Abstract: Data stream based event sequence anomaly detection for mobility customer fraud analysis is presented herein. A system obtains a sequence of events comprising respective modalities of communication that correspond to a subscriber identity associated with a communication service—the sequence of events having occurred within a defined period. Based on defined classifiers representing respective fraudulent sequences of events, the system determines, via a group of machine learning models corresponding to respective machine learning processes, whether the sequence of events satisfies a defined condition with respect to likelihood of representing a fraudulent sequence of events of the respective fraudulent sequences of events. In response to the sequence of events being determined to satisfy the defined condition, the system sends, via a user interface of the system, a notification indicating that the sequence of events has been determined to represent the fraudulent sequence of events.

Abstract: In one embodiment, there is provided a system for applying therapy to a human body. The system includes a coupling apparatus and a supporting apparatus. The coupling apparatus is configured to couple to an upper body portion of a user. The supporting apparatus is configured to support at least a portion of a weight of the user. An amount of weight that is supported is adjustable. In some embodiments, the system may further include a control apparatus configured to control at least one of a state and/or a position of the supporting apparatus.