Rakuten, Inc.

Provided are a method and system for verifying a liveness detection of a user. The method includes: obtaining a video of a user speaking a phrase in response to a question or a randomly generated phrase presented to the user; inputting video data and audio data of the obtained video to a first determination model to obtain a first determination indicative of whether a mouth movement of the user is synchronized with the audio data; inputting, to a second determination model, a first input corresponding to the audio data and a second input corresponding to a predetermined phrase, to obtain a second determination indicative of whether the predetermined phrase is spoken by the user; and determining whether the first determination indicates that the mouth movement is synchronized with the audio data and whether the second determination indicates that the predetermined phrase is spoken by the user.

A computer implemented search management process and platform include USER data to build libraries of keywords used to associate merchant selections in response to queries seeking merchant products and services.

A computer implemented search management process and platform include USER data to build libraries of keywords used to associate merchant selections in response to queries seeking merchant products and services.

The present disclosure relates to a recommender system and method. Unlike known systems, which learn neural network parameters during training and fix the input vectors, the recommender system learns both the input vectors and machine learning model parameters during training. In one embodiment, the initial user and item input vectors are interaction vectors that are based on known and unknown user feedback. The non-zero elements of the interaction vectors correspond user-item pairs for which feedback is known, and the zero elements corresponding to user-item pairs for which feedback is unknown. The non-zero elements of the interaction vectors are learnable parameters during the training phase. The user and item vectors, as well as the model parameters, learned during the training phase are used in a prediction and recommendation phase to make product recommendations for a user.

A method, computer program, and computer system is provided for predicting features in a binary file, such as an image, an animation, a video, or a sound. The binary file is received. A feature value corresponding to latent space of the binary file is calculated based on a dual-encoder attention-based U-net architecture. A new binary file is generated based on the calculated feature value.

Authentication means ( 102 ) of an authentication system (S) performs authentication based on authentication information acquired by using an authentication terminal ( 30 ) and authentication information registered in storage means ( 100 ) in association with identification information on a user in a vicinity of the authentication terminal ( 30 ). Possibility determination means ( 103 ) determines, when there are a plurality of users in the vicinity of the authentication terminal ( 30 ), whether there is a possibility of one user being authenticated as another user. Restriction means ( 104 ) restricts successful authentication when it is determined that there is the possibility.

For each of a plurality of images, a learning device acquires first training data including an image, a class of each of one or more objects shown in the image, and the number of objects, from a storage. The learning device trains a feature extraction part, a class estimation part, and an object number estimation part using the first training data. For each of images of which the number is smaller than the plurality of images, the learning device acquires second training data including an image and coordinates of each of one or more objects in the image, from the storage. The learning device causes the trained feature extraction part to output a feature map of the image included in the second training data, and trains a coordinate estimation part using the feature map and the coordinates included in the second training data.

In a processing system 101 , a first processing device 111 obtains first characteristics information indicating characteristics of users in a first environment, divides the users into first clusters according to the first characteristics information, and transmits, to a second processing device 121 , representation information representing characteristics of users belonging to each of the first clusters using a centroid vector based on the first characteristics information of the cluster. The second processing device 121 obtains second characteristics information indicating characteristics of the users in a second environment, and divides the users into second clusters according to the representation information and the second characteristics information. The present invention allows for dividing the users into clusters more accurately, although the second processing device 121 need not use the first characteristics information and the first processing device 111 need not use the second characteristics information.

Flight equipment according to an embodiment of the present invention includes: an aerial vehicle having a body and at least one rotor held on the body; a plurality of supporters mounted to the aerial vehicle and supporting a protective member stretched therebetween so as to surround an outside of the body; a support information detection unit configured to detect support information indicating a support state of the protective member; and a mounting state determiner configured to determine whether the support state of the protective member is suitable or unsuitable, based on a value detected by the support information detection unit.

In a state where a communication session between a terminal device and a mediating unit is established, an information processing device transmits information in accordance with a processing request generated by the mediating unit based on input data of the terminal device so that an output based on the transmitted information is performed in the terminal device. The information processing device receives the processing request from the mediating unit, obtains guide information in accordance with the processing request, and intermittently and sequentially transmits a plurality of pieces of process information and, in a case where the communication session is disconnected before transmission of all the process information is completed, transmits information for executing an output in accordance with a transmission situation of process information when the communication session is disconnected to the terminal device.