Nascent Papers - Revision history

Jadrian Miles: Added transclusion of Jadrian's paper list

2012-05-22T15:33:13Z

Added transclusion of Jadrian's paper list

← Older revision		Revision as of 15:33, 22 May 2012
Line 67:		Line 67:
	# We can evaluate what makes a "high rated" visualization on ManyEyes.		# We can evaluate what makes a "high rated" visualization on ManyEyes.
	## It’s easy to visually tell the difference between the “high rated” sorted list of visualizations there, and just the “most current”. I’m not sure the topic of the visualization is even that critical -- I could probably classify to a first approximation just by looking at the title syntax and the distribution of the visualization data.		## It’s easy to visually tell the difference between the “high rated” sorted list of visualizations there, and just the “most current”. I’m not sure the topic of the visualization is even that critical -- I could probably classify to a first approximation just by looking at the title syntax and the distribution of the visualization data.


			== Jadrian ==
			{{User:Jadrian Miles/Paper list}}

Hua Guo: /* Random Ideas */

2012-05-11T15:27:31Z

Random Ideas

← Older revision		Revision as of 15:27, 11 May 2012
Line 86:		Line 86:

	focus + context algorithm for circular layout		focus + context algorithm for circular layout

			Applying correlation to visual analytics
			- guides the display of visual elements (as variables in the DOI function)
			- good ways to visualize correlation in complex dataset? (not as correlation for 2D/nD points..)
			- can we use correlation to "compress" the visualization of data? (display the same amount of information using less screen estate)?

	== Eni ==		== Eni ==

Hua Guo at 15:22, 11 May 2012

2012-05-11T15:22:29Z

@@ Line 67: / Line 67: @@
 # We can evaluate what makes a "high rated" visualization on ManyEyes.
 ## It’s easy to visually tell the difference between the “high rated” sorted list of visualizations there, and just the “most current”.  I’m not sure the topic of the visualization is even that critical -- I could probably classify to a first approximation just by looking at the title syntax and the distribution of the visualization data.
 == Eni ==

Steven Gomez: /* Combining Topic Modeling with Crowdsourcing for Image Description */

2011-11-16T00:02:07Z

Combining Topic Modeling with Crowdsourcing for Image Description

← Older revision		Revision as of 00:02, 16 November 2011
Line 6:		Line 6:
	* Based on a conversation w/ Rebecca who is making a black box that can take in an image and output a distribution of likely noun phrases (or sentences extracted from training captions) that describe the image. She's got her topic modeler trained on images of shoes!		* Based on a conversation w/ Rebecca who is making a black box that can take in an image and output a distribution of likely noun phrases (or sentences extracted from training captions) that describe the image. She's got her topic modeler trained on images of shoes!
	* Thinking about integrating Turk with the topic modeling stuff. The TM stuff is good at labeling "expert" features of the shoes, but does not get basic things right, like color or when these shoes might be worn. These are things Turkers could easily do. Can we figure out what the "expert" terminology is versus normal terminology? Can we integrate that with a language model for better caption generation?		* Thinking about integrating Turk with the topic modeling stuff. The TM stuff is good at labeling "expert" features of the shoes, but does not get basic things right, like color or when these shoes might be worn. These are things Turkers could easily do. Can we figure out what the "expert" terminology is versus normal terminology? Can we integrate that with a language model for better caption generation?
			* Check out "Im2Text" paper by Tamara Berg (NIPS 2011)

	=== A Visual Survey of Evaluation Methods ===		=== A Visual Survey of Evaluation Methods ===

Steven Gomez: /* Steve */

2011-11-16T00:00:51Z

Steve

← Older revision		Revision as of 00:00, 16 November 2011
Line 1:		Line 1:
	== Steve ==		== Steve ==
	[updated 11/10/11]		[updated 11/10/11]

			=== Combining Topic Modeling with Crowdsourcing for Image Description ===
			* 19:00, 15 November 2011 (EST)
			* Based on a conversation w/ Rebecca who is making a black box that can take in an image and output a distribution of likely noun phrases (or sentences extracted from training captions) that describe the image. She's got her topic modeler trained on images of shoes!
			* Thinking about integrating Turk with the topic modeling stuff. The TM stuff is good at labeling "expert" features of the shoes, but does not get basic things right, like color or when these shoes might be worn. These are things Turkers could easily do. Can we figure out what the "expert" terminology is versus normal terminology? Can we integrate that with a language model for better caption generation?

	=== A Visual Survey of Evaluation Methods ===		=== A Visual Survey of Evaluation Methods ===

Steven Gomez: /* Steve */

2011-11-11T01:41:37Z

Steve

← Older revision		Revision as of 01:41, 11 November 2011
Line 21:		Line 21:
	Abstract: We introduce an interaction pattern for soliciting and integrating crowdsourced human feedback for data visualization. Human feedback in the visualization design process is often useful in building tools that are aesthetically pleasing and useful for data analysis. We hypothesize that even non-experts on the web can make significant contributions to visualization design. With a new framework X, we demonstrate that workers on Mechanical Turk can effectively guide designs by completing several kinds of tasks: selecting the preferred image in a side-by-side comparison of generated visualizations; answering a quantitative image analysis question with known ground truth; providing qualitative feedback about a visualization; reviewing (another worker's) criticism of a visualization and answering "Do you agree with this?"		Abstract: We introduce an interaction pattern for soliciting and integrating crowdsourced human feedback for data visualization. Human feedback in the visualization design process is often useful in building tools that are aesthetically pleasing and useful for data analysis. We hypothesize that even non-experts on the web can make significant contributions to visualization design. With a new framework X, we demonstrate that workers on Mechanical Turk can effectively guide designs by completing several kinds of tasks: selecting the preferred image in a side-by-side comparison of generated visualizations; answering a quantitative image analysis question with known ground truth; providing qualitative feedback about a visualization; reviewing (another worker's) criticism of a visualization and answering "Do you agree with this?"

	=== Modeling the ~~visual attention 'spotlight' by low-level image features~~ for ~~vector and tensor field visualizations~~ ===		=== Modeling the Visual Attention Spotlight for Vector Field Visualization Using Natural Image Descriptors ===

	Expected Contribution(s):		Expected Contribution(s):
Line 30:		Line 30:
	Abstract: ''TBD''		Abstract: ''TBD''

	=== Evaluating ~~analysts~~' ~~bias toward beauty~~ in ~~visual analytics~~' ===		=== Evaluating Analysts' Bias Toward Beauty in Visual Analytics' ===

	Expected Contribution(s):		Expected Contribution(s):
Line 41:		Line 41:
	## they are used for longer time periods than "ugly" ones, even when told that these ugly tools provide different features and may contain different insights.		## they are used for longer time periods than "ugly" ones, even when told that these ugly tools provide different features and may contain different insights.
	# Users felt more confident in their analyses using beautiful applications.		# Users felt more confident in their analyses using beautiful applications.

	~~Abstract: ''TBD''~~

	~~=== Crowdsourcing visualization interaction experiments: finding a taxonomy of interactions that Turkers do well and poorly ===~~

	~~Expected Contribution(s):~~
	~~# Demonstration of the use of crowdsourcing for interactive visual analytics.~~
	~~# A characterization of analytical interaction tasks one might reliably crowdsource, as well as those that should be not be crowdsourced.~~

	~~Expected Results:~~
	# We designed experiments to test user performance over a general taxonomy of interaction types for information visualization ([http://vgtc.org/wpmu/infovis07/2007/12/12/toward-a-deeper-understanding-of-the-role-of-interaction-in-information-visualization/ e.g., in this Yi and Stasko InfoVis paper]).
	~~# Found that X of these interactions can be reliably completed by novices, and Y cannot.~~

	~~In a sense this is a follow-up to Heer and Bostock's 2010 CHI paper ([http://vis.stanford.edu/papers/crowdsourcing-graphical-perception "Crowdsourcing Graphical Perception..."]).~~

	Abstract: ''TBD''		Abstract: ''TBD''

Steven Gomez: /* Paper Sketches */

2011-11-11T01:39:04Z

Paper Sketches

← Older revision		Revision as of 01:39, 11 November 2011
Line 1:		Line 1:
	== Steve ==		== Steve ==
	~~=== Paper Sketches ===~~
	[updated 11/10/11]		[updated 11/10/11]

	===~~= Tentative title: '''~~A Visual Survey of Evaluation Methods~~''' =~~===		=== A Visual Survey of Evaluation Methods ===

	Expected Contribution(s):		Expected Contribution(s):
Line 9:		Line 8:
	# I'm imaging a tool for people who don't know what kind of evaluation to do with a new tool. I think it would be awesome to read in a .bib file of related papers, and somehow be able to summarize (visualize?) the kinds of evaluations done by peers, as sort of a ''suggestion'' for an evaluation method.		# I'm imaging a tool for people who don't know what kind of evaluation to do with a new tool. I think it would be awesome to read in a .bib file of related papers, and somehow be able to summarize (visualize?) the kinds of evaluations done by peers, as sort of a ''suggestion'' for an evaluation method.

	===~~= Tentative title:~~ Domain-effects on Visual Thinking and Communication ====		=== Domain-effects on Visual Thinking and Communication ===
	* '''Experiment: How does an individual's academic discipline (e.g., math, physics, bio) affect the way s/he chooses representations to explain basic scientific phenomena'''		* '''Experiment: How does an individual's academic discipline (e.g., math, physics, bio) affect the way s/he chooses representations to explain basic scientific phenomena'''

	Expected Contribution(s):		Expected Contribution(s):

	===~~= Tentative title: '''~~Crowdsourcing Visualization Edits and Quality Assessment~~''' =~~===		=== Crowdsourcing Visualization Edits and Quality Assessment ===

	Expected Contribution(s):		Expected Contribution(s):
Line 22:		Line 21:
	Abstract: We introduce an interaction pattern for soliciting and integrating crowdsourced human feedback for data visualization. Human feedback in the visualization design process is often useful in building tools that are aesthetically pleasing and useful for data analysis. We hypothesize that even non-experts on the web can make significant contributions to visualization design. With a new framework X, we demonstrate that workers on Mechanical Turk can effectively guide designs by completing several kinds of tasks: selecting the preferred image in a side-by-side comparison of generated visualizations; answering a quantitative image analysis question with known ground truth; providing qualitative feedback about a visualization; reviewing (another worker's) criticism of a visualization and answering "Do you agree with this?"		Abstract: We introduce an interaction pattern for soliciting and integrating crowdsourced human feedback for data visualization. Human feedback in the visualization design process is often useful in building tools that are aesthetically pleasing and useful for data analysis. We hypothesize that even non-experts on the web can make significant contributions to visualization design. With a new framework X, we demonstrate that workers on Mechanical Turk can effectively guide designs by completing several kinds of tasks: selecting the preferred image in a side-by-side comparison of generated visualizations; answering a quantitative image analysis question with known ground truth; providing qualitative feedback about a visualization; reviewing (another worker's) criticism of a visualization and answering "Do you agree with this?"

	===~~= Tentative title: '''~~Modeling the visual attention 'spotlight' by low-level image features for vector and tensor field visualizations~~''' =~~===		=== Modeling the visual attention 'spotlight' by low-level image features for vector and tensor field visualizations ===

	Expected Contribution(s):		Expected Contribution(s):
Line 31:		Line 30:
	Abstract: ''TBD''		Abstract: ''TBD''

	===~~= Tentative title: '''~~Evaluating analysts' bias toward beauty in visual analytics'~~'' =~~===		=== Evaluating analysts' bias toward beauty in visual analytics' ===

	Expected Contribution(s):		Expected Contribution(s):
Line 45:		Line 44:
	Abstract: ''TBD''		Abstract: ''TBD''

	===~~= Tentative title: '''~~Crowdsourcing visualization interaction experiments: finding a taxonomy of interactions that Turkers do well and poorly~~''' =~~===		=== Crowdsourcing visualization interaction experiments: finding a taxonomy of interactions that Turkers do well and poorly ===

	Expected Contribution(s):		Expected Contribution(s):

Steven Gomez: /* Paper Sketches */

2011-11-11T01:36:29Z

Paper Sketches

← Older revision		Revision as of 01:36, 11 November 2011
Line 1:		Line 1:
	== Steve ==		== Steve ==
	=== Paper Sketches ===		=== Paper Sketches ===
	[updated 9/1/11]		[updated 11/10/11]

	Tentative title: '''~~Experiment: How does an individual's academic discipline (e.g., math, physics, bio) affect the way s/he chooses representations to explain basic scientific phenomena~~'''		==== Tentative title: '''A Visual Survey of Evaluation Methods''' ====

	Expected Contribution(s):		Expected Contribution(s):
			# A survey and visualization of evaluation methods for visualization tools.
			# I'm imaging a tool for people who don't know what kind of evaluation to do with a new tool. I think it would be awesome to read in a .bib file of related papers, and somehow be able to summarize (visualize?) the kinds of evaluations done by peers, as sort of a ''suggestion'' for an evaluation method.

	Tentative title: '''Crowdsourcing Visualization Edits and Quality Assessment'''		==== Tentative title: Domain-effects on Visual Thinking and Communication ====
			* '''Experiment: How does an individual's academic discipline (e.g., math, physics, bio) affect the way s/he chooses representations to explain basic scientific phenomena'''

			Expected Contribution(s):

			==== Tentative title: '''Crowdsourcing Visualization Edits and Quality Assessment''' ====

	Expected Contribution(s):		Expected Contribution(s):
Line 15:		Line 22:
	Abstract: We introduce an interaction pattern for soliciting and integrating crowdsourced human feedback for data visualization. Human feedback in the visualization design process is often useful in building tools that are aesthetically pleasing and useful for data analysis. We hypothesize that even non-experts on the web can make significant contributions to visualization design. With a new framework X, we demonstrate that workers on Mechanical Turk can effectively guide designs by completing several kinds of tasks: selecting the preferred image in a side-by-side comparison of generated visualizations; answering a quantitative image analysis question with known ground truth; providing qualitative feedback about a visualization; reviewing (another worker's) criticism of a visualization and answering "Do you agree with this?"		Abstract: We introduce an interaction pattern for soliciting and integrating crowdsourced human feedback for data visualization. Human feedback in the visualization design process is often useful in building tools that are aesthetically pleasing and useful for data analysis. We hypothesize that even non-experts on the web can make significant contributions to visualization design. With a new framework X, we demonstrate that workers on Mechanical Turk can effectively guide designs by completing several kinds of tasks: selecting the preferred image in a side-by-side comparison of generated visualizations; answering a quantitative image analysis question with known ground truth; providing qualitative feedback about a visualization; reviewing (another worker's) criticism of a visualization and answering "Do you agree with this?"

	Tentative title: '''Modeling the visual attention 'spotlight' by low-level image features for vector and tensor field visualizations'''		==== Tentative title: '''Modeling the visual attention 'spotlight' by low-level image features for vector and tensor field visualizations''' ====

	Expected Contribution(s):		Expected Contribution(s):
Line 24:		Line 31:
	Abstract: ''TBD''		Abstract: ''TBD''

	Tentative title: '''Evaluating analysts' bias toward beauty in visual analytics'''		==== Tentative title: '''Evaluating analysts' bias toward beauty in visual analytics''' ====

	Expected Contribution(s):		Expected Contribution(s):
Line 38:		Line 45:
	Abstract: ''TBD''		Abstract: ''TBD''

	Tentative title: '''Crowdsourcing visualization interaction experiments: finding a taxonomy of interactions that Turkers do well and poorly'''		==== Tentative title: '''Crowdsourcing visualization interaction experiments: finding a taxonomy of interactions that Turkers do well and poorly''' ====

	Expected Contribution(s):		Expected Contribution(s):

Steven Gomez: /* Paper Sketches */

2011-09-02T20:40:10Z

Paper Sketches

← Older revision		Revision as of 20:40, 2 September 2011
Line 2:		Line 2:
	=== Paper Sketches ===		=== Paper Sketches ===
	[updated 9/1/11]		[updated 9/1/11]

			Tentative title: '''Experiment: How does an individual's academic discipline (e.g., math, physics, bio) affect the way s/he chooses representations to explain basic scientific phenomena'''

			Expected Contribution(s):

	Tentative title: '''Crowdsourcing Visualization Edits and Quality Assessment'''		Tentative title: '''Crowdsourcing Visualization Edits and Quality Assessment'''

Steven Gomez: /* Paper Sketches */

2011-09-01T19:53:56Z

Paper Sketches

← Older revision		Revision as of 19:53, 1 September 2011
Line 9:		Line 9:
	# (What should the evaluation look like here? In Soylent, the evaluation is specific to the types of edits it makes available; for instance, the results explain how successful crowdsourced edits are at shortening text.)		# (What should the evaluation look like here? In Soylent, the evaluation is specific to the types of edits it makes available; for instance, the results explain how successful crowdsourced edits are at shortening text.)

	Abstract: We introduce an interaction pattern for soliciting and integrating crowdsourced human feedback for data visualization. Human feedback in the visualization design process is often useful in building tools that are aesthetically pleasing and useful for data analysis. We hypothesize that even non-experts on the web can make significant contributions to visualization design. With a new framework X, we demonstrate ~~the~~ workers on Mechanical Turk can effectively guide designs by completing 3 kinds of tasks: selecting the preferred ~~view~~ in a side-by-side comparison of generated visualizations; providing qualitative feedback about a visualization; reviewing (another worker's) criticism of a visualization and answering "Do you agree with this?"		Abstract: We introduce an interaction pattern for soliciting and integrating crowdsourced human feedback for data visualization. Human feedback in the visualization design process is often useful in building tools that are aesthetically pleasing and useful for data analysis. We hypothesize that even non-experts on the web can make significant contributions to visualization design. With a new framework X, we demonstrate that workers on Mechanical Turk can effectively guide designs by completing several kinds of tasks: selecting the preferred image in a side-by-side comparison of generated visualizations; answering a quantitative image analysis question with known ground truth; providing qualitative feedback about a visualization; reviewing (another worker's) criticism of a visualization and answering "Do you agree with this?"

	Tentative title: '''Modeling the visual attention 'spotlight' by low-level image features for vector and tensor field visualizations'''		Tentative title: '''Modeling the visual attention 'spotlight' by low-level image features for vector and tensor field visualizations'''