International Business Machines Corporation

Qpq Ltd

Emanuele Ragnoli

Fearghal O'Donncha

Stefano Braghin

Sean A McKenna

Gokhan Sagirlar

Sergiy Zhuk

Killian Levacher

Ernesto Arandia

Venkatraman Ramakrishna

Angelo De Caro

Francesco Fusco

Frank Suits

Charles Arthur Jochim

Seshu Tirupathi

John Sheehan

Noreen O'Brien

Eli M Dow

Ying Liu

Yassine Lassoued

Julien Monteil

Martin Mevissen

Kevin J Reilly

Yohei Ueda

Senthilnathan Natarajan

Harry Roger Kolar

Yu Chin Fabian Lim

Chun Hui Suen

Albert Akhriev

Jia Yuan Yu

Mark Purcell

Michael Barry

Zaira Pindado Tost

Fearghal ODonncha

Greg Chew

Roberto Ripamonti

Callum Sutherland

Mustafa Rafique

Massimiliano Meneghin

A method for providing a zero-knowledge membership proof is provided. The method comprises defining public parameters to be used for computing and verification of proof by a prover and a verifier, respectively. The method comprises computing, by the prover, a commitment to a given ordered set X using the public parameters, and sending it to the verifier. The method further comprises receiving, by the prover, a query from the verifier for proving membership of a subset S sampled from the given ordered set X. The method further comprises computing, by the prover, a proof of membership, including an inner product, commitments of polynomials defining the inner product and a proof of the inner product being correctly computed, and sending the proof of membership to the verifier. The method further comprises verifying, by the verifier, the proof of membership based on the defined public parameters.

System and method for proving membership of subset from given set and linear operation therefor

The present disclosure provides a self-adaptive sharding engine, SASE, which is configured to dynamically adapt the configuration of connected DL T platforms to ensure that their operational performance is maintained within target levels. The SASE is provided with a decision and optimisation, DO, module that is configured to receive and process a set of configuration parameters obtained from a sensor module to determine a set of corresponding performance metrics indicative of the operational performance of the target DLT platform. The DO module is configured to compare the values of the performance metrics with corresponding target values. Accordingly, when at least one of the performance metrics is within a value range from at least one corresponding target value, the DO module is configured to adjust an aspect of the DLT configuration of the DLT platform to optimise its operational performance.

System and method for self-adaptive and autonomous sharding of distributed ledger technology platforms

A system and a method for digital proof generation are provided. The system includes a data management module to manage dataset having plurality of data rows and a query execution and verification module including a commitment storage sub-module, a query execution sub-module and a result verification sub-module. The data management module receives query from the query execution sub-module, related to operation on data rows of plurality of data rows, which is processed to generate execution result associated with data rows. The execution result is transferred along with data rows to the query execution sub-module. A set of commitments associated with execution result is transferred to the commitment storage sub-module. The query execution sub-module transfers set of commitments, from the commitment storage sub-module, and data rows to the result verification sub-module for verification, and receives a verification result from the result verification sub-module.

System and method for digital proof generation

Embodiments for ensemble training in a distributed marketplace in a computing environment. One or more ensemble machine learning models may be provided from a plurality of machine learning models competing within the distributed marketplace that achieve a performance on ensemble training data equal to or greater than a selected performance threshold, wherein the distributed marketplace is a blockchain.

Ensemble training in a distributed marketplace

A system and method for providing power consumption data for a distributed system in blockchains in a computer environment are presented. Energy consumption of one or more blockchain nodes in a blockchain network that achieves a consensus by a proof of work on a plurality of transactions upon executing a cryptographic operation may be determined.

Estimation of power consumption of proof of work consensus based distributed systems

Embodiments for establishing trust in commerce negotiations are provided. Information associated with negotiation actions of each of a plurality of entities is stored in a shared ledger. A trustworthiness score is calculated for at least one of the plurality of entities based on the information associated with the negotiation actions of the at least one of the plurality of entities. The calculated trustworthiness score is provided to at least one other of the plurality of entities.

Methods and systems for establishing trust in commerce negotiations

A system and method for implementing a marketplace for risk assessed smart contracts issuers and execution providers in blockchains in a computer environment are presented. A smart contract, issued by one or more blockchain nodes to a secondary blockchain and accepted by a primary blockchain, may be executed following a risk assessment to recursive call attack vulnerabilities of the smart contract according to a reputation of both the one or more blockchain nodes and the one or more execution nodes.

Implementing a marketplace for risk assessed smart contracts issuers and execution providers in a blockchain

Various embodiments are provided for managing cryptographic bottlenecks for distributed multi-signature blockchain contracts in a computing environment. One or more cryptographic bottlenecks of cryptographic requests at a cryptographic accelerator may be resolved by switching between a blockchain node cryptographic library and an accelerator cryptographic library upon a number of the cryptographic requests at the accelerator exceeding a defined threshold.

Resolving cryptographic bottlenecks for distributed multi-signature contracts shared with cryptographic accelerators by switching between local and accelerator cryptographic libraries

Embodiments for dynamically distributing loads in computational rendering in a computing environment. A computational rendering model on a computational rendering to exploit nested recursive parallelism within a heterogenous computing architecture to enable communication overlap, memory transfer, and data and task management, wherein the computational rendering model is developed for the heterogenous computing architecture.

Dynamic distribution of loads across heterogeneous computing structures in computational rendering

A system and method for reducing risk of smart contracts in blockchains in a computer environment are presented. One or more smart contracts may be accepted or rejected from a secondary blockchain to a primary blockchain according to a risk assessment to recursive call attack vulnerabilities.

Reducing risk of smart contracts in a blockchain

The protection of water providing entities from loss due to environmental events is disclosed including receiving structured and unstructured data related to a water resource, generating time series for a plurality of index items, generating training data and testing data based on the time series for the plurality of index items, determining correlations between the time series of the index items using the generated training data, determining a classification boundary as a strike level based on the determined correlation, generating a protection strategy based on the strike level, applying the protection strategy to the testing data to generate a resulting data time series, and presenting the resulting data time series via a display of a computing device to at least one user of the computing device.

Protection of water providing entities from loss due to environmental events

Embodiments for estimating ice formation and depth by a processor. Ice formation and ice depth of a selected region of water may be cognitively forecasted using a prediction model such that the prediction model identifies similar characteristics between the selected region of water and one or more alternative regions of water by analyzing content of one or more data sources associated with an ontology of concepts representing a domain knowledge related to the selected region of water and the one or more alternative regions.

Ice formation and ice depth estimation system

Embodiments for implementing intelligent vessel trajectory planning and adapting by a processor. A trajectory of a vessel may be dynamically determined according to forecasted wave conditions using a surrogate wave model, a wave forecasting model, one or more user defined constraints, or a combination thereof.

Dynamic adaption of vessel trajectory using machine learning models

An example operation may include one or more of determining, by a first blockchain peer, a chaincode transaction execution needs to be offloaded, receiving bids from one or more second blockchain peers to execute the chaincode transaction, selecting a bid corresponding to a second blockchain peer, executing, by the second blockchain peer corresponding to the selected bid, the chaincode transaction, receiving, by the first blockchain peer, executed chaincode transaction results, and updating a reputation for the second blockchain peer corresponding to the selected bid.

Offloaded chaincode execution for a database

A computer system, method, and computer readable product are provided for cognitive initialization of large-scale advection-diffusion models. In various embodiments, parameters are identified for configuring an advection-diffusion model to be run. Then a natural language processing search is conducted for these parameters in relevant literature to find corresponding numerical values. These search results are compared, and values for the parameters are set for the advection-diffusion model, and the model is run.

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Dublin, Ireland

Emanuele Ragnoli