Bitcoin is a high-risk asset with a potentially high return. Predicting Bitcoin candlestick, i.e., open, high, low, and close (OHLC) prices, can help investors make trading decisions. The objective of this study is to develop a neural network model to predict the candlestick prices of Bitcoin for the next period. Additionally, this study investigates methods to enhance the model's forecasting performance by feature transformations, specifically data normalization. This study employs two neural network algorithms, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), to forecast daily Bitcoin OHLC prices. To enhance the model's performance, we compare sliding window normalization with whole set normalization techniques. The normalization techniques investigated for both whole set and sliding window data include z-score normalization, min-max normalization, and relative change normalization. Furthermore, this study compares two candlestick prediction methods, namely using OHLC prices and using candle wick (CULR) to predict OHLC prices. The models use historical OHLC prices over several days to predict the next day's OHLC prices. The results indicate that the best-performing model is the OHLC method using GRU algorithm with sliding window z-score normalization, which achieves an MAPE of 1.95% and an RMSE of 767.71. Moreover, the sliding window normalization generally outperforms the whole set normalization for both LSTM and GRU models in terms of RMSE and MAPE. Regarding the candlestick prediction methods, there was no significant difference in their performance in terms of accuracy and forecasting error. However, our results suggest that the OHLC method performs slightly better than the CULR method.
Blockchain technology has certainly revolutionized the world. A decentralized approach devoid of a centralized server/third party access provides complete anonymity, security, and integrity. But still, there is some vulnerability in Blockchain, one of them we discussed in this paper is Front-Running. Front running is mostly exercised in the stock market sector where the traders try to front-run to gain more profit. Now as blockchain technology is flourishing at an immense rate, new forms of front running techniques have been encountered. Thus creating transaction vulnerabilities. In this paper, we explained the front-running issues on MCS-Dapps deployed on Ethereum Blockchain. Furthermore, we will study its working on the system and how it affects clients and miners on their gas prices. Then we implemented a commit-reveal strategy to increase the transaction confidentiality to counter the issue of front running.
Muhammad Hussain Mughal, Zaffar Ahmed Shaikh, Khurshed Ali, Safdar Ali · 5 authors
The streamflow data acquisition with various techniques and dispersing of River’s locations demand improvement of reliability and frequency aspects. One of the reliability measurement characteristics is data ownership. The authority sharing data authorizes its quality and is also responsible for the wrong decision triggered by incorrect data. The consensus-based crowdsourcing data contribute to aggregated streamflow records’ generation. The aggregated streamflow records are stored on a streamflow ledger, a Hyperledger fabric-based ledger for rivers’ streamflow data. In contrast, InterPlanery File System(IPFS) distributed file storage system is helpful for policy documents and distribution scheme storage. Blockchain-based techniques for improvement of data-intensive decision support systems. The distributed river streamflow data measured and shared by distributed gauging officials and stored on the blockchain-based distributed storage system contributes to scalability, transparency, availability, and accessibility of shareable data. All stakeholders require quality data with trust and provenance management through a persistent, linked, and immutable copy of data. This technique resolves the conflict or disagreement on streamflow optimization and flood mitigation decisions. In a nutshell, the blockchain technology with IPFS for off-chain large files storage would contribute twofold to irrigation systems and flood mitigation domains. On one side, the streamflow data aggregation has consensus from streamflow assessment for software agents and stakeholders. Secondly, the persistent data copy is shared among distributed stakeholders using IPFS-based content addressed file-sharing protocol for a common operating picture to improve effective collaboration and coordination among managers of irrigation systems and flood mitigation activities.
Marco Di Gennaro, Lorenzo Italiano, Giovanni Meroni, Giovanni Quattrocchi
Thanks to built-in immutability and persistence, the blockchain is often seen as a promising technology to certify information. However, when the information does not originate from the blockchain itself, its correctness cannot be taken for granted. To address this limitation, blockchain oracles -- services that validate external information before storing it in a blockchain -- were introduced. In particular, when the validation cannot be automated, oracles rely on humans that collaboratively cross-check external information. In this paper, we present DeepThought, a distributed human-based oracle that combines voting and reputation schemes. An empirical evaluation compares DeepThought with a state-of-the-art solution and shows that our approach achieves greater resistance to voters corruptions in different configurations.
Blockchain finance has become a part of the world financial system, most typically manifested in the attention to the price of Bitcoin. However, a great deal of work is still limited to using technical indicators to capture Bitcoin price fluctuation, with little consideration of historical relationships and interactions between related cryptocurrencies. In this work, we propose a generic Cross-Cryptocurrency Relationship Mining module, named C2RM, which can effectively capture the synchronous and asynchronous impact factors between Bitcoin and related Altcoins. Specifically, we utilize the Dynamic Time Warping algorithm to extract the lead-lag relationship, yielding Lead-lag Variance Kernel, which will be used for aggregating the information of Altcoins to form relational impact factors. Comprehensive experimental results demonstrate that our C2RM can help existing price prediction methods achieve significant performance improvement, suggesting the effectiveness of Cross-Cryptocurrency interactions on benefitting Bitcoin price prediction.
Patrick Ocheja, Friday Joseph Agbo, Solomon Sunday Oyelere, Brendan Flanagan · 5 authors
The advent of blockchain technology over the last decade has led to the development of multiple use-cases of decentralization in various fields including education. This paper presents a unique bibliometric and qualitative analysis of the blockchain in education with novel contributions on temporal development, emerging themes and practical case studies on adoption and integration with existing educational technologies. We focus on identifying the major actors in the space, demographic participation and adoption, current hot topics, grey areas, and potential areas for innovation. Our analysis shows that while the blockchain has been around for about 13 years, blockchain in education only became prominent 5 years ago. This research also reveals that most of the efforts have been focused on reporting and verifying academic certificates and transcripts: only very few research focused on reporting and connecting in-depth academic records such as learning behaviour logs, learning contents and assessment data. This calls for concern as current education blockchain systems do not consider interoperability at the blockchain level and the heterogeneous nature in which institutes create and consume academic data. Finally, we present discussions on the implications of our findings, potential solutions and aspects of education blockchain research that can help to improve educational outcomes for various stakeholders.
Aspiring to achieve an accurate Bitcoin price prediction based on people's opinions on Twitter usually requires millions of tweets, using different text mining techniques (preprocessing, tokenization, stemming, stop word removal), and developing a machine learning model to perform the prediction. These attempts lead to the employment of a significant amount of computer power, central processing unit (CPU) utilization, random-access memory (RAM) usage, and time. To address this issue, in this paper, we consider a classification of tweet attributes that effects on price changes and computer resource usage levels while obtaining an accurate price prediction. To classify tweet attributes having a high effect on price movement, we collect all Bitcoin-related tweets posted in a certain period and divide them into four categories based on the following tweet attributes: $(i)$ the number of followers of the tweet poster, $(ii)$ the number of comments on the tweet, $(iii)$ the number of likes, and $(iv)$ the number of retweets. We separately train and test by using the Q-learning model with the above four categorized sets of tweets and find the best accurate prediction among them. Especially, we design several reward functions to improve the prediction accuracy of the Q-leaning. We compare our approach with a classic approach where all Bitcoin-related tweets are used as input data for the model, by analyzing the CPU workloads, RAM usage, memory, time, and prediction accuracy. The results show that tweets posted by users with the most followers have the most influence on a future price, and their utilization leads to spending 80\% less time, 88.8\% less CPU consumption, and 12.5\% more accurate predictions compared with the classic approach.
To facilitate the consumers’ online shopping, it is very necessary to build a reliable and sustainable product evaluation management (PEM) system to store, secure, and retrieve the evaluation data of products. To this end, we propose a novel PEM system based on blockchain technologies. In this PEM system, the evaluation data of products including evaluation scores and comments are stored in the interplanetary file system (IPFS), and then the data hash addresses are returned and stored on the blockchain. Then, the retrieval of evaluation data is implemented by designing and deploying smart contracts on the Ethereum blockchain. Since it is almost impossible to change any data stored in blockchain and IPFS, the proposed PEM system can protect the comment data from intentional or unintentional modifications. Moreover, the proposed PEM system allows users to efficiently and reliably retrieve the relevant evaluation data of a certain product by a given query keyword with the increase of data stored on the blockchain. In addition, to achieve high sustainability, the proposed system also contains an economic incentive mechanism, which is beneficial to form a virtuous circle in online shopping. The proposed system is simulated on the popular blockchain platform, i.e., Ethereum with Solidity programming language. The experimental results and analysis demonstrate that the proposed PEM system achieves desirable usability, reliability, and sustainability.
We show that knowledge of wallet addresses from the current time state of a blockchain network, such as Bitcoin, increases the performance of illicit activity detection. Based on this finding we introduce two new methods for the sampling of classifier training data so that precedence is given to transaction information from the recent past and the current time state. This sampling enables streaming classification in which a decision on the class of a transaction needs to be made based on data seen to date. Our new approach provides insight into how the dynamics of the blockchain network plays a central role in the detection of illicit transactions, and is independent of the classifier choice. Our proposed sampling methods enable graph convolution network (GCN) and random forest (RF) classifiers to better adapt to changes in the network due to significant events, such as the closure of a large ‘Darknet’ marketplace. We introduce Graphlet spectral correlation analysis for exposing the effect of such network re-organisation due to major events. Finally, based on our analysis, we propose a new two-stage random forest classifier that feeds back intermediate predictions of neighbours to improve the classification decision. Our methodology enables practical streaming classification, even in the scenario of very limited information on the feature space of each transaction.
Muhammad Milhan Afzal Khan, Hafiz Muhammad Azeem Sarwar, Muhammad Awais
Abstract In Ethereum blockchain, whenever a transaction of smart contract is executed, transaction fee is charged in terms of Ethers. To calculate the transaction fee, a computational unit, gas is introduced in smart contracts. Gas consumption is calculated against the smart contract source code execution. The transaction initiator sets the gas price against per unit of gas and the total gas limit. If the gas limit is sufficient, the transaction will be mined otherwise it will be reverted. Smart contracts of Ethereum can be written in any high‐level language such as Solidity, Vyper, Python, Java and so forth, but Solidity is massively used for smart contracts creation. In this article, we have examined the 5000 transactions of Solidity based smart contracts from Etherscan and performed statistical analysis on opcodes and source code parameters used in these transactions to identify gas costly patterns. Our statistical results (correlation and regression) analyze the relationship of Solidity parameters and opcodes with the gas consumption. Factors causing an increase or decrease in the gas consumption of smart contracts are highlighted in this article. The regression analysis showed that 87.8% of the variability in the response variable (gas consumption) is due to the parameters used in this analysis. Our results will help the smart contract developers to write the gas optimized smart contracts. The results can be beneficial for end users as they will have to pay gas price for less number of gas units.
Since the birth and open source of Bitcoin, there are more than 10,000 kinds of virtual cryptocurrencies in the market. Every day, virtual cryptocurrencies are born, but also virtual cryptocurrencies die out. In the life cycle of virtual cryptocurrencies, different periods of abnormal transactions will occur. However, there are still deficiencies in the definition and related studies of the life cycle of virtual cryptocurrencies in existing researches. Machine learning (ML) can dig out the hidden rules from a large amount of data. We use unsupervised learning in machine learning to detect the life cycle of virtual cryptocurrencies. In this work, we divide and define each stage of the life cycle of virtual cryptocurrencies in detail. Based on the popularity value system of virtual cryptocurrencies and the similarity comparison algorithm, we establish a virtual cryptocurrencies life cycle detection tool to detect the life cycle stage of virtual cryptocurrencies. Experimental results demonstrate the effectiveness of the proposed unsupervised learning based virtual cryptocurrency life cycle detection tool.
Industrial blockchain applications have recently risen to the top of the scientific and industrial communities' priority lists. This is due to their practical capabilities in resolving many issues in various industrial domains. Visibility and traceability to a large volume of trusted data benefit members of a consortium of industrial companies and associated organizations. Data scientists who query data and use statistics and Machine Learning to solve a wide range of interesting problems benefit as well. This paper introduces a blockchain application Query Engine based on upgradable Smart Contracts with a lightweight, flexible, and interoperable query framework. This query engine specifically is designed to facilitate the storage and querying of application data recorded on blockchain networks.
Lovemore Ngwira, Mpyana Mwamba Merlec, Youn Kyu Lee, Hoh Peter In
Context-awareness is very essential for blockchain-based IoT systems to guarantee successful system operations, even in presence of frequent external environment changes. However, current blockchain-based IoT solutions lack the adaptive ability required to accommodate external environment context changes during the system runtime. In this paper, we propose a context-aware smart contract system using rule-based reasoning for efficient energy consumption and environmental sustainability in blockchain-based IoT systems. Using smart contracts, the system collects contextual information, analyzes it by considering suitable control rules to perform required actions. To guarantee data authenticity and integrity, all transaction history and logs are stored in the blockchain-based distributed ledger for immutable data provenance evidence, accountability, and traceability. A smart grid home area network-based prototype was implemented on top of the Quorum blockchain to prove the feasibility of our concept. Besides, the performance evaluation of the experimental results shows that our system efficiently reduced energy consumption while maintaining environmental sustainability.
Ningyu He, Weihang Su, Zhou Yu, Xinyu Liu · 10 authors
The continuing expansion of the blockchain ecosystems has attracted much attention from the research community. However, although a large number of research studies have been proposed to understand the diverse characteristics of individual blockchain systems (e.g., Bitcoin or Ethereum), little is known at a comprehensive level on the evolution of blockchain ecosystems at scale, longitudinally, and across multiple blockchains. We argue that understanding the dynamics of blockchain ecosystems could provide unique insights that cannot be achieved through studying a single static snapshot or a single blockchain network alone. Based on billions of transaction records collected from three representative and popular blockchain systems (Bitcoin, Ethereum and EOSIO) over 10 years, we conduct the first study on the evolution of multiple blockchain ecosystems from different perspectives. Our exploration suggests that, although the overall blockchain ecosystem shows promising growth over the last decade, a number of worrying outliers exist that have disrupted its evolution.
Fátima Leal, Bruno Veloso, Benedita Malheiro, Juan C. Burguillo · 6 authors
Explainable recommendations enable users to understand why certain items are suggested and, ultimately, nurture system transparency, trustworthiness, and confidence. Large crowdsourcing recommendation systems ought to crucially promote authenticity and transparency of recommendations. To address such challenge, this paper proposes the use of stream-based explainable recommendations via blockchain profiling. Our contribution relies on chained historical data to improve the quality and transparency of online collaborative recommendation filters – Memory-based and Model-based – using, as use cases, data streamed from two large tourism crowdsourcing platforms, namely Expedia and TripAdvisor. Building historical trust-based models of raters, our method is implemented as an external module and integrated with the collaborative filter through a post-recommendation component. The inter-user trust profiling history, traceability and authenticity are ensured by blockchain, since these profiles are stored as a smart contract in a private Ethereum network. Our empirical evaluation with HotelExpedia and Tripadvisor has consistently shown the positive impact of blockchain-based profiling on the quality (measured as recall) and transparency (determined via explanations) of recommendations.
The blockchain technology is regarded as a significant trust-building technology and has attracted much attention from the public. The longest chain rule has been widely applied in blockchain systems to reach consensus on the distributed ledger. However, the longest chain rule cannot support a higher transaction throughput due to its lower security. As an alternative solution to the longest chain rule, GHOST is proposed as a safer consensus rule. Existing studies show that the longest chain rule can suffer from selfish mining attacks. However, it is unclear how selfish mining attacks perform on GHOST. In this paper, we explore the performance of selfish mining on GHOST. We first propose the original selfish mining (GHOST-SM) and stubborn mining (GHOST-StuM) for GHOST. We then evaluate these two selfish mining strategies on our blockchain simulation system. The experimental result shows that GHOST achieves better security than the longest chain rule. However, when the block generation rate increases, the security of GHOST is close to the longest chain rule. For example, the threshold for selfish mining attacks of GHOST is increased by 47.55% and 0.60% compared to the longest chain rule corresponding to the block generation interval of 1 second and 15 seconds.