Introducing Ethereum and Solidity
01 Jan 2017-
TL;DR: The first € price and the £ and $ price are net prices, subject to local VAT, while the €(D) includes 7% for Germany, the €A includes 10% for Austria.
Abstract: The first € price and the £ and $ price are net prices, subject to local VAT. Prices indicated with * include VAT for books; the €(D) includes 7% for Germany, the €(A) includes 10% for Austria. Prices indicated with ** include VAT for electronic products; 19% for Germany, 20% for Austria. All prices exclusive of carriage charges. Prices and other details are subject to change without notice. All errors and omissions excepted. C. Dannen Introducing Ethereum and Solidity
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TL;DR: A systematic review of the literature finds four main clusters in the co-citation analysis, namely Technology, Trust, Trade, and Traceability/Transparency, and discusses the emerging themes and applications of blockchains for supply chains, logistics and transport.
Abstract: This paper presents current academic and industrial frontiers on blockchain application in supply chain, logistics and transport management. We conduct a systematic review of the literature and find four main clusters in the co-citation analysis, namely Technology, Trust, Trade, and Traceability/Transparency. For each cluster, and based on the pool of articles included in it, we apply an inductive method of reasoning and discuss the emerging themes and applications of blockchains for supply chains, logistics and transport. We conclude by discussing the main themes for future research on blockchain technology and its application in industry and services.
437 citations
Cites background from "Introducing Ethereum and Solidity"
...Smart contracts are protocols on blockchain that are executed automatically by machine if the terms of the contract are met (Dannen 2017)....
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TL;DR: Results show that the proposed methodology can bring more advantages to CMfg than the security and scalability, as well as the qualitative and quantitative methods are utilized.
Abstract: New emerging manufacturing paradigms such as cloud manufacturing, IoT enabled manufacturing and service-oriented manufacturing, have brought many advantages to the manufacturing industry and metamorphosis the industrial IT infrastructure. However, all existing paradigms still suffer from the main problem related to centralized industrial network and third part trust operation. In a nutshell, centralized networking has had issues with flexibility, efficiency, availability, and security. Therefore, the main aim of this paper is to present a distributed peer to peer network architecture that improves the security and scalability of the CMfg. The proposed architecture was developed based on blockchain technology, this facilitated the development of a distributed peer to peer network with high security, scalability and a well-structured cloud system. The proposed architecture which was named as the “BCmfg” is made up of five layers namely; resource layer, perception layer, manufacturing layer, infrastructure layer and application layer. In this paper, the concept of its architecture, secure data sharing, and typical characteristic are discussed and investigated as well as the key technologies required for the implementation of this proposed architecture is explained based on demonstrative case study. The proposed architecture is explained based on a case study which contains five service providers and 15 end users with considering 32 OnCloud services. For evaluation purpose, the qualitative and quantitative methods are utilized and the results show that the proposed methodology can bring more advantages to CMfg than the security and scalability.
221 citations
TL;DR: A new decentralized P2P energy trading platform that guarantees a near-optimally efficient market solution, preserves players’ privacy, and allows inter-temporal market products trading is developed.
193 citations
Cites methods from "Introducing Ethereum and Solidity"
...The smart contract in DeTrade is implemented in the Solidity programming language [67]....
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TL;DR: The DAO experiment failed shortly after inception as an anonymous hacker stole over $50M USD worth of Ethers out of $168M invested as discussed by the authors, and the Ethereum community voted to return (or fork) the state of the network to one prior to the hack, returning Ethers back to investors and shuttingtering The DAO.
Abstract: In spring 2016, The Distributed Autonomous Organization (The DAO) was created on Ethereum. As with Bitcoin, Ethereum uses a P2P network, where distributed ledgers are implemented as daisy-chained blocks of data. Ethereum’s native cryptocurrency, Ethers, are spent to execute pieces of code called smart contracts. Investors paid their Ethers for The DAO to operate, and received the opportunity to vote on and become investors in venture projects proposed by Ethereum-based startups. Transactions and settlements between investors and startups executed autonomously. The DAO experiment failed shortly after inception as an anonymous hacker stole over $50M USD worth of Ethers out of $168M invested. The Ethereum community voted to return (or fork) the state of the network to one prior to the hack, returning Ethers back to investors and shuttering The DAO. However, this action arguably represented a bailout—ironically, Bitcoin was conceived as a reaction against the 2008 bailout of US banks—and violated the ledger immutability and “code is law” ethos of the blockchain community.
153 citations
TL;DR: This work proposes ContractWard to detect vulnerabilities in smart contracts with machine learning techniques and extracts bigram features from simplified operation codes of smart contracts to demonstrate the effectiveness and efficiency of ContractWard.
Abstract: Smart contracts are decentralized applications running on Blockchain. A very large number of smart contracts has been deployed on Ethereum. Meanwhile, security flaws of contracts have led to huge pecuniary losses and destroyed the ecological stability of contract layer on Blockchain. It is thus an emerging yet crucial issue to effectively and efficiently detect vulnerabilities in contracts. Existing detection methods like Oyente and Securify are mainly based on symbolic execution or analysis. These methods are very time-consuming, as the symbolic execution requires the exploration of all executable paths or the analysis of dependency graphs in a contract. In this work, we propose ContractWard to detect vulnerabilities in smart contracts with machine learning techniques. First, we extract bigram features from simplified operation codes of smart contracts. Second, we employ five machine learning algorithms and two sampling algorithms to build the models. ContractWard is evaluated with 49502 real-world smart contracts running on Ethereum. The experimental results demonstrate the effectiveness and efficiency of ContractWard. The predictive Micro-F1 and Macro-F1 of ContractWard are over 96% and the average detection time is 4 seconds on each smart contract when we use XGBoost for training the models and SMOTETomek for balancing the training sets.
126 citations