the agenda...that is still requiring some of the drudgery (and the agenda is a short text!)


 Learning about HathiTrust (large text archive/corpus)



Open Lab to work and collaborate.


11:40am Lunch


1:00pm Advanced Visualization 3D Lab


1:45pm: XSEDE (funding/support), Alan Craig

eXtreme Science & Engineering Discovery Environment

provide service to supercomputing & data analysis systems

75 consultants

currently 1290 projects

4 of 1290 are from the humanities


Don't define your project based on what your machine can handle. 


2:10pm: Conclusion

10 Things to Consider...

How to work in computational humanities or a collection of irresponsible metaphors

*Think like a fountain, not like a pond

*Knowthe branches of CS you want to work with...homework is good

*Think big, then work backwards...

*...Towards a proof of concept that can get you a publication or a grant

*You are a bat in a cave. Use your sonar.

*Your technical and scientific colleagues will learn from you

*Sometimes alliances of talented people make great research ideas, instead of great research ideas making alliances of talented people

*You are important specifically because you do not see everything as quantitative data

*But understanding which tiny pieces (or what kinds) of quantitative data (or descrete tasks) may fuel your broader analysis is crucial.

*Tech for discovery, not just implementation.  

*The bridging of low level data features to high level semantic concepts is not just a challenge facing DH; it is one of the signal challenges of the information age. 

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Day 4. Wed. June 13, 2012; 11:00-11:50am

Alex Yahja

Social Media & Networks


Social Media exists because of social needs and drivers. 


Social: it's who you know

Cognitive: it's who they think you know

Knowledge: what they think you know



Theories of Networking (I didn't get them all...hoping for slides)


Mutual Interest & Collective Action

Contagian Theories

Cognitive Theories

Exchange & Dependency Theories

Homophily and Proximity

Balance Theories

Coevolutionary Theory


Definition: Social network analysis is the mapping and measuring of realtionships and flows between people, groups, and organizations + <i>spacetime</i>. 


Data Representation

Matrix of relationship

Semantic Graph: 

*Node- a person or entities, w/attributes

*Link-a relationship, w/attributes ("friend w/): directed: one-way; undirected: two-way




Building the Graph

Each person is a node, a relationship is a link

Links can by directed or undirected

Graphs can have cycles. 


Degree of Separation. 


"Best" location in network:

most popular

best location

closest to everyone

not connected/think outside the box (less prone to group-think)

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Day 3. Tues. June 12, 2012; 1:00-4:30pm

Visit Fab Lab


Discussion of possible projects

Need to save data; talking about ways to "sell" data as well to get people to fund/keep it. 

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Day 2: Mon. June 11, 2012, 1:00-5:00pm

Day 2: Mon. June 11, 2012, 1:00-5:00pm | DHHpC12 @ICHASS | Scoop.it

Alan Craig: A Brief Introduction to 3D content

Discussed the various ways we see in 2 & 3D. 


Michael Vila: 3D Modeling

NCSA Katrina Visualization:
The Fantastic Flying Books of Mr. Morris Lessmore
Beginners’ Tutorial for 3dsMax Design
Cylinder Seal information:
Google Earth
Google Sketchup


Fascinating discussion about the ideas (and constraints) of Digital Humanities projects. 

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Day 1: Sun. June 10, 2012; 2:30-5:10pm

Day 1: Sun. June 10, 2012; 2:30-5:10pm | DHHpC12 @ICHASS | Scoop.it

Reminders that the point is to understand what goes on under the hood of serious image analysis applications so we have some sense of what/how/why to have conversations with CS folks in the future (we're not going to be coding this stuff ourselves). 


What can you do?

Matching: match an object in two images based on similar features.

Tracking: Follow an object in a video by following its features.

Object Recognition: Find objects based on known features it will posses.

Segmentation: Break an image up into more meaningful regions based on the seen features. 


Feature Descriptors: Shapes, Curves, Color, Mean, Distribution, Texture, Filter banks, Size, Statistics, Neighbors


Machine Learning:

Supervised learning (human groups elements and teaches the computer w/a bunch of examples)


ISDA Project: digitizing (w/OCR) census data


Project: Outcome goal?, what exactly is the data?, how will we constrain material? 


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Day 1: Sun. June 10, 2012; 9:00-11:00am

Day 1: Sun. June 10, 2012; 9:00-11:00am | DHHpC12 @ICHASS | Scoop.it



Kenton McHenry: Using Image Data in your Research

We have large collections of image data...what/how do we do with it? 


1. A high level understanding of what Computer Vision is and how we might use it.

a. A sense of what is currently possible.

b. A sense of how these things break

c. A sense of what might be possible

d. A sesne of what is pure science fiction

e. The looming opportunity in "Big Data"

2. A little bit of hands on experience. 


Computer Vision: What information can you get from a digital image? High level and low level (and mid). What kind of scene? Are there cars? Where are the cars? Is it day or night? What is the ground made of? 

What computer sees: raster Images...pixels, matrix of numbers. 


How/why is this hard? 

Image created: a scene, light bounces off scene into a sensor (eye/camera lens). Variables: 

Light/s: position, strength, geometry, color (Shows example of the light source of an image can change how the machine understands a color.)

Surface/s: orientation, color, material, nearby surfaces (absorption, diffuse reflection, specular reflection, transparency, refraction, fluorescence, surface interaction)

Sensor: lens, apeture, exposure, resolution, perspective (3D world onto a 2D plane)


Neighborhoods of Pixels: Individual pixels don't mean much on their own; need to look at them in context. Change in intensity from pixel to pixel. 


Make a computer understand images and video

*Lots of variables

*variables are not independent and interact

*problem is underconstrained (multiple scens can result in same image)

Shows videos of 3D objects where perspective makes us (and computers) interpret one way (confused). 


More human brain devoted to vision than anything else (is this part of the reason that vision trumps other senses in learning...Brain Rules)


So Far...

1. Barcodes...computer can read lines well (since 1950s)

2. Opitical Character Recognition (OCR)

3. Biometrics (fingerprints, faces)


*Image stitching

* Sports (lines on football field)

* Object Recognition (Google Goggles)

* Human Computer Interaction (object recognition, 3D reconstruction)



Vision is Hard!

Most apps are still "quirky"

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Day 4. Wed. June 13, 2012; 9:00-10:40am

Day 4. Wed. June 13, 2012; 9:00-10:40am | DHHpC12 @ICHASS | Scoop.it

Guest Lecture: Vernon Burton, Director, Clemson Cyberinstitute; Prof. of History;Prof. of Human-Centered Computing in the School of Computer (founder of ICHASS)


Cyberinfrastructure and the Humanities

Some history of ICHASS.

DH is interdisciplinary field that is interested in archiving, analyzing, and interpreting human activity

Humanities is about contextualzing information; we can help comptuers do that (and/or they help us do that).

CS folks like to work with difficult problems; nobody has more difficult problems than Humanities scholars.

Discuss that the technologies are accessible enough to use with students. 

Explicitly mentions that we need to be inclusive about CS folks as co-authors on the projects. 

Building a DH PhD program at Clemson. 


CC image of punchcard posted at Flickr by BinaryApe: http://www.flickr.com/photos/93001633@N00/5151286161/

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Day 3. Tues. June 12, 2012; 9:00am-12:00noon

Alan Craig

Started showing a picture of a smell sensory device/experiment (what smell versus what see & taste).



Alan is interested in interact/interface with computers & the world. 

Talking about "Caves": fully immersive virtual reality spaces. 


2 big tasks:

data management

decide what you want to do with it


Caves are only as good as the software.

If the person's body is not encoded it is diconcerting; discussion of easily making people trip/fall without ever touching them. 


Lot's of Twitter backchatter about the ethics of VR (esp. with the high cost of Caves and access). 


Alan is now talking about more accessible VR, things like a "fish tank" VR. 


Alan's Definition of VR: a medium composed of interactive computer simulations that sense the participant's position and replace or augment the feedback to one or more senses--giving the feeling of being immersed or being present in the simulation. 




Sensors: to determine pose and info about the world



Smartphone & Tables have them all!!!


Fiducial Symbols/Markers: something you put in the world that makes it easy for the camera to recognize. 


Natural Feature Tracking (NFT): Faces, Skylines, Natural Features, QCAR-Qualcomm (Qualcomm example allows for interaction with "buttons" that changed the color of the augmented object.) 

Alan: the whole world is an interface. 



Whole body

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Day 2: Mon. June 11, 2012; 9:00am-12:00noon

Alan B. Craig

Introduction to Visualization



* existed before the invention of computers

*representing information in a way that is maximally benefitial to the intended audience

a. information visualization

b. scientific visualization

c. other specific areas like Business Visualization, Humanities Visualization, etc.

*provides NEW insight OR a NEW way to communicate


1987 NSF Panel Initiative-Formal Definition

"Visualization is a method of computing. It transforms the symbolic into the geometric, enabling researchers to observe their simulations and computations. Visualization offers a method for seeing the unseen. It enriches the process of scientifice discovery and ofsters profound and unexpected insights."

A. Craig's interpretation: it's a method of doing something

Richard Hamming: "The purpose of computing is insight, not numbers."

 Goal of visualization: leverage existing scientific methods by providing new scientific insight through visual methods. 


Visualization: The choice of the appropriate representation of what we are trying to do.


Interpolation: Generating new data based on a given numbers. 


Inveractive vs. Batch Visualization

Interactive: allows the ability to control in real-time; limits the amount of data; useful for analysis & exploration (more likely to lead to new discovery)

Batch: High-Quality, complex representation; no control in real time; useful for presentation, communication, high complexity (focused on presentation)


Visualization Process/Flow:

Data, filter, map to geometry, viewing attributes, render, display, record, loop to appropriate step...


Ended w/playing in Many Eyes

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Day 1: Sun. June 10, 2012; 11:30am-2:15pm (w/Lunch)

Day 1: Sun. June 10, 2012; 11:30am-2:15pm (w/Lunch) | DHHpC12 @ICHASS | Scoop.it

Kenton McHenry: Image Features


Neighborhoods of Pixels...so how do we do something with this!



1. Interest points: Points or collections of points that are somehow relevant to indentifying the contents of an image.

2. Subset of the image: Far less data than the total number of pixels.

3. Repeatability: A feature detector should be likely to detect the feature in a scene under a variety of lighting, orientation, and other variable conditions. 



Changes in intensity: albedo, orientation, distance




McHenry uses MATLAB to demo how to read/manipulate/interpret digital images. 


Convolution: the concept of the function scanning the whole image


Continuous vs. Discrete...real world, smooth; image is static, discrete, pixels! So in MATLAB compare vector coordinates of given contrast (like vertical distinction) to vector coordinates from an image to mark what you are looking for (we're talking about borders of objects in images). To smooth out noise, average the difference of the surrounding pixals and make the center pixal that new average. 



Features: interest points, repeatables, subset images

Edges: lines, circles, ...


Blobs: superpixels

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