Cătălin Gheorghe, Oana Panazan, Hind Alnafisah, Ahmed Jeribi
This study investigates the asymmetric responses of AI and ESG Exchange Traded Funds (ETFs) to geopolitical and financial uncertainty, with a focus on resilience across market regimes. The NASDAQ-100 and MSCI ESG Leaders indices are used as proxies for thematic ETFs, and their dynamic interlinkages are examined in relation to volatility indicators (VIX, GPR), alternative assets (Bitcoin, Ethereum, gold, oil, natural gas), and safe-haven currencies (CHF, JPY). A daily dataset spanning the 2016–2025 period is analyzed using Quantile-on-Quantile Regression (QQR) and Wavelet Coherence (WCO), enabling a granular assessment of nonlinear, regime-dependent behaviors across quantiles. Results reveal that ESG ETFs demonstrate stronger downside resilience under extreme uncertainty, maintaining stability even during periods of elevated geopolitical and financial risk. In contrast, AI-themed ETFs tend to outperform under moderate-risk conditions but exhibit greater vulnerability during systemic stress, reflecting differences in asset composition and investor risk perception. The findings contribute to the literature on ETF resilience and cross-asset contagion by highlighting differential behavior patterns under varying uncertainty regimes. Practical implications emerge for investors and policymakers seeking to enhance portfolio robustness through thematic diversification during market turbulence.
Humans have the ability to incrementally learn, accumulate, update, and apply knowledge from dynamic environments. This capability, known as continual learning or lifelong learning, is also a long-term goal in the development of artificial intelligence. However, neural network-based continual learning suffers from catastrophic forgetting: the acquisition of new knowledge typically disrupts previously learned knowledge, leading to partial forgetting and a decline in the model’s overall performance. Most current continual learning methods can only mitigate catastrophic forgetting and fail to incrementally improve the overall performance. In this work, we aim to incrementally improve performance within sample incremental context by utilizing inter-stage edges as a pathway for explicit knowledge transfer in continual graph learning. Building on this pathway, we propose a knowledge-augmented replay method by leveraging evolving subgraphs of important nodes. This method enhances the distinction between patterns associated with different node classes and consolidates previously learned knowledge. Experiments on phishing detection in Ethereum transaction networks validate the effectiveness of the proposed method, demonstrating effective knowledge retention and augmentation while overcoming catastrophic forgetting and incrementally improving performance. The results also reveal the relationship between average accuracy and average forgetting. Lastly, we identify the key factor to incremental performance improvement, which lays a foundation for convergence of continual graph learning.
Bitcoin and Ethereum’s current combined 71% market dominance creates an unprecedented systemic risk as quantum computing threatens their cryptographic foundations. A successful quantum attack would not merely compromise individual chains but trigger cascading failures across exchanges, stablecoins, DeFi protocols, and tokenized assets—potentially destroying trillions in value. This paper presents a comprehensive framework for transitioning Bitcoin and Ethereum to post-quantum cryptography. We analyze vulnerabilities in ECDSA and SHA-256, evaluate NIST-standardized algorithms (ML-DSA, SLH-DSA, ML-KEM) alongside emerging alternatives, and propose a phased migration strategy using hybrid cryptographic schemes. Our proof-of-concept demonstrates quantum-safe transactions with acceptable performance trade-offs, including detailed soft fork mechanisms, backward compatibility solutions, and incentive structures to achieve network-wide adoption before quantum threats materialize. While theoretical models suggest a 42-month migration timeline, our analysis of real-world complexity, workforce constraints, and historical precedents indicates a more realistic 6–8-year timeline. The framework addresses critical challenges, including smart contract verification, cross-chain compatibility, and miner coordination, to ensure seamless transition while maintaining network security and functionality.
Asst. Prof. Panchami M Hegde, Asst. Prof. Swetha M
Carpooling has emerged as one of the most practical strategies for reducing the growing challenges of traffic congestion, fuel consumption, and environmental pollution, yet conventional carpooling systems that are operated through centralized platforms continue to face numerous issues that restrict their effectiveness and adoption. Existing solutions largely depend on intermediaries to coordinate between drivers and passengers, creating a system that lacks transparency, suffers from high service costs, and exposes user data to privacy risks and security breaches. Moreover, traditional systems are often criticized for inefficient dispute resolution, a reliance on single points of failure such as central servers, and the absence of mechanisms that foster accountability and long-term trust among users. These weaknesses make centralized carpooling platforms vulnerable to manipulation, biased practices, and technical outages, thereby limiting their scope as sustainable mobility solutions. To address these persistent challenges, blockchain technology—specifically the Ethereum ecosystem—offers a transformative alternative. Ethereum supports the development of decentralized applications (dApps) driven by smart contracts, which are self-executing agreements coded directly onto the blockchain. By embedding business logic into these contracts, processes such as ride creation, ride booking, payment settlements, user verification, and rating are automated, ensuring that interactions remain tamper-proof, transparent, and immune to third-party manipulation.
Benjamin Kraner, Luca Pennella, Nicolò Vallarano, Claudio J. Tessone
We introduce a micro-velocity framework for analysing the on-chain circulation of Lidos liquid-staking tokens, stETH, and its wrapped ERC-20 form, wstETH. By reconstructing full transfer and share-based accounting histories, we compute address-level velocities and decompose them into behavioural components. Despite their growing importance, the micro-level monetary dynamics of LSTs remain largely unexplored. Our data reveal persistently high velocity for both tokens, reflecting intensive reuse within DeFi. Yet activity is highly concentrated: a small cohort of large addresses, likely institutional accounts, are responsible for most turnover, while the rest of the users remain largely passive. We also observe a gradual transition in user behavior, characterized by a shift toward wstETH, the non-rebasing variant of stETH. This shift appears to align with DeFi composability trends, as wstETH is more frequently deployed across protocols such as AAVE, Spark, Balancer, and SkyMoney. To make the study fully reproducible, we release (i) an open-source pipeline that indexes event logs and historical contract state, and (ii) two public datasets containing every Transfer and TransferShares record for stETH and wstETH through 2024-11-08. This is the first large-scale empirical characterisation of liquid-staking token circulation. Our approach offers a scalable template for monitoring staking asset flows and provides new, open-access resources to the research community.
I WAYAN SUMARJAYA, RENOVAR JOJOR DELIMA SIMANULLANG, RATNA SARI WIDIASTUTI
Forecasting is the process of estimating future events using past data. Financial time series forecasting often prioritizes stock price variables. Apart from the stock price variable, inter-transaction time or duration is also an important variable to predict, because the timing of changes in financial prices cannot be predicted. Duration modeling and forecasting can be done using the autoregressive conditional duration (ACD) model. In this research, modeling and forecasting using the ACD model was carried out on Ethereum. This research aims to predict the duration of Ethereum in order to help traders know the time needed to reach the next price change. Several ACD models with four distributions, i.e., exponential, Weibull, Burr, and generalized gamma were fit to the Ethereum duration. The research results suggest that the Burr-ACD model produces the smallest AIC value compared to other distributed ACD models. However, the forecast results using the Burr-ACD models show increasing duration and hence are less accurate. The generalized gamma-ACD (2,2) model was then chosen as an alternative for forecasting Ethereum duration, showing that Ethereum duration forecast results are less than one second, which indicates the high frequency of transactions that occur on Ethereum.
Shafique Ahmed Awan, Muazzam A. Khan, Anwar Ali Sathio, Haleem Farman · 7 authors
Blockchain scalability is a pressing challenge affecting blockchain throughput, latency, and energy consumption. This study proposes a dynamic block size optimization framework for private blockchain networks using hybrid heuristic algorithms—Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO)—integrated with Merkle trees and Directed Acyclic Graphs (DAGs). The core contribution is a novel block size adjustment strategy that relocates the Merkle root from the block header to a local buffer, reducing header size from 80 to 48 bytes and enabling higher transaction capacity. The framework achieved a 33.33% increase in transactions per block, a 30% reduction in latency, a 25% reduction in energy consumption, and a 60% improvement in transactions per kilobyte (KB). These results were validated using Python-based simulations, Ethereum transaction datasets, and defined experimental settings. The proposed solution is currently applicable to private blockchains, with future validation planned for public blockchain networks.
The cryptocurrency market, which is extremely volatile and has high price fluctuations, is transforming the financial ecosystems in the world.In contrast to traditional markets, cryptocurrencies are characterized by the unprecedented volatility due to the complicated interaction of speculative trading, regulatory changes, technological breakthroughs, and macroeconomic forces.The purpose of the current study is to build and test machine learning models to predict the price trend of cryptocurrencies, including the most popular ones, Bitcoin (BTC), Ethereum (ETH), and other top altcoins that are traded in the United States.The analysis is based on a large amount of data on historical prices at daily, hourly, and minute-by-minute intervals, including the detailed data on opening, closing, high, and low prices, and trading volumes that indicate the liquidity and the activity of investors.The most important technical indicators such as moving averages, Relative Strength Index (RSI) and Bollinger Bands are incorporated to identify the most important market signals and momentum.It uses three machine learning models, including Logistic Regression, Random Forest Classifier, and XGBoost Classifier.Directional prediction capability (upward or downward price movements) is evaluated by accuracy, precision, recall, and F1-score measures of model performance.Logistic Regression was the most accurate among the models that were tested, which highlights its comparative effectiveness in this application.The introduction of AI-based predictive analytics into cryptocurrency trading can be a great way to improve the process of decision-making by traders and institutional investors and help them comply with regulations in the U.S. financial system.This study sheds light on the transformational nature of machine learning in cryptocurrency prediction and also points out the research opportunities in the future, especially the use of deep learning models like the Long Short-Term Memory (LSTM) network in time-series analysis.
This paper presents a comprehensive comparative analysis of three prominent blockchain networks: Solana (SOL), Ethereum (ETH), and SUI. The study examines transaction speeds, costs, transaction volumes, and evaluates the benefits and disadvantages of each cryptocurrency in real-world applications. Through detailed analysis of technical specifications, market performance, and ecosystem development, this research provides insights into the relative strengths and weaknesses of these blockchain platforms as they compete for market dominance in 2025.
Among the plethora of literature on interlinkages in markets, more focus has been on peripheral factors. This study attempts to fill this gap by exploring volatility as driver for interlinkages between Bitcoin, Ethereum, Tether, USD-Coin, Binance Coin (BNB), and the crypto-volatility-index (CVI) from April 2019 to August 2022. Using various wavelet techniques, the study depicts significant interlinkages across short-term, medium-term, and long-term horizons, with relatively stronger interlinkages in the long term. The findings confirm that while CVI does not drive these interlinkages, Ethereum, Bitcoin, and CVI play dominant roles in the short, interim, and medium-term periods, respectively, offering new insights into the dynamism of cryptocurrency markets.
Non-fungible tokens (NFTs) have become a significant digital asset class, each uniquely representing virtual entities such as artworks. These tokens are stored in collections within smart contracts and are actively traded across platforms on Ethereum, Bitcoin, and Solana blockchains. The value of NFTs is closely tied to their distinctive characteristics that define rarity, leading to a growing interest in quantifying rarity within both industry and academia. While there are existing rarity meters for assessing NFT rarity, comparing them can be challenging without direct access to the underlying collection data. The Rating over all Rarities (ROAR) benchmark addresses this challenge by providing a standardized framework for evaluating NFT rarity. This paper explores a dimension reduction approach to rarity design, introducing new performance measures and meters, and evaluates them using the ROAR benchmark. Our contributions to the rarity meter design issue include developing an optimal rarity meter design using non-metric weighted multidimensional scaling, introducing Dissimilarity in Trades (DIT) as a performance measure inspired by dimension reduction techniques, and unveiling the non-interpretable rarity meter DIT, which demonstrates superior performance compared to existing methods.
Recent advancements in money laundering detection have demonstrated the potential of using graph neural networks to capture laundering patterns accurately. However, existing models are not explicitly designed to detect the diverse patterns of off-chain cryptocurrency money laundering. Neglecting any laundering pattern introduces critical detection gaps, as each pattern reflects unique transactional structures that facilitate the obfuscation of illicit fund origins and movements. Failure to account for these patterns may result in under-detection or omission of specific laundering activities, diminishing model accuracy and allowing schemes to bypass detection. To address this gap, we propose the MPOCryptoML model to effectively detect multiple laundering patterns in cryptocurrency transactions. MPOCryptoML includes the development of a multi-source Personalized PageRank algorithm to identify random laundering patterns. Additionally, we introduce two novel algorithms by analyzing the timestamp and weight of transactions in high-volume financial networks to detect various money laundering structures, including fan-in, fan-out, bipartite, gather-scatter, and stack patterns. We further examine correlations between these patterns using a logistic regression model. An anomaly score function integrates results from each module to rank accounts by anomaly score, systematically identifying high-risk accounts. Extensive experiments on public datasets including Elliptic++, Ethereum fraud detection, and Wormhole transaction datasets validate the efficacy and efficiency of MPOCryptoML. Results show consistent performance gains, with improvements up to 9.13% in precision, up to 10.16% in recall, up to 7.63% in F1-score, and up to 10.19% in accuracy.
Introduction: Digital content, including images and videos, is increasingly ruling the online world, and so multimedia services form a part of this modern life. However, the digital resources face significant problems, especially regarding copyright infringement. In such an instance, any modification without authority infringes intellectual property rights. Methods: Based on Inter Planetary File System (IPFS) and blockchain technology, a decentralized and distributed framework has been proposed in this study for dealing with insecurity over digital assets and openness of multimedia resources. In this respect, secure, transparent, and immutable transactions in regard to the transfer and ownership of creative works have been facilitated by the use of such a framework. Results: This paper proposes novel decentralized and Blockchain enabled framework to address the problem of video copyright protection by employing solidity based smart contract in a Ethereum network, that allows the content creators to register their videos. The designed smart contract performs copyright checks and release copyright disputes by generating and comparing perceptual hash's (Phash) for original video and modified video. Discussion: Phash techniques play a crucial role in multimedia content analysis, particularly in verifying the integrity and similarity of the video data under various transformations. Additionally, the framework generates Inter Planetary File System (IPFS) main values that signifies the ownership of the video content. Then it compars the phash values, IPFS and similarly score in public Blockchain environment i.e. Ethereum. The framework performance was measured by simulating the contracts of the Application Binary Interface (ABI), JSON file in the Hyperledger Caliper environment. This result shows the performance in the form of video registration, the measured latency was 5.02 seconds with a throughput of 409.87 seconds. For video verification the latency was 4.57 seconds with a throughput of 484.23 seconds.
As blockchain technology advances, Ethereum based gambling decentralized applications (DApps) represent a new paradigm in online gambling. This paper examines the concepts, principles, implementation, and prospects of Ethereum based gambling DApps. First, we outline the concept and operational principles of gambling DApps. These DApps are blockchain based online lottery platforms. They utilize smart contracts to manage the entire lottery process, including issuance, betting, drawing, and prize distribution. Being decentralized, lottery DApps operate without central oversight, unlike traditional lotteries. This ensures fairness and eliminates control by any single entity. Automated smart contract execution further reduces management costs, increases profitability, and enhances game transparency and credibility. Next, we analyze an existing Ethereum based gambling DApp, detailing its technical principles, implementation, operational status, vulnerabilities, and potential solutions. We then elaborate on the implementation of lottery DApps. Smart contracts automate the entire lottery process including betting, drawing, and prize distribution. Although developing lottery DApps requires technical expertise, the expanding Ethereum ecosystem provides growing tools and frameworks, lowering development barriers. Finally, we discuss current limitations and prospects of lottery DApps. As blockchain technology and smart contracts evolve, lottery DApps are positioned to significantly transform the online lottery industry. Advantages like decentralization, automation, and transparency will likely drive broader future adoption.
Blockchain address poisoning is an emerging phishing attack that crafts "similar-looking" transfer records in the victim's transaction history, which aims to deceive victims and lure them into mistakenly transferring funds to the attacker. Recent works have shown that millions of Ethereum users were targeted and lost over 100 million US dollars. Ethereum crypto wallets, serving users in browsing transaction history and initiating transactions to transfer funds, play a central role in deploying countermeasures to mitigate the address poisoning attack. However, whether they have done so remains an open question. To fill the research void, in this paper, we design experiments to simulate address poisoning attacks and systematically evaluate the usability and security of 53 popular Ethereum crypto wallets. Our evaluation shows that there exist communication failures between 12 wallets and their transaction activity provider, which renders them unable to download the users' transaction history. Besides, our evaluation also shows that 16 wallets pose a high risk to their users due to displaying fake token phishing transfers. Moreover, our further analysis suggests that most wallets rely on transaction activity providers to filter out phishing transfers. However, their phishing detection capability varies. Finally, we found that only three wallets throw an explicit warning message when users attempt to transfer to the phishing address, implying a significant gap within the broader Ethereum crypto wallet community in protecting users from address poisoning attacks. Overall, our work shows that more efforts are needed by the Ethereum crypto wallet developer community to achieve the highest usability and security standard. Our bug reports have been acknowledged by the developer community, who are currently developing mitigation solutions.
Ye Li, Mengliang Li, Dehai Zhao, Jiamou Sun · 5 authors
Solidity, the dominant smart contract language for Ethereum, has rapidly evolved with frequent version updates to enhance security, functionality, and developer experience. However, these continual changes introduce significant challenges, particularly in compilation errors, code migration, and maintenance. Therefore, we conduct an empirical study to investigate the challenges in the Solidity version evolution and reveal that 81.68 % of examined contracts encounter errors when compiled across different versions, with 86.92 % of compilation errors. To mitigate these challenges, we conducted a systematic evaluation of large language models (LLMs) for resolving Solidity compilation errors during version migrations. Our empirical analysis across both open-source (LLaMA3, DeepSeek) and closedsource (GPT-4o, GPT-3.5-turbo) LLMs reveals that although these models exhibit error repair capabilities, their effectiveness diminishes significantly for semantic-level issues and shows strong dependency on prompt engineering strategies. This underscores the critical need for domain-specific adaptation in developing reliable LLM-based repair systems for smart contracts. Building upon these insights, we introduce SMCFIXER, a novel framework that systematically integrates expert knowledge retrieval with LLM-based repair mechanisms for Solidity compilation error resolution. The architecture comprises three core phases: (1) context-aware code slicing that extracts relevant error information; (2) expert knowledge retrieval from official documentation; and (3) iterative patch generation for Solidity migration. Experimental validation across Solidity version migrations demonstrates our approach's statistically significant 24.24% improvement over baseline GPT-4o on real-world datasets, achieving near-perfect 96.97% accuracy.
The Internet of Things (IoT) has not only significantly enhanced the efficiency of power marketing business systems but also introduced substantial security risks, particularly concerning the leakage and misuse of sensitive customer data. The current existence of a variety of data security auditing programs is more or less flawed, unable to comprehensively rule out the risk of data leakage. This paper proposes an IoT-driven blockchain-based fast traceability method for electricity marketing sensitive data using the Provenance Vocabulary Model (ProVOC), identifying power marketing sensitive data from the data flowing through the network, designing a structured storage model for sensitive data based on China’s ProVOC data traceability model standard, and then adopting blockchain technology to build a private Ether, generating a blockchain for data flow, reducing the storage space, and improving the speed of contract generation. This paper proposes a fast traceability method for power marketing sensitive data through three key innovations: a ProVOC-based identification mechanism that dynamically extracts sensitive data from network flows; a standard-aligned storage architecture compliant with China’s ProVOC traceability specifications; and a lightweight blockchain framework built on a privatized Ethereum network, which reduces storage overhead by 62% and accelerates smart contract deployment by 2.3 × compared to conventional approaches.
The manufacturing and distribution of counterfeit tablets, especially in developing countries, is an urgent and increasingly critical global problem. Falsified medicinal products may contain incorrect ingredients and doses. One of the reasons for drug counterfeiting is the imperfect supply chain system in the pharmaceutical industry. Medicinal products are moved between manufacturers, suppliers, wholesalers, retailers, and pharmaceutical firms before meeting consumers. This study proposes PharmChain, a scenario-oriented drug traceability and regulation blockchain framework that reconstructs the entire service infrastructure by splitting the service provider into three separate service components and ensuring the authenticity and privacy of traceability details. PharmChain can track medication development via patient supply in the pharmaceutical industry. An Ethereum-based blockchain stores the transactions, and only trusted parties can access the data through the chain. We create and test our smart contract code in the Remix environment. We present detailed cost and security analyses incurred by supply chain stakeholders. We also use cost analysis to assess the performance of the proposed solution and demonstrate its affordability. • Introduce PharmChain, a data-driven scenario-based drug traceability and regulation blockchain framework. • Ensure authenticity and privacy of traceability data, enhancing security. • Validate smart contract code rigorously in the Remix environment. • Show the feasibility and affordability of PharmChain through detailed analytics. • Alleviate counterfeit tablet distribution, which poses a pressing and escalating global challenge.
Ruba Islayem, Senay A. Gebreab, Walaa AlKhader, Ahmad Musamih · 7 authors
Traditional health insurance claim processing systems are plagued by inefficiencies and vulnerabilities, often resulting in significant financial losses due to fraudulent activities. Existing fraud detection methods are largely manual, time-consuming, and inadequate for handling the complexity and scale of modern fraudulent schemes. Moreover, the trust-based relationships between insurers and healthcare providers lack mechanisms to ensure data integrity and prevent manipulation. While several blockchain-based systems have been proposed to improve transparency and tamper resistance, they typically focus on structured data and predefined fraud types, offering limited adaptability and analytical insight. This paper proposes a novel solution leveraging blockchain technology and Large Language Models (LLMs) to transform fraud detection. The system uses Ethereum smart contracts (SCs) to securely store medical records and claim details on a decentralized, tamper-proof ledger that ensures data integrity, traceability, and accountability. This immutable data is accessed by an LLM via a Retrieval-Augmented Generation (RAG) system, which enables intelligent retrieval and analysis of relevant clinical information to detect fraud patterns and inconsistencies. To support complex scenarios involving free-text documents, unstructured clinical data, such as lab reports, are stored using decentralized off-chain storage and retrieved during LLM analysis. In addition, an LLM-powered chatbot also allows insurance providers to interact with the system in natural language for claim inquiries, explanations, and summaries. The architecture, sequence diagrams, and implementation algorithms outline the development process, while testing scenarios demonstrate the system's ability to detect fraud such as inflated costs, unnecessary treatments, and unrendered services. Evaluation using both synthetic and public clinical datasets showed strong performance, with the LLM achieving up to 99% fraud detection accuracy. Cost, security, and scalability analyses confirm the system's practicality and resilience, with the complete detection process executing in just 13 seconds. By overcoming the limitations of traditional systems, this framework offers a scalable and adaptable approach for healthcare and other domains. The SCs and source code are publicly available on GitHub.
Ruslan Kysil, István András Seres, Péter Kutas, Nándor Kelecsényi
This work explores the application and efficient deployment of (standardized) post-quantum (PQ) digital signature algorithms in the blockchain environment. Specifically, we implement and evaluate four PQ signatures in the Ethereum Virtual Machine: W-OTS+ , XMSS, SPHINCS+, and MAYO. We focus on optimizing the gas costs of the verification algorithms as that is the signature schemes’ only algorithm executed on-chain, thus incurring financial costs (transaction fees) for the users. Hence, the verification algorithm is the signature schemes’ main bottleneck for decentralized applications. We examine two methods to verify post-quantum digital signatures on-chain. Our practical performance evaluation shows that full on-chain verification is often prohibitively costly. Naysayer proofs (FC’24) allow a novel optimistic verification mode. We observe that the Naysayer verification mode is generally the cheapest, at the cost of additional trust assumptions. We release our implementation called poqeth as an open-source library.
Aydin Abadi, Basil Aldali, Benito Vincent, Elliot A. J. Hurley · 13 authors
Weather forecasting plays a vital role in disaster preparedness, agriculture, and resource management, yet current centralized forecasting systems are increasingly strained by security vulnerabilities, limited scalability, and susceptibility to single points of failure. To address these challenges, we propose a decentralized weather forecasting framework that integrates Federated Learning (FL) with blockchain technology. FL enables collaborative model training without exposing sensitive local data; this approach enhances privacy and reduces data transfer overhead. Meanwhile, the Ethereum blockchain ensures transparent and dependable verification of model updates. To further enhance the system's security, we introduce a reputation-based voting mechanism that assesses the trustworthiness of submitted models while utilizing the Interplanetary File System (IPFS) for efficient off-chain storage. Experimental results demonstrate that our approach not only improves forecasting accuracy but also enhances system resilience and scalability, making it a viable candidate for deployment in real-world, security-critical environments.
Pri razvoju decentraliziranih aplikacij (dApps) se tradicionalni razvojni procesi pogosto izkažejo za nezadostne. Tovrstne rešitve zahtevajo večji poudarek na tehničnih, varnostnih in uporabniških vidikih kakovosti aplikacij, kot smo jih sicervajeni pri razvoju klasičnih rešitev. Ker je spreminjanje pametnih pogodb po namestitvi v omrežje verig blokov zahtevno oziroma nemogoče, sta temeljito testiranje ter presoja programske kode ključnega pomena za uspešen razvoj tovrstnih rešitev. Optimizacija stroškov goriva, nujna za izvrševanje programov v javnih omrežjih, predstavlja enega ključnih razvojnih izzivov, ki ga je potrebnoustrezno obravnavati. Poleg tega specifično okolje omrežij veriženja blokov zahteva ustrezne ukrepe za obvladovanje tveganj povezanih z ranljivostmi aplikacij in morebitnimi povezanimi finančnimi izgubami. Nespremenljivost, stroški goriva in zagotavljanje varnosti so le nekateri izmed ključnih razvojnih izzivov, ki jih je treba uspešno nasloviti pri izgradnji kakovostnih in stabilnih decentraliziranih aplikacij. Prispevek obravnava izzive, sodobne pristope in strategije razvoja decentraliziranih aplikacij ter podaja priporočila za njihov zanesljivejši in učinkovitejši razvoj, s čimer naslavlja ključne izzive uvajanja tehnologij veriženja blokov v industrijska okolja ter razvoja pametnih pogodb. Poseben poudarek je namenjen pametnim pogodbam, ki temeljijo na omrežju Ethereum.
Pri razvoju decentraliziranih aplikacij (dApps) se tradicionalni razvojni procesi pogosto izkažejo za nezadostne.Tovrstne rešitve zahtevajo večji poudarek na tehničnih, varnostnih in uporabniških vidikih kakovosti
Blockchain technology, once limited to niche technological communities, has seen widespread global adoption in recent years, with the potential to reshape financial and social systems. Launched in July 2015, the Ethereum blockchain introduced programmable Smart Contracts. This innovation enabled the creation of user-defined crypto-assets adhering to the ERC-20 standard, supporting a wide range of decentralized applications beyond simple value transfer. We present a large-scale, temporally annotated dataset of ERC-20 token transactions recorded on the Ethereum blockchain. Spanning from November 2015 to December 2024, the dataset encapsulates the trading activity of 216,336,529 users trading 1,138,136 unique tokens, offering a detailed view of crypto-market activity over time. Uniquely, it enables the analysis of a financial ecosystem from its inception, providing rare insights into its structural evolution, participant dynamics, and emergent behaviors. As the largest publicly available resource of its kind, it supports research in blockchain analytics, market dynamics and temporal network analysis. The full dataset and accompanying code are released for public use.