Abstract: A method and apparatus for generating a temporal knowledge graph, a device and a medium. An embodiment comprises: acquiring corpus including time information; performing multivariate data extraction on the corpus, multivariate data including an entity pair, an entity relationship and a target time interval of the entity relationship, the target time interval being used to indicate a valid period of the entity relationship; and generating a temporal knowledge graph based on the entity pair, the entity relationship and the target time interval of the entity relationship.

Abstract: Provided are a method for preparing a microfluidic device, a microfluidic device and a microfluidic system. The method includes: providing a mold having a groove; injecting a liquid metal into the groove of the mold, and solidifying the liquid metal to obtain a solid metal; separating the solid metal from the mold; providing the solid metal with an electrode; providing a cladding layer on a surface of the solid metal provided with the electrode, such that the solid metal is wrapped by the cladding layer, and at least a part of the electrode extends outside the cladding layer, so as to obtain a preform; and fixing the preform in a substrate, melting the solid metal and extending at least a part of the electrode outside the substrate, to obtain the microfluidic device.

Abstract: An intelligent production line for motor pulley, include a processing mechanism; a feeding mechanism; a grasping mechanism; a detection mechanism, including a photographing device, a light source, a positioning block, and an image processing unit, the positioning block is used to place the pulley to be detected, the light source is used to illuminate the pulley to be detected, and the photographing device is arranged in the backlight direction of the light source, the image processing unit is used for acquiring the workpiece image of the pulley to be detected through the photographing device and detecting the workpiece parameters of the pulley to be detected through the workpiece image; a feedback mechanism, which communicates and connects the detection mechanism and the processing mechanism, and is used for acquiring the workpiece parameters and adjusting the processing parameters of the processing mechanism according to the parameters.

Abstract: A laser transfer printing device and a laser transfer printing method are provided. The laser transfer printing device includes a laser, a beam expander, a beam splitter and a focusing module. The laser is configured to generate a laser beam. The beam expander is disposed on an optical path of the laser beam generated by the laser to expand the laser beam generated by the laser. The beam splitter is disposed on an optical path of the laser beam expanded by the beam expander to reflect the expanded laser beam to the focusing module. The focusing module is disposed on an optical path of the laser beam reflected by the beam splitter to focus the laser beam and project the focused laser beam to a predetermined transfer processing position.

Abstract: A method of extracting a table information, an electronic device, and a storage medium are provided, which relate to fields of artificial intelligence and big data, in particular to fields of machine learning, knowledge graph, intelligent search and intelligent recommendation, and may be used for an intelligent extraction of an information in a table and other scenarios. The method includes: performing a clustering based on features of a plurality of rows of cells and/or features of a plurality of columns of cells in a table, so as to determine candidate header cells in the table; and performing an information extraction on the table based on the candidate header cells, so as to extract attribute-attribute value pairs in the table.

Abstract: Input data is received from an analysis of an application running in a real-world environment. The input data identifies one or more parameters that are associated with one or more test scripts that are used to test a new version of the application running in the real-world environment. One or more inputs are received that map the received input data to the one or more test scripts. The one or more test scripts are used to test the new version of the application. The one or more tests scripts are executed against the new version of the application based on the one or more parameters.

Abstract: The present disclosure provides a medical data verification method, apparatus and an electronic device, related to a field of artificial intelligence technologies, such as AI (artificial intelligence) medical treatment, deep learning, knowledge graphs, natural language processing. A specific implementation is: obtaining medical data to be verified and a candidate document; obtaining feature vectors respectively corresponding to the medical data to be verified and the candidate document by processing the medical data to be verified and the candidate document by using a nature language processing model; obtaining N correlation vectors by calculating correlation between the medical data to be verified and the candidate document based on the feature vectors by using N methods, N being a positive integer greater than 1; and determining a confidence degree of the medical data to be verified to the candidate document by performing fusion calculation on the N correlation vectors.

Abstract: A method and apparatus for processing a table, a device, a storage medium and a product. An implementation of the method comprise: receiving a content query request for a target table; acquiring a target tree structure of the target table according to the content query request; where, the target tree structure is obtained by performing absorbing processing and merging processing on at least one target cell in the target table; acquiring to-be-queried content in the content query request; and querying target content matching the to-be-queried content from the target tree structure.

Abstract: A method and an apparatus for processing information are provided. The method can include: acquiring a word sequence obtained by performing word segmentation on two paragraphs in a text; inputting the word sequence into a to-be-trained natural language processing model to generate a word vector corresponding to a word in the word sequence; inputting the word vector into a preset processing layer of the to-be-trained natural language processing model; predicting whether the two paragraphs are adjacent, and a replaced word in the two paragraphs; and acquiring reference information of the two paragraphs, and training the to-be-trained natural language processing model to obtain a trained natural language processing model, based on the prediction result and the reference information.

Abstract: Provided by the present disclosure is a new category tag mining method, involving the field of knowledge graph technology, and including: obtaining a plurality of queries during a current preset time period; labeling a category tag on each query of the plurality of queries, by using a pre-trained sequence labeling model, to extract the category tag currently corresponding to the query from the query; and removing a category tag already existing in a preset current category tag library from category tags currently corresponding to all the queries, and determining a remaining category tag as a new category tag. The present disclosure also provides an electronic device and a non-transitory computer-readable storage medium.

Abstract: Disclosed are an information extraction method, an electronic device and a readable storage medium, which relate to the field of artificial intelligence technologies, and particularly to the field of knowledge graph technologies. The information extraction method includes: acquiring to-be-processed text to obtain a semantic vector of each token in the to-be-processed text; generating a relationship prediction matrix, an entity prediction matrix and an alignment matrix according to each token in the to-be-processed text and the semantic vector of each token; and extracting a target triplet in the to-be-processed text using the relationship prediction matrix, the entity prediction matrix and the alignment matrix, and taking the target triplet as an information extraction result of the to-be-processed text.

Abstract: The present disclosure relates to the field of medical data processing based on natural language processing. Embodiments of the present disclosure disclose a method and apparatus for verifying a medical fact. The method may include: acquiring a description text of the medical fact; selecting a relevant paragraph related to the description text of the medical fact from a medical document; and inputting the description text of the medical fact and the corresponding relevant paragraph into a trained discrimination model for authenticity judgment, to obtain a verification result of the medical fact, the discrimination model being pre-trained based on a medical text paragraph pair extracted from the medical document, and being iteratively adjusted using a medical fact sample set including authenticity labeling information after the pre-training.

Abstract: A computer-implemented method for sample augmentation includes: acquiring a second sample corpus and second triplet information of the second sample corpus by performing data augmentation on a first sample corpus labeled with first triplet information; acquiring third triplet information of a third sample corpus by performing semi-supervised learning on the third sample corpus that is not labeled with triplet information; and generating a set of training corpora for a triplet information extraction network based on the first sample corpus and the first triplet information, the second sample corpus and the second triplet information, and the third sample corpus and the third triplet information.

Abstract: A method of extracting a table information, an electronic device, and a storage medium are provided, which relate to fields of artificial intelligence and big data, in particular to fields of machine learning, knowledge graph, intelligent search and intelligent recommendation, and may be used for an intelligent extraction of an information in a table and other scenarios. The method includes: performing a clustering based on features of a plurality of rows of cells and/or features of a plurality of columns of cells in a table, so as to determine candidate header cells in the table; and performing an information extraction on the table based on the candidate header cells, so as to extract attribute-attribute value pairs in the table.

Abstract: An intelligent production line for motor pulley, include a processing mechanism; a feeding mechanism; a grasping mechanism; a detection mechanism, including a photographing device, a light source, a positioning block, and an image processing unit, the positioning block is used to place the pulley to be detected, the light source is used to illuminate the pulley to be detected, and the photographing device is arranged in the backlight direction of the light source, the image processing unit is used for acquiring the workpiece image of the pulley to be detected through the photographing device and detecting the workpiece parameters of the pulley to be detected through the workpiece image; a feedback mechanism, which communicates and connects the detection mechanism and the processing mechanism, and is used for acquiring the workpiece parameters and adjusting the processing parameters of the processing mechanism according to the parameters.

Abstract: The present disclosure provides a method of processing triple data, a method of training a triple data processing model, an electronic device, and a storage medium. A specific implementation solution includes: performing a triple data extraction on text data to obtain a plurality of field data; normalizing the plurality of field data to determine target triple data, wherein the target triple data contains entity data, entity relationship data, and association entity data; and verifying a confidence level of the target triple data to obtain a verification result.

Abstract: A method and apparatus for processing a table, a device, a storage medium and a product. An implementation of the method comprise: receiving a content query request for a target table; acquiring a target tree structure of the target table according to the content query request; where, the target tree structure is obtained by performing absorbing processing and merging processing on at least one target cell in the target table; acquiring to-be-queried content in the content query request; and querying target content matching the to-be-queried content from the target tree structure.

Abstract: An infrared thermal imager and an OpenMV machine vision module provided in a data acquisition device are used to collect crop surface temperature data and image data, respectively, and transmit the data to a computer for processing by a Raspberry Pi module via a wireless transmission module. After reading the image data, the computer obtains color components and growth parameters of the crop, and merges the color components and temperature data to assist in building an early warning model. The system and method enable real-time, continuous and nondestructive monitoring of crop growth information to provide references for adjustment and control of greenhouse environmental parameters.

Abstract: A directed graph for an application under test (AUT) hosted at a hosting computing device is generated based on test flows. Each test flow traverses GUI objects of the AUT via GUI events. Each GUI event is associated with a transition from a first GUI object of the AUT to a second GUI object of the AUT. The GUI event represents interaction with the GUI of the AUT at a client computing device and that resulted in the transition. The directed graph includes nodes that each correspond to a GUI object of the AUT and edges that each correspond to a GUI event.

Abstract: The disclosure discloses a method and an apparatus for recognizing an entity word. The method includes: obtaining an entity word category and a document to be recognized; generating an entity word question based on the entity word category; segmenting the document to be recognized to generate a plurality of candidate sentences; inputting the entity word question and the plurality of candidate sentences into a question-answer model trained in advance to obtain an entity word recognizing result; and obtaining an entity word set corresponding to the entity word question based on the entity word recognizing result.