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Friday I leave for IUI 2009, where I’ll be presenting a formative study about end users debugging machine-learned programs.  This will be my first conference presentation, and I’m about equal parts excited and nervous.  At the very least, I should a chance to head out with my camera in sunny Florida.  I’ve hardly even taken it out of its bag this past month, and that needs to change.

I finally finished running participants for our current research study… now to start analyzing the data.  The paper deadline is March 8th, which feels like it’s approaching rudely fast.  But hey, at least we’re still on track.  I’d been worried that bringing in participants would become a 2 or 3-week nightmare like our last study, which would have pushed things way too close to the deadline.

This weekend I bought Henri T. Cat a new scratching board, which he promptly fell in love with.  Maybe now he’ll leave my couch alone.

Also, yesterday I learned that I’ll be going to CHI 2009 in Boston!  First big conference, eep!

Screenshot

Background

The purpose of this project is to visually explain a machine learning classifier’s logic to an end user.  Additionally, the user must be able to explain back to the classifier when its logic is faulty and how it should be fixed.

Implementation

This project was designed in C++ using OpenGL.  I used GLUT primitives such as glutSolidCube() to create the basic components, then reshaped and arranged them using OpenGL transformations.  In order to create the translucent regions, I needed to enable lighting and shading.  Blending was then used to create the illusion that each word’s bar is inside one or more of the translucent boxes.  The text was created using glutStrokeCharater() calls; regular bitmap fonts were not an option because I wanted to rotate the text beneath the word boxes.  The resulting words suffered from severe aliasing problems, so I used the OpenGL accumulation buffer to enable 8x anti-aliasing.

Usage

While this project is not actually hooked up to a classifier, the word importance can still be manipulated by dragging the bars up or down into the desired regions.  There are also controls in a secondary window for rotating the view or panning right and left.

Room for Improvement

Ideally, this explanation would be interacting with a classifier; changes made to word importance would then be instantly viewable in the application when it re-classifies based upon the user’s adjustments.  I would also have liked to make it possible to zoom in and out so that the user can fit exactly as many words on-screen as she would like.  Finally, the blending of the word boxes with the translucent boxes isn’t entirely right–the word boxes should slightly change color depending on which region they are.

Downloads

Source code (for Visual Studio 2008)

Windows Binary (Requires OpenGL, GLUT and GLUI libraries)

After another day of coaxing OpenGL to play nice, I’ve managed to make some progress on my final project.  The goal is to create a 3D version of the 2D visualization my research group designed for the logic underlying a Bayesian classifier.  So far I’ve got it displaying blocks representing the importance of each word to the classifier (only using three words at the moment, but I’d like to scale it up so that you can just zoom down a list of hundreds), and translucent regions denoting whether a word is required, forbidden, or unimportant to the classifier’s decision.  The blending isn’t quite right for that last part, but good enough to move on.  Now I need to make it interactive, so that the user can adjust the importance of each word.

Also, Ted and Judy dropped off an electric oil heater when they passed through town today.  My apartment’s furnace does a great job of heating the family room, but that’s about it.  Now maybe my bedroom won’t be freezing each morning!  Sadly they were just passing through while I was on campus; I want to take them out to dinner sometime before heading back to Detroit for the holidays.

Yesterday I was notified that my research group’s submission to IUI’09 was accepted!  This was the first research study and resulting publication that I led, and I’d honestly not been expecting it to be accepted on the first try.  So yay!  That’s two publications in a year and half–not a bad start, I suppose   The paper detailed a formative study of barriers end users encounter when attempting to “fix” [i.e. debug] machine-learned programs, like junk mail filters or hand-writing recognizers.  These sorts of machine-learned programs are becoming increasingly common in software, and since they need to tailor themselves to a particular user’s needs, that end user is the only person available to debug them if they misbehave.  It’s really a whole new research field that can build upon existing data about end-user programmers debugging in more traditional environments, like Microsoft Excel, but the machine-learned component is a significant change from such traditional models of source code.

Now we have three weeks to address the reviewers’ comments and get a camera-ready version to the conference organizers.  The conference itself is in Florida this February, so with a little luck, I might be able to afford to go and present the results in person