GestureBar is a novel, approachable UI for learning gestural interactions that enables a walk-up-and-use experience which is in the same class as standard menu and toolbar interfaces. GestureBar leverages the familiar, clean look of a common toolbar, but in place of executing commands, richly discloses how to execute commands with gestures, through animated images, detail tips and an out-of-document practice area. GestureBar's simple design is also general enough for use with any recognition technique and for integration with standard, non-gestural UI components. We evaluate GestureBar in a formal experiment showing that users can perform complex, ecologically valid tasks in a purely gestural system without training, introduction, or prior gesture experience when using GestureBar, discovering and learning a high percentage of the gestures needed to perform the tasks optimally, and significantly outperforming a state of the art crib sheet. The relative contribution of the major design elements of GestureBar is also explored. A second experiment shows that GestureBar is preferred to a basic crib sheet and two enhanced crib sheet variations.

We present an online system for recognizing handwritten mathematical matrices in the context of an interactive computational tool called MathPaper. Automatic segmentation and recognition of multiple expressions are supported based on a spacing algorithm that leverages recognized symbol identities, sizes, and relative locations of individual symbols. Matrices with ellipses can be recognized and instantiated with non-ellipsis elements. Both well- and non-well-formed matrices can also be recognized. Matrix elements can be any general mathematical expressions including imbedded matrices. Our recognizer also addresses the poor column alignment problem of handwritten matrices, and allows for slight horizontal overlaps between elements in neighboring columns and different rows.

We present results from a user study that compared six visualization methods for two-dimensional vector data. Users performed three simple but representative tasks using visualizations from each method: 1) locating all critical points in an image, 2) identifying critical point types, and 3) advecting a particle. Visualization methods included two that used different spatial distributions of short arrow icons, two that used different distributions of integral curves, one that used wedges located to suggest flow lines, and line-integral convolution (LIC). Results show different strengths and weaknesses for each method. We found that users performed these tasks better with methods that: 1) showed the sign of vectors within the vector field, 2) visually represented integral curves, and 3) visually represented the locations of critical points. Expert user performance was not statistically different from nonexpert user performance. We used several methods to analyze the data including omnibus analysis of variance, pairwise t-tests, and graphical analysis using inferential confidence intervals. We concluded that using the inferential confidence intervals for displaying the overall pattern of results for each task measure and for performing subsequent pairwise comparisons of the condition means was the best method for analyzing the data in this study. These results provide quantitative support for some of the anecdotal evidence concerning visualization methods. The tasks and testing framework also provide a basis for comparing other visualization methods, for creating more effective methods and for defining additional tasks to further understand the tradeoffs among the methods. In the future, we also envision extending this work to more ambitious comparisons, such as evaluating two-dimensional vectors on two-dimensional surfaces embedded in three-dimensional space and defining analogous tasks for three-dimensional visualization methods.

We present a range of novel interactions enabled by a simple modification in the design of a computer mouse. By converting each mouse button to pop through tactile push-buttons, similar to the focus/shutter-release buttons used in many cameras, users can feel, and the computer can sense, two distinct "clicks" corresponding to pressing lightly and pressing firmly to pop through. Despite the prototypical status of our hardware and software implementations, our current pop through mouse interactions are compelling and warrant further investigation. In particular, we demonstrate that pop through buttons not only yield an additional button activation state that is composable with, or even preferable to, techniques such as double-clicking, but also can endow a qualitatively novel user experience when meaningfully and consistently applied. We propose a number of software guidelines that may provide a consistent, systemic benefit; for example, light pressure may invoke default interaction (short menu), and firm pressure may supply more detail (long menu).

Presents results from a user study that compared six visualization methods for 2D vector data. Two methods used different distributions of short arrows, two used different distributions of integral curves, one used wedges located to suggest flow lines, and the final one was line-integral convolution (LIC). We defined three simple but representative tasks for users to perform using visualizations from each method: (1) locating all critical points in an image, (2) identifying critical point types, and (3) advecting a particle. The results show different strengths and weaknesses for each method. We found that users performed better with methods that: (1) showed the sign of vectors within the vector field, (2) visually represented integral curves, and (3) visually represented the locations of critical points. These results provide quantitative support for some of the anecdotal evidence concerning visualization methods. The tasks and testing framework also provide a basis for comparing other visualization methods, for creating more effective methods and for defining additional tasks to further understand tradeoffs among methods. They may also be useful for evaluating 2D vectors on 2D surfaces embedded in 3D and for defining analogous tasks for 3D visualization methods.

We describe the Scene‐Graph‐As‐Bus technique (SGAB), the first step in a staircase of solutions for sharing software components for virtual environments. The goals of SGAB are to allow, with minimal effort, independently‐designed applications to share component functionality; and for multiple users to share applications designed for single users.This paper reports on the SGAB design for transparently conjoining different applications by unifying the state information contained in their scene graphs. SGAB monitors and maps changes in the local scene graph of one application to a neutral scene graph representation (NSG), distributes the NSG changes over the network to remote peer applications, and then maps the NSG changes to the local scene graph of the remote application. The fundamental contribution of SGAB is that both the local and remote applications can be completely unaware of each other; that is, both applications can interoperate without code or binary modification despite each having no knowledge of networking or interoperability.

D interaction with a computer is hard. As human interaction in the real world is quite dependent on haptics, we believe that adding haptics will alleviate many of the problems of 3D interaction with a computer. The naive approach to incorporating haptics would be to simply find existing objects and imitate them. This, however, would not seem to adequately leverage what is possible with a rea- sonably general force feedback device, as it does not take advantage of the ability to provide forces that are not physically based, or to simulate objects and behavior that could have been physically real- ized but weren't, or which would have been prohibitively difficult to physically realize. From our extensive experience with design- ing haptic computer user interfaces and using haptic interfaces in the real world we have abstracted from those interactions certain qualities which we present in this paper. In addition, this paper demonstrates how to use these qualities in the analysis and design of a suite of 3D haptic widgets, most of which are novel and many of which include non-physically based forces.

This paper describes preliminary work in a project to add force feedback to user interface elements of the X Window System in an attempt to add true "feel" to the window system's "look and feel".Additions include adding ridges around icons and menu items to aid interaction, alignment guides for moving windows, and other enhancements to window manipulation.The motivation for this system is the observation that people naturally have many skills for and intuitions about a very rich environment of interaction forces in the non-computer world; however, these skills are largely unused in computer applications.We expect that haptic modifications to conventional graphical user interfaces, such as those we present, can lead to gains in performance, intuition, learnability, and enjoyment of the interface.This paper describes details of the implementation of the haptic window system elements, in addition to higher-level haptic design principles and informal observations of users of the system.