Abstract: In a computer-implemented method, computer program product, and computer system, program code executing on a processor(s) generates input-output pairs, to form a training set for machine learning and generates a machine learning model of a cyber physical system by utilizing a machine learning algorithm and the training set to train the machine learning model. The program code converts the trained machine learning model into an equivalent constraint and optimizes objective functions subject to the constraint by utilizing one or more constraint solvers to compute an input-output pair. The program code determines if the computed input-output pair, is within an error tolerance and based on determining that it is, either utilizes it as a solution or adds it to the training set (and continues training). If the pair is not within the tolerance, the program code utilizes it as a solution or adds it to the training set (and continues training).

Abstract: There is set forth herein obtaining data traffic monitoring data, the data traffic monitoring data being in dependence on monitoring of traffic received by a container of a protected computing environment; obtaining data traffic monitoring data, the data traffic monitoring data being in dependence on monitoring of traffic received by a processing resource of a computing environment; obtaining a state of the processing resource and provisioning a utility processing resource to include the state of the processing resource; and configuring the computing environment to route data traffic to the utility processing resource.

Abstract: There is set forth herein, in one embodiment, receiving, by a transceiver of a UE device an aggregate signal stream, the aggregate signal stream including an aggregate of base station downlink signal streams from a plurality of base stations; processing the aggregate signal stream and detecting, from the processing of the aggregate signal stream, a set of base stations in communication range of the UE device, wherein the set of base stations include a current serving base station and a scanning base station; determining, from the processing of the aggregate signal stream, a Doppler shifted center frequency of a downlink scanning signal stream received from the scanning base station; and enhancing, in dependence on the Doppler shifted center frequency, the downlink scanning signal stream associated to the scanning base station.

Abstract: There is set forth herein, in one embodiment, receiving, by a transceiver of a UE device an aggregate signal stream, the aggregate signal stream including an aggregate of base station downlink signal streams from a plurality of base stations; processing the aggregate signal stream and detecting, from the processing of the aggregate signal stream, a set of base stations in communication range of the UE device, wherein the set of base stations include a current serving base station and a scanning base station; determining, from the processing of the aggregate signal stream, a Doppler shifted center frequency of a downlink scanning signal stream received from the scanning base station; and enhancing, in dependence on the Doppler shifted center frequency, the downlink scanning signal stream associated to the scanning base station.

Abstract: Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: examining one or more parameter characterizing a prospective connection of the mobile airborne article to first through Nth base stations, the one or more parameter including a Doppler shift; determining based on the examining that a criterion is satisfied, the criterion having one or more factor; and initiating handoff of the mobile airborne article to one of the first through Nth base stations based on the determining.

Abstract: Examples herein include a computer system and methods. Some computer systems comprise two or more devices (each device comprises at least one processing circuit), where each computing device comprises or is communicatively coupled to one or more optical network interface controller (O-NIC) cards. Each O-NIC card comprises at least two bidirectional optical channels to transmit data and to receive additional data from each O-NIC card communicatively coupled to a device, over a channel. The system also includes one or more interfaces and a memory. Program instructions execute a method on one or more processors in communication with a memory, and the method includes modifying, during runtime of at least one application, a pairing over a given bidirectional optical channel of an interface of the interfaces to a given device.

Abstract: A computer program product, a computer-implemented method, and a computer system include a processor(s) that obtains side channel emanations from a device. The processor(s) analyzes the side channel emanations to identify distinct emanation patterns and timing characteristics, wherein the timing characteristics are associated with transitions between the distinct emanation patterns. The processor(s) generates a non-deterministic finite automaton (NFA) by correlating the distinct emanation patterns with states of the device, where the NFA captures states and state transitions of the device. The processor(s) identifies an anomaly in the device, based on deviation in emanations from the device.

Abstract: A computer-implemented method, a system, and a computer program product include program code that obtains, from a controller, temporal objectives and configuration specifications. The program code adjusts weighting for multiple concurrent modes. The program code optimizes each mode of the multiple concurrent modes. The program code discovers conflicts exist between the optimized multiple concurrent modes and resolves the conflicts. The program code allocates resources to the optimized multiple concurrent modes.

Abstract: A computer-program product, a system, and a computer-implemented method include a processor(s) obtaining a configuration of a network including configurations of multiple network nodes and configurations of the network communication devices. The program code automatically models the network to generate a system model. The program code derives, from the system model, a loop-free Bayesian inference model, by generating a loop-free Bayesian network from the network.