S-1 Corporation

Yva.ai, Inc.

Visier Solutions, Inc.

Victor Kuznetsov

David Yan

Anwar Adkhamovich Irmatov

Dmitry Yurievich Buryak

Aleksandr Mertvetsov

Hae-Kwang Yang

Yong-Jin Lee

Wang-Jin Mun

Anton Gordienko

Victor Bocharov

Polina Kananykina

Peter Ivanov

Dong Sung Lee

Viacheslav Seledkin

Marina Chilingaryan

Dmitry Vladimirovich Cherdakov

Mun Wang Jin

Eric Pelletier

Lee Yong Jin

Alexander B Murynin

Il-Jun Jeong

Peter Valerievich Bazanov

Anton Kirillov

An example method comprises: receiving a natural language text; transforming, by a neural network, the natural language text into a numeric representation comprising a plurality of numeric values; discarding the natural language text; and performing, using the numeric representation of the natural language text, an information extraction task.

Utilizing neural networks for data anonymization

A method to predict problematic actions in an organization, executed by a processing device, includes accessing stored employee-related data, such as at least one of emails, surveys, minutes, or records of conversations, identifying a subset of the employee-related data that is associated with an employee, and predicting, based on the subset of the employee-related data associated with the employee, at least one of a likelihood that the employee is engaged in an adverse relation with other employees or a likelihood that the employee is to resign from the organization within a period of time.

Method of using machine learning to predict problematic actions within an organization

A task assistant identifies a correspondence received by a source associated with a user and determines that the correspondence includes a request. The task assistant further determines a ranking associated with the request based on one or more characteristics of the request and of the correspondence. In response to the ranking of the request exceeding a threshold, the task assistant generates a notification associated with the request and provides the notification to a client device associated with the user.

Task assistant

The present invention relates to a figure recognition system and method for automatic detection, tracking and recognition of a human face image. 2D image data in the surveillance zone are remotely collected by using an optical sensor, the faces of all persons in the surveillance zone are detected, and corresponding positions are determined. The face is detected, the detected face's feature coordinate is estimated, and the detected face and the feature are tracked in the next frame while processing the video sequence. Image quality of each detected face is determined according to parameters of focus, brightness, contrast, and the presence of glasses. Recognition methods stored in the repository for each detected face are adjusted by considering the face image quality computation value, and a biometric feature set is generated by using the recognition method selected for each detected face. The figure is recognized according to the watch list by using the biometric feature generated by comparing each detected face and a template set stored in the database. A new user registration process is performed and the recognition method is adapted automatically by considering the watch list.

Method and system for automated face detection and recognition

A method and apparatus for recognizing a protrusion on a face is proposed. The apparatus forms two images, wherein one is formed in a condition in which a target is lighted and another is formed in a condition in which the target is non-lighted. Face regions are detected from the images respectively and then are compared each other. Then, analyzing change in intensity for indicating change in contrast of the compared regions is performed. Based on the result of the analyzing, it is determined whether there is a protrusion in a face presented in the images.

Method and apparatus for recognizing a protrusion on a face

An image quality control system and method is disclosed. At least one infrared camera takes a screen image of a room. When there are a plurality of cameras, images of the cameras are synchronized with respect to time, and a specific object of the image is tracked to estimate image quality of the object. When there are a plurality of cameras, a 3D screen model is reconfigured, and positions of the cameras and the infrared lighting tools are controlled. Infrared lighting and the cameras are controlled, and particularly, optical axis direction, optical magnification, exposure time, and the iris of the camera can be amended. Next, a high-quality object image list can be generated so as to process the images.

System and method for controlling image quality

Disclosed is a method of intruder's detection, comprising of using of more than 1 sensor, represented, for example, by video cameras that are arranged with fixed spatial orientation for stereo detector formation at that 2D distributions of light intensity that form stereo images registered by stereo detector are transmitting to blocks of processing, for example, blocks of digital processing at that mentioned blocks of digital processing perform processing of sequence of stereo images with different frequencies (high and low), to determine the presence of intruder, it's position and speed of transference.

Method of intruder detection and device thereof

Growing community of inventors

Moscow, Russia

Victor Kuznetsov