Abstract: Content for products can be identified. For each identified content, at least one class to which the first content pertains can be predicted using an artificial intelligence multiclass model. For each identified first content that corresponds to the at least one class, a support level of the product with regard to at least one class can be predicted using artificial intelligence binary class models. For each identified product, data indicating the product and the support level of the product with regard to the at least one class can be added to a data table. A product features map based on the data indicating the products and a support level of each product with regard to the at least one class can be generated and output.

Abstract: Content for products can be identified. For each identified content, at least one class to which the first content pertains can be predicted using an artificial intelligence multiclass model. For each identified first content that corresponds to the at least one class, a support level of the product with regard to at least one class can be predicted using artificial intelligence binary class models. For each identified product, data indicating the product and the support level of the product with regard to the at least one class can be added to a data table. A product features map based on the data indicating the products and a support level of each product with regard to the at least one class can be generated and output.

Abstract: A computer-implemented method, system and computer program product for identifying equivalent technical terms. A deep learning model is trained to identify equivalent technical terms. The deep learning model is then applied to a new document. The sentences of the document are analyzed to identify technical terms. Text is then analyzed surrounding the technical term identified in the document to determine the meaning of such text. A glossary list is then reviewed to determine if the identified meaning of the analyzed text matches a meaning/concept in the glossary list linked to a technical term. In response to determining that the meaning of the analyzed text matches a meaning/concept in the glossary list linked to an equivalent technical term, the technical term in the document is annotated with the equivalent technical term. In this manner, non-standard equivalent technical terms in different documents with the same meaning/concept are able to be identified.

Abstract: An example operation may include one or more of connecting, by a service discovery node, to a blockchain configured to store workloads and stakes of a plurality of endorser nodes, receiving, by the service discovery node, a discovery query that contains an endorsement policy from a user node, generating, by the service discovery node, cryptographic sortition parameters based on the endorsement policy, sending, by the service discovery node, the cryptographic sortition parameters to the plurality of the endorser nodes, collecting, by the service discovery node, sortition labels from the plurality of the endorser nodes, the sortition labels generated based on the sortition parameters, determining, based on the sortition labels, a set of the endorser nodes from the plurality of the endorser nodes that qualify the endorsement policy, and providing, by the service discovery node, identifiers of the set of the endorser nodes to the user node for an endorsement of a user transaction proposal.

Abstract: A computer-implemented method for fording depth estimation based on a real-time image recognition is provided according an embodiment of the present disclosure. In the method, a first image of a low-lying road being submerged under a bridge can be acquired. At least one second image of the low-lying road without being submerged can be determined. A fording depth of the low-lying road can be estimated based on the first image and the at least one second image.

Abstract: A computer-implemented method for fording depth estimation based on a real-time image recognition is provided according an embodiment of the present disclosure. In the method, a first image of a low-lying road being submerged under a bridge can be acquired. At least one second image of the low-lying road without being submerged can be determined. A fording depth of the low-lying road can be estimated based on the first image and the at least one second image.

Abstract: An example operation may include one or more of connecting, by a service discovery node, to a blockchain configured to store workloads and stakes of a plurality of endorser nodes, receiving, by the service discovery node, a discovery query that contains an endorsement policy from a user node, generating, by the service discovery node, cryptographic sortition parameters based on the endorsement policy, sending, by the service discovery node, the cryptographic sortition parameters to the plurality of the endorser nodes, collecting, by the service discovery node, sortition labels from the plurality of the endorser nodes, the sortition labels generated based on the sortition parameters, determining, based on the sortition labels, a set of the endorser nodes from the plurality of the endorser nodes that qualify the endorsement policy, and providing, by the service discovery node, identifiers of the set of the endorser nodes to the user node for an endorsement of a user transaction proposal.