Accenture Patente

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A device determines, from a network slice template associated with a network slice, a quality of service (QoS) profile for the network slice that includes performance metrics for corresponding QoS parameters associated with providing a service. The device monitors performance of the network slice in association with the QoS profile, and determines, based on the performance, that a performance indicator for a QoS parameter of the network slice is outside a threshold range of a performance metric. The device determines, based on the performance information and the QoS profile, a slice modification to the network slice template for the network slice, where the slice modification is configured to cause the performance indicator to be within the threshold range of the performance metric. The device causes a network slice orchestrator to update an instantiation of the network slice according to the slice modification and the network slice template.

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Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Zusammenfassung wird geladen …

Implementations are directed to an agile security platform for enterprise-wide cyber-security and performing actions of receiving, from an agile security platform, analytical attack graph (AAG) data representative of one or more AAGs, each AAG representing one or more lateral paths within an enterprise network for reaching a target asset from one or more assets within the enterprise network, determining, for each instance of a plurality of instances of the AAG, a graph value representing a measure of hackability of the enterprise network at respective times, providing a profile of the enterprise network based on a set of graph values determined for instances of the AAG, the profile representing changes in graph values over time, determining an effectiveness of one or more security controls based on the profile, and selectively executing one or more remedial actions in response to the effectiveness.

A device may be configured to obtain text from a document. The device may perform embedding to obtain a data structure indicating probabilities associated with characters included in the text and apply a first convolution to the data structure to obtain different representations of the characters included in the text. In addition, the device may apply parallel convolution to the different representations to obtain multiple sets of character representations, subsample the multiple sets of character representations, and pool the subsampled multiple sets of character representations into a merged data structure. The device may provide the merged data structure to a fully connected layer, of a convolutional neural network, to produce data representing features of the text; and provide the data representing features of the text to an inference layer, of the convolutional neural network, that provides data indicating a classification for the text.

This document describes a computing architecture that enables users to develop applications using building blocks that include micro frontends. In one aspect, a method includes providing a configuration user interface that enables a user to select and arrange building block user interface elements in a layout for a page. Data specifying a layout of one or more building block user interface elements that each include a micro frontend and corresponding backend logic is received from the configuration user interface. Each micro frontend includes one or more user interface components and a store that manages a model that represents a state of the one or more user interface components of the micro frontend. The corresponding backend for each micro frontend includes a function that updates the model of the micro frontend based on a previous model of the micro frontend and an action. User interface data is generated.

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Implementations are directed to a computer-implemented method that includes broadcasting, by one or more computing devices, a request that indicates a need for a number of carts at a location; receiving confirmation from one or more carts that accept the request, the one or more carts are carts that are close to the location; sending instructions to the one or more carts instructing the one or more carts to navigate to the location; receiving, from a scanner of a baggage handling system, scanned data of baggage that is loaded into the one or more carts assigned to transport the baggage; receiving, from each of the one or more carts, a message indicating the cart is full; and sending instructions to each cart to navigate to a destination of the baggage according to a list of bags inside each cart.

Systems and methods for providing an explainability framework for use with AI systems are described. In one example, such an AI explainability system for intent classification uses a surrogate Bert-Siamese model approach. For example, a prediction from an intent classification model is paired with a top matching sentence and used as input to train a Bert-Siamese model for sentence similarity. Using the sentence similarity, the token / word level embedding can be extracted from attention weights of the sentences and correlations between query tokens/words, and the best matching sentences may be used for explanations.

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Techniques are described for component configuration based on sensor data. Sensor data is collected by sensors in, or proximal to, a system under diagnosis (e.g., a vehicle), the sensor data describing the use of component(s) of the system by individual(s). The sensor data is analyzed (e.g., in real time) to determine an updated configuration for component(s) (e.g., an adjustment to the seat back, lumbar support of a car seat, etc.). The updated configuration may be communicated to the individual as a recommended configuration. In some implementations, the updated configuration may be communicated directly to the component which, on receiving and processing the configuration update, sends signals to various actuators to move the subcomponents of the component into the updated configuration. In some implementations, the sensor data is used to train, through machine learning, a model that provides configuration update(s) for component(s) based on the input sensor data.

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Embodiments of the present disclosure leverage near field communication (NFC) technology to provide dynamic and interactive monitoring of an environment. NFC devices may be used to check items in and out of a storage facility, obtain readings from instruments or other machinery present in the environment (e.g., to perform tests on the items, etc.), track movement of users and items within the environment, and to prompt users with information about the environment, such as information about the instruments or machinery currency being used to perform operations with respect to one or more items checked out to the user. Additionally, the NFC device may be used to configure the instruments with appropriate settings for the particular item(s) for which the instrument is currently being used or for other purposes.

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An upper-extremity prosthetic is adapted to engage with an athletic ball. The prosthetic includes one or more springs that provide energy return as a user is throwing the ball using the prosthetic. The springs can have a conductivity that changes in relation to an amount of strain or deformation of the spring. The change in conductivity can be used to provide haptic feedback to the user so the user can sense the amount of force being applied to throw the ball. In some embodiments, the springs are made by a multi-material 3D printing (additive manufacturing) process and include a first material that is electrically non-conductive and a second material that electrically conductive. In some embodiments, the prosthetic also includes one or more cantilevered springs that are also adapted to engage with the ball and to provide energy return while throwing the ball.

Methods, systems, and apparatuses, including computer programs encoded on computer-storage media, for prioritizing test cases. Processes may include obtaining test artifacts that were generated based on testing one or more legacy versions of a software application using multiple test cases, generating a risk index based at least on the test artifacts that were generated based on testing the one or more legacy versions of the software application using the multiple test cases, and training an ensemble model that is configured to identify likely unnecessary or redundant test cases in connection with in testing an updated version of the software application, based at least on the risk index.

A device obtains a machine readable document, and extracts plain text information from the machine readable document. The device extracts visual structural information from the machine readable document, and processes the plain text information with an artificial intelligence model to generate artificial intelligence-generated annotations. The device aligns the artificial intelligence-generated annotations and the visual structural information to generate a structured document, and provides the structured document for display.

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Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support leveraging of data cooperatives to facilitate configuration of execution environments in which processes may be performed using data supplied by various data sources of the data cooperative. Each data source may configure data storage privacy levels and execution privacy levels with their respective users. Information regarding the data storage and execution privacy levels may be utilized to generate mapping data that is used to configure a unified storage-and-execution privacy policy that meets or exceeds the data storage privacy levels configured by each individual data source and their respective users. The unified storage-and-execution policy may then be utilized to configure an execution environment, which may be centralized or distributed across multiple computing devices, and data provided by the data sources may be analyzed in a manner that preserves the privacy of the data.

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The disclosure enables latent space exploration of a dataset based on drug molecular-structure data and drug biological-treatment data for a set of drug compounds in order to determine optimal drug compounds for treating diseases. Regional interpolation, including a linear interpolation (LERP) operation and a spherical linear interpolation (SLERP) operation, along with quantitative structure-activity relationship (QSAR) models may be utilized to navigate through a latent space generated from variational auto-encoders (VAE), in accordance with certain embodiments.

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A device may receive a job request that requests performance of one or more operations by resources of a high-performance computing environment, and may process the job request, with a policy execution model trained with policy parameters, to identify policies to apply during execution of the job request. The device may process the job request, with a forecast object model trained with job data and profile data, to generate a forecast of resources and licenses required from the high-performance computing environment. The device may process the job request, other job requests, the one or more of the policies, and the forecast, with a heuristic model, to determine a schedule for the job request, and may process the schedule and current constraints on the resources and the licenses, with a linear programming model, to determine an optimized schedule for the job request.

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