Abstract: Techniques are described for linked blockchains that allow computing devices to access information from an acceptance blockchain about acceptance reports stored in an enterprise delivery blockchain that are also linked to a product blockchain that stores information of the products of the acceptance reports. An acceptance report is indicative of operability of a product in a service provided by a service provider. A processor is configured to access the block in the enterprise delivery blockchain, retrieve the acceptance report from the block in the enterprise delivery blockchain, determine an identifier, identified in the block in the enterprise delivery blockchain, to a block in an acceptance blockchain, access the block in the acceptance blockchain via the determined identifier, retrieve from the block in the acceptance blockchain contextual information of the acceptance report, and output the acceptance report and contextual information of the acceptance report.

Abstract: Managing network interactions by engaging a networked information broadcast service, receiving information from the networked information broadcast service, filtering the information according to a profile, and sending information according to the filtered information using another network communications connection.

Abstract: Techniques are provided for mapping natural language to code segments. In one embodiment, the techniques involve receiving a document and software code, wherein the document comprises a natural language description of a use of the code, generating, via a vectorization process performed on the document, at least one vector or word embedding, generating, via a natural language processing technique performed on the at least one vector or word embedding, a first label set, generating, via a machine learning analysis of the software code, a second label set, determining, based on a comparison of the first label set and the second label set, a match confidence between the document and the software code, wherein the match confidence indicates a measure of similarity between the first label set and the second label set, and upon determining that the match confidence exceeds a predefined threshold, mapping the document to the software code.

Abstract: A computer-implemented method, computer system, and computer program product for blockchain enabled service reservation and delegation. The present invention may include receiving one or more first or second trigger conditions defined by a user, detecting an occurrence of the one or more first trigger conditions, deploying a smart contract based on the received one or more first trigger condition, detecting an occurrence of the one or more second trigger conditions, and deactivating the smart contract based on the received one or more second trigger conditions. The present invention may include receiving a subject registration, the subject is the user, a service, or a device. The one or more first trigger conditions may be a condition that upon its occurrence may initiate the deployment of the smart contract. The one or more second trigger conditions may be a condition that upon its occurrence may initiate the deactivation of the smart contract.

Abstract: Managing security access in real-time to a computer system using control lists includes detecting a security event at a computer system. The security event is analyzed including an analysis of a historical corpus having historical data of security events. An access control list is generated based on the security event. A determination is made when the security event includes abnormal behavior based on the analysis of the security event and the historical corpus. The security event is published to a monitoring system for controlling access to the computer system, in response to the security event.

Abstract: Techniques are provided for mapping natural language to code segments. In one embodiment, the techniques involve receiving a document and software code, wherein the document comprises a natural language description of a use of the code, generating, via a vectorization process performed on the document, at least one vector or word embedding, generating, via a natural language processing technique performed on the at least one vector or word embedding, a first label set, generating, via a machine learning analysis of the software code, a second label set, determining, based on a comparison of the first label set and the second label set, a match confidence between the document and the software code, wherein the match confidence indicates a measure of similarity between the first label set and the second label set, and upon determining that the match confidence exceeds a predefined threshold, mapping the document to the software code.

Abstract: Methods and systems for selecting a tool for a project is described. In an example, a processor can run a machine learning model to generate a set of requirements to implement a project. The processor can identify a keyword from the set of requirements. The processor can search for the keyword on a search engine. The processor can receive a search result from the search engine corresponding to the keyword. The processor can identify, based on the search result, a tool that can be used to implement the project, where the tool can be in compliance with the set of requirements.

Abstract: In an approach for a text block recognition in a document, a processor detects characters in the document using an object detection technique. A processor identifies positions of the detected characters in the document. A processor analyzes semantic connectivity among the detected characters based on the positions and semantic connectivity of the characters. A processor recognizes text blocks of related characters based on the semantic connectivity analysis. A processor outputs the text blocks associated with the related characters.

Abstract: In an approach to blockchain management of cloud service provisioning failures, one or more computer processors capture one or more application programming interface (API) calls associated with a service provision. One or more computer processors submit the captured one or more API calls to a blockchain ledger. One or more computer processors detect a system failure during the service provision. One or more computer processors extract the submitted one or more API calls from the blockchain ledger. Based on the extracted one or more API calls, one or more computer processors identify a problematic system associated with the system failure.

Abstract: A method, system, and computer program product for implementing automated cognitive software application error detection is provided. The method includes receiving data associated with model based self-learning software code. The annotated data is automatically divided with respect to specified categorization and grouping attributes and categorized groups comprising portions of the annotated data are generated and analyzed. At least one incorrect annotation associated a group of the categorized groups is detected and filtered. Likewise, a correct annotation for the group is detected and retrieved from a database. The correct annotation is appended to the group.

Abstract: A computer-implemented method, computer system, and computer program product for blockchain enabled service reservation and delegation. The present invention may include receiving one or more first or second trigger conditions defined by a user, detecting an occurrence of the one or more first trigger conditions, deploying a smart contract based on the received one or more first trigger condition, detecting an occurrence of the one or more second trigger conditions, and deactivating the smart contract based on the received one or more second trigger conditions. The present invention may include receiving a subject registration, the subject is the user, a service, or a device. The one or more first trigger conditions may be a condition that upon its occurrence may initiate the deployment of the smart contract. The one or more second trigger conditions may be a condition that upon its occurrence may initiate the deactivation of the smart contract.

Abstract: Filtering incoming calls according to predicted preferences of a user. User preferences are predicted by analysis of user behavior, online activity, oral queues, and purchasing history. Data analysis includes weighting caller and user attributes according to a scheme that is dynamically updated by applying user feedback and/or machine learning processes.

Abstract: In an approach for a text block recognition in a document, a processor detects characters in the document using an object detection technique. A processor identifies positions of the detected characters in the document. A processor analyzes semantic connectivity among the detected characters based on the positions and semantic connectivity of the characters. A processor recognizes text blocks of related characters based on the semantic connectivity analysis. A processor outputs the text blocks associated with the related characters.

Abstract: In an approach to blockchain management of cloud service provisioning failures, one or more computer processors capture one or more application programming interface (API) calls associated with a service provision. One or more computer processors submit the captured one or more API calls to a blockchain ledger. One or more computer processors detect a system failure during the service provision. One or more computer processors extract the submitted one or more API calls from the blockchain ledger. Based on the extracted one or more API calls, one or more computer processors identify a problematic system associated with the system failure.

Abstract: A method, system, and computer program product for reconstructing training data and building a new incremental learning model with the reconstructed training data that can be further trained. The method may include receiving new data to be inputted into a previously trained machine learning model, where the previously trained machine learning model has inaccessible training data. The method may also include generating simulated training data using a reverse form of the previously trained machine learning model. The method may also include verifying the simulated training data. The method may also include creating a new machine learning model using the simulated training data, where the new machine learning model includes a same structure as the previously trained machine learning model. The method may also include inputting the new data into the new machine learning model, where the new machine learning model is further trained with the new data.

Abstract: An approach is provided for generating a scaling plan. Plans for onboarding first tenant(s) a cloud computing environment and offboarding second tenant(s) of the cloud computing environment are received. Historical data about behavior of tenants of the cloud computing environment is received. Based on the received plans and the historical data, a scaling plan for scaling computer resources of external systems during the onboarding and the offboarding is generated. The scaling plan specifies a timeline indicating dates and times at which changes in workloads associated with the external systems are required for the onboarding and the offboarding. Based on the scaling plan, a scaling is determined to be needed for computer resource(s) of one of the external systems. Responsive to determining that the scaling is needed, the scaling for the computer resource(s) is triggered at a date and a time indicated by the timeline.

Abstract: An approach is provided for generating a scaling plan. Plans for onboarding first tenant(s) a cloud computing environment and offboarding second tenant(s) of the cloud computing environment are received. Historical data about behavior of tenants of the cloud computing environment is received. Based on the received plans and the historical data, a scaling plan for scaling computer resources of external systems during the onboarding and the offboarding is generated. The scaling plan specifies a timeline indicating dates and times at which changes in workloads associated with the external systems are required for the onboarding and the offboarding. Based on the scaling plan, a scaling is determined to be needed for computer resource(s) of one of the external systems. Responsive to determining that the scaling is needed, the scaling for the computer resource(s) is triggered at a date and a time indicated by the timeline.

Abstract: Filtering incoming calls according to predicted preferences of a user. User preferences are predicted by analysis of user behavior, online activity, oral queues, and purchasing history. Data analysis includes weighting caller and user attributes according to a scheme that is dynamically updated by applying user feedback and/or machine learning processes.

Abstract: Methods and systems for selecting a tool for a project is described. In an example, a processor can run a machine learning model to generate a set of requirements to implement a project. The processor can identify a keyword from the set of requirements. The processor can search for the keyword on a search engine. The processor can receive a search result from the search engine corresponding to the keyword. The processor can identify, based on the search result, a tool that can be used to implement the project, where the tool can be in compliance with the set of requirements.

Abstract: Approaches presented herein enable generation of a multi-dimensional tag metric in a cloud resource management environment. More specifically, a tagging namespace is provided for managing a resource in the cloud resource management environment. This namespace comprises at least two dimensions and a plurality of positions. A set of tags associated with the resource are received into the tagging namespace. A match of each tag of the set of tags to a position within the namespace into which that tag was received is verified and an alert is triggered in the case verification fails. Alternatively, in the case verification is successful, the tag-containing namespace is validated as a multi-dimensional tag metric.