Abstract: Methods and systems for artificially intelligent security incident and event management using an attention-based deep neural network and transfer learning are disclosed. A method includes: collecting, by a computing device, system and network activity events in bulk; forming, by the computing device, a corpus using the collected system and network activity events; correlating, by the computing device, discrete events of the system and network activity events into offenses; adding, by the computing device, additional features to the corpus representing the offenses and disposition decisions regarding the offenses; training, by the computing device, a deep neural network using the corpus; and tuning, by the computing device, the deep neural network for a monitored computing environment using transfer learning.

Abstract: Managing anonymous network connections. In one aspect managing anonymous network connections by providing anonymous authentication credentials to a plurality of devices in a hierarchical network, registering a first set of devices at a first data aggregator, determining that the first set of devices at the first aggregator numbers less than a first threshold value, registering the first set of devices with a second aggregator upstream in the hierarchy from the first aggregator, causing data from the first set of devices to be received at the second aggregator.

Abstract: A computer-implemented method for the automatic generation of photorealistic 3D avatars. The method includes generating, using machine learning network, a plurality of 2D photorealistic facial images; generating, using a model agnostic meta-learner, a plurality of 2D photorealistic facial expression images based on the plurality of 2D photorealistic facial images; and generating a plurality of 3D photorealistic avatars based on the plurality of 2D photorealistic facial expression images.

Abstract: A computer-implemented method for the automatic generation of photorealistic 3D avatars. The method includes generating, using machine learning network, a plurality of 2D photorealistic facial images; generating, using a model agnostic meta-learner, a plurality of 2D photorealistic facial expression images based on the plurality of 2D photorealistic facial images; and generating a plurality of 3D photorealistic avatars based on the plurality of 2D photorealistic facial expression images.

Abstract: Methods and systems for artificially intelligent security incident and event management using an attention-based deep neural network and transfer learning are disclosed. A method includes: collecting, by a computing device, system and network activity events in bulk; forming, by the computing device, a corpus using the collected system and network activity events; correlating, by the computing device, discrete events of the system and network activity events into offenses; adding, by the computing device, additional features to the corpus representing the offenses and disposition decisions regarding the offenses; training, by the computing device, a deep neural network using the corpus; and tuning, by the computing device, the deep neural network for a monitored computing environment using transfer learning.

Abstract: Managing anonymous network connections. In one aspect managing anonymous network connections by providing anonymous authentication credentials to a plurality of devices in a hierarchical network, registering a first set of devices at a first data aggregator, determining that the first set of devices at the first aggregator numbers less than a first threshold value, registering the first set of devices with a second aggregator upstream in the hierarchy from the first aggregator, causing data from the first set of devices to be received at the second aggregator.