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We present SketchREAD, a multi-domain sketch recognition engine capable of recognizing freely hand-drawn diagrammatic sketches. Current computer sketch recognition systems are difficult to construct, and either are fragile or accomplish robustness by severely limiting the designer's drawing freedom. Our system can be applied to a variety of domains by providing structural descriptions of the shapes in that domain; no training data or programming is necessary. Robustness to the ambiguity and uncertainty inherent in complex, freely-drawn sketches is achieved through the use of context. The system uses context to guide the search for possible interpretations and uses a novel form of dynamically constructed Bayesian networks to evaluate these interpretations. This process allows the system to recover from low-level recognition errors (e.g., a line misclassified as an arc) that would otherwise result in domain level recognition errors. We evaluated Sketch-READ on real sketches in two domains--family trees and circuit diagrams--and found that in both domains the use of context to reclassify low-level shapes significantly reduced recognition error over a baseline system that did not reinterpret low-level classifications. We also discuss the system's potential role in sketch based user interfaces.

This paper describes a new technique for transferring data between computers, the synchronized clipboard. Multiple computers can share a synchronized clipboard for all clipboard operations, so that data copied to the clipboard from one computer, using the standard Copy command, can be pasted directly on another computer using the standard Paste command. Synchronized clipboards are well-suited for a single user moving data among several computers in close proximity. We describe an implementation of synchronized clipboards that works across a wide range of existing systems, including 3Com PalmPilots, Microsoft Windows PCs, Unix workstations, and other Java-capable platforms. Our implementation adds no noticeable overhead to local copy and paste operations.

Help-seeking communities have been playing an increasingly critical role in the way people seek and share information. However, traditional help-seeking mechanisms of these online communities have some limitations. In this paper, we describe an expertise-finding mechanism that attempts to alleviate the limitations caused by not knowing users' expertise levels. As a result of using social network data from the online community, this mechanism can automatically infer expertise level. This allows, for example, a question list to be personalized to the user's expertise level as well as to keyword similarity. We believe this expertise location mechanism will facilitate the development of next generation help-seeking communities.

Many computer operating systems provide seamless support for multiple display screens, but there are few cross-platform tools for collaborative use of multiple computers in a shared display environment. Mighty Mouse is a novel groupware tool built on the public domain VNC protocol. It is tailored specifically for face-to-face collaboration where multiple heterogeneous computers (usually laptops) are viewed simultaneously (usually via projectors) by people working together on a variety of applications under various operating systems. Mighty Mouse uses only the remote input capability of VNC, but enhances this with various features to support flexible movement between the various platforms, "floor control" to facilitate smooth collaboration, and customization features to accommodate different user, platform, and application preferences in a relatively seamless manner. The design rationale arises from specific observations about how people collaborate in meetings, which allows certain simplifying assumptions to be made in the implementation.