TrueChain

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0xBitcoin

Unification

Roobee (ROOBEE) is a blockchain-based investment platform that allows users to invest in products from both the traditional and crypto markets. The platform is under active development and is already operational. Behind the platform is a team of developers who won prizes in hackathons, including the 2017 and 2019 ETHWaterloo hackathons held in Canada by ETHGlobal.

The mission of Roobee is to give any person with any level income an opportunity to invest starting just from $10 in investment products that are only available to big institutional players, qualified investors, millionaires, and funds. With the help of the Roobee platform, the goal is to empower people from all over the world to invest in any of the many investment products available on the platform: crypto, stocks, ETFs, IPOs, real estate, and more.

ROOBEE

SwftCoin

Founded by Peter Kim, MovieBloc is a reverse ICO project driven by the Korean video streaming platform Pandora.tv. MovieBloc is a decentralized movie and content distribution platform.

MovieBloc

Validity

Everscale (EVER) is a fast and secure layer-one blockchain that aims to become a decentralized platform for high-performance applications with real-world relevance, such as Stablecoins, CBDCs, DEXes, Bridges, Gaming Platforms and more.

Everscale

High Performance Blockchain is the first permissionless public blockchain to innovate with hardware acceleration and provably random numbers:

acceleration delivers fast, ultra low-fee, on-chain transactions

provably random numbers support 100% fairness

HPB (the coin) is used to pay transaction gas fees and to reward node operators.

Hyperblox

Abyss

CyberVein's big-data solution is based on the PISR (private, interlink, secure, robust) distributed database, database operations are stored on CyberVein blockchain network which runs on a Proof-of-Contribution (PoC) consensus mechanism. The solution is focusing on enterprise-level "blockchain + big-data" customized services.

CVT is the native utility token that is used for:

Storage payment: The data owner pays the corresponding storage fee based on the file size and bandwidth consumed in such a process.

Computing power payment: Payments from user to owner for usage, and remuneration for software developers is going to be exclusively conducted in CVT.

Data exchange: On CyberVein's federated learning platform, the data acquirer trades the data at a price contracted between the two parties for distributed modeling applications.

CROSS NFT issuance payment: Dapp businesses are obligated to pay for Storage and Data Exchange in light of network resources occupied, and may set up CyberVein token as a means of payment to issue NFT or conduct business in accordance to their desired business model.

CyberVein tools include:

PISR Consortium database, creates better data management: through safe and reliable virtual machine operation and maintenance of user data, data is efficiently aggregated for upstream data, and data traceability and tracking are realized to ensure the reliability of data sources and outputs, and achieve cross-level, cross-departmental data sharing to improve the timeliness, diversity, stock and density of data to realize the commercial value of data, and truly solve the problems in data flow.

DAG storage chain, creates safer data storage, with better efficiency, no block confirmation, low transaction fees, and also eliminates miner, supports asynchronous verification and parallel processing of each node, the more nodes, the faster the speed, the stronger the scalability, which leads to better scalability. The database backup on DAG storage chain provides an extra layer of security for businesses.

Cytrix, allows GPU to be shared. Nodes can contribute part of their GPU to the network, in return for compensation through the Proof of Contribution mechanism. The contributed GPU can aid enterprises or individuals in data modeling with federated learning.

Federated Learning Platform, enables data to be monetized. Federated Learning is a distributed machine learning workflow. The datasets are first trained locally, and the data of all parties is kept locally, without revealing privacy or violating regulations.  Individual terminals return trained models to the server to create a shared model. Effectively protect data privacy and security, solve the problem of information silo, and thus realize the value of data. The platform will also provide algorithms to support data modeling operations by enterprises and individuals.

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