Abstract: A system and method for field extraction including determining a key position of a key in an electronic file, isolating candidate key values based on a distance from the key position, selecting a key value from the candidate key values based on an output of a trained neural network, and extracting the key and the key value from the electronic file, regardless of a key-value structure.

Abstract: A system, method, and computer program product for implementing cognitive natural language processing software framework optimization is provided. The method includes receiving instructions associated with an audible user input of a user. An AI input intention of the user is determined and key information is extracted from the audible user input. The key information is inputted into a generated database table and additional key information is retrieved from a dialog table. A supplementary database table comprising the additional key information is generated and the key information is spliced with the additional key information. A resulting spliced data structure is merged into a final database table and natural language is converted into a request code structure within an SQL structure and an interactive AI interface presenting results of the converting is generated. Operational functionality of an AI device is enabled for audibly presenting results of the conversion.

Abstract: In an approach for using an open source of existing text labeling models to label sentences that need to be clustered with multiple external tags and then to use the tags as auxiliary information to perform the clustering at a dual level, a processor receives a set of text, wherein the set of text contains one or more sentences. A processor tags each sentence of the set of text with one or more tags using a plurality of open-source text classification models. A processor performs a preliminary clustering of one or more nodes under strict conditions using a canopy clustering algorithm.

Abstract: The present invention provides a method for separation and recovery of boron trifluoride and complexes thereof in an olefin polymerization reaction.

Abstract: A method and related system detail a split of an architecture of a monolithic application into an architecture of a micro service application. The method receives source code for the monolithic application, and maps the source code into a directed graph. The graph is split into subgraphs and optimized. The method further provides the detailing of the micro service application split, based on the subgraphs.

Abstract: A computer-implemented process for modifying a training dataset includes the following operations. The training dataset is benchmarked using a State Of The Art (SOTA) neural network to determine a benchmark for the training dataset. The training set is divided into a plurality of slices. A sequence of a plurality of atomic operations are selected using a selection strategy generator operating on one of the plurality of slices. The sequence of the plurality of atomic operations is applied to modify the one of the plurality of slices to generate a revised one of the plurality of slices. Reverse reinforcement learning is performed on the revised one of the plurality of slices using the benchmark and the SOTA neural network. The training dataset is modified by replacing the one of the plurality of slices with the revised one of the plurality of slices to generate a modified training dataset.

Abstract: Training data models using machine learning can include training a computer data model of data distribution using a training data set. The training data set includes training data and additional training data, and the training data and the additional training data being represented by layers of data representing the data distribution of the training data set. The computer data model using the additional training data is iteratively trained for each of the layers of the training data set. Statistical noise is added randomly to each of the layers of the training data set. Data variations are detected in each of the layers of the additional training data. The data variations are diluted in each of the additional layers of the training data, and the computer data model is retrained for the training data set using the diluted data variations in each of the layers of the additional training data.

Abstract: A method, a computer program product, and a computer system compare images for content consistency. The method includes receiving a first image including a first document and a second image including a second document. The method includes performing a visual classification analysis on the first image and the second image. The visual classification analysis generates an overlap of the first image with the second image. The method includes determining whether a region of the overlap is indicative of a content inconsistency. As a result of the region of the overlap being indicative of a content inconsistency, the method includes performing a character recognition analysis on a first area of the first image and a second area of the second image corresponding to the region of the overlap to verify the content inconsistency.

Abstract: Systems, methods and/or computer program products for automating evaluations of code deliveries for software projects. Automated evaluations are generated by selecting software projects and dividing a project into a plurality of code blocks by analyzing the code, build file and build log. Attributes of code blocks are extracted and correlations between code blocks are calculated. Dynamic distance between code blocks is calculated by the correlation and code delivery history, to create a Dynamic General Distance Map and Dynamic User Distance Map for the code blocks. Code delivery distance indicating the code delivery behavior is generated by the Dynamic User Distance Map while the delivery assessment criteria is generated to evaluate code delivery and assess levels of risk associated with delivery of the code. High-level risk indicates that a code delivery may not follow best practices and users are alerted to pay more attention to the code delivery during review.

Abstract: Aspects include determining a coding intention and a dimension of interest to a user. A plurality of relevant projects that each include a logical code block that meets the coding intention are located. The locating includes searching a plurality of code repositories based at least in part on the coding intention. A score is assigned to each of the plurality of logical code blocks based at least in part on properties associated with the logical code blocks and on the dimension of interest to the user. A logical code block with the highest score is promoted to the user.

Abstract: Systems and methods are related to a memory device including a plate line. The memory device also includes a pair of ferroelectric layers implementing a pair of memory cells and coupled to opposite sides of the plate line. The memory device further includes a pair of digit lines each coupled to a respective ferroelectric layer of the pair of ferroelectric layers. The memory device also includes a sense amplifier coupled to the pair of digit lines and configured to sense and amplify voltages received at the digit lines from the respective memory cells. The sense amplifier includes a threshold voltage compensated latch that includes multiple p-channel transistors and is configured to compensate for process, voltage, or temperature variation mismatches between the threshold voltages of the multiple p-channel transistors.

Abstract: The present disclosure discloses a catalyst composition for polymerization of an ?-olefin and preparation and use thereof. The catalyst composition comprises boron trifluoride and at least one protic cocatalyst; the protic cocatalyst has a structural formula of X—(CH2)n—OH, where n is an integer selected from 1 to 10; X is selected from nitro, halogen, cyano, sulfonic acid group, aldehyde group, acyl, carboxyl and amino. The catalyst can be used in production of a poly(?-olefin) synthetic base oil, and is particularly suitable for a low viscosity poly(?-olefin) synthetic base oil with high selectivity of the target product.

Abstract: An FPGA for implementing data transmission by using a built-in edge module is provided. The FPGA is provided with a built-in edge module. A read port of each resource module connected to the edge module in the FPGA is separately connected to a winding architecture and the edge module, and/or a write port of each resource module connected to the edge module in the FPGA is separately connected to the winding architecture and the edge module. The edge module includes a read/write controller and a cache unit. The read/write controller simultaneously reads data from read ports of a plurality of resource modules and temporarily stores the read data in the cache unit. Alternatively, the read/write controller simultaneously writes temporarily stored data in the cache unit into write ports of the plurality of resource modules.

Abstract: Label-based document classification using artificial intelligence includes collecting, by one or more processors, a plurality of pre-trained classification models into a model pool and a plurality of documents into a document pool. The collected plurality of pre-trained classification models are applied in parallel to the plurality of documents in the document pool to generate a list of labels. Based on the list of labels, a final label result is generated according to which a baseline algorithm for document classification is generated by the one or more processors.

Abstract: Provided is a method, computer program product, and system for fusing knowledge graphs to generate a larger knowledgebase for responding to cross document questions. A processor may extract contextual information from a plurality of documents. The processor may generate, based on the extracted contextual information, a knowledge graph for each document of the plurality of documents. The processor may analyze each knowledge graph to determine if one or more entities of each knowledge graph are linked. The processor may fuse, in response to an entity in a first knowledge graph being linked to an entity in a second knowledge graph, the first knowledge graph with the second knowledge graph to create a fused knowledge graph.

Abstract: Based on a graph storing a current state of an engineering project consisting of modules, a graph neural network computes an embedding for each node. For each node embedding, a classifier determines a preliminary confidence score for each class, which represents a type of module that could be added to the engineering project. A topology-based measure is calculated at least for a current center node. A blank node is assigned to a bin depending on the topology-based measure that has been computed for the current center node. A post-processor calibrates all preliminary confidence scores for the blank node by applying a scaling factor depending on the assigned bin. Finally, a user interface outputs at least the class with the highest calibrated confidence score for the blank node as well as the respective calibrated confidence score. The binning scheme takes the graph structure into account and allows for adaptive calibration.

Abstract: Disclosed are techniques for modifying deep learning models (such as neural networks) to run more efficiently in computing environments with limited floating point computation resources. A deep learning model is trained using a set of training data. Input and output values are then recorded from the layers of the trained model when supplied with the training data, which are then used to generate deep forest decision tree models corresponding to individual layers of the trained model. Experimental versions of the trained model are then generated with different layers of the trained model replaced with their corresponding deep forest decision tree models. These experimental versions are then ranked according to the accuracy of their results compared to the results of the trained model. An updated trained model is then generated with one or more layers replaced with their corresponding deep forest decision tree models.

Abstract: What is disclosed are systems and methods for compensating for display OLED degradation. Correction factors k for OLED degradation of each sub-pixel is modelled and tracked based on grey level, temperature, and time, and used to correct image data provided to an OLED display.

Abstract: The present disclosure provides a method of determining a status of a position on a shelf, a shelf and a non-transitory computer-readable storage medium. The method includes: acquiring current detection data of the shelf in a current detection period; determining first status information of respective positions from the current detection data, in response to determining that the current detection data satisfies a preset condition; in response to the current detection data indicating that a target position whose first status information indicates the second status exists, acquiring first status information of the target position from previous detection data in a previous detection period; and determining second status information of the target position based on the first status information of the target position in the current detection data and the previous detection data respectively.