Abstract: In some examples, simulation-based software design and delivery attribute tradeoff identification and resolution may include receiving requirements specification, and generating, based on an analysis of the requirements specification, canonical sustainability requirements. Based on an analysis of the canonical sustainability requirements, sustainable software attribute decisions and an attribute optimization score may be generated, and used to generate a sustainable software attribute balance score and a tradeoff attributes list. Based on an analysis of the sustainable software attribute balance score and the tradeoff attributes list, a green quotient may be generated and used to generate an architecture document. Further, based on an analysis of the architecture document, software quality rules may be generated, and used to generate a software application.

Abstract: In some implementations, a device may receive a configuration associated with a machine learning model. The device may additionally receive a first hyperparameter set associated with the machine learning model. Accordingly, the device may estimate a first quantity of floating-point operations (FLOPs) associated with one or more epochs, for the machine learning model, based on the first hyperparameter set. The device may output, to a user, an indication of a first energy consumption associated with training the machine learning model based on the first quantity of FLOPs.

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 may receive application data identifying an application to be executed by a hybrid cloud computing environment and constraint data identifying constraints associated with the hybrid cloud computing environment. The device may select objectives based on the application data and the constraint data, and may identify factors associated with the objectives. The device may correlate the factors with potential data centers, and may apply weights to the correlated factors to generate correlated factor scores. The device may identify a list of data centers based on the correlated factor scores, and may determine a reduced list of data centers. The device may apply a preference technique to the reduced list of data centers to generate a ranked list of data centers, and may determine the hybrid cloud computing environment based on the ranked list of data centers. The device may perform actions based on the hybrid cloud computing environment.

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: Implementations include providing a robotic model representative of a robotic system, determining baseline data by executing a simulation using the robotic model and a simulation world that represents the environment, generating, by a perturbation system, perturbations to be included in a set of test cases, each test case including at least one perturbation to at least one object relative to a baseline of the at least one object determined from the baseline data, determining test result data for the set of test cases by modifying the at least one object in view of the at least one perturbation to provide a modified object and executing the simulation with the modified object, and displaying a graph representative of the at least one object based on the baseline data and test result data for a test case, the graph depicting variance of the object during the simulation using the test case.

Abstract: In some examples, simulation-based software design and delivery attribute tradeoff identification and resolution may include receiving requirements specification, and generating, based on an analysis of the requirements specification, canonical sustainability requirements. Based on an analysis of the canonical sustainability requirements, sustainable software attribute decisions and an attribute optimization score may be generated, and used to generate a sustainable software attribute balance score and a tradeoff attributes list. Based on an analysis of the sustainable software attribute balance score and the tradeoff attributes list, a green quotient may be generated and used to generate an architecture document. Further, based on an analysis of the architecture document, software quality rules may be generated, and used to generate a software application.

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: In some examples, user journey carbon footprint reduction may include generating, for a vehicle associated with a user, a carbon emission quota for user journey carbon footprint reduction. A predicted journey carbon emissions may be generated for the vehicle for a specified journey. Based on collaborative filtering, at least one goal-based and conditions-based recommendation may be generated for the user of the vehicle for the specified journey for the user journey carbon footprint reduction. Based on a user behavior model, a user-interface display may be generated for the specified journey for the user journey carbon footprint reduction. Further, based on the user behavior model, and real-time monitoring of the user and the vehicle, a real-time update of the user-interface display may be generated for the specified journey for the user journey carbon footprint reduction.

Abstract: A device may receive, from a user device, a request for an automation service for a project. The device may analyze the request to identify one or more input keywords. The device may obtain, by using the one or more input keywords to search one or more data sources, project information for a set of projects and/or automation services information for a set of automation services. The device may determine one or more similar projects and/or one or more similar automation services by analyzing the project information and/or the automation services information. The device may provide, to the user device, a recommendation that identifies one or more automation services associated with the one or more similar projects and/or the one or more similar automation services.

Abstract: According to an example, customized visualization based intelligence augmentation may include accessing, based on a user request, a domain model, and mapping the user request to the domain model. Based on the mapping, a guided query that includes a relevant refinement question may be generated. A response may be received to the refinement question. Based on the received response, a refined user request may be generated, and classified into an intelligence augmentation category. Based on the classification, an intelligence augmentation analyzer may be accessed to analyze the refined user request to generate an insight output that is classified to a visualization. Based on the classification of the insight output to the visualization, responsive to the user request, a display of the visualization may be generated.

Abstract: A device may receive a process model and a reference process model. The process model may include a first plurality of nodes corresponding to a first plurality of stages of a particular process. The reference process model may include a second plurality of nodes corresponding to a second plurality of stages of a reference process. The device may map the process model to the reference process model, identify that a first stage corresponds to a second stage, determine a difference between a value, of a metric, associated with the first stage and a value, of the metric, associated with the second stage, cause an interactive user interface to display a visual representation of the difference, identify automation service(s) that compensate for the difference, determine a score for the process model, and cause the interactive user interface to display the score to enable selection of optimal automation service(s) to deploy.

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, immersive and artificial intelligence based retail may include ascertaining attributes associated with a user, identifying a device to display an augmented reality display, a virtual reality display, and/or a mixed reality display, and generating, based on the ascertained attributes and for display on the identified device, a virtual store including a plurality of clothing articles. A clothing article may be selected from the plurality of clothing articles, and based on the selection of the clothing article, a design, a color combination, a texture, and/or a print pattern may be generated based on the clothing article. Further, immersive and artificial intelligence based retail may include generating a display of a plurality of three-dimensional hologram images of the user wearing the clothing article, and determining personalized clothing articles for the user based on selection of a clothing article from the displayed plurality of three-dimensional hologram images.

Abstract: According to an example, partitioning based migration of systems to container and micro-service based-platforms may include determining, based on an analysis of source code for an application that is to be partitioned, an entity model corresponding to the application, identifying resources associated with the application, and determining a mapping of the identified resources to entities of the entity model. Further, partitioning based migration of systems to container and micro-service based-platforms may include identifying dependencies for each of the mapped resources, generating dependency and control flow metrics for the application, generating affinity values between the mapped resources, generating a resource affinity graph, determining an affinity score between each of the mapped resources, and generating resource clusters that correspond to partitions of the application.

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: A device may perform a static analysis of a codebase associated with a software application to determine a structure of the codebase and one or more metrics associated with the codebase. The device may generate a structured format of the codebase based on the structure of the codebase. The device may generate a domain specific language format of the codebase based on the structured format of the codebase and the one or more metrics associated with the codebase. The device may generate an extended reality rendered view of the domain specific language format of the codebase. The device may provide, to an extended reality device, the extended reality rendered view of the domain specific language format of the codebase.

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 may evaluate first data to identify a plurality of users, where each user, of the plurality of users, is assigned to complete a portion of a group task. The device may evaluate the first data to select a first user, of the plurality of users, for completion of a particular portion of the group task based on a first set of decision criteria. The device may provide, to the first user, information identifying the particular portion of the group task. The device may receive second data relating to the particular portion of the group task after providing information identifying the particular portion of the group task. The device may evaluate the first data to select a second user, of the plurality of users, based on a second set of decision criteria. The device may provide, to the second user, information associated with the second data.