Wednesday, September 7, 2011

Paper Reading #5- A framework for robust and flexible handling of inputs with uncertainty

Title: A framework for robust and flexible handling of inputs with uncertainty
Reference Information:
Julia Schwarz, Scott Hudson, Jennifer Mankoff, and Andrew D. Wilson. "A framework for robust and flexible handling of inputs with uncertainty". UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology ACM New York, NY ©2010 ISBN: 978-1-4503-0271-5.
Author Bios:
Julia Schwarz is a PhD student at Carnegie Mellon University studying Human-Computer Interaction. She and Andrew Wilson work at Microsoft.
Scott Hudson is a Professor in the Human-Computer Interaction Institute within the School of Computer Science at Carnegie Mellon University. He earned his Ph.D. in Computer Science at the University of Colorado in 1986.
Jennifer Mankoff  is an Associate Professor in the Human Computer Interaction Institute at Carnegie Mellon University, where she joined the faculty in 2004. She earned her B.A. at Oberlin College and her Ph.D. in Computer Science at the Georgia Institute of Technology advised by Gregory Abowd and Scott Hudson.
Andrew D. Wilson is a senior researcher at Microsoft Research. He obtained his BA at Cornell University, and MS and PhD at the MIT Media Laboratory.
Summary:
  • Hypothesis: The device that the authors present is the solution to the all-too-common problem of "uncertain input" being fed into different UIs of devices- this device is a systematic, extensible, and easily manipulated device to allow pleasurable experiences for users where input may not be read 100% accurately all of the time.
  • Methods: There are many differences between conventional input and uncertain input. With uncertain input, the PMF (probability mass function) is injected into an algorithm to try to determine what a user was attempting to do (taking into account that where they touch on the screen may have overlapped with some other touch-sensitive area, causing a clash in actions.) This device needed to be able to handle 4 different kinds of actions/events: modeling, dispatch, interpretation, and action. In the case of event modeling, they implement the PMF for event type as a collection of separate event objects, one for each type, each with an associated probability. The collection of alternative events and their associated probabilities then serves as the PMF over possible event types. For the dispatching events problem, Each interactor's state and the event type are considered when passing off an event to the dispatcher. Scores are then returned for each interactor (which can be thought of like a PMF) as to whether each one is a possible candidate for that event input. For the interpretation problem, the interactor must determine what possible action to take based upon the internal state of the interactor, the nature of the event, and the possible actions it can take. As far as the action is concerned, the mediator determines what action to take based on priority, machine state, and information from input (if there is even enough to process to take an action).
  • Results: Using six areas of focus, the authors were able to improve upon / make more certain input that was inputted into the system in order to claim that the right event/action was taken for each method of inputting "uncertainty" into the system. For instance, they have a way to have text show up in a textbox on a screen even when the user did not have a textbox selected to type into (i.e., when you bring up google and start typing before the page fully loads- the cursor is not in the textbox but you might start typing anyway). The other areas in which the authors were able to excel include speech recognition, improved pointing for the motor impaired, buttons for touch input, smart window resizing, and view sliders on the page.
  • Conents: In this paper, the authors sought to show off their created work to improve on input into a system that might not be so clear or easy to deal with. They tested it out in practical situations which may arise during common use of kinds of devices like an iPad, speech recognition software, etc. After each experiment was conducted, results were gathered. The whole point of this creation was to build on the improvement of interpreting input so that users would not become frustrated with their experiences using smart devices.
Discussion:
After reading through the results they got from each of the 6 fields of improvement, I am convinced that the authors achieved their goals. Their uses of a PMF (or an abstract representation of one) was very well done and I believe that this could be implemented in smart devices in order to improve users' experiences. For example, iPhone users' fingers are not all the same size. Some users might become frustrated when trying to type on the on-screen keyboard because they have bigger fingers. The error correction and interpreter on this device would either allow the user to choose which letter they meant, or disregard the letter and let them try again (which would be better than inputting a typo and not realizing it until a while later in the text, and having to go back and fix it). There are definitely uses for this to be incorporated into. This could even be a springboard for even more advancements and improvements in "uncertain" inputs.

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