Abstract: An example operation may include one or more of obtaining a first biometric sample of a user from a user device. extracting, by an issuing node of a permissioned blockchain network, a biometric template from the first biometric sample, encrypting the biometric template, distributing an issuetoken proposal comprising the encrypted biometric template to the blockchain network, and generating and distributing a biometric token to the user device. In response to the user indicating to the user device to redeem the biometric token, the method includes one or more of presenting, by the user device, the biometric token to a verifying node of the blockchain network, validating, by the verifying node, the biometric token, receiving, by the verifying node, a second biometric sample from the user device, distributing a redeemtoken proposal to the blockchain network, committing a transaction corresponding to the biometric token, to the blockchain network, and invalidating the biometric token.

Abstract: Image data is run through a neural network, and the neural network produces a vector representation of the image data. Random sparse sampling masks are created. The vector representation of the image data is masked with each of the random sparse sampling masks, the masking generating corresponding sparsely sampled vectors. The sparsely sampled vectors are transmitted to nodes of a consensus network, wherein a sparsely sampled vector of the sparsely sampled vectors is transmitted to a node of the consensus network. Votes from the nodes of the consensus network are received. Whether a consensus is achieved in the votes is determined. Responsive to determining that the consensus is achieved, at least one of identification and verification of the image data may be provided.

Abstract: Image data is run through a neural network, and the neural network produces a vector representation of the image data. Random sparse sampling masks are created. The vector representation of the image data is masked with each of the random sparse sampling masks, the masking generating corresponding sparsely sampled vectors. The sparsely sampled vectors are transmitted to nodes of a consensus network, wherein a sparsely sampled vector of the sparsely sampled vectors is transmitted to a node of the consensus network. Votes from the nodes of the consensus network are received. Whether a consensus is achieved in the votes is determined. Responsive to determining that the consensus is achieved, at least one of identification and verification of the image data may be provided.

Abstract: Image data is run through a neural network, and the neural network produces a vector representation of the image data. Random sparse sampling masks are created. The vector representation of the image data is masked with each of the random sparse sampling masks, the masking generating corresponding sparsely sampled vectors. The sparsely sampled vectors are transmitted to nodes of a consensus network, wherein a sparsely sampled vector of the sparsely sampled vectors is transmitted to a node of the consensus network. Votes from the nodes of the consensus network are received. Whether a consensus is achieved in the votes is determined. Responsive to determining that the consensus is achieved, at least one of identification and verification of the image data may be provided.

Abstract: A method of managing lifelong learner events on a blockchain includes detecting an event related to a learner using a blockchain-enabled digital learning system, determining a concern/risk level of the learner by performing a risk assessment, determining parameters to generate a transaction related the learner's event based on the parameters and the concern/risk level, determining the values of the parameters by measuring the value or importance of the event and its associated metadata and documents, generating a list of transactions corresponding to the parameters, and validating the transactions using validating distributed peer-to-peer devices that run one or more chaincodes related to the management of the lifelong learner events.

Abstract: Image data is run through a neural network, and the neural network produces a vector representation of the image data. Random sparse sampling masks are created. The vector representation of the image data is masked with each of the random sparse sampling masks, the masking generating corresponding sparsely sampled vectors. The sparsely sampled vectors are transmitted to nodes of a consensus network, wherein a sparsely sampled vector of the sparsely sampled vectors is transmitted to a node of the consensus network. Votes from the nodes of the consensus network are received. Whether a consensus is achieved in the votes is determined. Responsive to determining that the consensus is achieved, at least one of identification and verification of the image data may be provided.

Abstract: An example operation may include one or more of obtaining a first biometric sample of a user from a user device. extracting, by an issuing node of a permissioned blockchain network, a biometric template from the first biometric sample, encrypting the biometric template, distributing an issuetoken proposal comprising the encrypted biometric template to the blockchain network, and generating and distributing a biometric token to the user device. In response to the user indicating to the user device to redeem the biometric token, the method includes one or more of presenting, by the user device, the biometric token to a verifying node of the blockchain network, validating, by the verifying node, the biometric token, receiving, by the verifying node, a second biometric sample from the user device, distributing a redeemtoken proposal to the blockchain network, committing a transaction corresponding to the biometric token, to the blockchain network, and invalidating the biometric token.

Abstract: A method of managing lifelong learner events on a blockchain includes detecting an event related to a learner using a blockchain-enabled digital learning system, determining a concern/risk level of the learner by performing a risk assessment, determining parameters to generate a transaction related the learner's event based on the parameters and the concern/risk level, determining the values of the parameters by measuring the value or importance of the event and its associated metadata and documents, generating a list of transactions corresponding to the parameters, and validating the transactions using validating distributed peer-to-peer devices that run one or more chaincodes related to the management of the lifelong learner events.

Abstract: A method of managing a school system using a blockchain includes receiving a transaction related to a school record along with chaincodes to validate the transaction and a set of record tokens that represent events with respect to a stakeholder, obtaining a historical block identifier from a blockchain of historical school records that is representative of historical record events in the blockchain, receiving validity requirements for the events on the record, obtaining a validation token that is indicative of a validity of the events based on the set of record tokens, computing a chaincode block for the transaction against the one or more validation requirements as a function of the validation token, the historical school record's block identifier, and the set of record tokens, and creating a new block and upadating the blockchain of historical school records based on the new block the transaction, when the validation succeeds,