Abstract: An example operation may include one or more of receiving, by a blockchain node or peer comprising hot asset storage and cold asset storage, an asset request to provide an asset, the asset comprising a key-value pair, determining if the asset request may be satisfied without accessing a blockchain of a shared ledger, providing the asset to a requesting node or peer from hot asset storage in response to the blockchain node or peer determining the asset request may be satisfied without accessing the blockchain, and in response to the blockchain node or peer determining the asset request cannot be satisfied without accessing the blockchain, the method further comprising utilizing a pointer in cold asset storage to obtain the requested asset from the blockchain.

Abstract: An embodiment establishes a designated attribute value as a semantic criterion for grouping records in a bucket, identifies a first set of records having attribute values that satisfy the semantic criterion, and adds the first set of records to the bucket. The embodiment detects that the first set of records represent a first series of events that occurred in succession at respective times. The embodiment derives a temporal attribute value representative of a time pattern formed by the times of the first series of events and designates the temporal attribute value as a temporal criterion for grouping records in the bucket. The embodiment identifies a second set of records that represent a second series of events and satisfy the temporal criterion and adds the second set of records to the bucket based at least in part on the second set of records satisfying the temporal criterion.

Abstract: A multi-purpose server cache directory in a computing environment is provided. One of a plurality of operation modes may be selectively enabled or disabled, by a cache directory, based on a computation phase, data type, and data pattern for caching data in a cache having a plurality of address tags in the cache directory greater than a number of data lines in a cache array.

Abstract: Embodiments are provided for generating a reasonable language model learning for text data in a knowledge graph in a computing system by a processor. One or more data sources and one or more triples may be analyzed from a knowledge graph. Training data having one or more candidate labels associated with one or more of the triples may be generated. One or more reasonable language models may be trained based on the training data.

Abstract: Embodiments for using learned physical knowledge to guide feature engineering in a computing environment by a processor. Physical knowledge data associated with a dataset may be learned. The physical knowledge data may be translated into a plurality of features for one or more automated feature engineering models to execute for one or more prediction and monitoring operations, wherein the plurality of features represent relationships between the physical knowledge data.

Abstract: Various embodiments are provided for providing enhanced data allocation for machine learning operations in a computing environment by one or more processors in a computing system. One or more data sampling strategies may be determined based on a dataset. One or more enhanced training data allocations may be suggested for machine learning operations in a cloud computing environment based on the one or more data sampling strategies.

Abstract: From an input image comprising a portion of text, the portion of text is extracted. The portion of text comprises text in a first natural language. The input image is classified into a candidate scene within a set of predefined scenes. The candidate scene is scored according to a quality measure. Using a predefined scene parameter of the scored candidate scene, the portion of text is translated. In the input image, the portion of text is replaced. with the translated portion of text.

Abstract: A first version of a model specified by a model execution request is executed, producing a first execution result. A second version of the model is selected according to an input data attribute specified by the model execution request. The second version of the model is executed, producing a first execution result. Using a natural language processing engine, responsive to the first execution result and the second execution result differing by more than a threshold amount, a natural language explanation of a difference between the first execution result and the second execution result is constructed.

Abstract: An embodiment generates a training batch of data points from training data for a plurality of classes and builds a multi-class classifier having a series of binary classifiers arranged in a first order. Each of the binary classifiers is associated with a respective class. The embodiment trains the multi-class classifier with the binary classifiers arranged in a first order and, at each binary classifier, the embodiment identifies data points as belonging to the class associated with the respective classifier and updates the training batch to exclude the classified data points. The embodiment then modifies the multi-class classifier by changing the order of classifiers and repeats the training of the multi-class classifier with the series of binary classifiers arranged in a second order. The embodiment then selects a final configuration of the multi-class classifier based at least in part on a comparison of first training results to the second training results.

Abstract: A system and technique are provided for providing a service address space. The system includes a service co-processor provided with a service address space. The service co-processor is attached to a main processor where the main processor is provided with a main address space. The service co-processor creates and maintains an independent copy of the main address space in the form of the service address space. The service co-processor receives from the main processor a command packet, determines a clock value for initiating a service function designated by the command packet, and updates the service address space until reaching the clock value. The service co-processor then performs the service function at the clock value.

Abstract: An efficient cloning mechanism is provided for a distributed storage environment, where, for example, a private cloud computing environment and a public cloud computing environment are included in a hybrid cloud computing environment (on-premise object storage to off-premise computation resources), to improve computation workloads. The disclosed algorithm forms an efficient cloning mechanism in a hybrid storage environment where the read/write speed of data from the disk is not limited by its angular velocity.

Abstract: An example operation may include one or more of receiving a gossip message originated from a domain anchor peer in a first security domain, verifying that block content within the gossip message does not violate a cross-domain security policy, in response to verifying the block content, updating an endpoint of the gossip message with an address of a domain anchor peer in a second security domain, and transmitting the updated gossip message to the domain anchor peer in the second security domain.

Abstract: A method of modifying a resonant frequency of a quantum device includes generating an ion beam having a beam energy and exposing a surface of the quantum device to the ion beam for an exposure time. The ion beam is incident onto the quantum device at an oblique angle that is less than 90 degrees as measured from the surface of the quantum device. The quantum device includes a Josephson junction, the ion beam exposing the quantum device proximate to the Josephson junction to modify a property of the Josephson junction, the property being associated with the resonant frequency of the quantum device.

Abstract: Motion-related 3D feature extraction by receiving a set of sequential radar signal data frames associated with a subject, determining radar signal amplitude and phase for each data frame, determining radar signal phase changes between sequential data frames, and extracting, by a trained machine learning model, one or more three-dimensional features from the sequential radar signal data frames according to the radar signal amplitude and the radar signal phase changes between sequential data frames.

Abstract: A computer-implemented system and related method process a smart contract. The method uses a processor for processing a smart contract that receives a transaction proposal (TP) from a blockchain client. The processor determines a pre-endorsement result (PER) that is associated with the TP, analyzes, according to a pre-endorsement agreement logic, the PER to produce an agreed result, and endorses the agreed result. The processor sends, to the blockchain client, the endorsed agreed result.

Abstract: A method for assigning weights to a knowledge graph includes extracting information from a knowledge graph. The information including entities extracted from nodes of the knowledge graph and relations extracted from edges of the knowledge graph. A shortest path generator receives the extracted entities and relations, and potential assigned weights from a heuristic data repository. Weights for the edges of the knowledge graph are determined. The weights are assigned to the edges of the knowledge graph.

Abstract: An indication is received. The indication is for a wireless communication being made by a computing device. Responsive to receiving the indication, an optimized location to make the wireless communication is determined based on a location of the computing device and an optimized locations list. The optimized location is communicated to the computing device making the wireless communication.

Abstract: A computer implemented method of administering a complex system includes receiving multivariate data from a plurality of sensors of the system in an ambient state. Event sequences in the received multivariate data are identified. The multivariate event sequences are projected to a lower stochastic latent embedding. A temporal structure of the sequences is learned in a lower latent space. A probabilistic prediction in the lower latent space is provided. The probabilistic prediction in the lower stochastic latent space is decoded to an event prediction in the ambient state.

Abstract: A method, computer system, and computer program product for selectively delivering missed content to a user are provided. The embodiment may include identifying a user's eye direction, awareness, and attentiveness using a wearable device. The embodiment may also include determining whether the user is in a physical place where the user is viewing a live event. The embodiment may further include determining a minimum level of an attentiveness score for the user. The embodiment may also include identifying the attentiveness score of the user while watching any content when the user's attentive score is below a preconfigured threshold value. The embodiment may further include delivering content that the user missed based on the attentive score.

Abstract: Techniques include updating a semantic search function with a custom ontology, the semantic search function initially supporting a separate ontology having been used to enrich a corpus. The custom ontology is used to augment input of a search query for the semantic search function, thereby providing a custom user experience for searching the corpus.