Abstract: An example operation may include one or more of receiving a request for trust information of an off-chain data source from a client, detecting that the trust information of the off-chain data source is not stored in a distributed ledger shared among a plurality of peer nodes, retrieving reliability data recursively identified and retrieved from a plurality of external sources having different reliability information of the off-chain data source, determining a reliability value based on a combination of the retrieved reliability data from the plurality of external sources, and transmitting the determined reliability value to the client.

Abstract: An example operation may include one or more of receiving a blockchain request comprising a timestamp added by one or more endorsing nodes included within a blockchain network, identifying that the timestamp added by an endorsing node from among the one or more endorsing nodes is a modification to a previously added timestamp provided by the computing node, determining a reputation value for the endorsing node based on a difference between the timestamp added by the endorsing node and the previously added timestamp provided by the computing node, and transmitting the determined reputation value of the endorsing node to an ordering node within the blockchain network.

Abstract: A computer-implemented method is provided for estimating output confidence of a black box Application Programming Interface (API). The method includes generating paraphrases for an input text. The method further includes calculating a distance between the input text and each respective one of the paraphrases. The method also includes sorting the paraphrases in ascending order of the distance. The method additionally includes selecting a top predetermined number of the paraphrases. The method further includes inputting the input text and the selected paraphrases into the API to obtain an output confidence score for each of the input text and the selected paraphrases. The method also includes estimating, by a hardware processor, the output confidence of the input text from a robustness of output scores of the input text and the selected paraphrases.

Abstract: A computer-implemented method is provided for estimating output confidence of a black box Application Programming Interface (API). The method includes generating paraphrases for an input text. The method further includes calculating a distance between the input text and each respective one of the paraphrases. The method also includes sorting the paraphrases in ascending order of the distance. The method additionally includes selecting a top predetermined number of the paraphrases. The method further includes inputting the input text and the selected paraphrases into the API to obtain an output confidence score for each of the input text and the selected paraphrases. The method also includes estimating, by a hardware processor, the output confidence of the input text from a robustness of output scores of the input text and the selected paraphrases.

Abstract: An example operation may include one or more of receiving a request for trust information of an off-chain data source from a client, determining a category type of the off-chain data source from among a plurality of category types based on the request, retrieving a reliability value of the off-chain data source linked to one or more of an identity of the off-chain data source and the determined category type from a reliability database implemented via a distributed ledger shared among a plurality of peer nodes, and transmitting the retrieved reliability value linked to the category type to the client.

Abstract: An example operation may include one or more of receiving a blockchain request from a client application, determining a network latency between the client application and the endorsing node based on a network path between the client application and the endorsing node, extracting a timestamp from the blockchain request, determining whether the extracted timestamp is invalid based on the network latency between the client application and the endorsing node, and in response to determining that the timestamp is valid, generating an endorsement for the blockchain request and transmitting the endorsement to the client application.

Abstract: A method is provided for determining support of a hypothesis by opinion sentences. The method converts sentence structures in the opinion sentences using various sentence structure conversion methods to obtain converted opinion sentences. For each converted opinion sentence, the method calculates a difference between a proximity label value indicating proximity to the hypothesis and an intermediate score before and after a conversion, adopts the conversion responsive to a condition being met relative to the difference, and adopts the opinion sentence instead responsive to the condition being unmet. The method creates sub-opinions using the various methods applied to adopted conversions and opinion sentences, and obtains an intermediate score for each sub-opinion.

Abstract: A method is provided for validating a hypothesis sentence. The method extracts, from a document database D using a hypothesis sentence that includes a causal part and a consequence part, a set D1 of documents related to the causal part. The method extracts, from set D1, a set S of sentences that include expressions of opinion. The method obtains a word list W of words that have a high co-occurrence in the set D1. The method selects, from the set S, a set S1 of sentences that are positionally close to any of the words in the word list W. The method selects, from the set S, a set S2 of sentences that are related to the words in the consequence part of the hypothesis. The method extracts and displays sentences included in both the set S1 and the set S2 as opinion sentences in relation to the hypothesis sentence.

Abstract: Verified snapshots are generated by obtaining, from one of a plurality of first nodes, a difference between a common data at a first time point and the common data at a second time point that is different from the first time point, generating a first snapshot of the common data at the first time point based on the difference, obtaining a hash of the common data at the first time point from one of the plurality of first nodes, and verifying the first snapshot at the first time point with the hash of the common data at the first time point.

Abstract: An example operation may include one or more of recording, by a snapshot node, a plurality of snapshots of a key-value storage based on a plurality of delta offsets from an initial snapshot of the key-value storage, receiving, by the snapshot node, an audit request from an audit node that contains an audit time, generating, by the snapshot node, a current snapshot based on an aggregation of the snapshots from the plurality of the snapshots up to a time of a transaction closest to the audit time, and executing, by the snapshot node, a chaincode based on a delta offset of the current snapshot from the time of the transaction to the audit time to restore a snapshot at the audit time.

Abstract: An example operation may include one or more of receiving a request for trust information of an off-chain data source from a client, determining a category type of the off-chain data source from among a plurality of category types based on the request, retrieving a reliability value of the off-chain data source linked to one or more of an identity of the off-chain data source and the determined category type from a reliability database implemented via a distributed ledger shared among a plurality of peer nodes, and transmitting the retrieved reliability value linked to the category type to the client.

Abstract: An example operation may include one or more of receiving a request to modify a reliability value of an off-chain data source to generate a modified reliability value, determining whether a consensus on the modified reliability value has been received among a plurality of peer nodes, and in response to a determination that the consensus has been received, updating a storage to reflect the modified reliability value associated with the off-chain data source, and storing an identification of the modified reliability value within a block among a hash-linked chain of blocks on a distributed ledger shared among the plurality of peer nodes.

Abstract: An example operation may include one or more of receiving a request for trust information of an off-chain data source from a client, determining a category type of the off-chain data source from among a plurality of category types based on the request, retrieving a reliability value of the off-chain data source linked to one or more of an identity of the off-chain data source and the determined category type from a reliability database implemented via a distributed ledger shared among a plurality of peer nodes, and transmitting the retrieved reliability value linked to the category type to the client.

Abstract: An example operation may include one or more of receiving a request for trust information of an off-chain data source from a client, detecting that the trust information of the off-chain data source is not stored in a distributed ledger shared among a plurality of peer nodes, retrieving reliability data recursively identified and retrieved from a plurality of external sources having different reliability information of the off-chain data source, determining a reliability value based on a combination of the retrieved reliability data from the plurality of external sources, and transmitting the determined reliability value to the client.

Abstract: An example operation may include one or more of receiving a request for trust information of an off-chain data source from a client, detecting that the trust information of the off-chain data source is not stored in a distributed ledger shared among a plurality of peer nodes, retrieving reliability data recursively identified and retrieved from a plurality of external sources having different reliability information of the off-chain data source, determining a reliability value based on a combination of the retrieved reliability data from the plurality of external sources, and transmitting the determined reliability value to the client.

Abstract: An example operation may include one or more of receiving a request to modify a reliability value of an off-chain data source to generate a modified reliability value, determining whether a consensus on the modified reliability value has been received among a plurality of peer nodes, and in response to a determination that the consensus has been received, updating a storage to reflect the modified reliability value associated with the off-chain data source, and storing an identification of the modified reliability value within a block among a hash-linked chain of blocks on a distributed ledger shared among the plurality of peer nodes.

Abstract: An example operation may include one or more of receiving a blockchain request from a client application, determining a network latency between the client application and the endorsing node based on a network path between the client application and the endorsing node, extracting a timestamp from the blockchain request, determining whether the extracted timestamp is invalid based on the network latency between the client application and the endorsing node, and in response to determining that the timestamp is valid, generating an endorsement for the blockchain request and transmitting the endorsement to the client application.

Abstract: A computer-implemented method is provided for estimating output confidence of a black box Application Programming Interface (API). The method includes generating paraphrases for an input text. The method further includes calculating a distance between the input text and each respective one of the paraphrases. The method also includes sorting the paraphrases in ascending order of the distance. The method additionally includes selecting a top predetermined number of the paraphrases. The method further includes inputting the input text and the selected paraphrases into the API to obtain an output confidence score for each of the input text and the selected paraphrases. The method also includes estimating, by a hardware processor, the output confidence of the input text from a robustness of output scores of the input text and the selected paraphrases.

Abstract: A method is provided for determining support of a hypothesis by opinion sentences. The method converts sentence structures in the opinion sentences using various sentence structure conversion methods to obtain converted opinion sentences. For each converted opinion sentence, the method calculates a difference between a proximity label value indicating proximity to the hypothesis and an intermediate score before and after a conversion, adopts the conversion responsive to a condition being met relative to the difference, and adopts the opinion sentence instead responsive to the condition being unmet. The method creates sub-opinions using the various methods applied to adopted conversions and opinion sentences, and obtains an intermediate score for each sub-opinion.

Abstract: An example operation may include one or more of receiving a blockchain request from a client application, determining a network latency between the client application and the endorsing node based on a network path between the client application and the endorsing node, extracting a timestamp from the blockchain request, determining whether the extracted timestamp is invalid based on the network latency between the client application and the endorsing node, and in response to determining that the timestamp is valid, generating an endorsement for the blockchain request and transmitting the endorsement to the client application.