Mcafee, Inc.

Craig Philip Olinsky

Marcin Dziduch

Nidhi Singh

Edward Dixon

Thamizhannal Paramasivam

Embodiments of the present disclosure are directed to updating categorization of online content. An analytics engine implemented at least partially in hardware can receive an engagement indicator across a network interface; identify a type of the engagement indicator, the type of the engagement indicator comprising one of a positive engagement indicator or a negative engagement indicator; and update the reputation data stored in memory based on the type of the engagement indicator. A safe harbor time window is described during with user activity with online content is not reported to system administrators.

Implicit crowdsourcing for untracked correction or verification of categorization information

In an example, there is disclosed a computing apparatus, including one or more logic elements, including at least one hardware logic element, comprising a classification engine to: receive a clean multi-labeled dataset comprising a plurality of document each assigned to one or more of a plurality of categories; receive an unclean multi-labeled dataset; and produce a recategorized and cleansed dataset from the unclean multi-labeled dataset, comprising predicting a number of labels {circumflex over (l)} for a document j, and comparing {circumflex over (l)} to an existing number of labels l. There is also disclosed a method of providing a classification engine.

Multi-label content recategorization

In an example, there is disclosed a computing apparatus, including one or more logic elements comprising a classification engine operable to identify at least one overlapping class pair comprising at least two classes selected from a class group, wherein the overlapping class pair may be characterized by a substantial mutual false positive rate; and assign an object to at least one assigned class selected from the class group.

Multi-label classification for overlapping classes

Systems, apparatus, computer-readable media, and methods to provide filtering and/or search based at least in part on semantic representations of words in a document subject to the filtering and/or search are disclosed. Furthermore key words for conducting the filtering and/or search, such as taboo words and/or search terms, may be semantically compared to the semantic representation of the words in the document. A common semantic vector space, such as a base language semantic vector space, may be used to compare the key word semantic vectors and the semantic vectors of the words of the document, regardless of the native language in which the document is written or the language in which the key words are provided.

Systems and methods for multilingual document filtering

Systems, apparatus, computer-readable media, and methods to provide translation of words or phrases from an initial language to a target language using multiple pathways are disclosed. The multiple pathways may have independent or near independent errors and the use of multiple pathways may reduce the errors that may be encountered in semantic vector based language translation. Cost values may be determined for translation to various potential words in the target language based at least in part on the multiple translation pathways between the initial language and the final language. The cost values may be used to select from among the various potential words in the target language.

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Paderborn, Germany

Craig Philip Olinsky