In particular embodiments, one or more computer systems of a social-networking system determine a first media device of a first user of a social-networking system and determine a second media device of a second user of the social-networking system. The one or more computer systems access a social graph of the social-networking system, update the social graph based on the determined first and second media devices, and stream media from the first media device to the second media device using the updated social graph. The social graph includes nodes and edges connecting the nodes, the nodes including user nodes that are each associated with a particular user of the social-networking system and concept nodes that are each associated with a particular media device.

In particular embodiments, one or more computer systems of a social-networking system determine a first media device of a first user of a social-networking system and determine a second media device of a second user of the social-networking system. The one or more computer systems access a social graph of the social-networking system, update the social graph based on the determined first and second media devices, and stream media from the first media device to the second media device using the updated social graph. The social graph includes nodes and edges connecting the nodes, the nodes including user nodes that are each associated with a particular user of the social-networking system and concept nodes that are each associated with a particular media device.

An information processing apparatus includes a use determination processing portion, a first acquisition processing portion, and a time notification processing portion. The use determination processing portion determines whether or not its associated information processing apparatus is in use. The first acquisition processing portion, when a use request of its associated information processing apparatus is received in a state where the use determination processing portion has determined that its associated information processing apparatus is in use, acquires a planned use time of its associated information processing apparatus via an input operation performed on an operation portion of its associated information processing apparatus. The time notification processing portion notifies a first information processing apparatus that is a transmission source of the use request, of the planned use time acquired by the first acquisition processing portion.

Systems and methods are disclosed for enriching a knowledge base for search queries. According to certain embodiments, images are assigned annotations that identify entities contained in the images. An object entity is selected among the entities based on the annotations and at least one attribute entity is determined using annotated images containing the object entity. A relationship between the object entity and the at least one attribute entity is inferred and stored in the knowledge base. In some embodiments, confidence may be calculated for the entities. The confidence scores may be aggregated across a plurality of images to identify an object entity.

An image-based transaction system includes a mobile device with an image sensor that is programmed to capture, via the image sensor, a digital images of a scene. The mobile device identifies a document using image characteristics from the captured images and acquires an image of at least a part of the document, and then identifies symbols in the image based on locations within the image of the document. The symbols can include alphanumeric symbols. The mobile device processes the symbols according to their type to obtain an address related to the document and the symbols and initiates a transaction associated with the identified document.

Each image of a plurality of user-defined collections of images has properties associated therewith. A seed image is defined as currently viewed or selected image and the properties associated with the seed image are retrieved. The seed image is used to identify seed collections as the collections of images which have the seed image as one of their images. A plurality of candidate images are identified from the seed collections. For each candidate image, a significance score is determined as a function of either the number of seed collections to which the candidate image belongs or the ratio of the number of seed collections to the set of all collections to which the candidate image belongs. Each candidate image is ranked based on the significance scores and the seed image properties. At least one candidate image is recommended to the user based on the ranking of the candidate images.

A computing platform that analyzes a captured video stream to identify a document depicted in the video stream, validates identification information corresponding to the document to display an information address associated with the document, and that initiates a transaction based on the validation of the identification information associated with the document.

An XR device and a method for controlling the same are disclosed. The XR device is applicable to 5G communication technology, robot technology, autonomous driving technology, and Artificial Intelligence (AI) technology. A method for controlling the XR device includes executing an electronic appliance arrangement application by a user who uses the XR device, displaying an image of an indoor space of a display screen of the XR device, overlapping an electronic appliance selected by the user with the image of the indoor space, and displaying the overlap result image, and if the electronic appliance is arranged at a specific position of the indoor space and the specific position is improper for arrangement of the electronic appliance, providing the user with an improper notification message including information about why the arrangement position is improper.

Today, artificial neural networks are trained on large sets of manually tagged images. Generally, for better training, the training data should be as large as possible. Unfortunately, manually tagging images is time consuming and susceptible to error, making it difficult to produce the large sets of tagged data used to train artificial neural networks. To address this problem, the inventors have developed a smart tagging utility that uses a feature extraction unit and a fast-learning classifier to learn tags and tag images automatically, reducing the time to tag large sets of data. The feature extraction unit and fast-learning classifiers can be implemented as artificial neural networks that associate a label with features extracted from an image and tag similar features from the image or other images with the same label. Moreover, the smart tagging system can learn from user adjustment to its proposed tagging. This reduces tagging time and errors.

An image evaluation apparatus which is capable of evaluating an image that can be given positive evaluations in a networking service. Images included in post data posted on a networking service and evaluating values for the post data are obtained at predetermined time intervals. When the post data has been obtained, first parameters are generated by applying an image analysis process to the images. The first parameters and the evaluation values are stored in association with each other. Upon input of image to be evaluated, a second parameter is generated by applying the image analysis process to the image to be evaluated. A first parameter corresponding to the second parameter is extracted from the plurality of stored first parameters. Parameter evaluation values representing variations in the evaluation values associated with the extracted first parameter in notifying order are calculated. Notification of the calculated parameter evaluation values is provided.