Microsoft Technology Licensing, LLC

Dharmanshu Kamra

Nicholas Jie Fang

Sudhakar Visweswara Prabhu

Ziyad Ibrahim

Stefan Francis Slivinski

Quinn Damerell

James David McCaffrey

Dylan Keith Garrett

Gerald Albert Pogue, Jr

Daniel Diniz Niemeyer

Alexander Robert Paul Grenier

Daniel James Carpenter

James David Mccaffrey

Ziyad Ahmad Ibrahim

Examples described herein generally relate to processing event logs where, for each of multiple events in an event log of the one or more event logs, a table of logged event instances can be generated for the event. For each of the multiple events, the table can be processed using an autoencoder to identify one or more of the logged event instances as anomalies, and an indication of at least a portion of the anomalies can be output. In addition, the event logs and/or corresponding tables of events can be used to train models for the autoencoders.

Techniques for detecting atypical events in event logs

A method for low-latency streaming of audio and video via a mobile computing device to facilitate a high-quality, real-time interactive streaming experience. The method includes receiving an application audio stream generated by execution of an application program, an application video stream generated by execution of the application program, a microphone audio stream, and a camera video stream. The application audio stream and the microphone audio stream are mixed to produce a mixed audio stream. The application video stream and the camera video stream are composited to produce a composited video stream. The mixed audio stream is encoded to produce an encoded audio stream, and the composited video stream is encoded to produce an encoded video stream. The encoded audio and video streams are packaged into one or more streaming packets, and output, to a remote computing device.

Low-latency mobile device audiovisual streaming

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Seattle, WA, United States of America

Dharmanshu Kamra