Abstract: Systems, computer program products, and methods are described herein for steganalysis detection of metadata in a video stream for providing real-time data. An encrypted steganalysis marker is positioned within a boarder of a video stream and, upon user device scanning and identification provides a communication channel for real-time data display on the user device. The data displayed on the user device is transmitted via narrowband internet-of-things communication linkage displaying product or service data associated with the video stream. Using a combination of NB-IoT and computational memory computing creates a unique, in user device computing structure with various variable inputs to provide specified data and recommendations to the user.

Abstract: A system and method for assuring digital asset integrity when integrating digital assets into a traditional financial asset management system is disclosed. The system and methods allow enterprises to integrate traditional asset management with newer distributed transaction ledgers (DTLs), i.e., blockchains. The platform's infrastructure couples traditional enterprise ledger systems with digital asset transaction systems thereby equipping a traditional enterprise with the ability to integrate digital assets into their traditional platforms giving them the ability to evaluate, buy, hold, monitor, and sell their assets, both digital and traditional using state of the art DTLs and asset tokenization by ensuring asset integrity.

Abstract: A system and method for integrating a tokenized digital asset into a traditional financial asset management system is disclosed. The system and methods allow enterprises to value and integrate tokenized digital assets into traditional asset ledgers. The platform's infrastructure couples traditional enterprise ledger systems with digital asset transaction systems thereby equipping a traditional enterprise with the ability to integrate digital assets into their traditional platforms giving them the ability to evaluate, buy, hold, monitor, and sell their assets, both digital and traditional using state of the art DTLs and asset tokenization.

Abstract: A method and system for integrating an enterprise governance system and a traditional asset management system with newer distributed transaction ledgers (DTLs), i.e., blockchains is described. The platform's infrastructure and interface couples traditional enterprise ledger systems with digital asset transaction systems thereby equipping a traditional enterprise with the ability to integrate digital assets into their traditional platforms giving them the ability to evaluate, buy, hold, monitor, and sell their assets, both digital and traditional using state of the art DTLs and asset tokenization.

Abstract: A platform that enterprises can use to integrate traditional asset management with newer distributed transaction ledgers (DTLs), i.e., blockchains is described. The platform's infrastructure couples traditional enterprise ledger systems with digital asset transaction systems thereby equipping a traditional enterprise with the ability to integrate digital assets into their traditional platforms giving them the ability to evaluate, buy, hold, monitor, and sell their assets, both digital and traditional using state of the art DTLs and asset tokenization.

Abstract: A system and method for continuous and combinatorial analysis of digital assets integrated into a traditional financial asset management system is disclosed. The system and methods allow enterprises to integrate traditional asset management with newer distributed transaction ledgers (DTLs), i.e., blockchains. The platform's infrastructure couples traditional enterprise ledger systems with digital asset transaction systems thereby equipping a traditional enterprise with the ability to integrate digital assets into their traditional platforms giving them the ability to evaluate, buy, hold, monitor, and sell their assets, both digital and traditional using state of the art DTLs and asset tokenization.

Abstract: Systems, computer program products, and methods are described herein for steganalysis detection of metadata in a video stream for providing real-time data. An encrypted steganalysis marker is positioned within a boarder of a video stream and, upon user device scanning and identification provides a communication channel for real-time data display on the user device. The data displayed on the user device is transmitted via narrowband internet-of-things communication linkage displaying product or service data associated with the video stream. Using a combination of NB-IoT and computational memory computing creates a unique, in user device computing structure with various variable inputs to provide specified data and recommendations to the user.

Abstract: Recursive endorsements in a decentralized database minimize validation effort for a smart contract because there are fewer signatures to verify. Collected endorsements required by an endorsement policy are verified and signed by a set of trusted peers representing different organizations, the set of trusted peers being fewer than the number of endorsing peers. Prior to validation, the target transaction is submitted with all required endorsements and the signatures of the set of trusted peers. Validation is performed by verifying only the signatures of the set of trusted peers.

Abstract: One or more machine learning models for a network of tensor time series can be provided. Co-evolving time series having multiple modes can be received. A tensor graph convolutional network can be trained, using the co-evolving time series and adjacency matrices associated with the multiple modes in the co-evolving time series, to generate node embeddings associated with a snapshot of the co-evolving time series at time t. A tensor recurrent neural network can be trained to generate temporal dynamics associated with the co-evolving time series based on the generated node embeddings. A neural network model can be trained to forecast a prediction for the co-evolving time series based on the generated node embeddings and the generated temporal dynamics. The tensor graph convolutional network, the tensor recurrent neural network and the neural network model can be trained jointly.

Abstract: Systems, methods, and computer programming products leveraging the use of machine learning, cryptographic keys and blockchain technology for validating blockchain transactions. The disclosed systems, methods and products improve detection of malicious cyberattacks and fraud, while reducing occurrences of falsely invalidated transactions and improving overall blockchain security in both permissioned and permissionless blockchain networks. Classifiers are trained using machine learning and other classification techniques by building a transaction history to learn how to identify suspicious transactions on the blockchain. In permissionless and order-execute models of permissioned blockchains, cryptographic keys are publicly registered to guardians residing out of band, who may co-sign requests and override or resubmit transactions marked as suspicious by the classifiers.

Abstract: An example operation includes one or more of receiving, by a data processing node, inference data object from a multi-channel data server over a blockchain, sorting, by the data processing node, longitudinal records contained in the inference data object, linking, by the data processing node, transaction outcomes and inferences data from the inference data object to the sorted longitudinal records, and recording linked data onto a blockchain ledger. The data processing node serves as a validator of data from a robo-advisory using natural language (NL) processing to reduce bias and measure effectiveness of inference from the robo-advisory.

Abstract: An example operation may include one or more of dividing a neural network that corresponds to an artificial intelligence (AI) model into a plurality of sub-models, assigning the plurality of sub-models to a plurality of blockchain peers, respectively, training the sub-models, via the plurality of blockchain peers, to generate training results within an iteration, and committing the training results to a blockchain which is accessible by the plurality of blockchain peers.

Abstract: A method includes: receiving, by a processor set, a request for a web-based service; generating, by the processor set, a feature vector including values based on parameters of the request; generating, by the processor set, an endpoint selection vector including plural probabilities corresponding to plural endpoints, wherein the endpoint selection vector is generated using the feature vector with a machine learning model; selecting, by the processor set, one of the plural endpoints based on the plural probabilities; and invoking, by the processor set, the selected endpoint.

Abstract: A system may enact a process including initiating an operation to transfer a secret, establishing n groups of nodes in a blockchain network, dividing a secret into n secret parts, creating n group keys, encrypting each secret part of the secret with a corresponding group key, and recording encrypted secret parts on the blockchain networks.

Abstract: A graph neural network can be built and trained to predict a risk of an entity. A multi-relational graph network can include a first graph network and a second graph network. The first graph network can include a first set of nodes and a first set of edges connecting some of the nodes in the first set. The second graph network can include a second set of nodes and a second set of edges connecting some of the nodes in the second set. The first set of nodes and the second set of nodes can represent entities, the first set of edges can represent a first relationship between the entities and the second set of edges can represent a second relationship between the entities. A graph convolutional network (GCN) can be structured to incorporate the multi-relational graph network, and trained to predict a risk associated with a given entity.

Abstract: Systems and methods for creating a Digital Directive and Conveyance contract to distribute digital assets, including cryptocurrency and NFTs are described. The systems and methods as described improve the digital asset owner's control over his digital assets and keys prior to distribution using one or more of guardians, custody partners and custodial accounts to facilitate the secure validation and transfer of digital assets at the behest of or upon the death of the owner.

Abstract: An example operation may include one or more of transmitting a user identifier to a plurality of blockchain peers of a blockchain network, receiving a plurality of public keys from the plurality of blockchain peers, receiving a plurality of ciphertexts from the plurality of blockchain peers generated based on the user identifier and a corresponding plurality of private keys, generating a combined ciphertext that includes the plurality of ciphertexts and generating a temporary blockchain address of the blockchain network based on the plurality of public keys, and storing the combined ciphertext and a transfer of an asset via the temporary blockchain address of the blockchain network.

Abstract: An example operation may include one or more of receiving, via a first blockchain peer, a request to verify an identity of a user, performing, via the first blockchain peer, an identity validation of the user via a know your customer (KYC) service provider of a blockchain network, determining whether a digital identity of the user has been previously stored by another blockchain peer on a blockchain of the blockchain network, in response to a determination that the digital identity of the user has not been stored on the blockchain, creating a non-fungible token (NFT) that identifies the user, and appending information about the identity validation of the user to a structure of the NFT and storing the NFT in a block of the blockchain.

Abstract: Systems, computer program products, and methods are described herein for steganalysis detection of metadata in a video stream for providing real-time data. An encrypted steganalysis marker is positioned within a boarder of a video stream and, upon user device scanning and identification provides a communication channel for real-time data display on the user device. The data displayed on the user device is transmitted via narrowband internet-of-things communication linkage displaying product or service data associated with the video stream. Using a combination of NB-IoT and computational memory computing creates a unique, in user device computing structure with various variable inputs to provide specified data and recommendations to the user.

Abstract: A node in a blockchain network may agree, on an authority accept a compliance module from the authority, accept the compliance module. The node may also receive an operation, verify a compliance of the operation based on the compliance module, add the verified operation to a ledger on the blockchain network.