Abstract: The present disclosure is directed to systems, methods and devices for replacing a static map image with an interactive map that approximates the geographic area depicted by the static map image. A static image may be inspected and a determination may be made as to whether the static image meets a map identification threshold value. At least one signal associated with the static image may be evaluated and based on that evaluation at least one geo-entity of intent may be identified. The static image may be replaced with a static image corresponding to at least one of the identified geo-entities of intent.

Abstract: The present disclosure is directed to systems, methods and devices for replacing a static map image with an interactive map that approximates the geographic area depicted by the static map image. A static image may be inspected and a determination may be made as to whether the static image meets a map identification threshold value. At least one signal associated with the static image may be evaluated and based on that evaluation at least one geo-entity of intent may be identified. The static image may be replaced with a static image corresponding to at least one of the identified geo-entities of intent.

Abstract: Content is proactively presented to a user, to enable the user to more efficiently access such content. A user context is correlated to content that is likely to be subsequently accessed. One such a correlation is specific to a given user, while another such correlation is general to a collection, or class, of users. Correlations between a current user context and content subsequently accessed are based on historical data and are defined in terms of mathematical functions or semantic relationships. Such correlations are then utilized to identify content that is likely to be subsequently accessed, and such content is proactively presented to a user. A user interface provides a defined area within which proactive presentations of content are made, including while the user is utilizing other application programs.

Abstract: Methods, systems, and computer programs for categorizing the sensitivity of web pages are presented. In one method, a space of sensitive pages is identified based on the sensitivity categorization of a first plurality of web pages and a second plurality of web pages. The first plurality of web pages is obtained by performing search queries using known sensitive words, and the second plurality of web pages includes randomly selected web pages. Additionally, the method identifies a third plurality of web pages that includes web pages on or near the boundary between the space of sensitive pages and the space of non-sensitive pages. The space of sensitive pages is then redefined based on the sensitivity categorization of the first, second, and third pluralities of web pages. Once the space of sensitive pages is defined, the method is used to determine that a given web page is sensitive when the given web page is in the space of sensitive pages.

Abstract: A system for training classifiers in multiple categories through an active learning system, including a computer having a memory and a processor, the processor programmed to: train an initial set of m binary one-versus-all classifiers, one for each category in a taxonomy, on a labeled dataset of examples stored in a database coupled with the computer; uniformly sample up to a predetermined large number of examples from a second, larger dataset of unlabeled examples stored in a database coupled with the computer; order the sampled unlabeled examples in order of informativeness for each classifier; determine a minimum subset of the unlabeled examples that are most informative for a maximum number of the classifiers to form an active set for learning; and use editorially-labeled versions of the examples of the active set to re-train the classifiers, thereby improving the accuracy of at least some of the classifiers.

Abstract: A system for training classifiers in multiple categories through an active learning system, including a computer having a memory and a processor, the processor programmed to: train an initial set of m binary one-versus-all classifiers, one for each category in a taxonomy, on a labeled dataset of examples stored in a database coupled with the computer; uniformly sample up to a predetermined large number of examples from a second, larger dataset of unlabeled examples stored in a database coupled with the computer; order the sampled unlabeled examples in order of informativeness for each classifier; determine a minimum subset of the unlabeled examples that are most informative for a maximum number of the classifiers to form an active set for learning; and use editorially-labeled versions of the examples of the active set to re-train the classifiers, thereby improving the accuracy of at least some of the classifiers.

Abstract: Methods, systems, and computer programs for categorizing the sensitivity of web pages are presented. In one method, a space of sensitive pages is identified based on the sensitivity categorization of a first plurality of web pages and a second plurality of web pages. The first plurality of web pages is obtained by performing search queries using known sensitive words, and the second plurality of web pages includes randomly selected web pages. Additionally, the method identifies a third plurality of web pages that includes web pages on or near the boundary between the space of sensitive pages and the space of non-sensitive pages. The space of sensitive pages is then redefined based on the sensitivity categorization of the first, second, and third pluralities of web pages. Once the space of sensitive pages is defined, the method is used to determine that a given web page is sensitive when the given web page is in the space of sensitive pages.

Abstract: Multiple features of email traffic are analyzed and extracted. Feature vectors comprising the multiple features are created and cluster analysis is utilized to track spam generation even from dynamically changing or aliased IP addresses.

Abstract: Multiple features of email traffic are analyzed and extracted. Feature vectors comprising the multiple features are created and cluster analysis is utilized to track spam generation even from dynamically changing or aliased IP addresses.

Abstract: Machine learning techniques are employed to build and evolve classifiers (e.g., decision trees or other rule-based classifiers) which generate scores representing confidence values associated with particular paths through a classifier (rather than discrete class labels), and then compare those scores to tunable thresholds to effect classification.