Abstract: An artificial intelligence (AI) platform to support selective replacement of one or more image layers of a container image build. A metadata file is subject to natural language processing and one or more corresponding vector representations are created and subject to evaluation by a set of artificial neural networks (ANNs). A first ANN assesses each vector representation with respect to compliance and operability, and the second ANN selectively assesses the vector representation(s) with respect to similarity with one or more compliant vector representations. In response to the assignment of the second score, at least one vector representation of the received metadata file is selectively replaced with at least one compliant vector representation. The metadata file is selectively provisioned with the selectively replaced vector representation(s).

Abstract: A processor may run a background process to identify a first task being initiated by a first user on a device, where the first task is associated with a first application. The processor may identify the first user of the device. The processor may analyze one or more interactions of the first user associated with the first application on the device. The processor may allocate, based at least in part on identification of the first user, identification of the first task, or analysis of the one or more interactions of the first user, computing resources to one or more hardware components on the device.

Abstract: Embodiments are provided for intelligent recommendations for program code. In some embodiments, a system can include a processor that executes computer-executable components stored in memory. The computer-executable components can include an evaluation component that determines that first program code pertains to a defined category representing a defined cost to execute the first program code by a cloud computing service. The computer-executable components also can include a recommendation component that generates a recommendation for second program code that satisfies a similarity criterion with respect to the first program code. The second program code pertains to a category representing a cost to execute the second program code by the cloud computing service, where the cost is less than the defined cost.

Abstract: An artificial intelligence (AI) platform to support optimization of container builds and virtual machine mounts in a distributed computing environment. A provisioning file is subject to natural language processing (NLP) and a corresponding vector representation of the file is created and subject to evaluation by a set of artificial neural networks (ANN). A first ANN assesses the representation of the file with respect to compliance and operability, and the second ANN selectively assesses the representation of the file with respect to provisioning efficiency. The provisioning file is selectively process based on the provisioning efficiency, with the processing directed at provisioning a container build or mounting a VM.

Abstract: A computer-implemented method, system, and computer program product for generating an aggregated user interface display. The aggregated user interface display includes a physical display of a computing device and a projected display from an electronic pen. Operator interactions with the aggregated user interface display are saved as historical information. The historical information is used to determine a configuration for the aggregated user interface display. The computing device and the electronic pen are controlled to implement the configuration for the aggregated user interface display.

Abstract: A method, computer system, and a computer program product for host validation is provided. The present invention may include receiving a job from a user. The present invention may include selecting, by a scheduler, a host in a hybrid cloud environment to run the received job. The present invention may include classifying, by a learning component, the selected host's subsystems. The present invention may include determining, based on the classification, that the selected host can run the received job.

Abstract: An artificial intelligence (AI) platform to support optimization of workload scheduling in a distributed computing environment. Unstructured data corresponding to one or more application artifacts related to a workload in the distributed computing environment is leveraged. NLP is applied to the unstructured data to identify one or more host requirements corresponding to the application artifacts. One or more hosts in the computing environment compatible with the identified host requirements are selectively identified and compatibility between the application artifacts and the identified hosts is assessed. The workload is selectively scheduled responsive to the selective host identification based on the assessed compatibility. The scheduled workload is selectively executed on at least one of the selectively identified hosts responsive to the assessment workload compatibility.

Abstract: An example operation includes one or more of receiving a request associated with a software library, identifying metadata of the software library, determining, via chaincode, whether data of the software library has been previously stored on a blockchain, and in response to a determination that the software library has not been previously stored, storing the identified metadata of the software library and a notarization value generated by the chaincode within the blockchain.

Abstract: Embodiments are provided for intelligent recommendations for program code. In some embodiments, a system can include a processor that executes computer-executable components stored in memory. The computer-executable components can include an evaluation component that determines that first program code pertains to a defined category representing a defined cost to execute the first program code by a cloud computing service. The computer-executable components also can include a recommendation component that generates a recommendation for second program code that satisfies a similarity criterion with respect to the first program code. The second program code pertains to a category representing a cost to execute the second program code by the cloud computing service, where the cost is less than the defined cost.

Abstract: According to one embodiment, a method, computer system, and computer program product for detecting one or more loose or malfunctioning components within a machine is provided. The present invention may include measuring, by one or more sensors, one or more vibration signals and one or more acoustic signals of the machine; determining one or more joint signals, wherein the one or more joint signals comprise one or more relationships between the one or more vibration signals and the one or more acoustic signals; and responsive to one or more new signals deviating from the one or more vibration signals, one or more acoustic signals, and/or one or more joint signals by an amount exceeding at least one threshold, triggering one or more ameliorative actions.

Abstract: The present invention may include an embodiment that receives a deployment declaration in a natural language. The embodiment may detect one or more sequencing entities and one or more parameter entities using trained natural language processing. The embodiment may sequence a configuration file based on the one or more sequencing entities. The embodiment may determine a plurality of configuration parameters in the sequenced configuration file. The embodiment may substitute a configuration parameter from the plurality of configuration parameters of the sequenced configuration file with the one or more parameter entities. The embodiment may align the plurality of configuration parameters of the sequenced configuration file based on organization compliance data and deploys a tuned cloud service using the sequenced configuration file.

Abstract: The present invention may include an embodiment that receives a deployment declaration in a natural language. The embodiment may detect one or more sequencing entities and one or more parameter entities using trained natural language processing. The embodiment may sequence a configuration file based on the one or more sequencing entities. The embodiment may determine a plurality of configuration parameters in the sequenced configuration file. The embodiment may substitute a configuration parameter from the plurality of configuration parameters of the sequenced configuration file with the one or more parameter entities. The embodiment may align the plurality of configuration parameters of the sequenced configuration file based on organization compliance data and deploys a tuned cloud service using the sequenced configuration file.

Abstract: A computer-implemented method for configuring a blockchain network, a computer program product for integrating device identity into a permissioning framework of a blockchain network, and a blockchain network. One embodiment may comprise registering a device at a delineate node of a blockchain network, creating, by a processor of the delineate node based on the registering, a profile for the device; an performing, by the processor of the delineate node, a pass-through service for the device. The registering may include receiving, by a network interface, an immutable device identity from the device.

Abstract: A plurality of ledger nodes within a blockchain infrastructure is continuously updated via a set of conduit nodes. The set of conduit nodes share a secure provision ledger. The secure provision ledger includes a set of updates for the blockchain infrastructure. Each conduit node within the set propagates the set of updates to a subset of the plurality of ledger nodes.

Abstract: A scheduler node in a blockchain network may receive data associated with a machine learning model. The scheduler node may measure a drift of the machine learning model for a first aspect of the data. The scheduler node may determine if the drift of the machine learning model is greater than a threshold. The scheduler node may schedule, in response to the drift being greater than the drift threshold, a retraining transaction for the machine learning model.

Abstract: A computer-implemented method, system and computer program product for sharing and utilizing healthcare data, by: providing one or more computer-implemented machine learning models for analyzing the healthcare data; and recording transactions involving the machine learning models using a blockchain as a distributed ledger that is shared, replicated and synchronized. Healthcare data is also used to train the machine learning models. The healthcare data comprises research data or patient data such as Electronic Medical Records (EMRs). A smart contract that is a computer-implemented protocol is used to facilitate, verify or enforce negotiation of the transactions involving the machine learning models or healthcare data.

Abstract: A processor may receive a request for storage in a blockchain network. The request may include information as to a specific storage that is required. The processor may commit the request to the blockchain network. The processor may provide the request to one or more proposing entities. The processor may receive, from the one or more proposing entities, respective proposals in response to the request for storage. The processor may determine which of the respective proposals best match the information included in the request. The processor may automatically accept the best matched proposal.

Abstract: An example operation may include one or more of: receive a first set of annotation information, determine a first approval decision for the first set of annotation information, record the first approval decision in a blockchain, retrieve a first set of approval decisions from the blockchain, generate a consensus based on the first set of approval decisions, and record the consensus in the blockchain.

Abstract: A processor may run a background process to identify a first task being initiated by a first user on a device, where the first task is associated with a first application. The processor may identify the first user of the device. The processor may analyze one or more interactions of the first user associated with the first application on the device. The processor may allocate, based at least in part on identification of the first user, identification of the first task, or analysis of the one or more interactions of the first user, computing resources to one or more hardware components on the device.

Abstract: An example operation includes one or more of receiving a request associated with a software library, identifying metadata of the software library, determining, via chaincode, whether data of the software library has been previously stored on a blockchain, and in response to a determination that the software library has not been previously stored, storing the identified metadata of the software library and a notarization value generated by the chaincode within the blockchain.