Abstract: In one embodiment, a network quality assessment service that monitors a network obtains multimodal data indicative of a plurality of measurements from the network and subjective perceptions of the network by users of the network. The network quality assessment service uses the obtained multimodal data as input to one or more neural network-based models. The network quality assessment service maps, using a conceptual space, outputs of the one or more neural network-based models to symbols. The network quality assessment service applies a symbolic reasoning engine to the symbols, to generate a conclusion regarding the monitored network. The network quality assessment service provides an indication of the conclusion to a user interface.

Abstract: Presented herein are techniques to analyze network anomaly signals based on both a spatial component and a temporal component. A method includes identifying a plurality of factors that trigger a first anomaly signal by a first network node and a second anomaly signal by a second network node in a network comprising a plurality of network nodes, determining that the first network node is adjacent to the second network node in the plurality of network nodes, calculating an anomaly severity score for the first network node based on a number of co-occurring factors from among the plurality of factors that trigger both the first anomaly signal and the second anomaly signal, and adjusting the anomaly severity score for the first network node based on a value of a prior anomaly severity score for the first network node.

Abstract: Presented herein are techniques to analyze network anomaly signals based on both a spatial component and a temporal component. A method includes identifying a plurality of factors that trigger a first anomaly signal by a first network node and a second anomaly signal by a second network node in a network comprising a plurality of network nodes, determining that the first network node is adjacent to the second network node in the plurality of network nodes, calculating an anomaly severity score for the first network node based on a number of co-occurring factors from among the plurality of factors that trigger both the first anomaly signal and the second anomaly signal, and adjusting the anomaly severity score for the first network node based on a value of a prior anomaly severity score for the first network node.

Abstract: In one embodiment, a network quality assessment service that monitors a network obtains multimodal data indicative of a plurality of measurements from the network and subjective perceptions of the network by users of the network. The network quality assessment service uses the obtained multimodal data as input to one or more neural network-based models. The network quality assessment service maps, using a conceptual space, outputs of the one or more neural network-based models to symbols. The network quality assessment service applies a symbolic reasoning engine to the symbols, to generate a conclusion regarding the monitored network. The network quality assessment service provides an indication of the conclusion to a user interface.

Abstract: In one embodiment, a network quality assessment service that monitors a network obtains multimodal data indicative of a plurality of measurements from the network and subjective perceptions of the network by users of the network. The network quality assessment service uses the obtained multimodal data as input to one or more neural network-based models. The network quality assessment service maps, using a conceptual space, outputs of the one or more neural network-based models to symbols. The network quality assessment service applies a symbolic reasoning engine to the symbols, to generate a conclusion regarding the monitored network. The network quality assessment service provides an indication of the conclusion to a user interface.

Abstract: In one embodiment, a device obtains radio frequency (RF) characteristic data for a wireless client. The device inputs the RF characteristic data for the wireless client to a deep learning-based encoder. The device learns a latent space representation of the RF characteristic data from the encoder. The device uses the learned latent space representation as a unique signature to identify the wireless client in a wireless network.

Abstract: Various systems and methods for counting people. For example, one method involves receiving input data at an analytics system that includes a neural network. The input data includes a representation of an environment, including representations of several people. The method also includes identifying the representations of the people in the representation of the environment. The method also includes updating an output value that indicates the number of people identified as being present in the environment.

Abstract: In one embodiment, a network quality assessment service that monitors a network obtains multimodal data indicative of a plurality of measurements from the network and subjective perceptions of the network by users of the network. The network quality assessment service uses the obtained multimodal data as input to one or more neural network-based models. The network quality assessment service maps, using a conceptual space, outputs of the one or more neural network-based models to symbols. The network quality assessment service applies a symbolic reasoning engine to the symbols, to generate a conclusion regarding the monitored network. The network quality assessment service provides an indication of the conclusion to a user interface.

Abstract: Presented herein are techniques for detecting anomalies in micro-service communications that are indicative of security issues/problems for the application. More specifically, a computing device receives a plurality of micro-service communication records each associated with traffic sent between pairs of executables (nodes) that are related to a micro-services application. Each of the micro-service communication records includes a time series entry and an associated trace sequence identifier and each of the micro-service communication records are generated during a time period. The computing device analyzes the plurality of micro-service communications to detect possible anomalous communication patterns associated with the micro-services application during the time period.

Abstract: Presented herein are techniques for detecting anomalies in micro-service communications that are indicative of security issues/problems for the application. More specifically, a computing device receives a plurality of micro-service communication records each associated with traffic sent between pairs of executables (nodes) that are related to a micro-services application. Each of the micro-service communication records includes a time series entry and an associated trace sequence identifier and each of the micro-service communication records are generated during a time period. The computing device analyzes the plurality of micro-service communications to detect possible anomalous communication patterns associated with the micro-services application during the time period.

Abstract: Various systems and methods for counting people. For example, one method involves receiving input data at an analytics system that includes a neural network. The input data includes a representation of an environment, including representations of several people. The method also includes identifying the representations of the people in the representation of the environment. The method also includes updating an output value that indicates the number of people identified as being present in the environment.