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DeFi Project HUMAN Protocol Launches Routing Protocol for Data Contribution - Blockchain.News

DeFi Project HUMAN Protocol Launches Routing Protocol for Data Contribution

Annie Li Jul 21, 2022 09:00

DeFi project HUMAN Protocol launched a new blockchain coordination layer called Routing Protocol, which aims to improve the data contribution function on the blockchain.

DeFi Project HUMAN Protocol Launches Routing Protocol for Data Contribution

DeFi project HUMAN Protocol launched a new blockchain coordination layer called Routing Protocol, which aims to improve the data contribution function on the blockchain.

HUMAN Protocoldecentralisedalized and permissionless protocol facilitates the exchange of HUMAN work, knowledge, and contribution.

The protocol reduces the impact of robots on the network and products through blockchain incentives and reasonable task distribution applied to major companies and data platforms worldwide.

The routing protocol will handle key network components, including proof-of-balance, compensation negotiation, and detection of network generators, thereby increasing data processing capabilities.

Human Protocol also introduced two functions, record oracles and reputation oracles, to assess the quality of work.

When workers submit their respective processing results, the marketplace submits the results to the record oracle, which makes an initial evaluation and aggregates the work into blocks.

In the Human Protocol ecosystem, the token rewards (HMT tokens) are awarded by the Human Protocol system to motivate users through smart contracts.

Reputation oracles oversee the entire work and are responsible for evaluating blocks that record oracle aggregation.

Routing protocols will improve the ability of suppliers, humans and machines to define and deploy jobs by recording worker-provided answers and assessments through oracles and reputation oracles, increasing transaction volume speed and fidelity.

In Dec 2020, Human Protocol announced the integration of the Polkadot parachain Moonbeam to improve machine learning data labelling capabilities.

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