Omar Farouk Al Mashhour, Ahmad Shamsul Abd Aziz, Nor Azlina Mohd Noor
As one of the fundamental technologies of blockchain, smart contracts have become one of popular and indispensable for certain businesses. Smart contracts have been introduced by Nick Szabo in 1997. However, the actual implementation was after the introduction of Blockchain, in particular, Ethereum. The enforceability of these contracts has always been dubious due to their unique characteristics and their considerable potential for abuse in criminal activities. The uncertainty stems mostly from these unique qualities and their connection to blockchain technology and cryptocurrencies. Consequently, determining the legal approach governing smart contracts is vital to ensuring the legal validity and the legal viability of such technology. This paper aims to provide a thorough overview and synthesis of prior literature on smart contracts and related regulatory implications (2016-2021). A thorough literature search of Scopus, Web of Science (WoS), Springer, and Emerald was done using two different combinations of key terms: “Smart Contracts AND Legality” and “Smart Legal Contracts AND Regulatory”. The final relevant articles were 22 articles found from 2016 to 2021. The narrowing of the literature search shows that smart contracts are a relatively new area that must be legally studied rather than merely focusing on the technical aspects. Ignoring the legal aspect would create uncertainly and inconsistency which might result in various legal dispute due to the nature of smart contracts. the novelty and lack of research represent one of the most major limitations of this research. However, it is crucial to highlight that the number of scientific journals published has increased significantly over the past five years, particularly in 2019 and 2020. This paper offers some guiding principles for academics, legal advisors, practitioners, and policymakers for their future research on smart contracts from a legal perspective.
Muhammad Rafi, Qublai Khan Ali Mirza, Muhammad Izaan Sohail, Maria Aliasghar · 6 authors
A cryptocurrency is a digitized, encrypted, and decentralized virtual currency, which is impossible to counterfeit or double-spend. It is one of the very popular investment instruments and traded in blockchain based crypto exchanges on ever growing volume. It is quite volatile due to imbalance of supply and demand, government regulations, investor sentiment and above all media hype. Cryptocurrency price forecasting is an active area of research and several approaches have been proposed recently. This study proposed a price forecasting model based on three vital characteristics (i) a feature selection and weighting approach based on Mean Decrease Impurity(MDI) features. (ii) Bi-directional LSTM and (iii) with a trend preserving model bias correction (CUSUM control charts for monitoring the model performance over time) to forecast Bitcoin and Ethereum values for long and short term spans. The data for both currencies were analyzed in three different intervals: (i) April 01, 2013 to April 01, 2016 (ii) April 01, 2013 to April 01, 2017 and (iii) April 01, 2013 to December 31, 2019. Extensive series of experiments were performed and evaluated on Root Mean Square Errors (RMSE). Comparing with the prevalent forecasting models we report a new state of the art in cryptocurrency forecasting.
Muhammad Hasan Danish Khan, Junaid Imtiaz, Muhammad Najam-ul-Islam
Energy markets are being transformed rapidly all over the world due to an increased integration of renewable energy sources. Blockchain technology is emerging as a prime contender, as it can provide a secure and efficient transactional platform for such markets. In a typical microgrid energy market, the consumers and prosumers belonging to the microgrid have the ability to trade energy in a peer-to-peer fashion. However, the existing energy markets suffer from multiple issues security and privacy issues. To handle the aforementioned issues, this research work proposes a blockchain based secure Decentralized Transaction System (DTS) for energy trading in microgrids. The proposed system comprises of a secure market model that facilitates energy trade between energy users. A simplistic energy exchange mechanism has been formulated that ensures data integrity and privacy of the participating energy users. A prosumer centric consensus mechanism has been employed to incentivize the prosumers and ensure the availability of energy in the microgrid at all times. An efficient and dynamic pricing mechanism has been used to reduce the supply and demand disparity. A comprehensive trust model based on commitments has been adopted for ensuring the reliability of the participating energy user. Additionally, a hardware based access control mechanism has been utilized to make the proposed DTS a physical and cyber secure system. Other than this, a framework of smart contracts has been deployed to provide a comprehensive solution that ensures privacy, security, anonymity, auditability and confidentiality of the generated energy information. To demonstrate its practicality, the system has been implemented on Ethereum platform. The proposed DTS is validated using realistic data with the Ethereum Virtual Machine (EVM) environment of Goerli Test Network.
Distributed ledger technologies, together with AI, smart systems and robotics could provide a scalable and robust platform for the smart underwater and surface marine infrastructures of the close future. They will be harbours or ports, marine farms or remote tourism facilities - only accessible through robotic avatars - for protected areas or for the elders. Autonomous Marine Vehicles, IoT networks, and humans will coexist in highly heterogeneous multivendor multiplatform environments where market transactions and complex administrative procedures will be ubiquitous. Some blockchains such as the Ethereum network are able to provide distributed scalable computing and trustable functionalities, capable of managing both technical interactions and market transactions among very diversified autonomous agents. For these reasons they seem to provide a valuable backbone for the smart marine infrastructure of the coming decades. In this paper we outline our research and innovation strategy and present our results showing the potential benefits of a subsidiary architecture integrating distributed ledger technologies with swarms of autonomous surface robots implemented by a Belief Space Planning approach.
With the expansion of social networks, sentiment analysis has become one of the hot topics in machine learning. However, in traditional sentiment analysis, the text is considered of a general nature and ignores the different aspects that may exist in the text. This paper presents a hybrid model of transfer deep learning methods for the aspect-oriented sentiment analysis of influencers’ tweets to predict the trend of cryptocurrencies. In the first model, different aspects of tweets are extracted using the Concept Latent Dirichlet Allocation (Concept-LDA). Then, by using the pre-trained RoBERTa network and combining it with the Bidirectional Gated Recurrent Unit (BiGRU) deep learning network and attention layer, sentiments of different aspects of tweets are determined. In the following, the price trend of seven cryptocurrencies, Bitcoin, Ethereum, Binance, Ripple, Dogecoin, Cardano, and Solana, is determined using the historical price and the polarity of tweets with BiGRU combined deep neural network and the attention layer. Also, we used the gridsearch method to select dropout hyper-parameters, learning rate, and the number of GRU units, and the Akaike Information Criterion (AIC) criterion confirmed the results of this proposed combination. The results show that the proposed model in the aspect-based sentiment analysis section has been able to achieve 5.94% accuracy and 9.9% improvement in the f1-score on the SemEval 2015 dataset and 2.61% improvement on the SemEval 2016 dataset in f1-score compared to the state-of-arts. Also, the results of predicting the price trend of cryptocurrencies show that the proposed model has correctly recognized the price trend in the next five days in 77% of cases according to the ROC-AUC criterion.
Cryptocurrency as an alternative method of payment that acts both as a type of currency and as a virtual accounting system has always been of interest to investors. Since the public sentiment of a society about cryptocurrencies can affect the cryptocurrencies’ prices, a machine learning model based on sentiment analysis has been proposed to forecast the future prices of cryptocurrencies such as Bitcoin, Ethereum, EOS, Cardano, and Ripple using machine learning models that are suitable for time series data analysis to reduce the risk of investing in this market. It was shown that by applying weights to the sentiment scores of tweets according to the influence factor of the individuals, the accuracy of the prediction will increase and a significant difference between the accuracy scores was observed using the LSTM model according to the MAPE indicator (P=0.045). Also, a hybrid model is proposed based on the combination of features extracted from the texts by one of the dictionary-based text analysis models and the feature of weighted sentiment scores. It was shown that our proposed hybrid model outperformed the other models in predicting the prices of Ethereum, EOS, and Cardano according to the MSE indicator. Also, our proposed model based on weighted sentiment scores according to the influence factor of the Twitterers outperformed the other models in the prediction of the future prices of Bitcoin and Ripple, which indicates that the increase in the number of features will not always lead to an increase in the accuracy of our prediction models.
Ray Neiheiser, Gustavo Inácio, Luciana Rech, Carlos Montez · 6 authors
Most permissionless blockchains inherently suffer from throughput limitations. Layer-2 systems, such as side-chains or Rollups, have been proposed as a possible strategy to overcome this limitation. Layer-2 systems interact with the main-chain in two ways. First, users can move funds from/to the main-chain to/from the layer-2. Second, layer-2 systems periodically synchronize with the main-chain to keep some form of log of their activity on the main-chain - this log is key for security. Due to this interaction with the main-chain, which is necessary and recurrent, layer-2 systems impose some load on the main-chain. The impact of such load on the main-chain has been, so far, poorly understood. In addition to that, layer-2 approaches typically sacrifice decentralization and security in favor of higher throughput. This paper presents an experimental study that analyzes the current state of Ethereum layer-2 projects. Our goal is to assess the load they impose on Ethereum and to understand their scalability potential in the long-run. Our analysis shows that the impact of any given layer-2 on the main-chain is the result of both technical aspects (how state is logged on the main-chain) and user behavior (how often users decide to transfer funds between the layer-2 and the main-chain). Based on our observations, we infer that without efficient mechanisms that allow users to transfer funds in a secure and fast manner directly from one layer-2 project to another, current layer-2 systems will not be able to scale Ethereum effectively, regardless of their technical solutions. Furthermore, from our results, we conclude that the layer-2 systems that offer similar security guarantees as Ethereum have limited scalability potential, while approaches that offer better performance, sacrifice security and lead to an increase in centralization which runs against the end-goals of permissionless blockchains.
OpenID Connect (OIDC) is one of the most widely used delegated authentication protocols in web and mobile applications providing a single sign-on experience. It allows third-party applications, called Relying Parties (RP), to securely request and receive information about authenticated sessions and end-users from an identity provider. The OIDC specification defines several parameters, including the client_id, client_secret, authorization code, access token, id token, state, and redirect_uri, as keys to the protocol operation, with significant security and privacy implications. Therefore, securing these parameters is critical to prevent attackers from impersonating legitimate entities, gaining unauthorized access, having complete control over users’ accounts, and/or violating their privacy. To enhance OIDC security and preserve its users’ privacy, we propose a novel model for OIDC based on the Ethereum Blockchain and the non-fungible token (ERC721) standard. To prove the robustness and safety of the proposed system, we perform a detailed security analysis formally using the most widely accepted protocols security verification tools, AVISPA and Scyther, and informally by discussing various attacks. The analysis results show that the proposed system is resilient against well-known attacks. Furthermore, we evaluate the cost and performance of the proposed solution, confirming its affordability and assuring that our approach does not impact the user experience and performance of existing OIDC-based systems. Finally, we conduct a security and privacy comparative analysis with similar existing systems, proving the superiority and efficiency of our proposed Blockchain-based OIDC system.
Non-Fungible Token (NFT) is a digital asset that cannot be exchanged or used, and uses Crypto currency values according to the type of digital money used, for example Bitcoin, Ethereum.The NFT Marketplace is a platform for buying and selling NFT like Tokopedia.This common problem is often encountered in e-commerce, especially in the NFT Marketplace, among other buyers often having difficulty finding products.This makes it difficult for the NFT Marketplace and sellers to promote products that match the preferences of potential buyers.A recommendation system that is very much needed in overcoming these problems, responding to these problems the author tries to make a recommendation system using the Content Based Filtering approach using the cosine similarity.The results of this study indicate that the Machine Learning model can provide Top-N recommendations from the product being sought.
Mohammad Madine, Khaled Salah, Raja Jayaraman, Mohamed Jamal Zemerly
Software licenses are legal agreements of sale and usage among software developers and clients. Such legal agreements are crucial to effectively manage ownership and protect the rights of involved parties. Today’s software licensing mechanisms are mostly centralized and do not address the ever-increasing issues and complexities of modern software that may include multiple licenses, open-source distribution, rewarding other contributors of external software libraries, and utilizing royalty payments for monetization. As a result, developers have lost confidence in the existing software licensing models, and many software projects are failing due to lack of funding and royalty payments. This paper addresses such issues and complexities by proposing a novel decentralized software licensing system based on Non-Fungible Tokens (NFTs) and blockchain. The proposed licensing system is applicable to both commercial and open-source software. We use NFTs as digital tokens that encapsulate software code and their artifacts by minting them as unique valuable assets that allow developers to store and manage them on a blockchain ledger. With NFTs, developers can register and license their code, monetize it on NFT marketplaces, and earn royalties from other software projects that use their code. We present system architecture, relevant sequence diagrams, and develop aggregation algorithms for Ethereum smart contracts with ERC-1155 NFTs. Furthermore, we perform functional validation of our system and analyze the cost of its adoption. We also analyze the security of the solution and show how its applicability can be generalized and extended. We have made our smart contract code and related testing scripts publicly available on GitHub.
This research examines the correlations between the return volatility of cryptocurrencies, global stock market indices, and the spillover effects of the COVID-19 pandemic. For this purpose, we employed a two-stage multivariate volatility exponential GARCH (EGARCH) model with an integrated dynamic conditional correlation (DCC) approach to measure the impact on the financial portfolio returns from 2019 to 2020. Moreover, we used value-at-risk (VaR) and value-at-risk measurements based on the Cornish–Fisher expansion (CFVaR). The empirical results show significant long- and short-term spillover effects. The two-stage multivariate EGARCH model’s results show that the conditional volatilities of both asset portfolios surge more after positive news and respond well to previous shocks. As a result, financial assets have low unconditional volatility and the lowest risk when there are no external interruptions. Despite the financial assets’ sensitivity to shocks, they exhibit some resistance to fluctuations in market confidence. The VaR performance comparison results with the assets portfolios differ. During the COVID-19 outbreak, the Dow (DJI) index reports VaR’s highest loss, followed by the S&P500. Conversely, the CFVaR reports negative risk results for the entire cryptocurrency portfolio during the pandemic, except for the Ethereum (ETH).
The distribution of royalties associated with the exchange of digital assets, especially Non-Fungible Tokens (NFTs), is now more than ever a strong point of contention. Between conceptual disagreements and technical limitations, actors have implemented a variety of solutions tailored to their needs. In the process, creators and buyers have lost the possibility of transparent, trusted, and interoperable exchanges of said assets, often having to compromise to connect with the rest of the community. This study deals with the automatic distribution of royalty payments. We first investigate the current day limitations, and formally state their underlying requirements, before advancing a royalty-friendly NFT marketplace-agnostic trading framework. The advanced solution, referred to as the RM-TLSC–Royalty Management Token-LevelSmart Contract, establishes synergies between the token and Smart Contract paradigms, thus ensuring royalties are managed throughout the life cycle of the asset. A comprehensive, open-source software implementation is provided for the Ethereum blockchain, while the generality of the approach is cross-checked by an open-source proof of concept for the Tezos blockchain. The effectiveness of the results is illustrated through a case-study related to ISO 21000–23 Smart Contracts for Media standard.
This paper investigates the price discovery relationships between FTT Token, issued by the cryptocurrency exchange FTX, and a set of assets and liabilities held by FTX amid a period of catastrophic financial decline by applying novel information flow measurement techniques. Results indicate that during key phases associated with the collapse of FTX, FTT Token had an informational lead over multiple assets, including cryptocurrencies such as Ethereum. Furthermore, we identify significant interactions between the FTT Token and both Robinhood shares and the token Serum, raising concerns about the direct influence of permissionless, technically valueless tokens on other assets and the potential challenges to market stability and investor protection. Our findings underscore the need for stronger policy-making, regulatory, and ethical considerations in cryptocurrency markets.
Hasib Shamshad, Fasee Ullah, Asad Ullah, Victor R. Kebande · 6 authors
The digital market trend is rapidly expanding due to key characteristics like decentralization, accessibility, and market diversity enabled by blockchain technology. This study proposes a Predictive Analytics System to provide simplified reporting for the three most popular cryptocurrencies with varying digits, namely ADA Cardano, Ethereum, and Binance coin, for ten days to contribute to this emerging technology. Thus, this proposed system employs a data science-based framework and six highly advanced data-driven Machine learning and Deep learning algorithms: Support Vector Regressor, Auto-Regressive Integrated Moving Average (ARIMA), Facebook Prophet, Unidirectional LSTM, Bidirectional LSTM, Stacked LSTM. Moreover, the research experiments are repeated several times to achieve the best results by employing hyperparameter tuning of each algorithm. This involves selecting an appropriate kernel and suitable data normalization technique for SVR, determining ARIMA’s (p, d, q) values, and optimizing the loss function values, number of neurons, hidden layers, and epochs in LSTM models. For the model validation, we utilize widely used evaluation techniques: Mean Absolute Error, Root Mean Squared Error, Mean Absolute Percentage Error, and R-squared. Results demonstrate that ARIMA outperforms the other models in all cases, accurately projecting the price variability within the actual price range. Conversely, Facebook Prophet exhibits good performance to some extent. The paper suggests that the ARIMA technique offers practical implications for market analysts, enabling them to make well-informed decisions based on accurate price projections.
Ethereum has recently surged in popularity, as it can hold various digital tokens and decentralized applications.This paper aims to predict UNI's price in USD through dynamic network analysis and time-series analysis.Previous research in this field rarely considers comprehensive network analysis while predicting token price.This paper puts forward a strengthened Bidirectional LSTM model that includes token economical features and network features.We use Root Mean Squared Error (RMSE) to verify the validity and compare it with other LSTM and GRU models on performance.Lastly, a logarithm difference method for data preprocessing was introduced to resolve the lag problems.
K. V. Raghavender, S. Alankruthi, A. Akhila, T. Preethi · 5 authors
Traditional paper certificates and electronic certificates are difficult to handle and preserve, may need third parties to authenticate the certificate, take a lot of time, and have a potential of being tampered with.People routinely fabricate certificates to represent their credentials and degrees.A false certificate created by a skilled con artist is never easy to spot and address as the real one.As a result, it is imperative to enhance the certification and verification procedure.So, to avoid such issues, we want to develop a project where we can use blockchain technology for verification of certificates.Initially University will enter the students roll number and upload their college certificate and it will be stored on Interplanetary File System (IPFS) by generating a hash which uniquely identifies that block.Now any person like the student or recruiter or an administrator can fetch and verify the college certificate by providing a unique hash value and roll number of the student.And we can also validate the certificate by providing the certificate and roll number of the student in case we forget the generated hash value.This can result in increased security, lower costs, and a quicker platform for verifying educational certificates.
Mithilesh Pawar, Rudraksh Balkar, Vijay Pawar, Saurav Roy
Blockchain technology is poised to bring about a paradigm shift in the business environment, offering substantial benefits in the near future. Non-fungible Tokens are digital representations of material possessions like artwork, recordings, assets within the game, and films. These tokens are traded via the internet with multiple cryptocurrencies. NFTs have their own distinct characteristics from fungible tokens, which may be traded without restriction on numerous centralized or decentralized exchanges.Each NFT carries a unique digital signature that precludes its exchange for another NFT, and its value is determined by factors such as metadata, creator, and features.