Abstract: A computer-implemented method for increasing a recognition rate of an optical character recognition (OCR) system is provided. The method includes preprocessing by receiving an image, and extracting all vertical lines from the image. The method includes adding vertical lines at character areas of the image, extracting all horizontal lines from the image, and creating an unlined image removing all the vertical/horizontal lines from the image. The method further includes determining a border of a vertical direction of the unlined image based on the total of pixels of rows in each column, and adding vertical/horizontal auxiliary lines between characters of the unlined image. The method also includes postprocessing by receiving garbled words of OCR output, removing noise after morphologically analyzing, replacing garbled letters with correct ones based on a frequent edit operation, and outputting the correct word, weighting results of image distance calculations based on machine learning.

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: A method for emergency communication includes encoding a message for visual display including a message to field and a message from field. The visual display is revealed to a reading device in communication with a broadcast center, which stores the visual display. The messages are decoded and sorted from visual displays at the broadcast center. On an index channel, a time and channel number for when a message body of the message will be broadcast on a message channel is broadcasted. The message body is broadcasted on the message channel.

Abstract: There is provided a generating apparatus that generates an estimation model for estimating an attribute of an unknown target, the generating apparatus including a training-data acquiring unit acquiring training data including an attribute of a known target, attributes of other targets related to the known target, and a plurality of sets of types of relations between the known target and the other targets and a training processing unit training, using the training data, on the basis of the attribute of the known target and a type of a relation between the known target and the unknown target related to the known target, an estimation model for estimating the attribute of the unknown target, a generating method using the generating apparatus, and a program used in the generating apparatus.

Abstract: An example operation may include one or more of connecting, by a broker node, to a blockchain comprised of an arranger node and a plurality of client nodes, retrieving from the blockchain, by the broker node, a request for information sent by a client node of the plurality of the client nodes, decrypting, by the broker node, the request for the information with a private key of the broker node, extracting, by the broker node, a public key of a client associated with the client node from the decrypted request for the information, selecting, by the broker node, a set of client properties based on the public key of the client, generating, by the broker node, a modified request for information based on the request for the information and the set of the client properties, and sending, by the broker node, the modified request for the information to the arranger node.

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: Text analysis includes determining one or more global analysis parameters based on backtranslation of a first corpus between supported languages. A new text analysis model is determined for an unsupported language based on the one or more global analysis parameters and a text analysis model for a first supported language. An input text is analyzed in the unsupported language with the new text analysis model.

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: 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: A computer-implemented method for increasing a recognition rate of an optical character recognition (OCR) system is provided. The method includes preprocessing by receiving an image, and extracting all vertical lines from the image. The method includes adding vertical lines at character areas of the image, extracting all horizontal lines from the image, and creating an unlined image removing all the vertical/horizontal lines from the image. The method further includes determining a border of a vertical direction of the unlined image based on the total of pixels of rows in each column, and adding vertical/horizontal auxiliary lines between characters of the unlined image. The method also includes postprocessing by receiving garbled words of OCR output, removing noise after morphologically analyzing, replacing garbled letters with correct ones based on a frequent edit operation, and outputting the correct word, weighting results of image distance calculations based on machine learning.

Abstract: An example operation may include one or more of receiving a blockchain request from a client application in a blockchain network, receiving validity information of a timestamp included in the blockchain request from one or more endorsing nodes in the blockchain network, modifying the timestamp included in the blockchain request based on the validity information received from the one or more endorsing nodes, ordering the blockchain request among a group of blockchain requests based on the modified timestamp with respect to timestamps of other blockchain requests in the group, and storing the ordered group of blockchain requests within a data block among a hash-linked chain of data blocks.

Abstract: A method and system are provided for collecting social media users who have a specific profile. The method includes retrieving a set of lists connected by at least one criterion to a particular list that is included in a set of reliable lists whose users have already been reliably deemed to have a specific profile. The method includes calculating a list name based confidence value and a list member based confidence value for each list in the retrieved set of lists. The method includes updating the set of reliable lists by adding all lists in the retrieved set of lists that have the list name based confidence value above a first threshold value and the list member based confidence value above a second threshold value. The method includes outputting a listing of users belonging to set of reliable lists as the social media users who have the specific profile.

Abstract: An example operation may include one or more of receiving, by a participating peer, an accident certificate associated with a claim, extracting, by the participating peer, an accident type data from the accident certificate, receiving, by the participating peer, a repair estimate associated with the accident certificate, the repair estimate containing a value and repair steps, executing, by the participating peer, a smart contract to obtain from a blockchain an average value and a standard deviation for the value of the repair estimate based on the accident type data, subtracting, by the participating peer, the average value from the value of the repair estimate and dividing a subtraction result by the standard deviation to obtain a deviation of the value of the repair estimate, and in response to the deviation of the value of the repair estimate exceeding a threshold value, declining the repair estimate for the claim by the participating peer.

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: An example operation may include one or more of receiving, by a participating peer, an accident certificate associated with a claim, extracting, by the participating peer, an accident type data from the accident certificate, receiving, by the participating peer, a repair estimate associated with the accident certificate, the repair estimate containing a value and repair steps, executing, by the participating peer, a smart contract to obtain from a blockchain an average value and a standard deviation for the value of the repair estimate based on the accident type data, subtracting, by the participating peer, the average value from the value of the repair estimate and dividing a subtraction result by the standard deviation to obtain a deviation of the value of the repair estimate, and in response to the deviation of the value of the repair estimate exceeding a threshold value, declining the repair estimate for the claim by the participating peer.

Abstract: An example operation may include one or more of connecting, by a broker node, to a blockchain comprised of an arranger node and a plurality of client nodes, retrieving from the blockchain, by the broker node, a request for information sent by a client node of the plurality of the client nodes, decrypting, by the broker node, the request for the information with a private key of the broker node, extracting, by the broker node, a public key of a client associated with the client node from the decrypted request for the information, selecting, by the broker node, a set of client properties based on the public key of the client, generating, by the broker node, a modified request for information based on the request for the information and the set of the client properties, and sending, by the broker node, the modified request for the information to the arranger node.

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, 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, 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.