Amount Of Transactions Research Articles

A Temporal–Spatial network embedding model for ICT supply chain market trend forecasting

Market trend forecasting for the information and communication technology (ICT) supply chain strengthens external regulation. The existing models treat the influence weight and time granularity equally, ignoring the timeliness and accuracy of trading information, which influences the result of prediction. In addition, these methods do not consider the topological and sector hierarchical relationship of enterprises. In this work, a Temporal–Spatial hybrid market trend forecasting model (TSMTF) is proposed. First, in time domain instead of modeling time-varying transaction amount, transaction event probability prediction is modeled by Hawkes​ process. Furthermore, the attention mechanism is used to optimize the accuracy of weight allocation. Second, in spacial domain, the topological dependency relation between the different enterprises with transaction information, share information, and sector information is constructed by network embedding. The experimental results show that the model is superior to other baseline algorithms in ICT data sets. The effectiveness and applicability of this method are verified by ablation experiments and examples of products in the communication industry, and the model provides a practical tool for the external management of ICT supply chain market supervision.

Increasing Non-Interest Income in Banks from the Use of Credit and Debit Cards in ATMs: A Novel Approach

Most of the online banking channels have a favourable impact on the fee based ROA and ROE of both SBI and ICICI; except for the use of credit and debit cards in ATMs; Guha et al (2020). In this paper, regression model has been used to show the impact of use of credit and debit cards at ATMs on the commission, excise and brokerage of both banks; and a multiple regression model has been developed which will help in increasing fee-based income from the use of credit and debit cards in ATMs. This has important implications for the banking sector as well as the government; because, online banking channels contribute directly towards commission, exchange and brokerage of banks and these are a major source of non-interest income. Fees on the use of credit cards and debit cards in ATMs are levied on the basis of the number of transactions. However, no fees are levied on the basis of the amount of transactions. This lacuna that exists in the banking system demands attention. The aim of this paper is to make appropriate suggestions to increase the fee-based income from the use of credit and debit cards in ATMs.

BlockMaze: An Efficient Privacy-Preserving Account-Model Blockchain Based on zk-SNARKs

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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BlockMaze</i> , 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BlockMaze</i> . Finally, we implement a prototype of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BlockMaze</i> 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.

Phishing Detection on Ethereum via Attributed Ego-Graph Embedding

In recent years, the losses caused by phishing scams on Ethereum have reached a level that cannot be ignored. In such a phishing detection scenario, network embedding is seen as an effective solution. In this brief, we propose an attributed ego-graph embedding framework to distinguish phishing accounts. We first obtain the account labels from an authority site and the transaction records from Ethereum on-chain blocks. Then we extract ego-graphs for each labeled account to represent it. To learn representations for ego-graphs, we utilize non-linear substructures sampled from ego-graphs and use a skip-gram model. Finally, a classifier is applied to graph embeddings to predict phishing accounts. To overcome the limit that transaction attributes are not encoded into ego-graph embeddings, we give nodes and subgraphs with richer attribute-based semantics. Specifically, we propose a novel node relabeling strategy based on Ethereum transaction attributes including transaction amount, number, and direction, and differentiating nodes and subgraphs by new labels. Through this, structural and attributed features of the Ethereum transaction networks can be learned at the same time. Experimental results show that our framework achieves effective performance on class imbalanced phishing detection on Ethereum.

Prediction Algorithm of Digital Economy Development Trend Based on Big Data

With the development of economic globalization, people have higher requirements for economic analysis and prediction, while the prediction accuracy of the traditional economic analysis and prediction model is quite low. Therefore, with the adaptation of the times, the “big data and artificial intelligence” technology is presented for researchers and data analysts. Based on the “big data and artificial intelligence” technology, economic analysis and prediction have unique advantages in many aspects. This study analyses the impact of “big data and artificial intelligence” technology on economic analysis and prediction. Based on the mean square error criterion, the variable selection method for constructing the interval data model is given. The interval financial time-series data of stock market, fund market, futures market, and money market are used to predict and analyze the macroeconomy, and the macroeconomic interval prediction method is given, which is different from the traditional point data model. The empirical results show that the Shenzhen Component Index, Shanghai fund index, futures market transaction amount, and narrow money supply in the interval financial data have little fitting error to the macroeconomic interval prediction model. Through the first mock examination, we get the macroeconomic forecasting model based on interval financial time-series data. We also use the single model structure and the combined model structure to give the macroeconomic change interval of 2020–2023 years in China. The experimental research results show that the proposed model has good performance in the prediction of economic development trends and that it can be used for forecasting subsequent economic development forecasts.

Information construction of construction project management based on internet of things blockchain

AbstractBecause of the complexity of construction projects and the frequency of project transactions, smart project management has a huge amount of transaction and operation data. Massive transaction and operation data urgently need a safe, efficient and low–cost storage and data analysis to realize fine management of buildings, low–cost transaction and interconnection of project information. Therefore, the construction project management system based on internet of things blockchain has been gradually improved, and more and more researches have been made on it. This article proposes a parallel design scheme. Parallel algorithms disperse the repeated calculation work on each node, reduce the calculation load and calculation time of a single node, effectively improve the efficiency of the algorithm, and enable it to process large–scale data of different sizes in parallel. Improve the algorithm according to the experiment, and analyze the performance of the parallel algorithm according to the experimental results. Experiments show that Apriori algorithm and K‐means algorithm based on MapReduce are more than 85° optimized than traditional data mining.

Convenient prices and cash usage

ABSTRACT In the decision to use cash instead of an alternative payment method, quite a few factors influence consumers’ choice and the driving forces behind the cash payment preference are associated with two main topics: the socio-demographical factors of consumers and the characteristics of the payment method. However, beyond these factors pricing behaviour is also a crucial factor that affects the consumers’ cash payment preference. This study aims to examine the impact of convenient prices on cash usage behaviour in Turkey by using the micro-level data of the Methods of Payment Survey carried out in 2020 by the Central Bank of the Republic of Turkey (CBRT). Results show that the likelihood of cash usage increases in convenient prices, and it decreases for the amounts for which the individual receives a coin change. The results also show that the consumers’ cash usage decreases with the transaction amount, which shows that cash is preferred mostly in low-value transactions. Alongside the low-value payments primarily paid in cash, an important finding of the study shows that the likelihood of consumers’ cash usage increases with the amounts that match the currency denomination.

Fintech Operations and Banks’ Profitability

The study examined how Fin Techs affect the banks’ profit in Nigeria. The intent of this research was to see how the advancement of Fin Techs has affected banking performance in Nigeria. Annual transaction volumes from digital payment platforms, digital banking platforms, digital lending and loans platforms, and digital investment and crowd contributory platforms were used to achieve this, while profitability in Nigeria was measured using the aggregate ROA for all deposit money banks (DMBs). Secondary data on the selected FinTech platforms and banks for a period of 11 years, from 2010 to 2020 were used for this study. The ordinary least square (OLS) regression estimation technique was utilized for data analysis, with diagnostic tests such as ADF for unit root detection, Jarque-Bera for data normality, and Relation matrix for suspected multicollinearity being employed to assess the data's and model's fitness. The results showed no evidence of auto-correlation and indicated that the model is fit with 74 percent. Furthermore, due to the increased amount of transactions flowing through these technologies in conjunction with banks, the regression result demonstrated that the introduction of FinTechs has greatly boosted banks’ profit. Thus, Nigerian banks have reaped enormous benefits from financial innovations. The paper recommends that banks should take advantage of financial innovations to immediately boost their profitability by reaching out to the unbanked. These FinTechs make it possible for banking to be initiated and completed without emphases on physical appearance.

Improved Particle Swarm Algorithm Using Rubik’s Cube Topology for Bilevel Building Energy Transaction

Abstract Following the rapid growth of distributed energy resources (e.g., renewables, battery), localized peer-to-peer energy transactions are receiving more attention for multiple benefits, such as reducing power loss and stabilizing the main power grid. To promote distributed renewables locally, the local trading price is usually set to be within the external energy purchasing and selling price range. Consequently, building prosumers are motivated to trade energy through a local transaction center. This local energy transaction is modeled in bilevel optimization game. A selfish upper level agent is assumed with the privilege to set the internal energy transaction price with an objective of maximizing its arbitrage profit. Meanwhile, the building prosumers at the lower level will response to this transaction price and make decisions on electricity transaction amount. Therefore, this non-cooperative leader-follower trading game is seeking for equilibrium solutions on the energy transaction amount and prices. In addition, a uniform local transaction price structure (purchase price equals selling price) is considered here. Aiming at reducing the computational burden from classical Karush–Kuhn–Tucker (KKT) transformation and protecting the private information of each stakeholder (e.g., building), swarm intelligence-based solution approach is employed for upper level agent to generate trading price and coordinate the transactive operations. On one hand, to decrease the chance of premature convergence in global-best topology, Rubik’s Cube topology is proposed in this study based on further improvement of a two-dimensional square lattice model (i.e., one local-best topology-Von Neumann topology). Rotating operation of the cube is introduced to dynamically changing the neighborhood and enhancing information flow at the later searching state. Several groups of experiments are designed to evaluate the performance of proposed Rubik’s Cube topology-based particle swarm algorithm. The results have validated the effectiveness of proposed topology and operators comparing with global-best version PSO and Von Neumann topology-based PSO and its scalability on larger scale applications.

Dangerous liasons and hot customers for banks

Understanding the correlation between different customers’ loss of creditworthiness is crucial to credit risk analysis. This paper describes a novel method, based on a weighted network model, in which a set of firms, customers of the same bank, represent the nodes while their links and weights derive from the total transaction amounts. We explore the contagion mechanism deriving from the transmission of the difficulties of one customer to other clients of the same bank so highlighting areas where contagion risk is higher. We use a real proprietary data set provided by a bank to illustrate the proposed approach.

Who Are the Phishers? Phishing Scam Detection on Ethereum via Network Embedding

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 making 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 paper 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 oneclass 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.

Corporate governance and ownership structure on performance

The aim of this study is to investigate how Corporate Governance structures, Related Party Transactions, and Family Ownership could influence Firm Performance in Indonesia. The data is collected from companies listed in LQ45 index from 2016 to 2018. Multiple regression analysis is used to examine the relationship between Corporate Governance, Related Party Transaction and Family Ownership towards Firm Performance, specifically on ROE. Corporate Governance can be measured by Independent Directors and Board Size in which the ratio of independent directors to the total of directors in the board whereas Board Size is the number of members sitting on the board. For Related Party Transaction, the measurements used is by utilizing the total amount of related party transactions by total of assets. Dummy variable will be used to indicate a Family Ownership, that is whether there is an affiliation member in the board. If there is a family member in the board, this will indicate as 1, and if there is no family member in the board, it will be indicated as 0. ROE is used to measure Firm Performance, that is net income divided by shareholder’s equity. The results concluded that only Independent Directors have positive significant to Firm Performance. Due to Independent Directors have higher control compare to other directors. This indicates the level of supervision of the company will also certainly increase which implies whenever a firm company is well monitored it will improve the firm performance especially the profit factor measured, i.e. ROE.

MSCCS: A Monero-based security-enhanced covert communication system

Existing public blockchain-based covert communication systems are suffering the issues of insufficient robustness, anti-temper modification, and anonymity at both the transaction and network layers. In this paper, to overcome this problem, we propose a novel Monero-based security-enhanced covert communication system, in which a new storage-type covert channel is developed. This channel makes Monero transaction amount as data carrier for covert communication. Then, we devise two new algorithms, respectively for resisting Eclipse attacks and the two existing node crawling attacks. Extensive simulation experiments show that the developed new covert communication channel can achieve higher robustness, anti-detection, and anonymity. The new security-enhanced algorithms can effectively mitigate Eclipse attacks by 37.6%, and the two existing node crawling attacks by 21.1% and 17.1% respectively.

Cell Based Raft Algorithm for Optimized Consensus Process on Blockchain in Smart Data Market

Due to the explosive increase in IoT devices and traffic, big data is developing into smart data that helps the data science experts understand human activities, through the relationship between mobility and resource application of the users in public spaces. For example, smart data markets help to predict crimes or understand the infection the cause of COVID-19 infections. For these smart services, the users agree to the privacy policy so that the personal and sensitive information can be collected by a third party. But the conditions of the privacy policy do not specify whether the information of the users can be tracked. To ensure data transparency, many systems are applying consortium/private blockchains with raft algorithm. The raft algorithm requires nodes to check countless messages for a single transaction. Eventually, as the number of nodes increases, the overall system degradation is derived from the burden of the leader node. This paper proposes a method to process the collected transactions by dividing a certain amount of transactions into cells, without any extra protocol. The proposed scheme also uses the federated learning model with high accuracy and data privacy, in order to determine the optimized cell size in a blockchain system that should lead to consensus on multiple servers. Therefore, the proposed CBR(Cell-based Raft) consensus algorithm proposes a protocol that reduces the number of messages, without interfering with the concept of the existing raft algorithm, in order to maintain stable throughput in the smart data market where massive transactions occur.

A Survey on Technologies Which Make Bitcoin Greener or More Justified

According to recent estimates, one bitcoin transaction consumes as much energy as 1.5 million Visa transactions. Why is bitcoin using so much energy? Most of the energy is used during the bitcoin mining process, which serves at least two significant purposes: a) distributing new cryptocurrency coins to the cryptoeconomy and b) securing the Bitcoin blockchain ledger. In reality, the comparison of bitcoin transactions to Visa transactions is not that simple. The amount of transactions in the Bitcoin network is not directly connected to the amount of bitcoin mining power nor the energy consumption of those mining devices; for example, it is possible to multiply the number of bitcoin transactions per second without increasing the mining power and the energy consumption. Bitcoin is not only &#x201C;digital money for hackers&#x201D;. It has very promising future potential as a global reserve currency and a method to make the World Wide Web (WWW) immune to cyberattacks such as the Distributed Denial-of-Service attacks. This survey approached cryptocurrencies&#x2019; various technological and environmental issues from many different perspectives. To make various cryptocurrencies, including bitcoin (BTC) and ether (ETH), greener and more justified, what technological solutions do we have? We found that cryptocurrency mining might be cleaner than is generally expected. There is also a plan to make a vast renewable energy source available by combining Ocean Thermal Energy Conversion and Bitcoin mining. There are plans to use unconventional computing methods (quantum computing, reversible computing, ternary computing, optical computing, analog computing) to solve some of the issues regarding the vast energy consumption of conventional computing (including cryptocurrency mining). We think using spare computing cycles for grid computing efforts is justified. For example, there are billions of smartphones in the world. Many smartphones are being recharged every day. If this daily recharging period of twenty to sixty minutes would be used for grid computing, for example, finding new cures to cancer, it would probably be a significant breakthrough for medical research simulations. We call cryptocurrency communities to research and develop grid computing and unconventional computing methods for the most significant cryptocurrencies: bitcoin (BTC) and ether (ETH).

A Certificateless Homomorphic Encryption Scheme for Protecting Transaction Data Privacy of Post-Quantum Blockchain

Blockchain has a profound impact on all areas of society by virtue of its immutability, decentralization and other characteristics. However, blockchain faces the problem of data privacy leakage during the application process, and the rapid development of quantum computing also brings the threat of quantum attack to blockchain. In this paper, we propose a lattice-based certificateless fully homomorphic encryption (LCFHE) algorithm based on approximate eigenvector firstly. And we use the lattice-based delegate algorithm and preimage sampling algorithm to extract part of the private key based on certificateless scheme, which is composed of the private key together with the secret value selected by the user, thus effectively avoiding the problems of certificate management and key escrow. Secondly, we propose a post-quantum blockchain transaction privacy protection scheme based on LCFHE algorithm, which uses the ciphertext calculation characteristic of homomorphic encryption to encrypt the account balance and transaction amount, effectively protecting the transaction privacy of users and having the ability to resist quantum attacks. Finally, we analyze the correctness and security of LCFHE algorithm, and the security of the algorithm reduces to the hardness of learning with errors (LWE) hypothesis.

Retracted] Research on Multiparty Payment Technology Based on Blockchain and Smart Contract Mechanism

As a peer‐to‐peer “P2P” distributed ledger, the blockchain has the advantages of decentralization, no trust, open autonomy, and nontampering. Therefore, many users are willing to conduct transactions in blockchain cryptocurrency systems such as Bitcoin and Ethereum. However, the throughput of traditional blockchain is extremely low, and the transaction is so delayed. The payment channel network is the most promising solution to expand the blockchain for widespread use. Achieving secure instant payment on the payment channel can significantly increase transaction throughput and reduce transaction delays. When the payment channel is closed, the balance in the channel will be returned to an account on the blockchain. In this paper, we discuss the design and the implementation of a multiparty payment channel network based on smart contracts. Where a two‐party payment channel is designed based on blockchain and smart contracts, a new multiparty payment channel is established on the basis of the payment channel. A detailed definition and description are given, and the creation, update, and closing functions of the multiparty payment channel are designed. Moreover, we design a multiparty payment channel smart contract, deploy it to the local private blockchain, and conduct simulation and testing. The delay time of different transaction methods is counted, and the network topology type, transaction amount, and other factors are studied. The impact of transaction success rate and the gas consumption of different transaction methods are analyzed through multiple sets of experimental statistics.

Retracted] A Blockchain Technique for Trade Credit Maintainability Using the Role of Information and Communication Technology

Recent advances in data analysis and processing methods can improve the ability of computational applications to perform complex steps of different tasks. With the progress of information and communication technologies (ICT), Blockchain‐based complex data processing for transaction analysis and smart contract agreement has become a new research area in the fields of mathematics and computation. Stability of financial sector based on the ICT is a core component for growing the economics of medium and small enterprises. This stability brings the innovation to businesses productivity, while the management of information takes more prospective for improving the efficiency and more ways for innovating the business of products. In this study, we use the autoregressive distribution lag (ARDL) model with Blockchain‐based complex data processing approach to emphasize the role of ICT in the field of trade credit maintainability. Actually, the ICT connects the industries in the entire world and makes business sectors that use its technologies be more advanced. Based on the ARDL model conducted on the records gathered from 2000 to 2019, the analysis concludes that the ICT‐based complex data processing is a critical component of trade credit. The statistics of ICT are chosen based on the economy penetrations through the Internet and mobile phones. The causality exposed between the trade credit and ICT is bidirectional in nature. Also, it is found that the usage of mobile phones has a substantial influence on the business sectors, as a substantial amount of trading and business transactions are conducted over the phone. Therefore, the primary concern is the association between the Blockchain and trade credit, which is thoroughly discussed in this work. The trade credit improves the stability of financial sector and the Blockchain supports its maintainability by the role of ICT. The results of the study can help the business stakeholders and investors to estimate the marketing for future useful execution.

Digital and advanced electronic signature: the security function, especially in electronic commerce

The purpose of Directive 1999/93 / EC on the Community framework for electronic signatures is to facilitate the use of electronic signatures. Digital signatures are incredibly important as they prevent fraud in e-commerce transactions. A legal framework for electronic signatures is established to ensure the smooth functioning of the internal market. This article aims to interpret the European framework for digital signatures. Member States shall at least ensure that, when issuing a certificate, the certification body is liable for any damage caused to any entity or natural or legal person reasonably based on the certificate: (a) the accuracy, at the time of issue, of all the information contained in the recognized certificate. (b) ensure that, at the time of issue of the certificate, the signatories identified on the recognized certificate were the holders of the signature-creation data corresponding to the signature verification data referred to or specified in the certificate; Member States shall at least ensure that the certified provider is liable for any damage caused to any entity or natural person who reasonably relies on the certificate, unless the certifying provider proves that it did not act negligently. Member States shall ensure that a certified service provider may indicate in a recognized certificate restrictions on the use of that certificate. Member States shall ensure that a certification-service-provider may indicate on the recognized certificate the limits on the amount of transactions for which the certificate may be used. For all of the above, the certification service provider is not liable for damages resulting from exceeding these limits.

Transforming supply chain network and logistics using blockchain - a survey

The supply chain is the fastest growing sector of business technology. As it grows day by day, the difficulty of handling vast amount of transaction records thus increases. To effectively manage the supply chain, the participants in the system need to record the price, quantity, location, date, and other pertinent details. In addition to that the asymmetric information transfer in the production process, limitations of the quality monitoring, losses from the counterfeit market and the self-interests of supply chain participants are the major challenges affecting the supply chain management system. Even a small change in the production process can have an enormous smash on the financial growth, trust of the supply chain system. This survey aims to study how blockchain technology helps supply chain networks to overcome these challenges. This survey discusses the current status of blockchain technology and its potential benefits. This survey also discusses various implementations on blockchain-based supply chain networks and how those implementation frameworks solve the challenges in the traditional supply chain system.