Disclosed are various embodiments for providing dynamically generated summaries of media content, such as electronic books, audiobooks, audio series, video content series, and so on. An elapsed time since consumption of media content was ended is determined. A summary of a portion of the media content occurring prior to a point of resumption in the media content is dynamically generated. The content of the summary is selected according to a length of the elapsed time. The summary is presented via an output device.

A computer-implemented method is disclosed for segmenting a binarized document. The method includes extracting connected components from the binarized document and discriminating (for at least one of the connected components) whether it is a text component based on a homogeneity level value. The homogeneity level value is representative of the level of homogeneity within the local region of the connected component. The local region includes the connected component and at least one adjacent connected component. The homogeneity level value is based on at least one value representative of at least one image characteristic parameter determined for the connected component and on at least one value representative of the image characteristic parameter of the at least one adjacent connected component.

A computer-implemented method is disclosed for segmenting a binarized document. The method includes extracting connected components from the binarized document and discriminating (for at least one of the connected components) whether it is a text component based on a homogeneity level value. The homogeneity level value is representative of the level of homogeneity within the local region of the connected component. The local region includes the connected component and at least one adjacent connected component. The homogeneity level value is based on at least one value representative of at least one image characteristic parameter determined for the connected component and on at least one value representative of the image characteristic parameter of the at least one adjacent connected component.

An image retrieval system receives an image for which to identify relevant images from an image repository. Relevant images may be of the same environment or object and features and other characteristics. Images in the repository are represented in an image retrieval graph by a set of image nodes connected by edges to other related image nodes with edge weights representing the similarity of the nodes to each other. Based on the received image, the image traversal system identifies an image in the image retrieval graph and alternatively explores and traverses (also termed “exploits”) the image nodes with the edge weights. In the exploration step, image nodes in an exploration set are evaluated to identify connected nodes that are added to a traversal set of image nodes. In the traversal step, the relevant nodes in the traversal set are added to the exploration set and a query result set.

A scene aware dialog system includes an input interface to receive a sequence of video frames, contextual information, and a query and a memory configured to store neural networks trained to generate a response to the input query by analyzing one or combination of input sequence of video frames and the input contextual information. The system further includes a processor configured to detect and classify objects in each video frame of the sequence of video frames; determine relationships among the classified objects in each of the video frame; extract features representing the classified objects and the determined relationships for each of the video frame to produce a sequence of feature vectors; and submit the sequence of feature vectors, the input query and the input contextual information to the neural network to generate a response to the input query.

Provided herein is technology relating to selecting an element in a software application and particularly, but not exclusively, to systems and methods for identifying a target element for testing a software application using artificial intelligence.

A computer-implemented prediction method of making time-series predictions for controlling and/or monitoring a computer-controlled system, such as a semi-autonomous vehicle. The method uses a time series of one or more observed states. A state comprises values of measurable quantities of multiple interacting objects. Based on the observed states, values of time-invariant latent features for the multiple objects are determined, for example, according to an encoder model. A decoder model is then used to predict at least one next state. This involves applying a trained graph model to obtain a first prediction contribution based on an object's interactions with other objects, and applying a trained function to obtain a second prediction contribution based just on information about the object itself. Based on the predicted next state, output data is generated for use in controlling and/or monitoring the computer-controlled system.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using surfels for vehicle localization. One of the methods includes obtaining surfel data comprising a plurality of surfels, wherein each surfel corresponds to a respective different location in an environment, and each surfel has associated data that comprises a stability measure, wherein the stability measure characterizes a permanence of a surface represented by the surfel; obtaining sensor data for a plurality of locations in the environment, the sensor data having been captured by one or more sensors of a first vehicle; determining a plurality of high-stability surfels from the plurality of surfels in the surfel data; and determining a location in the environment of the first vehicle using the plurality of selected high-stability surfels and the sensor data.

An autonomous vehicle (AV) planning method comprises: receiving sensor inputs pertaining to an AV; processing the AV sensor inputs to determine an encountered driving scenario; in an AV planner, executing a tree search algorithm to determine a sequence of AV manoeuvres corresponding to a path through a constructed game tree; and generating AV control signals for executing the determined sequence of AV manoeuvres; wherein the game tree has a plurality of nodes representing anticipated states of the encountered driving scenario, and the anticipated driving scenario state of each child node is determined by updating the driving scenario state of its parent node based on (i) a candidate AV manoeuvre and (ii) an anticipated behaviour of at least one external agent in the encountered driving scenario.

An equity system receives input from multiple data subsystem module. The data is filtered and weighted. The filtered and weighted data is received by a visualization subsystem that generates a visual representation of the weighted, filtered data. The visualization is rendered to a user and may be a cluster web. The visualization includes connections defining the relatedness of the nodes in the visualization. A client device is configured to view the displayed visualized representation to a user, wherein the client device is configured to receive input from a user requesting execution of one of a trade or research based upon the displayed visualized representation.