A computer-implemented method for streaming multimedia data includes receiving a request to stream, by a first computing device, a first multimedia data file stored on a second computing device. The computer-implemented method further includes determining a first recommendation to skip viewing of a first logical segment included in the multimedia data file, wherein the first recommendation is based, at least in part, on analyzing a set of user insight information corresponding to a first classification associated with the first logical segment. The computer-implemented method further includes determining that a first confidence score associated with the first recommendation is below a predetermined threshold level. The computer-implemented method further includes providing, in response to determining that the first confidence score is below the predetermined threshold level, the first recommendation to a user while streaming the first multimedia data file via the first computing device.

Provided is a method and an apparatus for displaying a live broadcast room, a device, and a storage medium. The method includes: acquiring a live broadcast screenshot of at least one live broadcast room under a target classification label; inputting the live broadcast screenshot of the at least one live broadcast room into an image detection model to obtain a live broadcast screenshot that satisfies a set type condition; adding a display identifier to a live broadcast room corresponding to the live broadcast screenshot of the set type condition; and arranging and displaying the at least one live broadcast room according to the display identifier in a display interface corresponding to the target classification label.

Apparatus, systems, and articles of manufacture are disclosed to categorize audience members by age. An example apparatus includes memory including instructions, and a processor to execute the instructions to split audience member records from an initial node into child nodes based on comparisons of attribute-value pairs of the audience member records to a first value threshold, the attribute-value pairs representative of database subscriber activity data of audience members subscribed to a database proprietor, designate a first child node of the child nodes as a terminal node when a quantity of the audience member records of the first child node of the child nodes does not satisfy a minimum leaf size, generate an age-correction model based on the terminal node, and correct, based on the age-correction model, the age characteristic associated with the media impression, the media impression indicative of a person exposed to media presented by a media presentation device.

An embodiment of the present disclosure provides a data processing method, a device, and a pair of racing drone goggles. The method includes receiving image transmission data; determining a current image frame of the image transmission data; and storing images frames determined as a first type in a first storage device, and storing the images frames determined as a second type in a second storage device. By classifying and storing the image transmission data, the image transmission data can be protected from other terminal devices, and the data security is improved.

In a video quality assessment method, an assessment model is first generated based on a subjective assessment result of a user on each sample in a sample set and based on a parameter set (a parameter type in the parameter set may include at least one of a packet loss rate, a delay, and a jitter) of each sample. Therefore, when video quality is being assessed, a parameter set of a to-be-assessed video is obtained first, where the parameter set of the to-be-assessed video has a same parameter type as the parameter set of each sample that is used to generate the assessment model; and then video quality of the to-be-assessed video is assessed based on the assessment model and the parameter set of the to-be-assessed video, to obtain an assessment result.

Techniques for efficiently detecting similarity among electronic content items are provided. A vector is generated for each of multiple content items and is used to assign its corresponding content item to a group among multiple groups. A set of content items that is assigned to a particular group of the plurality of groups is identified. For each pair of content items in the set, a similarity score is generated and used to determine whether to classify the pair as similar to each other. Similarity data is updated if to identify the pair if it is determined to classify the pair of content items as similar to each other. The similarity data associates a first content item with a second content item. The second content item is prevented from being presented to a particular user for a period of time after the first content item is presented to the particular user.

A computerized system providing parental supervision of child consumers of digital media, comprising an electronic repository of digital content; user interface for children, adults, digital content providers and guidance providers where digital content providers may upload digital content items into the repository and guidance providers may provide guidance objects for adults which are specific for subsets of children end-users being defined in terms of metadata, and for specific spots within, or portions of, specific items in the digital contents items; and logic including a central processor and configured to control interactions of the user interfaces with the processor and between various end-users at least partly depending on the category of each end-user wherein the logic presents overlaid digital content item/s to child C, including generating the overlaid item by selecting guidance object G specific to subset to which C belongs, and integrating G into the digital content as an overlay.

A computer-implemented method comprising: receiving, at a communications network interface, telemetry data representing a streaming service instance provided to an end-device by a remote streaming server over the Internet; and recursively, with respect to each current time window: (i) dividing, based on the telemetry data, the time window into segments, wherein each of the segments is classified based on its buffering status as a buffering segment or a non-buffering segment, (ii) calculating a current streaming service score, based on the buffering status of at least one of the segments within the current time window, and (iii) updating a quality of service (QoS) rating for the streaming media service instance, based, at least in part, on the current streaming service score.

Methods, apparatus, systems and articles of manufacture are disclosed to project ratings for future broadcasts of media. Disclosed example methods include normalizing, with a processor, audience measurement data corresponding to media exposure data, social media exposure data and programming information associated with a future quarter to determine normalized audience measurement data. Disclosed example methods also include classifying a media asset based on the programming information to determine a media asset classification. Disclosed example methods also include building, with the processor, a projection model based on a first subset of the normalized audience measurement data, the first subset of the normalized audience measurement data associated with a first time frame relative to the future quarter, the first subset of the normalized audience measurement data based on the media asset classification, and applying, with the processor, the programming information to the projection model to project ratings for the media asset.

In accordance with the technique described herein, a home shopping content suitable for a specific viewer can be dynamically selected and provided using a product classification group of interest extracted based on the time at which a viewer selects a home shopping channel and the time at which the viewer leaves the home shopping channel. In particular, since the product classification group of interest can be extracted using an interest index calculated based on a bounce rate defined according to the technique described herein, the more personalized home shopping content can be provided to the viewer.