Abstract: One aspect of systems and methods for data correction includes identifying a false label from among predicted labels corresponding to different parts of an input sample, wherein the predicted labels are generated by a neural network trained based on a training set comprising training samples and training labels corresponding to parts of the training samples; computing an influence of each of the training labels on the false label by approximating a change in a conditional loss for the neural network corresponding to each of the training labels; identifying a part of a training sample of the training samples and a corresponding source label from among the training labels based on the computed influence; and modifying the training set based on the identified part of the training sample and the corresponding source label to obtain a corrected training set.

Abstract: Systems and methods for document classification are described. Embodiments of the present disclosure generate classification data for a plurality of samples using a neural network trained to identify a plurality of known classes; select a set of samples for annotation from the plurality of samples using an open-set metric based on the classification data, wherein the annotation includes an unknown class; and train the neural network to identify the unknown class based on the annotation of the set of samples.

Abstract: Systems and techniques for privacy preserving document analysis are described that derive insights pertaining to a digital document without communication of the content of the digital document. To do so, the privacy preserving document analysis techniques described herein capture visual or contextual features of the digital document and creates a stamp representation that represents these features without included the content of the digital document. The stamp representation is projected into a stamp embedding space based on a stamp encoding model generated through machine learning techniques capturing feature patterns and interaction in the stamp representations. The stamp encoding model exploits these feature interactions to define similarity of source documents based on location within the stamp embedding space. Accordingly, the techniques described herein can determine a similarity of documents without having access to the documents themselves.

Abstract: Systems and techniques for privacy preserving document analysis are described that derive insights pertaining to a digital document without communication of the content of the digital document. To do so, the privacy preserving document analysis techniques described herein capture visual or contextual features of the digital document and creates a stamp representation that represents these features without included the content of the digital document. The stamp representation is projected into a stamp embedding space based on a stamp encoding model generated through machine learning techniques capturing feature patterns and interaction in the stamp representations. The stamp encoding model exploits these feature interactions to define similarity of source documents based on location within the stamp embedding space. Accordingly, the techniques described herein can determine a similarity of documents without having access to the documents themselves.

Abstract: Systems and techniques for privacy preserving document analysis are described that derive insights pertaining to a digital document without communication of the content of the digital document. To do so, the privacy preserving document analysis techniques described herein capture visual or contextual features of the digital document and creates a stamp representation that represents these features without included the content of the digital document. The stamp representation is projected into a stamp embedding space based on a stamp encoding model generated through machine learning techniques capturing feature patterns and interaction in the stamp representations. The stamp encoding model exploits these feature interactions to define similarity of source documents based on location within the stamp embedding space. Accordingly, the techniques described herein can determine a similarity of documents without having access to the documents themselves.

Abstract: The technical field of the invention relates to magnetic sealing devices used for transfer of torque involving rotating parts. Industries that deal in chemicals and pharmaceutics have a requirement to mix materials such as gases and liquids. This is carried out by mixers and agitators and similar other equipment. All devices involve a shaft that comes out of the body of the equipment, that is to say it pierces the equipment. The location at which the shaft enters the equipment is a potential point of leakage. One of the technical problems that the present invention tackles is that of providing a secure sealing that will reduce leakage. The magnetic seal assembly disclosed in the present invention provides a magnetic sealing device that gives a safe, trouble-free, robust, and leak-proof contactless seal for agitator drives and similar equipment used for industrial scale applications.

Abstract: The technical field of the invention relates to magnetic sealing devices used for transfer of torque involving rotating parts. Industries that deal in chemicals and pharmaceutics have a requirement to mix materials such as gases and liquids. This is carried out by mixers and agitators and similar other equipment. All devices involve a shaft that comes out of the body of the equipment, that is to say it pierces the equipment. The location at which the shaft enters the equipment is a potential point of leakage. One of the technical problems that the present invention tackles is that of providing a secure sealing that will reduce leakage. The magnetic seal assembly disclosed in the present invention provides a magnetic sealing device that gives a safe, trouble- free, robust, and leak-proof contactless seal for agitator drives and similar equipment used for industrial scale applications.