In order to display web pages designed for desktop-sized monitors, some small-screen web browsers provide singlecolumn or thumbnail views. Both have limitations. Singlecolumn views affect page layouts and require users to scroll significantly more. Thumbnail views tend to reduce contained text beyond readability, so differentiating visually similar areas requires users to zoom. In this paper, we present Summary Thumbnails—thumbnail views enhanced with readable text fragments. Summary Thumbnails help users identify viewed material and distinguish between visually similar areas. In our user study, participants located content in web pages about 41 % faster and with 71% lower error rates when using the Summary Thumbnail interface than when using the Single-Column interface, and zoomed 59 % less than when using the Thumbnail interface. Nine of the eleven participants preferred Summary Thumbnails over both the Thumbnail and Single-Column interfaces. ACM Classifiction: H5.2 [Information interfaces and

Decision-theoretic optimization is becoming a popular tool in the user interface community, but creating accurate cost (or utility) functions has become a bottleneck --- in most cases the numerous parameters of these functions are chosen manually, which is a tedious and error-prone process. This paper describes ARNAULD, a general interactive tool for eliciting user preferences concerning concrete outcomes and using this feedback to automatically learn a factored cost function. We empirically evaluate our machine learning algorithm and two automatic query generation approaches and report on an informal user study.

Abstract. We present Supple, a novel toolkit which automatically generates interfaces for ubiquitous applications. Designers need only specify declarative models of the interface and desired hardware device and Supple uses decision-theoretic optimization to automatically generate a concrete rendering for that device. This paper provides an overview of our system and describes key extensions that barred the previous version (reported in [3]) from practical application. Specifically, we describe a functional modeling language capable of representing complex applications. We propose a new adaptation strategy, split interfaces, which speeds access to common interface features without disorienting the user. We present a customization facility that allows designers and end users to override Supple’s automatic rendering decisions. We describe a distributed architecture which enables computationally-impoverished devices to benefit from Supple interfaces. Finally, we present experiments and a preliminary user-study that demonstrate the practicality of our approach. 1

We evaluate two systems for automatically generating personalized interfaces adapted to the individual motor capabilities of users with motor impairments. The first system, SUPPLE, adapts to users ’ capabilities indirectly by first using the ARNAULD preference elicitation engine to model a user’s preferences regarding how he or she likes the interfaces to be created. The second system, SUPPLE++, models a user’s motor abilities directly from a set of one-time motor performance tests. In a study comparing these approaches to baseline interfaces, participants with motor impairments were 26.4 % faster using ability-based user interfaces generated by SUPPLE++. They also made 73 % fewer errors, strongly preferred those interfaces to the manufacturers’ defaults, and found them more efficient, easier to use, and much less physically tiring. These findings indicate that rather than requiring some users with motor impairments to adapt themselves to software using separate assistive technologies, software can now adapt itself to the capabilities of its users.

Most of today’s GUIs are designed for the typical, able-bodied user; atypical users are, for the most part, left to adapt as best they can, perhaps using specialized assistive technologies as an aid. In this paper, we present an alternative approach: SUPPLE++ automatically generates interfaces which are tailored to an individual’s motor capabilities and can be easily adjusted to accommodate varying vision capabilities. SUPPLE++ models users ’ motor capabilities based on a onetime motor performance test and uses this model in an optimization process, generating a personalized interface. A preliminary study indicates that while there is still room for improvement, SUPPLE++ allowed one user to complete tasks that she could not perform using a standard interface, while for the remaining users it resulted in an average time savings of 20%, ranging from an slowdown of 3 % to a speedup of

Web image search is difficult in part because a handful of keywords are generally insufficient for characterizing the visual properties of an image. Popular engines have begun to provide tags based on simple characteristics of images (such as tags for black and white images or images that contain a face), but such approaches are limited by the fact that it is unclear what tags end-users want to be able to use in examining Web image search results. This paper presents CueFlik, a Web image search application that allows end-users to quickly create their own rules for re-ranking images based on their visual characteristics. End-users can then re-rank any future Web image search results according to their rule. In an experiment we present in this paper, end-users quickly create effective rules for such concepts as “product photos”, “portraits of people”, and “clipart”. When asked to conceive of and create their own rules, participants create such rules as “sports action shot ” with images from queries for “basketball ” and “football”. CueFlik represents both a promising new approach to Web image search and an important study in end-user interactive machine learning.

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Designers still often create a specific user interface for every target platform they wish to support, which is timeconsuming and error-prone. The need for a multi-platform user interface design approach that designers feel comfortable with increases as people expect their applications and data to go where they go. We present Gummy, a multiplatform graphical user interface builder that can generate an initial design for a new platform by adapting and combining features of existing user interfaces created for the same application. Our approach makes it easy to target new platforms and keep all user interfaces consistent without requiring designers to considerably change their work practice.

Many end-users wish to customize their applications, automating common tasks and routines. Unfortunately, this automation is difficult today — users must choose between brittle macros and complex scripting languages. Programming by demonstration (PBD) offers a middle ground, allowing users to demonstrate a procedure multiple times and generalizing the requisite behavior with machine learning. Unfortunately, many PBD systems are almost as brittle as macro recorders, offering few ways for a user to control the learning process or correct the demonstrations used as training examples. This paper presents CHINLE, a system which automatically constructs PBD systems for applications based on their interface specification. The resulting PBD systems have novel interaction and visualization methods, which allow the user to easily monitor and guide the learning process, facilitating error recovery during training. CHINLE-constructed PBD systems learn procedures with conditionals and perform partial learning if the procedure is too complex to learn completely. ACM Classification D.2.2 [Design Tools and Techniques]: User Interfaces, H1.2. [Models and principles]: User/Machine

A problem with many of today’s appliance interfaces is that they are inconsistent. For example, the procedure for setting the time on alarm clocks and VCRs differs, even among different models made by the same manufacturer. Finding particular functions can also be a challenge, because appliances often organize their features differently. This paper presents a system, called Uniform, which approaches this problem by automatically generating remote control interfaces that take into account previous interfaces that the user has seen during the generation process. Uniform is able to automatically identify similarities between different devices and users may specify additional similarities. The similarity information allows the interface generator to use the same type of controls for similar functions, place similar functions so that they can be found with the same navigation steps, and create interfaces that have a similar visual appearance. Author Keywords Automatic interface generation, consistency, familiarity, handheld computers, personal digital assistants, mobile phones, personal universal controller (PUC), Pebbles