Abstract: An example operation may include one or more of generating a plurality of successive data points of an iterative simulation based on predefined checkpoints, each data point identifying an evolving state of the iterative simulation with respect to a previous data point among the successive data points, transmitting a blockchain request for validating state data within a first data point among the plurality of successive data points to a first subset of endorsing nodes of a blockchain network, and transmitting a blockchain request for validating state data within a second data point among the plurality of successive data points to a second subset of endorsing nodes which are mutually exclusive from the first subset of endorsing nodes of the blockchain network for parallel endorsement of the first and second data points.

Abstract: An example operation may include one or more of obtaining data of a simulation, identifying checkpoints within the simulation data, generating a plurality of sequential data structures based on the identified checkpoints, where each data structure identifies an evolving state of the simulation with respect to a previous data structure among the sequential data structures, and transmitting the generated sequential data structures to nodes of a blockchain network for inclusion in one or more data blocks within a hash-linked chain of data blocks.

Abstract: An example operation may include one or more of obtaining data of a simulation, identifying checkpoints within the simulation data, generating a plurality of sequential data structures based on the identified checkpoints, where each data structure identifies an evolving state of the simulation with respect to a previous data structure among the sequential data structures, and transmitting the generated sequential data structures to nodes of a blockchain network for inclusion in one or more data blocks within a hash-linked chain of data blocks.

Abstract: An example operation may include one or more of generating a data frame storing content of a simulation, compressing the simulation content within the data frame based on previous simulation content stored in another data frame to generate a compressed data frame, and transmitting the compressed data frame via a blockchain request to one or more endorsing peer nodes of a blockchain network for inclusion of the compressed data frame within a hash-linked chain of blocks of the blockchain network.

Abstract: A method for automatically generating documentation for an artificial intelligence model includes receiving, by a computing device, an artificial intelligence model. The computing device accesses a model facts policy that indicates data to be collected for artificial intelligence models. The computing device collects artificial intelligence model facts regarding the artificial intelligence model according to the model facts policy. The computing device accesses a factsheet template. The factsheet template provides a schema for an artificial intelligence model factsheet for the artificial intelligence model. The computing device populates the artificial intelligence model factsheet using the factsheet template with the artificial intelligence model facts related to the artificial intelligence model.

Abstract: The system, method, and computer program product are disclosed that track the evolution of a network over time through the analysis of media corpora associated with nodes of the network at each time slice. The media corpora may be analyzed to generate word clusters for each time slice that are then compared across time slices to determine how the network has evolved. The evolution may be tracked by determining the similarity of each word cluster of a particular time slice to each word cluster of another time slice. The similarity may be measured by a similarity score for each comparison that may be combined to determine an overall similarity of the network between the two time slices.

Abstract: An example operation may include one or more of generating a data frame storing content of a simulation, compressing the simulation content within the data frame based on previous simulation content stored in another data frame to generate a compressed data frame, and transmitting the compressed data frame via a blockchain request to one or more endorsing peer nodes of a blockchain network for inclusion of the compressed data frame within a hash-linked chain of blocks of the blockchain network.

Abstract: An example operation may include one or more of obtaining data of a simulation, identifying checkpoints within the simulation data, generating a plurality of sequential data structures based on the identified checkpoints, where each data structure identifies an evolving state of the simulation with respect to a previous data structure among the sequential data structures, and transmitting the generated sequential data structures to nodes of a blockchain network for inclusion in one or more data blocks within a hash-linked chain of data blocks.

Abstract: Software that uses personalized information pertaining to a user to determine how familiar (or “novel” or “surprising”) a new artifact will be to the user, by performing the following steps: (i) receiving a first dataset pertaining to a first user; (ii) building, utilizing the first dataset, an ontology of artifacts known to the first user, where the ontology includes a domain of food and a plurality of artifacts that include food recipes, and where the artifacts have corresponding characteristics that include food ingredients; (iii) calculating a prior probability distribution for each artifact of the ontology using a probabilistic familiarity algorithm; and (iv) calculating a probabilistic familiarity value for the first artifact with respect to the first user by adding the first artifact to the set of artifacts and calculating the first artifact's prior probability distribution using the probabilistic familiarity algorithm.

Abstract: An example operation may include one or more of obtaining data of a simulation, identifying checkpoints within the simulation data, generating a plurality of sequential data structures based on the identified checkpoints, where each data structure identifies an evolving state of the simulation with respect to a previous data structure among the sequential data structures, and transmitting the generated sequential data structures to nodes of a blockchain network for inclusion in one or more data blocks within a hash-linked chain of data blocks.

Abstract: A method for automatically generating documentation for an artificial intelligence model includes receiving, by a computing device, an artificial intelligence model. The computing device accesses a model facts policy that indicates data to be collected for artificial intelligence models. The computing device collects artificial intelligence model facts regarding the artificial intelligence model according to the model facts policy. The computing device accesses a factsheet template. The factsheet template provides a schema for an artificial intelligence model factsheet for the artificial intelligence model. The computing device populates the artificial intelligence model factsheet using the factsheet template with the artificial intelligence model facts related to the artificial intelligence model.

Abstract: Methods and systems for determining a time dependent relevancy score of an agent node among an evolving heterogeneous network are described. A processor may expand the heterogeneous network by generating temporal heterogeneous networks representing states of the heterogeneous network at different times. The processor may extract a set of agent nodes from each temporal heterogeneous network and may generate a relationship network based on the extracted agent nodes for each temporal heterogeneous network. The processor may remove the agent node from the temporal heterogeneous network to generate a conditional relationship network excluding the removed agent node. The processor may determine a relevancy score for the agent node based on the corresponding relationship network and the conditional relationship network. Each relevancy score for the agent node may correspond to a temporal heterogeneous network and may indicate an impact of removing the agent node from the corresponding temporal heterogeneous network.

Abstract: A network is crawled using a trained learning model to identify a set of secondary-source documents related to an event. A hub page from the set of secondary-source documents is identified that includes a link predicted to link to a new relevant secondary-source document. The new document is added to the set of secondary-source documents. Information is extracted from the set of secondary-source documents. Feedback is received indicative of a relevancy level for the extracted information as applied to the event. Each document is classified into one or more categories related to the event, based on the extracted information and the received feedback information. A learning model is trained based on the received feedback.

Abstract: A method includes performing contextual association of entities using multi-source data. For each context the method performs co-clustering to identify distinct expert-skill associations; constructing single-entity unipartite graph representations and performing a random walk within each single-entity unipartite graph; for each single-entity unipartite graph, obtaining steady state distributions using the random walks to obtain clusters of experts and skills; performing a weighted two-way random walk across entity graphs (graph edges), giving preference to traversal within members of the same co-cluster; and performing link prediction for each context by dynamically adding edges, and obtaining overall skills predictions, analyses and inferences by merging the contexts and weighting the links of each context.

Abstract: An example operation may include one or more of generating a plurality of successive data points of an iterative simulation based on predefined checkpoints, each data point identifying an evolving state of the iterative simulation with respect to a previous data point among the successive data points, transmitting a blockchain request for validating state data within a first data point among the plurality of successive data points to a first subset of endorsing nodes of a blockchain network, and transmitting a blockchain request for validating state data within a second data point among the plurality of successive data points to a second subset of endorsing nodes which are mutually exclusive from the first subset of endorsing nodes of the blockchain network for parallel endorsement of the first and second data points.

Abstract: An example operation may include one or more of generating a data frame storing content of a simulation, compressing the simulation content within the data frame based on previous simulation content stored in another data frame to generate a compressed data frame, and transmitting the compressed data frame via a blockchain request to one or more endorsing peer nodes of a blockchain network for inclusion of the compressed data frame within a hash-linked chain of blocks of the blockchain network.

Abstract: An example operation may include one or more of obtaining data of a simulation, identifying checkpoints within the simulation data, generating a plurality of sequential data structures based on the identified checkpoints, where each data structure identifies an evolving state of the simulation with respect to a previous data structure among the sequential data structures, and transmitting the generated sequential data structures to nodes of a blockchain network for inclusion in one or more data blocks within a hash-linked chain of data blocks.

Abstract: The system, method, and computer program product are disclosed that track the evolution of a network over time through the analysis of media corpora associated with nodes of the network at each time slice. The media corpora may be analyzed to generate word clusters for each time slice that are then compared across time slices to determine how the network has evolved. The evolution may be tracked by determining the similarity of each word cluster of a particular time slice to each word cluster of another time slice. The similarity may be measured by a similarity score for each comparison that may be combined to determine an overall similarity of the network between the two time slices.

Abstract: Methods and systems for determining a time dependent relevancy score of an agent node among an evolving heterogeneous network are described. A processor may expand the heterogeneous network by generating temporal heterogeneous networks representing states of the heterogeneous network at different times. The processor may extract a set of agent nodes from each temporal heterogeneous network and may generate a relationship network based on the extracted agent nodes for each temporal heterogeneous network. The processor may remove the agent node from the temporal heterogeneous network to generate a conditional relationship network excluding the removed agent node. The processor may determine a relevancy score for the agent node based on the corresponding relationship network and the conditional relationship network. Each relevancy score for the agent node may correspond to a temporal heterogeneous network and may indicate an impact of removing the agent node from the corresponding temporal heterogeneous network.

Abstract: A method includes predicting availability of a plurality of constituents for one or more future epochs, obtaining one or more metrics for each of a plurality of existing work products, each of the plurality of existing work products using at least one constituent, and generating at least one work product for each of the one or more future epochs based in part on the predicted availability of the constituents and the one or more metrics for the existing work products. The metrics for the existing work products may include quality metrics and novelty metrics.