In this study, an ensemble neural network (ENN) for Ethereum time series prediction was optimized using particle swarm optimization and genetic algorithms. Additionally, Type-1, Type-2, and Type-3 fuzzy inference systems, of both Mamdani and Sugeno types, were designed for achieving the prediction. The integration performed with these fuzzy systems is achieved by utilizing the results from optimizing the ENN with each optimization algorithm. In this case, the Ethereum data is the series being used for testing the proposal. This approach aims to minimize prediction error by combining the responses of the ENN with Type-1, Type-2, and Type-3 fuzzy systems, each consisting of five inputs and consequently 32 fuzzy rules are utilized. The results show that the Type-1, Type-2, and Type-3 fuzzy system approach yields an accurate prediction of the Ethereum series, as further validated by statistical tests on the results of the fuzzy systems.
Maria S. Aguiar, Elvira Albert, Samir Genaim, Pablo Gordillo · 7 authors
Context: Superoptimization is a synthesis technique that, given a loop-free sequence of instructions, searches for an equivalent sequence that is optimal wrt. an objective function. Superoptimization of Ethereum smart contracts aims at minimizing the size of their bytecode and the gas consumption of executing the contract’s functions. The search for the optimal solution poses huge computational demands –as the search space to find the optimal sequence is exponential on the given size-bound – being the main challenge for superoptimization today to scale up to real, industrial software. Even if the underlying problem for finding the optimal solution is decidable, practical tools often prioritize efficiency over completeness. This means they might be implemented to find a sub-optimal solution or even time out. Objective: This work aims at leveraging superoptimization to a real setting: Ethereum blockchain. This paper proposes a neural-guided superoptimization (NGS) approach which incorporates deep neural networks using (supervised) learning into superoptimization to improve scalability by predicting: (1) if a sequence is already optimal and hence the search can be skipped; (2) the size-bound for the optimal solution in order to reduce the search space. Method: We have downloaded over 13,000 smart contracts deployed on the blockchain for training and testing the machine learning models, and a disjoint set with 100 of the smart contracts with more transactions to prove our scalability gains and impact for the Ethereum community. Results: Incorporating DNNs resulted in a 16x overall speedup (12x for gas) with only 12% optimization loss (14% for gas), or a 3-4x speedup with no optimization loss. For the 100 analyzed contracts, this approach reduced the average compilation time to 3 min per contract and achieved monetary savings of $1.24M. Conclusions: The integration of machine learning models mitigates several limitations of traditional superoptimization by drastically reducing execution times while maintaining most of the original optimization gains.
As applications based on Distributed Ledger Technology (DLT) gain popularity, the wide range of vulnerabilities that have affected existing blockchains (most notably Ethereum and Solidity-based applications) has motivated renewed interest in the design of programming languages capable of providing more adequate abstractions for managing digital assets and their access control policies. These mechanisms are crucial to certify that applications are safe and secure before deploying them on the target blockchains.Venturing into this challenge, we focus on Move, currently one of the most promising programming languages for resources and digital assets management with the aim to investigate its effectiveness in the realm of general-purpose smart contract development, and the long-term goal to identify the design principles and language-based techniques for the safe and secure development of asset-based DLT applications. As a first step in that direction, in the present paper, we develop ALGOMOVE, a Move embedding on Algorand. In addition to providing new insight into the nature of digital assets, the embedding is noteworthy in its own right. It provides Algorand/TEAL developers with a friendly API that aligns with their familiar programming patterns, while at the same time leveraging Move’s mechanisms of static typing and security verification to offer certified, language-level protection against double spending and other resource-related pitfalls commonly encountered in DLT applications.
There has been great interest in developing hierarchical structures of fuzzy rule-based systems due to their flexibility allowing to model complex problems. To cope with the high degree of uncertainty arising from the characteristics of cryptocurrency markets, this paper proposes a hierarchical intuitionistic TSK (Takagi-Sugeno-Kang) fuzzy system equipped with a feature selection and feature ranking component. The proposed system uses intuitionistic fuzzy sets, allowing to effectively model investor uncertainty in the decision-making on cryptocurrency markets. The hierarchical structure is a parallel tree-like fuzzy system that is based on relevant features while considering feature dependencies. Computational efficiency is achieved by using fuzzy c-means clustering to produce rule antecedents. The proposed system is validated using multivariate bitcoin data for the period 2018 to 2022, showing that the proposed system can accurately predict bitcoin prices while retaining an interpretable hierarchical structure.
Because of its potential to upend established industries and alter how apps are developed, run, used, and promoted in the near future, blockchain technology has recently gained growing interest on a global scale.Although this technology was initially intended to be an immutable and distributed ledger for avoiding cryptocurrency double spending, it is currently anticipated to serve as the main support system for businesses by facilitating interoperability and collaboration across firms.In this setting, consortium blockchains have come to light as an intriguing architectural idea that makes use of the decentralized governance of public blockchains while gaining the efficiency and anonymity of private blockchains for transactions.Despite the fact that blockchain technology has been the subject of several research, the idea of consortium blockchains has received very little attention in the literature.This article offers a thorough examination of consortium blockchains' topologies, technological underpinnings, and applications in order to close this gap.