There has been tremendous interest invested by researchers and academics in Bitcoin since it's introduction to the financial market. However, in recent years there has been an advancement of the cryptocurrency market where other cryptocurrencies such as Ethereum, Litecoin and Ripple have grown relatively quickly and could potentially challenge the dominant placement of Bitcoin. These cryprocurrencies have been utilized globally as a virtual currency for multiple transactions. The returns of cryptocurrencies are known to be volatile and have been observed to fluctuate quite a bit in recent times. This study assesses and differentiates the performance of generalized autoregressive score (GAS) models integrated with a few heavy-tailed distributions in Value-at-Risk (VaR) estimation of the four most popular cryptocurrencies' returns, i.e. Bitcoin returns, Ethereum returns, Litecoin returns and Ripple returns. This paper proposed VaR models for Bitcoin, Ethereum, Litecoin and Ripple returns, i.e. GAS models combined with the generalized hyperbolic distribution (GHD), the variance gamma (VG) distribution, the normal inverse Gaussian (NIG) distribution and the generalized lambda distribution (GLD). The Kupiec likelihood ratio test was adopted to evaluate the proposed models' adequacy and Backtesting VaR was used to select the superior set of models.
This paper presents a comprehensive analysis of the historical progression, current trends, and prospects of Big Data. It explores the technological advancements that have established Big Data as a critical element of contemporary analytics, its extensive impact across various sectors, and the ethical challenges it poses. Beginning with the early recognition of Big Data's potential in the 2000s, the paper traces the development of foundational technologies such as Hadoop and the subsequent diversification of tools and methods. It delves into the integration of advanced analytics and machine learning, the rise of cloud-based Big Data services, and the transformative effects on sectors including healthcare, finance, agriculture, and education. The study also examines ethical considerations such as privacy, bias, transparency, and regulatory compliance, emphasizing the need for robust governance frameworks. It investigates the potential of emerging technologies like AI, IoT, and quantum computing to enhance Big Data capabilities further. It highlights future directions, including decentralized data ecosystems, advanced analytical techniques, and enhanced data privacy measures. By providing a panoramic view of Big Data's development, this paper aims to showcase its potential to revolutionize decision-making processes, improve operational efficiency, and drive innovation across industries; it underscores the importance of balancing technological innovation with ethical responsibility to ensure positive societal advancement and global progress. To add a novelty to the discussion, an AI agent Big D was created to provide a relevant analysis of trends in Big Data. The agent uses a multimodal ChatGPT-4o Large Language Model (LLM) from OpenAI and provides its review based on uploaded files and LLM knowledge.
Abstract Bitcoin and Ethereum are the two largest cryptocurrencies in the world by market capitalization and trading volume and the most popular despite high price fluctuations. This paper analyzes the relationship between Bitcoin and Ethereum metrics and the internet search interest on cryptocurrencies. As the literature shows, Google searches signal investor attention and Google Trends has proven useful for nowcasting economic and financial indicators. We aim to find the impact of Google Trends on Bitcoin and Ethereum prices, trading volumes and market capitalization since 2015 and discuss the potential correlations and patterns that may exist between these metrics and Google search interest. Through correlation and time-series analysis, we provide insights into the dynamics of this relationship and its implications for understanding cryptocurrency market behavior. The interest in cryptocurrencies tracked by Google Trends is a good indicator of measuring the social interest in the cryptocurrency market that drives a price movement. On the other hand, the price fluctuations of Bitcoin and Ethereum generate media and social attention and increase the interest in these cryptocurrencies. We also observe a positive effect of Google Trends values on trading volumes. The findings could help investors to understand the cryptocurrencies dynamics and build their trading strategies and could be of special interest to policymakers.
This research paper explores the intersection of zero-knowledge proofs (ZKPs) and machine learning (ML), presenting a comprehensive overview of recent advancements, applications, and challenges in this fast growing area. The jointers of ZKPs and ML techniques shall go a meter further to fuse privacy, security, and integrity in a number of solutions, which include forming of groups for data sharing and safe machine learning. Through the investigation of the well-respected sites in that area and also the thorough description of formulas and their experimental outcome, this paper looks for the clarification of the current state of affairs and the possible future directions of ZKPs in the AI world. By inserting the verification mechanism of ZKPs into machine learning ecosystem, it allows devising novel solutions for the problems of privacy and confidentiality that have for long been not solved. With this approach, the concatenation of parties collectively performs the process of dealing with private inputs without revealing any of these data and this, in return, opens the possibilities of secure multi-party computation. Furthermore, ZKPs protect data sharing as it gives people the opportunity to construct confidential data and share them to model training without compromising any one’s private details. Being a part of the dynamic conversations, which focus on the game-changing capacity of transparent zero-knowledge proofs (ZKPs), this paper brings the role of ZKPs in preserving the confidentiality and integrity of artificial intelligence (AI) applications into the centre of attention. As scientists still fight to improve protocols and circumvent computational complications, ZKPs are likely to establishment as critical tools in the effort to increase ML systems in the digital sphere.
Open access
Online Learning and Analytics
Intelligent Tutoring Systems and Adaptive Learning
Yaoyue Tang, Karina Arias-Calluari, M. N. Najafi, Michael Harré · 5 authors
This paper analyses the high-frequency intraday Bitcoin dataset from 2019 to 2022. During this time frame, the Bitcoin market index exhibited two distinct periods, 2019-20 and 2021-22, characterized by an abrupt change in volatility. The Bitcoin price returns for both periods can be described by an anomalous diffusion process, transitioning from subdiffusion for short intervals to weak superdiffusion over longer time intervals. The characteristic features related to this anomalous behavior studied in the present paper include heavy tails, which can be described using a $q$-Gaussian distribution and correlations. When we sample the autocorrelation of absolute returns, we observe a power-law relationship, indicating time dependence in both periods initially. The ensemble autocorrelation of the returns decays rapidly. We fitted the autocorrelation with a power law to capture the decay and found that the second period experienced a slightly higher decay rate. The further study involves the analysis of endogenous effects within the Bitcoin time series, which are examined through detrending analysis. We found that both periods are multifractal and present self-similarity in the detrended probability density function (PDF). The Hurst exponent over short time intervals shifts from less than 0.5 ($\sim$ 0.42) in Period 1 to closer to 0.5 in Period 2 ($\sim$ 0.49), indicating that the market has gained efficiency over time.
Bitcoin is one of those less common financial innovations that threatens to up-end the status quo in a more general sense. This not only instils fear in some decision-makers but loathing too. Which leads to an interesting question. That is, how does fear work itself into the construction of a Savage “Small World”? What happens if something terrifying is thought to be lurking in one of Weitzman’s boxes? Fear disrupts the very nature, the very structure, of search and decision.
Owen Chaffard, Pablo Mollá, Marc Cavazza, Helmut Prendinger
In the recent advancements in application of deep learning to time series forecasting, focus has shifted from training transformers end-to-end to efficiently leveraging the predictive capabilities of Large Language Models (LLMs). Models that encode the time series data to interact with a frozen LLM backbone have been shown to outperform transformers on all benchmark datasets. However, their efficiency on complex datasets, which do not show clear seasonality or trend, remains an open question. In this work, we seek to evaluate the performance of reprogrammed LLMs on the Bitcoin price chart, a financial time series known for its complexity and high volatility. We propose effective methods to improve the performance of Time-LLM, a State-of-the-art (SOTA) method, on such a time series. First, we propose structural improvements to Time-LLM. Second, we suggest an efficient way to handle the non-stationarity of the dataset. Finally, we propose an efficient method for passing additional financial information to the LLM. Our results demonstrate a 50% improvement on the average percentage loss and a 5% increase on accuracy of our adapted Time-LLM architecture on Bitcoin data when compared to SOTA models, including the original Time-LLM model. This highlights the impact on forecast accuracy of domain-specific decision making in data processing and feature selection.
When migrating smart contracts from one blockchain platform to another, there are potential security risks. This is because different blockchain platforms have different environments and characteristics for executing smart contracts. The focus of this paper is to study the security risks associated with the migration of smart contracts from Ethereum to Arbitrum. We collected relevant data and analyzed smart contract migration cases to explore the differences between Ethereum and Arbitrum in areas such as Arbitrum cross-chain messaging, block properties, contract address alias, and gas fees. From the 36 types of smart contract migration cases we identified, we selected four typical types of cases and summarized their security risks. The research shows that smart contracts deployed on Ethereum may face certain potential security risks during migration to Arbitrum, mainly due to issues inherent in public blockchain characteristics, such as outdated off-chain data obtained by the inactive sequencer, logic errors based on time, failed permission checks, and denial of service (DOS) attacks. To mitigate these security risks, we proposed avoidance methods and provided considerations for users and developers to ensure a secure migration process. It is worth noting that this study is the first to conduct an in-depth analysis of the secure migration of smart contracts from Ethereum to Arbitrum.
Development of management system standards for the cryptocurrency industry is an ongoing research project. This document is the first draft of a proposed standard for mining. It defines the obligations of firms engaged in cryptocurrency mining with respect to carbon footprint and safety. It follows the structure of ISO 27001. Firms could potentially be audited for adherence to such a standard (among potentially others) as a way of signaling their ethics.
Bao Doan, Dulani Jayasuriya, John B. Lee, Jonathan J. Reeves
In this study, we analyse systematic risk associated with the two leading cryptocurrencies - Bitcoin and Ethereum, from 2015 to 2023. Our findings show a significant escalation in the systematic risk levels, with beta estimates rising from 0.032 to 0.834 for Bitcoin, and from 0.087 to 1.003 for Ethereum. This hike in risk levels has dramatically reduced the diversification benefits of cryptocurrency that were documented in prior studies. In addition, we also identify increased autocorrelation of cryptocurrency systematic risk.
Crypto Pi is a hardware crypto wallet that has different benefits. The wallet aims to show the users how a hardware wallet works. It is an alternative to Trezor Model One, so it has all its benefits. It is not hard to use or set up. We chose to work on this project because we believe in the future potential of using cryptocurrencies as a form of payment.
Stablecoins are cryptocurrencies whose price is pegged to that of another asset (typically one with low price volatility). The market for stablecoins has grown tremendously - up to almost $200 billion USD in 2022. These coins are being used extensively in newly developing paradigms for digital money and commerce as well as for decentralized finance technology. This work provides a technical description of stablecoin technology to enable reader understanding of the variety of ways in which stablecoins are architected and implemented. This includes a descriptive definition, commonly found properties, and distinguishing characteristics, as well as an exploration of stablecoin taxonomies, descriptions of the most common types, and examples from a list of top stablecoins by market capitalization. This document also explores related security, safety, and trust issues with an analysis conducted from a computer science and information technology security perspective as opposed to the financial analysis and economics focus of much of the stablecoin literature.
In decentralized finance (DeFi), lenders can offer flash loans to borrowers, i.e., loans that are only valid within a blockchain transaction and must be repaid with fees by the end of that transaction. Unlike normal loans, flash loans allow borrowers to borrow large assets without upfront collaterals deposits. Malicious adversaries use flash loans to gather large assets to exploit vulnerable DeFi protocols. In this paper, we introduce a new framework for automated synthesis of adversarial transactions that exploit DeFi protocols using flash loans. To bypass the complexity of a DeFi protocol, we propose a new technique to approximate the DeFi protocol functional behaviors using numerical methods (polynomial linear regression and nearest-neighbor interpolation). We then construct an optimization query using the approximated functions of the DeFi protocol to find an adversarial attack constituted of a sequence of functions invocations with optimal parameters that gives the maximum profit. To improve the accuracy of the approximation, we propose a novel counterexample driven approximation refinement technique. We implement our framework in a tool named FlashSyn. We evaluate FlashSyn on 16 DeFi protocols that were victims to flash loan attacks and 2 DeFi protocols from Damn Vulnerable DeFi challenges. FlashSyn automatically synthesizes an adversarial attack for 16 of the 18 benchmarks. Among the 16 successful cases, FlashSyn identifies attack vectors yielding higher profits than those employed by historical hackers in 3 cases, and also discovers multiple distinct attack vectors in 10 cases, demonstrating its effectiveness in finding possible flash loan attacks.
The construction of smart grid has triggered comprehensive changes in power equipment. At the same time, massive data are waiting to be screened, analyzed and processed. However, there are many data sources and complex data structure in smart grid, and smart grid requires higher security, reliability, traceability, non-repudiation and real-time performance of data processing, which poses unprecedented challenges to the traditional data management. Blockchain technology has the advantages of decentralization, autonomy, leakage prevention and openness. It can match the demands of power data management in terms of operation mode, security protection, complement power data, and better meet the security and credibility requirements of energy Internet. Therefore, this paper designs the smart grid data management system based on blockchain, and constructs the system security evaluation model by using AHP and expert consultation method. Firstly, the characteristics of smart grid big data and blockchain technology are analyzed; Then the technical architecture of data management system is designed by using alliance chain, and the technical architecture is analyzed in detail; Finally, the index factors affecting system security are combed, and the system security evaluation model is constructed by AHP. Distributed storage, asymmetric encryption, consensus mechanism, and other technologies of blockchain technology can well solve the problems of power grid data storage and application, and better ensure the security of smart grid data.
Oluwatobi A. Adekunle, Adedeji Daniel Gbadebo, Joseph Akande
Bitcoin price exhibits patterns predictable on its historical pasts. We adopt ARIMA(auto), ARIMA(fix)models and the Holt-Winters filter (HWF) with trend plus additive seasonal HWF (𝛾[0,1]), and no seasonality HWF (𝛾[False]) to forecast the price of Bitcoin under three datasets–Actual (observed), Polynomial (fitted) and STL-Trend (fitted). We apply daily time-series from 1/09/2014–28/12/2020,and establish 18 models to forecast the price of Bitcoin. The results show that HWF (𝛾[0,1]) with lower limit fitted on STL-Trend provides the best prediction on the first training-sample, while ARIMA(fix) fitted on actual data outperform in the second training-set with the smallest Mean-Absolute-Error (MAE). The training-set forecast performance of the ARIMA(fix) for the actual function provides better performance with the least MAE. The HWF is appropriate for prediction of the daily Bitcoin price with the generalise STL-Trend function, but ARIMA(fix) is more accurate for the actual series.
This exploration paper is about the conception of a World Wide Web grounded for the conception grounded around machine-readability, also called Web3.0. Web3.0 will review how we interact with the digital world and the change won't just be for individualities. The effect of Web3.0 blockchain on businesses – both traditional and disruptive will be inversely massive. The transition from Web2.0 to Web3.0 still, won't be overnight. This means businesses will have time to look back at their process and see where they fit on the decentralization and translucency radar. But indeed though Web3.0 is in the future, the reality of the moment is that businesses need to start preparing. Let our Blockchain experts help you. Some technologists and intelligencers have varied it with Web2.0, wherein they say data and content are consolidated in a small group of companies occasionally appertained to as"Big Tech". The term"Web3" was chased in 2014 by Ethereumco- founder Gavin Wood, and the idea gained interest in 2021 from cryptocurrency suckers, large technology companies, and adventure capital enterprises. Some experts argue that web3 will give increased data security, scalability, and sequestration for druggies and combat the influence of large technology companies. Others have raised enterprises about a decentralized web, citing the eventuality of low temperance and the proliferation of dangerous content, the centralization of wealth to a small group of investors and individualities, or a loss of sequestration due to further extensive data collection. Crucial Words World Wide Web; Web3.0; Big Tech; cryptocurrency; decentralized web