Jan 1, 2020·Proceedings of the 2nd International Scientific and Practical Conference “Modern Management Trends and the Digital Economy: from Regional Development to Global Economic Growth” (MTDE 2020)
The subject of this paper is the review of the feasibility and advisability of using smart contracts based on blockchain technologies for the legal regulation of relations to provide the exclusive rights to means of individualization (trademarks). Blockchain technology is one of the very promising fields in the area of digitalization of the economy, which continues to be constantly and actively developed. The use of selfexecutable transactions by subjects of civil (commercial) circulation is becoming increasingly relevant. However, it should be noted that law enforcement practice has yet to be formed in this area. The existing civil (commercial) law has recently begun to develop provisions defining the specifics of the circulation of so-called digital assets, which may also include exclusive rights and the results of intellectual activity, including trademark rights. So far, the existing legal regulation regarding the rights to digital assets (tokens) and the introduction of smart contracts into the circulation can hardly be called sufficient. The paper discusses the rationale for the feasibility and advisability of using smart contracts for registration of license agreements regarding the use of trademark rights. As a result of the study and practical proposal, the authors focus attention on the need for an internationally unified approach to using blockchain technologies for fixing the transfer (assignment) of exclusive rights to the results of intellectual activity.
This survey develops a dual analysis, consisting, first, in a bibliometric examination and, second, in a close literature review of all the scientific production around cryptocurrencies conducted in economics so far. The aim of this paper is twofold. On the one hand, proposes a methodological hybrid approach to perform comprehensive literature reviews. On the other hand, we provide an updated state of the art in cryptocurrency economic literature. Our methodology emerges as relevant when the topic comprises a large number of papers, that make unrealistic to perform a detailed reading of all the papers. This dual perspective offers a full landscape of cryptocurrency economic research. Firstly, by means of the distant reading provided by machine learning bibliometric techniques, we are able to identify main topics, journals, key authors, and other macro aggregates. Secondly, based on the information provided by the previous stage, the traditional literature review provides a closer look at methodologies, data sources and other details of the papers. In this way, we offer a classification and analysis of the mounting research produced in a relative short time span.
In this paper, two univariate generalised autoregressive conditional heteroskedasticity (GARCH) option pricing models are applied to Bitcoin and the Cryptocurrency Index (CRIX). The first model is symmetric and the other takes asymmetric effects into account. Furthermore, the accuracy of the GARCH option pricing model applied to Bitcoin is tested. Empirical results indicate that asymmetry is not an important factor to consider when pricing options on Bitcoin or CRIX, this is consistent with findings in the literature. In addition, the GARCH option pricing model provides realistic price discovery within the bid-ask spreads suggested by the market.
In recent years, cryptocurrency, and its foundational technology, blockchain, have become increasingly popular.As the value of some cryptocurrencies has cataclysmically risen and fallen, many people are curious about what the future of cryptocurrency prices look like, and what means are available to predict that future.We conducted experiments on three RNN models to determine whether one performed better than the others when predicting the price of Bitcoin, Ripple, and Litecoin.We also collected Google trends data for these three cryptocurrencies and ran additional experiments to explore whether this additional data significantly boosted the prediction accuracy of these models.After performing all our experiments, we found that among the three RNN models we tested, none performed significantly better than the others.Additionally, we concluded that supplementing the three models with Google trends data did not significantly boost the prediction accuracy of the models.We discuss implications of this research in the paper.
The objective of the study is to determine whether the Bitcoin forks have produced significant effects on the cryptocurrency market. The event study methodology is used in this paper in order to determine the statistical significance of the abnormal return of leading cryptocurrencies after three Bitcoin forks. The forks were viewed as three isolated events, with the estimations windows and the event windows constructed separately for each of them. There were statistically significant negative effects related to the creation of Bitcoin Gold and Bitcoin SV. Contrary to expectations, there was no statistically important effect throught out the most famous Bitcoin forking and emergence of Bitcoin Cash. Although cryptocurrencies are a current topic, the literature lacks quantitative research dealing with price changes. Without quantitative analysis, it is difficult to conclude whether the return change is a consequence of a statistically significant event The analysis would therefore provide the tool to determine the statistical significance of their impact on the market. A small number of observed cryptocurrencies is the main limitation of this research. Future researches could cover a wider scope of the market and include other famous cases of forking, for example, the Ethereum forks.
Distributed ledger technology is the driving force behind the blockchain technology and is proving its usefulness in various types of transaction processing systems. Fast, secure, reliable and efficient transactions are the key features of the blockchain based applications. A suitable or optimal size of block used by an application is dependent on the number of transactions in each block. Block size optimization is an important issue for any blockchain based application as it directly affects the performance of the application as scalability bottlenecks could prevent higher throughput and cause congestion. A larger block size will require higher transmission time compared to the smaller block size. A smaller block is more efficient but building too small a block will require higher block composition time to clear all the transactions. Both performance factors are contradictory to each other. An efficient blockchain network requires a suitable block size that demands lesser transmission time and block composition time. This paper proposes meta-heuristic algorithm based techniques for finding the suitable block size. It uses meta-heuristic algorithms to find the optimal number of transaction in each block. These algorithms are multi-objective particle swarm optimization and strength Pareto evolutionary algorithm. Experimental results reveal that the suitable block size is 213 transactions per block. Since size of a transaction is taken as 1.2 Kb, hence the results show that an optimal block size is of 255 Kbytes when network bandwidth of miners varies from 250 kbps to 1200 kbps. This shall achieve lower block transmission time and block composition time.
Skills gap between company needs and competencies occupied by the workforce can be the source of inefficiencies. The purpose of this research is to develop a blockchain-based human resource (HR) framework to match the needs from the company and workforce competencies This framework will help Corporate Training Centre to standardized the competencies which then used by HR Department to develop the training material. In order to get valid information regarding skills that are needed from the company, we develop a prototype based on Blockchain. Hence, blockchain-based HRM is built to improve the quality of workforce competency in an organization. The current organizations are struggling to fulfil the needs of the workforce in accordance with industry quality standards. Therefore, this will help all parties to create a consensus between the needs of the industry with the labour market. Corporate Training Centre through the competent institution will be the mediator or intermediary to unite the information from companies, training institutions, and Professional Certification Institutions. As a result, in the long term, the needs of the workforce with the qualification required by the company in such industries will always fit the current situation. Blockchain helps to process the information and data needed by each party so that the connection between parties will be assisted efficiently and effectively.
Electronic medical data have significant advantages over paper-based patient records when it comes to storage and retrieval. However, most existing medical data sharing schemes have security risks, such as being prone to data tampering and forgery, and do not support the ability to verify the authenticity of the data source. To solve these problems, we propose a medical data sharing scheme based on attribute cryptosystem and blockchain technology in this paper. First, the encrypted medical data are stored in the cloud, and the storage address and medical-related information are written into the blockchain, which can ensure efficient storage and eliminate the possibility of irreversible modification of the data. Second, the proposed scheme combines attribute-based encryption (ABE) and attribute-based signature (ABS), which achieves the sharing of medical data in many-to-many communications. The ABE achieves data privacy and fine-grained access control, and the ABS verifies the authenticity of the source of the medical data while protecting the signer's identity. Moreover, the data user outsources most of the operations of medical data ciphertext decryption to the cloud service provider (CSP), which can greatly reduce the computational burden. Finally, results of the analysis show that our scheme satisfies the requirements for confidentiality and unforgeability in the random oracle model, and that the proposed scheme offers higher computational performance than other similar schemes.
Blockchain has the potential to accelerate the deployment of emissions trading systems (ETS) worldwide and improve upon the efficiency of existing systems. In this paper, we present a model for a permissioned blockchain implementation based on the successful European Union (EU) ETS and discuss its potential advantages over existing technology. We propose an ETS model that is both backwards compatible and future-proof, characterised by interconnectedness, transparency, tamper-resistance and high liquidity. Further, we identify key challenges to implementation of a blockchain ETS, as well as areas of future work required to enable a fully-decentralised blockchain ETS.
With the decreasing reserves of fossil energy and the increasing capacity of renewable energy generation, the scale of microgrid based on distributed generations is expanding. However, more operation data and transaction information of microgrids will also bring several problems: the sufficient capacity of the server in the central management needed, the crises of trust among members, the transparency of transaction information and the confidentiality of data storage. In this paper, blockchain technology is used to deal with these problems as distributed data storage technology. A double-layer framework of energy transactions based on blockchain in multi-microgrids is proposed to provide decentralized trading, information transparency and mutual trust system of each node in the trading market. The central node within the microgrid collects the demand information of the trading market in lower layer and sends them to the trading market of multi-microgrids in higher layer to seek the energy transaction. The continuous double auction mechanism is used in the trading market to guarantee free and fair transactions among nodes. The proposed transaction framework effectively reduces the transaction volume with the main grid which improves energy utilization. Comprehensive simulation results are presented to prove the feasibility of the proposed transaction framework.
Takuzu and Juosan are logical Nikoli games in the spirit of Sudoku. In Takuzu, a grid must be filled with 0’s and 1’s under specific constraints. In Juosan, the grid must be filled with vertical and horizontal dashes with specific constraints. We give physical algorithms using cards to realize zero-knowledge proofs for those games. The goal is to allow a player to show that he/she has the solution without revealing it. Previous work on Takuzu showed a protocol with multiple instances needed. We propose two improvements: only one instance needed and a soundness proof. We also propose a similar proof for Juosan game.
This work provides a short but technical introduction to the main building blocks of a blockchain. It argues that a blockchain is not a revolutionary technology but rather a clever combination of three fields: cryptography, decentralization and game theory. In addition, it summaries the differences between a public, private and federate blockchain model and the two prominent consensus mechanism Proof-of-Work (POW) and Proof-of-Stake (POS).
Decentralization reform includes a number of other reforms, including education reform, which is one of the most important. The main results of the reform of secondary education are: the introduction of the New Ukrainian School, the change of the system of management and financing of institutions, as well as the creation of educational districts and basic schools. Optimization of the network of general secondary education institutions (GSEI) is an important element on the way to quality educational services. When optimizing the network of GSEI, it is necessary to take into account a number of factors, among which the most important are: quality of transport routes, distance of transportation of students, number of students who will need transportation, material and technical base of institutions (availability of computer classes, gym and classrooms), staffing of the library fund, the quality of teaching staff and others. As of September,1 2019 there were 402 educational establishments with 105483 students in Chernivtsi oblast. Currently, 14 basic educational institutions and 22 branches have been established in Chernivtsi oblast. Among the raions, the largest number of basic educational institutions have been established in Hertsa raion. No basic educational institutions have been established in Putyla and Hlyboka raions, as well as in the city of Chernivtsi. A total of 7,354 students study in basic institutions and their branches. To test the method of optimization of the network of GSEI, we chose Kitsman raion of Chernivtsi oblast, which is optimal for the oblast and Ukraine in general on various indicators: demographic characteristics; the size of the raion; features of the transport network; the number of GSEI and students enrolled in them and others. Currently, there are 2 basic institutions and 2 branches in Kitsman raion. After analyzing a number of indicators (level of institutions, peculiarities of the institution location, number of students who will need transportation, area of student premises, material and technical base, staffing of the library fund, qualification of pedagogical staff and quality of graduates’ knowledge), we propose to optimize Kitsman raion network of GSEI, by means of establishing of 12 educational districts, 11 basic institutions and 26 branches. In most educational districts of Kitsman raion we propose to create one basic institution, only in Kitsman educational district – two, and in Shypyntsi and Luzhany educational districts not to create any basic institution at the moment.
Guglielmo Maria Caporale, Woo-Young Kang, Fabio Spagnolo, Nicola Spagnolo
This paper provides some comprehensive evidence on the effects of cyber-attacks on the returns, realized volatility and trading volume of five of the main cryptocurrencies (Bitcoin, Ethereum, Litecoin, XRP and Stellar) in 99 developed and developing countries. More specifically, it investigates the effects of four different types of cyber-attacks (cyber-crime, cyber-espionage, hacktivism and cyber-warfare) on four target sectors (government, industry, finance and cryptocurrency exchange). We find that in the US cyber security firms tend to overreact to cyberattacks affecting cryptocurrencies and more wealth is spent on cyber security compared to other countries. Both hacktivism and cyber-warfare have a significant impact on cryptocurrencies. Cryptocurrency exchanges are more vulnerable to cyber-attacks in non-US countries and in the presence of high economic uncertainty and less so if the industry sector is already being targeted. Finally, cryptocurrency investors exhibit risk-loving behaviour when the hash rate and cryptocurrency returns increase and risk-averse one when cyber-attacks target the financial and industry sectors and economic uncertainty is high.
Muhammad Abubakr Naeem, Kashif Saleem, Sheraz Ahmed, Naeem Muhammad · 5 authors
We explore extreme return-volumes dependence among different cryptocurrencies such as Bitcoin, Ethereum, Ripple, and Litecoin by using the Copula approach. We use Student-t, Frank, Clayton, Survival Clayton, Gumbel, and SJC copulas. We filter out margins by using the EGARCH model for return series and GARCH model for volume series. Evidence of significant symmetric dependence between return-volume is not found due to insignificance of student-t and Frank copula parameters. In a return-volume relationship, coefficients of lower tail dependence are significant for Bitcoin, Ripple, and Litecoin which means that low returns are followed by low volumes. Lower tail dependence for the return-volume relationship is stronger than the upper tail dependence for Bitcoin, Ripple, and Litecoin. Moreover, for negative return-volume, left tail dependence coefficients are significant for Ripple and Litecoin, which means that high returns are followed by low volumes for Ripple and Litecoin. Our investigation shows that investors (buyer or seller) are very careful in extreme market conditions for both Ripple and Litecoin. Extreme upper tail and lower tail dependence coefficients are insignificant for Ethereum.
The increasing electric vehicle (EV) penetration in a distribution network triggers the need for EV charging coordination. This paper firstly proposes a hierarchical EV charging coordination model and an algorithm based on Lagrangian relaxation. A barrier to the implementation of the coordination algorithm is that there usually does not exist a reliable coordinator of charging stations. This paper shows that an unreliable coordinator may collude with some charging stations and behave dishonestly by disobeying the coordination algorithm. Thus, the collusion coalition can gain more profits while lowering the profits of others and the total social welfare. To provide reliable coordination of charging stations, a novel blockchain-based coordination platform via Ethereum is established, including a coordination structure and a smart contract. A mathematical analysis is given to show that the proposed platform can mitigate the collusion behaviors in the coordination. Simulation results show the consequence of collusion and how blockchain can prevent the collusion.