Abstract:
In one embodiment, a method includes training a deep neural network using a first set of network characteristics corresponding to a first time and a second set of network characteristics corresponding to a second time, generating, using the deep neural network, a predictive set of network characteristics corresponding to a future time, and assigning a task of a distributed application to a processing unit based on the predictive set of network characteristics.
Abstract:
In one embodiment, a method includes receiving current data, the current data including time series data representing a plurality of time instances. The method includes storing at least a recent portion of the current data in a buffer. The method includes reducing the dimensionality of the current data to generate dimensionality-reduced data. The method includes generating a reconstruction error based on the dimensionality-reduced data and a plurality of neural network metrics. At least one of a size of the recent portion of the current data stored in the buffer or an amount of the reducing the dimensionality of the current data is based on the reconstruction error.
Abstract:
A method comprises collecting, by a computing device located at an edge of a network, data items corresponding to information transmitted by endpoints using the network, generating, by the computing device, a probabilistic hierarchy using the data items, generating, by the computing device using the probabilistic hierarchy and natural language data, a similarity metric, generating, by the computing device using the probabilistic hierarchy, the natural language data, and the similarity metric, an ontology, detecting, by the computing device using the ontology, an anomaly, and in response to detecting the anomaly, sending a notification.
Abstract:
In one embodiment, a method includes obtaining a plurality of tracklets, each of the plurality of tracklets including tracklet data representing a position of a respective one of a plurality of people at a plurality of times. The method includes generating a behavioral analytic metric based on the plurality of tracklets. The method includes generating a notification in response to determining that the behavioral analytic metric is greater than a threshold.
Abstract:
Techniques are provided for establishing a videoconference session between participants at different endpoints, where each endpoint includes at least one computing device and one or more displays. A plurality of video streams is received at an endpoint, and each video stream is classified as at least one of a people view and a data view. The classified views are analyzed to determine one or more regions of interest for each of the classified views, where at least one region of interest has a size smaller than a size of the classified view. Synthesized views of at least some of the video streams are generated, wherein the synthesized views include at least one view including a region of interest, and views including the synthesized views are rendered at one or more displays of an endpoint device.
Abstract:
A video coder includes a forward coder and a reconstruction module determining a motion compensated predicted picture from one or more previously decoded pictures in a multi-picture store. The reconstruction module includes a reference picture predictor that uses only previously decoded pictures to determine one or more predicted reference pictures. The predicted reference picture(s) are used for motion compensated prediction. The reference picture predictor may include optical flow analysis that uses a current decoded picture and that may use one or more previously decoded pictures together with affine motion analysis and image warping to determine at least a portion of at least one of the reference pictures.
Abstract:
In one embodiment, a method includes receiving current data, the current data including time series data representing a plurality of time instances. The method includes storing at least a recent portion of the current data in a buffer. The method includes reducing the dimensionality of the current data to generate dimensionality-reduced data. The method includes generating a reconstruction error based on the dimensionality-reduced data and a plurality of neural network metrics. At least one of a size of the recent portion of the current data stored in the buffer or an amount of the reducing the dimensionality of the current data is based on the reconstruction error.
Abstract:
A system for collision avoidance includes memory storing instructions which, when executed, cause one or more processors to perform determining a direction of flight of a first drone, causing broadcasting, in the direction of flight based, a beamformed signal of beacon frames, determining a new flight direction of the same first drone, in response to the new flight direction, causing broadcasting of the beacon frames in the new flight direction, detecting second beacon frames from a second drone associated with a direction from which the second beacon frames are arriving; in response, causing the first drone to perform, without input from a pilot, one or more of a change in elevation, heading, speed, or type of operation, directed toward causing the first drone to follow a flight path that is separated from the second drone.
Abstract:
Various implementations disclosed herein provide a method for performing one or more transactions between application containers. In various implementations, the method includes transmitting a key request to a first network node within a cluster of network nodes that are configured to generate and maintain a distributed ledger. In some implementations, the key request indicates that the requested key is for one or more transactions between a first application container and a second application container. In various implementations, the method includes receiving a key in response to transmitting the key request. In some implementations, the key is valid for the one or more transactions between the first application container and the second application container. In various implementations, the method includes synthesizing, at the first application container, transaction data with the key. In various implementations, the method includes transmitting, by the first application container, the transaction data to the second application container.
Abstract:
In one embodiment, a method includes training a deep neural network using a first set of network characteristics corresponding to a first time and a second set of network characteristics corresponding to a second time, generating, using the deep neural network, a predictive set of network characteristics corresponding to a future time, and assigning a task of a distributed application to a processing unit based on the predictive set of network characteristics.