In order to give people ubiquitous access to software applications, device controllers, and Internet services, it will be necessary to automatically adapt user interfaces to the computational devices at hand (e.g., cell phones, PDAs, touch panels, etc.). While previous researchers have proposed solutions to this problem, each has limitations. This paper proposes a novel solution based on treating interface adaptation as an optimization problem. When asked to render an interface on a specific device, our Supple system searches for the rendition that meets the device’s constraints and minimizes the estimated effort for the user’s expected interface actions. We make several contributions: 1) precisely defining the interface rendition problem, 2) demonstrating how user traces can be used to customize interface rendering to particular user’s usage pattern, 3) presenting an efficient interface rendering algorithm, 4) performing experiments that demonstrate the utility of our approach.

The study of complex social systems has traditionally been an arduous process, involving extensive surveys, interviews, ethnographic studies, or analysis of online behavior. Today, however, it is possible to use the unprecedented amount of information generated by pervasive mobile phones to provide insights into the dynamics of both individual and group behavior. Information such as continuous proximity, location, communication and activity data, has been gathered from the phones of 100 human subjects at MIT. Systematic measurements from these 100 people over the course of eight months have generated one of the largest datasets of continuous human behavior ever collected, representing over 300,000 hours of daily activity. In this thesis we describe how this data can be used to uncover regular rules and structure in behavior of both individuals and organizations, infer relationships between subjects, verify selfreport

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

In this paper we present our experience in building a visual file manager, VennFS2, that offers to users an adaptive interface toward access to files. Our file manager was originally designed to overcome some of limitations of hierarchical file systems, since it allows users to categorize files in such a way that files may belong multiple categories at once. Based on the past history of the files that were opened and modified by the user, VennFS2 graphically presents the user a small number of choices of the next file the user will modify. Some preliminary testing with interesting hints are also reported.

Abstract. Supple is an application and device-independent system, currently under development at University of Washington, that automatically generates user interfaces for a wide variety of display devices. Supple uses decision-theoretic optimization to render an interface from an abstract functional specification and an interchangeable device model. Supple also provides adaptation and customization mechanisms that allow for system- and user-initiated modifications to the appearance, organization and navigational structure of the user interface. 1

SUPPLE uses decision-theoretic optimization to render an abstract functional specification into an adaptive interface, which is personalized both to an individual's usage pattern and the characteristics of a target device. This paper briefly describes three enhancements to SUPPLE: 1) light-weight utility elicitation, 2) the ability to adapt to user's behavior by generating interfaces with multiple ways to access the same functionality, and 3) generalization-based customization.

Modern application software contains increasingly more customization features. Yet studies have shown that users often fail to adapt a system to their needs even if it makes their work more efficient. Two main barriers to customization are the difficulty in making adaptations and the lack of awareness that these adaptations exist. As a solution, systems that automatically recommend customizations when they appear to be useful have been proposed. In our work, we consider the problem of recommending software extensions – small components that provide additional functionality to an application. For applications like Emacs, Office, or Firefox, there exist thousands of such extensions on the web. However, classical recommender algorithms based on context or collaborative features often turn out to be insufficient, since the extensions are sometimes of highly varying quality. Existing systems that recommend software functions or customizations have also not succeeded for various other reasons. They are application-dependent, do not solve the privacy problem, and their mechanisms do not evolve in case the underlying application changes. We propose to improve recommendations by estimating the relevance and quality of an extension based on web link structure, extension authorship, and its dependencies with respect to other extensions. Our system is extensible and can be used with any application. We address the privacy issue by giving the user full control over the collected information. Finally, our feature computations evolve with the set of available extensions. ACM Classification H5.2 [Information interfaces and presentation]:

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We present a novel approach to the creation of user-generated, documentary video using a distributed network of sensorenabled video cameras and wearable on-body sensor devices. The wearable sensors are used to identify the subjects in view of the camera system and label the captured video with real-time human-centric social and physical behavioral information. With these labels, massive amounts of continually recorded video can be browsed, searched, and automatically stitched into cohesive multimedia content. This system enables naturally occurring human behavior to drive and control a multimedia content creation system in order to create video output that is understandable, informative, and/or enjoyable to its human audience. The collected sensor data is further utilized to enhance the created multimedia content such as by using the data to edit and/or generate audio score, determine appropriate pacing of edits, and control the length and type of audio and video transitions directly from the content of the captured media. We present the design of the platform, the design of the multimedia content creation application, and the evaluated results from several live runs of the complete system.