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Information
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Blockchain and Smart Contract Technologies
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Blockchain and Smart Contract Technologies

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Processes".

Deadline for manuscript submissions: closed (30 January 2020) | Viewed by 26401

Special Issue Editors

Assoc. Prof. Dr. Saulius Masteika

E-Mail Website
Guest Editor
Institute of Social Sciences and Applied Informatics, Kaunas Faculty, Vilnius University, Vilnius, Lithuania
Interests: computational finance and engineering; cryptocurrencies; blockchain; technical analysis and high frequency trading
Prof. Erich Schweighofer

E-Mail Website
Guest Editor
Section for Legal Informatics, University of Vienna, Wien, Austria
Interests: legal informatics; international law
Dr. Piotr Stolarski

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Guest Editor
Department of Information Systems, Poznań University of Economics, Poznań, Poland
Interests: cryptocurrencies; blockchain; natural language processing; knowledge representation; information systems security

Special Issue Information

Dear Colleagues,

The 2nd International Workshop on Blockchain and Smart Contract Technologies (BSCT 2019) will be held in June 2019 in Seville in conjunction with the 22nd International Conference on Business Information Systems. The goal of the workshop is to bring together participants both from academia and business practice working on innovative ideas and the application of blockchain and smart contracts. The workshop calls for sharing research experiences and knowledge related to the adoption of distributed ledgers as well as associated technologies. We invite papers that provide methodologies, techniques, or empirical evidence of vital theoretical and practical aspects of blockchain-based architectures and share views on challenges that limit the broader use and implementation of such decentralized systems including various legal, organizational, or socio-economic factors.

This Special Issue of Information intends to attract submissions on such exemplary topics as consensus protocols, information security, (pseudo) anonymity, distributed applications, initial coin offering or token economy, etc. There is no Article Processing Charge for extended versions of the accepted papers at the BSCT 2019 workshop.

Assoc. Prof. Dr. Saulius Masteika
Prof. Erich Schweighofer
Dr. Piotr Stolarski
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • blockchain cconsensus protocols
  • distributed ledger analytics
  • macro and microeconomic models for cryptocurrencies
  • legal and financial aspects of ICO
  • smart contracts development and testing
  • performance measures of blockchain
  • blockchain and big data
  • identity management in distributed or anonymous environments
  • blockchain vulnerabilities and attacks schemes
  • DAO and governance
  • blockchain and data protection
  • blockchain industrial applications (fintech, insurtech, IoT, and asset digitization)

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

26 pages, 2019 KiB  
Article
Cryptocurrencies Perception Using Wikipedia and Google Trends
by Piotr Stolarski, Włodzimierz Lewoniewski and Witold Abramowicz
Information 2020, 11(4), 234; https://doi.org/10.3390/info11040234 - 24 Apr 2020
Cited by 9 | Viewed by 12487
Abstract
In this research we presented different approaches to investigate the possible relationships between the largest crowd-based knowledge source and the market potential of particular cryptocurrencies. Identification of such relations is crucial because their existence may be used to create a broad spectrum of [...] Read more.
In this research we presented different approaches to investigate the possible relationships between the largest crowd-based knowledge source and the market potential of particular cryptocurrencies. Identification of such relations is crucial because their existence may be used to create a broad spectrum of analyses and reports about cryptocurrency projects and to obtain a comprehensive outlook of the blockchain domain. The activities on the blockchain reach different levels of anonymity which renders them hard objects of studies. In particular, the standard tools used to characterize social trends and variables that describe cryptocurrencies’ situations are unsuitable to be used in the environment that extensively employs cryptographic techniques to hide real users. The employment of Wikipedia to trace crypto assets value need examination because the portal allows gathering of different opinions—content of the articles is edited by a group of people. Consequently, the information can be more attractive and useful for the readers than in case of non-collaborative sources of information. Wikipedia Articles often appears in the premium position of such search engines as Google, Bing, Yahoo and others. One may expect different demand on information about particular cryptocurrency depending on the different events (e.g., sharp fluctuations of price). Wikipedia offers only information about cryptocurrencies that are important from the point of view of language community of the users in Wikipedia. This “filter” helps to better identify those cryptocurrencies that have a significant influence on the regional markets. The models encompass linkages between different variables and properties. In one model cryptocurrency projects are ranked with the means of articles sentiment and quality. In another model, Wikipedia visits are linked to cryptocurrencies’ popularity. Additionally, the interactions between information demand in different Wikipedia language versions are elaborated. They are used to assess the geographical esteem of certain crypto coins. The information about the legal status of cryptocurrency technologies in different states that are offered by Wikipedia is used in another proposed model. It allows assessment of the adoption of cryptocurrencies in a given legislature. Finally, a model is developed that joins Wikipedia articles editions and deletions with the social sentiment towards particular cryptocurrency projects. The mentioned analytical purposes that permit assessment of the popularity of blockchain technologies in different local communities are not the only results of the paper. The models can show which country has the biggest demand on particular cryptocurrencies, such as Bitcoin, Ethereum, Ripple, Bitcoin Cash, Monero, Litecoin, Dogecoin and others. Full article
(This article belongs to the Special Issue Blockchain and Smart Contract Technologies)
►▼ Show Figures

Figure 1

Figure 1
<p>Number of publications in subsequent years (logarithmic scale).</p> Full article ">Figure 2
<p>Popularity of cryptocurrencies on English (EN) Wikipedia and the timeline of the article creation events.</p> Full article ">Figure 3
<p>Comparison of Wikipedia, Google Trends and Internet rankings of Bitcoin visitors.</p> Full article ">Figure 4
<p>Ratio of jurisdictions in which Bitcoin (and other crypto coins) are permitted or forbidden to use.</p> Full article ">Figure 5
<p>Venn diagram of sets of cryptocurrency-related articles in selected editions of Wikipedia. See <a href="http://data.lewoniewski.info/crypto/" target="_blank">http://data.lewoniewski.info/crypto/</a> for interactive version.</p> Full article ">
12 pages, 1296 KiB  
Article
Execution Plan Control in Dynamic Coalition of Robots with Smart Contracts and Blockchain
by Nikolay Teslya and Semyon Potryasaev
Information 2020, 11(1), 28; https://doi.org/10.3390/info11010028 - 4 Jan 2020
Cited by 7 | Viewed by 2915
Abstract
The paper presents an approach of the blockchain and smart contracts utilization for dynamic robot coalition creation. The coalition is forming for solving complex tasks in industry applications that requires sequential united actions from the several robots. The main idea is that the [...] Read more.
The paper presents an approach of the blockchain and smart contracts utilization for dynamic robot coalition creation. The coalition is forming for solving complex tasks in industry applications that requires sequential united actions from the several robots. The main idea is that the process is split into two stages: scheduling and dynamic execution. On the scheduling stage, the coalition is defined based on the correlation of existing tasks and robot equipment, and the execution plan is formed and stored in smart contracts. The second stage is the plan execution. During this stage, smart contract controls how each robot solves its sub-task and whether it solves the sub-task due to the planned moment of time. In case of any deviation from the plan, smart contacts will provide a solution for returning to the plan or for changing the coalition composition with new robots and an execution plan. The prototype for execution control system has been developed based on the Hyperledger Fabric platform. Full article
(This article belongs to the Special Issue Blockchain and Smart Contract Technologies)
►▼ Show Figures

Figure 1

Figure 1
<p>Example of robot ontology.</p> Full article ">Figure 2
<p>Scheme of joint task solving.</p> Full article ">Figure 3
<p>Fragment of the manufacturing process in BPMN.</p> Full article ">Figure 4
<p>Framework for robot coalition interaction.</p> Full article ">Figure 5
<p>Process for adjustment.</p> Full article ">Figure 6
<p>Process for rescheduling.</p> Full article ">
18 pages, 902 KiB  
Article
Studying Transaction Fees in the Bitcoin Blockchain with Probabilistic Logic Programming
by Damiano Azzolini, Fabrizio Riguzzi and Evelina Lamma
Information 2019, 10(11), 335; https://doi.org/10.3390/info10110335 - 30 Oct 2019
Cited by 18 | Viewed by 6560
Abstract
In Bitcoin, if a miner is able to solve a computationally hard problem called proof of work, it will receive an amount of bitcoin as a reward which is the sum of the fees for the transactions included in a block plus an [...] Read more.
In Bitcoin, if a miner is able to solve a computationally hard problem called proof of work, it will receive an amount of bitcoin as a reward which is the sum of the fees for the transactions included in a block plus an amount inversely proportional to the number of blocks discovered so far. At the moment of writing, the block reward is several orders of magnitude greater than the sum of transaction fees. Usually, miners try to collect the largest reward by including transactions associated with high fees. The main purpose of transaction fees is to prevent network spamming. However, they are also used to prioritize transactions. In order to use the minimum amount of fees, users usually have to find a compromise between fees and urgency of a transaction. In this paper, we develop a probabilistic logic model to experimentally analyze how fees affect confirmation time and miner’s revenue and to predict if an increase of average fees will generate a situation when the miner gets more reward by not following the protocol. Full article
(This article belongs to the Special Issue Blockchain and Smart Contract Technologies)
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Figure 1

Figure 1
<p>The graph relates the average Bitcoin fee rate and the miner profit. Data are computed by setting the parameters for the Gaussian distribution for block size as <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> <mn>700</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msup> <mi>σ</mi> <mn>2</mn> </msup> <mo>=</mo> <mn>25</mn> </mrow> </semantics></math> and for rewards as <math display="inline"><semantics> <mrow> <msup> <mi>σ</mi> <mn>2</mn> </msup> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mi>μ</mi> </semantics></math> according to the legend. The dashed lines represent the values computed with <tt>mc_expectation/3</tt> (without observations).</p> Full article ">Figure 2
<p>The graph shows how an increasing size of a block influences the average profit obtained from fees. The parameters for the distribution for block size are <math display="inline"><semantics> <mrow> <msup> <mi>σ</mi> <mn>2</mn> </msup> <mo>=</mo> <mn>25</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mi>μ</mi> </semantics></math> variable and for the reward <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> <mn>17</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msup> <mi>σ</mi> <mn>2</mn> </msup> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>. Dashed lines represent the values computed without observations.</p> Full article ">Figure 3
<p>The graph shows how transaction fees influence the probability of confirmation in <span class="html-italic">N</span> blocks. We selected a value of fee rate (<math display="inline"><semantics> <mi>ϕ</mi> </semantics></math>) for the transaction under consideration and then computed how probability changes according observed fee rate.</p> Full article ">Figure 4
<p>Results for threshold experiment. The first two graphs are obtained by considering that a successful attack was due to the fact that the attacker was able to create a chain with a length equal to the main chain. The last two (bottom) consider a successful attack a private chain one block longer than the main chain. Graphs on the left have <math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, for the graphs on the right, <math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>.</p> Full article ">Figure 5
<p>Results for value test. Values on the left are computed with <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> <mn>0.15</mn> </mrow> </semantics></math> while graphs on the right have <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> <mn>0.25</mn> </mrow> </semantics></math>. As before, graphs at the top contain values computed considering a successful attack, the creation of a private chain with length equal to the main chain. At the bottom, the attacker succeeds only if its private chain is one block longer than the honest one.</p> Full article ">Figure 6
<p>The graphs show that positive expected values can be obtained only with significant values of <math display="inline"><semantics> <mi>σ</mi> </semantics></math> and <math display="inline"><semantics> <mi>β</mi> </semantics></math>.</p> Full article ">
30 pages, 1821 KiB  
Article
Analysis and Comparison of Bitcoin and S and P 500 Market Features Using HMMs and HSMMs †
by David Suda and Luke Spiteri
Information 2019, 10(10), 322; https://doi.org/10.3390/info10100322 - 18 Oct 2019
Cited by 1 | Viewed by 3522
Abstract
We implement hidden Markov models (HMMs) and hidden semi-Markov models (HSMMs) on Bitcoin/US dollar (BTC/USD) with the aim of market phase detection. We make analogous comparisons to Standard and Poor’s 500 (S and P 500), a benchmark traditional stock index and a protagonist [...] Read more.
We implement hidden Markov models (HMMs) and hidden semi-Markov models (HSMMs) on Bitcoin/US dollar (BTC/USD) with the aim of market phase detection. We make analogous comparisons to Standard and Poor’s 500 (S and P 500), a benchmark traditional stock index and a protagonist of several studies in finance. Popular labels given to market phases are “bull”, “bear”, “correction”, and “rally”. In the first part, we fit HMMs and HSMMs and look at the evolution of hidden state parameters and state persistence parameters over time to ensure that states are correctly classified in terms of market phase labels. We conclude that our modelling approaches yield positive results in both BTC/USD and the S and P 500, and both are best modelled via four-state HSMMs. However, the two assets show different regime volatility and persistence patterns—BTC/USD has volatile bull and bear states and generally weak state persistence, while the S and P 500 shows lower volatility on the bull states and stronger state persistence. In the second part, we put our models to the test of detecting different market phases by devising investment strategies that aim to be more profitable on unseen data in comparison to a buy-and-hold approach. In both cases, for select investment strategies, four-state HSMMs are also the most profitable and significantly outperform the buy-and-hold strategy. Full article
(This article belongs to the Special Issue Blockchain and Smart Contract Technologies)
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Figure 1

Figure 1
<p>Daily closing prices (top) and log returns (bottom) of Bitcoin/US dollar (BTC/USD) from 1 January 2016 to 28 January 2019.</p> Full article ">Figure 2
<p>State dwell-time distributions for the homogeneous four-state normal-HSMM (red) and for the stationary four-state normal-HMM (black). Figure reproduced from [<a href="#B1-information-10-00322" class="html-bibr">1</a>].</p> Full article ">Figure 3
<p>Expanding window: state persistence for the homogeneous four-state normal-HMM on BTC/USD daily log returns.</p> Full article ">Figure 4
<p>Expanding window: state-dependent means and volatilities for the homogeneous four-state normal-HMM on BTC/USD daily log returns.</p> Full article ">Figure 4 Cont.
<p>Expanding window: state-dependent means and volatilities for the homogeneous four-state normal-HMM on BTC/USD daily log returns.</p> Full article ">Figure 5
<p>Expanding window: four-state normal HMM filtering via the Viterbi algorithm on BTC/USD. Upper figure: Colours vary by the value of the state-dependent mean (see legend), and the larger the state-dependent volatility, the larger the dot size. Lower figure: The plot indicates the inferred state at each time-point, and the colour code also indicates the value of the state-dependent mean as per the legend. Figure reproduced from [<a href="#B1-information-10-00322" class="html-bibr">1</a>].</p> Full article ">Figure 6
<p>Expanding window: dwell-time parameters for the homogeneous four-state normal HSMM on BTC/USD daily log returns.</p> Full article ">Figure 7
<p>Expanding window: state-dependent means and volatilities for the homogeneous four-state normal HSMM on BTC/USD daily log returns.</p> Full article ">Figure 8
<p>Expanding window: four-state normal HSMM filtering via the Viterbi algorithm on BTC/USD. Upper figure: Colours vary by the value of the state-dependent mean (see legend), and the larger the state-dependent volatility, the larger the dot size. Lower figure: The plot indicates the inferred state at each time point, and the colour code also indicates the value of the state-dependent mean as per the legend. Figure reproduced from [<a href="#B1-information-10-00322" class="html-bibr">1</a>].</p> Full article ">Figure 9
<p>Daily close prices (top) and log returns (bottom) of the S and P 500 from 1 January 2000 to 28 January 2019.</p> Full article ">Figure 10
<p>State dwell-time distributions for the homogeneous four-state normal HSMM (red) and for the stationary four-state normal HMM (black). Figure reproduced from [<a href="#B1-information-10-00322" class="html-bibr">1</a>].</p> Full article ">Figure 11
<p>Expanding window: state persistence for the homogeneous four-state normal HMM on S and P 500 daily log returns.</p> Full article ">Figure 12
<p>Expanding window: state-dependent means and volatilities for the homogeneous four-state normal HMM on S and P 500 daily log returns.</p> Full article ">Figure 12 Cont.
<p>Expanding window: state-dependent means and volatilities for the homogeneous four-state normal HMM on S and P 500 daily log returns.</p> Full article ">Figure 13
<p>Expanding window: four-state normal HMM filtering via the Viterbi algorithm on the S and P 500 Index. Upper figure: Colours vary by the value of the state-dependent mean (see legend), and the larger the state-dependent volatility, the larger the dot size. Lower figure: The plot indicates the inferred state at each time-point, and the colour code also indicates the value of the state-dependent mean as per the legend. Figure reproduced from [<a href="#B1-information-10-00322" class="html-bibr">1</a>].</p> Full article ">Figure 14
<p>Expanding window: dwell-time parameters for the homogeneous four-state normal HSMM on S and P 500 daily log returns.</p> Full article ">Figure 15
<p>Expanding window: state-dependent means and volatilities for the homogeneous four-state normal HSMM on S and P 500 daily log returns.</p> Full article ">Figure 16
<p>Expanding window: four-state normal HSMM filtering via the Viterbi algorithm on the S and P 500 Index. Upper figure: Colours vary by the value of the state-dependent mean (see legend), and the larger the state-dependent volatility, the larger the dot size. Lower figure: The plot indicates the inferred state at each time-point, and the colour code also indicates the value of the state-dependent mean as per the legend. Figure reproduced from [<a href="#B1-information-10-00322" class="html-bibr">1</a>].</p> Full article ">Figure 17
<p>HSMM-based best strategy for BTC/USD actions. Colours vary by the value of the state-dependent mean (see legend), and buy and sell signals are denoted in the figure.</p> Full article ">Figure 18
<p>Best HSMM-based strategy for S and P 500 Index actions. Colours vary by the value of the state-dependent mean (see legend), and buy and sell signals are denoted in the figure.</p> Full article ">
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