Abstract: A computer-implemented method for classification of data by a machine learning system using a logic constraint for reducing a data labeling requirement. The computer-implemented method includes: generating a first embedding space from a first partially labeled training data set, wherein in the first embedding space, content-wise related training data of the first partially labeled training data are clustered together, determining at least two clusters in the first embedding space formed from the first partially labeled training data, and training a machine learning model based, at least in part, on a second partially labeled training data set and the at least two clusters, wherein the at least two clusters are used as training constraints.

Abstract: The present disclosure relates to a method for query expansion. The method comprises: a) receiving a current query having at least one search term; b) inputting the at least one search term of the current query to a set of one or more query expansion modules, wherein the query expansion modules are configured to predict expansion terms of input terms; c) receiving from the set of expansion modules candidate expansion terms of the search term; d) modifying the current query using at least part of the candidate expansion terms, resulting in a modified query having at least one modified search term, The method further comprises repeating steps b) to d) using the modified query as the current query, the repeating being performed until a predefined stopping criterion is fulfilled.

Abstract: Computer-implemented methods and systems are provided for digitally signing predetermined arrays of digital data. Such a method may provide a secret neural network model trained to classify arrays of digital data in dependence on data content of the arrays. The array of the arrays may be signed by supplying the array to the secret neural network model to obtain an initial classification result; and effecting a modification of data in the array to change the initial classification result to a predetermined, secret classification result, the modification being effected via a backpropagation process in the secret neural network model to progressively modify the array in response to backpropagated errors dependent on a difference between a current classification result for the array and the secret classification result.

Abstract: Computer-implemented methods and systems are provided for digitally signing predetermined arrays of digital data. Such a method may provide a secret neural network model trained to classify arrays of digital data in dependence on data content of the arrays. The array of the arrays may be signed by supplying the array to the secret neural network model to obtain an initial classification result; and effecting a modification of data in the array to change the initial classification result to a predetermined, secret classification result, the modification being effected via a backpropagation process in the secret neural network model to progressively modify the array in response to backpropagated errors dependent on a difference between a current classification result for the array and the secret classification result.

Abstract: An approach for determining a concatenated confidence value of a first class using an artificial-intelligence module (AI-module) for performing a classification based on the concatenated confidence value of the first class. The AI-module comprises a knowledge graph module, a machine learning module, and a weighting module. A processor determines a first confidence value of the first class as a first function of an input dataset using the machine learning module. A processor determines a second confidence value of the first class as a second function of the input dataset using the knowledge graph module. A processor determines the concatenated confidence value of the first class as a third function of the first confidence value of the first class, the second confidence value of the first class, and a value of a weighting parameter of the weighting module.

Abstract: A computer-implemented method for classification of data by a machine learning system using a logic constraint for reducing a data labeling requirement. The computer-implemented method includes: generating a first embedding space from a first partially labeled training data set, wherein in the first embedding space, content-wise related training data of the first partially labeled training data are clustered together, determining at least two clusters in the first embedding space formed from the first partially labeled training data, and training a machine learning model based, at least in part, on a second partially labeled training data set and the at least two clusters, wherein the at least two clusters are used as training constraints.

Abstract: The present disclosure relates to a method for query expansion. The method comprises: a) receiving a current query having at least one search term; b) inputting the at least one search term of the current query to a set of one or more query expansion modules, wherein the query expansion modules are configured to predict expansion terms of input terms; c) receiving from the set of expansion modules candidate expansion terms of the search term; d) modifying the current query using at least part of the candidate expansion terms, resulting in a modified query having at least one modified search term, The method further comprises repeating steps b) to d) using the modified query as the current query, the repeating being performed until a predefined stopping criterion is fulfilled.