Abstract: Systems, methods, and computer-readable media for automatically seeding an API into a natural language conversational interface are described herein. An API is automatically seeded into a natural language conversational interface by mapping a set of API calls to a set of intents, mapping the set of intents to a collection of example utterances, and using the collection of example utterances as input training data to train a natural language classifier. The trained classifier may then be used to determine an intent associated with a received query such that an action associated with the determined intent can then be performed.

Abstract: A method, system, and computer program product for random number generation using a network of mobile devices are provided in the illustrative embodiments. From a set of mobile devices, a corresponding set of data packets is received. A presence of raw sensor data is detected in a first data packet received from a first mobile device in the set of mobile devices. The raw sensor data comprises data corresponding to changing value of an output of a sensor in a set of sensors installed in the first mobile device. The raw sensor data is separated from the first data packet, resulting in an original data packet. A first random number is generated using the raw sensor data.

Abstract: A method, system, and computer program product for random number generation using a network of mobile devices are provided in the illustrative embodiments. From a set of mobile devices, a corresponding set of data packets is received. A presence of raw sensor data is detected in a first data packet received from a first mobile device in the set of mobile devices. The raw sensor data comprises data corresponding to changing value of an output of a sensor in a set of sensors installed in the first mobile device. The raw sensor data is separated from the first data packet, resulting in an original data packet. A first random number is generated using the raw sensor data.

Abstract: A processor chip may have a built-in hardware lock and deterministic exclusive locking of the hardware lock by execution units executing in parallel on the chip. A set of software locks may be maintained, where the execution units set and release the software locks only by first acquiring a lock of the hardware lock. A first execution unit sets a software lock after acquiring a lock of the hardware lock, and other execution units, even if exclusively locking the hardware lock, are unable to lock the software lock until after the first execution unit has reacquired a lock of the hardware lock and possibly released the software lock while exclusively locking the hardware lock. An execution unit may release a software lock after and while holding a lock of the hardware lock. The hardware lock is released when a software lock has been set or released.

Abstract: A processor chip may have a built-in hardware lock and deterministic exclusive locking of the hardware lock by execution units executing in parallel on the chip. A set of software locks may be maintained, where the execution units set and release the software locks only by first acquiring a lock of the hardware lock. A first execution unit sets a software lock after acquiring a lock of the hardware lock, and other execution units, even if exclusively locking the hardware lock, are unable to lock the software lock until after the first execution unit has reacquired a lock of the hardware lock and possibly released the software lock while exclusively locking the hardware lock. An execution unit may release a software lock after and while holding a lock of the hardware lock. The hardware lock is released when a software lock has been set or released.