Amaç –Bu çalışmada Türkiye’de yaşayan bireylerin kripto para farkındalık düzeylerini ölçmek ve kripto para sahipliğinin bireylerin demografik özelliklerine göre farklılık gösterip göstermediğini tespit etmek hedeflenmiştir. Çalışmanın bir diğer amacı ise bireylerin hangi kripto paraları daha fazla tanıdığını ve kullandığını saptamaktadır. Yöntem –Türkiye’de yaşayan bireylere; kripto paralar hakkındaki farkındalıkları, kripto paraya sahip olup olmadıkları ve eğer kripto paralara sahipseler bunların hangileri olduğu ile ilgili sorular sorulmuş ve alınan cevaplar istatistiki yöntemler ile analiz edilmiştir. Toplamda 1211 bireyden online anket tekniği ile veri toplanmıştır. Elde edilen veriler SPSS 28 istatistik paket programı ile analiz edilmiştir. Verilere; güvenilirlik analizi, frekans analizi, t-testi ve ANOVA analizi uygulanmıştır.Bulgular –Frekans analizleri sonucunda; katılımcıların cinsiyetlerinin büyük ölçüde yakın olduğu, yaş gruplarının 18-31 aralığında çoğunluk gösterdiği, katılımcıların çoğunluğunun aktif olarak çalıştığı, gelir düzeylerinin asgari ücret düzeyinde olduğu, eğitim düzeylerinin ise lisans seviyesinde olduğu tespit edilmiştir. Katılımcıların kripto paralar konusunda bilinirliklerinin çok yüksek olduğu (%91,4) ve büyük çoğunluğunun son 1 yıl içerisinde bilgi sahibi olduğu saptanmıştır. Herhangi bir kripto paraya sahip olan katılımcıların oranı ise (%22) bilinirliğin aksine çok daha düşük olduğu görülmüştür. Katılımcılar tarafından en çok bilinen kripto para birimleri sırasıyla; Bitcoin, Ethereum, Litecoin ve Ripple şeklinde olduğu saptanmıştır.Tartışma –Kanada’da kripto para bilinirliğinin %89, kripto para sahipliğinin ise %5, Hindistan’da kripto para sahipliğinin %37,5 ve Malezya’da bireylerin %94,7’sinin kripto paralar konusunda temel seviyede bilgisinin ve kripto para kullanımının %80 olduğu daha önce yapılan çalışmalarda tespit edilmiştir. Bu bilgiler ışığında, gelişmekte olan ülkelerde kripto para sahipliğinin gelişmiş ülkelere kıyasla daha fazla olduğu söylemek mümkündür.
These days, businesses, in both online and offline, have started accepting cryptocurrencies as payment methods. Even in countries like <italic>El Salvador</italic>, cryptocurrencies are recognized as fiat currencies. Meanwhile, publicly known, but not patched software vulnerabilities are security threats to not only software users but also to our society in general. As the status of cryptocurrencies has gradually increased, the impact of security vulnerabilities related to cryptocurrencies on our society has increased as well. In this paper, we first analyze vulnerabilities from the two major cryptocurrency vendors of Bitcoin and Ethereum in a quantitative manner with the respect to the CVSS, to see how the vulnerabilities are roughly structured in those systems. Then we introduce a modified AML vulnerability discovery model for the vulnerability datasets from the two vendors, after showing the original AML dose not accurately represent the vulnerability discovery trends on the datasets. The analysis shows that the modified model performs better than the original AML model for the vulnerability datasets from the major cryptocurrencies.
Real-world application development through Smart Contracts on the Ethereum Blockchain platform is one of the emerging technologies. It also has much vulnerability, and reentrancy is among the most popular ones. In our work, we have reviewed the tools based on ML for vulnerability detection in Ethereum smart contracts. Based on that, we proposed a framework that can dynamically monitor threats based on the blockchain platform's transaction meta-data and balance data. It does not require any changes or updates to the existing system and does not require expertise to implement. This framework will extract features for machine learning classifier models from the transaction data and identify the transaction as agreeable or unfavorable. It will help to identify the reentrancy threat as well as the cause of it and help the developer to trace it from where the attack is generated. In the ML classifier for the framework, random forest and decision tree are used. The cumulative performance of both is 98 percent on 540 transactions.
Danilo Bizarria de Oliveira, Geraldo Lucas Fernandes do Amaral, Markel Pedrosa Duarte de Macedo, Matheus Ferreira Mesquita · 5 authors
Sistemas de processamento de transações são parte essencial de negócios online, e exigem tanto confiança quanto desempenho para entregar o serviço esperado. Uma ferramenta de blockchain é capaz de oferecer escalabilidade e confiança na validação de transações. A abordagem proposta neste trabalho é da avaliação do potencial de escalabilidade da rede Ethereum, e seu comportamento em cenários progressivamente mais distribuídos. Com destaque para um aumento observado de cerca de 10× o número de transações por segundo, quando comparados um nó isolado e uma rede de 16 nós, esta que também proporcionou uma queda de mais da metade da latência com relação ao cenário centralizado.
A. N. M. Sajedul Alam, Junaid Bin Kibria, Al Hasib Mahamud, Arnob Kumar Dey · 7 authors
In the realm of digital art and collectibles, NFTs are sweeping the board. Because of the massive sales to a new crypto audience, the livelihoods of digital artists are being transformed. It is no surprise that celebs are jumping on the bandwagon. It is a fact that NFTs can be used in multiple ways, including digital artwork such as animation, character design, digital painting, collection of selfies or vlogs, and many more digital entities. As a result, they may be used to signify the possession of any specific object, whether it be digital or physical. NFTs are digital tokens that may be used to indicate ownership of one of a-kind goods. For example, I can buy a shoe or T shirt from any store, and then if the store provides me the same 3D model of that T-Shirt or shoe of the exact same design and color, it would be more connected with my feelings. They enable us to tokenize items such as artwork, valuables, and even real estate. NFTs can only be owned by one person at a time, and they are protected by the Ethereum blockchain no one can alter the ownership record or create a new NFT. The word non-fungible can be used to describe items like your furniture, a song file, or your computer. It is impossible to substitute these goods with anything else because they each have their own distinct characteristics. The goal was to find all the existing implementations of Non-fungible Tokens in different fields of recent technology, so that an overall overview of future implementations of NFT can be found and how it can be used to enrich user experiences.
This research investigates the effects of several measures of Twitter-based sentiment on cryptocurrencies during the COVID-19 pandemic. Innovative economic, as well as market uncertainty measures based on Tweets, along the lines of Baker et al. (2021), are employed in an attempt to measure how investor sentiment influences the returns and volatility of major cryptocurrencies, developing on non-linear Granger causality tests. Evidence suggests that Twitter-derived sentiment mainly influences Litecoin, Ethereum, Cardano and Ethereum Classic when considering mean estimates. Moreover, uncertainty measures non-linearly influence each cryptocurrency examined, at all quantiles except for Cardano at lower quantiles, and both Ripple and Stellar at both lower and higher quantiles. Cryptocurrencies with lower values are found to be unaffected by investor sentiment at extreme values, however, prove to be profitable due to more aligned investor behaviour.
Erik Mauricio Muñoz Henríquez, Francisco A. Gálvez-Gamboa
<p>El objetivo de este trabajo es analizar el efecto spillover entre el mercado de las criptomonedas, los mercados financieros y commodities, utilizando índices de volatilidad realizada de las diez criptomonedas con mayor capitalización de mercado y la volatilidad implícita de las cotizaciones del Oro (GVZ) y el Petróleo (OVX), y el mercado financiero norteamericano (VIX) y europeo (VSTOXX) a través del Spillover Index basado en un Vector Autorregresivo (VAR). Los resultados indican que Ethereum es el mayor transmisor de volatilidad, seguido por Cardamo, mientras que los mayores receptores de volatilidad son ChainLink y BinanceCoin. Además, demostramos a través de la utilización de la volatilidad implícita que la contribución de los mercados financieros al spillover no excede el 3%, incluso resultados menores se evidencian con ambos commodities. El análisis de impulso-respuesta muestra el mayor efecto sobre las criptomonedas proviene del VIX, junto con una respuesta negativa ante un shock en el OVX y GVZ.</p>
Introduction. In the last decade, the cryptocurrency market has shown significant development. The active growth of this market has created significant investment opportunities for entrepreneurs. In order to form highly profitable investments, one should pay attention to the price behavior of cryptocurrency coins and their niche in the cryptocurrency market. Scientific research analysis. Despite the impact of investments in cryptocurrencies on the economic development of many countries, scientific research in the field of cryptocurrencies is still inconsistent. Investment activities on the primary and secondary cryptocurrency markets were analyzed in the scientific publications by M. Duchenko, T. Pavlenko, Z. Dvulit, H. Peredal, M. Rebryk, I. Gonak and other scientists. However, scientific research on cryptocurrency investing is at an initial stage since a significant number of aspects of their implementation have not yet been worked out and require systematic development due to the rapid transformation of both the cryptocurrency market and the legislative provision of their functioning in the conditions of dynamic geo-economic changes. The purpose and tasks of the research. The purpose of studying cryptocurrencies as an investment field is to find out the advantages of investing in cryptocurrencies in comparison with other investment objects, as well as to evaluate the effectiveness of such investment through the use of mathematical modeling methods. Research methodology and methods. The following special research methods were used: direct observation; applied information processing methods for the development of tables of linear dependence; to determine the closeness of the relationship between the dynamics of the value of the main cryptocoins, the method of conducting analytical work on the calculation of the correlation coefficient was used. Research results. Cryptocurrencies have taken an important place in the development of investment activities. As an investment object in the primary cryptocurrency market, it is advisable to mine the Ethereum cryptocurrency, and in the secondary market, it is advisable to buy Ethereum and Bitcoin for investments, which occupy two thirds of the cryptocurrency market and between which there is a significant correlation. Investing in cryptocurrencies is advisable due to the significant potential of mining, the growth of the value and limits of the cryptocurrencies use. Investing in cryptocurrency coins is advisable both in the short term and in the long term. There is an average (not high) correlation between the primary and secondary markets of cryptocurrency, so an investor is recommended to focus on one of the forms of investment in order to obtain a high profit, and to reduce risks, you can diversify your business by investing in the primary and secondary markets. Scientific novelty of the research results. The possibilities of cryptocurrency investment in short-term and long-term periods were considered, and a slight correlation between the primary and secondary markets of cryptocurrencies was revealed. Cryptocurrency is a relatively new object of savings and investment, therefore, with dynamic changes in the legal regulation of the cryptocurrency market, the transformed conditions for the attractiveness of cryptocoins will be formed, and this will require reflection in further scientific publications. Practical significance of the obtained research results. According to the authors, investing in cryptocurrencies is appropriate both in the primary and secondary markets of cryptocurrencies (although the correlation between them is not high) in both the short-term and long-term perspectives. The obtained research results will be useful for use in the practical activities of conducting cryptocurrency business. The article contains: formulas: 6, fig. 2, table 5, bibl. 24).
The Internet of Things (IoT) and blockchain technologies characterizing the era of the fourth industrial revolution have enabled smart home networks to support their various systems and services. In a blockchain-based smart-home network environment, all connected IoT devices must be controlled safely and efficiently. Nevertheless, existing block-chain-based smart-home IoT systems pose a delay issue due to the necessary block generation time. In addition, IoT devices installed in smart homes should be able to prevent forgery attacks such as spoofing because they are often directly associated with personal information. In this study, we proposed an enhanced method to control smart home devices safely and efficiently by applying the zero-knowledge proof combined with a blockchain-based IoT system to protect the public keys of home network devices and the communication among them. The proposed model was approximately 10 s faster than the block generation-based model when it communicated three times in rinkeby, which is one of the test networks of Ethereum.
Blockchain technology is a platform for transactions and investment. It includes Cryptocurrencies, of which there are dozens of investment vehicles. Bitcoin and Ethereum are the best known. Crypto is an emerging asset class and offers some portfolio diversification benefits and attractive return rates. Some crypto investments are stablecoins and are tethered to underlying currencies like the US dollar. Blockchain allows ease of access and transparency and provides a cloak of pseudonymity, which may be incompatible with owning real estate, which generally has ownership and transactions as public records. This article provides a brief primer on the intersection of blockchain, cryptocurrencies, and real property. It reviews the various ways that these emerging technologies and instruments are used in real estate investment today. We investigate a blockchain-oriented investment platform, RealT, and report on their business model and customer-servicing interface. Their model offers tokenized real estate to a wide range of qualified investors, and many of the occupied houses feature secure cash flows in the form of Section 8 tenants. If properly developed, the model appears sustainable and can rejuvenate inner-city residential property markets and open the door for a greater ROE for not-for-profit groups.
Non-Fungible Tokens (NFTs), digital certificates of ownership for virtual art, have until recently been traded on a highly lucrative and speculative market. Yet, an emergence of misconceptions, along with a sustained market downtime, are calling the value of NFTs into question. This project (1) describes three properties that any valuable NFT should possess (permanence, immutability and uniqueness), (2) creates a quantitative summary of permanence as an initial criteria, and (3) tests our measures on 6 months of NFTs on the Ethereum blockchain, finding 45% of ERC721 tokens in our corpus do not satisfy this initial criteria. Our work could help buyers and marketplaces identify and warn users against purchasing NFTs that may be overvalued.
Are conventional and sustainable cryptocurrencies effective hedging instruments for high cryptocurrency uncertainty? This paper examines co-movements between conventional (Bitcoin, Ethereum, Binance Coin, Tether) and sustainable (Cardano, Powerledger, Stellar, Ripple) cryptocurrencies and two cryptocurrency uncertainty indices (UCRY price and UCRY policy). Using weekly returns from 1 October 2017 to 30 March 2021, the paper employs the bivariate wavelet coherence method considering three investment horizons, short-term, medium-term, and long-term. The results confirm that conventional and sustainable cryptocurrencies show consistent positive and identical co-movements with both cryptocurrency uncertainty indices at the short-term horizon during COVID-19 and negative co-movement at the medium-term investment horizon, suggesting the short-term hedging ability of dirty/green cryptocurrencies for high UCRY price and policy. Evidence of negative coherences shows that higher cryptocurrency prices and policy uncertainties lead to lower cryptocurrency returns, reflecting the adverse impact of higher uncertainties on the trust of crypto traders and investors. Weak co-movement is found between dirty/green cryptocurrencies and UCRY price/policy indices, which suggests the possible role of dirty/green cryptocurrencies as a weak hedge for UCRY price and policy indices. These findings provide potential avenues to hedge cryptocurrency uncertainties using conventional and sustainable cryptocurrencies across multiple investment horizons.
In this paper we analyze dynamic demand elasticity for Bitcoin and Ethereum in terms of price, transaction fees, and energy usage. We find that while both BTC and ETH have significantly positive price elasticities, transaction fee elasticity is negative and positive for BTC and ETH respectively, indicating differences in potential uses for these cryptocurrencies.
Mehedi Masud, Gurjot Singh Gaba, Pardeep Kumar, Andrei Gurtov
Ambient Intelligence (AmI) in Internet of Things (IoT) has empowered healthcare professionals to monitor, diagnose, and treat patients remotely. Besides, the AmI-IoT has improved patient engagement and gratification as doctors’ interactions have become more comfortable and efficient. However, the benefits of the AmI-IoT-based healthcare applications are not availed entirely due to the adversarial threats. IoT networks are prone to cyber attacks due to vulnerable wireless mediums and the absentia of lightweight and robust security protocols. This paper introduces computationally-inexpensive privacy-assuring authentication protocol for AmI-IoT healthcare applications. The use of blockchain & fog computing in the protocol guarantees unforgeability, non-repudiation, transparency, low latency, and efficient bandwidth utilization. The protocol uses physically unclonable functions (PUF), biometrics, and Ethereum powered smart contracts to prevent replay, impersonation, and cloning attacks. Results prove the resource efficiency of the protocol as the smart contract incurs very minimal gas and transaction fees. The Scyther results validate the robustness of the proposed protocol against cyber-attacks. The protocol applies lightweight cryptography primitives (Hash, PUF) instead of conventional public-key cryptography and scalar multiplications. Consequently, the proposed protocol is better than centralized infrastructure-based authentication approaches.
An innovation that was sparked is an alternative currency to replace conventional currency, namely digital currency that is secured using a cryptographic method called Cryptocurrency. However, cryptocurrency prices cannot be controlled, causing massive fluctuations. Cryptocurrency price changes are very stable due to several factors such as speculation from users and the nature of the follow-up from new to one of the causes. The cause of this bandwagon is due to extreme price increases in a short time, even within a day the price of Crypto can experience a difference of millions of rupiah for each increase and decrease. To answer this question, we need a software that can predict the Rupiah exchange rate. By using the Triple Exponential Smoothing prediction method. In implementing Triple Exponential Smoothing, historical data is needed which will later be processed to produce prediction results, for that we need parameters that can be used as historical data counters, in this study used parameters 0.40 as parameters Xt and S't and 0.60 as Beta parameters. In the process, starting from calculating the first to third smoothing, ensuring Constants, Slopes, and Parabolics, to completing the process by producing Forecasting, the results of the measurement of the margin error level with a margin of error of 0.94638% using data for the final 2021 period, which is between November - December on the Triple Exponential implementation simulation and on the implementation into the system that has been developed is 3.7249% using data from May 2022 to June 2022.
Virtual digital assets including cryptocurrencies, non-fungible tokens and decentralized financial asset have been initially used as an alternative currency but are currently being purchased as an asset and hedging instruments. Exponentially growing trading volume witnesses the growing inclination of investors towards these assets, and this calls for volatility analysis of these assets. In this reference, the present study assessed and compared the volatility of returns from investment in virtual digital assets, equity and commodity market. Daily closing prices of selected cryptocurrencies, non-fungible tokens and decentralized financial assets, stock indices and commodities have been analysed for the post-covid period. Since returns were observed to be heteroscedastic, autoregressive conditional heteroscedastic models have been used to assess the volatility. The results indicate a low correlation of commodity investment with all other investment opportunities. Also, Tether and Dai have been observed to be negatively correlated with stock market. This indicates the possibility of minimizing risk through portfolio diversification. In terms of average returns, virtual digital assets are discerned to be better options than equity stock or commodity yet the variance scenario of these investment avenues is not very rosy. The volatility parameters reveal that unlike commodity market, virtual digital assets have got a significant impact of external shocks in the short-run. Further, the long run persistency of shocks is observed to be higher for the UK stock market, followed by Ethereum, Tether and Dai. The present analysis is crucial as the decision about its acceptance as legal tender money is still sub-judice in some countries. The results are expected to provide insight to regulatory bodies about these assets.
Smart contracts are the key software components to realize blockchain applications, from single encrypted digital currency to various fields. Due to the immutable nature of blockchain, any bugs or errors will become permanent once published and could lead to huge economic losses. Recently, a great number of security problems have been exposed in smart contracts. It is important to verify the correctness of smart contracts before they are deployed on the blockchain. This paper aims to verify the correctness of smart contracts in Ethereum transactions, and the model checker Spin is adopted for the formal verification of smart contracts in order to ensure their execution with respect to parties’ willingness, as well as their reliable interaction with clients. In this direction, we propose a formal method to construct the models for smart contracts. Then, the method is applied to a study case in the Ethereum commodity market. Finally, a case model is implemented in Spin, which can simulate the process’s execution and verify the properties that are abstracted from the requirements. Compared with existing techniques, formal analysis can verify whether smart contracts comply with the specifications for given behaviors and strengthen the credibility of smart contracts in the transaction.
Amal Abid, Saoussen Cheikhrouhou, Slim Kallel, Mohamed Jmaïel
Blockchain presents a promising and revolutionary technology for organizations' collaboration, particularly for Inter-Organizational Business Processes (IOBP). It addresses the lack-of-trust problem thanks to its transparency and decentralized features. However, while the adoption of Blockchain technology can alleviate some of IOBP's challenges, it does so at the expense of significant privacy issues. In fact, some process execution data, such as customers' data or business secrets, cannot be shared across the collaborating organizations owing to regulatory restrictions such as the General Data Protection Regulation (GDPR). To address trust and privacy issues in IOBP, this paper presents a Blockchain-based Self-Sovereign Identity (SSI) approach. The SSI concept is combined with a registry proof smart contract to provide an efficient privacy-preserving solution. The proposed approach is applied to the pharmaceutical supply chain case study and implemented on the Ethereum Blockchain.
Eventual consistency is a consistency model that favors liveness over safety. It is often used in large-scale distributed systems where models ensuring a stronger safety incur performance that are too low to be deemed practical. Eventual consistency tends to be uniformly applied within a system, but we argue a demand exists for differentiated eventual consistency, e.g. in blockchain systems. We propose update-query consistency with primaries and secondaries (UPS) to address this demand. UPS is a novel consistency mechanism that works in pair with our novel two-phase epidemic broadcast protocol gossip primary-secondary (GPS) to offer differentiated eventual consistency and delivery speed. We propose two complementary analyses of the broadcast protocol: a continuous analysis and a discrete analysis based on compartmental models used in epidemiology. Additionally, we propose the formal definition of a scalable consistency metric to measure the consistency trade-off at runtime. We evaluate UPS in two simulated worldwide settings: a one-million-node network and a network emulating that of the Ethereum blockchain. In both settings, UPS reduces inconsistencies experienced by a majority of the nodes and reduces the average message latency for the remaining nodes.
Svrha je istraživanja u ovom diplomskom radu utvrditi što je potrebno za pravno i tehnološko unapređenje i usklađivanje pametnih ugovora kako bi pametni ugovori imali širu primjenu i pravnu prihvaćenost te bili ravnopravni s tradicionalnim ugovorima u pisanom obliku. Cilj je istraživanja dokazati spremnost tehnologije koja omogućava pametne ugovore te objasniti zašto pametni ugovori nisu do sad pravno regulirani i prihvaćeni. Analizom i usporedbom definicija za blockchain tehnologiju i pametne ugovore ponuđena je vlastita definicija za blockchain tehnologiju i pametne ugovore. Usporedili su se tradicionalni i pametni ugovori kako bi se istaknule prednosti i nedostaci pametnih ugovora te su se ponudila rješenja za nedostatke pametnih ugovora. Prijedlozi za tehnološko unapređenje pametnih ugovora su ponuđeni za Ethereum blockchain jer je to prva platforma za pametne ugovore koja je najveća i najpopularnija. Ponuđena su rješenja za prepreke koje su detektirali regulatori pojedinih država i organizacije. Pravna unapređenja pametnih ugovora su predložena na temelju analizi stava sudionika u pravnom sustavu.
Recently, security issues of smart contracts are arising great attention due to the enormous financial loss caused by vulnerability attacks. There is an increasing need to detect similar codes for hunting vulnerability with the increase of critical security issues in smart contracts. Binary similarity detection that quantitatively measures the given code diffing has been widely adopted to facilitate critical security analysis. However, due to the difference between common programs and smart contract, such as diversity of bytecode generation and highly code homogeneity, directly adopting existing graph matching and machine learning based techniques to smart contracts suffers from low accuracy, poor scalability and the limitation of binary similarity on function level. Therefore, this paper investigates graph neural network to detect smart contract binary code similarity at the program level, where we conduct instruction-level normalization to reduce the noise code for smart contract pre-processing and construct contract control flow graphs to represent smart contracts. In particular, two improved Graph Convolutional Network (GCN) and Message Passing Neural Network (MPNN) models are explored to encode the contract graphs into quantitatively vectors, which can capture the semantic information and the program-wide control flow information with temporal orders. Then we can efficiently accomplish the similarity detection by measuring the distance between two targeted contract embeddings. To evaluate the effectiveness and efficient of our proposed method, extensive experiments are performed on two real-world datasets, i.e., smart contracts from Ethereum and Enterprise Operation System (EOS) blockchain-based platforms. The results show that our proposed approach outperforms three state-of-the-art methods by a large margin, achieving a great improvement up to 6.1% and 17.06% in accuracy.
The advent of Ethereum opened up a pandora box of decentralized possibilities. While allowing for the replicated, decentralized computation of Turing-complete instructions, platforms such as Ethereum do not offer the possibility of direct, interactive, real-time processing of users’ inputs that could later affect the decentralized state machine. They cannot directly observe, replicate and authenticate users’ actions performed in real-time while presenting the results of these. They lack mechanics that would incentivize full-nodes to provide low-latency-constrained services to users in-between epochs of a decentralized state machine, thus pushing dApps’ developers towards hybrid architectures—ones employing centralized servers while not even considering certain applications, due to the aforementioned limitations. In this research paper, we explore our results of an attempt to create a ‘decentralized operating system’ user experience a reality. We propose an architecture which solves the problems of the responsiveness and finalization of multiple actions performed by users in real-time—without the need for users to pre-authenticate but after having presented a single, unitary consent to commit—through the hereby proposed Deferred Authentication mechanism. To allow for this, we employ an in-house developed #GridScript programming language, used by our decentralized state machine, along with a computer-vision-enabled and AI-aided mobile app (available for both iOS and Android). We introduce the concept of Decentralized Processing Threads (DPTs) and see how these enable fascinating possibilities. In addition, we look into how Access-Control-Lists (ACLs)-enabled, incentivized storage, incentivized Sybil-proof communication, embedded firewall apparatus, integrated off-the-chain payments, and crypto-incentivized off-the-chain storage aid such a system and thus render it as feasible. We highlight various interesting troubles we have encountered, such as state recovery after disconnects of the UI and the replication of its state across both nodes maintaining the network and web browsers. We depict ‘off-the-chain’ mechanics, which we use to reward for real-time services provided to users by nodes maintaining the network. We tackle crypto-incentivized WebRTC swarms not needing centralized servers for signaling. We look into a user-friendly approach to Non-Fungible Tokens (NFTs). The test-bed is readily available with multiple functional UI dApps already in place. Indeed, the paper presents UI and UX design decisions we have undertaken based on conclusions from statistical research results on a group of 50,341 volunteers over 4 years, which we have used to formulate what we codenamed as the Venice UI/UX design paradigm. We extend upon the notion of Token Pools to allow for the Sybil-proof incentivization of multiple-peers from a single data structure stored on the decentralized state machine.
Ethereum, a typical application of blockchain technology, has attracted extensive attention from all walks of life since its release. Owing to imperfections in existing supervision technology, illegal and criminal activities on blockchain platforms are becoming increasingly frequent. The most typical Ethereum fraud is the Ponzi scheme, which causes blockchain investors to lose millions of assets and severely impacts social development. Currently, Ponzi scheme detection primarily focuses on machine learning and data mining. However, existing detection methods still have two problems in data imbalance processing and feature extraction: (1) data enhancement using an oversampling algorithm produces noise and (2) feature redundancy existing in extracted feature data. The SMOTEENN algorithm is introduced to solve data imbalance. The PD-SECR method, the Convolutional Neural Network (CNN) feature extraction, and random forest (RF) classification models are used for detection, but the two models are independently trained. The results show that the detection method proposed in this study is more suitable for the Ethereum Ponzi scheme.