Showing papers on "Database transaction published in 2022"
TL;DR: Wang et al. as discussed by the authors investigated the time-varying relationship between metro accessibility and residential property prices and found that the implicit price of metro accessibility modestly decreases in COVID-19, which can be explained by the declining role of metro.
74 citations
TL;DR: Wang et al. as discussed by the authors applied the difference-in-differences method and mediating effect model to estimate the impact of China's ETS on the market power of high-carbon enterprises.
53 citations
01 Feb 2022
TL;DR: Wang et al. as mentioned in this paper proposed an approach to detect phishing scams on Ethereum by mining its transaction records, which crawled the labeled phishing addresses from two authorized websites and reconstruct the transaction network according to the collected transaction records.
Abstract: Recently, blockchain technology has become a topic in the spotlight but also a hotbed of various cybercrimes. Among them, phishing scams on blockchain have been found to make a notable amount of money, thus emerging as a serious threat to the trading security of the blockchain ecosystem. In order to create a favorable environment for investment, an effective method for detecting phishing scams is urgently needed in the blockchain ecosystem. To this end, this article proposes an approach to detect phishing scams on Ethereum by mining its transaction records. Specifically, we first crawl the labeled phishing addresses from two authorized websites and reconstruct the transaction network according to the collected transaction records. Then, by taking the transaction amount and timestamp into consideration, we propose a novel network embedding algorithm called trans2vec to extract the features of the addresses for subsequent phishing identification. Finally, we adopt the one-class support vector machine (SVM) to classify the nodes into normal and phishing ones. Experimental results demonstrate that the phishing detection method works effectively on Ethereum, and indicate the efficacy of trans2vec over existing state-of-the-art algorithms on feature extraction for transaction networks. This work is the first investigation on phishing detection on Ethereum via network embedding and provides insights into how features of large-scale transaction networks can be embedded.
40 citations
TL;DR: This article presents an elaborated structure of DT, namely, spiral DT-framework, and proposes a new variant of blockchain, namely twinchain, which is quantum-resilient and offers immediate transaction confirmation and a framework for deployment of twinchain for manufacturing of a robot surgical machine.
Abstract: Digital twins (DT) have been proposed to support and enhance manufacturing processes of the industries. The outcome of adopting DT is so encouraging that it is hoped that more than 50% of the large industries will benefit from DT by the end of 2021. Unfortunately, DT lacks a single publicly accepted narrative. In order to help researchers for building a common narrative about DT, we present an elaborated structure of DT, namely, spiral DT-framework. Furthermore, for a secure and reliable management of the DT data, we propose using the blockchain technology rather than cloud or fog. As the classical blockchain suffers from transaction confirmation delays and is vulnerable to the quantum attacks, therefore, we propose a new variant of blockchain, namely twinchain, which is quantum-resilient and offers immediate transaction confirmation. This article also presents a framework for deployment of twinchain for manufacturing of a robot surgical machine.
39 citations
TL;DR: In this paper , the authors investigated the influence of platform usage on three aspects of buyers' performance, including trust, transaction cost, and moderating effects of transaction frequency, and found that platform usage increases trust and decreases transaction cost for buyer firms, both of which in turn improve the buyers' relationship performance, supplier performance and market performance.
Abstract: Platforms are observed to gradually replace traditional pipeline supply chains. To better understand how platforms can assist business buyers in improving their bottom-line, this study investigates the influence of platform usage on three aspects of buyers’ performance. We build a theoretical framework focused on the roles of trust and transaction cost, and the moderating effects of transaction frequency. Using a sample of 314 buyer firms who are enrolled on a major e-commerce platform within the past ten years, we find that platform usage increases trust and decreases transaction cost for buyer firms, both of which in turn improve the buyers’ relationship performance, supplier performance and market performance. We also reveal a positive moderating effect of transaction frequency in this framework. The findings highlight the substitution effect of platforms in replacing traditional supply chains and what it means for business buyers.
36 citations
TL;DR: A game-theoretic model develops to investigate how a blockchain platform's decision on its settings affects the competition between blockchain platforms as well as the participation behavior of customers and miners suggests that increasing the transaction fee alleviates congestion on the platform when customers have a relatively balanced need between efficiency and safety.
32 citations
01 Apr 2022
TL;DR: Wang et al. as discussed by the authors proposed a feature-based network analysis framework to identify statistical properties of mixing services from three levels, namely, network level, account level, and transaction level.
Abstract: As the first decentralized peer-to-peer (P2P) cryptocurrency system allowing people to trade with pseudonymous addresses, Bitcoin has become increasingly popular in recent years. However, the P2P and pseudonymous nature of Bitcoin make transactions on this platform very difficult to track, thus triggering the emergence of various illegal activities in the Bitcoin ecosystem. Particularly, mixing services in Bitcoin, originally designed to enhance transaction anonymity, have been widely employed for money laundry to complicate trailing illicit fund. In this paper, we focus on the detection of the addresses belonging to mixing services, which is an important task for anti-money laundering in Bitcoin. Specifically, we provide a feature-based network analysis framework to identify statistical properties of mixing services from three levels, namely, network level, account level and transaction level. To better characterize the transaction patterns of different types of addresses, we propose the concept of Attributed Temporal Heterogeneous motifs (ATH motifs). Moreover, to deal with the issue of imperfect labeling, we tackle the mixing detection task as a Positive and Unlabeled learning (PU learning) problem and build a detection model by leveraging the considered features. Experiments on real Bitcoin datasets demonstrate the effectiveness of our detection model and the importance of hybrid motifs including ATH motifs in mixing detection.
30 citations
TL;DR: In this paper , the authors show that labor market transaction costs explain why the smallest farms are more efficient than slightly larger farms in most low-income countries and that increases in machine capacity with operational scale result in the globally observed rising upper tail of productivity.
Abstract: We show that labor market transaction costs explain why the smallest farms are more efficient than slightly larger farms in most low-income countries and that increases in machine capacity with operational scale result in the globally observed rising upper tail of productivity. We find evidence consistent with these mechanisms using Indian data, and we show that if all Indian farms were at the minimum scale required to maximize the return on land, the number of farms would be reduced by 82% and income per farm worker would rise by 68%.
29 citations
14 Jan 2022
TL;DR: A rigorous and comprehensive empirical study to examine the effect of EIP-1559, one of the earliest-deployed TFMs that depart from the traditional first-price auction paradigm, and finds that when Ether’s price is more volatile, the waiting time is significantly higher.
Abstract: A transaction fee mechanism (TFM) is an essential component of a blockchain protocol. However, a systematic evaluation of the real-world impact of TFMs is still absent. Using rich data from the Ethereum blockchain, the mempool, and exchanges, we study the effect of EIP-1559, one of the earliest-deployed TFMs that depart from the traditional first-price auction paradigm. We conduct a rigorous and comprehensive empirical study to examine its causal effect on blockchain transaction fee dynamics, transaction waiting times, and consensus security. Our results show that EIP-1559 improves the user experience by mitigating intrablock differences in the gas price paid and reducing users' waiting times. However, EIP-1559 has only a small effect on gas fee levels and consensus security. In addition, we find that when Ether's price is more volatile, the waiting time is significantly higher. We also verify that a larger block size increases the presence of siblings. These findings suggest new directions for improving TFMs.
29 citations
22 Mar 2022
TL;DR: Inthissystematization of knowledge (SoK) categorize and analyze state-of-the-art transaction reordering manipulation mitigation schemes and finds that currently no scheme fully meets all the demands of the blockchain ecosystem.
Abstract: User transactions on Ethereum's peer-to-peer network are at risk of being attacked. The smart contracts building decentralized finance (DeFi) have introduced a new transaction ordering dependency to the Ethereum blockchain. As a result, attackers can profit from front- and back-running transactions. Multiple approaches to mitigate transaction reordering manipulations have surfaced recently. However, the success of individual approaches in mitigating such attacks and their impact on the entire blockchain remains largely unstudied. In this systematization of knowledge (SoK), we categorize and analyze state-of-the-art transaction reordering manipulation mitigation schemes. Instead of restricting our analysis to a scheme's success at preventing transaction reordering attacks, we evaluate its full impact on the blockchain. Therefore, we are able to provide a complete picture of the strengths and weaknesses of current mitigation schemes. We find that currently no scheme fully meets all the demands of the blockchain ecosystem. In fact, all approaches demonstrate unsatisfactory performance in at least one area relevant to the blockchain ecosystem.
27 citations
TL;DR: In this article , the authors quantitatively describe the main events that led to the Terra project's failure in May 2022 and identify the crash's trigger events, analyzing hourly and transaction data for Bitcoin, Luna, and TerraUSD.
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TL;DR: Narwhal as mentioned in this paper separates the task of reliable transaction dissemination from transaction ordering, to enable high-performance Byzantine fault-tolerant quorum-based consensus, and it is designed to easily scale-out using multiple workers at each validator, and demonstrate that there is no foreseeable limit to the throughput we can achieve.
Abstract: We propose separating the task of reliable transaction dissemination from transaction ordering, to enable high-performance Byzantine fault-tolerant quorum-based consensus. We design and evaluate a mempool protocol, Narwhal, specializing in high-throughput reliable dissemination and storage of causal histories of transactions. Narwhal tolerates an asynchronous network and maintains high performance despite failures. Narwhal is designed to easily scale-out using multiple workers at each validator, and we demonstrate that there is no foreseeable limit to the throughput we can achieve.
TL;DR: In this paper , the authors identify and implement the relationships between the enablers of blockchain adoption in renewable energy supply chains (RESC), and propose that the comparison among the identified enabler shows the most superior of secure database for BT implementation in RESC tracked by immutability and decentralized database.
TL;DR: In this article , the authors presented a way of improving the resulted clusters generated by the K-means algorithm by post processing the resulting clusters with a supervised learning algorithm, which is focused on improving the quality of the resulting clustering and not on reducing the processing time.
TL;DR: Two novel classifiers are presented, based on lasso-regularized logistic regression and gradient tree boosting, which directly minimize the proposed instance-dependent cost measure when learning a classification model.
TL;DR: In this article , a game-theoretic model was developed to investigate how a blockchain platform's decision on its settings, such as block size and transaction fee, affects the competition between blockchain platforms as well as the participation behavior of customers and miners.
TL;DR: In this paper , the authors employ the continuous difference-in-differences (DID) model and conduct an evaluation of the CO2 reduction effect of China's emission trading scheme (ETS) pilot markets from the dual perspectives of price and scale.
TL;DR: In this article , a spiral digital twins (DT) framework is proposed to support and enhance manufacturing processes of the industries, and for a secure and reliable management of the DT data, they propose using the blockchain technology rather than cloud or fog.
Abstract: Digital twins (DT) have been proposed to support and enhance manufacturing processes of the industries. The outcome of adopting DT is so encouraging that it is hoped that more than 50% of the large industries will benefit from DT by the end of 2021. Unfortunately, DT lacks a single publicly accepted narrative. In order to help researchers for building a common narrative about DT, we present an elaborated structure of DT, namely, spiral DT-framework. Furthermore, for a secure and reliable management of the DT data, we propose using the blockchain technology rather than cloud or fog. As the classical blockchain suffers from transaction confirmation delays and is vulnerable to the quantum attacks, therefore, we propose a new variant of blockchain, namely twinchain, which is quantum-resilient and offers immediate transaction confirmation. This article also presents a framework for deployment of twinchain for manufacturing of a robot surgical machine.
TL;DR: In this article , the authors examined whether mergers and acquisitions operations impact firms' performances on triple ESG pillars (environment, social, and governance) using a large panel covering 41 countries and 12 economic sectors between 2002 and 2020.
08 Feb 2022
TL;DR: It is unveiled that most broadcasted transactions can avoid sandwich attacks while simultaneously only experiencing a low risk of transaction failure, and it is demonstrated that a constant auto-slippage cannot adjust to varying trade sizes and pool characteristics.
Abstract: Predatory trading bots lurking in Ethereum's mempool present invisible taxation of traders on automated market makers (AMMs). AMM traders specify a slippage tolerance to indicate the maximum price movement they are willing to accept. This way, traders avoid automatic transaction failure in case of small price movements before their trade request executes. However, while a too-small slippage tolerance may lead to trade failures, a too-large slippage tolerance allows predatory trading bots to profit from sandwich attacks. These bots can extract the difference between the slippage tolerance and the actual price movement as profit. In this work, we introduce the sandwich game to analyze sandwich attacks analytically from both the attacker and victim perspectives. Moreover, we provide a simple and highly effective algorithm that traders can use to set the slippage tolerance. We unveil that most broadcasted transactions can avoid sandwich attacks while simultaneously only experiencing a low risk of transaction failure. Thereby, we demonstrate that a constant auto-slippage cannot adjust to varying trade sizes and pool characteristics. Our algorithm outperforms the constant auto-slippage suggested by the biggest AMM, Uniswap, in all performed tests. Specifically, our algorithm repeatedly demonstrates a cost reduction exceeding a factor of 100.
TL;DR: A user-oriented model for the Oracles’ Gas price prediction is proposed, based on two Gas price categories actually corresponding to the users’ interests and a new method to estimate the Gas price.
08 Jun 2022
TL;DR: This paper measures the popularity of Flashbots and evaluates if it is meeting its chartered goals and finds that Flashbots miners account for over 99.9% of the hashing power in the Ethereum network.
Abstract: The rise of Ethereum has lead to a flourishing decentralized marketplace that has, unfortunately, fallen victim to frontrunning and Maximal Extractable Value (MEV) activities, where savvy participants game transaction orderings within a block for profit. One popular solution to address such behavior is Flashbots, a private pool with infrastructure and design goals aimed at eliminating the negative externalities associated with MEV. While Flashbots has established laudable goals to address MEV behavior, no evidence has been provided to show that these goals are achieved in practice. In this paper, we measure the popularity of Flashbots and evaluate if it is meeting its chartered goals. We find that (1) Flashbots miners account for over 99.9% of the hashing power in the Ethereum network, (2) powerful miners are making more than 2X what they were making prior to using Flashbots, while non-miners' slice of the pie has shrunk commensurately, (3) mining is just as centralized as it was prior to Flashbots with more than 90% of Flashbots blocks coming from just two miners, and (4) while more than 80% of MEV extraction in Ethereum is happening through Flashbots, 13.2% is coming from other private pools.
TL;DR: An evaluation index system based on the development of cross-border e-commerce is constructed andResponsiveness is the most important factor found by artificial neural networks, and the descending order of importance of other factors is fulfillment, diversity, privacy, reliability, compensation, and ease of use.
Abstract: The transaction scale of cross-border import e-commerce has grown rapidly around the world. Platform-style cross-border e-commerce does not control the quality, source and transaction process of goods strictly and comprehensively. In terms of customer service quality, the seller's customer service often ignores the customer's problems, and some customer service solutions cannot solve the customer's problems. Serving customers through the network has changed the traditional offline service form without distance, and the service process has a time and space distance. This paper constructs an evaluation index system based on the development of cross-border e-commerce. Through questionnaires, relevant data were obtained and analyzed. Analyze the results based on the collected data on the factors that affect the quality of cross-border import e-commerce services. Responsiveness is the most important factor found by artificial neural networks. The descending order of importance of other factors is fulfillment, diversity, privacy, reliability, compensation, and ease of use.
TL;DR: In this paper , a new model of AI-influenced decision-making (AIDM) processes is introduced to demonstrate consumers' increasing tendency to outsource decisions to AI.
TL;DR: In this article , the authors examined the role of accessibility to metro in shaping house prices and examined the moderating effect of COVID-19 on the price effects of to-metro and bymetro accessibility.
Abstract: Previous studies extensively examined the role of accessibility to metro in shaping house prices but largely overlooked the contribution of accessibility by metro. In addition, limited studies examined the moderating effect of COVID-19 on the price effects of to-metro and by-metro accessibility. Based on multilevel hedonic price and quantile regression models, this study scrutinizes the association between to-metro accessibility, by-metro accessibility, and house prices in Chengdu, China, and examines the moderating role of COVID-19 in this association. We show that by-metro accessibility significantly influences house prices. COVID-19 significantly influences the value of to-metro accessibility but marginally affects that of by-metro accessibility. The value of to-metro accessibility is disproportionately affected by the pandemic. Specifically, small or low-priced houses are less affected than big or high-priced houses. In other words, the flattening of the to-metro price gradient is more discernible for big or high-priced houses. The changing preference of residents has also been verified by the decreases in house transaction volume in metro-adjacent areas.
TL;DR: In this article, the authors argue that the transaction attributes of asset specificity, transaction uncertainty, and transaction frequency have all changed fundamentally in digital platform-based transactions and make curvilinear moderating hypotheses.
TL;DR: BlockMaze as discussed by the authors is an efficient privacy-preserving account-model blockchain based on zk-SNARKs, which achieves strong privacy guarantees by hiding account balances, transaction amounts, and linkage between senders and recipients.
Abstract: The disruptive blockchain technology is expected to have broad applications in many areas due to its advantages of transparency, fault tolerance, and decentralization, but the open nature of blockchain also introduces severe privacy issues. Since anyone can deduce private information about relevant accounts, different privacy-preserving techniques have been proposed for cryptocurrencies under the UTXO model, e.g., Zerocash and Monero. However, it is more challenging to protect privacy for account-model blockchains (e.g., Ethereum) since it is much easier to link accounts in the account-model blockchain. In this article, we propose BlockMaze , an efficient privacy-preserving account-model blockchain based on zk-SNARKs. Along with dual-balance model, BlockMaze achieves strong privacy guarantees by hiding account balances, transaction amounts, and linkage between senders and recipients. Moreover, we provide formal security definitions and prove the security of BlockMaze . Finally, we implement a prototype of BlockMaze based on Libsnark and Go-Ethereum, and conduct extensive experiments to evaluate its performance. Our 300-node experiment results show that BlockMaze has high efficiency in computation and transaction throughput: one transaction verification takes about 14.2 ms, one transaction generation takes 6.1-18.6 seconds, and its throughput is around 20 TPS.
TL;DR: In this article , the authors explored the relationship between novelty and worries and travel satisfaction, as well as examined how tourists enhance their quality of trips with the use of smart tourism technologies, finding that tourists' novelty seeking would enhance the trip experience, leading to overall travel satisfaction.
Abstract: Tourists deal with two intrinsic, uncertainty-driven attributes of travel, tourist worries and novelty seeking, that simultaneously affect their transaction and travel experience satisfaction. Rapid technological advances coupled with uncertainties caused by momentous events such as COVID-19 highlight the increasing significance of smart technologies in the tourism industry. This study explores the relationships between novelty and worries and travel satisfaction, as well as examines how tourists enhance their quality of trips with the use of smart tourism technologies. We find the tourists’ novelty seeking would enhance the trip experience, leading to overall travel satisfaction. In contrast, tourist worries, particularly in trip planning, would negatively affect tourists’ transaction satisfaction, which in turn impacts the overall travel experience satisfaction. As a moderator in its ambidextrous role, smart tourism technologies help tourists to develop a sense of novelty when planning and visiting a destination and mitigate the worries emanated from the uncertainty of transaction made during the pre-trip planning. Insights and implications of such findings are discussed for both theory and practice.
TL;DR: In this article , the authors provide a first view of vulnerable informal economy after the blows from COVID-19, using transaction-level business data of around 80 million offline micro businesses (OMBs) owners from the largest Fintech company in China and employing machine learning method for causal inference.
TL;DR: In this article , a credit-based P2P electricity trading model in a blockchain environment is proposed, where the default users are provided a waiting time in the default query stage or penalized in the payment stage.
Abstract: Peer-to-peer (P2P) electricity trading promotes the local consumption of renewable energy. However, it suffers from high transaction costs and mutual distrust among users. To address these issues, we propose a credit-based P2P electricity trading model in a blockchain environment. First, the P2P electricity trading process is introduced, which involves six stages: order generation, default query, order picking, trading execution, trading verification, and payment. Credit management is also introduced in this model to manage the default behavior of users. In particular, the default users are provided a waiting time in the default query stage or penalized in the payment stage. Finally, the model is simulated on the Hyperledger Fabric platform using Docker and Go. Experimental results show that the proposed model can facilitate cost reduction for users in the blockchain and realize credit management in P2P electricity trading, thereby enhancing trading stability and efficiency.