Abstract: A rule-based attribution mechanism analyzes documents having different types of data in different formats through the application of script-based rules that apply a tag to the document identifying the type of sensitive data that is contained in the document. Documents having similar tags are aggregated so that the sensitive data is scrubbed from the document leaving the telemetric data available for downstream processing. The scrubbing entails different actions, such as, eliminating the sensitive data, obfuscating the sensitive data, and converting the sensitive data into a non-sensitive value.

Abstract: Runtime errors in a source code program are detected in advance of execution by machine learning models. Features representing a context of a runtime error are extracted from source code programs to train a machine learning model, such as a random forest classifier, to predict the likelihood that a code snippet has a particular type of runtime error. The features are extracted from a syntax-type tree representation of each method in a program. A model is generated for distinct runtime errors, such as arithmetic overflow, and conditionally uninitialized variables.

Abstract: A rule-based attribution mechanism analyzes documents having different types of data in different formats through the application of script-based rules that apply a tag to the document identifying the type of sensitive data that is contained in the document. Documents having similar tags are aggregated so that the sensitive data is scrubbed from the document leaving the telemetric data available for downstream processing. The scrubbing entails different actions, such as, eliminating the sensitive data, obfuscating the sensitive data, and converting the sensitive data into a non-sensitive value.

Abstract: Runtime errors in a source code program are detected in advance of execution by machine learning models. Features representing a context of a runtime error are extracted from source code programs to train a machine learning model, such as a random forest classifier, to predict the likelihood that a code snippet has a particular type of runtime error. The features are extracted from a syntax-type tree representation of each method in a program. A model is generated for distinct runtime errors, such as arithmetic overflow, and conditionally uninitialized variables.

Abstract: A rule-based attribution mechanism analyzes documents having different types of data in different formats through the application of script-based rules that apply a tag to the document identifying the type of sensitive data that is contained in the document. Documents having similar tags are aggregated so that the sensitive data is scrubbed from the document leaving the telemetric data available for downstream processing. The scrubbing entails different actions, such as, eliminating the sensitive data, obfuscating the sensitive data, and converting the sensitive data into a non-sensitive value.

Abstract: Runtime errors in a source code program are detected in advance of execution by machine learning models. Features representing a context of a runtime error are extracted from source code programs to train a machine learning model, such as a random forest classifier, to predict the likelihood that a code snippet has a particular type of runtime error. The features are extracted from a syntax-type tree representation of each method in a program. A model is generated for distinct runtime errors, such as arithmetic overflow, and conditionally uninitialized variables.

Abstract: Runtime errors in a source code program are detected in advance of execution by machine learning models. Features representing a context of a runtime error are extracted from source code programs to train a machine learning model, such as a random forest classifier, to predict the likelihood that a code snippet has a particular type of runtime error. The features are extracted from a syntax-type tree representation of each method in a program. A model is generated for distinct runtime errors, such as arithmetic overflow, and conditionally uninitialized variables.

Abstract: An offline batch processing system classifies sensitive data contained in consumer data, such as telemetric data, using a manual classification process and a machine learning model. The machine learning model is used to recheck the policy settings used in the manual classification process and to learn relationships between the features in the consumer data in order to identify sensitive data. The identified sensitive data is then scrubbed so that the remaining data may be used.

Abstract: A rule-based attribution mechanism analyzes documents having different types of data in different formats through the application of script-based rules that apply a tag to the document identifying the type of sensitive data that is contained in the document. Documents having similar tags are aggregated so that the sensitive data is scrubbed from the document leaving the telemetric data available for downstream processing. The scrubbing entails different actions, such as, eliminating the sensitive data, obfuscating the sensitive data, and converting the sensitive data into a non-sensitive value.

Abstract: A cable that comprises a cable core that includes a plurality of insulated pairs of twisted conductors wherein, the insulation of at least one pair of the plurality of insulated pairs of twisted conductors has inner and outer layers, the inner layer being formed of a zero halogen material that is not flame retardant, the outer layer being formed of a zero halogen material that is substantially flame retardant, and the insulation of at least another pair of the plurality of insulated pairs of twisted conductors is formed of a zero halogen material that is not flame retardant, wherein at least a portion of one of the inner layer and the outer layer is foamed.

Abstract: A cable that comprises a cable core that includes a plurality of insulated pairs of twisted conductors wherein, the insulation of at least one pair of the plurality of insulated pairs of twisted conductors has inner and outer layers, the inner layer being formed of a zero halogen material that is not flame retardant, the outer layer being formed of a zero halogen material that is substantially flame retardant, and the insulation of at least another pair of the plurality of insulated pairs of twisted conductors is formed of a zero halogen material that is not flame retardant.

Abstract: A cable that comprises a cable core that includes a plurality of insulated pairs of twisted conductors wherein, the insulation of at least one pair of the plurality of insulated pairs of twisted conductors is formed of a zero halogen material that is substantially flame retardant, and the insulation of at least another pair of the plurality of insulated pairs of twisted conductors is formed of a zero halogen material that is not flame retardant.