Abstract: System that allows devices of an autonomous store, such as sensors in product display areas, to receive power and communicate data over conductive rails of store fixtures. By using fixtures to transmit data and power, the need for cabling to these devices, or for batteries to power the devices, is eliminated or greatly reduced, thereby dramatically simplifying installation and maintenance. Illustrative fixtures over which devices can communicate include slatwalls, pegboards, and rectangular support bars. Power and data may be multiplexed onto the same pair of conductive rails. Embodiments may use device hubs to coordinate communication with devices and to act as gateways between devices and centralized store servers. Devices may communicate their identities and locations to store servers to facilitate installation; for example, they may display their identities on electronic labels that are imaged by store cameras, so that the store server can learn the location of each device automatically.

Abstract: The disclosure relates to systems and methods for real-time detection of a very large number of items in a given constrained volume. Specifically, the disclosure relates to systems and methods for retrieving an optimized set of classifiers from a self-updating classifiers' database, configured to selectively and specifically identify products inserted into a cart in real time, from a database comprising a large number of stock-keeping items, whereby the inserted items' captured images serve simultaneously as training dataset, validation dataset and test dataset for the recognition/identification/re-identification of the product.

Abstract: Systems and methods are disclosed for image-based object detection and classification. For example, methods may include accessing an image from an image sensor; applying a convolutional neural network to the image to obtain localization data to detect an object depicted in the image and to obtain classification data to classify the object, in which the convolutional neural network has been trained in part using training images with associated localization labels and classification labels and has been trained in part using training images with associated classification labels that lack localization labels; annotating the image based on the localization data and the classification data to obtain an annotated image; and storing, displaying, or transmitting the annotated image.

Abstract: Disclosed herein is a method of detecting a vehicle speed. The method includes receiving a first vehicle image photographed by a camera at a first time; identifying an area occupied by the license plate from the first vehicle image and extracting a first number; receiving a second vehicle image photographed by the camera at a second time; identifying an area occupied by the license plate from the second vehicle image and extracting a second number; comparing the first number with the second number to determine whether the first number is equal to the second number; deciding an actual size of the number of plate; calculating a distance between the vehicle and the camera at first and second times; and calculating a vehicle speed.

Abstract: Systems and methods discussed herein include, among other things, a method comprising quantifying analyte staining of a biological compartment in a region in which said staining is intermixed with analyte staining of an analytically-distinct distinct biological compartment. Disclosed systems and methods include, for example, a system and method for identifying membrane staining of an analyte of interest in regions where diffuse membrane staining is intermixed with cytoplasmic staining and/or punctate staining is disclosed. Disclosed systems and methods include, for example, a system and method for quantifying membrane staining of an analyte of interest in tissue or cytological samples having regions in which membrane staining is intermixed with cytoplasmic staining and/or punctate staining.

Abstract: Aspects of the disclosure relate to anomaly detection in cybersecurity training modules. A computing platform may receive information defining a training module. The computing platform may capture a plurality of screenshots corresponding to different permutations of the training module. The computing platform may input, into an auto-encoder, the plurality of screenshots corresponding to the different permutations of the training module, wherein inputting the plurality of screenshots corresponding to the different permutations of the training module causes the auto-encoder to output a reconstruction error value. The computing platform may execute an outlier detection algorithm on the reconstruction error value, which may cause the computing platform to identify an outlier permutation of the training module. The computing platform may generate a user interface comprising information identifying the outlier permutation of the training module.

Abstract: Systems and methods that use machine learning to optimize the execution of micro-tasks, by partially automating the generation and validation actions are disclosed. The system uses a combination of automatic intelligent model-based decision systems and human-in-the-loop for generating annotated task instances with respect to an identified task. Before a task is executed, the system can compute the crowd effort to generate data for each task instance, as well as the effort to validate and/or correct them. These computations can occur multiple times during the execution of task. The generation, validation and correction effort are measures that allow the system to design more efficient workflows that combine machine learning models and human input because the system can decide automatically what is the most efficient next step to obtain the best results.

Abstract: Methods and systems are provided for implementing source separation techniques, and more specifically performing source separation on mixed source single-channel and multi-channel audio signals enhanced by inputting lip motion information from captured image data, including selecting a target speaker facial image from a plurality of facial images captured over a period of interest; computing a motion vector based on facial features of the target speaker facial image; and separating, based on at least the motion vector, audio corresponding to a constituent source from a mixed source audio signal captured over the period of interest. The mixed source audio signal may be captured from single-channel or multi-channel audio capture devices. Separating audio from the audio signal may be performed by a fusion learning model comprising a plurality of learning sub-models. Separating the audio from the audio signal may be performed by a blind source separation (“BSS”) learning model.

Abstract: An in-vehicle device includes a camera, a passenger detector, a goods detector, an association unit, and an in-vehicle controller. The camera images the inside of a vehicle. The passenger detector detects the passenger inside the vehicle based on an image captured by the camera. The goods detector detects the goods inside the vehicle based on the image captured by the camera. The association unit generates association information. The association information is obtained by associating specific goods detected by the goods detector with a specific passenger detected by the passenger detector based on the goods detected by the goods detector and the passenger detected by the passenger detector. The in-vehicle controller performs predetermined control based on the association information generated by the association unit.

Abstract: A correlithm object processing system that includes a trainer configured to receive a real world input value and a real world output value. The trainer is further configured to send the real world input value to a sensor engine and to receive a source correlithm object in response to sending the real world value to the sensor engine. A source correlithm object is a point in an n-dimensional space represented by a binary string. The trainer is further configured to send a real world output value to an actor engine and to receive a target correlithm object in response to sending the real world output value to the actor engine. A target correlithm object is a point in the n-dimensional space represented by a binary string. The trainer is further configured to generate an entry in a node table linking the source correlithm object with the target correlithm object.

Abstract: A face posture analysis method, an electronic device, and a computer-readable storage medium are provided. The face posture analysis method includes: obtaining a face key point of a to-be-processed face image; and inputting the face key point of the to-be-processed face image into a neural network, to obtain face posture information of the to-be-processed face image output by the neural network.

Abstract: An in vivo of insitu bio-matter sample magnified digital image creating device for realtime biopsy determinations having a sharp edged window pocket coupled to housing having an optical and digital magnification path coupled to image detecting sensor logic. The device has an optical two blade window pocket rotatably coupled to a stem mechanically controlled and extendable from mechanism within the housing, the stem having source for at least one fiber optical channel for selected frequency and wavelength light various light sources, an optical two blade window pocket slide component optically coupled to the two blade window pocket fiber optic channel stem distal end, providing light through the two blade window pocket slide normal axis surface for illumination penetrating a bio matter assay or sample for imaging through an optical microscopy magnification path axis to a micrograph image sensor. The resulting image micrographs for digital pathology realtime result determination.

Abstract: An object detection device detects a predetermined object from an image. The object detection device includes a first detection unit configured to detect a plurality of candidate regions where the predetermined object exists from the image, a region integrating unit configured to determine one or a plurality of integrated regions according to the plurality of candidate regions detected by the first detection unit, and a second detection unit configured to detect, in the one or the plurality of integrated regions, the predetermined object by using a detection algorithm different from an algorithm of the first detection unit. As a result, it is possible to detect the predetermined object faster and more accurately than before.

Abstract: A computing system receives a request to generate computer-generated data for an experiment. The computer-generated data comprises generated inputs defining setting(s) for a plurality of factors for a design of the experiment. The generated inputs are generated to be representative of a respective design space of different design spaces for the design of the experiment. The system receives first characteristic(s) for specifying generation of the computer-generated data associated with a first design space. The system receives second characteristic(s) for specifying generation of the computer-generated data associated with a second design space. The system, responsive to the request, generates a design suite that comprises the computer-generated data that represents, in a first set of design cases of the design suite, settings constrained by the first design space, and represents, in a second set of design cases of the design suite, settings constrained by the second design space.

Abstract: A system and method of extracting knowledge from a plurality of documents by at least one processor may include: receiving a domain-specific schema data structure, comprising a definition of one or more domain entity types; using at least one first machine-learning (ML) based model to fetch one or more mentions from the plurality of documents; using at least one second ML model to extract, from the one or more mentions, at least one domain entity that corresponds to the one or more domain entity types; and integrating the at least one extracted domain entity into a knowledge graph, based on the domain schema.

Abstract: A system for detecting invisible human emotion in a retail environment is provided. The system comprises a camera and an image processing unit. The camera is configured in a retail environment to capture an image sequence of a person before and during when a price of a product or service becomes visible. The image processing unit is trained to determine a set of bitplanes of a plurality of images in the captured image sequence that represent the hemoglobin concentration (HC) changes of the person, and to detect the person's invisible emotional states based on HC changes. The image processing unit is trained using a training set comprising a set of subjects for which emotional state is known.

Abstract: A method for detecting a dim and small object based on a discriminant feature of video satellite data is provided. The method makes full use of the discriminant feature to improve detection accuracy of the dim and small object. A symmetric semantic segmentation model and an autoencoder model are used to extract the discriminant feature of an object. A top-down adjustment mechanism is used to fuse an image feature and the discriminant feature of the object. Then an attention mechanism is used to enhance a background to further increase contrast between the object and the background. A multi-scale semantic analysis strategy is introduced, and a pyramid model is used to extract the dim and small object in the video satellite data.

Abstract: A system for optimizing a machine learning model. The machine learning model generates predictions based on at least one input feature vector, each input feature vector having one or more vector values; and an optimization module with a processor and an associated memory, the optimization module being configured to: create at least one slice of the predictions based on at least one vector value, determine at least one optimization metric of the slice that is based on at least a total number of predictions for the vector value, and optimize the machine learning model based on the optimization metric.

Abstract: The invention relates to a method for training a machine learning model to identify a subject having at least one machine readable identifier providing a subject ID, said method comprising: providing a computer vision system with an image capturing system comprising at least one image capturing device, and a reader system comprising at least one reader for reading said at least one machine readable identifier; defining said machine learning model in said computer vision system; capturing a first image using said image capturing system, said first image showing said subject; reading said subject ID using said reader system when capturing said first image, and linking said subject ID with said first image, said linking providing said first image with a linked subject ID, providing a first annotated image; capturing at least one further image showing said subject, linking said linked subject ID to said at least one further image providing at least one further annotated image, and subjecting said first annotat

Abstract: A system includes a first analysis apparatus, a second analysis apparatus, and a sensor fusion apparatus. The individual analysis apparatuses analyze outputs of their respective sensors and generate preciseness information about preciseness of the positions of their respective objects. The sensor fusion apparatus calculates the distance between a determination target object and an association candidate object per directional axis that is determined from a position of a sensor corresponding to the determination target object. The sensor fusion apparatus normalizes the distance calculated per directional axis by using the preciseness information corresponding to the determination target object. The sensor fusion apparatus calculates the distance between the determination target object and the association candidate object by using the distance normalized per directional axis as a post-normalization distance.