Abstract: According to one aspect, anomalous event detection based on deep learning may include a system for anomalous event detection for a device. The system includes a computing device having a processor, an encoding module, and a decoding module. The processor is configured to receive sensor data. The encoding module generates reconstruction data based on the sensor data, identifies at least one reconstruction error in the reconstruction data, and determines an anomaly score based on the at least one reconstruction error. The decoding module generates an action prediction based on the sensor data and determines a likelihood value based on the action prediction. The processor can then calculate a scaled anomaly score based on the anomaly score and the likelihood value and causes the processor to execute an action based on the scaled anomaly score.

Abstract: Various embodiments comprise systems, methods, architectures, mechanisms, and apparatus providing location safety management implemented by cooperating mobile devices that operate in a privacy-secured manner to identify respective proximate infectious areas, build corresponding local datasets of the infectious areas, and share the datasets or relevant portions thereof with each other, such as in response to hierarchical location-based requests for such data. The datasets may be used to adapt the operation of navigation applications and the like so as to avoid routes, seats or locations associated with infectious areas and/or persons.

Abstract: According to one aspect, anomalous event detection based on deep learning may include a system for anomalous event detection for a device. The system includes a computing device having a processor, an encoding module, and a decoding module. The processor is configured to receive sensor data. The encoding module generates reconstruction data based on the sensor data, identifies at least one reconstruction error in the reconstruction data, and determines an anomaly score based on the at least one reconstruction error. The decoding module generates an action prediction based on the sensor data and determines a likelihood value based on the action prediction. The processor can then calculate a scaled anomaly score based on the anomaly score and the likelihood value and causes the processor to execute an action based on the scaled anomaly score.