Abstract: A method and system for assisting program code development are disclosed. The method may include obtaining multimodal data of a meeting discussing a program code development, extracting a plurality of topics and a plurality of concepts from the multimodal data, identifying a plurality of meeting segments for the plurality of concepts. The method may further include determining a coding intent from program codes for the program code development, aligning the coding intent to a set of topics, identifying the set of concepts associated with the topic aligned with the coding intent, identifying a set of meeting segments associated with the concept. The method may further include determining an alignment metric of the meeting segment based on an alignment metric between the coding intent and the topic, and outputting one or more meeting segments for the coding intent based on alignment metrics of the one or more meeting segments.

Abstract: In some implementations, a device may receive first energy consumption information relating to a set of hardware components of a computing system. The device may receive second energy consumption information relating to a set of virtual machines associated with the computing system. The device may receive third energy consumption information relating to a set of software elements associated with the computing system. The device may determine an energy consumption of the computing system based on the first energy consumption information, the second energy consumption information, and the third energy consumption information. The device may identify, based on the energy consumption of the computing system, an energy optimization associated with a usage context of the computing system. The device may transmit a set of instructions to alter one or more parameters of the computing system to implement the energy optimization for the computing system.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support intelligent re-use of knowledge (e.g., across an organization) using a natural text-based querying framework. A knowledge representation of prior work performed for the organization may be generated based on organizational knowledge (e.g., historical work record data that identifies a plurality of work items across an organization). The knowledge representation may include individual work-record entities for each respective work item and individual knowledge graphs corresponding to the individual work-record entities. For each individual knowledge graph, operations may be performed to identity and store project name, subgraph, sentence embedding, and word embedding information.

Abstract: In some implementations, a device may receive information identifying a computing system for energy management, the computing system having a set of hardware components, a set of virtual machines, and a set of software entities. The device may generate a digital twin of the computing system for simulation of the set of hardware components, the set of virtual machines, and the set of software entities. The device may determine, using the digital twin of the computing system, a set of energy consumption metrics, for the computing system, associated with a set of candidate parameters. The device may generate, using a recommendation engine, one or more recommendations for the computing system based on the set of energy consumption metrics associated with the set of candidate parameters. The device may transmit information associated with identifying the one or more recommendations.

Abstract: Disclosed herein is a system and method for generating computer code for a plurality of components of a software development project. An artificial intelligence code generator can generate computer code in response to a natural language text input describing a component of the software development project. A first database can store natural language text describing components of the software development project. A second database can store computer code generated at least partially by the artificial intelligence code generator and defining components of the software development project and corresponding to the natural language text stored in the first database. Using a pre-trained language model, the system can generate a natural language summary text based on the code intent of the first component, the identified characteristics of the objects, and the natural language text retrieved from the first database.

Abstract: A remote location monitoring system identifies a place of interest and a subset of artificial satellites that can capture images of the place of interest within a threshold period. The next available artificial satellite with image sensors that can capture images of the place of interest earliest is selected and the cache heating signal is transmitted to be stored in a cache associated with the next available image sensor. The cache heating signal activates particular image sensors for image capture and enables the transmission of meaningful images that enable monitoring the place of interest.

Abstract: In some implementations, a device may receive first energy consumption information relating to a set of hardware components of a computing system. The device may receive second energy consumption information relating to a set of virtual machines associated with the computing system. The device may receive third energy consumption information relating to a set of software elements associated with the computing system. The device may determine an energy consumption of the computing system based on the first energy consumption information, the second energy consumption information, and the third energy consumption information. The device may identify, based on the energy consumption of the computing system, an energy optimization associated with a usage context of the computing system. The device may transmit a set of instructions to alter one or more parameters of the computing system to implement the energy optimization for the computing system.

Abstract: A method and system for assisting program code development are disclosed. The method may include obtaining multimodal data of a meeting discussing a program code development, extracting a plurality of topics and a plurality of concepts from the multimodal data, identifying a plurality of meeting segments for the plurality of concepts. The method may further include determining a coding intent from program codes for the program code development, aligning the coding intent to a set of topics, identifying the set of concepts associated with the topic aligned with the coding intent, identifying a set of meeting segments associated with the concept. The method may further include determining an alignment metric of the meeting segment based on an alignment metric between the coding intent and the topic, and outputting one or more meeting segments for the coding intent based on alignment metrics of the one or more meeting segments.

Abstract: In some examples, collective application portfolio migration control may include determining, for a plurality of applications that are to be clustered for migration to a cloud environment, a coupling coefficient that represents a type of coupling between pairs of applications from the plurality of applications, a proximity coefficient that represents application proximities, and a connectedness coefficient that represents application connections. A combined application relatedness coefficient may be determined based on the coupling coefficient, the proximity coefficient, and the connectedness coefficient. A portfolio graph may be generated based on the combined application relatedness coefficient to generate migration application clusters to duster the plurality of applications. Migration of the plurality of applications to the cloud environment may be controlled based on the migration application clusters.

Abstract: Intelligent AI-based systems and methods of generating architecture diagrams for cloud computing-based infrastructures. The system can ingest and process requirement data and identify intents associated with the software. Based on the classifications of the requirement data, the system can automatically extract dependencies between software layers and microservices in order to identify the most appropriate components for the application. In some embodiments, validation can be performed in which a digital twin model is implemented. Implementation of such as system can eliminate manual errors and variability based on human skill sets, as well as enable risk-free testing of the architecture based on the application goals.

Abstract: A method and system for evaluating and enhancing a prompt for use by a generative artificial intelligence processing model are disclosed. The method may include obtaining a prompt representing a natural language text for use by a generative artificial intelligence processing model, obtaining a prompt classifier trained to evaluate a classification of the prompt, and inputting the prompt to the prompt classifier to generate a classification of the prompt. The method may further include identifying an intent underlying the prompt and detecting an implicit constraint for the prompt based on the intent. The method may further include transforming the intent in the prompt into a constraint-enhanced intent based on the implicit constraint, generating an enhanced prompt based on the constraint-enhanced intent, and outputting the enhanced prompt for the generative artificial intelligence processing model.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support ontology driven processes to generate and/or update digital twins using a partially automated process. To generate the digital twin, an ontology may be obtained and used to generate a hierarchy model, from which a knowledge graph is generated and represents a digital twin. Similarity measurements may be performed on the knowledge graph, such as components thereof, to determine a similarity between the components. Components are ranked and clustered to identify clusters of similar components and new relationships between components. This new information may be used to update the knowledge model and/or knowledge graph. Updating the knowledge graph or model may enable generation of an updated digital twin and enable continued updating of additional different data structures for other similar elements.

Abstract: A method and system for training a federated learning model are disclosed. The method may include receiving the global federated learning model from the server via the client. The client may control remote computing resources. The method may further include identifying a spare computing instance from the remote computing resources and determine a threshold training load for training the global federated learning model based on a training load assigned to the client. The method may further include, in response to a processing capacity of the spare computing instance being sufficient to process the threshold training load, offloading the threshold training load to the spare computing instance and training the global federated learning model on the spare computing instance with the training dataset stored in a data source maintained by the client.

Abstract: A retraining monitoring system maintains the sustainability of a production machine learning (ML) model system that includes a production ML model retraining platform. The retraining monitoring system collects contextual data from the production ML model system and determines if one or more of a currently-selected architectural options has to be changed for sustainability. An architectural option of the production ML model retraining platform, such as, a processing location is selected from a cloud retraining platform or an on-premises retraining platform by a selection process based on a multi-armed bandit problem. An evaluation of the retraining architecture is dealt with as a reinforcement learning problem to implement one of a periodic retraining architecture or a reactive retraining architecture.

Abstract: In some examples, policy-based application architecture generation may include generating, based on a knowledge model schema, data residency policies, and a data classification ontology, a knowledge model, and determining, based on the knowledge model, whether an application includes regulated data. Based on an analysis of application data and user data, user location and regulated data insights may be generated to determine location specific data residency policies. Location specific regulated data in-flow and data source hosted location insights may be analyzed to determine a location compliance assessment that includes an indication of whether a location associated with the application is compliant or not compliant with the location specific data residency policies.

Abstract: A device may receive system data identifying computational components and software components of a distributed and heterogeneous system executing a hybrid cloud application, and may create digital twins for the computational components and the software components. The device may create a central digital twin to receive functional data, operational data, and key performance indicators (KPIs) from the digital twins, and may create complex KPIs based on the functional data, the operational data, and the KPIs. The device may modify, based on the complex KPIs, one or more of the digital twins to generate additional KPIs, and process the additional KPIs, with a principal component analysis model and a self-organizing maps model, to detect anomalies in the distributed and heterogeneous system. The device may generate, based on the anomalies, a KPI cause vector identifying a root cause associated with the anomalies, and may perform actions based on the root cause.

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: 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 first device may provide, via a global digital twin of the first device, a communication mode assignment, of a communication mode, to a local digital twin of a second device. The communication mode assignment is to cause the local digital twin to communicate with the global digital twin via the communication mode. The first device may generate, via the global digital twin, a task assignment, and may provide, via the global digital, the task assignment to the local digital twin. The first device may update, via the global digital twin, a model based on the task assignment, and may receive, via the global digital twin and from the local digital twin, a model update associated with the local digital twin. The first device may update, via the global digital twin, the model based on the model update.

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.