We examine three biometric authentication modalities -- voice, face and gesture -- as well as password entry, on a mobile device, to explore the relative demands on user time, effort, error and task disruption. Our laboratory study provided observations of user actions, strategies, and reactions to the authentication methods. Face and voice biometrics conditions were faster than password entry. Speaking a PIN was the fastest for biometric sample entry, but short-term memory recall was better in the face verification condition. None of the authentication conditions were considered very usable. In conditions that combined two biometric entry methods, the time to acquire the biometric samples was shorter than if acquired separately but they were very unpopular and had high memory task error rates. These quantitative results demonstrate cognitive and motor differences between biometric authentication modalities, and inform policy decisions in selecting authentication methods.

Visual representations are common in communicating about artifacts such as computer programs, software architectures, and business rules. Yet, generally speaking, these representations seem much harder to learn and to use than many of the representations in other domains. Moody [1] has pointed this out and proposed a set of principles for visual representations based on a wide review of relevant literature in cognitive psychology and software engineering. The real test of this framework is to use it. In this paper, we apply the principles set forth in Moody to examine and improve a proposed representation for business rules and business decisions.

As digital communities grow in size their feature sets also grow with them. Different users have different experiences with the same tools and communities. Enterprises and other organizations seeking to leverage these communities need a straightforward way to analyze and compare a variety of salient attributes of these communities. We describe a taxonomy and tool for crowd-sourcing user based evaluations of enterprise relevant attributes of digital communities and present the results of a small scale study on its usefulness and stability across multiple raters.

Sketching facilitates design in the exploration of ideas about concrete objects and abstractions. In fact, throughout the software engineering process when grappling with new ideas, people reach for a pen and start sketching. While pen and paper work well, digital media can provide additional features to benefit the sketcher. Digital support will only be successful, however, if it does not detract from the core sketching experience. Based on research that defines characteristics of sketches and sketching, this paper offers three preliminary tool examples. Each example is intended to enable sketching while maintaining its characteristic experience.

In this paper we present a novel method for computing superhistograms to represent video segments. We build cumulative histograms for video shots and scenes. The underlying assumption is that a TV program has a consistent color palette, which can be derived as a family of merged individual shot histograms. We present the experimental results computed from several hours of different TV programs such as sitcoms, serials and news. The superhistograms representing episodes of the same sitcom look strikingly similar. The histograms extracted from TV news are not similar to the histograms extracted from sitcoms. However, there are superhistograms extracted from different sitcoms, which show certain resemblance to each other. This method can be used in a variety of applications that need video classification and retrieval methods such as video editing, studio archival, digital libraries, consumer products, and web crawling.

Methods for video content analysis are necessary for the growing amount of video information delivered to consumers today. In this paper, we present a system for video content analysis called Vitamin, which provides management of a home video library. The system presents the user with a visual table of contents that provides an overview of the video content and direct access to particular points in the stored video. In this process, we apply a computationally inexpensive, simple, yet powerful mechanism for cut detection and keyframe filtering. Our initial implementation and results show that this system can perform video content extraction in real time on a low-end platform that matches a visual table of contents extracted by an expert.