Interactive end-user training of machine learning systems has received significant attention as a tool for personalizing recognizers. However, most research limits end users to training a fixed set of application-defined concepts. This paper considers additional challenges that arise in end-user support for defining the number and nature of concepts that a system must learn to recognize. We develop BeatBox, a new system that enables end-user creation of custom beatbox recognizers and interactive adaptation of recognizers to an end user's technique, environment, and musical goals. BeatBox proposes rapid end-user exploration of variations in the number and nature of learned concepts, and provides end users with feedback on the reliability of recognizers learned for different potential combinations of percussive vocalizations. In a preliminary evaluation, we observed that end users were able to quickly create usable classifiers, that they explored different combinations of concepts to test alternative vocalizations and to refine classifiers for new musical contexts, and that learnability feedback was often helpful in alerting them to potential difficulties with a desired learning concept.

A lack of support for active listening undermines discussion and deliberation on the web. We contribute a design frame identifying potential improvements to web discussion were listening more explicitly encouraged in interfaces. We explore these concepts through a novel interface, Reflect, that creates a space next to every comment where others can summarize the points they hear the commenter making. Deployments on Slashdot, Wikimedia's Strategic Planning Initiative, and a local civic effort suggest that interfaces for listening may have traction for general use on the web.

The success of a computer system depends upon a user choosing it, but the field of Human-Computer Interaction has little ability to predict this user choice. We present a new method that measures user choice, and quantifies it as a measure of utility. Our method has two core features. First, it introduces an economic definition of utility, one that we can operationalize through economic experiments. Second, we employ a novel method of crowdsourcing that enables the collection of thousands of economic judgments from real users.

We present reform, a step toward write-once apply-anywhere user interface enhancements. The reform system envisions roles for both programmers and end users in enhancing existing websites to support new goals. First, a programmer authors a traditional mashup or browser extension, but they do not write a web scraper. Instead they use reform, which allows novice end users to attach the enhancement to their favorite sites with a scraping by-example interface. reform makes enhancements easier to program while also carrying the benefit that end users can apply the enhancements to any number of new websites. We present reform's architecture, user interface, interactive by-example extraction algorithm for novices, and evaluation, along with five example reform enabled enhancements.

Today, tools like Greasemonkey and Chickenfoot already provide the raw technology for modifying a Website in the end user's Web browser instead of the Webmaster's Web server by injecting it with a special browser-side script. The problem, however, is that it takes a programmer to enhance a Website with such a script. The paper presents reform, an interactive machine learning technique designed for novice end users, allowing them to scrape a variety of data layouts by example, without seeing the underlying Webpage representation.

Controlling the privacy of online content is difficult and often confusing. We present a social access control where users devise simple questions testing shared knowledge instead of constructing authenticated accounts and explicit access control rules. We implemented a prototype and conducted studies to explore the context of photo sharing security, gauge the difficulty of creating shared knowledge questions, measure their resilience to adversarial attack, and evaluate user ability to understand and predict this resilience.

We present Linked Editing, a novel, lightweight editor-based technique for managing duplicated source code. Linked Editing is implemented in a prototype editor called Codelink. We argue that the use of programming abstractions like functions and macros-the traditional solution to duplicated code-has inherent cognitive costs, leading programmers to chronically copy and paste code instead. Our user study compares functional abstraction with Linked Editing and shows that Linked Editing can give the benefits of abstraction with orders of magnitude decrease in programming time.