Blockchain Papers

Jingwen Zhang, Meiting Guo, Biyan Li, Ruimin Lu

No abstract is available for this record.

Isaiah Michael Omame, Juliet C. Alex-Nmecha

This chapter explored the concept and application of blockchain technology in libraries and information centers. Blockchain is one of the emerging technologies thriving in the fourth industrial revolution. It is the application of cryptography for creating a time-stamped, immutable, and dynamic database, distributed across nodes in a network. Although its emergence began with cryptocurrencies, advancement in this technology has given birth to a fourth generation of blockchain with industrial disruptive capabilities, cutting across various fields including library and information science. Accordingly, the application of blockchain in libraries and information centers was thoroughly examined. Specifically, the chapter underscored the application of blockchain in circulation services, collection development, storage and archiving of records, research data management, cataloging and classification, indexing and abstracting, digital first right (DFR), etc. Lastly, the merits and demerits of blockchain in libraries and information centers were furnished accordingly.

Gausiya Momin, Trupti Ingle, Vaishnavi Mirajkar, Anand Magar

Bitcoin is the most profitable in the cryptocurrency market. However, the prices of Bitcoin have highly fluctuated which makes them very difficult to predict. This research aims to discover the most efficient accuracy model to predict Bitcoin prices from various machine learning algorithms. Using one-minute interval trading data on the exchange website name is bit stamp from January 1, 2012, to January 8, 2018, some different regression models with sci-kit- learn and Keras libraries had experimented. The best results showed that the Mean Squared Error (MSE) was as low as 0.00002 and the R-Square (R2) was as high as 99.2 Percentage.

Liping Yang

In recent years, Bitcoin price prediction has attracted the interest of researchers and investors. However, the accuracy of previous studies is not well enough. Machine learning and deep learning methods have been proved to have strong prediction ability in this area. This paper proposed a method combined with Ensemble Empirical Mode Decomposition (EEMD) and a deep learning method called long short-term memory (LSTM) to research the problem of next-day Bitcoin price forecast.

Wenzheng Li, Mingsheng He, Sang Hai-quan

Starting from the basic concept of blockchain technology, the paper introduces the architecture of blockchain technology and the typical application of blockchain technology, and finally summarizes the challenges faced by blockchain technology, so as to provide some reference for the technological path innovation and data value reconstruction of deep integration of blockchain technology and various fields.

Mayuri Kulkarni, Khalid Alfatmi

Today's era is the smart era where every person is trying to execute the process smartly. Then how the education system will be in a back place. The online conduction of courses either by engaging online classes or by introducing the MOOC courses. This made learning easier but the major concerning issue in the online system is the conduction of online examination. This paper discusses the current treads in an online examination system as well as the new approach introduced for smooth conduction of online examination at any place anywhere at any time. This new approach is based on blockchain technology such as the smart contract. The smart contract will be helpful to the universities, institutes for conduction of online examination at any place at any time. This will keep regress monitoring on the examinee such as posture analysis as well as control panel processes. By using Compare Hash And Password() the authentication of the examinee password will be possible. GoCV package is used to authenticate the examinee through video capturing. dlib toolkit used to monitor the continuous posture of the examinee during the examination.

Zhihao Hao, Guancheng Wang, Dianhui Mao, Bob Zhang · 8 authors

As a part of food safety research, researches on food transactions safety has attracted increasing attention recently. Food choice is an important factor affecting food transactions safety: It can reflect consumer preferences and provide a basis for market regulation. Therefore, this paper proposes a food market regulation method based on blockchain and a deep learning model: Stacked autoencoders (SAEs). Blockchain is used to ensure the fairness of transactions and achieve transparency within the transaction process, thereby reducing the complexity of the trading environment. In order to enhance the usability, relevant Web pages have been developed to make it more friendly and conduct a security analysis for using blockchain. Consumers' reviews after the transactions are finished can be used to train SAEs in order to perform emotional tendencies predictions. Compared with different advanced models for predictions, the test results show that SAEs have a better performance. Furthermore, in order to provide a basis for the formulation of regulation strategies and its related policies, case studies of different traders and commodities have also been conducted, proving the effectiveness of the proposed method.

Zhenguang Liu, Peng Qian, Xiang Wang, Lei Zhu · 6 authors

Smart contracts hold digital coins worth billions of dollars, their security issues have drawn extensive attention in the past years. Towards smart contract vulnerability detection, conventional methods heavily rely on fixed expert rules, leading to low accuracy and poor scalability. Recent deep learning approaches alleviate this issue but fail to encode useful expert knowledge. In this paper, we explore combining deep learning with expert patterns in an explainable fashion. Specifically, we develop automatic tools to extract expert patterns from the source code. We then cast the code into a semantic graph to extract deep graph features. Thereafter, the global graph feature and local expert patterns are fused to cooperate and approach the final prediction, while yielding their interpretable weights. Experiments are conducted on all available smart contracts with source code in two platforms, Ethereum and VNT Chain. Empirically, our system significantly outperforms state-of-the-art methods. Our code is released.

Vishnu Gopal P G, George Mathew

The automotive industry is one of the lucrative markets in the world. With the advancement of the human lifestyle, everybody needs a car for their daily business. Before buying a used vehicle, the history of the vehicle should be verified. Currently, the history of the vehicle is obtained from the second-hand vehicle dealers. The data provided by dealers need not be correct because they do not maintain a proper record. The only way for the buyer is to believe the dealer and proceed with the deal. To resolve these problems, we proposed a distributed framework using blockchain technology to verify the history of the vehicles. The creator or deployer in the system is responsible for creating the registration of the vehicle with a unique VIN and it will be updated to the shared ledger. Then the vehicles are distributed to the trusted dealers in the system with the execution of the smart contract in the supply chain and finally released to the user. In the running phase, the proposed model records insurance, real-time data and maintenance services, and legal issues if any. The main advantage of the vehicle verification on the blockchain is that it can reduce the possibility of fraud and counterfeiting documents.

Shivam Singh, Gaurav Choudhary, Shishir Kumar Shandilya, Vikas Sihag · 5 authors

Since the invention of the Blockchain technology in 2008, it has been used in many domains to ensurehigh security and reliability of data, like from the use of Bitcoin to BaaS (Blockchain as a Service)which is a new blockchain trend and is a sort of cloud-based network for the organizations in thebusiness of building blockchain-based applications. This paper implements the combined approachof the decentralized Blockchain technology and the Supply Chain to establish that the end-users ina supply chain do not completely rely on the trader to establish that the product is counterfeited ornot and this can be done by authenticating the product at every stage in the Supply Chain by usingOne Time Passwords on the receiver’s mobile phone along with a deployed personnel who willbe responsible for assuring the quality of products. Furthermore, using this combined technical approachcan considerably lower down the cost of product quality assurance and this proposed systemwill track the authenticity of the product from its origin from the manufacturer to the end-user as well.

Grace. LK. Joshila, P. Asha, D. Usha Nandini, G. Kalaiarasi

This work aims to enhance the existing analysis made on bitcoin and predict the price of a Bitcoin by taking some parameters into consideration. After a huge research taking all the parameters which affect the price of the bitcoin value and identified daily changes in the bitcoin market. In this work all the data consists of different features over the past few year's daily records. This work is started by gaining all the information that all are needed to predict the bitcoin price. All the information was collected from the past few years and implemented the data into this work. In this work Support Vector Machine (SVM) algorithm is used as it gives much more accuracy better than previous algorithms. This study predicts sign of change in the price of bitcoin to the investors so that they can invest in this easily and also for the newcomers to this market or business.

Joshi Padma Narasimhachari, B Sharon Angel, B. Shwetha Bindu

The goal of this paper is to determine how well the Bitcoin volume per USD can be predicted. The Bitcoin Price Index contains price information. The work is accomplished to varying degrees of success by employing the Bayesian optimised recurrent neural network (RNN) and the Long Short Term Memory (LSTM) network. LSTM achieves a maximum accuracy of 52% and an RMSE of 8%. The popular ARIMA model for time series is used to compare with in-depth learning models. In-depth offline learning methods outperform ARIMA's poor performance forecast, as expected. Finally, both in-depth learning models are marked on both GPU and CPU, with GPU training time improving CPU implementation by 67.7%. Bitcoin, Deep Learning, GPU, Recurrent Neural Network, Long-Term Memory, ARIMA are index terms.

Alara Altay, Robert Learney, Firat Güder, Can Dincer

No abstract is available for this record.

Hyunjun Jung, Dongwon Jeong

Central Bank Digital Currency (CBDC) is a digital currency issued by a central bank. Motivated by the financial crisis and prospect of a cashless society, countries are researching CBDC. Recently, global consideration has been given to paying basic income to avoid consumer sentiment shrinkage and recession due to epidemics. CBDC is coming into the spotlight as the way to manage the public finance policy of nations comprehensively. CBDC is studied by many countries. The bank of the Bahamas released Sand Dollar. Each country’s central bank should consider the situation in which CBDCs are exchanged. The transaction of the CDDB is open data. Transaction registers CBDC exchange information of the central bank in the blockchain. Open data on currency exchange between countries will provide information on the flow of money between countries. This paper proposes a blockchain system and management method based on the ISO/IEC 11179 metadata registry for exchange between CBDCs that records transactions between registered CBDCs. Each country’s CBDC will have a different implementation and time of publication. We implement the blockchain system and experiment with the operation method, measuring the block generation time of blockchains using the proposed method.

Ajay Kumar, Kumar Abhishek, Pranav Nerurkar, Mohammad R. Khosravi · 6 authors

No abstract is available for this record.

Qiutong Guo, Shun Lei, Qing Ye, Zhiyang Fang

Bitcoin, one of the major cryptocurrencies, presents great opportunities and\nchallenges with its tremendous potential returns accompanying high risks. The\nhigh volatility of Bitcoin and the complex factors affecting them make the\nstudy of effective price forecasting methods of great practical importance to\nfinancial investors and researchers worldwide. In this paper, we propose a\nnovel approach called MRC-LSTM, which combines a Multi-scale Residual\nConvolutional neural network (MRC) and a Long Short-Term Memory (LSTM) to\nimplement Bitcoin closing price prediction. Specifically, the Multi-scale\nresidual module is based on one-dimensional convolution, which is not only\ncapable of adaptive detecting features of different time scales in multivariate\ntime series, but also enables the fusion of these features. LSTM has the\nability to learn long-term dependencies in series, which is widely used in\nfinancial time series forecasting. By mixing these two methods, the model is\nable to obtain highly expressive features and efficiently learn trends and\ninteractions of multivariate time series. In the study, the impact of external\nfactors such as macroeconomic variables and investor attention on the Bitcoin\nprice is considered in addition to the trading information of the Bitcoin\nmarket. We performed experiments to predict the daily closing price of Bitcoin\n(USD), and the experimental results show that MRC-LSTM significantly\noutperforms a variety of other network structures. Furthermore, we conduct\nadditional experiments on two other cryptocurrencies, Ethereum and Litecoin, to\nfurther confirm the effectiveness of the MRC-LSTM in short-term forecasting for\nmultivariate time series of cryptocurrencies.\n

Wenlong Yi, Ximeng Huang, Hua Yin, Shiming Dai

Abstract The traditional traceability system for agricultural product transactions is susceptible to information alteration and damage, which may lead to issues regarding product quality and food safety. And blockchain is a technology that boasts tamper-proofing, complete traceability and time-stamped storage. Considering the above, this study proposes a new blockchain-based approach to the quality management of agricultural products and introduces Solidity-based prototype smart contracts for agricultural product transactions. Test results show that the new traceability system can offer good performance in terms of data upload and block response time. The method proposed in this paper can be used as a solution for quality management of agricultural product that boasts whole process transparency, full-link reliability and joint supervision by all nodes in the system.

Harsha R. Vyawahare

Blockchain technology and distributed ledger has attracted massive attention and has triggered multiple projects in different industries. Blockchain is one of the most important technical invention in the recent years. It serves as an immutable ledger which allows transactions to take place in a decentralized manner. Blockchain based applications are springing up and are covering numerous fields including financial services, reputation system and Internet of Things (IoT), and so on. However, there are still many challenges of blockchain technology such as scalability and security problems waiting to be overcome. This paper presents a comprehensive overview on blockchain technology.

Monika di Angelo, Gernot Salzer

Crypto tokens are digital assets, similar to the coins of a cryptocurrency, except that they do not have their own blockchain or distributed ledger. Rather, they are built on top of an existing one. Areas of application include their use as means of investment, as a local currency in a decentralized application, as well as means for building an ecosystem or a community. Depending on the purpose, it is common to categorize tokens into payment tokens, security tokens and utility tokens. The distinction is of interest since in most jurisdictions, security tokens are more heavily regulated than other tokens. In this paper, we present a heuristic approach towards automatic detection of security tokens from blockchain data. To this end, we first discuss several methods for the (semi-) automatic identification of token contracts. Then we attempt to identify the token type. For our analysis, we examine both the deployed bytecode and the calls to token contracts that we extract from transaction data of the Ethereum main chain up to block 9500000, mined on Feb 17, 2020.

Remzi Gürfidan, Mevlüt Ersoy

The works produced within the music industry arepresented to their listeners on a digital platform,taking advantage of technology. The problems of thepast, such as pirated cassettes and CDs, have left theirplace to the problem of copyright protection on digitalplatforms today. Block chain is one of the mostreliable and preferred technologies in recent timesregarding data integrity and data security. In thisstudy, a blockzincir-based music wallet model isproposed for safe and legal listening of audio files.The user's selected audio files are converted intoblock chain structure using different techniques andalgorithms and are kept securely in the user's musicwallet. In the study, performance comparisons aremade with the proposed model application in terms ofthe length of time an ordinary audio player can addnew audio files to the list and the response times ofthe user. The findings suggest that the proposedmodel implementation has acceptable differences inperformance with an ordinary audio player.

Zeinab Shahbazi, Yung-Cheol Byun

The growth of data production in the manufacturing industry causes the monitoring system to become an essential concept for decision-making and management. The recent powerful technologies, such as the Internet of Things (IoT), which is sensor-based, can process suitable ways to monitor the manufacturing process. The proposed system in this research is the integration of IoT, Machine Learning (ML), and for monitoring the manufacturing system. The environmental data are collected from IoT sensors, including temperature, humidity, gyroscope, and accelerometer. The data types generated from sensors are unstructured, massive, and real-time. Various big data techniques are applied to further process of the data. The hybrid prediction model used in this system uses the Random Forest classification technique to remove the sensor data outliers and donate fault detection through the manufacturing system. The proposed system was evaluated for automotive manufacturing in South Korea. The technique applied in this system is used to secure and improve the data trust to avoid real data changes with fake data and system transactions. The results section provides the effectiveness of the proposed system compared to other approaches. Moreover, the hybrid prediction model provides an acceptable fault prediction than other inputs. The expected process from the proposed method is to enhance decision-making and reduce the faults through the manufacturing process.

Rohan Kumar C L, Ali M. Zain, Ali M. Zain, A V Prajwal · 5 authors

Fraudulent transactions have a huge impact on the economy and trust of a blockchain network. Consensus algorithms like proof of work or proof of stake can verify the validity of the transaction but not the nature of the users involved in the transactions or those who verify the transactions. This makes a blockchain network still vulnerable to fraudulent activities. One of the ways to eliminate fraud is by using machine learning techniques. Machine learning can be of supervised or unsupervised nature. In this paper, we use various supervised machine learning techniques to check for fraudulent and legitimate transactions. We also provide an extensive comparative study of various supervised machine learning techniques like decision trees, Naive Bayes, logistic regression, multilayer perceptron, and so on for the above task.

Bogahawatte W.W.M.K. A, Isuri Samanmali A.H. L, Perera K.D. M, Kavindi M.A. T · 6 authors

Cheque Truncation System (CTS) is an image-based cheque clearing framework used in Sri Lanka. This semi manual process has certain limitations and takes up to 3 working days to clear an inter-bank national cheque in Sri Lanka. Faced with the limitations of this system, cheque users and commercial banks must need an efficient and a secured system which can clear a cheque within less than 24 hours along with providing integrity and confidentiality to the system. This research portrays an automated solution, which is feasible for any commercial bank in Sri Lanka, to address above-mentioned issues. The proposed system is based on the blockchain where all banks willing to take an interest in this framework must connect the proposed blockchain based system to supply the quicker cheque clearance to its clients. Answers were proposed with a complete framework consisting of four main phases: (i) paper cheque clearing process, (ii) digital cheque issuing and clearing process, (iii) cheque fraud detection process and (iv) cheque transaction securing process. Python along with Flutter framework and Ethereum were the major technologies used for implementing the system. The proposed system is highly scalable as Ethereum provides added integrity to the system. The approach advocates the customer as well as the bank with much simpler and speedier cheque clearing process with increased security. It also contributes with a paper cheque fraud detection system with faster and reliable results. The proposed system provides benefits to the user as well as the bank by addressing the requirement of producing a secure, effective and environment friendly system. Finally, CheckMate permits a consistent stream of cheque clearance operation for the payer and the payee without any mediators.

S. Aarif Ahamed, Chandrasekar Ravi

Blockchain, a shared digital ledger, operates on a peer-to-peer network which is used for storing the transactions. Cryptocurrencies are used for transactions in blockchain. The most popular breed among cryptocurrency was bitcoin. Predicting the day-to-day value of bitcoin is a challenging task due to nonlinear and market volatility. There are many statistical methods and machine learning algorithms proposed to forecast the cost of bitcoin, but they were lacking to predict the correct result when the input data set is larger and has more noise. To handle large data set, a deep learning technique has been used. The deep learning algorithms, especially LSTM network, also have some drawbacks such as high computational time, inability to generate higher quality prediction result. To avoid these shortcomings and make LSTM a better model for bitcoin prediction, it is necessary to optimize LSTM network. This paper presents a comparative study of numerous optimized deep learning techniques to forecast the price of bitcoin.