Abstract: Methods, systems, and computer storage media for providing a tailored meeting-video segment associated with a meeting-video management engine of a meeting-video management system. The tailored meeting-video segment corresponds to a portion of meeting-video content that is programmatically generated based on features associated with video data, meeting data, and user data. A tailored meeting-video segment—or a plurality of tailored meeting-video segments—can be generated by employing a meeting-video tailoring machine learning model of the meeting-video management engine. In particular, the features—associated with video data comprising the plurality of clips, meeting data of the meeting, and user data of the user—are meeting-video tailoring features used by the meeting-video tailoring machine learning model to generate the tailored meeting-video segment.

Abstract: A system and method and for searching for an element in and replacement a portion of a video is conducted by retrieving the video and generating a video index for the video, the video index indexing a plurality of elements of the video such as text, audio and video. To search in the vide, a search query is received that includes at least one of a text element, an audio element, an image element, or a video element. Features and embeddings are extracted from the search query before comparing the search query features with the video index to identify a portion of the video that includes one of the text element, audio element, image element or video element of the search query. The identified portion is then automatically replaced with a replacement portion to generate a revised video.

Abstract: Methods, systems, and computer storage media for providing a tailored meeting-video segment associated with a meeting-video management engine of a meeting-video management system. The tailored meeting-video segment corresponds to a portion of meeting-video content that is programmatically generated based on features associated with video data, meeting data, and user data. A tailored meeting-video segment—or a plurality of tailored meeting-video segments—can be generated by employing a meeting-video tailoring machine learning model of the meeting-video management engine. In particular, the features—associated with video data comprising the plurality of clips, meeting data of the meeting, and user data of the user—are meeting-video tailoring features used by the meeting-video tailoring machine learning model to generate the tailored meeting-video segment.

Abstract: Methods and systems for controlling tintable windows based on cloud detection.

Abstract: Methods and systems for controlling tintable windows based on cloud detection.

Abstract: This technique improves energy efficiency of MapReduce system by using system performance model without changing any component of the MapReduce system. This involves determining presence of any hardware bottleneck in any node of MapReduce system based on a system performance model and if any hardware bottleneck is present in any node, then the maximum bandwidth value of hardware associated with the bottleneck of each node is determined. Thereafter, an energy efficient value of Central Processing Unit (CPU) frequency of each node having the bottleneck is determined by using the system performance model and the maximum bandwidth value of hardware associated with the bottleneck. Further, the CPU frequency of each node having the bottleneck is set at the energy efficient value determined in the earlier step.

Abstract: This technique improves energy efficiency of MapReduce system by using system performance model without changing any component of the MapReduce system. This involves determining presence of any hardware bottleneck in any node of MapReduce system based on a system performance model and if any hardware bottleneck is present in any node, then the maximum bandwidth value of hardware associated with the bottleneck of each node is determined. Thereafter, an energy efficient value of Central Processing Unit (CPU) frequency of each node having the bottleneck is determined by using the system performance model and the maximum bandwidth value of hardware associated with the bottleneck. Further, the CPU frequency of each node having the bottleneck is set at the energy efficient value determined in the earlier step.

Abstract: The present technique is an apparatus and method evaluating software performance. The method identifies performance scenarios using a knowledge base and selects the performance scenarios from a context module using operational laws. The system analyzes performance criticality of an application workload. Furthermore, the system comprises a context module that ranks the performance criterions depending on the input and the knowledge base stores and retrieves the performance criterions using the operational laws. The knowledge base comprises the criterions and their relevant ranks based of the application context. The present technique automates classification of performance criterions into benefit and cost categories with the usage of the operational laws.

Abstract: The present technique is an apparatus and method evaluating software performance. The method identifies performance scenarios using a knowledge base and selects the performance scenarios from a context module using operational laws. The system analyzes performance criticality of an application workload. Furthermore, the system comprises a context module that ranks the performance criterions depending on the input and the knowledge base stores and retrieves the performance criterions using the operational laws. The knowledge base comprises the criterions and their relevant ranks based on the application context. The present technique automates classification of performance criterions into benefit and cost categories with the usage of the operational laws.