Abstract: Systems, methods and/or computer program products optimizing network policies between microservices of a service mesh. The service mesh tracks incoming API calls of applications and based on the historical transactions, the context of API calls, and the microservices in the microservice chain being invoked, network controls and policy configurations are set to optimize the transactions performed by the service mesh. Dimensions of the communications between microservices of the service mesh are dynamically optimized via the service mesh control plane using a policy optimizer. Optimized dimensions of service mesh transactions includes automated policy adjustments to retries between microservices, circuit breaking between microservices, automated timeout adjustments between microservices and intelligent rate limiting between microservices and/or rate limiting applied to user profiles.

Abstract: In an approach to optimize server connection timeout errors in a cloud environment, embodiments create a knowledge corpus associated with connection timeout patterns based on historical learning of transaction parameters and predicts a criticality of a transaction based on one or more identified contextual situations. Further, embodiments dynamically adjust a connection timeout range of the transaction based on the predicted criticality and one or more identified contextual situations of the transaction, and selectively identify a connection timeout range for the transaction based on an evaluation of the one or more contextual situations. Additionally, embodiments analyze generated timeout errors on a remote server from within a service mesh, and adjust timeout values of the transaction based on the analyzed generated timeouts errors. Responsive to the transaction receiving a timeout error, embodiments output a recommended timeline detailing when the transaction can be reinitiated.

Abstract: A computer-implemented method for optimizing reinforcement learning based on a stability of a reinforcement learning state is disclosed. The computer-implemented method includes determining whether a stability of a next reinforcement learning state of a reinforcement learning problem is above a predetermined threshold. The computer-implemented method further includes responsive to determining that the stability of the next reinforcement state is below the predetermined threshold, determining a stability of an alternate next reinforcement learning state of the reinforcement learning problem. The computer-implemented method further includes responsive to determining that the stability of the next reinforcement state is above the predetermined threshold, transitioning from a current reinforcement learning state to the next reinforcement learning state based, at least in part, on determining that the stability of the next reinforcement learning state is above the predetermined threshold.

Abstract: A system for electrically charging a first autonomous vehicle (that is, the charged vehicle) by a second autonomous vehicle (that is, charging vehicle) through a wired connection while the vehicles are travelling over a roadway (for example, an interstate highway). In some embodiments, multiple charged vehicles may be charged at the same time while driving in tandem with the charging vehicle.

Abstract: A dynamic user interface (UI) input system and method for smart glasses based on availability states of a user is provided. In embodiments, a method includes determining, by a computing device of smart glasses, an initial availability state of a user's hands for a user interface (UI) navigation event based on incoming image data; selecting, by the computing device, a UI configuration and a control profile for the UI navigation event from stored UI configurations and control profiles of the user based on the initial availability state of the user's hands; and initiating, by the computing device, the UI navigation event based on the selected UI configuration and the control profile, wherein content is displayed in a virtual UI of the smart glasses according to the UI configuration, and UI navigation by the user is controlled based on the control profile.

Abstract: According to one embodiment, a method, computer system, and computer program product for parking allocation is provided. The embodiment may include measuring a plurality of network optimization factors corresponding to a given area. The embodiment may also include mapping a plurality of priority factors for network optimization across the given area. The embodiment may further include identifying one or more mobile nodes within the given area. The embodiment may also include selecting a location for each mobile node based on one or more priority factors. The embodiment may further include transmitting an instruction directing the mobile node to the location.

Abstract: Propagation of pathogens is reduced by configuring internet of things (IoT) sensors along a supply chain of package; and analyzing the packages in the supply chain using the IoT sensors to determine handling requirements of products. The packages can be tracked with a package handling confirmation engine including a Region Based Convolutional Neural Network (RCNN) to determine with the IoT sensors measuring interactions with the packages that parties in the supply chain are handling the packages in accordance with the handling requirements. Product distribution can be stopped through the supply chain in response to the interactions with the packages failing to meet the handling requirements.

Abstract: A computer-implemented method, system and computer program product for testing a software application. User interactions with the functional and non-functional aspects of software applications are monitored. User experience profiles are then generated for the monitored users based on the monitored interactions. The best user experiences in connection with interacting with specific functional and non-functional aspects of the software applications are then identified based on the generated user experience profiles. Virtual users having different types of best user experiences in connection with interacting with different functional and non-functional aspects of the software applications are created. The software application is then tested using the virtual users to determine if the functional and non-functional aspects of the software application satisfy the best user experiences associated with the virtual users.

Abstract: A processor may receive sound data associated with a bounded environment. The sound data may be associated the sound data is associated with a sound field. The processor may analyze the sound data associated with the sound field to identify one or more external sound sources and one or more internal sound sources. The processor may generate one or more simulations of the sound field based, at least in part, on a user preference set. The processor may modify a sound field within the bounded environment. The modified sound field may be based, at least in part, on the one or more simulations of the sound field and the user preference set.

Abstract: A capture operation is identified. The capture operation is of a video stream of a subject located in an environment. The capture operation is performed by a first capture device from a first perspective of the subject. A perspective update request of the video stream is received. In response to the perspective update request, one or more candidate capture devices in the environment is detected. A second capture device is selected as a selected capture device, from the one or more candidate capture devices. The second capture device is instructing to perform the capture operation based on the selection. The video stream is transferred from the first capture device to the second capture device. The transferring updates the video stream to a second perspective of the subject.

Abstract: A processor may receive an air dataset associated with a smart environment having one or more storage objects. The processor may simulate the smart environment using the air dataset. The processor may apply an optimization criteria to the simulation of the smart environment. The processor may generate an optimum smart environment design associated with an improved air condition level of the smart environment and the optimization criteria.

Abstract: A navigation instruction of an autonomous vehicle is detected. The autonomous vehicle is in an environment. The autonomous vehicle is communicatively coupled to a plurality of sensors configured to capture environmental information of the environment. An anomalous sensor status of a first sensor of the plurality of sensors is determined based on the plurality of sensors. An air measurement is identified in response to the anomalous sensor status and based on a second sensor of the plurality of sensors. The air measurement is adjacent to the autonomous vehicle. Autonomous movement operation of the autonomous vehicle is directed in response to the movement instruction and based on the air measurement.

Abstract: According to one embodiment, a method, computer system, and computer program product for solar power generation is provided. The embodiment may include identifying relative positions of co-located electric autonomous vehicles (EAVs) within a group. The embodiment may include identifying solar power generation metrics for each EAV. The embodiment may include determining whether at least two EAVs have a same side unexposed to direct sunlight. In response to determining at least two EAVs have a same side unexposed to direct sunlight, the embodiment may include identifying a direction and an angle of solar light impacting the group. Based on the relative positions and the direction and angle of solar light, the embodiment may include instructing a collaborative movement among the group such that solar light is reflected from an exterior surface of at least one EAV to an exterior surface of at least one other EAV which was unexposed to sunlight.

Abstract: Systems and methods synchronize content of a virtual environment with a state of a physical environment. In aspects, a method includes obtaining sensor data from a network of remote sensors measuring a physical state of a location at a time; generating context specific parameter data based on the sensor data; obtaining context data from a remote virtual reality (VR) system, wherein the context data reflects a current state of virtual content in a virtual environment displayed by the remote VR system; selecting virtual content to be displayed in the virtual environment by the remote VR system based on the context specific parameter data, the context data, and rules; and sending the virtual content to the remote VR system to be displayed to a user, wherein the virtual content reflects a state of the physical location at the time.

Abstract: A processor may receive component data associated with the machine. The component data may be associated one or more machine auxiliary components configured within one or more environments. The processor may generate a digital twin associated with the machine. The digital twin associated with the machine area may be based, at least in part, on the component data. The processor may simulate the digital twin of the machine. The processor may generate, responsive to simulating the digital twin associated with the machine, the optimized assembly plan for the machine.

Abstract: A method, computer system, and a computer program product for robot assistance are provided. A computer identifies a position of a first robot in an environment and a range of movement of an arm of the first robot. The computer generates an alignment plan for moving a first conveyor to be aligned with the first robot so that the first conveyor is engageable with the arm of the first robot within the range of movement of the arm. The first conveyor and the first robot include respective supports that are separate from each other. The computer transmits the alignment plan for execution so that the first conveyor is moved according to the alignment plan.

Abstract: A first plurality of machine learning operations are performed on Internet of Things (IoT) input data in an IoT ecosystem. The first plurality of machine learning operations are performed using a first machine learning level. One or more first machine learning outputs are received from the first machine learning level. A second plurality of machine learning operations are executed on the one or more first machine learning outputs. The second plurality of machine learning operations are executed using a second machine learning level. One or more second machine learning outputs are obtained from the second machine learning level. A third plurality of machine learning operations run on the one or more second machine learning outputs. The third plurality of machine learning operations run using a third machine learning level. An IoT output is identified from the third machine learning level.

Abstract: A processor may receive worker data associated with a smart environment having one or more workers. The processor may generate a digital twin of the smart environment. The digital twin may be based, at least in part, on the worker data. The processor may simulate the digital twin of the smart environment. The processor may identify, responsive to simulating, a safety score. The safety score may be associated with the one or more workers.

Abstract: A capture operation is identified. The capture operation is performed by a first capture device. The capture operation is of a video steam of a subject that is located in an environment. The video stream includes a perspective of the subject. A capture limitation of the first capture device, related to capturing the subject, is determined. In response to the capture limitation one or more candidate capture devices in the environment are detected. A second capture device is selected as a selected capture device, from the one or more candidate capture devices. The second capture device is instructed to perform the capture operation. The video stream is transferred from the first capture device to the second capture device; the transfer maintains the perspective.

Abstract: Embodiments of the present invention determine a curiosity of a user based on data received from an electronic device associated with the user, where the data includes audible speech captured from user and one or more facial expressions of the user. Embodiments of the present invention identify a first wavelength for audible speech from the user to initiate a command detection mode based on a plurality of wavelengths associated with a user profile for the user. Embodiments of the present invention identify a topic for the audible speech from the user and responsive to determining an intelligent virtual assistant is an intended recipient based on the topic, suspend an activation word for the intelligent virtual assistant.