Abstract: A black box optimization method, system, and computer program product include implementing an average gradient estimator using a forward difference of function values at multiple random directions, performing variance reduction via gradient blending with an output of the average gradient estimator using a control variate, and performing binary quantization of a result of the variance reduction.

Abstract: A certification method, system, and computer program product include certifying an adversarial robustness of a convolutional neural network by deriving an analytic solution for a neural network output using an efficient upper bound and an efficient lower bound on an activation function and applying the analytic solution in computing a certified robustness.

Abstract: A computing system is configured to learn data-augmentations from unlabeled media. The system includes an extracting unit and an embedding unit. The extracting unit is configured to receive media data that includes moving images of an object and audio generated by the object. The extracting unit extracts an image frame of the object among the moving images and extracts an audio segment from the audio. The embedding unit is configured to generate first embeddings of the image frame and second embeddings of the audio segment, and to concatenate the first and second embeddings together to generate concatenated embeddings. The computing system labels the media data based at least in part on the concatenated embeddings.

Abstract: An adversarial robustness testing method, system, and computer program product include testing a robustness of a black-box system under different access settings via an accelerator.

Abstract: A method (and structure and computer product) of temporal action localization in video data includes receiving a stream of video data and determining all proposals in the video data stream, the proposals being candidate regions for temporal action in the video data stream. Values for a pair-wise relation function are calculated for relating the proposals, wherein the pair-wise relation function calculates a scalar value representing a pair-wise relation weight for pairs of the proposals.

Abstract: The disclosure relates to technology for identifying a root cause of anomalous behavior in a communications network. A key quality indicator (KQI) indicative of a performance level associated with a source is received in the communication network. The KQI includes a performance measurement value to identify a performance level of the source having the anomalous behavior. An improved KQI indicative of a level of degradation is calculated based on the anomalous behavior at the source by recovering the KQI to a historical value. Sources of the anomalous behavior are clustered into subsets according to the level of degradation based on the calculated KQI improvement. A global or partial degradation type of the root cause source in the subset having a severe level of degradation is determined using hypothesis testing, and a confidence value is provided for the accepted degradation type.

Abstract: The disclosure relates to technology for identifying a root cause of anomalous behavior in a communications network. A key quality indicator (KQI) indicative of a performance level associated with a source is received in the communication network. The KQI includes a performance measurement value to identify a performance level of the source having the anomalous behavior. An improved KQI indicative of a level of degradation is calculated based on the anomalous behavior at the source by recovering the KQI to a historical value. Sources of the anomalous behavior are clustered into subsets according to the level of degradation based on the calculated KQI improvement. A global or partial degradation type of the root cause source in the subset having a severe level of degradation is determined using hypothesis testing, and a confidence value is provided for the accepted degradation type.

Abstract: A user displayed interactive GUI widget (icon) provides two or more regions. A first region provides widget manipulation function for editing the widget, editing including moving or sizing functions. A second region provides widget interaction function for user interaction with the widget including interaction lists or interaction graphics. The regions may comprise one or more icon border regions and one or more icon internal regions.

Abstract: A user displayed interactive GUI widget (icon) provides two or more regions. A first region provides widget manipulation function for editing the widget, editing including moving or sizing functions. A second region provides widget interaction function for user interaction with the widget including interaction lists or interaction graphics. The regions may comprise one or more icon border regions and one or more icon internal regions.

Abstract: A user displayed interactive GUI widget (icon) provides two or more regions. A first region provides widget manipulation function for editing the widget, editing including moving or sizing functions. A second region provides widget interaction function for user interaction with the widget including interaction lists or interaction graphics. The regions may comprise one or more icon border regions and one or more icon internal regions.