205 results found
Matsui T, Al-Ali A, Knottenbelt WJ, 2022, On the Dynamics of Solid, Liquid and Digital Gold Futures
This paper examines the determinants of the volatility of futures prices and basis for three commodities: gold, oil and Bitcoin - often dubbed solid, liquid and digital gold - by using contract-by-contract analysis which has been previously applied to crude oil futures volatility investigations. By extracting the spot and futures daily prices as well as the maturity, trading volume and open interest data for the three assets from 18th December 2017 to 30th November 2021, we find a positive and significant role for trading volume and a possible negative influence of open interest (when significant) in shaping the volatility in all three assets, supporting earlier findings in the context of oil futures. Additionally, we find maturity has a relatively positive significance for Bitcoin and oil futures price volatility. Furthermore, our analysis demonstrates that maturity affects the basis of Bitcoin and gold positively - confirming the general theory that the basis converges to zero as maturity nears for Bitcoin and gold - while oil is affected in both directions.
Pan S, Finlay C, Besenbruch C, et al., 2021, Three gaps for quantisation in learned image compression, New Trends in Image Restoration and Enhancement (NTIRE 2021) (CVPR Workshop), Publisher: IEEE
Learned lossy image compression has demonstrated impressive progress via end-to-end neural network training. However, this end-to-end training belies the fact that lossy compression is inherently not differentiable, due to the necessity of quantisation. To overcome this difficulty in training, researchers have used various approximations to the quantisation step. However, little work has studied the mechanism of quantisation approximation itself. We ad-dress this issue, identifying three gaps arising in the quantisation approximation problem. These gaps are visualised, and show the effect of applying different quantisation approximation methods. Following this analysis, we propose a Soft-STE quantisation approximation method, which closes these gaps and demonstrates better performance than other quantisation approaches on the Kodak dataset.
Koutsouri A, Petch M, Knottenbelt W, 2021, Performance of the CoinShares Gold and Cryptoassets Index under different market regimes, Cryptoeconomic Systems, Vol: 1
Regime-switching models are frequently used to explain the tendency of financial markets to change their behavior, often abruptly. Such changes usually translate to structural breaks in the average means and volatilities of financial indicators, and partition their time-series into distinct segments, each with unique statistical properties. In this paper, we address the problem of identifying the presence of such regimes in the constituents of diversified, cryptoasset-containing portfolios, ultimately to define high-risk market conditions and assess portfolio resilience. For each portfolio component, we first consider a Gaussian Hidden Markov Model (HMM) in order to extract intermediate trend-related states, conditional on the weekly returns distributions. We further apply a Markov-switching GARCH model to the demeaned daily returns to describe changes in the conditional variance dynamics and isolate volatility-related states. We combine the former approaches to generate a number of price paths for each constituent, simulate the portfolio allocation strategy and obtain a risk profile for each combination of the trend and volatility regimes. We apply the proposed method to the CoinShares Gold and Cryptoassets Index, a diversified, monthly-rebalanced index which includes two main risk-weighted components; a cryptoassets basket and physical gold. Results demonstrate an overall stable risk-reward profile when compared against the individual components and suggest a superior performance in terms of Omega ratio for investors that target wealth preservation and moderate annual returns. We detect underperformance regions in bear-low volatility market regimes, where diversification is hindered.
Ilie D, Werner S, Stewart I, et al., 2021, Unstable throughput: when the difficulty algorithm breaks, 2021 IEEE Conference on Blockchain and Cryptocurrency (ICBC 2021), Publisher: IEEE, Pages: 1-5
In Proof-of-Work blockchains, difficulty algorithms serve the crucial purpose of maintaining a stable transaction throughput by dynamically adjusting the block difficulty in response to the miners’ constantly changing computational power. Blockchains that may experience severe hash rate fluctuations need difficulty algorithms that quickly adapt the mining difficulty. However, without careful design, the system could be gamed by miners using coin-hopping strategies to manipulate the block difficulty for profit. Such miner behavior results in an unreliable system due to the unstable processing of transactions. We provide an empirical analysis of how Bitcoin Cash’s difficulty algorithm design leads to cyclicality in block solve times as a consequence of a positive feedback loop. In response, we mathematically derive a difficulty algorithm using a negative exponential filter which prohibits the formation of positive feedback and exhibits additional desirable properties, such as history agnosticism. We compare the described algorithm to that of Bitcoin Cash in a simulated mining environment and verify that the former would eliminate the severe oscillations in transaction throughput.
Koutsouri A, Petch M, Knottenbelt W, 2021, Diversification benefits of commodities for cryptoasset portfolios, 2021 International Conference on Blockchain and Cryptocurrency (ICBC 2021), Publisher: IEEE, Pages: 1-9
The aim for balance between risk and reward in investment portfolios often requires studying the diversification contribution of its constituents. This objective requires to specify whether investors can extend their exposure in certain asset classes and benefit their portfolios in a statistically significant way. In this paper, we address this issue of diversification in the context of cryptoasset portfolios and examine whether their risk-adjusted performance can be enhanced through seeking exposure into the commodities class. For an equally-weighted portfolio of five cryptoassets, we first consider the addition of physical gold, as conceptualised by the CoinShares Gold and Cryptoassets Index, a diversified, monthly-rebalanced index that seeks exposure to both asset classes. We further consider modifying the index composition by replacing physical gold with a basket of five commodities. Mean-variance spanning tests reveal that the addition of physical gold in the original cryptoasset portfolio translates to a significant shift in the efficient frontier, both in terms of the global minimum variance and the tangency portfolios. Additionally, expanding the exposure in the commodity side confirms a statistically significant improvement, with the diversification benefit arising from a shift in the tangency portfolio. We further generate a number of price paths for the original index, the modified index and their components, according to a Dynamic Conditional Correlation GARCH specification, to assess the efficiency of the index weighted risk contribution scheme. Results demonstrate a superior performance of the two indices when compared against their constituents in terms of Omega ratio. The modified index appears more appropriate for investors that seek higher annual returns, while the original composition would be more appropriate for individuals with mod
Knottenbelt W, Wolter K, 2021, Message from the Chairs, ACM SIGMETRICS Performance Evaluation Review, Vol: 48, Pages: 2-2, ISSN: 0163-5999
<jats:p>This volume presents the proceedings of the 2nd Symposium of Cryptocurrency Analysis (SOCCA 2020), originally scheduled to be held in Milan, Italy, on November 6, 2020. The COVID-19 pandemic has necessitated, in common with many other conferences, that SOCCA will be held entirely virtual.</jats:p>
Werner SM, Perez D, Gudgeon L, et al., 2021, SoK: Decentralized Finance (DeFi)., Publisher: arXiv
Decentralized Finance (DeFi), a blockchain powered peer-to-peer financial system, is mushrooming. One year ago the total value locked in DeFi systems was approximately 600m USD, now, as of January 2021, it stands at around 25bn USD. The frenetic evolution of the ecosystem makes it challenging for newcomers to gain an understanding of its basic features. In this Systematization of Knowledge (SoK), we delineate the DeFi ecosystem along its principal axes. First, we provide an overview of the DeFi primitives. Second, we classify DeFi protocols according to the type of operation they provide. We then go on to consider in detail the technical and economic security of DeFi protocols, drawing particular attention to the issues that emerge specifically in the DeFi setting. Finally, we outline the open research challenges in the ecosystem.
Chong QZ, Knottenbelt WJ, Bhatia KK, 2021, Evaluation of Active Learning Techniques on Medical Image Classification with Unbalanced Data Distributions, Pages: 235-242, ISSN: 0302-9743
In supervised image classification, convolutional deep neural networks have become the dominant methodology showing excellent performance in a number of tasks. These models typically require a very large number of labelled data samples to achieve required performance and generalisability. While data acquisition is relatively easy, data labelling, particularly in the case of medical imaging where expertise is required, is expensive. This has led to the investigation of active learning methods to improve the effectiveness of choosing which data should be prioritised for labelling. While new algorithms and methodologies continue to be introduced for active learning, each reporting improved performance, one key aspect that can be overlooked is the underlying data distribution of the dataset. Many active learning papers are benchmarked using curated datasets with balanced class distributions. This is not representative of many real-world scenarios where the data acquired can be heavily skewed towards a certain class. In this paper, we evaluate the performance of several established active learning techniques on an unbalanced dataset of 15153 chest X-Ray images, forming a more realistic scenario. This paper shows that the unbalanced dataset has a significant impact on the performance of certain algorithms, and should be considered when choosing which active learning strategy to implement.
Zamyatin A, Al-Bassam M, Zindros D, et al., 2021, SoK: Communication Across Distributed Ledgers, 25th International Conference on Financial Cryptography and Data Security (FC), Publisher: SPRINGER-VERLAG BERLIN, Pages: 3-36, ISSN: 0302-9743
Gudgeon L, Werner S, Perez Hernandez D, et al., 2020, DeFi protocols for loanable funds: interest rates, liquidity and market efficiency, 2nd ACM Conference on Advances in Financial Technologies (AFT 2020), Publisher: ACM, Pages: 92-112
We coin the term Protocols for Loanable Funds (PLFs)to refer to pro-tocols which establish distributed ledger-based markets for loanable funds. PLFs are emerging as one of the main applications within De-centralized Finance (DeFi), and use smart contract code to facilitate the intermediation of loanable funds. In doing so, these protocols allow agents to borrow and save programmatically. Within these protocols, interest rate mechanisms seek to equilibrate the supply and demand for funds. In this paper, we review the methodologies used to set interest rates on three prominent DeFi PLFs, namely Compound, Aave and dYdX. We provide an empirical examination of how these interest rate rules have behaved since their inception in response to differing degrees of liquidity. We then investigate the market efficiency and inter-connectedness between multiple protocols, examining first whether Uncovered Interest Parity holds within a particular protocol and second whether the interest rates for a particular token market show dependence across protocols,developing a Vector Error Correction Model for the dynamics.
Koutsouri A, Knottenbelt WJ, 2020, Stress Testing Diversified Portfolios: The Case of the CoinShares Gold and Cryptoassets Index, 2nd International Conference on Mathematical Research for Blockchain Economy, Publisher: Springer Verlag
Stress testing involves the use of simulation to assess the resilience of investment portfolios to changes in market regimes and extreme events. The quality of stress testing is a function of the realism of the market models employed, as well as the strategy used to determine the set of simulated scenarios. In this paper, we consider both of these parameters in the context of diversified portfolios, with a focus on the emerging class of cryptoasset-containing portfolios. Our analysis begins with univariate modelling of individual risk factors using ARMA and GJR--GARCH processes. Extreme Value Theory is applied to the tails of the standardised residuals distributions in order to account for extreme outcomes accurately. Next, we consider a family of copulas to represent the dependence structure of the individual risk factors. Finally, we combine the former approaches to generate a number of plausibility-constrained scenarios of interest, and simulate them to obtain a risk profile. We apply our methodology to the CoinShares Gold and Cryptoassets Index, a monthly-rebalanced index which comprises two baskets of risk-weighted assets: one containing gold and one containing cryptoassets. We demonstrate a superior risk-return profile as compared to investments in a traditional market-cap-weighted cryptoasset index.
Koutsouri A, Poli F, Alfieri E, et al., 2020, Balancing Cryptoassets and Gold: A Weighted-Risk-Contribution Index for the Alternative Asset Space, 1st International Conference on Mathematical Research for Blockchain Economy, Publisher: Springer Verlag, Pages: 217-232, ISSN: 0302-9743
Bitcoin is foremost amongst the emerging asset class knownas cryptoassets. Two noteworthy characteristics of the returns of non-stablecoin cryptoassets are their high volatility, which brings with it ahigh level of risk, and their high intraclass correlation, which limits thebenefits that can be had by diversifying across multiple cryptoassets. Yetcryptoassets exhibit no correlation with gold, a highly-liquid yet scarceasset which has proved to function as a safe haven during crises affectingtraditional financial systems. As exemplified by Shannon’s Demon, a lackof correlation between assets opens the door to principled risk controlthrough so-called volatility harvesting involving periodic rebalancing.In this paper we propose an index which combines a basket of five cryp-toassets with an investment in gold in a way that aims to improve therisk profile of the resulting portfolio while preserving its independencefrom mainstream financial asset classes such as stocks, bonds and fiatcurrencies. We generalise the theory of Equal Risk Contribution to allowfor weighting according to a desired level of contribution to volatility. Wefind a crypto–gold weighting based on Weighted Risk Contribution to behistorically more effective in terms of Sharpe Ratio than several alterna-tive asset allocation strategies including Shannon’s Demon. Within thecrypto-basket, whose constituents are selected and rebalanced monthly,we find an Equal Weighting scheme to be more effective in terms of thesame metric than a market capitalisation weighting.
Marchenko Y, Knottenbelt WJ, Wolter K, 2020, EthExplorer: A Tool for Forensic Analysis of the Ethereum Blockchain, Pages: 100-117, ISSN: 0302-9743
This paper presents EthExplorer, a graph-based tool for analysing the Ethereum blockchain. EthExplorer has been designed for the assessment of Ethereum transactions, which represent diverse and complex activities in a large-scale distributed system. EthExplorer shows Ethereum addresses as nodes and transactions as directed arcs between addresses. The graph is annotated in several ways: arcs are scaled according to the amount of Ether they carry and the nodes are colour encoded to indicate types of addresses, such as exchanges, miners or mining pools. Ether transfer transactions and smart contracts are distinguished by line styles. EthExplorer can be used to trace the flow of Ether between addresses. For a given address all its output or input transactions with the corresponding receiver or sender addresses can be found. The set of considered addresses can be increased by adding selected addresses to the set of analysed addresses.
Ilie DI, Knottenbelt WJ, Stewart ID, 2020, Committing to Quantum Resistance, Better: A Speed-and-Risk-Configurable Defence for Bitcoin Against a Fast Quantum Computing Attack, 1st International Conference on Mathematical Research for Blockchain Economy (MARBLE), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 117-132, ISSN: 2198-7246
Pardalos P, Kotsireas I, Guo Y, et al., 2020, MARBLE 2019 Conference Proceedings Volume: Preface, Pages: v-vi, ISSN: 2198-7246
Ilie DI, Karantias K, Knottenbelt WJ, 2020, Bitcoin Crypto–Bounties for Quantum Capable Adversaries, Pages: 9-25, ISSN: 2198-7246
With the advances in quantum computing taking place over the last few years, researchers have started considering the implications on cryptocurrencies. As most digital signature schemes would be impacted, it is somewhat reassuring that transition schemes to quantum resistant signatures are already being considered for Bitcoin. In this work, we stress the danger of public key reuse, as it prevents users from recovering their funds in the presence of a quantum enabled adversary despite any transition scheme the developers decide to implement. We emphasize this threat by quantifying the damage a functional quantum computer could inflict on Bitcoin (and Bitcoin Cash) by breaking exposed public keys.
Wolter K, Pesu T, van Moorsel A, et al., 2020, Black-box models for restart, reboot and rejuvenation, Handbook Of Software Aging And Rejuvenation: Fundamentals, Methods, Applications, And Future Directions, Pages: 155-194, ISBN: 9789811214578
This chapter discusses black-box models for retries, where no distinction is made between the purpose of the retry. Retries can be used as restart, to improve userobserved performance, as reboot, for fault-tolerance, or as rejuvenation, to treat the aging of the system. The chapter derives stochastic models and shows the results we obtained for optimising moments of the user-observed job completion time as well as the probability of meeting a deadline. The second part of the chapter provides a review of the literature in this area of the past decade. The chapter closes with a discussion of open problems.
Pardalos P, Kotsireas I, Guo Y, et al., 2020, Preface, Pages: v-vi, ISSN: 2198-7246
Zamyatin A, Avarikioti Z, Perez D, et al., 2020, TxChain: Efficient Cryptocurrency Light Clients via Contingent Transaction Aggregation, Pages: 269-286, ISSN: 0302-9743
Cryptocurrency light- or simplified payment verification (SPV) clients allow nodes with limited resources to efficiently verify execution of payments. Instead of downloading the entire blockchain, only block headers and selected transactions are stored. Still, the storage and bandwidth cost, linear in blockchain size, remain non-negligible, especially for smart contracts and mobile devices: as of April 2020, these amount to 50 MB in Bitcoin and 5 GB in Ethereum. Recently, two improved sublinear light clients were proposed: to validate the blockchain, NIPoPoWs and FlyClient only download a polylogarithmic number of block headers, sampled at random. The actual verification of payments, however, remains costly: for each verified transaction, the corresponding block must too be downloaded. This yields NIPoPoWs and FlyClient only effective under low transaction volumes. We present TxChain, a novel mechanism to maintain efficiency of light clients even under high transaction volumes. Specifically, we introduce the concept of contingent transaction aggregation, where proving inclusion of a single contingent transaction implicitly proves that n other transactions exist in the blockchain. To verify n payments, TxChain requires a only single transaction in the best (n≤ c ), and transactions in the worst case (n> c ), where c is a blockchain constant. We deploy TxChain on Bitcoin without consensus changes and implement a hard fork for Ethereum. To demonstrate effectiveness in the cross-chain setting, we implement TxChain as a smart contract on Ethereum to efficiently verify Bitcoin payments.
, 2020, Mathematical Research for Blockchain Economy, 1st International Conference, MARBLE 2019, Santorini, Greece, May 6-9, 2019., Publisher: Springer
Ilie DI, Karantias K, Knottenbelt WJ, 2020, Bitcoin Crypto - Bounties for Quantum Capable Adversaries., IACR Cryptol. ePrint Arch., Vol: 2020, Pages: 186-186
Ilie DI, Knottenbelt WJ, Stewart I, 2020, Committing to quantum resistance, better: a speed - and - risk - configurable defence for bitcoin against a fast quantum computing attack., Publisher: Cryptology ePrint Archive
In light of the emerging threat of powerful quantum computers appearing in the near future, we investigate the potential attacks onBitcoin available to a quantum-capable adversary. In particular, we illustrate how Shor’s quantum algorithm can be used to forge ECDSA basedsignatures, allowing attackers to hijack transactions. We then proposea simple commit–delay–reveal protocol, which allows users to securelymove their funds from non-quantum-resistant outputs to those adheringto a quantum-resistant digital signature scheme. In a previous paper we presented a similar scheme with a long fixed delay. Here we improveon our previous work, by allowing each user to choose their preferreddelay – long for a low risk of attack, or short if a higher risk is acceptableto that user. As before, our scheme requires modifications to the Bitcoinprotocol, but once again these can be implemented as a soft fork.
Zamyatin A, Avarikioti Z, Perez D, et al., 2020, TxChain: efficient cyptocurrency light clients via contingent transaction aggregation., Publisher: Cryptology ePrint Archive
Cryptocurrency light- or simplified payment verification (SPV) clientsallow nodes with limited resources to efficiently verify execution of payments.Instead of downloading the entire blockchain, only block headers and selectedtransactions are stored. Still, the storage and bandwidth cost, linear in blockchainsize, remain non-negligible, especially for smart contracts and mobile devices: asof April 2020, these amount to 50 MB in Bitcoin and 5 GB in Ethereum.Recently, two improved sublinear light clients were proposed: to validate theblockchain, NIPoPoWs and FlyClient only download a polylogarithmic numberof block headers, sampled at random. The actual verification of payments, however, remains costly: for each verified transaction, the corresponding block musttoo be downloaded. This yields NIPoPoWs and FlyClient only effective underlow transaction volumes.We present TXCHAIN, a novel mechanism to maintain efficiency of light clientseven under high transaction volumes. Specifically, we introduce the concept ofcontingent transaction aggregation, where proving inclusion of a single contingent transaction implicitly proves that n other transactions exist in the blockchain.To verify n payments, TXCHAIN requires a only single transaction in the best(n ≤ c), and dnc + logc(n)e transactions in the worst case (n > c). We deployTXCHAIN on Bitcoin without consensus changes and implement a soft fork forEthereum. To demonstrate effectiveness in the cross-chain setting, we implementTXCHAIN as a smart contract on Ethereum to efficiently verify Bitcoin payments.
Zamyatin A, Al-Bassam M, Zindros D, et al., 2019, SoK: communication across distributed ledgers., Publisher: Cryptology ePrint Archive
Communication across distributed systems, each running its own consensus, is a problem previously studied under the assumption of trust across systems. With the appearance of distributed ledgers or blockchains, numerous protocols have emerged, which attempt to achieve trustless communication between distrusting ledgers and participants. Cross-chain communication thereby plays a fundamental role in cryptocurrency exchanges, sharding, bootstrapping and extension of distributed ledgers. Unfortunately, existing proposals are designed ad-hoc for specific use-cases, making it hard to gain confidence on their correctness and to use them as building blocks for new systems.
Harz D, Gudgeon L, Gervais A, et al., 2019, Balance: dynamic adjustment of cryptocurrency deposits, 2019 ACM SIGSAC Conference on Computer & Communications Security (CCS '19), Publisher: ACM, Pages: 1485-1502
In cryptoeconomic protocols, nancial deposits are fundamental totheir security. Protocol designers and their agents face a trade-owhen choosing the deposit size. While substantial deposits might in-crease the protocol security, for example by minimising the impactof adversarial behaviour or risks of currency uctuations, locked-up capital incurs opportunity costs for agents. Moreover, someprotocols require over-collateralization in anticipation of futureevents and malicious intentions of agents. We presentBalance,an application-agnostic system that reduces over-collateralizationwithout compromising protocol security. InBalance, maliciousagents receive no additional utility for cheating once their depositsare reduced. At the same time, honest and rational agents increasetheir utilities for behaving honestly as their opportunity costs forthe locked-up deposits are reduced.Balanceis a round-basedmechanism in which agents need tocontinuouslyperform desiredactions. Rather than treating agents’ incentives and behaviour asancillary, we explicitly model agents’ utility, proving the conditionsfor incentive compatibility.Balanceimproves social welfare givena distribution of honest, rational, and malicious agents. Further,we integrateBalancewith a cross-chain interoperability protocol,XCLAIM, reducing deposits by 10% while maintaining the sameutility for behaving honestly. Our implementation allows any num-ber of agents to be maintained for at most 55,287 gas (≈USD 0.07)to update the agents’ scores, and at a cost of 54,948 gas (≈USD0.07) to update the assignment of agents to layers.
Zamyatin A, Harz D, Lind J, et al., 2019, XCLAIM: trustless, interoperable, cryptocurrency-backed assets, 40th IEEE Symposium on Security and Privacy (IEEE S&P 2019), Publisher: IEEE, Pages: 193-210, ISSN: 2375-1207
Building trustless cross-blockchain trading protocols is challenging. Centralized exchanges thus remain the preferred route to execute transfers across blockchains. However, these services require trust and therefore undermine the very nature of the blockchains on which they operate. To overcome this,several decentralized exchanges have recently emerged which offer support for atomic cross-chain swaps (ACCS). ACCS enable the trustless exchange of cryptocurrencies across blockchains,and are the only known mechanism to do so. However, ACCS suffer significant limitations; they are slow, inefficient and costly,meaning that they are rarely used in practice.We present XCLAIM: the first generic framework for achieving trustless and efficient cross-chain exchanges using cryptocurrency-backed assets(CBAs). XCLAIM offers protocols for issuing,transferring, swapping and redeeming CBAs securely in anon-interactive manner on existing blockchains. We instanti-ate XCLAIM between Bitcoin and Ethereum and evaluate our implementation; it costs less than USD 0.50 to issue an arbi-trary amount of Bitcoin-backed tokens on Ethereum. We show XCLAIMis not only faster, but also significantly cheaper than atomic cross-chain swaps. Finally, XCLAIMis compatible with the majority of existing blockchains without modification, and enables several novel cryptocurrency applications, such as cross-chain payment channels and efficient multi-party swaps
Harz D, Gudgeon L, Gervais A, et al., 2019, Balance: dynamic adjustment of cryptocurrency deposits., Publisher: Cryptology ePrint Archive
Financial deposits are fundamental to the security of cryptoeconomic protocols as they serve as insurance against potential misbehaviour of agents. However, protocol designers and their agents face a trade-off when choosing the deposit size. While substantial deposits might increase the protocol security, for example by minimising the impact of adversarial behaviour or risks of currency fluctuations, locked-up capital incurs opportunity costs. Moreover, some protocols require over-collateralization in anticipation of future events and malicious intentions of agents. We present Balance, an application-agnostic system that reduces over-collateralization without compromising protocol security. In Balance, malicious agents receive no additional utility for cheating once their deposits are reduced. At the same time, honest and rational agents increase their utilities for behaving honestly as their opportunity costs for the locked-up deposits are reduced. Balance is a round-based mechanism in which agents need to continuously perform desired actions. Rather than treating agents' incentives and behaviour as ancillary, we explicitly model agents' utility, proving the conditions for incentive compatibility. Balance improves social welfare given a distribution of honest, rational, and malicious agents. Further, we integrate Balance with a cross-chain interoperability protocol, XCLAIM, reducing deposits by 10% while maintaining the same utility for behaving honestly. Our implementation allows any number of agents to be maintained for at most 55,287 gas (ca. USD 0.07) to update all agents' scores, and at a cost of 54,948 gas (ca. USD 0.07) to update the assignment of all agents to layers.
Wu H, Knottenbelt W, Wolter K, 2019, An efficient application partitioning algorithm in mobile environments, IEEE Transactions on Parallel and Distributed Systems, Vol: 30, Pages: 1464-1480, ISSN: 1045-9219
Application partitioning that splits the executions into local and remote parts, plays a critical role in high-performance mobile offloading systems. Mobile devices can obtain the most benefit from Mobile Cloud Computing (MCC) or Mobile Edge Computing (MEC) through optimal partitioning. Due to unstable resources at the wireless network (network disconnection, bandwidth fluctuation, network latency, etc.) and at the service nodes (different speeds of mobile devices and cloud/edge servers, memory, etc.), static partitioning solutions with fixed bandwidth and speed assumptions are unsuitable for offloading systems. In this paper, we study how to dynamically partition a given application into local and remote parts effectively, while keeping the total cost as small as possible. For general tasks (i.e., arbitrary topological consumption graphs), we propose a Min-Cost Offloading Partitioning (MCOP) algorithm that aims at finding the optimal partitioning plan (determine which portions of the application to run on mobile devices and which portions on cloud/edge servers) under different cost models and mobile environments. Simulation results show that the MCOP algorithm provides a stable method with low time complexity which significantly reduces execution time and energy consumption by optimally distributing tasks between mobile devices and servers, besides it well adapts to mobile environmental changes.
Werner S, Pritz P, Zamyatin A, et al., 2019, Uncle traps: harvesting rewards in a queue-based ethereum Mining Pool, 12th EAI International Conference on Performance Evaluation Methodologies and Tools, Publisher: ACM, Pages: 127-134
Mining pools in Proof-of-Work cryptocurrencies allow miners topool their computational resources as a means of reducing payoutvariance. In Ethereum,uncle blocksare valid Proof-of-Work solu-tions which do not become the head of the blockchain, yet yieldrewards if later referenced by main chain blocks. Mining pool opera-tors are faced with the non-trivial task of fairly distributing rewardsfor both block types among pool participants.Inspired by empirical observations, we formally reconstruct aSybil attack exploiting the uncle block distribution policy in a queue-based mining pool. To ensure fairness of the queue-based payoutscheme, we propose a mitigation. We examine the effectiveness ofthe attack strategy under the current and the proposed policy via adiscrete-event simulation. Our findings show that the observed attackcan indeed be obviated by altering the current reward scheme.
Seakhoa-King S, Balaji P, Alvarez NT, et al., 2019, Revenue-Driven Scheduling in Drone Delivery Networks with Time-sensitive Service Level Agreements, 12th EAI International Conference on Performance Evaluation Methodologies and Tools (VALUETOOLS), Publisher: ASSOC COMPUTING MACHINERY, Pages: 183-186
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