Abstract: Approaches presented herein enable automatically fulfilling an obligation under a smart contract. A block is added to a blockchain ledger in response to an event that triggers the obligation. The block includes data related to the event. Inferences related to the event are derived based on an analysis of event data and historical data incorporated in prior blocks in the blockchain ledger. Based on the inferences, a potential cause of the event is derived. Based on the potential cause, an interested stakeholder to the fulfilling of the obligation is identified. The interested stakeholder is added to a group that is allowed access to the blockchain ledger corresponding to the smart contract.

Abstract: In an approach to robot vision configuration, one or more computer processors receive a command for image capture by a robot. The one or more computer processors determine one or more cameras of a plurality of cameras to respond to the command. The one or more computer processors configure the one or more cameras to respond to the command.

Abstract: Aspects of the present invention provide an approach for controlling an operation of a video capture device (e.g., in a cognitive robotic device). In an embodiment, a set of conditions is obtained using a cognitive computer system. Each of the obtained conditions includes a specific individual and a potential action that may be performed by the individual. The cognitive computer system analyzes video being captured by the video capture device to determine whether the video satisfies any of the set of conditions (e.g., the individual in the video is performing the action). If the cognitive computer system determines that one of the set of conditions has been satisfied, the operation of the video capture device (e.g., capture rate of the video) is modified to account for the satisfied condition.

Abstract: Approaches presented herein enable automatically fulfilling an obligation under a smart contract. A block is added to a blockchain ledger in response to an event that triggers the obligation. The block includes data related to the event. Inferences related to the event are derived based on an analysis of event data and historical data incorporated in prior blocks in the blockchain ledger. Based on the inferences, a potential cause of the event is derived. Based on the potential cause, an interested stakeholder to the fulfilling of the obligation is identified. The interested stakeholder is added to a group that is allowed access to the blockchain ledger corresponding to the smart contract.

Abstract: Aspects of the present invention provide an approach for controlling an operation of a video capture device (e.g., in a cognitive robotic device). In an embodiment, a set of conditions is obtained using a cognitive computer system. Each of the obtained conditions includes a specific individual and a potential action that may be performed by the individual. The cognitive computer system analyzes video being captured by the video capture device to determine whether the video satisfies any of the set of conditions (e.g., the individual in the video is performing the action). If the cognitive computer system determines that one of the set of conditions has been satisfied, the operation of the video capture device (e.g., capture rate of the video) is modified to account for the satisfied condition.

Abstract: Aspects of the present invention provide an approach for controlling an operation of a video capture device (e.g., in a cognitive robotic device). In an embodiment, a set of conditions is obtained using a cognitive computer system. Each of the obtained conditions includes a specific individual and a potential action that may be performed by the individual. The cognitive computer system analyzes video being captured by the video capture device to determine whether the video satisfies any of the set of conditions (e.g., the individual in the video is performing the action). If the cognitive computer system determines that one of the set of conditions has been satisfied, the operation of the video capture device (e.g., capture rate of the video) is modified to account for the satisfied condition.

Abstract: In an approach to non-functional requirement testing of a robot, a computer determines one or more kinematic actions included in a received command. The computer determines at least one component of the robot included in an operation of the one or more kinematic actions. The computer determines at least one agent associated with the at least one component of the robot included in the one or more kinematic actions. The computer sends the one or more kinematic actions to the robot. In response to the robot performing the one or more kinematic actions, the computer receives, from the at least one agent, at least one characteristic of the performed one or more kinematic actions associated with the at least one component of the robot included in the one or more kinematic actions. The computer, based on the received at least one characteristic, generates one or more test results.

Abstract: Aspects of the present invention provide an approach for controlling an operation of a video capture device (e.g., in a cognitive robotic device). In an embodiment, a set of conditions is obtained using a cognitive computer system. Each of the obtained conditions includes a specific individual and a potential action that may be performed by the individual. The cognitive computer system analyzes video being captured by the video capture device to determine whether the video satisfies any of the set of conditions (e.g., the individual in the video is performing the action). If the cognitive computer system determines that one of the set of conditions has been satisfied, the operation of the video capture device (e.g., capture rate of the video) is modified to account for the satisfied condition.

Abstract: Aspects of the present invention provide an approach for controlling an operation of a video capture device (e.g., in a cognitive robotic device). In an embodiment, a set of conditions is obtained using a cognitive computer system. Each of the obtained conditions includes a specific individual and a potential action that may be performed by the individual. The cognitive computer system analyzes video being captured by the video capture device to determine whether the video satisfies any of the set of conditions (e.g., the individual in the video is performing the action). If the cognitive computer system determines that one of the set of conditions has been satisfied, the operation of the video capture device (e.g., capture rate of the video) is modified to account for the satisfied condition.

Abstract: A system for classifying and tagging digital images includes: a CPU, a computer readable memory, and a computer readable storage medium associated with a computer device; program instructions defining plural pipelines each configured to classify and tag aspects of the image, wherein a first one of the plural pipelines is configured to classify and tag an object in the image, and a second one of the plural pipelines is configured to classify and tag a kinematic aspect of the object in the image; program instructions defining a controller configured to: pass the image to each of the plural pipelines in a predefined order; and output an annotation of the image to a user interface. The program instructions are stored on the computer readable storage medium for execution by the CPU via the computer readable memory.

Abstract: Aspects of the present invention provide an approach for controlling an operation of a video capture device (e.g., in a cognitive robotic device). In an embodiment, a set of conditions is obtained using a cognitive computer system. Each of the obtained conditions includes a specific individual and a potential action that may be performed by the individual. The cognitive computer system analyzes video being captured by the video capture device to determine whether the video satisfies any of the set of conditions (e.g., the individual in the video is performing the action). If the cognitive computer system determines that one of the set of conditions has been satisfied, the operation of the video capture device (e.g., capture rate of the video) is modified to account for the satisfied condition.

Abstract: A method for classifying and annotating an image includes: receiving, by a computer device and from a user interface, an input of an image; generating an annotation of the image, by the computer device, by passing the image to plural separate pipelines and tag libraries, wherein the plural separate pipelines and tag libraries include: a pipeline configured to classify and tag objects in the image; and a pipeline configured to tag kinematic aspects of the objects in the image; and outputting, by the computer device, the annotation to the user interface.

Abstract: In an approach to robot vision configuration, one or more computer processors receive a command for image capture by a robot. The one or more computer processors determine one or more cameras of a plurality of cameras to respond to the command. The one or more computer processors configure the one or more cameras to respond to the command.

Abstract: The factual accuracy of an event is verified. Event data is received by a computer, whereby the event data includes actor data related to at least one actor involved in the event and location data related to a location of the event. A factual scenario is created based on the event data. A cognitive reasoning and analysis of the event data is performed to derive inferences regarding the event and a time-sequenced series of inferences is composed based on the cognitive reasoning and analysis of the event data. Integrity of the event data is validated by comparing a data points from different sources and at least one flag is prompted when an instance of factual inconsistency is identified by the step of validating the integrity. A rendering of the event is generated based on the factual scenario and the time-sequenced series of inferences.

Abstract: In an approach to non-functional requirement stimulus testing of a robot, one or more computer processors receive one or more stimulus parameters to test. The one or more computer processors trigger the one or more stimulus parameters in the robot. The one or more computer processors determine at least one response time to the one or more stimulus parameters.

Abstract: In an approach to non-functional requirement testing of a robot, a computer determines one or more kinematic actions included in a received command. The computer determines at least one component of the robot included in an operation of the one or more kinematic actions. The computer determines at least one agent associated with the at least one component of the robot included in the one or more kinematic actions. The computer sends the one or more kinematic actions to the robot. In response to the robot performing the one or more kinematic actions, the computer receives, from the at least one agent, at least one characteristic of the performed one or more kinematic actions associated with the at least one component of the robot included in the one or more kinematic actions. The computer, based on the received at least one characteristic, generates one or more test results.

Abstract: A method, computer program product, and system includes a processor(s) to obtain, over a communications network, media comprising at least one audio file, The processor(s) determines that the audio file includes human speech and extract the human speech from the audio file. The processor(s) contextualizes general elements of the human speech, based on analyzing metadata of the file. The processor(s) generates an unannotated textual representation of the human speech, where the unannotated textual representation includes spoken words. The processor(s) annotates the unannotated textual representation of the human speech, with indicators, where each indicator identifies a granular contextual element in the unannotated textual representation of the human speech. The processor(s) generates a textual representation of the human speech, by applying a template to the annotated textual representation, where the template defines values for the indicators in the annotated textual representation.

Abstract: For data integration using APIs, a request for data is analyzed to determine a set of functional characteristics and a set of non-functional characteristics expected in the data. A first API entry is selected in a registry of API entries, the first API entry corresponding to a first API of the first data source. The first API entry includes a first metadata corresponding to a first functional characteristic in the set of functional characteristics. The first API is invoked to obtain a first portion of the data, the first portion having the first functional characteristic. Using a second API entry in the registry, a second API is invoked to obtain a second portion of the data. The first portion and the second portion are returned in a response to the request.