Abstract: Systems and techniques are provided for aggregation of asynchronous trust outcomes in a mobile device. Trust levels may be determined from the signals. Each trust level may be determined independently of any other trust level. Each trust level may be determined based on applying to the signals heuristics, mathematical optimization, decisions trees, machine learning systems, or artificial intelligence systems. An aggregated trust outcome may be determined by aggregating the trust levels. Aggregating the trust levels may include applying heuristics, mathematical optimization, decisions trees, machine learning systems, or artificial intelligence systems to the trust levels, and wherein the aggregated trust outcome; and sending the aggregated trust outcome to be implemented by the enabling, disabling, or relaxing of at least one security measure based on the aggregated trust outcome.

Abstract: Systems and techniques are provided for aggregation of asynchronous trust outcomes in a mobile device. Trust levels may be determined from the signals. Each trust level may be determined independently of any other trust level. Each trust level may be determined based on applying to the signals heuristics, mathematical optimization, decisions trees, machine learning systems, or artificial intelligence systems. An aggregated trust outcome may be determined by aggregating the trust levels. Aggregating the trust levels may include applying heuristics, mathematical optimization, decisions trees, machine learning systems, or artificial intelligence systems to the trust levels, and wherein the aggregated trust outcome; and sending the aggregated trust outcome to be implemented by the enabling, disabling, or relaxing of at least one security measure based on the aggregated trust outcome.

Abstract: The present disclosure relates to techniques for data classification. The techniques for data classification may include a machine learning (ML)-based classifier for classification of potentially objectionable content and differentiating responses based on a confidence score and classification category outputted by the ML-based classifier.

Abstract: Systems and techniques are provided for aggregation of asynchronous trust outcomes in a mobile device. Trust levels may be determined from the signals. Each trust level may be determined independently of any other trust level. Each trust level may be determined based on applying to the signals heuristics, mathematical optimization, decisions trees, machine learning systems, or artificial intelligence systems. An aggregated trust outcome may be determined by aggregating the trust levels. Aggregating the trust levels may include applying heuristics, mathematical optimization, decisions trees, machine learning systems, or artificial intelligence systems to the trust levels, and wherein the aggregated trust outcome; and sending the aggregated trust outcome to be implemented by the enabling, disabling, or relaxing of at least one security measure based on the aggregated trust outcome.

Abstract: Systems and techniques are provided for aggregation of asynchronous trust outcomes in a mobile device. Trust levels may be determined from the signals. Each trust level may be determined independently of any other trust level. Each trust level may be determined based on applying to the signals heuristics, mathematical optimization, decisions trees, machine learning systems, or artificial intelligence systems. An aggregated trust outcome may be determined by aggregating the trust levels. Aggregating the trust levels may include applying heuristics, mathematical optimization, decisions trees, machine learning systems, or artificial intelligence systems to the trust levels, and wherein the aggregated trust outcome; and sending the aggregated trust outcome to be implemented by the enabling, disabling, or relaxing of at least one security measure based on the aggregated trust outcome.

Abstract: Methods, systems, and computer-readable media for a method of propagating signals across a web graph. A signal describes a document or otherwise provides useful information about a document in a web graph. A web graph is a collection of documents that are related to one another through links, such as hyperlinks. The signals are propagated in the sense that information from the related pages is associated with the target page even though the information may not be directly found in the target page. This information may then be used by a search engine to determine that a particular page is relevant to a search query.

Abstract: Methods, systems, and computer-readable media for a method of propagating signals across a web graph. A signal describes a document or otherwise provides useful information about a document in a web graph. A web graph is a collection of documents that are related to one another through links, such as hyperlinks. The signals are propagated in the sense that information from the related pages is associated with the target page even though the information may not be directly found in the target page. This information may then be used by a search engine to determine that a particular page is relevant to a search query.