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4758 results about "Fine grain" patented technology

Fine grain refers to the types of wood that have very small, tight lines in the wood. It may also be referred to as a smooth grain. Fine grain woods have natural lines in the wood that are small and very close together.

Method and system for automatically extracting relations between concepts included in text

A method and system for automatically extracting relations between concepts included in electronic text is described. Aspects the exemplary embodiment include a semantic network comprising a plurality of lemmas that are grouped into synsets representing concepts, each of the synsets having a corresponding sense, and a plurality of links connected between the synsets that represent semantic relations between the synsets. The semantic network further includes semantic information comprising at least one of: 1) an expanded set of semantic relation links representing: hierarchical semantic relations, synset / corpus semantic relations verb / subject semantic relations, verb / direct object semantic relations, and fine grain / coarse grain semantic relationship; 2) a hierarchical category tree having a plurality of categories, wherein each of the categories contains a group of one or more synsets and a set of attributes, wherein the set of attributes of each of the categories are associated with each of the synsets in the respective category; and 3) a plurality of domains, wherein one or more of the domains is associated with at least a portion of the synsets, wherein each domain adds information regarding a linguistic context in which the corresponding synset is used in a language. A linguistic engine uses the semantic network to performing semantic disambiguation on the electronic text using one or more of the expanded set of semantic relation links, the hierarchical category tree, and the plurality of domains to assign a respective one of the senses to elements in the electronic text independently from contextual reference.
Owner:EXPERT AI SPA

Method and system for detecting intrusive anomalous use of a software system using multiple detection algorithms

A method of detecting an intrusion into (or an anomaly in a behavior of) a target software system begins by instrumenting the target software system to generate behavior data representing a current observation or observation aggregate. The method then determines whether the current observation or observation aggregate warrants a second level examination; preferably, this determination is made by processing the current observation or observation aggregate through a first level detection algorithm that provides a first, provisional indication of a possible intrusion. If a result of executing the first level detection algorithm indicates that the current observation or observation aggregate warrants a second level examination, the method continues by processing the current observation or observation aggregate through at least one or more second level detection algorithms to provide a second, more definite, fine grain indication of a possible intrusion. The observation aggregates used by the first and second level detection algorithms may be the same or different. The first and second level detection algorithms may be executed in the same or different systems, machines or processors. The target software system operation may be suspended as the current observation or observation aggregate is processed through the one or more second level detection algorithms. A given action (e.g., sending an alert, logging the event, activating a countermeasure, or the like) may be taken if the result of the second level examination indicates a possible intrusion. Multiple algorithms may be executed together within a single examination level, with the individual results then analyzed to obtain a composite result or output indicative of intrusive or anomalous behavior.
Owner:STRATACLOUD
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