Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for performing adaptive audio bitrate selection. An example embodiment operates by receiving a data streaming request. The embodiment then streams data at a first audio bitrate. The embodiment then determines whether there is a streaming discontinuity associated with the data. The embodiment then determines, in response to the determination that there is a streaming discontinuity, whether a network condition satisfies one or more conditions based on one or more streaming parameters. The embodiment then, in response to the determination that the network condition satisfies the one or more conditions, adjusts the first audio bitrate to a second audio bitrate during the streaming discontinuity.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for utilizing a single storage buffer for a dynamic number of players, each using a dynamically sized virtual buffer. For example, a system includes a buffer management controller that receives a request to initiate at least one player instance for displaying a content item. The buffer management controller creates a virtual buffer having a maximum capacity for the player instance. Finally, the buffer management controller identifies one or more available regions of the single storage buffer and maps the virtual buffer to the one or more available regions in response to determining that address space in the one or more available regions matches or exceeds the maximum capacity of the virtual buffer.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for modifying one or more parameters of a data streaming bitrate selection algorithm based on machine learning. An example embodiment operates by training and operating a first machine learning model to predict a sustainable network bandwidth. A second machine learning model is trained to receive the sustainable network bandwidth and predict a likelihood that this network bandwidth will not empty a data buffer of streaming data. A bitrate is selected based on the likelihood being below a threshold percentage, such as 50%.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for performing adaptive audio bitrate selection. An example embodiment operates by receiving a data streaming request. The embodiment then streams data at a first audio bitrate. The embodiment then determines whether there is a streaming discontinuity associated with the data. The embodiment then determines, in response to the determination that there is a streaming discontinuity, whether a network condition satisfies one or more conditions based on one or more streaming parameters. The embodiment then, in response to the determination that the network condition satisfies the one or more conditions, adjusts the first audio bitrate to a second audio bitrate during the streaming discontinuity.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for evaluating bitrate selection models. An example embodiment operates by aggregating data associated with a data streaming session comprising a plurality of segments. The embodiment then determines, for each segment of the plurality of segments, segment data comprising streaming parameters by analyzing the aggregated data. The embodiment then generates a simulation input based on the segment data for each segment. The embodiment then performs a first simulation, based at least on the segment data for each segment and a first bitrate selection model, to simulate streaming each segment. The embodiment then generates a first simulation result for each segment. The embodiment then aggregates each first simulation result to provide a first aggregate simulation result of the data streaming session.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for modifying one or more parameters of a data streaming bitrate selection algorithm based on machine learning. An example embodiment operates by training and operating a first machine learning model to predict a sustainable network bandwidth. A second machine learning model is trained to receive the sustainable network bandwidth and predict a likelihood that this network bandwidth will not empty a data buffer of streaming data. A bitrate is selected based on the likelihood being below a threshold percentage, such as 50%.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for performing adaptive audio bitrate selection. An example embodiment operates by receiving a data streaming request. The embodiment then streams data at a first audio bitrate. The embodiment then determines whether there is a streaming discontinuity associated with the data. The embodiment then determines, in response to the determination that there is a streaming discontinuity, whether a network condition satisfies one or more conditions based on one or more streaming parameters. The embodiment then, in response to the determination that the network condition satisfies the one or more conditions, adjusts the first audio bitrate to a second audio bitrate during the streaming discontinuity.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for modifying one or more parameters of a data streaming bitrate selection algorithm based on machine learning. An example embodiment operates by training and operating a first machine learning model to predict a sustainable network bandwidth. A second machine learning model is trained to receive the sustainable network bandwidth and predict a likelihood that this network bandwidth will not empty a data buffer of streaming data. A bitrate is selected based on the likelihood being below a threshold percentage, such as 50%.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for modifying one or more parameters of a data streaming bitrate selection algorithm based on machine learning. An example embodiment operates by training and operating a first machine learning model to predict a sustainable network bandwidth. A second machine learning model is trained to receive the sustainable network bandwidth and predict a likelihood that this network bandwidth will not empty a data buffer of streaming data. A bitrate is selected based on the likelihood being below a threshold percentage, such as 50%.

Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for modifying one or more parameters of a data streaming bitrate selection algorithm based on machine learning. An example embodiment operates by training and operating a first machine learning model to predict a sustainable network bandwidth. A second machine learning model is trained to receive the sustainable network bandwidth and predict a likelihood that this network bandwidth will not empty a data buffer of streaming data. A bitrate is selected based on the likelihood being below a threshold percentage, such as 50%.

Abstract: Methods and systems for activity based video recording are provided. Exemplary methods include: receiving an amplified signal to improve sensitivity, the amplified signal being produced by a circuit using an analog differential signal, the analog differential signal being received from a microphone; converting the amplified signal to a digital signal; detecting the digital signal; activating a video camera to record video for a predetermined amount of time using the detecting; storing the recorded video; placing the video camera into power saving mode.

Abstract: Systems and methods for activity based recording by a smart camera system are described herein. Activity based recording provides a smart and automated method to enable and disable the camera recording, such that potentially significant events or activities are detected and recorded. A camera located in a physical space detects a triggering event and begins recording video and/or audio for a predetermined time period, resulting in a captured video. The triggering event may be detected by one or more sensors associated with the camera, including but not limited to, a camera sensor, a passive infrared sensor, and a microphone. A metadata file is added to the captured video. Certain video frames are added to the beginning of the captured video, resulting in a finalized video recording. The finalized video recording is transmitted and presented to a user computing device.

Abstract: Systems and methods for continuously recognizing and determined a threat level via a smart camera system are described herein. A camera located in a physical space may recording video and/or audio for a predetermined amount of time. The recorded video may be processed to determine a background, activity, identify human faces, and expressions, and evaluate those inputs to determine a predictive threat level to persons and/or property present in the physical space where the camera is located.

Abstract: Methods and systems for activity based video recording are provided. Exemplary methods include: receiving an amplified signal to improve sensitivity, the amplified signal being produced by a circuit using an analog differential signal, the analog differential signal being received from a microphone; converting the amplified signal to a digital signal; detecting the digital signal; activating a video camera to record video for a predetermined amount of time using the detecting; storing the recorded video; placing the video camera into power saving mode.

Abstract: Systems and methods for automatically detecting and recognizing human faces by a smart camera system are described herein. A camera located in a physical space may detect a triggering event and begin recording video and/or audio for a predetermined time period. The camera also processes the video to determine if any human faces are present. If so, the camera updates the metadata file associated with the recorded video with information identifying specific video frames that contain a human face. The recorded video and metadata file are transmitted to video analysis software, which performs further facial analysis on the selected video frames, in an attempt to identify the person(s) detected in the video. Results of the facial recognition process are presented on a user computing device.

Abstract: Systems and methods for activity based recording by a smart camera system are described herein. Activity based recording provides a smart and automated method to enable and disable the camera recording, such that potentially significant events or activities are detected and recorded. A camera located in a physical space detects a triggering event and begins recording video and/or audio for a predetermined time period, resulting in a captured video. The triggering event may be detected by one or more sensors associated with the camera, including but not limited to, a camera sensor, a passive infrared sensor, and a microphone. A metadata file is added to the captured video. Certain video frames are added to the beginning of the captured video, resulting in a finalized video recording. The finalized video recording is transmitted and presented to a user computing device.

Abstract: Systems and methods for use in parallelization of computer program code are provided. One method includes determining line dependency data indicating a dependency of a plurality of lines of the sequential computer program code. The method further includes determining time data indicating a time required for the execution of a plurality of functions of the sequential computer program code and determining parallelizability of the sequential computer program code using the line dependency data and the time data. The method further includes generating parallel executable computer program code by inserting program instructions in the sequential computer program code based on the determined parallelizability. The step of generating the parallel executable computer program code includes identifying and routing at least a portion of one or more of the plurality of functions to different processors to achieve parallelization of sequential computer program code using the line data and time dependency data.

Abstract: The present invention provides a method and apparatus for selectively enhancing regions in an image. In one embodiment, a digital image is read from an image source and is converted into a desired image model. One or more regions in the image having intensity values of pixels falling outside a pre-determined optimal intensity range are determined. The one or more regions in the image are then enhanced using a modeled light source of an optimal intensity such that the intensity value of pixels corresponding to the one or more regions in the image fall within the pre-determined optimal intensity range.

Abstract: Systems and methods for use in parallelization of computer program code are provided. One method includes determining line dependency data indicating a dependency of a plurality of lines of the sequential computer program code. The method further includes determining time data indicating a time required for the execution of a plurality of functions of the sequential computer program code and determining parallelizability of the sequential computer program code using the line dependency data and the time data. The method further includes generating parallel executable computer program code by inserting program instructions in the sequential computer program code based on the determined parallelizability. The step of generating the parallel executable computer program code includes identifying and routing at least a portion of one or more of the plurality of functions to different processors to achieve parallelization of sequential computer program code using the line data and time dependency data.

Abstract: The present invention provides a method and apparatus for selectively enhancing regions in an image. In one embodiment, a digital image is read from an image source and is converted into a desired image model. One or more regions in the image having intensity values of pixels falling outside a pre-determined optimal intensity range are determined. The one or more regions in the image are then enhanced using a modeled light source of an optimal intensity such that the intensity value of pixels corresponding to the one or more regions in the image fall within the pre-determined optimal intensity range.