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: 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: A method and system for cloud distributed hybrid data storage and normalization are disclosed. The method may include obtaining a data set comprising data entities. A data entity may comprise data fields each containing a data element. The method may further include determining policy constraint meta-data for each of the data elements based on the storage policy constraint. The policy constraint meta-data may include a first meta-tag indicating the storage policy constraint for the data element. The method may further include determining whether a server satisfies the storage policy constraint based on the first meta-tag for the data element. When the server satisfies the storage policy constraint, the method may further include transmitting the data element to the server to store the data element on the server. When the server fails to satisfy the storage policy constraint, the method may further include, storing the data element on the client.

Abstract: A device may generate a knowledge model based on a knowledge model schema, data residency constraints, and a data classification ontology associated with a cloud application, and may perform a dynamic flow analysis of the cloud application data and the data source identifiers to generate a data flow graph. The device may process the data flow graph, with the knowledge model, to determine sensitive attributes in the data flow graph, and may identify sensitive data sources that include the sensitive attributes and sensitive assets based on the data flow graph and the sensitive data sources. The device may process the sensitive data sources and the sensitive assets, with a machine learning model, to determine methods for identifying misconfigurations, and may utilize the methods to identify misconfigurations and severities of the misconfigurations. The device may generate remediation actions for correcting the cloud application based on the severities of the misconfigurations.

Abstract: Systems and methods supporting discovery and quantification of vulnerabilities in software code are disclosed. The systems and methods provide functionality for using software code analysis and other types of tools to analyze the software code and determine whether it can be trusted. The software code tools may be able to discover various hidden issues in the software code and the outputs of such tools may be normalized to quantify the risk associated with vulnerabilities identified by the different tools. A labeling strategy is provided to label the software code to enable users to identify the best software among various available software options based on the label(s) and a set of criteria.

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: 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: A method and system for cloud distributed hybrid data storage and normalization are disclosed. The method may include obtaining a data set comprising data entities. A data entity may comprise data fields each containing a data element. The method may further include determining policy constraint meta-data for each of the data elements based on the storage policy constraint. The policy constraint meta-data may include a first meta-tag indicating the storage policy constraint for the data element. The method may further include determining whether a server satisfies the storage policy constraint based on the first meta-tag for the data element. When the server satisfies the storage policy constraint, the method may further include transmitting the data element to the server to store the data element on the server. When the server fails to satisfy the storage policy constraint, the method may further include, storing the data element on the client.

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, 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: A method and system for cloud distributed hybrid data storage and normalization are disclosed. The method may include obtaining a data set comprising data entities. A data entity may comprise data fields each containing a data element. The method may further include determining policy constraint meta-data for each of the data elements based on the storage policy constraint. The policy constraint meta-data may include a first meta-tag indicating the storage policy constraint for the data element. The method may further include determining whether a server satisfies the storage policy constraint based on the first meta-tag for the data element. When the server satisfies the storage policy constraint, the method may further include transmitting the data element to the server to store the data element on the server. When the server fails to satisfy the storage policy constraint, the method may further include, storing the data element on the client.

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: 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, 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: 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: 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: 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.