Abstract: Systems and methods are disclosed for parsing resume documents using computer vision and optical character recognition technology in combination with a user feedback interface system to facilitate user feedback to improve the overall processing quality of the resumes that are imported into computer resume processing systems. In at least one embodiment, the system and method prompt a user to upload an input resume document, which is processed with a first parsing pass to generate initial resume data by extracting a plurality of resume text blocks. Further processing identifies an initial set of bounding blocks and to visually displays the initial resume data for user review and feedback to regroup one or more of the initial set of bounding blocks into a regrouped bounding block. Additional processing consolidates into a group text block each of the resume text blocks corresponding to the regrouped one or more of the initial set of bounding blocks.

Abstract: Systems and methods are provided for identifying a product in an image and outputting stock keeping units of the product. The system comprises three main components: a database server, a data analytics system and a standard dashboard. The database server contains real-time inventory images as well as historical images of each product type. The data analytics system is executed by a computer processor configured to apply a multi-head self-supervised learning-based classifier to detect product information captured by the image. The data analytics system is also configured to determine hierarchical classification categories for the product. The standard dashboard is configured to output a report regarding the product information.

Abstract: A technique that automatically adjusts a motion given to a projection target using a perceptual model is provided. A motion vector generation apparatus includes a first parameter generation unit that generates a first parameter that is a parameter for scaling a motion vector based on a perceptual difference between a projection result reproduction image which is an image that is obtained when a projection target onto which a projection image obtained based on the motion vector has been projected is photographed and a warped image which is an image generated by distorting an image obtained when the projection target is photographed by a perceptual amount of motion perceived when the projection result reproduction image is viewed, and a motion vector reduction unit that scales the motion vector using the first parameter.

Abstract: An artificial intelligence (AI) system utilizing a machine learning algorithm such as deep learning for controlling an electronic device when a video is reproduced and a user's voice instruction is received, to acquire a frame corresponding to the time point when the input of the user's voice instruction is received, and obtain a search result for information on objects in the frame using an AI model trained according to at least one of machine learning, a neural network or a deep learning algorithm.

Abstract: An example system for performing segmentation of data based on tensor inputs includes memory storing computer-executable instructions defining a learning network, where the learning network includes a plurality of sequential encoder down-sampling blocks. A processor is configured to execute the computer-executable instructions to receive a multi-dimensional input tensor including at least a first dimension, a second dimension and a plurality of channels. The processor is also configured to process the received multi-dimensional input tensor by passing the received multi-dimensional input tensor through the plurality of sequential encoder down-sampling blocks of the learning network, and to generate an output tensor in response to processing the received multi-dimensional input tensor. The output tensor includes at least one segmentation classification.

Abstract: Described herein is a mechanism for visual recognition of items or visual search using Optical Character Recognition (OCR) of text in images. Recognized OCR blocks in an image comprise position information and recognized text. The embodiments utilize a location-aware feature vector created using the position and recognized information in each recognized block. The location-aware features of the feature vector utilize position information associated with the block to calculate a weight for the block. The recognized text is used to construct a tri-character gram frequency, inverse document frequency (TGF-IDP) metric using tri-character grams extracted from the recognized text. Features in location-aware feature vector for the block are computed by multiplying the weight and the corresponding TGF-IDF metric. The location-aware feature vector for the image is the sum of the location-aware feature vectors for the individual blocks.

Abstract: A system for enhanced sports analytics and/or content creation includes: an object tracking system that generates coordinate data corresponding to object motion in a sports event; a data processing module that receives the coordinate data from the object tracking system, analyzes the coordinate data with an event recognition algorithm that identifies and characterizes events and outcomes of interest, and catalogs the data in accordance with the identified events and outcomes into event profile data; a database that receives and stores the event profile data generated by the data processing module; a user application that accesses the event profile data from the database; and at least one processing unit that executes instructions stored in at least one non-transitory medium to implement at least one of the object tracking system, the data processing module, or the user application.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for automatically determining accuracy of entity recognition of text. An example method includes segmenting a service entity recognition analysis of the text and a gold entity recognition analysis of the text into common superstrings that define entity spans. The example method further includes classifying each of the entity spans based on an accuracy of entity recognition in the service analysis of the text corresponding to the entity spans using a classification system that differentiates accurately identified but improperly bounded entities into at least three subcategories to obtain an entity accuracy classification. The example method also includes obtaining a score report based on the entity accuracy classification. The example method additionally includes performing an action set based on the entity accuracy classification.

Abstract: A detection system includes a filter and a detector. The filter receives input packets from one or more dynamic vision sensors (DVSs) deployed in an area. The filter reduces the bandwidth of the input packets to produce compressed data packets while maintaining good end-to-end detection capability. The compressed data packets are transmitted to a detector in a low bandwidth transmission. The detector may collect compressed data packets from many filters. The detector determines if an object of interest is represented in the information of the input packets. If an object of interest is present, the detector activates an alarm, sends a notification to a person associated with the area and/or stores an indication in a database.

Abstract: Methods for prediction and inhibition of calcium carbonate scale in hydrocarbon wells using machine learning include extracting training data including parameters from aqueous samples. Each aqueous sample is collected from a respective hydrocarbon well. The training data is classified in accordance with hydrocarbon production conditions of each hydrocarbon well. The classified training data is labeled in accordance with whether calcium carbonate scale has formed in each aqueous sample within a particular time period. A feature vector is determined from the labeled training data based on the parameters extracted from each aqueous sample. The feature vector is indicative of whether the respective hydrocarbon well contains calcium carbonate scale. A trained machine learning model is generated, wherein the machine learning model is trained based on the feature vector, to predict a number of the hydrocarbon wells containing calcium carbonate scale within the particular time period.

Abstract: Provided herein is technology relating to analysis of images and particularly, but not exclusively, to methods and systems for determining the area and/or volume of a region of interest using optical coherence tomography data. Some embodiments provide for determining the area and/or volume of a lesion in retinal tissue using three-dimensional optical coherence tomography data and a two-dimensional optical coherence tomography fundus image.

Abstract: Provided is a method for detecting a biomarker indicating states of a target biological system based on data acquired by measuring the target biological system. The method includes the steps of: preparing a reference dataset based on data acquired from one or more reference biological systems; generating a target dataset by adding, to the reference dataset, target biological data acquired from the target biological system; acquiring first correlation coefficients between a plurality of factor items in the reference dataset; acquiring second correlation coefficients between the plurality of factor items in the target dataset; acquiring difference correlation coefficients that are differences between the first correlation coefficients and the second correlation coefficients; acquiring indexes respectively for the plurality of factor items based on the difference correlation coefficients; and selecting the biomarker based on the indexes.

Abstract: One embodiment provides a method, including: receiving a sample set for training a machine-learning model, wherein the sample set includes a plurality of classes, wherein classes within the plurality of classes have an imbalance in a number of samples; creating an enlarged minority class by generating new samples from the samples within the minority class and adding the new samples to the minority class; selecting subset samples from both the samples within the enlarged minority class and the majority class; weighting each of the subset samples based upon user input defining goals for attributes of a training sample set to be used in training the machine-learning model; and generating, using the neural network, the training sample set by re-running the selecting in view of the weighting.

Abstract: Methods for compressing shape data for a set of electronic designs include inputting a set of shape data, where the shape data comprises mask designs. A convolutional autoencoder encodes the set of shape data, where the encoding compresses the set of shape data to produce a set of encoded shape data. The convolutional autoencoder is tuned for increased accuracy of the set of encoded shape data based on design rules for the set of shape data. The convolutional autoencoder comprises a set of parameters comprising weights, and the convolutional autoencoder has been trained to determine what information to keep based on the weights.

Abstract: A method is provided. The method may include, in response to electronically receiving a document, automatically classifying the document and different parts of the document, by electronically identifying a document type associated with the document and electronically tagging data associated with the different parts of the document based on classification rules. The method may further include automatically extracting the tagged data associated with the automatically classified document based on data extraction rules. The method may further include detecting first feedback associated with the classification rules and second feedback associated with the data extraction rules. The method may further include automatically generating and updating validation rules based on the identified document type, the detected first feedback, and the detected second feedback to validate the automatically classified document and the automatically tagged and extracted data.

Abstract: An embodiment of the present application provides an optical fingerprint identification apparatus, which includes: at least one non-visible light source, configured to emit non-visible light to a human finger to provide excitation light for fingerprint identification; an optical component, disposed under a fingerprint detecting area of the display screen, and configured to receive signal light scattered and reflected by the human finger; and a fingerprint sensor configured to perform imaging based on the signal light passing through the optical component, where a working wave band of the fingerprint sensor is the same as a light emitting wave band of the non-visible light source, a number of the working wave bands is the same as the number of the light emitting wave bands, and the fingerprint sensor is connected to the non-visible light source, and is configured to control the non-visible light source to actively emit light.

Abstract: The invention relates to a method of determining the sagittal rotation of a patient's pelvis based on a standard anterior posterior X-ray-image with known image parameters and a calibration of the image, for example by using at least one King-Mark calibration object. The angle of the pelvic rotation is determined between a pelvic plane which is orthogonal to the midsagittal plane of the pelvis, and the image plane of the X-ray-image. Assuming the patient's position shown on the X-ray-image represents a standard neutral position, the X-ray-image plane can be used as a functional reference plane for further calculations, for example during hip-replacement surgery. The present invention further relates to a corresponding computer program and system.

Abstract: Disclosed are an apparatus and method for configuring spatial information required to visualize a space represented based on a hierarchical structure having a plurality of levels each of which includes one or more tiles, configuring spatial information. The method includes checking tile visibility of each level according to whether a tile exists in the corresponding level; grouping one or more levels in which tile visibility is consistently maintained into one or more groups; and assigning a tile visibility index to each group.

Abstract: Disclosed herein are system, method, and computer program product embodiments for locating, identifying, and tracking a known criminal, fugitive, missing person, and/or any other person of interest. An embodiment operates by deploying an unmanned aerial vehicle, determining the mode of operation of the UAV, operating the UAV in accordance with the mode of operation of the UAV, determining whether a subject has been detected, capturing a first voice sample associated with the subject, authenticating the identity of the subject, and transmitting the GPS location of the unmanned aerial vehicle to a computing device.

Abstract: A highly versatile data processing is implemented on data collected in a manufacturing process. A data processing device includes: a calculation part configured to collect a plurality of data groups associated with a predetermined step of a process, and calculate effects in the predetermined step for each of the plurality of data groups; a dividing part configured to divide a feature space such that a distribution of each of the plurality of data groups associated with the predetermined step in the feature space is classified for each of the calculated effects; and an output part configured to output specific data that specifies respective regions of the divided feature space.