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\n\ncoinbase pro tech ☎️1806-318-0965☎️ support number,coinbase pro support number, coinbase pro phone number, coinbase pro helpline number,coinbase pro customer support number At Coinbase, we’re focused on offering more ways for customers to earn crypto rewards. Today, we’re expanding our staking offerings to include Cardano (ADA) with plans to continue to scale our staking portfolio in 2022.Cardano is one of the top ten most valuable cryptocurrencies by market cap. It’s a proof-of-stake blockchain designed to be a next-gen evolution of Ethereum — with a blockchain that seeks to be more flexible, sustainable, and scalable. Cardano aims to enable smart contracts to allow developers to build a wide range of decentralized finance (DeFi) apps, new crypto tokens, games, and more. When users stake their crypto, they make the underlying blockchain of that asset more secure and more efficient. And in exchange, they are rewarded with additional assets from the network, which are paid out as rewards. While it has been possible for individuals to stake Cardano on their own, or via a delegated staking service, the process can be confusing and complicated. With today’s launch, Coinbase is offering an easy, secure way for any retail user to actively participate in the Cardano network and earn rewards.
Uniswap, as with other DEXs, has gained much attention this year because it is a non-custodial and publicly verifiable exchange that allows users to trade digital assets without trusted third parties. However, its simplicity and lack of regulation also make it easy to execute initial coin offering scams by listing non-valuable tokens. This method of performing scams is known as rug pull, a phenomenon that already exists in traditional finance but has become more relevant in DeFi. Various projects have contributed to detecting rug pulls in EVM compatible chains. However, the first longitudinal and academic step to detecting and characterizing scam tokens on Uniswap was made. The authors collected all the transactions related to the Uniswap V2 exchange and proposed a machine learning algorithm to label tokens as scams. However, the algorithm is only valuable for detecting scams accurately after they have been executed. This paper increases their dataset by 20K tokens and proposes a new methodology to label tokens as scams. After manually analyzing the data, we devised a theoretical classification of different malicious maneuvers in the Uniswap protocol. We propose various machine-learning-based algorithms with new, relevant features related to the token propagation and smart contract heuristics to detect potential rug pulls before they occur. In general, the models proposed achieved similar results. The best model obtained accuracy of 0.9936, recall of 0.9540, and precision of 0.9838 in distinguishing non-malicious tokens from scams prior to the malicious maneuver.
In the research, the causal relationships between Bitcoin, gold and oil prices were examined. The data of the research covers the period from 2015 to July 2020 and consists of daily price values. Augmented Dickey-Fuller Unit Root Test was used to see whether the stochastic process changes with time. Bitcoin and gold series do not contain a unit root since the oil series is stationary at the level while the difference is stationary. The reason why the series containing unit roots are not stationary is due to structural breaks or not, was investigated by Bai-Perron Unit Root Test with Multiple Structural Breaks. According to the test, it was determined that the Bitcoin series has one break and two regimes, while the gold series has two structural breaks and three different regimes. Whether the research series are cointegrated or not was investigated with the Gregory and Hansen test. The causality between the series was examined with the Toda-Yamamoto causality test, which is based on the VAR (Vector Autoregression) model and examines the causality in the series regardless of the unit root. A two-way causality relationship was determined between the eight lag-long Gold series and the Bitcoin series. In other cases, a causal relationship has not been established. As a result, we give an evidence that Bitcoin and gold prices series followed a parallel pattern while with oil not. Therefore, investors can add Bitcoin into their portfolios to make balance of the risk and return.
Bitcoin is one of the most valuable Crypto currency in the world with the prices as high as 68,078 United States Dollar (USD) in November of 2021. It made Bitcoin a very profitable market for investment but Bitcoin saw many ups and down as well. Prices of Bitcoin have highly fluctuated which make them very difficult to predict. Hence, this research aims to discover the most efficient and highest accuracy model to predict Bitcoin prices from various machine learning algorithms. By using 1-minute interval trading data on the Bitcoin exchange website named bitstamp some different regression models with Scikit- learn and Keras libraries had experimented. The dataset used contains minute by minute prices of Bitcoin of over 5 years and contains almost 3 Million entries. Since, the dataset used is a big data, evaluating the performance of algorithms over a large dataset will give accurate results.
Ninuk Wiliani, Rizki Hesananda, Nidya Sari Rahmawati, Erdham Hestiadhi Prianggara
Predicting a currency Exchange rate and performing analysis is an action to try to determine the price valuation of a currency or other financial instrument traded on an exchange platform. Bitcoin is a consensus network that enables new payment systems and fully digital money. Bitcoin is the first decentralized peer to peer payment network that is fully controlled by its users without any central authority or intermediary. From the user's point of view, Bitcoin is like cash in the internet world. Bitcoin can also be viewed as the most prominent triple bookkeeping system in existence today. The change in Bitcoin's behavior against the US dollar is influenced by many factors. Basic or economic factors that may be affected include inflation rates and money supply. In this study, data was collected by obtaining all data through the API provided by binance.com and labeled with the specified attribute. The modeling is done by using the rapidminer application. The process begins by taking training data that has been provided previously. The next stage is the data testing process, all operators that have been previously determined are connected and tested using the Linear Regression operator. The purpose of testing this data is to predict stock prices from the testing data that has been made by the Split Data operator, which is 19% of the total data that has been prepared.
The globally acknowledged accelerating crypto hype has put a lot of work on researchers and analysts. This new marvel of digital currencies requires a lot of attention, for it to become conventional worldwide. Virtual coins or cryptographic forms of money utilize encryption framework, so called the Block Chain technology, that manage the formation and supply of coins and exchanges must be recognized from a financial examination point of view. Subsequently, it is essential to inspect which social, money related & macroeconomic components decide its cost with a specific end goal to know the degree and outcomes of the economy. This paper aims to study different internal and external factors that affect cryptocurrencies’ prices. A sample of four digital coins with largest market capitalization has been selected. Daily price data from the years 2015 to 2020 of Bitcoin with other altcoins such as Ethereum, Ripple and Litecoin has been taken. Internal factors consist of demand and supply variables and also the attractiveness associated with its increasing hype. Other factors include KSE-100 Index (Karachi Stock Exchange), USD-PKR (Dollar to Pakistani Rupee) exchange rate and oil prices from PSO (Pakistan State Oil). ARDL analysis has been done to study the effect of these factors on the prices of crypto coins. Our analysis shows that circulating supply has a significant effect on Ethereum and Ripple prices in the long run. Attractiveness has been significant on the prices of Ethereum only.
Blockchain technology has been widely used in digital currency, Internet of Things, and other important fields because of its decentralization, nontampering, and anonymity. The vigorous development of blockchain cannot be separated from the security guarantee. However, there are various security threats within the blockchain that have shown in the past to cause huge financial losses. This paper aims at studying the multi-level security threats existing in the Ethereum blockchain, and exploring the security protection schemes under multiple attack scenarios. There are ten attack scenarios studied in this paper, which are replay attack, short url attack, false top-up attack, transaction order dependence attack, integer overflow attack, re-entrancy attack, honeypot attack, airdrop hunting attack, writing of arbitrary storage address attack, and gas exhaustion denial of service attack. This paper also proposes protection schemes. Finally, these schemes are evaluated by experiments. Experimental results show that our approach is efficient and does not bring too much extra cost and that the time cost has doubled at most.
Open access
Blockchain Technology Applications and Security
Currency Recognition and Detection
Advanced Steganography and Watermarking Techniques
Miguel Pincheira, Mattia Antonini, Massimo Vecchio
Inspection of mining assets is a crucial part of the maintenance process and is of interest to several stakeholders (e.g., OEMs, owners, users, and inspectors). Inspections require an inspector to verify several characteristics of the assets onsite, typically using legacy and poorly digitized procedures. Thus, many research opportunities arise from the adoption of digital technologies to make these procedures more efficient, reliable, and straightforward. In addition to cloud computing, the ubiquitous presence of modern mobile devices, new measurement tools with embedded connectivity capabilities, and blockchain technologies could greatly improve trust and transparency between the stakeholders interested in the inspection. However, there has been little discussion on integrating these technologies into the mining domain. This paper presents and evaluates an end-to-end system to conduct inspections using mobile devices that directly interact with constrained IoT sensor devices. Furthermore, our proposal provides a method to integrate constrained IoT devices as smart measuring tools that directly interact with a blockchain system, guaranteeing data integrity and increasing the trustworthiness of the data. Finally, we highlight the benefits of our proposed architecture by evaluating a real case study in a mining inspection scenario.
Blockchain has been found of great use in various sectors and this technology promises much more because of the high level of security it provides and blockchain provides us trade without the need of any mediators so Land holding system seems to be a very promising area of interest. All the countries around the world are now implementing blockchain in the area of land registrations in India, we should also consider it as a viable option. The blockchain has provided major security benefits in diverse fields. Therefore, this land holding system is implemented using a blockchain architecture. In India main problems in traditional land holding system include space constraints, fraud in land registry, lack of uniformity and poor maintenance of land records, lack of single window title verification and investigation system, fear of destruction of records by force majeure events. Therefore, a blockchain is prepared to aid these problems in order to solve these problems we have analyzed, identified and developed an application with the help of blockchain smart contracts. In this paper we are developing a land holding System to encounter the problems in traditional systems.
With the advent of globalization and the evergrowing rate of technology, the volume of production as well as ease of procuring counterfeit goods has become unprecedented. Be it food, drug or luxury items, all kinds of industrial manufacturers and distributors are now seeking greater transparency in supply chain operations with a view to deter counterfeiting. This paper introduces a decentralized Blockchain based application system (DApp) with a view to identifying counterfeit products in the supply chain system. With the rapid rise of Blockchain technology, it has become known that data recorded within Blockchain is immutable and secure. Hence, the proposed project here uses this concept to handle the transfer of ownership of products. A consumer can verify the product distribution and ownership information scanning a Quick Response (QR) code generated by the DApp for each product linked to the Blockchain.
For traditional agricultural product traceability system, there are some problems, such as information asymmetry and serious centralized storage. To solve the problems, a set of an agricultural product traceability information analysis system based on blockchain is designed. The overall framework of the agricultural product information traceability system is built, and the traceability model and process are designed in detail from the data layer, consensus layer, and contract layer. Collaborative verification function modules are added in the data layer to ensure the authenticity of data on the blockchain. The integral penalty mechanism is introduced to improve the practical Byzantine fault tolerance algorithm (PBFT), which ensures the security of the block network and the validity of data. Smart contracts are adopted to ensure that changes in agricultural information are actually recorded on blockchain. The results show that the designed blockchain‐based agricultural product information traceability system solves the problem of difficult sharing and traceability of agricultural product information, which realizes the traceability of agricultural product production information, processing information, transportation information, and transaction query information, and has certain theoretical and practical value for the information traceability of agricultural products and other products.
Purpose:The objectives of this paper are, first, to synthesize the main events that have influenced the development and volatility of bitcoin, and second to present and apply the Rogers and Satchell range model (1993) for the measurement of bitcoin volatility.Desigh/methodology/Approach: Findings: The results are useful for risk management, searching profitable investments, diversification of portfolios, and the application of a reliable risk parameter for the valuation of bitcoin financial and real options.The evidence suggests further studies extending it, considering leptokurtosis and other moments of the distribution of the series. Practical implications: Finally, the study highlights the need for clear regulations on bitcoin and other cryptocurrencies to ensure a fruitful future co-existence with digital currencies created by the central banks. The work suggests the creation of additional IMF's SpecialDrawing Rights in lieu of a global cryptocurrency.That would help to overcome the problems created by the Covid-19 pandemic and member countries would retain their monetary sovereignty.Originality value: These objectives underline the originality and contribution of the work.This is the first time this model is fully recognized and used, highlighting its advantages.
D. Ramı́rez, Javier Andres Muñoz Romero, Carlos Alberto Vanegas
Objective: This article presents one of the Blockchain applications in power systems employing the electrical variables monitoring delivered by a simulated relay.
 Methodology: The electrical variables are verified, compared, and uploaded to a blockchain network created by a network of servers; these are responsible for uploading, validating, and exporting information to the system. This study is carried out through the Python programming language.
 Results: An algorithm capable of integrating these variables from a simulated relay is created, 4 servers are created that are in charge of taking the relay signals every minute, validating that they are unique in the blockchain and uploading them, the first node that completes this task uploads its identification and the information of the relay, the processing times of the information once obtained are approximately 10 seconds
 Conclusions: It is concluded that the algorithm is capable of decentralizing the information collected by the servers in times equal to or greater than one minute, which can be very useful when saving information, in applications such as control by the network operator it falls short by having times greater than or equal to one minute, it should be taken into account that, for the development of a blockchain with a greater number of equipment, it is advisable to use machines with high levels of processing, much higher RAM memories, these characteristics allow the algorithm to run smoothly and in the shortest possible time.
 Financing: Universidad Distrital Francisco José de Caldas
Nowadays, digital cryptocurrencies are the most popular asset, especially for international exchanges. Bitcoin is the earliest cryptocurrency that succeeded in being used in financial transactions. Bitcoin stores the transactions in Blockchain technology. Bitcoin price has been unstable during the time from 0.5$ to about 60,000$ since 2010. Many efforts exist to predict Bitcoin value or its fluctuations using machine learning techniques. The price prediction is usually more challenging than fluctuations prediction, and its performance metrics are improved. This study introduces a methodology to predict Bitcoin price in a dataset, including four intervals to evaluate the proposed method in different situations. The experimental results show that the generalized linear model and Long Short-Term Memory (LSTM) were the best machine learning techniques. The proposed model outperforms the deep learning baseline model with about 18% and 20% relative improvement in mean absolute error and means absolute percentage error, respectively. Deep learning approaches have achieved much better results than other approaches due to the automatic selection of features. Compared to the results reported in the literature, the 1D-CNN+IndRNN proposed approach has reached 81% accuracy, with an 18% improvement. In the proposed approach, 1D-CNN is responsible for feature extraction and IndRNN is responsible for learning features in the form of time series.
The remarkable increase in the number of interconnected smart devices in today’s Internet of things networks introduces more challenges related to security, trust, and centralization, which require more effective solutions. Fortunately, blockchain technology has recently emerged as a potential rescuer for IoT-based solutions due to its decentralization and enhanced security features. It is usual for smart contracts to arise in handling and processing the generated data when IoT devices are combined with blockchain. However, blockchain and smart contracts need to interact with input data of the same level of trust to guarantee correct applications execution. This implies using oracles to provide trust compatibility between inserted information collected from IoT devices and blockchain and smart contracts. Therefore, this study adopts a methodology that was shaped based on current literature and design and experiments to provide a full narrative of the process of combining two of the most intriguing systems in today’s world of technology, namely, blockchain and IoT including a very important part of the comprehensive system, viz. blockchain oracle. Moreover, it was found that the literature lacks a complete view of the IoT-blockchain integration process that covers all its important and related aspects. Therefore, this work is an attempt to fill the gap in literature and contribute to the body of knowledge by surveying the literature about existing IoT-blockchain architectures and shed light on the role of blockchain in addressing IoT issues while demonstrating the concept of oracles as well as their functions in addition to the main operating blockchain oracles. Additionally, this work illustrates a CO2measuring use case where a smart contract is developed and tested as part of two proposed oracle-based designs. The obtained results demonstrate a full picture of a practical integrated IoT-blockchain system architecture.
We constructed a cryptographic interaction method museum art exchange protocol (MAXP) for museum digital collections on the basis of blockchain technology. Using our method, we build a digital collection exchange system on Ethereum to realize the digital collection’s online exchange between two museums. Compared with the traditional centralized collection digital resource database method, MAXP can avoid the security risks caused by subjective factors and force majeure factors in the exchange process of digital collections, such as hackers and network viruses. In our exchange system we have built, the expression of content covered by digital collections is more convenient, and copyright disputes can be quickly resolved. Concurrently, given the decentralization and anonymity of the blockchain, a regulatory mechanism has been added to MAXP to avoid fraud, illegal fundraising, money laundering and smuggling. The regulatory mechanism we constructed is a dual receiver public key encryption scheme based on the Diffie-Hellman algorithm and the SM2 elliptic curve public key encryption algorithm. The sender encrypts the collection data, and both receivers can decrypt the messages using their respective private keys. One of the receivers is the museum that obtained the collection information, and the other receiver is the regulator. These two receivers can decrypt simultaneously, and the regulator can regulate the information exchange on the blockchain. The Beijing Planetarium and the Beijing Museum of Natural History have completed the exchange of collections through the system we have built. The analysis results show that the regulatory scheme based on the exchange blockchain system of the museum’s digital collections proves to be feasible, with security and expansibility. Our new encrypted exchange management method of digital collections in museums can effectively promote the exchange of collections between museums, and is of great significance to the promotion of cultural heritage and the dissemination of scientific knowledge.
Open access
2 source records
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Jan 1, 2022·Proceedings of the International Conference on Information Economy, Data Modeling and Cloud Computing, ICIDC 2022, 17-19 June 2022, Qingdao, China
As a financial asset, bitcoin has attracted the attention of many financial financial advisors and investors. This paper aims to analyze the dynamic correlation between bitcoin and two important financial assets, i.e., crude oil and gold. This paper selects weekly data from January 2014 to April 202
Recently, there has been a remarkable amount of research being done in both, the fields of Blockchain and Internet of Things (IoT). Blockchain technology synergises well with IoT, solving key problems such as privacy, concerns with interoperability and security. However, the consensus mechanisms that allows trustless parties to maintain an agreement, the same algorithms that underpins cryptocurrency mining, are usually extremely computationally expensive, making implementation on low-power IoT devices difficult. More importantly, mining requires downloading and synchronizing hundred of gigabytes worth of blocks which is far beyond the capabilities of most IoT devices. In this paper, we present an efficient, portable and platform-agnostic cryptocurrency mining algorithm using the Stratum protocol to avoid downloading the entire blockchain. We implement the algorithm in four different platforms- PC, ESP32, an emulator and an old PlayStation Portable (PSP) to demonstrate that it is indeed possible for any device to mine cryptocurrencies with no assumptions except the ability to connect to the internet. To make sure of ease of portability on any platform and for reproducibility of the reported results we make the implementation publicly available with detailed instructions at: https://anonymous.4open.science/r/cryptominer.
This paper answers three of the most frequently asked questions received in response to the paper crypt into crypto. We also received questions about stable coins, alt coins and Non-Fungible Tokens (NFTs), which we’ll cover in a future paper.
Ting Li, Mudassir Khan, Ashutosh Sharma, Mohd Dilshad Ansari
Abstract An intelligent climate and watering agriculture system is presented that is controlled with Android application for smart water consumption considering small and medium ruler agricultural fields. Data privacy and security as a big challenge in current Internet of Things (IoT) applications, as with the increase in number of connecting devices, these devices are now more vulnerable to security threats. An intelligent fuzzy logic and blockchain technology is implemented for timely analysis and securing the network. The proposed design consists of various sensors that collect real-time data from environment and field such as temperature, soil moisture, light intensity, and humidity. The sensed field information is stored in IoT cloud platform, and after the analysis of entries, watering is scheduled by implementing the intelligent fuzzy logic and blockchain. The intelligent fuzzy logic based on different set of rules for making smart decisions to meet the watering requirements of plant and blockchain technology provides necessary security to the IoT-enabled system. The implementation of blockchain technology allows access only to the trusted devices and manages the network. From the experimentation, it is observed that the proposed system is highly scalable and secure. Multiple users at the same time can monitor and interact with the system remotely by using the proposed intelligent agricultural system. The decisions are taken by applying intelligent fuzzy logic based on input variables, and an alert is transmitted about watering requirements of a field to the user. The proposed system is capable of notifying users for turning water motor on and off. The experimental outcomes of the proposed system also reveal that it is an efficient and highly secure application, which is capable of handling the process of watering the plants.
Supply chain management frequently faced issues such as service redundancy, poor coordination between several departments, and lack of standardization as a result of the lack of transparency. Product counterfeiting is something which is very common now-a-days and it’s almost impossible to detect a counterfeit product just by looking at it. Counterfeiters cause significant challenges for legitimate firms, yet far too many people have no idea of the entire amount of counterfeit items’ influence on brands. There are several methods devised in the past to get away with this problem of product counterfeiting. The most popular methods are using RFID tags, Artificial Intelligence, QR code based systems, etc. But each of them had few disadvantages such as the QR code can be copied from a genuine product and placed on a fake product, artificial intelligence uses CNN and machine learning which needs heavy computational power and so on. The idea of this project is to improve detection of fake products by tracking its supply chain history. This is achieved with Blockchain technology which ensures the identification and traceability of real products throughout the supply chain. Blockchain based system, makes everything decentralized that may be accessed by several parties at the same time. One of its main advantages is that the recorded data is difficult to change without the consent of all parties concerned which makes the data extremely secure and protect from all vulnerabilities. This paper presents system designed using blockchain technology for detection of counterfeit products.
Digital currency is considered a form of currency which is used in the digital world such as digital forms or electronic devices. Several terms are synonyms for digital currency like digital money, electronic money, and cyber cash. Accurate prediction of the digital currency is an urgent necessity due to its impacts on the economic community. The electronic economy is very dangerous and must be approached with great caution, so as to avoid or minimize the risks that occur in such cases. Deep neural network (DNN) algorithm was improved to predict the Bitcoin price and then achieve the main goal of reducing financial risks to proceed with electronic business, and good estimation was achieved by using informative data such as transactions and currency return. The proposed method extracted features of related Bitcoin and used the informative ones. Transaction plan considered building nodes in terms of network. Development of deep learning algorithms opens the horizons for the development of electronic businesses that use digital currency. The proposed method achieved worthy results in terms of accuracy (53.4%) and correct prediction (MSE 1.02) and offers the prospect of other research in this area.
Potreba izdavanja maloprodajnog digitalnog novca od strane središnjih banaka za građanstvo sve se češće spominje kao nužnost, ponajprije zbog financijske inkluzije i smanjenja korištenja gotovine u proteklih nekoliko godina. Osim toga, središnje banke razmatraju korištenje tehnologije raspodijeljenih glavnih knjiga zbog ubrzavanja veleprodajnog plaćanja i trgovanja imovinom te izbjegavanja korištenja pružatelja usluga u tu svrhu. S tim u vidu niz središnjih banaka u svijetu u posljednjih nekoliko godina pokrenuo je projekte i pilote za testiranje DLT sustava u svrhu izdavanja, trgovanja i plaćanja CDBC-om. Ovaj rad donosi načine provedbe i zaključke iz najznačajnijih te najbolje dokumentiranih istraživanja i pilota provedenih u tu svrhu. Nakon toga, rad ukratko prolazi kroz specifičnosti najčešće korištenih DLT platformi u navedenim istraživanjima i pilotima. Naposljetku se rad bavi sigurnosnim aspektima izdavanja CDBC-a na DLT platformi, uključujući tehnike za unaprjeđenje povjerljivosti te moguće ranjivosti i programske greške pametnih ugovora, te najboljim praksama koje bi trebalo slijediti kako bi se osigurala sigurnost takvih sustava.