We describe the design, prototyping and evaluation of ARC, a

The World Wide Web grows through a decentralized, almost anarchic process, and this has resulted in a large hyperlinked corpus without the kind of logical organization that can be built into more tradit,ionally-created hypermedia.To extract, meaningful structure under such circumstances, we develop a notion of hyperlinked communities on the www t,hrough an analysis of the link topology.By invoking a simple, mathematically clean method for defining and exposing the structure of these communities, we are able to derive a number of themes: The communities can be viewed as containing a core of central, "authoritative" pages linked tog&her by "hub pages" ; and they exhibit a natural type of hierarchical topic generalization that can be inferred directly from the pat,t,ern of linkage. Our investigation shows that although the process by which users of the Web create pages and links is very difficult to understand at a "local" level, it results in a much greater degree of orderly high-level structure than has typically been assumed.

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Search obstacles As we consider the types of pages we hope to discover, and to do so automatically, we quickly confront some difficult problems. First, it is insufficient to apply purely text-based methods to collect many potentially Sifting through the growing mountain of Web data demands an increasingly discerning search engine, one that can reliably assess the quality of sites, not just their relevance.

Background matching is the most familiar and widespread camouflage strategy: avoiding detection by having a similar colour and pattern to the background. Optimizing background matching is straightforward in a homogeneous environment, or when the habitat has very distinct sub-types and there is divergent selection leading to polymorphism. However, most backgrounds have continuous variation in colour and texture, so what is the best solution? Not all samples of the background are likely to be equally inconspicuous, and laboratory experiments on birds and humans support this view. Theory suggests that the most probable background sample (in the statistical sense), at the size of the prey, would, on average, be the most cryptic. We present an analysis, based on realistic assumptions about low-level vision, that estimates the distribution of background colours and visual textures, and predicts the best camouflage. We present data from a field experiment that tests and supports our predictions, using artificial moth-like targets under bird predation. Additionally, we present analogous data for humans, under tightly controlled viewing conditions, searching for targets on a computer screen. These data show that, in the absence of predator learning, the best single camouflage pattern for heterogeneous backgrounds is the most probable sample.