Abstract: In some examples, software CoBot engineering, execution, and monitoring may include extracting a CoBot requirement from a requirement specification for a CoBot that is to be implemented. Based on application of a CoBot description language to the CoBot requirement, a CoBot workflow that specifies a plurality of tasks to be performed by the CoBot may be generated. A determination may be made as to whether a task is to be performed by a bot or by a human. A team that includes a plurality of bots and at least one human may be generated to execute the CoBot workflow. The bots of the team may be prioritized to identify a bot that is a best match to the CoBot requirement. The CoBot that includes configured bots may be deployed in an operational environment to perform the CoBot workflow.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating customized recommendations for environmentally-conscious cloud computing frameworks for replacing computing resources of existing datacenters. One of the methods involves receiving, through a user interface presented on a display of a computing device, data regarding a user's existing datacenter deployment and the user's preferences for the new cloud computing framework, generating one or more recommendations for environmentally-conscious cloud computing frameworks based on the received data, and presenting such recommendations through the user interface for the user's review and consideration.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating customized recommendations for environmentally-conscious cloud computing frameworks for replacing computing resources of existing datacenters. One of the methods involves receiving, through a user interface presented on a display of a computing device, data regarding a user's existing datacenter deployment and the user's preferences for the new cloud computing framework, generating one or more recommendations for environmentally-conscious cloud computing frameworks based on the received data, and presenting such recommendations through the user interface for the user's review and consideration.

Abstract: In some examples, continuous learning-based application related trade-off resolution and implementation may include generating, based on a plurality of historical tradeoff instances, an application feature matrix. Further, association rules for historical tradeoff instances for which decisions are not known, and a decision tree for historical tradeoff instances for which decisions are known may be generated. Decision rules may be induced, and default rules may be applied to a cold start scenario. The decision rules and the default rules may be refined to generate refined rules, and a confidence level may be determined for the refined rules. The refined rules may be prioritized based on the confidence level and applied to a new tradeoff instance to generate a resolution associated with the new tradeoff instance. The resolution may be implemented with respect to the new tradeoff instance.

Abstract: System and methods for cryptologic digital twin segregation with blockchain for development operations are provided. By way of introductory example, a system may receive a first development object from a first devops tool and a second development object from a second devops tool. The system may identify, in a segregation model graph, a first node and a second node, the first node corresponding to the first development object and the second node corresponding to the second development object. The system may determine the first node is mapped to an off-chain storage classification and the second node is mapped to an on-chain storage classification. The system may store the first development object in an off-chain storage, generate a hash value of the first development object, and store the hash value of the first development object on a blockchain. The system may store the second development object on the blockchain.

Abstract: In some examples, data classification and modelling based application compliance analysis may include generating, for application data for an application, and based on data laws, a knowledge graph schema that is used to generate an instantiated knowledge graph. Based on a plurality of data sources, domain knowledge and patterns, and the instantiated knowledge graph, classified data may be generated to generate an annotated knowledge graph. Based on the annotated knowledge graph, an indication of applicable sovereign laws and compliance related to the application data for the application may be generated. Based on the annotated knowledge graph and the applicable sovereign laws and compliance related to the application data for the application, a sovereign cloud strategy related to the application data for the application may be generated. Further, the sovereign cloud strategy may be applied to the application data for the application.

Abstract: A device may obtain information identifying a base application. The device may extract a set of sub-application artifacts associated with the base application based on structural information associated with the base application. The device may define a set of metadata attributes associated with the set of sub-application artifacts associated with the base application. The device may generate a set of hash tuples for the set of metadata attributes associated with the set of sub-application artifacts associated with the base application. The device may generate a base composite identity of the base application based on the set of hash tuples. The device may store the base composite identity in a blockchain and in connection with storage of the base application in the blockchain to enable subsequent identification and verification of the base application.

Abstract: In some examples, extended reality based positive affect implementation for product development may include determining, for a user wearing or utilizing a device, characteristics and an environment of the user. Based on the characteristics and the environment of the user, a plurality of augmentation elements may be determined. A recommendation of a plurality of augmentation elements from the determined plurality of augmentation elements may be generated. Based on selection of an augmentation element from the recommended plurality of augmentation elements, the augmentation element may be rendered in an extended environment and/or an immediate environment of the user. Interaction of the user may be controlled with the rendered augmentation element. Feedback of the user may be analyzed with respect to the rendered augmentation element.

Abstract: System and methods for cryptologic digital twin segregation with blockchain for development operations are provided. By way of introductory example, a system may receive a first development object from a first devops tool and a second development object from a second devops tool. The system may identify, in a segregation model graph, a first node and a second node, the first node corresponding to the first development object and the second node corresponding to the second development object. The system may determine the first node is mapped to an off-chain storage classification and the second node is mapped to an on-chain storage classification. The system may store the first development object in an off-chain storage, generate a hash value of the first development object, and store the hash value of the first development object on a blockchain. The system may store the second development object on the blockchain.

Abstract: In some examples, extended reality based positive affect implementation for product development may include determining, for a user wearing or utilizing a device, characteristics and an environment of the user. Based on the characteristics and the environment of the user, a plurality of augmentation elements may be determined. A recommendation of a plurality of augmentation elements from the determined plurality of augmentation elements may be generated. Based on selection of an augmentation element from the recommended plurality of augmentation elements, the augmentation element may be rendered in an extended environment and/or an immediate environment of the user. Interaction of the user may be controlled with the rendered augmentation element. Feedback of the user may be analyzed with respect to the rendered augmentation element.

Abstract: In some examples, extended reality based immersive project workspace creation may include obtaining sensing data from at least one sensor of a device worn by a user, and analyzing, based on the sensing data, an environment of the user. A plurality of insights associated with the user may be determined, and prioritized based on a plurality of prioritization criteria. An insight of the plurality of prioritized insights associated with the user may be rendered in the environment of the user. Further, the rendering of the insight may be controlled based on an interaction of the user with the rendered insight.

Abstract: A device receives a machine learning model, model data associated with the machine learning model, and identifier generation data. The identifier generation data includes data utilized to generate identifier pairs that may be used to authenticate the machine learning model. The device selects an identifier model, for generating the identifier pairs, based on the machine learning model, the model data, and the identifier generation data. The device processes the machine learning model, the model data, and the identifier generation data, with the selected identifier model, to generate the identifier pairs and identifier pair data. The device stores the identifier pairs and the identifier pair data in one or more data structures, and utilizes the identifier pairs to identify and provide authentication for the machine learning model.

Abstract: A device may obtain information identifying a base application. The device may extract a set of sub-application artifacts associated with the base application based on structural information associated with the base application. The device may define a set of metadata attributes associated with the set of sub-application artifacts associated with the base application. The device may generate a set of hash tuples for the set of metadata attributes associated with the set of sub-application artifacts associated with the base application. The device may generate a base composite identity of the base application based on the set of hash tuples. The device may store the base composite identity in a blockchain and in connection with storage of the base application in the blockchain to enable subsequent identification and verification of the base application.