Abstract: A method of cryptographically secured decentralized testing includes receiving, by a computing device and from a secure test apparatus, an output of a cryptographic function of a secret test result identifier, authenticating the output, and recording, in a data repository, an indication of a test result as a function of the output.

Abstract: A method of cryptographically secured decentralized testing includes receiving, by a computing device and from a secure test apparatus, an output of a cryptographic function of a secret test result identifier, authenticating the output, and recording, in a data repository, an indication of a test result as a function of the output.

Abstract: A system and method provide a sequence learning model. The method for training the sequence learning model comprises retrieving input sequence data. The input sequence data includes one or more input time sequences. The method also encodes the input sequence data into output symbol data using a sequence learning model. The output symbol data includes one or more symbolic representations. The method decodes, based on a neural network, the output symbol data to decoded sequence data, where the decoded sequence data includes one or more decoded time sequences that are to match the one or more input time sequences in the input sequence data. The method further compares the decoded sequence data with the input sequence data and updates the sequence learning model based on the comparison.