Abstract: Data verification in federate learning is faster and simpler. As artificial intelligence grows in usage, data verification is needed to prove custody and/or control. Electronic data representing an original version of training data may be hashed to generate one or more digital signatures. The digital signatures may then be incorporated into one or more blockchains for historical documentation. Any auditor may then quickly verify and/or reproduce the training data using the digital signatures. For example, a current version of the training data may be hashed and compared to the digital signatures generated from the current version of the training data. If the digital signatures match, then the training data has not changed since its creation. However, if the digital signatures do not match, then the training data has changed since its creation. The auditor may thus flag the training data for additional investigation and scrutiny.

Abstract: Due diligence of mortgage documents is faster and simpler. An electronic mortgage application often contains or references a collection of many separate electronic mortgage documents. Electronic data representing an original version of an electronic mortgage document and its current version may be hashed to generate digital signatures. Any auditor may then quickly conduct the due diligence by comparing the digital signatures. If the digital signatures match, then the due diligence reveals that the electronic mortgage document has not changed since its creation. However, if the digital signatures do not match, then the electronic mortgage document has changed since its creation. The auditor may thus flag the electronic mortgage document for additional due diligence. Regardless, a result of the due diligence may be incorporated into one or more blockchains.

Abstract: Data verification in federate learning is faster and simpler. As artificial intelligence grows in usage, data verification is needed to prove custody and/or control. Electronic data representing an original version of training data may be hashed to generate one or more digital signatures. The digital signatures may then be incorporated into one or more blockchains for historical documentation. Any auditor may then quickly verify and/or reproduce the training data using the digital signatures. For example, a current version of the training data may be hashed and compared to the digital signatures generated from the current version of the training data. If the digital signatures match, then the training data has not changed since its creation. However, if the digital signatures do not match, then the training data has changed since its creation. The auditor may thus flag the training data for additional investigation and scrutiny.

Abstract: Due diligence of mortgage documents is faster and simpler. An electronic mortgage application often contains or references a collection of many separate electronic mortgage documents. Electronic data representing an original version of an electronic mortgage document and its current version may be hashed to generate digital signatures. Any auditor may then quickly conduct the due diligence by comparing the digital signatures. If the digital signatures match, then the due diligence reveals that the electronic mortgage document has not changed since its creation. However, if the digital signatures do not match, then the electronic mortgage document has changed since its creation. The auditor may thus flag the electronic mortgage document for additional due diligence. Regardless, a result of the due diligence may be incorporated into one or more blockchains.

Abstract: A two-coin mechanism for maintaining a stable value of cryptographic coinage traded in a decentralized market exchange without requiring a reserve. A pegged cryptographic token and a variable-priced cryptographic token are both traded in the reserveless decentralized market exchange. The pegged cryptographic token and the variable-priced cryptographic token are value related based on a cryptographic exchange rate. Whenever a market transaction is processed (such as a buy or sell order), at least one of a destruction operation and a creation operation are performed. The destruction operation destroys at least one of the pegged cryptographic token and/or the variable-priced cryptographic token, while the creation operation creates new ones of the pegged cryptographic token and/or the variable-priced cryptographic token.

Abstract: A multi-coin mechanism for maintaining a stable value of cryptographic coinage traded in a decentralized market exchange without requiring a reserve. Multiple, pegged cryptographic tokens are traded in the reserveless decentralized market exchange. Each of the multiple, pegged cryptographic tokens may be pegged to a different asset (such as different currencies and/or commodities). The multiple, pegged cryptographic tokens are value related based on cryptographic exchange rates. Whenever a market transaction is processed (such as a buy or sell order), at least one of a destruction operation and a creation operation are performed. The destruction operation destroys at least one of the pegged cryptographic tokens, while the creation operation creates new ones of the pegged cryptographic tokens n.

Abstract: A pool cleaner generator module with magnetic coupling. The generator module can include a housing, a paddle wheel, a magnetic follower, and a generator. The generator can power components of the pool cleaner, such as light emitting diode (LEDs). The housing can be removably coupled to the pool cleaner and can include a flow directing portion positioned in a fluid path of the pool cleaner. The paddle wheel can be located adjacent to the flow directing portion and can rotate in response to fluid flow through the fluid path. The generator can be magnetically coupled to the paddle wheel and can generate power through rotation of the paddle wheel. The LED can be coupled to the generator and can receive the generated power from the generator to illuminate an area adjacent to the pool cleaner.

Abstract: Authentication of electronic document is based on multiple digital signatures incorporated into a blockchain. Structured data, metadata, and instructions may be hashed to generate the multiple digital signatures for distribution via the blockchain. Any peer receiving the blockchain may then verify an authenticity of an electronic document based on any one or more of the multiple digital signatures incorporated into the blockchain.

Abstract: Confidential, secret data may be shared via one or more blockchains. Mortgage applications, medical records, financial records, and other electronic documents often contain social security numbers, names, addresses, account information, and other personal data. A secret sharing algorithm is applied to any secret data to generate shares. The shares may then be integrated or written to one or more blockchains for distribution.

Abstract: Authentication of electronic document is based on multiple digital signatures incorporated into a blockchain. Structured data, metadata, and instructions may be hashed to generate the multiple digital signatures for distribution via the blockchain. Any peer receiving the blockchain may then verify an authenticity of an electronic document based on any one or more of the multiple digital signatures incorporated into the blockchain.

Abstract: Confidential, secret data may be shared via one or more blockchains. Mortgage applications, medical records, financial records, and other electronic documents often contain social security numbers, names, addresses, account information, and other personal data. A secret sharing algorithm is applied to any secret data to generate shares. The shares may then be integrated or written to one or more blockchains for distribution.

Abstract: Data verification in federate learning is faster and simpler. As artificial intelligence grows in usage, data verification is needed to prove custody and/or control. Electronic data representing an original version of training data may be hashed to generate one or more digital signatures. The digital signatures may then be incorporated into one or more blockchains for historical documentation. Any auditor may then quickly verify and/or reproduce the training data using the digital signatures. For example, a current version of the training data may be hashed and compared to the digital signatures generated from the current version of the training data. If the digital signatures match, then the training data has not changed since its creation. However, if the digital signatures do not match, then the training data has changed since its creation. The auditor may thus flag the training data for additional investigation and scrutiny.

Abstract: Data verification in federate learning is faster and simpler. As artificial intelligence grows in usage, data verification is needed to prove custody and/or control. Electronic data representing an original version of training data may be hashed to generate one or more digital signatures. The digital signatures may then be incorporated into one or more blockchains for historical documentation. Any auditor may then quickly verify and/or reproduce the training data using the digital signatures. For example, a current version of the training data may be hashed and compared to the digital signatures generated from the current version of the training data. If the digital signatures match, then the training data has not changed since its creation. However, if the digital signatures do not match, then the training data has changed since its creation. The auditor may thus flag the training data for additional investigation and scrutiny.

Abstract: Data verification in federate learning is faster and simpler. As artificial intelligence grows in usage, data verification is needed to prove custody and/or control. Electronic data representing an original version of training data may be hashed to generate one or more digital signatures. The digital signatures may then be incorporated into one or more blockchains for historical documentation. Any auditor may then quickly verify and/or reproduce the training data using the digital signatures. For example, a current version of the training data may be hashed and compared to the digital signatures generated from the current version of the training data. If the digital signatures match, then the training data has not changed since its creation. However, if the digital signatures do not match, then the training data has changed since its creation. The auditor may thus flag the training data for additional investigation and scrutiny.

Abstract: Due diligence of mortgage documents is faster and simpler. An electronic mortgage application often contains or references a collection of many separate electronic mortgage documents. Electronic data representing an original version of an electronic mortgage document and its current version may be hashed to generate digital signatures. Any auditor may then quickly conduct the due diligence by comparing the digital signatures. If the digital signatures match, then the due diligence reveals that the electronic mortgage document has not changed since its creation. However, if the digital signatures do not match, then the electronic mortgage document has changed since its creation. The auditor may thus flag the electronic mortgage document for additional due diligence. Regardless, a result of the due diligence may be incorporated into one or more blockchains.

Abstract: Authentication of mortgage documents is based on one or more digital signatures incorporated into a blockchain. Structured data, metadata, and instructions may be hashed to generate the multiple digital signatures for distribution via the blockchain. Any peer receiving the blockchain may then verify an authenticity of the mortgage documents based on the digital signature(s) incorporated into the blockchain.

Abstract: Confidential, secret data may be shared via one or more blockchains. Mortgage applications, medical records, financial records, and other electronic documents often contain social security numbers, names, addresses, account information, and other personal data. A secret sharing algorithm is applied to any secret data to generate shares. The shares may then be integrated or written to one or more blockchains for distribution.

Abstract: Possessory claims may be verified. When any entity claims to possess an electronic document, a verification scheme may require verification numbers associated with the electronic document. If the correct verification numbers are provided, then a claimant may actually possess the electronic document. However, if the correct verification numbers cannot be provided, then the verification scheme may deny that the claimant has the electronic document.

Abstract: Authentication of electronic document is based on multiple digital signatures incorporated into a blockchain. Structured data, metadata, and instructions may be hashed to generate the multiple digital signatures for distribution via the blockchain. Any peer receiving the blockchain may then verify an authenticity of an electronic document based on any one or more of the multiple digital signatures incorporated into the blockchain.

Abstract: Embodiments of the invention provide a pool cleaner receiving fluid flow from a pool hose, the pool cleaner comprising a supply mast configured for connection to a pool hose and directing fluid flow from the pool hose and a generator positioned within the pool cleaner. The pool cleaner further includes a paddle wheel coupled to the generator, the paddle wheel and the generator generating electric power using the fluid flow directed through the pool cleaner and control circuitry coupled to the generator, the control circuitry receiving the generated power from the generator for providing energy to operate at least one function of the swimming pool cleaner.