Accenture Patente

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An automatic image annotation system receives a reference image with one or more parts annotated along with one or more query images and automatically identifies portions from the query images that are similar to the annotated parts of the reference image. The S-matrices of the reference image and the query images are obtained via singular value decomposition (SVD). Lower-dimensional images are also obtained for the reference image and the query images using a pre-trained deep learning model. The S-matrices and the lower-dimensional images of the corresponding images are combined to generate vector representations. A distance metric is calculated for the vector representation of the reference image with that of the query image. A preliminary output image with a preliminary annotation is initially generated. The preliminary annotation is further optimized to generate an optimized annotation that adequately covers the region of interest (ROI).

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Methods, systems, and computer-readable storage media for receiving, by an aromatic simulation platform, a recipe including descriptions indicative of ingredients of a consumable, processing the recipe through a first neural network to provide a recipe embedding, processing an ingredients profile to determine an aroma compounds profile representing the ingredients of the ingredients profile, processing the aroma compounds profile through a second neural network to provide an aroma embedding, and processing the recipe embedding and the aroma embedding through a third neural network to provide an aroma profile representative of the consumable of the recipe.

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A participant node of a distributed ledger network may identify a distributed federated learning (DFL) smart contract stored on a blockchain. The DFL smart contract may include an aggregation sequence. The aggregation sequence may include an ordered sequence of participant node identifiers. The participant node may generate a trained model by training a global model with training data. The participant node may detect, on the blockchain, a first transition token indicative of a first model previously aggregated by another participant node. The participant node may receive the first model. The participant node may aggregate the first model with the trained model to generate a second model. The participant node may store, on the blockchain, a second transition token indicative of the second model. A successor node identified in the aggregation sequence may further aggregate the second model with an additional model in response to detection of the second transition token.

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Systems and methods for facilitating an automated detection of an object in a test document are disclosed. A system may include a processor including a dataset generator. The dataset generator may obtain a first input image and a first original document from a data lake. The dataset generator may prune a portion of the first original document to obtain a pruned image. The dataset generator may blend the first input image with the pruned image to generate a modified image. The modified image may include the pruned image bearing the first pre-defined representation. The modified image may be combined with the first original document to generate a training dataset. The training dataset may be utilized to train a neural network based model to obtain a trained model for the automated detection of the object in the test document.

The present disclosure describes methods and systems for generating an approximated embedding of an out-of-knowledge-graph entity based on a knowledge graph. The method includes: receiving a target entity, a dataset associated with the target entity, and an embeddings space of a knowledge graph comprising a set of structured data, wherein the target entity is out of the knowledge graph and the embeddings space includes a set of vectors representing the set of structured data in the embeddings space; selecting a set of elements from the knowledge graph, each element being related to the target entity according to the dataset associated with the target entity; constructing a set of descriptory triples based on the target entity and the set of elements; obtaining an embedding matrix based on the descriptory triples and the embeddings space; and generating an approximated embedding for the target entity based on the embedding matrix.

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The present disclosure relates to systems, methods, and computer readable medium for automatically managing API documentation and code generation. The disclosure provides an IDE plug-in that may import a machine readable reference documentation file from a documentation database, and create automatically generated code from the machine readable reference documentation file. Generally, the IDE plug-in may then allow a developer to validate their project back against the machine readable reference documentation file to ensure that all expected code is present in the project. This may allow the developer to ensure that the project properly meets the functional objectives intended for the API, before their implementation is deployed.

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This document describes neuromorphic controllers. In one aspect, a method for controlling one or more joints of a robotic arm includes receiving, by neuromorphic controller comprising a spiking neural network (SNN), a target value of a joint control variable for a joint of the robotic arm. The SNN includes two position proprioceptor neurons, two-speed proprioceptor neurons, a presynaptic inhibitory neuron, an extensor motor neuron, and a flexor motor neuron. The neuromorphic controller updates an actual value of the joint control variable for the joint of the robotic arm based on the target value of the joint control variable. The updating includes generating, by one of the two position proprioceptor neurons, the first spikes to one of the extensors motor neurons or the flexor motor neuron based on a difference between the actual value of the joint control variable and the target value of the joint control variable.

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Implementations include determining a set of components within the connected vehicle ecosystem, components within the set of components representing at least one layer within the connected vehicle ecosystem, for each component in the set of components: providing a set of facts representative of the respective component, and providing a component digital twin using the set of facts, defining a set of digital twins including digital twins of components in the set of components, generating, using the set of digital twins, at least one AAG representative of potential lateral movement between components of the at least one layer within the connected vehicle ecosystem, the at least one AAG representing a contextual digital twin of components operating within the connected vehicle ecosystem, and evaluating the connected vehicle ecosystem using the at least one AAG.

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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.