This paper aims to compare the empirical performance of two approaches in detecting structural breaks and outliers due to the significant frequent price changes seen in cryptocurrencies.The two approaches are indicator saturation (IS) and Bai and Perron (BP).The cryptocurrency data employed in this study are Bitcoin and Ethereum.In comparing the performance of the two approaches, this study performed multiple empirical comparisons using various significant levels, different data frequencies, as well as the original and log series (price).The findings showed that the prices contained structural breaks and outliers and that the IS approach performed significantly better than the BP test in terms of the identified structural breaks as well as outliers across different settings.The contribution of this study is providing empirical comparisons between IS and BP approaches using cryptocurrency data.These findings are important to the potential stakeholders, in particular, for quality control in industries, for setting price targets, and for confirming trading signals to reduce potential losses.
Healthcare organizations exchange sensitive health records, including behavioral health data, across peer-to-peer networks, and it is challenging to find and fix compliance issues proactively. The Healthcare industry anticipates a growing need to audit substance use disorder patient data, commonly referred to as Part 2 data, having been shared without a release of information signed by the patient. To address this need, we developed and evaluated a novel methodology to detect Part 2 data exchanged between organizations that integrates Blockchain technologies with knowledge graphs. We detect substance use disorder data in patient encounters exchanged using clinical terminology based upon the value sets provided by the National Institutes of Health for the Substance Abuse and Mental Health Services Administration. Generally, we consider sharing Part 2 data without consent as Byzantine medical faults, as they represent data shared between known and trusted network participants, that is valid, but is not relevant, and sharing it causes a breach. In this paper, we present our methodology in detail along with the experiment results. We model a medical network of hospitals based upon the most recent healthcare legislation, TEFCA, and generate synthetic patient encounter data dynamically in HL7 format. We convert exchanged encounter data into a knowledge graph data model so that we can use SNOMED-CT for identifying Part 2 data. For cohorts of 1,000 patients, we detect Part 2 data in a subset of their encounter data shared between organizations and log that securely on an Ethereum-based blockchain.
In this dissertation, I investigate the existing smart contract problems that limit cognitive abilities. I use Taylor's serious expansion, polynomial equation, and fraction-based computations to overcome the limitations of calculations in smart contracts. To prove the hypothesis, I use these mathematical models to compute complex operations of naive Bayes, linear regression, decision trees, and neural network algorithms on Ethereum public test networks. The smart contracts achieve 95\% prediction accuracy compared to traditional programming language models, proving the soundness of the numerical derivations. Many non-real-time applications can use our solution for trusted and secure prediction services.
Disruption and shutdown of exchanges frequently happen in the cryptocurrency market, though its potential impacts are relatively under-investigated due to several empirical challenges. This study employs 20-h of service interruption on October 15th at Upbit , the dominant cryptocurrency exchange in Korea, as an exogenous shock to examine the effect of unexpected service interruption at the exchange on cryptocurrency market. Event study estimation using price data from Binance, the largest cryptocurrency exchange globally, shows the sharp and negative reactions to cryptocurrencies mostly traded at Upbit . Major currencies such as Bitcoin and Ethereum also presented limited reactions, implying that service interruption could be interpreted as vulnerability of overall cryptocurrencies.
El presente trabajo busca indagar las comunidades NFT (Non-Fungible Tokens), a través del estudio de caso de la colección CryptoPunks como ensamblajes desterritorializados con una clara postura ética de descentralización de los mercados (financiero y del arte), teniendo en cuenta las interpretaciones del sentido(s) de la acción, tanto a través de teoría filosófica cercana a la comunidad Crypto como la percepción de usuarios y expertos en la materia. La investigación busca encontrar dicho sentido a través de un análisis, tanto del nivel semántico como pragmático, distinguiendo entre los discursos manifiestos y las prácticas que se llevan a cabo en la Blockchain, con particular énfasis en la red de Ethereum, para hacer énfasis en los mecanismos que permiten dicho ensamblaje. De igual manera, se espera esbozar la importancia de la interacción humano-no humano en el campo sociológico, enfocándose en las potencias de sociabilidad que son establecidas tanto semántica como pragmáticamente en los desarrollos técnicos y tecnológicos, teniendo en cuenta la pertenencia a la época actual, de creciente automatización y algoritmización de las interacciones. A manera de conclusión, encontramos que la eticidad se distribuye diferencialmente entre el momento semántico y el pragmático, pues los discursos fundacionales y los usos que llevan a cabo los coleccionistas distan en sus sentidos. Los discursos suelen argumentar a favor de la descentralización de la economía, privilegiando formas heterónomas y heterárquicas de construcción de valor y subjetividades; mientras que los usos llevados a cabo por los coleccionistas suelen ir en el sentido de maximizar inversiones que fueron puestas en los bienes digitales atados a los contratos NFT, por ende, la comunidad es sujetada en la medida en que la especulación de una colección mantenga su momento ascendente. La comunidad, gestada como un ensamblaje a partir de su motivación por la velocidad, desterritorialización y descodificación es captada por usuarios cooptados por los discursos del capital en cuanto empresario de sí, caracterizando los bienes de arte digitales como maximizadores de capital y encerrándose en un bucle similar a las prácticas bursátiles.
Smart contract vulnerability can be exploited to cause serious financial loss. While there are many logic-based techniques for detecting vulnerabilities, few have focused on the dynamic stack of the Ethereum virtual machine (EVM) in the process of transactions. This motivates us to raise an intriguing question: What will happen when the dynamic EVM stack meets a transaction for smart contract vulnerability detection? To answer it, we propose a novel data-driven framework, dubbed TxMirror, to detect smart contract vulnerability at the bytecode level by simulating transactions symmetrically. Beyond logic-based wisdom, TxMirror customizes EVM for smart contract vulnerability detection, and stores its dependency between the stack data and logic relation in a new manner, that is, all the data are stored in a double link forest and can index the logic that creates them; it directly inspects customized EVM stack data in a transaction without replaying history transactions repeatedly or recording EVM bytecode-level traces. Furthermore, it extends detection rules defined by user interests, possessing good adaptability for developers. Extensive experiments demonstrate that TxMirror effectively detects attacks and vulnerabilities in unpredictable smart contracts.
This paper presents a general-purpose approach for the drug supply chain management, by proposing a DLT-based methodology to facilitate and make more efficient the development of such applications. For specific domains, such as drug management and traceability, a system based on Django Python framework, on Ethereum blockchain, and on web3.py library has been developed that can be customized for most real supply chains, automatically generating the specific applications (database schema, smart contracts, apps). A case study about a simple drug traceability system for a producer to hospital wards is described, to show how this approach works.
DEX, or decentralized exchange, is a prominent class of decentralized finance (DeFi) applications on blockchains, attracting a total locked value worth tens of billions of USD today.This paper presents the first large-scale empirical study that uncovers unfair trades on popular DEX services on Ethereum and Binance Smart Chain (BSC). By joining and analyzing 60 million transactions, we find 671, 400 unfair trades on all six measured DEXes, including Uniswap, Balancer, and Curve. Out of these unfair trades, we attribute 55, 000 instances, with high confidence, to token thefts that cause a value loss of more than 3.88 million USD. Furthermore, the measurement study uncovers previously unknown causes of extractable value and real-world adaptive strategies to these causes. Finally, we propose countermeasures to redesign secure DEX protocols and to harden deployed services against the discovered security risks.
The role of record keeping and information sharing in the health sector cannot be overemphasized.Such records comprise an individual's health history and other information that facilitates healthcare decisions, therefore easy access to a patient's health information is an important aspect of health-service delivery that must be regulated and monitored because of the sensitivity of the information.Some approaches adopted in many hospitals face challenges of missing files or records, lack of information sharing between healthcare providers, insecure records, and also inaccessibility of patient's health information for healthcare providers that are needed to make informed health decisions.To overcome these challenges, this work proposes an Electronic Health Record (EHR) using Blockchain to store information as well as enhance data privacy and data security.The proposed solution includes Ethereum and smart contracts to establish a medical record system to ensure the privacy of patients.Also, there exists the privilege to deal with and authorize personal medical records in the proposed framework.The practical findings demonstrate that our proposed system offers a practical approach for trustworthy data exchanges in healthcare while safeguarding private health data from dangers.When compared to the current data sharing models, the system evaluation and security exploration show performance gains in the design, minimize delays in patient data retrieval, and high levels of security and data privacy.
Vera Mita Nia, Ossi Ferli, Irvan Novikri, Roy Sembel · 5 authors
Increasing market capitalization is characterized by high volatility but doesn’t have the ability and potential for monetary function, Crypto world eventually shifted into the most attractive investment in the digital economy. Numerous published studies have required some improvement in the consistent relationship between commodities and financial assets and the authors proposed an alternative assessment with demonstrating the relationship between the trading volume activity of the most traded cryptocurrency in Indonesia (i.e., Ethereum) and other investment assets in Indonesia such as market indexes, rupiah exchange rate against the dollar, and gold, and related to cryptocurrencies in Indonesia which observed in over the last three years. A Var model as a quantitative and statistical approach introduced and tested the stationary data with significancy value to identify the level of acceptance model. Consistency results from previous studies where Ethereum has the largest average return but higher risk and Gold as safer investment, ultimately diversification of the investment portfolio is suggested considering the degree of risk aversion. ABSTRAK Kapitalisasi pasar yang meningkat ditandai dengan volatilitas yang tinggi namun tidak memiliki kemampuan dan potensi fungsi moneter, dunia Crypto akhirnya bergeser menjadi investasi paling menarik di ekonomi digital. Sejumlah penelitian yang diterbitkan memerlukan beberapa perbaikan dalam hubungan yang konsisten antara komoditas dan aset keuangan dan penulis mengusulkan penilaian alternatif dengan menunjukkan hubungan antara aktivitas volume perdagangan mata uang kripto yang paling banyak diperdagangkan di Indonesia (yaitu, Ethereum) dan aset investasi lainnya di Indonesia seperti indeks pasar, nilai tukar rupiah terhadap dolar, dan emas, serta terkait cryptocurrency di Indonesia yang diamati selama tiga tahun terakhir. Model Var sebagai pendekatan kuantitatif dan statistik memperkenalkan dan menguji data stasioner dengan nilai signifikansi untuk mengidentifikasi tingkat penerimaan model. Hasil konsistensi dari studi sebelumnya di mana Ethereum memiliki pengembalian rata-rata terbesar tetapi risiko lebih tinggi dan Emas sebagai investasi yang lebih aman, pada akhirnya diversifikasi portofolio investasi disarankan dengan mempertimbangkan tingkat penghindaran risiko.
Sunčica Stanković, Bojan Đorđević, Nataša Milojević
The increase in the value of cryptocurrencies, market capitalization, and volume of trading on crypto exchanges resulted in a significant increase in the interest of researchers in this decentralized financial system. The two most popular cryptocurrencies today - bitcoin and ethereum - have captured the greatest attention of researchers. Given that cryptocurrency trading is similar to stock trading, the author's assumption is that their returns are determined by the price of gold and the volatility index – VIX, representing this paper's research hypothesis. Testing through vector autoregression (VAR) models, Granger causality tests, and impulse response function (IRF) shows that gold returns do not impact, unlike the VIX volatility index and Ethereum, indicating a significant relationship between cryptocurrencies bitcoin and US stock markets. On the other hand, Bitcoin returns and the volatility index cause ethereum returns, while gold returns do not.
This research was conducted to determine the impact of the crisis caused by Russia and Ukraine on the condition of various war instrument investments, such as the stock market represented by the JCI and the Nasdaq Index, world gold prices, crypto assets such as bitcoin, Ethereum, ripple as well as against the US dollar. From these results, it is hoped that later the most stable investment instrument can be found when there is a crisis shock, so that it can provide input to investors to be able to make the safest investment. The study was conducted testing the difference between pre- and post-war conditions of Russia-Ukraine. The test uses a Wilcoxon test that is adjusted to the normality test results. Based on the tests carried out, it shows that there is a difference between the Stock Market (JCI); Stock Market (NASDAQ); Dollars; Crypto (Bitcoin); Crypto (Ethereum); Crypto (Ripple); World Gold before (Pre) with after (Post) Russia-Ukraine War.
Recent advancements in digital accident forensics, a conceptual evidence management paradigm developed using smart contracts and interplanetary file system in iov. This paper comprehensively summarizes the Smart contract implementation blockchain framework for vehicle accident investigation in IoV. We investigate comparing some review papers to find the classification of the smart contract. Using blockchain, evidence management provides an immutable and auditable method for investigating and resolving accident cases. Precisely we first investigate the security and privacy threats; therefore, Smart contracts provide effective access control for proof data and reports. On both the public and private Ethereum blockchains, the cost of setting up and executing transactions using smart contracts is assessed. However, we utilized the Inter Planetary File System most efficiently while minimizing memory and execution costs. Finally, we draw open research directions for building future digital-proof management.
“Ethereum ha tomado lo que era una calculadora de cuatro funciones de un lenguaje de programación en Bitcoin y lo convirtió en una computadora completa” (Fred Ehrsam, s.f.) Ethereum es la segunda criptomoneda de mayor capitalización del mercado, solo detrás de Bitcoin; sin embargo, la naturaleza y potencial es distinta y de una mayor utilidad en el largo plazo. Como se está conociendo en la actualidad, Bitcoin se está perfilando como el dinero de las próximas generaciones; por dicha razón, y ante el auge de nuevas criptomonedas, muchos se preguntan qué es Ethereum. Realidad Empresarial No. 15, 2023: 45-49.
Blockchain is a growing technology nowadays. This technology is often associated with the terms "distributed" and "decentralized" technology that does not require a central authority (CA) to control certain processes Therefore, the acceptance of this technology is increasing with the introduction of some of the latest applications in the world of decentralized financial (DeFi), non-fungible tokens (NFT), and metaverse. Previously, blockchain technology was widely adopted in several popular domains, such as digital government [2], health [3], business management [4], and smart cities The understanding of blockchain is linked to several main functions: public key, private key, hash value, and peer-to-peer (P2P) network.
Marcin Wątorek, Maria Skupień, Jarosław Kwapień, Stanisław Drożdż
This paper investigates the temporal patterns of activity in the cryptocurrency market with a focus on Bitcoin, Ethereum, Dogecoin, and WINkLink from January 2020 to December 2022. Market activity measures - logarithmic returns, volume, and transaction number, sampled every 10 seconds, were divided into intraday and intraweek periods and then further decomposed into recurring and noise components via correlation matrix formalism. The key findings include the distinctive market behavior from traditional stock markets due to the nonexistence of trade opening and closing. This was manifest in three enhanced-activity phases aligning with Asian, European, and U.S. trading sessions. An intriguing pattern of activity surge in 15-minute intervals, particularly at full hours, was also noticed, implying the potential role of algorithmic trading. Most notably, recurring bursts of activity in bitcoin and ether were identified to coincide with the release times of significant U.S. macroeconomic reports such as Nonfarm payrolls, Consumer Price Index data, and Federal Reserve statements. The most correlated daily patterns of activity occurred in 2022, possibly reflecting the documented correlations with U.S. stock indices in the same period. Factors that are external to the inner market dynamics are found to be responsible for the repeatable components of the market dynamics, while the internal factors appear to be substantially random, which manifests itself in a good agreement between the empirical eigenvalue distributions in their bulk and the random matrix theory predictions expressed by the Marchenko-Pastur distribution. The findings reported support the growing integration of cryptocurrencies into the global financial markets.
This paper explores the sociological and cultural implications of blockchain technology, specifically focusing on three prominent blockchain ecosystems: Bitcoin, Ethereum, and Algorand. The study utilizes the concept of the lifeworld, which encompasses collective human perceptions and everyday communicative social interaction, to analyze the formation and perpetuation of lifeworlds within these ecosystems. By employing scene theory as an analytical framework, the research identifies structural and thematic aspects of the lifeworlds represented in the discourse on Reddit and Twitter. The analysis reveals how these virtual spaces shape the unique social orderings, normative politics, and cultural identities associated with each blockchain. The study emphasizes the role of identity expression, cultural attitudes towards money, and the dynamics of boundary work within these scenes. Overall, the paper provides insights into the distinct lifeworlds and dynamics of Bitcoin, Ethereum, and Algorand, showcasing the significance of sociocultural factors in blockchain ecosystems and illustrating how a scenes lens offers insights into dynamics at the ecosystem level that may not be visible in an exploration of blockchain technology at the level of technological category.
<em>Electronic commerce (e-commerce) has revolutionized the way businesses operate worldwide. This technology has enabled online trading and economic services across various industries. Third-party marketplaces, known for their trusted intermediation and secure online transaction services to anonymous internet traders, are among the drivers of e-commerce. These centralized corporations charge a fee for their services, but online trading is not immune to various crimes such as identity theft, non-payment, delayed service delivery, and other attendant risks. To mitigate these problems, this research proposes a prototype system that utilizes blockchain technology to achieve decentralization in internet trade. The system's proof-of-concept utilizes the Ethereum Virtual Machine (EVM) and Interplanetary File System (IPFS). The proposed system can work with EVM-compatible blockchains such as Tron, the Binance Smart Chain, Matic network, Tomo chain, and Rootstock (RSK) network. The study demonstrates the potential of blockchain technology in e-commerce by providing a decentralized marketplace that is free from centralized control. By removing intermediaries, the proposed system eliminates the need for trust in third-party marketplaces, which in turn reduces transaction costs, and ensures transparency and accountability. The findings of this research highlight the practicality of using blockchain technology in e-commerce, and how it can significantly reduce transaction costs, improve security, and promote decentralization</em><em>.</em>
Kasliono Kasliono, Niken Candraningrum, Kartika Sari
The price of cryptocurrencies such as Ethereum often experiences high fluctuations and is difficult to predict. This study aims to predict Ethereum prices using the Extreme Learning Machine (ELM) algorithm which is a fast and efficient machine learning method. Ethereum price data is collected from CoinMarketCap by scraping the data using CoinmarketCap Scraper from the cryptocmd library using Python. An ELM model is built by changing the number of hidden nodes to determine the optimal prediction model of Ethereum prices based on the smallest average MAPE. Model performance was evaluated using the mean absolute percentage error (MAPE) on the test data set. The results show that the ELM model built can predict Ethereum prices with an accuracy of 96.96%. The MAPE obtained is 3.035334%, with 9 hidden nodes in the ELM network architecture model that was built. This shows that the model can explain about 96.96% of the variation in Ethereum price data. Therefore, the ELM model can be used as an aid in making investment decisions
The use of blockchain technology is expanding in various industries, including finance, supply chain management, food, energy, IoT, and healthcare. The article aims to address the challenges of complex medical procedures, large-scale medical data management, and cost optimization in the healthcare industry. By employing blockchain technology, the article aims to enhance data security and privacy while ensuring the integrity and efficiency of the healthcare system. This article focuses on the application of blockchain technology in the healthcare system by reviewing the existing literature and proposing multiple workflows for better data management. These workflows were implemented using the Ethereum blockchain platform and involve complex medical procedures such as surgery and clinical trials, as well as managing a large amount of medical data. The feasibility of the proposed system is analyzed in terms of associated costs, and a model-driven engineering approach is used to recover the architecture of traditional healthcare systems. The aim is to provide stakeholders in the healthcare system with better healthcare services and cost optimization. The solution being proposed automates interactions between different parties involved. Smart contracts were created using Solidity language, and their functions were tested using the Remix IDE. This paper illustrates that our smart contract code was designed to avoid common security vulnerabilities and attacks. To test the framework, a prototype of the smart contract was deployed on an Ethereum TESTNET blockchain in a Windows environment. This study found that the proposed approach is both practical and efficient.
In this paper, we present a measure of time irreversibility using trend pattern statistics. We define the irreversibility index as the Kullback-Leibler divergence between the distribution of uptrends subsequences (increasing trends) and the corresponding downtrends subsequences distribution (decreasing trends) in a time series. We use this index to analyze the degree of irreversibility in log return series over time, specifically focusing on five cryptocurrencies: Bitcoin, Ethereum, Ripple, Litecoin, and Bitcoin Cash. Our analysis reveals a strong indication of irreversibility in all these cryptocurrencies and the characteristic evolves over time. We additionally evaluate the market efficiency for these cryptocurrencies based on a recently proposed information-theoretic measure. By comparing inefficiency and irreversibility, we explore the relationship between these statistical features. This comparison provides insight into the non-trivial relationship between inefficiency and irreversibility.
Stanisław Barański, Julian Szymański, Higinio Mora
Abstract Lawyers, laboratories, auditors, and banks often need access to sensitive personal data to provide services such as genetic testing, paternity testing, STD testing, credit scoring, or legal advice. Processing such data exposes both service providers (SPs) and users to privacy risks: SPs risk violating laws like the General Data Protection Regulation (GDPR) and the Consumer Protection Act (CPA), while users risk losing their privacy. We observe that personal data is often only needed for logistical purposes like payment or communication and could be provided anonymously if suitable methods existed. To address this, we present a solution that enables services to be delivered without collecting personal data. Our protocol combines anonymous payment methods (e.g., cash, privacy-preserving cryptocurrencies), blockchain for fairness, and distributed content-addressable storage networks to deliver results. Compared to existing approaches, our protocol achieves anonymity under weaker assumptions, supports the transfer of physical materials and conflict resolution, and eliminates the need for customer interaction with a trusted arbiter in conflict-free cases-making it more practical. We analyze the protocol’s fairness and implement a prototype using Ethereum as a message board, Monero for anonymous payments, and Powergate (IPFS/Filecoin) as a decentralized storage solution.
Richard De Freitas Pinto, Viviane Leite Dias de Mattos, Luiz Ricardo Nakamura
Esse trabalho apresenta a modelagem do volume mensal de transações da criptmoeda Ethereum por meio da metodologia de Box-Jenkins, envolvendo as etapas: análise exploratória, identificação, estimação e validação, algumas das quais executadas com a utilização de diferentes técnicas. O modelo encontrado pela modelagem SARIMA (Modelo autoregressivo integrado de médias móveis sazonal) conseguiu descrever o comportamento linear dos dados de forma satisfatória, mas não foi suficiente para descrever o comportamento da série, composta por movimento linear e não linear, sendo melhor representada por um modelo híbrido.
This research aims to analyze and explain the importance of diversification benefit of cryptocurrencies and its nature in accordance with its relation with other financial assets before and especially during the Covid-19 pandemic era. This paper will help investors to understand that how to manage a portfolio of cryptocurrencies in parallel with other financial assets and mainly cryptocurrencies since they were a safe investment option during the pandemic period due to the good defense these digital currencies activated against the covid-19 shock back in 2020. Paper used DCC-GARCH model to examine the safety of Bitcoin and Ethereum with financial market of S & P 500 and FTSE100.