Abstract: Systems and methods for analyzing machine learning of cognitive software robots (CogBots) over time are provided. In implementations, a method includes generating, by a computing device, a graph of historic learning curves based on historic learning data over time for a subject obtained from a primary cognitive software robot (CogBot) and at least one secondary CogBot; generating, by the computing device, a best probable learning curve based on the historic learning curves of the graph, wherein the best probable learning curve is predictive of future learning by the primary CogBot for the subject; and generating, by the computing device, information regarding a current status of the learning of the primary CogBot based on the best probable learning curve.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: obtaining historical data of demand response programs and demand response agreements respective to each of the users regarding a subject energy. Training dataset for a DR user pooling model includes attributes of the demand response data collected that are relevant to responsivities of the demand response programs. The DR user pooling model is trained by the training dataset by machine learning. A DR user pool is identified amongst users of the demand response program by the DR user pooling model. Users in the DR user pool respond to demands as a group and the DR user pool is adjusted to improve responsivities of the demand response programs.

Abstract: An approach to generating a learning path framework may be provided. A Cognitive Bot may monitor the knowledge stream of a subject matter expert (SME) to glean insights from the activities and events performed by the SME. The CogBot determine categories within the subject matter. The CogBot may tune a grade scoring engine using the insights gleaned from the knowledge stream as a threshold for the grade scoring module. The knowledge stream of a second user may be monitored by a CogBot. A grade score of the subject matter for the second user may be generated by the grade scoring engine. An expertise level associated with the categories may be determined. A learning path framework may be generated based on the generated grade score and expertise level.

Abstract: A method and associated systems use DVFS performance-scaling technology to satisfy quality-of-service performance requirements when recovering a job that had been scheduled to run on a failed virtual machine. A Buffer Time specifies a duration of time remaining, at the time of failure, for the job to complete in order to satisfy the quality-of-service requirements. Depending on relative durations of time required to repair the failed virtual machine, to perform the job on an unscaled active-mode virtual machine, and to transfer the job to another virtual machine, the system determines whether to repair the failed virtual machine or to transfer the job. If the latter, the system then determines whether to select a destination virtual machine provisioned on a DVFS-compliant platform and, if so, the system scales the DVFS-compliant platform's performance to a level sufficient to complete the job within the Buffer Time.

Abstract: Systems and methods for monitoring of utility assets using crowd-sourced digital image data are disclosed. In embodiments, a method includes: monitoring, by a computing device, incoming real-time utility data; determining, by the computing device, that a triggering event has occurred based on the monitoring; sending, by the computing device, an event message to one or more agent devices comprising a request for digital image collection at a location based on the triggering event, the event message including event information, wherein the one or more agent devices are associated with respective agents in a crowd-sourcing network; receiving, by the computing device, event data from the one or more agent devices, the event data including one or more digital images; and processing, by the computing device, the one or more digital images to determine next steps with respect to the triggering event.

Abstract: A computer implemented method is provided that includes creating an industry force graph meta model; and establishing a relationship for each maturity dimension to determine most relevant content. The most relevant content is graphed using a chromatic polynomial to map strongest industry trends in an industry force. The method continues with building traversal logic to determine most relevant technologies for the strongest industry trends in the industry force. Most relevant components of an component business model are identified, and linkages between the strongest industry trends in the industry force are made to the most relevant components of the component business model.

Abstract: Generating a work-flow diagram from technology specific literature includes using an analysis of technology specific literature which includes using natural language processing (NLP), to derive semantics for a plurality of components defined in the technology specific literature. The plurality of components are associated to symbols in a component symbol database. The associated symbols are assigned to each of the plurality of components, respectively. A relationship is determined for each of the plurality of components. Nodes are assigned, and vectors are assigned when applicable, for each of the symbols. A work-flow diagram is generated for the technology specific literature using the assigned associated symbols for each of the plurality of components and the nodes and the vectors for each of the symbols.

Abstract: A computer-implemented method includes: receiving, by a computing device, a request from a requester; determining, by the computing device, one or more delegates that are currently capable of handling the request; sending, by the computing device, a request package to each of the one or more delegates, the request package including an authentication challenge; receiving, by the computing device, a solution to the authentication challenge from a supplier, the solution being provided by a particular delegate of the one or more delegates; determining, by the computing device, the solution to the authentication challenge is valid; and instructing, by the computing device and in response to determining the solution to the authentication challenge is valid, the particular delegate to proceed with handling the request.

Abstract: A system and method for managing the provisioning of electricity to an electric vehicle includes identifying an electric vehicle when the electric vehicle connects to a charging point; determining whether the electric vehicle is associated with a smart contract; validating the smart contract associated with the electric vehicle; creating a charging transaction for charging the electric vehicle; and applying the smart contract associated with the electric vehicle against the charging transaction. The electric vehicle electricity contract management system and method may use a consensus system such as a distributed ledger system or blockchain.

Abstract: A computer system message generated by an application programming interface (API) or addressed to the API can be received. A first data sensitivity score for at least a first of a plurality of data elements in a payload of the computer system message and at least second data sensitivity score for at least a second of the plurality of data elements in the payload of the computer system message can be determined. Based on the first data sensitivity score and at least the second data sensitivity score, a differential security can be applied to the computer system message. Applying the differential security can include masking the first of the plurality of data elements and not masking the second of the plurality of data elements. The computer system message can be electronically communicated the to a destination to which the message is addressed.

Abstract: A method, computer program product, and a system where a processor(s) obtains a universe of social media items posted to a social media platform at a given time. The processor(s) determines a likelihood of each item of the universe of social media items becoming viral by linking each item of the universe to a breakout pattern based on predefined dimensions and bucketing each item into a defined grouping by a measure of anticipated virality of the item. The processor(s) applies a susceptible-infected-recovered (SIR) model as a differential equation to the bucketed items to determine an equilibrium and one or more breakout points. The processor(s) determines if a social media item will become viral based on whether the item matches at least one or the one or more breakout points.

Abstract: A power-distribution routing system of a power-transmission company receives a request to route electrical power to users through a power-grid infrastructure during a specified future period of time. The system retrieves time-stamped blockchain data that identifies past fluctuations in energy demand, service agreements between energy companies, and energy-production and demand-fulfilment histories of energy-generating sources like power plants. The system also retrieves extrinsic contextual and socioeconomic data from online sources and various business applications. An artificially intelligent cognitive framework uses a sliding-frame mechanism to infer patterns in the rate of change of user demand during past time periods similar to the period specified by the request. The system ranks each source by its demonstrated ability to satisfy the patterns of demand in consideration of the contextual data.

Abstract: A power-distribution routing system of a power-transmission company receives a request to route electrical power to users through a power-grid infrastructure during a specified future period of time. The system retrieves time-stamped blockchain data that identifies past fluctuations in energy demand, service agreements between energy companies, and energy-production and demand-fulfilment histories of energy-generating sources like power plants. The system also retrieves extrinsic contextual and socioeconomic data from online sources and various business applications. An artificially intelligent cognitive framework uses a sliding-frame mechanism to infer patterns in the rate of change of user demand during past time periods similar to the period specified by the request. The system ranks each source by its demonstrated ability to satisfy the patterns of demand in consideration of the contextual data.

Abstract: Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: assigning resources of a K-tier resource pool to a certain job residing in a job queue, wherein the certain job residing in the job queue features job coupling characterized by an independent job and a dependent job which for completion depends on an output of the independent job, wherein the K-tier resource pool includes at least one foreground virtual machine (VM) having a first central processing unit (CPU) priority and at least one background virtual machine (VM) having a second CPU priority, wherein the first CPU priority is higher than the second CPU priority, wherein the assigning resources of the K-tier resource pool to the certain job includes assigning one or more foreground VM to the independent job and assigning one or more background VM to the dependent job.

Abstract: Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: assigning resources of a K-tier resource pool to a certain job residing in a job queue, wherein the certain job residing in the job queue features job coupling characterized by an independent job and a dependent job which for completion depends on an output of the independent job, wherein the K-tier resource pool includes at least one foreground virtual machine (VM) having a first central processing unit (CPU) priority and at least one background virtual machine (VM) having a second CPU priority, wherein the first CPU priority is higher than the second CPU priority, wherein the assigning resources of the K-tier resource pool to the certain job includes assigning one or more foreground VM to the independent job and assigning one or more background VM to the dependent job.

Abstract: Systems and methods for monitoring of utility assets using crowd-sourced digital image data are disclosed. In embodiments, a method includes: monitoring, by a computing device, incoming real-time utility data; determining, by the computing device, that a triggering event has occurred based on the monitoring; sending, by the computing device, an event message to one or more agent devices comprising a request for digital image collection at a location based on the triggering event, the event message including event information, wherein the one or more agent devices are associated with respective agents in a crowd-sourcing network; receiving, by the computing device, event data from the one or more agent devices, the event data including one or more digital images; and processing, by the computing device, the one or more digital images to determine next steps with respect to the triggering event.

Abstract: A method, system and computer program product for determining API pricing. Consumption parameters are identified using a supervised learning model. The API consumption parameters refer to any parameters that can be used to describe an API (functionality or otherwise) and can be used to compare other comparable APIs in similar domains provided by other providers. Furthermore, reference pricing is determined using machine learning using the identified API consumption parameters. Additionally, the API price is determined dynamically using the identified API consumption parameters and the determined reference pricing. An API pricing score is then derived for the API price using the supervised learning model. The API price is selected as the suggested price for the API in response to the API pricing score exceeding a threshold value. In this manner, an API price is established that reflects the true value of the API assessed by the API consumer.

Abstract: A method, system and computer program product are disclosed for creating a dynamic heterogeneous IoT network from existing IoT network devices to identify and implement a dynamic response to an event. In an embodiment, the method comprises identifying an occurrence of a specified event; and creating a heterogeneous IoT network to respond to the event, including selecting a master IoT device and identifying one or more participatory IoT devices for the created network from multiple IoT devices from one or more existing IoT networks. The master IoT device is in bidirectional communications with the participatory IoT devices. The master device identifies a response to the specified event, translates the response into commands, and sends the commands to the participatory IoT devices to implement the response. The master IoT device may pass control to another master device in another heterogeneous IoT network if the response is not accomplished.

Abstract: A method (and structure and computer product) of recognizing and tracking an object includes receiving image data from at least two camera sensors and analyzing the image data to determine attributes of an object detected as present in the image data. A first decision tree (DT) is developed to classify the object, based on the attributes determined from the image data. A second DT is developed to refine the first DT, wherein the second DT is used to refine the first DT by determining which attributes are most significant in the classification of the object.

Abstract: A method, system and computer program product for determining API pricing. Consumption parameters are identified using a supervised learning model. The API consumption parameters refer to any parameters that can be used to describe an API (functionality or otherwise) and can be used to compare other comparable APIs in similar domains provided by other providers. Furthermore, reference pricing is determined using machine learning using the identified API consumption parameters. Additionally, the API price is determined dynamically using the identified API consumption parameters and the determined reference pricing. An API pricing score is then derived for the API price using the supervised learning model. The API price is selected as the suggested price for the API in response to the API pricing score exceeding a threshold value. In this manner, an API price is established that reflects the true value of the API assessed by the API consumer.