International Business Machines Corporation

University of Science and Technology Beijing

Li Juan Gao

Zhong Fang Yuan

Tong Liu

Ming Jin Chen

Yi Chen Zhong

Xiang Yu Yang

Yu Pan

Na Liu

Si Heng Sun

Xiao Ming Ma

Zheng Zhang

Yue Hua Zhang

Shan Gao

Dan Wu

Zhuo Hua Li

Hai B Zou

Chen Gao

Bin Chen

Qiong Wu

Peng HuangFu

Ming Xia Shi

Xiankun Zhang

Qiang He

Mengting Huang

Wenhui Tang

Xiaofu Wei

Huihui Yu

Ting Ting Tang

Qian Ren

Ting Yao Liu

An example operation may include one or more of executing a machine learning model on training data, where the training data comprises a plurality of word strings, identifying words within the training data that are extracted by the machine learning model during the executing, determining a color for the machine learning model based on the identified words and a predefined mapping of words to colors, and rendering, via a user interface, a label associated with the machine learning model in the determined color for the machine learning model.

Visualization of the impact of training data

A computer-implemented method, according to one embodiment, includes: receiving an image having characters that correspond to a language, and using a text recognition algorithm to determine a first language believed to correspond to the characters. A first confidence level associated with the first language is also computed, and a determination is made as to whether the first confidence level associated with the first language is outside a predetermined range. In response to determining that the first confidence level associated with the first language is not outside the predetermined range, the first language is output as the given language. The text recognition algorithm is trained using a simple shallow neural network and a generated mixed language corpus. The generated mixed language corpus is formed by: randomly sampling libraries having vocabulary and/or characters therein, and combining the randomly sampled vocabulary and/or characters to form the generated mixed language corpus.

Rapid language detection for characters in images of documents

A system and method for table conversion including converting a table containing text in tabular form to an image, labeling each text area of the image with a bounding box, determining for each bounding box, a position information, a semantic information, and an image information, reconstructing the image into a graph form having a plurality of nodes, wherein each node represents the bounding box of the text areas of the image, inputting at least two nodes into a trained neural network to determine a relative relationship between the at least two nodes, building a knowledge graph using the relative relationship of the at least two nodes, and translating the knowledge graph into machine readable natural language.

Conversion of tabular format data to machine readable text for QA operations

A method, computer program product, and computer system for generating and using a basic state layer. N task models are provided (N≥2). Each task model was trained on a same pre-trained backbone model. Each task model includes M feature layers and a task layer (M≥1). Each feature layer of each task model includes a parameter matrix that is different for the different models. An encoder-decoder model is trained. The encoder-decoder model includes sequentially: an input layer, an encoder, M hidden layers, a decoder, and an output layer. The encoder is a neural network that maps and compresses the parameter matrices in the input layer into the M hidden layers, which generates a basic state model. The decoder is a neural network that receives the basic state model as input and generates the output layer to be identical to the input layer.

Parameter redundancy reduction method

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.

Semantic template matching

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.

Cognitive method to split monolithic architecture into microservice architecture

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.

Visual mode image comparison

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.

Label-based document classification using artificial intelligence

Disclosed is a metal-semiconductor contact structure based on two-dimensional (2D) semimetal electrodes, including a semiconductor module and a metal electrode module, where the semiconductor module is a 2D semiconductor material, and the metal electrode module is a 2D semimetal material with no dangling bonds on its surface; the 2D semiconductor material and the 2D semimetal material are interfaced with a Van der Waals interface with a surface roughness of 0.01-1 nanometer (nm) and no dangling bonds on the surface, the 2D semiconductor material and the 2D semimetal material are spaced less than 1 nm apart.

Metal-semiconductor contact structure based on two-dimensional semimetal electrodes

A method, system, and computer program product for segmenting and processing documents for optical character recognition is provided. The method includes receiving a document and detecting different types of text data. The document is divided into a plurality of text regions associated with the different types of said text data. Optical noise is removed from each text region and differing optical character recognition software code is selected for application to each text region. The differing optical character recognition software code is executed with respect to each text region resulting in extractable computer readable text located within each said text region.

Optical character recognition segmentation

The present disclosure provides a method, computer system and computer program product for branch optimization. According to the method, execution possibilities of instruction blocks corresponding to at least one branch of in a program can be determined. Then, the instruction blocks can be loaded according to the execution possibilities.

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Xi'an, China

Li Juan Gao