Abstract: The invention relates to a method for generating data, wherein the method is implemented by computer. The method includes implementing a 3D engine to produce metadata relating to a reference scene generated using the 3D engine, wherein the reference scene is representative of a predetermined target situation. The method also includes providing, as input to a control model coupled to a generative model, at least part of the metadata produced by the 3D engine; calculating, using the generative model, at least one synthetic image representative of the target situation, from an output of the control model and descriptive data relating to the target situation; and storing, in a data set, at least one calculated synthetic image.

Abstract: The invention relates to a data management system including a storing unit and a processing unit. The storing unit includes a dataset memory that stores at least one dataset; a metadata memory that stores, for each dataset stored in the dataset memory, respective metadata; and an annotation memory that stores at least one predetermined annotation value. The processing unit, upon storing of an additional dataset in the dataset memory, determines whether the additional dataset includes dataset annotations descriptive of the data comprised in said additional dataset; performs a mapping of each dataset annotation onto the at least one predetermined annotation value; and writes, in the metadata memory, in relation to the additional dataset, metadata representative of a result of the mapping.

Abstract: The invention relates to a method for automatically analyzing a transaction log of a distributed computing system comprising a plurality of lines. The method includes, for each line, cutting the line into words, constructing a comparison vector by comparing the line with the other lines of the same size as the line, constructing a pattern from the comparison vector, and creating an event per pattern. The invention includes constructing a prediction model by training an artificial neural network on a group of training events, the prediction model being configured to predict the next event in the transaction log. The invention includes, for at least one event, using the prediction model to predict the event, from a group of prediction events, and generating from the prediction model, a causal graph of the event comprising a causal relation for each event of the group of prediction events responding to a relevance condition.

Abstract: The invention relates to a method (200) for training a dynamic-architecture convolutional neural network for semantic image segmentation, comprising a phase (102) of auto-annotation of images, to constitute a database (B1), called the first database, of auto-annotated images, comprising the following steps: applying (104) at least one transformation to at least one source image, and adding (106), to each transformed image thus obtained, a tag, called the first tag, corresponding to said transformation; said method (200) comprising at least one iteration of a phase (110) of training said neural network with the first image database (B1), and a second image database (B2) annotated with a tag, called a second tag, relating to the content of said image. It also relates to a computer program and a device implementing such a method, and a deep-learning dynamic-architecture convolutional neural network obtained by such a method.

Abstract: An aspect of the invention relates to a method for automatically analysing a transaction log of a distributed computing system, comprising a plurality of lines, the method comprising the following steps: For each line: Cutting the line into words; Constructing a comparison vector by comparing the line with the other lines of the same size as the line; Constructing a pattern from the comparison vector; Creating an event per pattern; Constructing at least one prediction model by training an artificial neural network on a group of training events, the prediction model being configured to predict the next event in the transaction log; For at least one event: Using the prediction model to predict the event, from a group of prediction events; Generating from the prediction model, a causal graph of the event comprising a causal relation for each event of the group of prediction events responding to a relevance condition.