2024-12-27 06:00:14
Protocol
Security Analysis
\ A. Codes
B. Proofs
Currently, many on-chain options trading platforms are available in the market. They price the options using an Automatic Market Maker (AMM). Lyra [10] stands as the preeminent decentralized options trading platform, commanding around a third of the market’s TVL, and employs an AMM with a Black76 [4] pricing model. However, its operations hinge on external data feeds from oracle, such as spot prices and implied volatility. Hegic [44] decentralizes the writers’ risk and employs a fixed pricing rate based on option expiry date and target prices, which leads to less accurate pricing.
\ In traditional markets, the price of options is determined by supply and demand. Devising an effective pricing model for options by AMM faces challenges due to the lack of accurate supply and demand modeling. Therefore, an order-book based decentralized exchange shows up. Aevo [2] is a high-performance, order-book based decentralized exchange, which closely resembles the traditional options market. However, its current implementation employs an off-chain orderbook coupled with on-chain settlement, which introduces a higher degree of centralized risk into the protocol. Opyn [30] provides users with the ability to sell European options by minting ERC20 tokens as the option. These tokens can be destroyed to exercise rights or transacted in the market. However, the system faces challenges due to high gas fees on Ethereum and a lack of necessary liquidity for exchanges. All of above on-chain protocols lack universality, most [2, 10, 30, 44] currently only support ETH and BTC options trading and lack the flexibility of customized pricing to meet the needs of the traders
\ A Hashed Timelock Contract (HTLC) can address the aforementioned issues by enabling two parties to create option contracts across two chains. These contracts lock assets, agreed upon by both parties, on two chains at a predetermined price. HTLCs [23, 29, 48] were originally designed for cross-chain atomic swaps. Subsequently, Han et al. [14] highlighted the optionality and fairness aspects for one party, demonstrating that an atomic crosschain swap is equivalent to a premium-free American call option. They estimate premiums with Cox-Ross-Rubinstein option pricing model [8]. They addresses the unfairness by incorporating a premium mechanism. In [47], the authors define a sore loser attack in cross-chain swaps and let participants escrow assets along with a negotiated option premium, which acts as compensation. Nadahalli et al. [26] separate the premium protocol from the collateral protocol, employs upfront communication of off-chain unspent transaction outputs as the option premium and collateral. [21, 42] introduce cross-chain atomic options, incorporating concepts such as the holder’s late margin deposit and early cancellation of the option. In [12], the authors introduce transferability of options. However, their approach requires long transfer times and does not support concurrent trading involving multiple buyers, which may lead to phantom bid attacks. An adversary can create multiple fake buyers who offer higher prices but fail to complete the transfer. Consequently, the option holder is unable to sell their position
\ None of these protocols eliminate the holder’s collateral in crosschain options. To eliminate the holder’s upfront collateral requirement, cross-chain transaction confirmation can be adopted to verify the collateral deposition on one chain when the option is exercised. This approach can employ cross-chain bridges. Some cross-chain bridges rely on external verification and introduces a trusted third party to facilitate message transmission. This approach is vulnerable to many attacks [40, 50], such as rug pulls [18], code vulnerabilities [28] and private key leakage [24]. Some bridges employ native verification and use light clients on both chains to verify proofs. This method requires complex smart contracts and incurs high verification and storage costs [9, 39, 46, 49]. The diversity and heterogeneity of blockchains significantly increase the time and cost of implementing a light client for each chain. An alternative is using Trusted Execution Environments (TEE) for cross-chain transactions [3, 41]. Those solutions are susceptible to both software and hardware vulnerabilities, including side-channel attacks, which present significant security risks [7, 20, 25, 36, 43].
\ Our proposed approach provides an efficient cross-chain option protocol by combining HTLC logic with a signature scheme. This combination facilitates the transfer of positions and replacement of hashlocks in option contracts. Our approach eliminates the need for option holders to provide upfront collateral. Instead of relying on cross-chain bridges, we achieve this through a distributed protocol design bolstered by economic incentives.
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:::info Authors:
(1) Zifan Peng, The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China ([email protected]);
(2) Yingjie Xue, The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China ([email protected]);
(3) Jingyu Liu, The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China ([email protected]).
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:::info This paper is available on arxiv under CC BY 4.0 license.
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2024-12-27 05:30:17
Protocol
Security Analysis
\ A. Codes
B. Proofs
Implementation. We implemented, tested, and evaluated our proposed protocol, Efficient Cross-Chain Options without Upfront Holder Collateral, in Section 4.2.1. This was conducted within a simulated environment using the Ethereum Virtual Machine (EVM) (Remix VM Cancun) and the Solidity Compiler version 0.8.22. We employ the same signature algorithm as Ethereum, utilizing the secp256k1 curve and the Elliptic Curve Digital Signature Algorithm (ECDSA) [17]. Since EVM does not support direct on-chain verification of a public-private key pair, we implement the proof of the private key 𝑠𝑘 by signing a specific message with it. The corresponding codes are provided in Appendix A.
\ Expected Transfer Time Evaluation. We compared our work with that of Engel and Xue [12]. Assuming that the probability of an option being transferred and ultimately finalized within the current network is 𝑝, the total number of transfers is 𝑋. Then, in their protocol, 𝑋 follows a geometric distribution, i.e. 𝑋 ∼ 𝐺(𝑝). The relationship between the expected successful transfer time and the successful transfer probability of each phase is illustrated in Figure 3, where mutate lock phase in Engel and Xue’s protocol to corresponds to the reveal phase in our protocol. When a large number of malicious nodes exist in the current network, say, the finalization probability is 10%, the duration of the mutate lock phase and the consistency phase in their protocol becomes significantly prolonged, reaching 45Δ, which is approximately equivalent to 2 days in Bitcoin. By initiating the replace phase earlier and consolidating the mutate and consistency phases, we significantly reduce the duration of these phases.
\ Gas Consumption Evaluation. Figure 4 lists the gas consumption for contract deployment, option operations, and gas consumption in different phases, where gas price is 4.131 Gwei and ETH price is $2274.87 (Sep 7, 2024). We calculate the maximum gas used for each operation. In the transfer failure case, we only calculate the gas consumption of conforming parties.
\ Notably, as shown in Figure 4a, compared to Engel and Xue’s protocol, the gas consumption for the holder transfer process has significantly decreased from 714,867 to 510,857 gas (a reduction of approximately 28.5% for successful transfers). For failed transfers, the gas consumption decreases from 330,350 gas to 248,388 gas (a reduction of approximately 3.4%). The gas consumption of the transferring writer also decreases to a similar level. The gas consumption for exercising an option increases from 96,916 to 145,337, while the gas consumption for abandonment decreased. This is because, during an exercise, Alice needs to deposit funds and Bob must fulfill the request by revealing the exercise secret. In contrast, for an abandonment, Bob only needs to perform a refund operation.
\ Figure 4b illustrates the gas consumption of a successful transfer across different phases. In our protocol, the reveal phase only requires the seller to reveal a signature in one contract, significantly reducing gas consumption compared to the mutate and replace/revert phases, lowering the gas for the holder and the writer to 123,158 and 123,435 gas, respectively. However, gas usage in the consistency phase is higher than that of their protocol, as we verify signatures in both contracts to ensure consistency.
\ The gas consumption for contract deployment in our protocol is generally higher compared to Engel and Xue’s protocol due to additional security measures and DAPS support. For instance, the deployment costs for 𝐶𝑜𝑛𝑡𝑟𝑎𝑐𝑡𝐴 and 𝐶𝑜𝑛𝑡𝑟𝑎𝑐𝑡𝐵 in our protocol are 2,549,610 and 2,220,156 gas, respectively. Nonetheless, this is acceptable, as deploying a transferable HTLC contract in Engle and Xue’s protocol consumes around 2.0M gas, while our protocol adds more secure operations and reduces the cost of option transfer.
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:::info Authors:
(1) Zifan Peng, The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China ([email protected]);
(2) Yingjie Xue, The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China ([email protected]);
(3) Jingyu Liu, The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China ([email protected]).
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:::info This paper is available on arxiv under CC BY 4.0 license.
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2024-12-27 05:00:17
Protocol
Security Analysis
\ A. Codes
B. Proofs
In addition to the properties of the protocol during the option transfer process, here we explore the properties of option contracts.
\ • Option correctness: If both the holder and writer are conforming, either the exercise does not occur, the holder does not lose their collateral and the writer does not lose their collateral and guarantee; or upon completion of the exercise, they will each receive the other’s collateral, and the writer will reclaim their guarantee.
\ • Exercisablity: During the transfer of the writer position, the option holder can exercise the option without experiencing any delays or obstructions.
\ • Failure compensation: If the holder initiates the exercise before expiration, he will either successfully exercise the option or receive the pre-agreed compensation guarantee.
\ Theorem 6. Protocol 4.2 satisfies option correctness: If both the Alice and Bob are conforming, then if Alice does not exercise the right, Alice doesn’t lose the 𝐴𝑠𝑠𝑒𝑡𝐴 and Bob doesn’t lose the 𝐴𝑠𝑠𝑒𝑡𝐺 and 𝐴𝑠𝑠𝑒𝑡𝐵; or if Alice exercise the right, then Alice will receive 𝐴𝑠𝑠𝑒𝑡𝐵 and Bob will receive 𝐴𝑠𝑠𝑒𝑡𝐴 and 𝐴𝑠𝑠𝑒𝑡𝐺 .
\ Theorem 7. Protocol 4.2 satisfies exercisablity: During the transfer from Bob to Dave, the option remains active, allowing Alice to exercise the option without any delays.
\ Theorem 8. Protocol 4.2 satisfies failure compensation: Before expiration, Alice can exercise the option successfully, or if the exercise fails, she is compensated with the guarantee deposited by Bob.
\ The proofs are included in the Appendix Section B.2.
\
:::info Authors:
(1) Zifan Peng, The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China ([email protected]);
(2) Yingjie Xue, The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China ([email protected]);
(3) Jingyu Liu, The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, Guangdong, China ([email protected]).
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:::info This paper is available on arxiv under CC BY 4.0 license.
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2024-12-27 05:00:13
2. Preliminaries and 2.1. Blind deconvolution
2.2. Quadratic neural networks
3.1. Time domain quadratic convolutional filter
3.2. Superiority of cyclic features extraction by QCNN
3.3. Frequency domain linear filter with envelope spectrum objective function
3.4. Integral optimization with uncertainty-aware weighing scheme
4. Computational experiments
4.1. Experimental configurations
4.3. Case study 2: JNU dataset
4.4. Case study 3: HIT dataset
5. Computational experiments
5.2. Classification results on various noise conditions
5.3. Employing ClassBD to deep learning classifiers
5.4. Employing ClassBD to machine learning classifiers
5.5. Feature extraction ability of quadratic and conventional networks
5.6. Comparison of ClassBD filters
Previously, we have theoretically demonstrated that quadratic networks possess superior cyclostationary feature extraction ability to conventional networks. It is also necessary to validate the performance in practice. Therefore, we construct two time-domain filters using quadratic convolutional layers and conventional convolutional layers with an identical structure and then evaluate their feature-extraction performance on the JNU dataset subjected to -10 dB noise.
\ The signals are analyzed using the Fast-SC method [84]. The results, as depicted in Figure 8, clearly demonstrate that the quadratic network outperforms in terms of feature extraction capability. The bright lines in the spectral coherence, highlight the quadratic network can extract cyclic frequency across high and low frequency bands. Despite the severe attenuation of the signal amplitude due to the noise, the quadratic network effectively recovers the cyclic frequency of the signal. Remarkably, the amplitude of the initial few cyclic frequencies is even higher than that of the raw signal.
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:::info Authors:
(1) Jing-Xiao Liao, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China and School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China;
(2) Chao He, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, China;
(3) Jipu Li, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China;
(4) Jinwei Sun, School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China;
(5) Shiping Zhang (Corresponding author), School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China;
(6) Xiaoge Zhang (Corresponding author), Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China.
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:::info This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license.
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2024-12-27 04:34:08
NICOSIA, Cyprus, December 26th, 2024/Chainwire/--BC. GAME has launched its latest slot game, Wukong, inspired by the highly popular AAA game Black Myth: Wukong. Released on December 24, this 6-reel, 5-row slot game invites players to join Wukong on his epic journey, with the chance to win up to 10,000 times their bet.
\ The game features key symbols from Black Myth: Wukong, including Wukong's Ruyi staff and treasure pearls, bringing the game's mythical world to life. Players can also enjoy the convenience of using cryptocurrency for gameplay, adding extra flexibility to the experience.
The BC.GAME gaming platform continues to integrate cryptocurrency, offering players the ability to play Wukong using $BC, the platform's native token, and other popular cryptocurrencies.
\ In celebration of the holiday season, BC.GAME is hosting a large-scale event, distributing a total of 200 million $BC tokens. Players can claim free $BC tokens directly to their accounts, which can then be used to play the Wukong slot game.
\ Key Features of the Wukong Slot Game:
\
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\
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Olivia D
:::tip This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here
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\ \
2024-12-27 04:00:13
2. Preliminaries and 2.1. Blind deconvolution
2.2. Quadratic neural networks
3.1. Time domain quadratic convolutional filter
3.2. Superiority of cyclic features extraction by QCNN
3.3. Frequency domain linear filter with envelope spectrum objective function
3.4. Integral optimization with uncertainty-aware weighing scheme
4. Computational experiments
4.1. Experimental configurations
4.3. Case study 2: JNU dataset
4.4. Case study 3: HIT dataset
5. Computational experiments
5.2. Classification results on various noise conditions
5.3. Employing ClassBD to deep learning classifiers
5.4. Employing ClassBD to machine learning classifiers
5.5. Feature extraction ability of quadratic and conventional networks
5.6. Comparison of ClassBD filters
In comparison of deep learning models, classical machine learning (ML) classifiers offer some distinct advantages, including robust interpretability and lightweight models. However, these “shallow” ML methods invariably rely on
\
\ human-engineered features for bearing fault diagnosis, thereby exhibiting limited generalization ability in the design of end-to-end diagnozing models [6]. Given that ClassBD can enhance the performance of deep learning classifiers, we posit that it can also serve as a feature extractor to augment the performance of classical ML classifiers.
\ Consequently, in this experiment, we utilize the pre-trained ClassBD as a feature extractor and feed the output of ClassBD into several ML classifiers for comparison: support vector machine (SVM) [88], k-nearest neighbor (KNN) [89], random forest (RF) [90], logistic regression (LR) [91], and a highly efficient gradient boosting decision tree (LightGBM) [92].
\ The results are presented in Table 14. Evidently, ClassBD significantly facilitates the performance of ML methods. Directly inputting raw signals into these ML classifiers results in markedly poor performance. On the JNU and HIT datasets, SVM and RF even fail to converge. However, with the incorporation of ClassBD, the classification performance experiences a substantial improvement. For instance, the KNN achieves a 90.71% F1 score on the JNU dataset, compared to a mere 2.89% F1 score without ClassBD. This performance even surpasses some deep learning methods. Nonetheless, ML methods exhibit instability across different datasets. The best-performing ML methods can only achieve a 49.77% score on the PU dataset. Despite this, we believe that the combination of ClassBD and ML methods presents a promising solution, promoting the study of high interpretability and efficiency in diagnostic approaches. We will explore this topic further in our future work.
\
\
:::info Authors:
(1) Jing-Xiao Liao, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China and School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China;
(2) Chao He, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, China;
(3) Jipu Li, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China;
(4) Jinwei Sun, School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China;
(5) Shiping Zhang (Corresponding author), School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China;
(6) Xiaoge Zhang (Corresponding author), Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China.
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:::info This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license.
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