Natural language interfaces for visualizations have emerged as a promising new way of interacting with data and performing analytics. Many of these systems have fundamental limitations. Most return minimally interactive visualizations in response to queries and often require experts to perform modeling for a set of predicted user queries before the systems are effective. Eviza provides a natural language interface for an interactive query dialog with an existing visualization rather than starting from a blank sheet and asking closed-ended questions that return a single text answer or static visualization. The system employs a probabilistic grammar based approach with predefined rules that are dynamically updated based on the data from the visualization, as opposed to computationally intensive deep learning or knowledge based approaches.

As traditional media and information devices integrate with the web, they must suddenly support a vastly larger database of relevant items. Many devices use remote controls with on-screen keyboards which are not well suited for text entry but are difficult to displace. We introduce a text entry method which significantly improves text entry speed for on-screen keyboards using the same simple Up/Down/Left/Right/Enter interface common to remote controls and gaming devices used to enter text. The paper describes QuickSuggest's novel adaptive user interface, demonstrates quantitative improvements from simulation results on millions of user queries and shows ease of use and efficiency with no learning curve in user experiments.

We present a new CAPTCHA which is based on identifying an image's upright orientation. This task requires analysis of the often complex contents of an image, a task which humans usually perform well and machines generally do not. Given a large repository of images, such as those from a web search result, we use a suite of automated orientation detectors to prune those images that can be automatically set upright easily. We then apply a social feedback mechanism to verify that the remaining images have a human-recognizable upright orientation. The main advantages of our CAPTCHA technique over the traditional text recognition techniques are that it is language-independent, does not require text-entry (e.g. for a mobile device), and employs another domain for CAPTCHA generation beyond character obfuscation. This CAPTCHA lends itself to rapid implementation and has an almost limitless supply of images. We conducted extensive experiments to measure the viability of this technique.

Public exhibits or displays are widely used to present information in public and semipublic places. Users may pass by several of these and observe a significant amount of interesting content, much of which may be forgotten. Unfortunately, there is no widely adopted way for users to acquire related information in locale for their subsequent perusal. We present ContentCascade, a mechanism that allows users to implicitly download various levels of detail of summary information about the content available at the public displays to their personal devices. It also naturally and informally enables users to trade off quality of summary information with presence time. This way, ContentCascade can potentially enhance the overall user experience with public displays as users can recall the summary information later.

Digital books can significantly enhance the reading experience, providing many functions not available in printed books. In this paper we study a particular augmentation of digital books that provides readers with customized recommendations. We systematically explore the application of spreading activation over text and citation data to generate useful recommendations. Our findings reveal that for the tasks performed in our corpus, spreading activation over text is more useful than citation data. Further, fusing text and citation data via spreading activation results in the most useful recommendations. The fused spreading activation techniques outperform traditional text-based retrieval methods. Finally, we introduce a preliminary user interface for the display of recommendations from these algorithms.

Several visualizations have emerged which attempt to visualize all or part of the World Wide Web.Those visualizations, however, fail to present the dynamically changing ecology of users and documents on the Web.We present new techniques for Web Ecology and Evolution Visualization (WEEV).Disk Trees represent a discrete time slice of the Web ecology.A collection of Disk Trees forms a Time Tube, representing the evolution of the Web over longer periods of time.These visualizations are intended to aid authors and webmasters with the production and organization of content, assist Web surfers making sense of information, and help researchers understand the Web.

This paper describes a mechanism for improving rendering rates dynamically during runtime in an interactive three-dimensional graphics application. Well-known techniques such as transforming hierarchical geometry into a flat list and removing redundant graphics primitives are often performed off-line on static databases, or continuously every rendering frame. In addition, these optimizations are usually performed over the whole database. We observe that much of the database remains static for a fixed period of time, while other portions are modified continuously (e.g. the camera position), or are repeatedly modified during some finite interval (e.g. during user interaction). We have implemented a runtime optimization mechanism which is sensitive to repeated, local database changes. This mechanism employs timing strategies which optimize only when the cost of optimization will be amortized over a sufficient number of frames. Using this optimization scheme, we observe a rendering speedup of roughly 2.5 in existing applications. We discuss our initial implementation of this mechanism, the improved timing measurements, the issues and assumptions we made, and future improvements.