Abstract: Execution of one or more actions executing on at least one device is monitored to protect access to a selected digital asset. The monitoring includes determining whether a chosen action to be initiated by one entity to claim a chosen digital asset of another entity has completed. Based on determining that the chosen action has not completed, execution of a selected action, initiated by the other entity, to claim a selected digital asset of the one entity is prevented. Based on preventing execution of the selected action, a determination is made as to whether the chosen action has completed the claim of the chosen digital asset of the other entity. Based on determining that the chosen action has completed, completion of execution of the selected action to claim the selected digital asset of the one entity is facilitated.

Abstract: A system and method for a multi-party computation (MPC) is provided. In implementations, a method includes identifying a blockchain network and a computing device to perform an MPC based on an index; generating an MPC request including a function to be performed by the blockchain network and the computing device, data required for the function, and a verification policy defining a verification protocol to be performed by the blockchain network and the computing device; sending the MPC request to the blockchain network and the computing device; and receiving responses from a representative computing node of the blockchain network and the computing device, wherein each of the responses includes: an output of an MPC protocol performed by the blockchain network and the computing device to jointly compute the function while keeping the inputs private from one another and private from the computing system; and a proof based on the verification policy.

Abstract: A computer implemented method for assigning a request in a multi-cluster system. A processor set receives the request for processing by the multi-cluster system. The processor set determines whether a first pod on a node meeting a carbon intensity threshold is available to process the request without a service level agreement violation over a violation threshold. The processor set assigns the request to the first pod on the node meeting the carbon intensity threshold in response to the first pod being available and a service level agreement violation over the violation threshold being absent. The processor set assigns the request to a second pod that does not result in a service level agreement violation over the violation threshold in response to a service level agreement violation being present for assigning the request to the first pod.

Abstract: A computer program product comprises a computer readable storage medium having program instructions embodied therewith. The program instructions are executable by one or more processors to cause the one or more processors to construct an interaction graph based in part on a distributed application of a cloud platform comprised of a plurality of services; extract one or more sub-graphs from the interaction graph by mining the interaction graph using one or more feasibility validation tests; estimate a resource pressure metric for the one or more sub-graphs, wherein the resource pressure metric is at least one of a prospective resource pressure metric and a retrospective resource pressure metric, the resource pressure metric being associated with each service on one or more respective dependent services; construct a degradation plan for a least-cost sub-graph in accordance with the estimated resource pressure metric; and apply the degradation plan to the distributed application and the cloud platform.

Abstract: A computer-implemented method for automatically and digitally securing an asset is disclosed. The computer-implemented method includes hashing a secret preimage to produce a secret hash. The computer-implemented method further includes locking a digital collateral asset with the secret hash for a first predetermined period of time. The computer-implemented method further includes locking a digital loan asset with the secret hash for a second predetermined period of time, wherein the second predetermined period of time is less than the first predetermined period of time. The computer-implemented method further pledging a digital loan repayment up to the amount of time of the first predetermined period of time plus the second predetermined period of time. The computer-implemented method further includes responsive to receiving a pledge for the digital loan repayment, automatically unlocking the digital collateral asset for a borrower of the digital loan asset.

Abstract: A method of replicating the volume of stateful-sets by receiving input request types for each container of a number of containers, extracting control flow paths and mapping the input request types to the extracted control flow paths. The method learns control flow path-to-volume mappings by computing, for each control flow path of the number of control flow paths, (i) one or more associations between the control flow path and one or more volumes and (ii) a nature of access of the one or more volumes. For each pod replica a dominant control flow path is computed by computing a most common control flow path and a most common volume corresponding to the most common control flow path. A request for volume management of an identified most common volume is received and the identified most common volume is replicated or a its view is created.

Abstract: A computer-implemented method includes locking a shared digital collateral asset with a first hash for a first time period. The shared digital collateral asset is owned by a plurality of borrowers. The first hash is generated by hashing a plurality of secrets corresponding to the plurality of borrowers. The method includes locking a digital loan asset with the first hash for a second time period. The second time period is less than the first time period. The method includes releasing the digital loan asset to the plurality of borrowers in response to receiving a first secret preimage prior to expiration of the second time period. The first secret preimage is generated using the plurality of secrets. The method includes, prior to expiration of the first time period, claiming the shared digital collateral asset using the first secret preimage and pledging the shared digital collateral asset for a loan term.

Abstract: A group of transactions to be rolled back in a blockchain ledger can be identified. Whether the transactions in the group are eligible to be rolled back together can be determined. Other transactions can be recommended, and the other transactions may be added to the group to be rolled back together. A rollback transaction can be added to the blockchain ledger that rolls back effect of the transactions, where the rollback transaction has zero or more inputs and zero or more output, the rollback transaction having at least one selected from at least one input and at least one output. An unspent output of a transaction in the group is provided as an input of the rollback transaction. Input of a transaction in the group, that is not an output of another transaction in the group, is provided as an output of the rollback transaction.

Abstract: An example operation may include one or more of connecting, by a lead peer, to a blockchain network of an organization configured to store assets, receiving, by the lead peer, from an ordering server, a block that contains a plurality of chaincode transactions belonging to plurality of peers of the blockchain network, applying, by the lead peer, a filter to the block, determining, by the lead peer, a recipient peer of the block in response to the application of the filter, receiving, by the lead peer, a validation result of the block from the recipient peer, and committing, by the lead peer, the block to the blockchain network.

Abstract: An embodiment classifies, by analyzing data generated by a first edge device, the data into a sensitivity category in a set of sensitivity categories. An embodiment classifies, by analyzing the data, the data into a redundancy category in a set of redundancy categories. An embodiment classifies, into a region category in a set of region categories, a region in which the first edge device is located. An embodiment selects, by applying a set of allocation rules, a storage category of the data generated by the first edge device, the selecting resulting in a selected storage category, the set of allocation rules applied according to the sensitivity category, the redundancy category, and the region category. An embodiment causes storage of the data generated by the first edge device in a storage device in the selected storage category.

Abstract: A computer-implemented method, a computer program product, and a computer system for accelerated learning. A computer partitions an independent variable of input data into partitions. A computer creates data samples in each of the partitions, where each of the data samples has less granularity. For each of the partitions, a computer trains a machine learning model independently on each of the data samples and compares results of training on the data samples. For each of one or more partitions in which results of training on a predetermined number of the data samples are statistically identical at a predetermined confidence level, a computer outputs a result of training on one of the data samples. For each of one or more rest partitions in which no result of training has been outputted, a computer merges each pair of the data samples and uses merged data samples to train the machine learning model.

Abstract: An embodiment establishes a product design database based at least in part on product design data received from a product image repository, wherein the product design data is representative of a plurality of product designs. The embodiment ranks each product design based on a ranking metric derived from market data corresponding to existing retail products. The embodiment constructs a product design ranking based on a relative ranking between each of the product designs that have been ranked. The embodiment generates a new product design using a machine learning model based on the plurality of product designs and the product design ranking. The embodiment evaluates whether the new product design satisfies a design constraint. The embodiment, upon an evaluation that the new product design satisfies the design constraint, stores the new product design in the product design database. The embodiment displays the new product design via an interface.

Abstract: An example operation may include one or more of connecting, by a sparse peer, to a blockchain network of a plurality of peers controlled by orderers, selecting, by the sparse peer, a subset of data to be replicated, specifying, by the sparse peer, data selection filters based on the selected subset of the data, providing, by the sparse peer, the selection filters to the plurality of the peers, receiving, by the sparse peer, blocks matching the selection filters over a gossip protocol from the plurality of the peers, constructing, by the sparse peer, a local ledger of the sparse peer from the received blocks, and bootstrapping the sparse peer based on the local ledger.

Abstract: An example operation may include one or more of detecting, by a blockchain (BC) node, a contract update on a blockchain, determining, by the BC node, at least one charge affected by the contract update, executing, by the BC node, a smart contract to re-generate an invoice based on the at least one charge, and recording the invoice on a ledger of the blockchain.

Abstract: An example operation may include one or more of connecting, by a lead peer, to a blockchain network of an organization configured to store assets, receiving, by the lead peer, a block from an ordering server, that contains a plurality of chaincode transactions belonging to a plurality of peers of the blockchain, applying, by the lead peer, a filter to the block, determining, by the lead peer, a recipient peer of the block in response to the application of the filter, and receiving, by the lead peer, a notification of validation and commitment of the block to the blockchain by the recipient peer.

Abstract: A group of transactions to be rolled back in a blockchain ledger can be identified. Whether the transactions in the group are eligible to be rolled back together can be determined. Other transactions can be recommended, and the other transactions may be added to the group to be rolled back together. A rollback transaction can be added to the blockchain ledger that rolls back effect of the transactions, where the rollback transaction has zero or more inputs and zero or more output, the rollback transaction having at least one selected from at least one input and at least one output. An unspent output of a transaction in the group is provided as an input of the rollback transaction. Input of a transaction in the group, that is not an output of another transaction in the group, is provided as an output of the rollback transaction.

Abstract: Execution of one or more actions executing on at least one device is monitored to protect access to a selected digital asset. The monitoring includes determining whether a chosen action to be initiated by one entity to claim a chosen digital asset of another entity has completed. Based on determining that the chosen action has not completed, execution of a selected action, initiated by the other entity, to claim a selected digital asset of the one entity is prevented. Based on preventing execution of the selected action, a determination is made as to whether the chosen action has completed the claim of the chosen digital asset of the other entity. Based on determining that the chosen action has completed, completion of execution of the selected action to claim the selected digital asset of the one entity is facilitated.

Abstract: An example operation may include one or more of querying, via an application programming interface (API), a blockchain ledger for attributes of a shipment by a carrier from an origin location to a destination location, predicting, via an artificial intelligence (AI) model, one or more future events that will occur during the shipment based on the attributes of the shipment retrieved from querying the blockchain ledger, generating, via a smart contract, an accelerated e-invoice based on the one or more future events predicted by the AI model, and storing the accelerated e-invoice on the blockchain ledger.

Abstract: A method of replicating the volume of stateful-sets by receiving input request types for each container of a number of containers, extracting control flow paths and mapping the input request types to the extracted control flow paths. The method learns control flow path—to—volume mappings by computing, for each control flow path of the number of control flow paths, (i) one or more associations between the control flow path and one or more volumes and (ii) a nature of access of the one or more volumes. For each pod replica a dominant control flow path is computed by computing a most common control flow path and a most common volume corresponding to the most common control flow path. A request for volume management of an identified most common volume is received and the identified most common volume is replicated or a its view is created.

Abstract: A computer-implemented method for automatically and digitally securing an asset is disclosed. The computer-implemented method includes hashing a secret preimage to produce a secret hash. The computer-implemented method further includes locking a digital collateral asset with the secret hash for a first predetermined period of time. The computer-implemented method further includes locking a digital loan asset with the secret hash for a second predetermined period of time, wherein the second predetermined period of time is less than the first predetermined period of time. The computer-implemented method further pledging a digital loan repayment up to the amount of time of the first predetermined period of time plus the second predetermined period of time. The computer-implemented method further includes responsive to receiving a pledge for the digital loan repayment, automatically unlocking the digital collateral asset for a borrower of the digital loan asset.