Abstract: A system for verifying ambiguous entities in documents. The system comprises a processor configured to tag at least one entity in at least one page of a document, create an entity relationship among the tagged entities of each of the at least one page, receive a question from a user for the document, generate at least one corresponding answer for said question, generate an embedded question and an embedded corresponding answer for said question and each of the at least one corresponding answer, respectively, retrieve k-similar answers with associated labels for each of the at least one corresponding embedded answer for said question, and validate each of the corresponding at least one embedded answer if the labels associated with the embedded question, the corresponding at least one embedded answer, and each of the k-similar answers match.

Abstract: Disclosed are a positive electrode plate and a battery. The positive electrode plate includes a positive active material and a conductive agent; the positive active material includes lithium iron phosphate; in the positive electrode plate, within a unit area of 1 ?m×1 ?m, a ratio of a number of lithium iron phosphate to a number of the conductive agent is 1:(0.5-50). When adjusting the ratio of the number of lithium iron phosphate to the number of conductive agent in the positive electrode plate within the specific range of 1:(0.5-50), it helps form a good conductive network, reduces an internal resistance of the lithium-ion battery, improves a cycle performance and capacity retention rate of the battery, and further enhances a low-temperature cold start performance of the battery.

Abstract: System for optimizing training dataset comprising sample documents. The system comprises one or more processors configured to create graph embedding vector for each of the sample documents of the training dataset and cluster the graph embedding vectors of the sample documents of the training dataset into clusters based on the similarity between the graph embedding vectors. Further, the processor is configured to select a first set of training data, using an optimization model, wherein the first set of training data comprises a finite number of graph embedding vectors of the sample documents from the clustered training dataset. Finally, the first set of training data is fed as for a machine learning model.

Abstract: A double-track welding system is provided. A first track is detachably mounted on a first side of a weld seam of a workpiece, and a second track is detachably mounted on a second side of the weld seam of the workpiece. The first track and the second track are both mounted on a fixed support, and the fixed support can drive the first track and second track to move. At least one welding trolley is arranged on each of the first track and the second track. An image acquisition component is used for acquiring an image of a part to be welded; a weld seam scanning device is used for acquiring the weld seam morphology; and a control system is used for controlling, according to the image of the part to be welded, the fixed support to move.

Abstract: A system for verifying ambiguous entities in documents. The system comprises a processor configured to tag at least one entity in at least one page of a document, create an entity relationship among the tagged entities of each of the at least one page, receive a question from a user for the document, generate at least one corresponding answer for said question, generate an embedded question and an embedded corresponding answer for said question and each of the at least one corresponding answer, respectively, retrieve k-similar answers with associated labels for each of the at least one corresponding embedded answer for said question, and validate each of the corresponding at least one embedded answer if the labels associated with the embedded question, the corresponding at least one embedded answer, and each of the k-similar answers match.

Abstract: A welding workstation includes an integrated welding device. The integrated welding device includes a welding rail frame for enclosing the circumferential direction of a pipeline to be welded, a welding apparatus provided on the welding rail frame, a locomotive apparatus connected to the welding rail frame, and a girth weld driving apparatus connected to the welding rail frame or the welding apparatus; the locomotive apparatus is provided with driving wheels for roll-fitting pipeline to be welded or the ground, and the driving wheels moves along the axial direction of the pipeline to be welded; and the girth weld driving apparatus drives the welding apparatus to rotate around a seam to be welded of the pipeline to be welded. The welding workstation can realize automatic locomotion and automatic welding.

Abstract: A system for template invariant information extraction. The system comprises of processor, a first neural network model and a second neural network model. The processor is configured to recognize and extract entities and location of the entities in the input document using the first neural network model. The processor is further configured to classify whether the input document belongs to at least a template of the documents of the first training dataset using the second neural network model. The second neural network model comprises a linear classifier configured to generate a plurality of confidence scores for the input document corresponding to a unique template of the documents of the first training dataset. A threshold value to classify the input document belonging to template of the documents of the first training dataset is determined, Classification is done by comparing the confidence score with the threshold value.

Abstract: A system to calculate a reconfigured confidence score is configured to receive a text, a plurality of labels, and a plurality of confidence scores from a plurality of models and assign a weightage to the inputs received from the plurality of models. The system is configured to select a first text with a first label and retrieve a second text, a third text, and a second label. The system is further configured to generate a first, second and third output confidence score for the first text, second text and third text, and corresponding labels. The system compares the plurality of output confidence scores and generates an output which comprises of the first text, the first label, and a final confidence score, wherein the final confidence score is one among the first, second and third output confidence scores.

Abstract: A system to calculate a reconfigured confidence score is configured to receive a text, a plurality of labels, and a plurality of confidence scores from a plurality of models and assign a weightage to the inputs received from the plurality of models. The system is configured to select a first text with a first label and retrieve a second text, a third text, and a second label. The system is further configured to generate a first, second and third output confidence score for the first text, second text and third text, and corresponding labels. The system compares the plurality of output confidence scores and generates an output which comprises of the first text, the first label, and a final confidence score, wherein the final confidence score is one among the first, second and third output confidence scores.

Abstract: System for optimizing training dataset comprising sample documents. The system comprises one or more processors configured to create graph embedding vector for each of the sample documents of the training dataset and cluster the graph embedding vectors of the sample documents of the training dataset into clusters based on the similarity between the graph embedding vectors. Further, the processor is configured to select a first set of training data, using an optimization model, wherein the first set of training data comprises a finite number of graph embedding vectors of the sample documents from the clustered training dataset. Finally, the first set of training data is fed as for a machine learning model.

Abstract: System for graph-based clustering of documents. The system comprises one or more processors configured to receive a digital copy of a document to convert the document into a graph object. Further, the processor is configured to identify and label entities in the document, wherein each of the entities is represented as a node of the graph object. Further, the processor is configured to create the graph object for the received digital copy of the document and generate a graph embedding vector using a graph embedding neural network trained to receive the graph object as input and generate the graph embedding vector for the graph object as output. Finally, the processor is configured to cluster the graph embedding vector to a cluster comprising similar looking templates of the document.

Abstract: A system for template invariant information extraction. The system comprises of processor, a first neural network model and a second neural network model. The processor is configured to recognize and extract entities and location of the entities in the input document using the first neural network model. The processor is further configured to classify whether the input document belongs to at least a template of the documents of the first training dataset using the second neural network model. The second neural network model comprises a linear classifier configured to generate a plurality of confidence scores for the input document corresponding to a unique template of the documents of the first training dataset. A threshold value to classify the input document belonging to template of the documents of the first training dataset is determined, Classification is done by comparing the confidence score with the threshold value.