We present a video outage detection algorithm (VODA) that detects catastrophic failures in video systems by mimicking human behavior. VODA uses a continuity detector for video, audio, and motion, along with blackness, silence, and stillness detection. An outage is declared when all individual features exhibit a sudden drop; an alarm is generated when an outage lasts over two seconds. We analyze the performance of VODA on a large corpus of movie and TV episodes, and we show it has good performance when applied to 160 hours of recorded broadcast TV.

Networks are critical to modern society, and a thorough understanding of how they behave is crucial to their efficient operation. Fortunately, data on networks is plentiful; by visualizing this data, it is possible to greatly improve our understanding. Our focus is on visualizing the data associated with a network and not on simply visualizing the structure of the network itself. We begin with three static network displays; two of these use geographical relationships, while the third is a matrix arrangement that gives equal emphasis to all network links. Static displays can be swamped with large amounts of data; hence we introduce direct manipulation techniques that permit the graphs to continue to reveal relationships in the context of much more data. In effect, the static displays are parameterized so that interesting views may easily be discovered interactively. The software to carry out this network visualization is called SeeNet.>

The authors describe several dynamic graphics tools for visualizing network data involving statistics associated with the nodes or links in a network. The authors suggest a number of ideas for the static display of network data, while motivating the need for interaction through dynamic graphics. A brief discussion of dynamic graphics in general is presented. The authors specialize this to the case of network data. An example is presented.>

We present a method for tracing a curve that is represented as the contour of a function in Euclidean space of any dimension. The method proceeds locally by following the intersections of the contour with the facets of a triangulation of space. The algorithm does not fail in the presence of high curvature of the contour; it accumulates essentially no round-off error and has a well-defined integer test for detecting a loop. In developing the algorithm, we explore the nature of a particular class of triangulations of Euclidean space, namely, those generated by reflections.