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1,242 results on '"Kim, Hyun-Sik"'

4. Evaluating and Managing Tokenomics for Non-Fungible Tokens in Game-Based Blockchain Networks

Author

Kim, Hyoungsung, Kim, Hyun-Sik, and Park, Yong-Suk

Subjects
Computer Science - Computer Science and Game Theory
Abstract

Non-fungible tokens (NFTs) are becoming increasingly popular in Play-to-Earn (P2E) Web3 applications as a means of incentivizing user engagement. In Web3, users with NFTs ownership are entitled to monetize them. However, due to lack of objective NFT valuation, which makes NFT value determination challenging, P2E applications ecosystems have experienced inflation. In this paper, we propose a method that enables NFT inflation value management in P2E applications. Our method leverages the contribution-rewards model proposed by Curve Finance and the automated market maker (AMM) of decentralized exchanges. In decentralized systems, P2E Web3 applications inclusive, not all participants contribute in good faith. Therefore, rewards are provided to incentivize contribution. Our mechanism proves that burning NFTs, indicating the permanent removal of NFTs, contributes to managing inflation by reducing the number of NFTs in circulation. As a reward for this contribution, our method mints a compensation (CP) token as an ERC-20 token, which can be exchanged for NFTs once enough tokens have been accumulated. To further increase the value of the CP token, we suggest using governance tokens and CP tokens to create liquidity pools for AMM. The value of the governance token is determined by the market, and the CP token derives its value from the governance token in AMM. The CP token can determine its worth based on the market value of the governance token. Additionally, since CP tokens are used for exchanging NFTs, the value of the NFT is ultimately determined by the value of the CP token. To further illustrate our concept, we show how to adjust burning rewards based on factors such as the probability of upgrading NFTs' rarity or the current swap ratio of governance and CP tokens in AMM.

Published
2023

6. Perpetual Contract NFT as Collateral for DeFi Composability

Author

Kim, HyoungSung, Kim, Hyun-Sik, and Park, Yong-Suk

Subjects
Computer Science - Computer Science and Game Theory
Abstract

Ethereum and its standardized token interface have formed decentralized finance (DeFi), an open financial system based on blockchain smart contracts. The DeFi ecosystem has become richer with the introduction of DeFi composability projects, such as Lido finance and Curve finance. DeFi composability denotes the concatenation of DeFi services in which each DeFi service locks assets as collateral and gives another asset as liquidity of locked assets to providers. Providers use the tokens given for other concatenated DeFi services, such as lending, decentralized exchanges (DEXs), and derivatives. The DeFi ecosystem uses ERC-20 tokens which can represent the value of an asset. ERC-721 non-fungible tokens (NFTs) are not widely adopted in DeFi, since they represent rights to an asset and are not considered appropriate for valuation. In this paper, we propose a new concept, perpetual contract NFT, which exploits perpetual future contracts in the cryptocurrency derivatives market. Unlike futures contracts in a traditional derivatives market, in the cryptocurrency derivatives market, most futures contracts are perpetual. In addition, the value of futures contract is backed by collateral. Therefore, if we mint the rights to perpetual contracts as NFT, we can use the perpetual contract NFT as collateral for DeFi composability. To validate our proposal and its profitability, we experiment with the position NFT of Uniswap v3. Through validation, we show that our concept works in real-world scenarios.

Published
2022
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34. Supervised Contrastive Learning for 3D Cross-Modal Retrieval.

Author

Choo, Yeon-Seung, Kim, Boeun, Kim, Hyun-Sik, and Park, Yong-Suk

Subjects
SUPERVISED learning, POINT cloud, ASSETS (Accounting)
Abstract

Interoperability between different virtual platforms requires the ability to search and transfer digital assets across platforms. Digital assets in virtual platforms are represented in different forms or modalities, such as images, meshes, and point clouds. The cross-modal retrieval of three-dimensional (3D) object representations is challenging due to data representation diversity, making common feature space discovery difficult. Recent studies have been focused on obtaining feature consistency within the same classes and modalities using cross-modal center loss. However, center features are sensitive to hyperparameter variations, making cross-modal center loss susceptible to performance degradation. This paper proposes a new 3D cross-modal retrieval method that uses cross-modal supervised contrastive learning (CSupCon) and the fixed projection head (FPH) strategy. Contrastive learning mitigates the influence of hyperparameters by maximizing feature distinctiveness. The FPH strategy prevents gradient updates in the projection network, enabling the focused training of the backbone networks. The proposed method shows a mean average precision (mAP) increase of 1.17 and 0.14 in 3D cross-modal object retrieval experiments using ModelNet10 and ModelNet40 datasets compared to state-of-the-art (SOTA) methods. [ABSTRACT FROM AUTHOR]

Published
2024
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