Abstract: Disclosed are techniques for automatically identifying significant events. During a training phase, a method can include: training a neural network (NN) to automatically identify significant events, the training including: receiving training data including labeled weather event data for a group of locations over a group of time periods, generating, by the NN, a probability factor representing a chance that a weather event in the training data will be significant for at least one location amongst the locations, and comparing the probability to the received labels, and during a runtime phase: receiving data from sensors, each sensor generating weather event data about a respective location(s), providing the weather event data as inputs to the NN, receiving, as output from the NN, weather event data having a respective significance level greater than a respective predetermined threshold value, and correlating weather events with the outputted weather event data.

Abstract: A computer-based method, system, and computer program product for automatically identifying significant events for food traceability. The method may comprise receiving a series of events from an agriculture supply chain entity, automatically determining, at a machine learning model of an event analysis module, one or more events in the series having a significance for food traceability greater than a threshold, and automatically reporting the one or more events to a ledger.

Abstract: A computer-implemented method includes: obtaining, by a computing device, data items from data sources; classifying, by the computing device, the data items into categories using a first machine learning (ML) model; generating, by the computing device, a risk score of a first data center based on the classified data items and using a second machine learning (ML) model; determining, by the computing device, the risk score of the first data center exceeds a threshold; and in response to the determining the risk score of the first data center exceeds the threshold, initiating, by the computing device, a migration of the first data center to a second data center.

Abstract: Computer systems, methods and program products for automating pseudonymization of personal identifying information (PII) using machine learning, metadata, and crowdsourcing patterns to identify and replace PII. Machine learning models are trained for classifying known column names or key names for processing, using metadata. Column or key names are classified to be unprocessed, anonymized or pseudonymized by a pseudonymizer without revealing PII or scrubbing data into a useless format. A library of crowdsourced patterns are utilized for matching PII to data values within column or key names and PII is mapped to replacement methods. Feedback from user annotations retrains the algorithms to improve classification accuracy and Deep Learning algorithms automate the identification of PII using regular expression generation to concisely articulate how pseudonymizers search for PII patterns within a data set.

Abstract: A computer-based method, system, and computer program product for automatically identifying significant events for food traceability. The method may comprise receiving a series of events from an agriculture supply chain entity, automatically determining, at a machine learning model of an event analysis module, one or more events in the series having a significance for food traceability greater than a threshold, and automatically reporting the one or more events to a ledger.

Abstract: Computer systems, methods and program products for automating pseudonymization of personal identifying information (PII) using machine learning, metadata, and crowdsourcing patterns to identify and replace PII. Machine learning models are trained for classifying known column names or key names for processing, using metadata. Column or key names are classified to be unprocessed, anonymized or pseudonymized by a pseudonymizer without revealing PII or scrubbing data into a useless format. A library of crowdsourced patterns are utilized for matching PII to data values within column or key names and PII is mapped to replacement methods. Feedback from user annotations retrains the algorithms to improve classification accuracy and Deep Learning algorithms automate the identification of PII using regular expression generation to concisely articulate how pseudonymizers search for PII patterns within a data set.

Abstract: A method for automatically adjusting vehicle media may include obtaining personal data of a vehicle operator and obtaining contextual data about the vehicle operating. The method may further include generating a distraction-level value that represents a degree to which the vehicle operator may be distracted from the media. The method may further include generating a distraction threshold value. The method may further include determining that the distraction-level value exceeds the distraction threshold value and adjusting the media in response.

Abstract: A method for automatically adjusting vehicle media may include obtaining personal data of a vehicle operator and obtaining contextual data about the vehicle operating. The method may further include generating a distraction-level value that represents a degree to which the vehicle operator may be distracted from the media. The method may further include generating a distraction threshold value. The method may further include determining that the distraction-level value exceeds the distraction threshold value and adjusting the media in response.