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UIST2.0 Archive - 20 years of UIST
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form

form design

In Proceedings of UIST 2010
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Designing adaptive feedback for improving data entry accuracy (p. 239-248)

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Data quality is critical for many information-intensive applications. One of the best opportunities to improve data quality is during entry. Usher provides a theoretical, data-driven foundation for improving data quality during entry. Based on prior data, Usher learns a probabilistic model of the dependencies between form questions and values. Using this information, Usher maximizes information gain. By asking the most unpredictable questions first, Usher is better able to predict answers for the remaining questions. In this paper, we use Usher's predictive ability to design a number of intelligent user interface adaptations that improve data entry accuracy and efficiency. Based on an underlying cognitive model of data entry, we apply these modifications before, during and after committing an answer. We evaluated these mechanisms with professional data entry clerks working with real patient data from six clinics in rural Uganda. The results show that our adaptations have the potential to reduce error (by up to 78%), with limited effect on entry time (varying between -14% and +6%). We believe this approach has wide applicability for improving the quality and availability of data, which is increasingly important for decision-making and resource allocation.

sketching of interactive physical form

In Proceedings of UIST 2006
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Rapid construction of functioning physical interfaces from cardboard, thumbtacks, tin foil and masking tape (p. 289-298)

web form

In Proceedings of UIST 2009
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Mining web interactions to automatically create mash-ups (p. 203-212)

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The deep web contains an order of magnitude more information than the surface web, but that information is hidden behind the web forms of a large number of web sites. Metasearch engines can help users explore this information by aggregating results from multiple resources, but previously these could only be created and maintained by programmers. In this paper, we explore the automatic creation of metasearch mash-ups by mining the web interactions of multiple web users to find relations between query forms on different web sites. We also present an implemented system called TX2 that uses those connections to search multiple deep web resources simultaneously and integrate the results in context in a single results page. TX2 illustrates the promise of constructing mash-ups automatically and the potential of mining web interactions to explore deep web resources.

In Proceedings of UIST 2010
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Mixture model based label association techniques for web accessibility (p. 67-76)

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An important aspect of making the Web accessible to blind users is ensuring that all important web page elements such as links, clickable buttons, and form fields have explicitly assigned labels. Properly labeled content is then correctly read out by screen readers, a dominant assistive technology used by blind users. In particular, improperly labeled form fields can critically impede online transactions such as shopping, paying bills, etc. with screen readers. Very often labels are not associated with form fields or are missing altogether, making form filling a challenge for blind users. Algorithms for associating a form element with one of several candidate labels in its vicinity must cope with the variability of the element's features including label's location relative to the element, distance to the element, etc. Probabilistic models provide a natural machinery to reason with such uncertainties. In this paper we present a Finite Mixture Model (FMM) formulation of the label association problem. The variability of feature values are captured in the FMM by a mixture of random variables that are drawn from parameterized distributions. Then, the most likely label to be paired with a form element is computed by maximizing the log-likelihood of the feature data using the Expectation-Maximization algorithm. We also adapt the FMM approach for two related problems: assigning labels (from an external Knowledge Base) to form elements that have no candidate labels in their vicinity and for quickly identifying clickable elements such as add-to-cart, checkout, etc., used in online transactions even when these elements do not have textual captions (e.g., image buttons w/o alternative text). We provide a quantitative evaluation of our techniques, as well as a user study with two blind subjects who used an aural web browser implementing our approach.