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Modeling the behavior of bloggers is an important problem with various applications in recommender systems, targeted advertising, and event detection. In this paper, we propose three models by combining content, temporal, social dimensions: the general blogging-behavior model, the profile-based blogging-behavior model and the social- network and profile-based blogging-behavior model. The models are based on two regression techniques: Extreme Learning Machine (ELM), and Modified General Regression Neural Network (MGRNN). We choose one of the largest blogs, a political blog, DailyKos <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , for our empirical evaluation. Experiments show that the social network and profile-based blogging behavior model with ELM regression techniques produce good results for the most active bloggers and can be used to predict blogging behavior.

By analyzing a set of access attempts by teenagers to pornographic Web sites, we found that more than half of them are image searches and visits to Web sites with little text information. It is obvious that textual content-based filters cannot correctly categorize such access attempts. This paper describes a novel URL-based objectionable content categorization approach and its application to Web filtering. In this approach, we break the URL into a sequence of n-grams with a range of n's and then a machine learning algorithm is applied to the n-gram representation of URLs to learn a classifier of pornographic Web sites. We showed empirically that the URL-based approach is able to correctly identify many of the objectionable Web pages. We also demonstrated that the optimum Web filtering results could be achieved when it was used with a content-based approach in a production environment

This paper provides a synopsis of a server-driven link prefetching mechanism that we have designed and implemented for the Mozilla web browser, a popular Open Source web browser. The mechanism depends on the origin server or an intermediate proxy server determining the best set of documents for the browser to prefetch. The browser follows prefetch directives provided by the server, either embedded in an HTML document using the <LINK> tag or specified via Link HTTP response headers. The browser determines when best to prefetch the specified URLs based on its own heuristics. In this paper, we describe the mechanism and discuss some of the practical issues that impacted its design and implementation.KeywordsProxy ServerOrigin ServerContent AuthorUSENIX SymposiumPrefetch RequestThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.