Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for utilizing an artificial intelligence framework for generating enhanced digital content and improving digital content campaign design. In particular, the disclosed systems can utilize a metadata neural network, a summarizer neural network, and/or a performance neural network to generate metadata for digital content, predict future performance metrics, generate enhanced digital content, and provide recommended content changes to improve performance upon dissemination to one or more client devices.

Abstract: Techniques for incorporating social-network connections information into estimated user-ratings of videos for video recommendations are described. A user-rating that a user is likely to assign to a video is predicted based on first and second predictions. The first prediction is based on ratings assigned to the video by a first set of users who have rated other videos with ratings substantially similar to the user's ratings of the other videos. The second prediction is based on ratings assigned to the video by a second set of users who have established social-network connections with the user and who have rated at least one same video that the user has previously rated. Additionally, the estimated user-rating is added to additional estimated user-ratings of other videos for comparison, and top-rated videos are identified. Then, a notification is communicated via a client device of the user to recommend the top-rated videos to the user.

Abstract: In various implementations, a server is configured to execute instructions stored in storage that when executed perform operations that include receiving a hypertext transfer protocol (HTTP) request to stream a video segment of multimedia content to a client device. The video segment is of a video sub-stream of the multimedia content. The operations further include sending the video segment and an audio segment to the client device based on the HTTP request for the video segment. The sending pushes the video segment and/or the audio segment to the client device. The audio segment is of an audio sub-stream of the multimedia content. A plurality of segment sets may be pushed based on the HTTP request for the video segment. Each segment set can include an additional video segment and an additional audio segment that correspond to at least partially concurrent portions of the multimedia content.

Abstract: The present disclosure relates to systems, methods, and computer readable media that utilize a trained multi-modal combination model for content and text-based evaluation and distribution of digital video content to client devices. For example, systems described herein include training and/or utilizing a combination of trained visual and text-based prediction models to determine predicted performance metrics for a digital video. The systems described herein can further utilize a multi-modal combination model to determine a combined performance metric that considers both visual and textual performance metrics of the digital video. The systems described herein can further select one or more digital videos for distribution to one or more client devices based on combined performance metrics associated with the digital videos.

Abstract: A buffer model in an HTTP streaming client may include receiving a first content fragment of a first content stream in response to a first HTTP request. It may also include receiving a second content fragment of a second content stream in response to a second HTTP request. The buffer model may further include storing the first and second content fragments in first and second buffers of a plurality of configurable buffers. The first and second content fragments may be multiplexed into a third buffer of the plurality of buffers. The multiplexed first and second content fragments may be stored in a fourth buffer of the plurality of buffers for playback. The buffer model may be implemented by an application. The buffers may be designed based on one or more constraints.

Abstract: In implementations of trajectory-based viewport prediction for 360-degree videos, a video system obtains trajectories of angles of users who have previously viewed a 360-degree video. The angles are used to determine viewports of the 360-degree video, and may include trajectories for a yaw angle, a pitch angle, and a roll angle of a user recorded as the user views the 360-degree video. The video system clusters the trajectories of angles into trajectory clusters, and for each trajectory cluster determines a trend trajectory. When a new user views the 360-degree video, the video system compares trajectories of angles of the new user to the trend trajectories, and selects trend trajectories for a yaw angle, a pitch angle, and a roll angle for the user. Using the selected trend trajectories, the video system predicts viewports of the 360-degree video for the user for future times.

Abstract: In implementations of trajectory-based viewport prediction for 360-degree videos, a video system obtains trajectories of angles of users who have previously viewed a 360-degree video. The angles are used to determine viewports of the 360-degree video, and may include trajectories for a yaw angle, a pitch angle, and a roll angle of a user recorded as the user views the 360-degree video. The video system clusters the trajectories of angles into trajectory clusters, and for each trajectory cluster determines a trend trajectory. When a new user views the 360-degree video, the video system compares trajectories of angles of the new user to the trend trajectories, and selects trend trajectories for a yaw angle, a pitch angle, and a roll angle for the user. Using the selected trend trajectories, the video system predicts viewports of the 360-degree video for the user for future times.

Abstract: The present disclosure is directed toward systems and methods for identifying user segments. In particular, the systems and methods described herein evaluate user session logs to gather media content consumption history information associated with a plurality of users. Additionally, the systems and methods described herein analyze items of media content to identify keywords, genres, and other attributes, and further represent the items of media content as vectors. The systems and methods follow an algorithm to group items of media content into clusters and, based on the clusters of media content, further group users of media content into user clusters (e.g., user segments).

Abstract: Techniques and systems are provided for generating a video from texture images, and for reconstructing the texture images from the video. For example, a texture image can be divided into a number of tiles, and the number of tiles can be sorted into a sequence of ordered tiles. The sequence of ordered tiles can be provided to a video coder for generating a coded video. The number of tiles can be encoded based on the sequence of ordered tiles. The encoded video including the encoded sequence of ordered tiles can be decoded. At least a portion of the decoded video can include the number of tiles sorted into a sequence of ordered tiles. A data file associated with at least the portion of the decoded video can be used to reconstruct the texture image using the tiles.

Abstract: In an aspect of the disclosure, a method, a computer-readable medium, and a computer system are provided. The computer system includes a service processor. The service processor monitors events of one or more ACPI compliant devices of a host of the service processor. The service processor maintains device data associated with the one or more ACPI compliant devices based on the events in a data store of the service processor. The service processor emulates an ACPI controller to monitor a communication channel for detecting one or more ACPI commands from the host. The service processor processes the device data in the data store in response to detecting the one or more ACPI commands on the communication channel.

Abstract: A metadata generation system utilizes machine learning techniques to accurately describe content of videos based on multi-model predictions. In some embodiments, multiple feature sets are extracted from a video, including feature sets showing correlations between additional features of the video. The feature sets are provided to a learnable pooling layer with multiple modeling techniques, which generates, for each of the feature sets, a multi-model content prediction. In some cases, the multi-model predictions are consolidated into a combined prediction. Keywords describing the content of the video are determined based on the multi-model predictions (or combined prediction). An augmented video is generated with metadata that is based on the keywords.

Abstract: The present disclosure includes methods and systems for streaming high-performance virtual reality video using adaptive rate allocation. In particular, an adaptive rate allocation system partitions a panorama video into segments or tiles and assigns priorities to each tile or segment based on input (e.g., a viewport of field-of-view) from a user client device. Further, the adaptive rate allocation system streams each tile or segment to the user client device according to the adaptive rate allocation, which maximizes bandwidth efficiency and video quality. In this manner, the adaptive rate allocation system delivers higher quality content to regions in the panorama video where a user is currently looking/most likely to look.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating digital video summaries based on analyzing a digital video utilizing a relevancy neural network, an aesthetic neural network, and/or a generative neural network. For example, the disclosed systems can utilize an aesthetics neural network to determine aesthetics scores for frames of a digital video and a relevancy neural network to generate importance scores for frames of the digital video. Utilizing the aesthetic scores and relevancy scores, the disclosed systems can select a subset of frames and apply a generative reconstructor neural network to create a digital video reconstruction. By comparing the digital video reconstruction and the original digital video, the disclosed systems can accurately identify representative frames and flexibly generate a variety of different digital video summaries.

Abstract: In an aspect of the disclosure, a method, a computer-readable medium, and a computer system are provided. The computer system includes an initialization component of a host. The initialization component obtains a process file for dynamically creating a processing component on a management platform on a BMC of the host, the process file defining a logic to be implemented by the processing component, the initialization component operating to load an operating system of the host. The initialization component sends the process file to the BMC. The initialization component further sends a message to the BMC, the message including data to be processed by the processing component.

Abstract: In an aspect of the disclosure, a method, a computer-readable medium, and a computer system are provided. The computer system includes an initialization component of a host. The initialization component obtains a process file for dynamically creating a processing component on a management platform on a BMC of the host, the process file defining a logic to be implemented by the processing component, the initialization component operating to load an operating system of the host. The initialization component sends the process file to the BMC. The initialization component further sends a message to the BMC, the message including data to be processed by the processing component.

Abstract: The present disclosure includes methods and systems for streaming high-performance virtual reality video using adaptive rate allocation. In particular, an adaptive rate allocation system partitions a panorama video into segments or tiles and assigns priorities to each tile or segment based on input (e.g., a viewport of field-of-view) from a user client device. Further, the adaptive rate allocation system streams each tile or segment to the user client device according to the adaptive rate allocation, which maximizes bandwidth efficiency and video quality. In this manner, the adaptive rate allocation system delivers higher quality content to regions in the panorama video where a user is currently looking/most likely to look.

Abstract: Systems and methods to control a controller, such as a baseboard management controller (BMC), and its host computer with voice. The system may include a mobile device, which is communicatively connected to the controller via a network. The mobile device may receive a voice input, and generate a corresponding instruction by performing voice recognition to the voice input. The instruction may be sent to the controller for controlling the controller or its host computer. For example, the controller may perform a corresponding operation based on the instruction to determine whether the instruction is for the controller or for the host computer. When the instruction is for the host computer, in response to determining the target as the host computing device, the controller may perform keyboard-video-mouse (KVM) redirection to redirect the instruction to the host computer.

Abstract: Content consumption session progress is predicted based on historical observations of how users have interacted with a repository of digital content. This is approached as a matrix completion problem. Information extracted from tracking logs maintained by one or more content providers is used to estimate the extent to which various content items are consumed. The extracted session progress data is used to populate a session progress matrix in which each matrix element represents a session progress for a particular user consuming a particular content item. This matrix, which in principle will be highly (?95%) sparse, can be completed using a collaborative filtering matrix completion technique. The values obtained as a result of completing the session progress matrix represent predictions with respect to how much of a given content item will be consumed by a given user.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for utilizing an artificial intelligence framework for generating enhanced digital content and improving digital content campaign design. In particular, the disclosed systems can utilize a metadata neural network, a summarizer neural network, and/or a performance neural network to generate metadata for digital content, predict future performance metrics, generate enhanced digital content, and provide recommended content changes to improve performance upon dissemination to one or more client devices.

Abstract: A metadata generation system utilizes machine learning techniques to accurately describe content of videos based on multi-model predictions. In some embodiments, multiple feature sets are extracted from a video, including feature sets showing correlations between additional features of the video. The feature sets are provided to a learnable pooling layer with multiple modeling techniques, which generates, for each of the feature sets, a multi-model content prediction. In some cases, the multi-model predictions are consolidated into a combined prediction. Keywords describing the content of the video are determined based on the multi-model predictions (or combined prediction). An augmented video is generated with metadata that is based on the keywords.