Abstract: A computer-implemented method for determining a set of target items to be annotated for training a machine learning application. The method comprises providing a training data set with a set of data samples and an auto-encoder with a classifier. The auto-encoder comprises an embedding model that maps the set of data samples to a set of compressed feature vectors. The set of compressed feature vectors define a compressed feature matrix. Further provided are: a definition of a graph associated to the compressed feature matrix, applying a clustering-algorithm to identify node clusters of the graph and applying a centrality algorithm to identify central nodes of the node clusters, retrieving from an annotator node labels for the central nodes, propagating the annotated node labels to other nodes of the graph and performing a training of the embedding model and the classifier with the annotated and the propagated node labels.

Abstract: A computer-implemented method, computer program product, and computer system are provided. The method comprises training a machine-learning model using an initial set of training data samples, receiving a new training data sample, and predicting a label for the new training data sample. The method also comprises, upon determining that a prediction quality value for the predicted label of the new training data sample is below a predefined quality value, adding the new training data sample to the initial set, thereby building an extended training data set. The method also comprises retraining the machine-learning model using the extended training data set.

Abstract: Computer-implemented methods and apparatus are provided for annotating digital images of line plots with ground truth labels. For each digital image, such a method includes supplying image data defining the image of a line plot to a machine-learning model trained to generate a set of control points defining a spline corresponding to the line plot. The method further comprises displaying the spline, and the set of control points, superimposed on the image in a graphical user interface and, in response to user manipulation via the graphical user interface of one or more control points, dynamically adjusting the displayed spline in accordance with manipulated control points whereby the displayed spline can be adjusted for conformity with the line plot. The set of control points for the adjusted spline is then stored as a ground truth label for the image.

Abstract: A computer-implemented method, computer program product, and computer system are provided. The method comprises training a machine-learning model using an initial set of training data samples, receiving a new training data sample, and predicting a label for the new training data sample. The method also comprises, upon determining that a prediction quality value for the predicted label of the new training data sample is below a predefined quality value, adding the new training data sample to the initial set, thereby building an extended training data set. The method also comprises retraining the machine-learning model using the extended training data set.

Abstract: Computer-implemented methods and apparatus are provided for annotating digital images of line plots with ground truth labels. For each digital image, such a method includes supplying image data defining the image of a line plot to a machine-learning model trained to generate a set of control points defining a spline corresponding to the line plot. The method further comprises displaying the spline, and the set of control points, superimposed on the image in a graphical user interface and, in response to user manipulation via the graphical user interface of one or more control points, dynamically adjusting the displayed spline in accordance with manipulated control points whereby the displayed spline can be adjusted for conformity with the line plot. The set of control points for the adjusted spline is then stored as a ground truth label for the image.

Abstract: A computer-implemented method for determining a set of target items to be annotated for training a machine learning application. The method comprises providing a training data set with a set of data samples and an auto-encoder with a classifier. The auto-encoder comprises an embedding model that maps the set of data samples to a set of compressed feature vectors. The set of compressed feature vectors define a compressed feature matrix. Further provided are: a definition of a graph associated to the compressed feature matrix, applying a clustering-algorithm to identify node clusters of the graph and applying a centrality algorithm to identify central nodes of the node clusters, retrieving from an annotator node labels for the central nodes, propagating the annotated node labels to other nodes of the graph and performing a training of the embedding model and the classifier with the annotated and the propagated node labels.

Abstract: The present invention relates to a process for the hydrogenation of substituted or unsubstituted, monocyclic or polycyclicaromatics to form the corresponding cycloaliphatics, in particular of benzene to cyclohexane, by bringing the aromatic into contact with a hydrogen-containing gas in the presence of a catalyst, where hydrogen in which residual gases are present is used.

Abstract: Fuels for internal combustion engines or lubricants contain small amounts of polyisobutylaminoalcohols of the formulae Ia and Ib where R is polyisobutyl having a number average molecular weight of 500 to 5,000 and in each of the two formulae one of the radicals X is OH and the other is the group where the radicals R1 may be identical or different and are each hydrogen, alkyl, hydroxyalkyl or aminoalkyl which may be substituted by further hydroxyl- or amino-carrying alkyl radicals, or the two radicals R1 may form a nonaromatic ring.

Abstract: Process for the preparation of methylpyrazoles from diacetylene and hydrazines in which the diacetylene is removed from cracked gas by absorption and is then reacted with hydrazines.