5 results about "String representation" patented technology

The present disclosure provides a system and a method for authoring and automatically digitizing the printed and well as handwritten educational content inside an input image with a plurality of input characters. The method comprises the steps of receiving and processing an input image by a pre-processing module to identify and obtain a plurality of input characters from the input image, digitizing the plurality of input characters into the output string representations by a digitizing module, combining the plurality of output string representations using a post-processing module which is outputted in a computer-readable format, delineative text, and a markup language. Further, the system is based on the Convolution Neural Network (CNN) that may provide an embedded editing module allowing one or more users to make corrections and create new content. The CNN system may be retrained iteratively based on the editing performed by the users on the output strings representations to ensure high accuracy, better performance, and to avoid / reduce the error rate.

This invention discloses a three-dimensional pipeline path design method and system based on discretized string representation and optimization. It establishes a globally fixed world coordinate system and a locally updated coordinate system that dynamically updates with construction operations. The continuous initial three-dimensional pipeline path space in the world coordinate system is discretized and converted into a world string. A multi-objective optimization algorithm is used for iterative optimization to generate a set of optimized schemes containing multiple path heterogeneities. Structural mutations are performed on the world strings to form path heterogeneities. The optimal solution in the optimization scheme set is then converted into a locally generated string in the local coordinate system according to the construction operation sequence, using predefined mapping rules, to generate output results for construction operations. This method has very high computational efficiency, can generate multiple technical solutions and obtain a globally optimal topology, and can directly generate construction instructions from the optimal solution.

The present disclosure provides a system and a method for authoring and automatically digitizing the printed and well as handwritten educational content inside an input image with a plurality of input characters. The method comprises the steps of receiving and processing an input image by a pre-processing module to identify and obtain a plurality of input characters from the input image, digitizing the plurality of input characters into the output string representations by a digitizing module, combining the plurality of output string representations using a post-processing module which is outputted in a computer-readable format, delineative text, and a markup language. Further, the system is based on the Convolution Neural Network (CNN) that may provide an embedded editing module allowing one or more users to make corrections and create new content. The CNN system may be retrained iteratively based on the editing performed by the users on the output strings representations to ensure high accuracy, better performance, and to avoid / reduce the error rate.

The invention discloses an intelligent molecular design method based on a mathematical programming method and skeleton-group coupling. According to the algorithm, a Beis-Murcko skeleton and a group fragment are taken as structural members, a potential skeleton subset is obtained through skeleton similarity retrieval, and a candidate structure space is constructed; on the basis, fragment selection and counting of candidate molecules are modeled as a mixed integer nonlinear programming (MINLP) problem, and structural feasibility constraints, property threshold constraints and scores / probabilities output by a deep learning model are uniformly introduced as target functions or constraint conditions. Meanwhile, an improved SMILES-based structure generation mechanism is adopted in the algorithm to establish consistency bridging between a fragment set and structure character string representation in the solving process, a fragment feasible solution is generated in combination with a decomposition solving strategy, and then structure representation generation and nonlinear property / model evaluation screening are carried out. And finally, outputting a molecular structure which meets the constraint and is optimal or approximately optimal in target.

Techniques are described herein for using machine learning to learn vector representations of DNS requests such that the resulting embeddings represent the semantics of the DNS requests as a whole. Techniques described herein perform pre-processing of tokenized DNS request strings in which hashes, which are long and relatively random strings of characters, are detected in DNS request strings and each detected hash token is replaced with a placeholder token. A vectorizing ML model is trained using the pre-processed training dataset in which hash tokens have been replaced. Embeddings for the DNS tokens are derived from an intermediate layer of the vectorizing ML model. The encoding application creates final vector representations for each DNS request string by generating a weighted summation of the embeddings of all of the tokens in the DNS request string. Because of hash replacement, the resulting DNS request embeddings reflect semantics of the hashes as a group.