Accenture Patente

Implementations of the present disclosure include receiving a color image and an IR image, the IR image taken contemporaneously with the color image, providing a set of facial landmarks depicted in the color image, determining a depth value for each facial landmark in the set of color landmarks, depth values being provided from the IR image, determining an average depth difference based on at least a sub-set of facial landmarks in the set of facial landmarks, comparing the average depth difference to a difference threshold to provide a comparison, and selectively authenticating the person based on the comparison.

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The present disclosure relates to a system, a method, and a product for optimizing hyper-parameters for generation and execution of a machine-learning model under constraints. The system includes a memory storing instructions and a processor in communication with the memory. When executed by the processor, the instructions cause the processor to obtain input data and an initial hyper-parameter set; for an iteration, to build a machine learning model based on the hyper-parameter set, evaluate the machine learning model based on the target data to obtain a performance metrics set, and determine whether the performance metrics set satisfies the stopping criteria set. If yes, the instructions cause the processor to perform an exploitation process to obtain an optimal hyper-parameter set, and exit the iteration; if no, perform an exploration process to obtain a next hyper-parameter set, and perform a next iteration with using the next hyper-parameter set as the hyper-parameter set.

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A device may receive historical cloud data associated with resources of a cloud computing environment, and may receive historical customer data associated with requested resource usage by customers of the cloud computing environment. The device may determine a usage growth profile based on the historical cloud data and the historical customer data, and may determine, based on the historical cloud data and the historical customer data, usage deviation data indicating deviations between actual and planned resource usage. The device may train a model, with the usage growth profile and the usage deviation data, to generate a trained model, and may receive a request for new resource usage by a customer associated with the cloud computing environment. The device may process the request for the new resource usage, with the trained model, to generate projected resource usage data, and may perform actions based on the projected resource usage data.

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In some examples, source code differential pruning-based dataset creation may include receiving source code that includes at least one vulnerability and at least one remediation that remediates the at least one vulnerability, extracting at least one remediated section, and identifying each sentence of the remediated section. A plurality of clusters may be generated based on an analysis of each identified sentence of the remediated section to determine a score with respect to a specified cluster that includes the identified sentence. Further, a determination may be made as to whether the score is greater than a specified threshold. Each identified sentence for which the score is greater than the specified threshold may be designated as a relevant sentence. An auxiliary dataset may be generated based on a plurality of relevant sentences and include at least one relevant vulnerability and at least one relevant remediation that remediates the relevant vulnerability.

The present disclosure describes methods and systems for generating an explanation for a prediction based on a knowledge graph. The method includes receiving a target triple and a knowledge graph including a set of structured data; converting the knowledge graph to an embeddings space and outputting a plausibility prediction for the target triple; sampling a set of neighbors of elements of the target triple in the embeddings space; obtaining a set of example triples based on the set of neighbors according to the target triple; obtaining a prototype graph based on the set of the example triples according to the target triple; generating an explanation graph based on the prototype graph, the set of example triples, and the target triple; and generating an explanation for the plausibility prediction based on the explanation graph.

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A system may facilitate distributed ledger technology (DLT) record based (for example, blockchain-based) voting. A node configured to support a voter role may access a DLT record to determine answers and/or parameters for a ballot. An organizer of the ballot may provide the voter with a voter token that confers vote-value to the voter. The voter may distribute the vote-value to the answers using committed tokens that bind the voter to a particular vote-value without divulging the particular vote value while in a cryptographic form. The voter may distribute committed tokens to multiple answers. In some cases, the distribution of the committed tokens to multiple answers may frustrate attempts to determine the one or more targets to which the voter delivers a non-null vote-value.

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A dynamic tagging system facilitates the control, monitoring, and operation of technical components, such as virtual machines, in cloud environments. The dynamic tagging system performs an analysis on unstructured metadata tags linked to the technical components. The unstructured metadata tags may be created for technical components by authorized operators of the technical components, for instance. The dynamic tagging system may analyze an individual unstructured metadata tag or a combination of unstructured metadata tags assigned to a technical component to automatically trigger any pre-configured control, monitoring or operational actions for the technical component. As one example, the dynamic tagging system detects a trigger condition for an action trigger to perform automated electronic communications between any selected entities to convey information about the technical component.

A method and system for fault analysis and prediction in an enterprise environment is described. In one embodiment, a method includes obtaining data from a plurality of sources in the enterprise environment. The plurality of sources includes at least one or more systems, users, or applications. The obtained data is associated with identifiers that include a theme selected from a set of themes and one or more keywords that are specific to each theme. The method includes generating a workflow for a user based on a session identifier and/or timestamps associated with activity by the user. The workflow identifies a time-based sequence of interactions by the user with the at least one or more systems or applications in the enterprise environment. The method also includes determining at least one fault identification or fault prediction based on the workflow and identifiers associated with the obtained data that corresponds to the workflow.

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