András Nagy, János Tapolcai, István András Seres, Bence Ladóczki
Proof-of-stake consensus protocols often rely on distributed randomness beacons (DRBs) to generate randomness for leader selection. This work analyses the manipulability of Ethereum's DRB implementation, RANDAO, in its current consensus mechanism. Even with its efficiency, RANDAO remains vulnerable to manipulation through the deliberate omission of blocks from the canonical chain. Previous research has shown that economically rational players can withhold blocks known as a block withholding attack or selfish mixing when the manipulated RANDAO outcome yields greater financial rewards.
Layer 2 rollups offer promising solutions to address Ethereum's scalability issues. However, the centralized nature of the sequencer in these rollups makes them vulnerable to denial of service attacks, in which adversaries overwhelm the sequencer with invalid transactions that cannot be included in blocks, thereby exhausting its computational resources for transaction processing. To mitigate such threat, layer 2 rollups implement the legality check mechanism to filter out invalid transactions before they reach the sequencer.
M. Zeeshan Haider, Tayyaba Noreen, M. D. Assuncao, Kaiwen Zhang
Sharding has emerged as a key technique to address blockchain scalability by partitioning the ledger into multiple shards that process transactions in parallel. Although this approach improves throughput, static or heuristic shard allocation often leads to workload skew, congestion, and excessive cross-shard communication diminishing the scalability benefits of sharding. To overcome these challenges, we propose the Predictive Shard Allocation Protocol (PSAP), a dynamic and intelligent allocation framework that proactively assigns accounts and transactions to shards based on workload forecasts. PSAP integrates a Temporal Workload Forecasting (TWF) model with a safety-constrained reinforcement learning (Safe-PPO) controller, jointly enabling multi-block-ahead prediction and adaptive shard reconfiguration. The protocol enforces deterministic inference across validators through a synchronized quantized runtime and a safety gate that limits stake concentration, migration gas, and utilization thresholds. By anticipating hotspot formation and executing bounded, atomic migrations, PSAP achieves stable load balance while preserving Byzantine safety. Experimental evaluation on heterogeneous datasets, including Ethereum, NEAR, and Hyperledger Fabric mapped via address-clustering heuristics, demonstrates up to 2x throughput improvement, 35\% lower latency, and 20\% reduced cross-shard overhead compared to existing dynamic sharding baselines. These results confirm that predictive, deterministic, and security-aware shard allocation is a promising direction for next-generation scalable blockchain systems.
Purpose Cement manufacturing is a vital yet emission-intensive industry that faces challenges in certifying sustainable production practices, driven by the need for transparency, accountability and regulatory compliance. This paper provides a blockchain-based certification framework to enhance traceability and sustainability in cement production. Design/methodology/approach Leveraging Ethereum smart contracts (SCs) and blockchain technology, our solution ensures decentralized, immutable tracking of emissions data, production processes and compliance through secure interactions among regulators, manufacturers and auditors. The framework facilitates deployment, registration, reporting, auditing and certification. Detailed insights into system architecture, algorithms, SC implementation and validation are provided. Security analysis evaluates access controls, data privacy and vulnerability mitigation, while cost analysis highlights the framework's economic feasibility by examining gas costs for key functions. Findings The blockchain-based framework successfully produced a scalable solution with a modular design to allow for flexible deployment by different regulatory bodies. Transparency was achieved through blockchain events announcement, and accuracy was ensured through periodic auditing rounds. Security analysis results show no serious vulnerabilities. The developed framework proves a cost-effective solution for certifying sustainable production practices in the cement industry, with potential applications across other heavy manufacturing sectors. Research limitations/implications Cement manufacturing is a vital yet emission-intensive industry that faces significant challenges in certifying sustainable production practices, driven by the need for transparency, accountability and regulatory compliance. Practical implications We believe that this paper provides the following practical implications: Innovative Framework: A blockchain-based certification system promoting adherence to sustainable processes in cement manufacturing. SC Implementation: Detailed insights into the system architecture, implementation and validation of algorithms and SCs. Comprehensive Analysis: A thorough security analysis of SC coding and a cost analysis highlighting the economic feasibility of our proposed framework. Social implications The proposed methodology will help all stakeholder involved in the cement production and supply chain have a better understanding and a robust tool to observe and learn about operations, tasks and other activities made through sustainable cement production Originality/value The study contributes a novel blockchain-based method to certify sustainable production in energy-intensive industries, especially cement, offering a valuable tool that ensures transparency and immutability.
R. Li, Srisht Fateh Singh, Andreas Park, Andreas Veneris
This paper presents a securities tokenization solution that brings the accessibility, transparency, efficiency, and innovation of blockchain and decentralized finance to real-world securities. Tokenization in principle seems straightforward—an intermediary holds assets and issues 1:1 tokens—but decentralized finance applications (DeFi) introduce significant complications. Even basic DeFi mechanisms, such as liquidity pools, pose challenges for tokenizing stocks and bonds because when assets are pooled in smart contracts, ownership becomes unclear, hindering asset owners to access their entitlements, such as dividends, coupons, or voting rights. Existing solutions often fail to address these challenges and are typically limited to specific security types. Our solution, by contrast, generalizes to any security and any holding rights through fungible tokens and using separate smart contracts for shareholders to redeem their entitlements. To address the decentralized ownership issue, our solution employs off-chain accounting with additional logic for liquidity pools. We implement this on Ethereum, demonstrating that it is 27% cheaper in gas costs than current alternatives. We also analyze the liquidity logic of over 90% of Ethereum's liquidity pools, confirming compatibility with our solution. Finally, we demonstrate its use for dividend-paying stocks, common stock, mergers, and coupon-paying bonds.
Omer Aziz, Muhammad Shoaib Farooq, Junaid Nasir Qureshi, Muhammad Faraz Manzoor · 5 authors
(1) Background: A blockchain-based framework for distributed agile Open-Source Software for Archaeological Photogrammetry (OSSAP) testing life cycle is an innovative approach that uses blockchain technology to optimize the Open-Source Software for Archaeological Photogrammetry process. Previously, various methods have been employed to address communication and collaboration challenges in Open-Source Software for Archaeological Photogrammetry, but they were inadequate in aspects such as trust, traceability, and security. Additionally, a significant cause of project failure was the non-completion of unit testing by developers, leading to delayed testing. (2) Methods: This article discusses the integration of blockchain technology in Open-Source Software for Archaeological Photogrammetry and resolves critical concerns related to transparency, trust, coordination, testing and communication. A novel approach is proposed based on a blockchain framework named Open-Source Software for Archaeological Photogrammetry Testing-Plus. (3) Results: The Open-Source Software for Archaeological Photogrammetry Testing-Plus framework utilizes blockchain technology to provide a secure and transparent platform for acceptance testing and payment verification. Moreover, by leveraging smart contracts on a private Ethereum blockchain, Open-Source Software for Archaeological Photogrammetry Testing-Plus ensures that both the testing team and the development team are working towards a common goal and are compensated fairly for their contributions. (4) Conclusions: The experimental results conclusively show that this innovative approach substantially improves transparency, trust, coordination, testing and communication and provides security for both the testing team and the development team engaged in the distributed agile Open-Source Software for Archaeological Photogrammetry (Open-Source Software for Archaeological Photogrammetry) testing life cycle.
Audio piracy detection is increasingly complex in decentralised distribution settings, where mainstream approaches fail to ensure robustness, verifiability, or computational efficiency. Conventional Digital Rights Management (DRM) systems mainly enforce licensed access, but once content is copied or redistributed outside their control they offer little protection. Classical fingerprinting approaches such as MFCC based hashes can detect near-exact duplicates, yet they often fail under signal edits like pitch shifting, time stretching or equalisation. Deep learning embeddings improve robustness but demand heavy computation and centralised resources, making them less suitable for edge or decentralised deployments. These limitations call for a solution that is both edit resilient and verifiable. We propose HashWave, a blockchain-integrated perceptual hashing framework that combines robust audio fingerprinting with tamper-proof verification. The system fuses MFCC, chroma and chroma CENS, CQT, spectral contrast, and lightweight tempo/energy cues, applying operation-aware weighting via [Formula: see text] and constrained DTW for time-scale edits. Evaluated across GTZAN, FMA-A Dataset for Music Analysis, and MUSAN (SLR17) with over twenty signal-processing transformations, HashWave achieves AUC 0.957 and TPR@1%FPR 0.952, outperforming MFCC-only baselines and approaching deep embeddings at lower CPU cost. The blockchain layer, built on Ethereum and IPFS, ensures decentralised hash storage, duplication control, and verifiable authorship with average upload and contract execution times of 0.017 s and 0.044 s. Together, these results establish HashWave as a practical, scalable, and secure framework for piracy detection across streaming, podcasting, and Web3 ecosystems.
Open access
Advanced Steganography and Watermarking Techniques
Early diagnosis of cardiac abnormalities depends on accurate classification of heart sounds, but centralized training methods run the danger of violating patient privacy. We thus propose a privacy-preserving and reliable heart sound abnormality detection system combining Blockchain Technology with Federated Learning (FL). Training is spread among seven clients, each simulating an independent data source, using a preprocessed dataset from the PhysioNet Challenge 2016 to enable distributed learning without sharing raw data. CNN-LSTM model using FedAvg achieved the best performance: 94\% accuracy, 0.90 precision, 0.96 recall, and an AUC of 0.98 among five deep learning architectures evaluated with FedAvg and FedProx strategies. Along with metadata including client ID and round number, SHA-256 hashes of local and global model weights were recorded on a local Ethereum blockchain following every communication round to guarantee model integrity. The hash of the final model is revalidated against the blockchain to confirm authenticity prior to deployment. It then guarantees safe, distributed, clinically valuable AI-based diagnostics by real-time classification of heart sounds as normal or abnormal.
The NFT ecosystem represents an interconnected, decentralized environment that encompasses the creation, distribution, and trading of Non-Fungible Tokens (NFTs), where key actors, such as marketplaces, sellers, and buyers, utilize smart contracts to facilitate secure, transparent, and trustless transactions. Scam tokens are deliberately created to mislead users and facilitate financial exploitation, posing significant risks in the NFT ecosystem. Prior work has explored the NFT ecosystem from various perspectives, including security challenges, actor behaviors, and risks from scams and wash trading, leaving a gap in understanding the semantics and interactions of smart contracts during transactions, and how the risks associated with scam tokens manifest in relation to the semantics and interactions of contracts. To bridge this gap, we conducted a large-scale empirical study on smart contract semantics and interactions in the NFT ecosystem, using a curated dataset of nearly 100 million transactions across 20 million blocks on Ethereum. We observe a limited semantic diversity among smart contracts in the NFT ecosystem, dominated by proxy, token, and DeFi contracts. Marketplace and proxy registry contracts are the most frequently involved in smart contract interactions during transactions, engaging with a broad spectrum of contracts in the ecosystem. Token contracts exhibit bytecode-level diversity, whereas scam tokens exhibit bytecode convergence. Certain interaction patterns between smart contracts are common to both risky and non-risky transactions, while others are predominantly associated with risky transactions. Based on our findings, we provide recommendations to mitigate risks in the blockchain ecosystem, and outline future research directions.
Jennifer Bala, Sikiru O. SUBAIRU, Noel M. DOGONYARO, Joseph A. OJENIYI · 5 authors
Blockchain technology, particularly Ethereum, has revolutionized decentralized finance by enabling transparent, secure, and programmable smart contracts. However, these same features have created avenues for financial crimes such as Ponzi schemes, where fraudulent actors exploit pseudonymity and the absence of centralized oversight to deceive investors. This study develops an optimized hybrid detection model that combines eXtreme Gradient Boosting (XGBoost) and Gated Recurrent Units (GRU) to identify Ponzi schemes in Ethereum transaction networks. The model integrates XGBoost’s capability for structured feature learning with GRU’s temporal sequence modeling to capture both static and dynamic behavioral patterns of smart contracts. Using a dataset of 3,866 labeled Ethereum contracts obtained from Kaggle, the research employed advanced preprocessing, temporal sequence enrichment, and class balancing through SMOTE-TS to mitigate data imbalance. Bidirectional optimization, incorporating attention-enhanced GRUs and Bayesian hyperparameter tuning for XGBoost, further improved learning performance and generalization. The model was evaluated using precision, recall, F1-score, ROC-AUC, and PR-AUC, achieving higher detection accuracy of 99% (F1-score = 0.945, ROC-AUC = 0.983) than standalone XGBoost or GRU models. Results demonstrate the hybrid model’s superior ability to detect temporal and statistical anomalies, reducing false negatives and improving early detection of fraudulent contracts. The approach contributes a scalable and interpretable framework for real-time Ponzi detection in blockchain ecosystems. This research not only enhances the reliability of Ethereum’s financial ecosystem but also offers regulators and developers a novel tool for proactive fraud prevention. Future work could extend this framework to multi-chain detection systems and real-time forensic monitoring.
Abstract - Donation fraud and lack of transparency are major challenges in traditional charity systems, where donors often have limited visibility into how their contributions are utilized. Centralized platforms are prone to data manipulation, unauthorized fund usage, and security breaches, reducing donor confidence. This study explores blockchain-based approaches for securing and accurately managing donation transactions. We review various systems that implement smart contracts, decentralized ledgers, and cryptographic techniques to ensure transparency, traceability, and accuracy in fund distribution. The analysis compares architectural designs, data validation mechanisms, accuracy levels, and security models across existing frameworks. Finally, we highlight current limitations and propose future enhancements to improve scalability, privacy, and real-world implementation of blockchain-based donation management systems. Keywords: Blockchain, Smart Contracts, Donation Security, Transparency, Decentralized Ledger, Cryptography, Ethereum, Zero-Knowledge Proofs, Data Accuracy, Trust Management.
La información es un insumo vital para el desarrollo de cualquier proceso en la sociedad. Por supuesto, las actividades relativas a la atención y servicio de salud en el país se basan en los datos de cada uno de los actores y procesos que intervienen en el sistema de salud. De esta manera, los datos se convierten en activos de información fundamentales para garantizar el correcto funcionamiento del sistema y, a su vez, garantizar la calidad en el servicio prestado a las personas. Con el objeto de salvaguardar la integridad, autenticidad, custodia, trazabilidad, secuencialidad, inmutabilidad y confidencialidad de la información existen, además de los sistemas de información centralizados, aquellos sistemas que operan bajo una arquitectura descentralizada que permite cumplir con estas premisas, incluyendo además capas de encriptación anidadas entre bloques de información definidos como el caso concreto de blockchain. Se propone utilizar este sistema descentralizado de almacenamiento seguro de datos para el alojamiento de información relativa a las “Historias Clínicas” en el país. Este trabajo busca integrar, a modo académico, las propiedades y cualidades del sistema blockchain para el alojamiento de información de estos registros clínicos en Colombia.
<p>Document forgery remains a pervasive problem across education, government, and trade sectors. This paper presents a blockchain-based digital document verification system built on the Internet Computer Protocol (ICP). The approach computes SHA‑256 hashes of documents and anchors them to ICP canister smart contracts, ensuring integrity and non-repudiation without storing document contents. The system manages a registry of approved verifiers so that only trusted institutions can enroll documents. In evaluation with 15 documents (85–3025 KB) and five repeated trials per document, the prototype achieved an average verification time of 1.54 s and an accuracy of 99%. Compared with Ethereum-based baselines in prior work, the ICP-based design avoids gas fees and reduces verification latency. The proposed architecture supports future integration of zero-knowledge proofs (ZKP) to validate authenticity while preserving privacy.</p>
Open access
Blockchain Technology Applications and Security
Big Data and Digital Economy
Physical Unclonable Functions (PUFs) and Hardware Security
The rise of counterfeit medicines poses serious health risks to the public and threatens the credibility of pharmaceutical companies. Blockchain technology is gaining attention for its potential to enhance the security and transparency of the pharmaceutical supply chain. This study compares the performance of two blockchain platforms, Ethereum and Nexus, in detecting and preventing counterfeit drugs. A virtual observation system was used to examine how Ethereum and Nexus addressed major challenges in counterfeit drug prevention. Two specialists with expertise in both blockchain technology and healthcare evaluated the platforms using two assessment tools: The Global Quality Scale (GQS) to measure overall effectiveness and the modified DISCERN scale to assess credibility of the information. Nexus outperformed Ethereum in both effectiveness and credibility. Evaluator 1 reported median GQS scores of 4.5 for Nexus and 3.8 for Ethereum, while Evaluator 2 reported scores of 4.2 and 3.8, respectively. These findings indicate that Nexus demonstrated higher efficiency in detecting fake drugs and ensuring supply chain integrity. Blockchain technology shows promise in strengthening pharmaceutical supply chain security. Between the two platforms studied, Nexus was found to be more effective than Ethereum in preventing counterfeit drugs. These results provide valuable insights for pharmaceutical stakeholders and policymakers seeking to implement blockchain-based solutions for drug tracking and security.
Ebuka Chinaechetam Nkoro, Love Allen Chijioke Ahakonye, Dong‐Seong Kim
Smart Contracts (SCs), which are the backbone of automated transactions and digital assets within the Metaverse, ironically suffer from their own share of security vulnerabilities. While detecting these SC vulnerabilities using Artificial Intelligence (AI) and Deep Neural Networks (DNNs) has demonstrated remarkable performance and gained wide adoption, a critical limitation remains: the lack of explainability in these black box models. To facilitate meaningful progress in this field, our study addresses this gap by introducing a model-agnostic explanation framework that is both visual and quantitative, with human stakeholders actively involved to govern, verify, and interpret SC model predictions. The explainable SC outputs can be utilized for reward issuance and digital assets governance in the Metaverse. The effectiveness of our proposed Explainable AI (XAI) approach is validated using benchmark datasets, BCCC SCsVul 2024 and BCCC SCsVul 2023, comprising Ethereum SC entropy source codes, where it achieves an optimal detection accuracy of 97.13% alongside comprehensive explainability. To the best of our knowledge, this represents the first attempt at making Ethereum SC vulnerability detection within the Metaverse explainable, offering a valuable foundation for blockchain researchers, Metaverse security experts, and practitioners seeking verifiable, trustworthy, and auditable Ethereum SC vulnerability detection.
Smart contracts have significantly advanced blockchain technology, and digital signatures are crucial for reliable verification of contract authority. Through signature verification, smart contracts can ensure that signers possess the required permissions, thus enhancing security and scalability. However, lacking checks on signature usage conditions can lead to repeated verifications, increasing the risk of permission abuse and threatening contract assets. We define this issue as the Signature Replay Vulnerability (SRV). In this paper, we conducted the first empirical study to investigate the causes and characteristics of the SRVs. From 1,419 audit reports across 37 blockchain security companies, we identified 108 with detailed SRV descriptions and classified five types of SRVs. To detect these vulnerabilities automatically, we designed LASiR, which utilizes the general semantic understanding ability of Large Language Models (LLMs) to assist in the static taint analysis of the signature state and identify the signature reuse behavior. It also employs path reachability verification via symbolic execution to ensure effective and reliable detection. To evaluate the performance of LASiR, we conducted large-scale experiments on 15,383 contracts involving signature verification, selected from the initial dataset of 918,964 contracts across four blockchains: Ethereum, Binance Smart Chain, Polygon, and Arbitrum. The results indicate that SRVs are widespread, with affected contracts holding $4.76 million in active assets. Among these, 19.63% of contracts that use signatures on Ethereum contain SRVs. Furthermore, manual verification demonstrates that LASiR achieves an F1-score of 87.90% for detection. Ablation studies and comparative experiments reveal that the semantic information provided by LLMs aids static taint analysis, significantly enhancing LASiR's detection performance.
This study explores how integrating cryptocurrencies into traditional financial portfolios can influence investment performance. Focusing on Bitcoin and Ethereum alongside key European stock indices (BUX, DAX, and FTSE), the analysis examines whether blockchain-based assets can enhance diversification and improve the balance between risk and return. Using weekly market data from 2019 to 2023, the research applies Markowitz mean–variance optimization to identify optimal asset allocations under different objectives such as maximizing the Sharpe ratio, minimizing risk, and maximizing returns. The findings reveal that cryptocurrencies show weak correlations with European stock indices, suggesting meaningful diversification potential. When included in portfolios, Bitcoin and Ethereum can significantly boost returns, though they also increase volatility. Portfolios optimized for risk reduction favored traditional indices, while those targeting higher returns relied predominantly on cryptocurrencies. Overall, combining digital and conventional assets produced a more balanced performance, with the Sharpe-ratio–maximized portfolio demonstrating the best trade‐off between stability and profitability. These results indicate that cryptocurrencies can play a valuable complementary role in modern portfolio construction. They are most suitable for investors willing to accept higher risk in exchange for potentially greater rewards, while more risk‐averse investors may benefit from maintaining a stronger focus on traditional equity indices. The study contributes to understanding how blockchain‐driven assets can expand financial opportunities and supports a broader view of diversification in contemporary investment strategies.
Crowdfunding websites tend to have centralized escrow infrastructure, which can create concerns over the lack of transparency, security threats, and fraud vulnerability. The proposed system, a hybrid blockchain–AI architecture, combines Ethereum-based smart contracts and machine learning-based fraud detection to result in a decentralized and transparent crowdfunding space. The blockchain layer ensures accountability with controlled release of funds based on milestones, limiting the tendency to spend funds more due to the cryptocurrency nature with the AI module detecting fraudulent activity based on analysis of textual, transactional, temporal, and reputation data. Experimentation proves that the proposed system promotes higher trust, reduces transaction cost, and signifies a robust fraud detection model compared to conventional crowdfunding models. The results suggest creating a combination of the immutability of blockchains with the analytical power of AI as a potential route to safer and more effective decentralized finance apps.
Polygon Chain Development Kit (CDK) Validium is a Layer 2 blockchain scaling solution that processes transactions off-chain. It uses Polygon’s distinctive approach to Zero-Knowledge Proofs (ZKPs) implemented within their Zero-Knowledge Ethereum Virtual Machine (zkEVM). A key factor in its successful deployment is robustness, ensuring that users can trust their transactions will be processed accurately and promptly. This research concerns developing robust validation methodologies and comprehensive testing strategies targeting the “double-spending” problem within Polygon CDK Validium. We indicate theoretical scenarios where double-spending vulnerabilities could arise in Polygon CDK Validium by identifying how execution errors can combine with a specific category of flawed constraints to create vulnerabilities. When combined with what we classify as Invalid PIL Constraints For EVM Specification Vulnerabilities (IPCFESV), these errors can trigger problematic behaviours. We further illustrate how erroneous behaviour resulting from IPCFESV can lead to cascading involvement in withdrawal operations resulting in irreversible cross-layer double-spending. We also illustrate how a protocol anti-censorship mechanism bypasses standard validation checks, thereby intensifying reliance on constraint correctness. We then propose ways to determine the correct behaviour. We propose a method to utilise Polygon’s integration testing framework for generating execution traces for de-facto ERC-20 fungible token standard. The outcomes of this study will form the foundational basis for the subsequent development of practical testing and verification methods for Polygon CDK Validium. Implementation and empirical validation remain as future work.
Prof. S. H. Thengil, Tanmay Sadanshiv, A. M. Patil, Shreyash Trimbake · 5 authors
Abstract - With the increasing volume of digital evidence in law-enforcement and judicial processes, ensuring integrity, traceability and tamper-resistance has become paramount. This paper presents the Blockchain Evidence Archive System (BEAS), a decentralized application that leverages blockchain technology, smart contracts and the InterPlanetary File System (IPFS) to provide a secure, immutable and transparent evidence- management platform. Evidence metadata is stored on an Ethereum-based blockchain while the associated large files (images, videos, documents) are stored on IPFS with their cryptographic hashes recorded on-chain. Role-based access control ensures only authorized users such as police officers and court officials can upload, verify or access evidence. We describe the system architecture, implementation details, security features and evaluate the performance of the system in terms of upload time, verification latency and resistance to tampering. The results demonstrate that BEAS significantly improves evidence integrity and auditability when compared to conventional centralized systems. We conclude with a discussion on future enhancements including biometric integration, mobile accessibility and enterprise-scale deployment. l Key Words: Blockchain Technology, IPFS, Digital EvidenceManagement, Decentralized Application, Smart Contracts, Ethereum Network, Cryptographic Hashing, Data Integrity, Tamper- Proof Storage, Role-Based Access Control, Chain of Custody, Evidence Verification, Immutable Ledger, Secure File Storage, Decentralized Architecture, Forensics Technology, Law Enforcement Data Security, Distributed Ledger Technology
Mohd. Sultan Ahammad, Maisha Maliha, Nilufa Easmin Nila, Md Shofiqul Islam
Blockchain technology is revolutionizing industries by fundamentally transforming data management and storage practices. Traditional banking systems, however, continue to face challenges such as dependency on intermediaries, lack of transparency, vulnerability to fraud, and restricted accessibility. To overcome this limitation, we propose an innovative blockchain framework built on the Ethereum platform to enhance security and efficiency in banking. The proposed system eliminates intermediaries by using Ethereum-based smart contracts to enable secure, automated peer-to-peer (P2P) deposits, withdrawals, and transfers while incorporating a user-friendly interface with MetaMask and custom wallets for accessibility. The architecture was implemented and tested on the Sepolia Ethereum Testnet using Solidity, Ether.js, and React.js, ensuring seamless interaction between the smart contract and the user interface. Our experimental evaluation demonstrated significant improvements in transaction speed, transparency, and operational efficiency compared to traditional systems, with near real-time processing and automated verification. Performance benchmarking showed competitive latency and throughput, while gas cost analysis highlighted trade-offs in transaction expenses compared to conventional banking. These findings suggest that our blockchain framework has strong potential to address long-standing inefficiencies in the financial sector. While challenges remain, including scalability and regulatory considerations, this work offers a concrete and impactful step toward the practical adoption of blockchain in mainstream banking.
This research examines deep-learning and machine-learning models for cryptocurrency price prediction, with a keen focus on Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), and Solana (SOL). Cryptocurrencies exhibit high volatility, non-linear behavior and are able to react strongly to exogenous events, making their prediction and forecasting challenging. The primary aim of this research is to determine which predictive models yield optimal performance in characterizing these complexities and to provide empirical guidance on real-life investment and risk-management applications. Four approaches were used for this forecasting: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), a combination of LSTM-GRU models, and Stochastic Gradient Descent (SGD) regression. The daily historical data were used to train and test each model on different forecast horizons, and performance was measured accordingly by Mean Squared Error (MSE) and Mean Absolute Error (MAE) values. As shown in the results, it can be observed that GRU exhibited the lowest error rates in the majority of the assets, particularly in short-term predictions. LSTM demonstrated a promising ability to capture long dependencies, whereas the hybrid LSTM-GRU system showed a similar performance proficiency by combining the relative superiorities of the two respective models. On the other hand, the conventional SGD regression was the worst among all the deep-learning algorithms, thereby demonstrating the extreme capability of these algorithms in modelling non-linear time sequences. The results confirm GRU as the most viable model for AI-powered crypto prediction and demonstrate the potential of hybrid architecture, at least in certain situations. This study will contribute to the existing debates about the role of deep learning in predicting financial outcomes and provide valuable insights to traders, analysts, and researchers navigating the uncertainties of the digital asset world.
Joel Poncha Lemayian, Ghyslain Gagnon, Kaiwen Zhang, Pascal Giard
Ethereum leverages smart contracts (SCs) to power decentralized applications (dApps), with execution handled by the Ethereum virtual machine (EVM) within an Ethereum client. Other blockchain platforms, including Avalanche, Polkadot, Aurora, and Cardano, have also adopted the EVM. However, the performance of the EVM is often constrained by the limitations of general-purpose processors, a challenge that has been explored in the literature. This work aims to further address the limitation by proposing EVMx, a dedicated single-core SC execution engine implemented on a field programmable gate array (FPGA). EVMx follows a processor-like architecture inspired by the RISC philosophy. By exploiting the parallelism and high-speed processing capabilities of FPGA hardware, EVMx achieves a 61% to 99% reduction in execution time for commonly used operation codes compared to traditional central processing unit (CPU)-based environments. Furthermore, EVMx executes entire Ethereum blocks with a percentage reduction in execution time between 6% and 56% against comparable FPGA implementations and 98% to 99% compared to CPU-based EVMs in the literature. These results demonstrate the potential of EVMx to significantly accelerate SC execution and enhance the performance of EVM-compatible blockchains.
A presente dissertação propõe o desenvolvimento de uma plataforma designada SoundSlice, que visa automatizar a gestão de direitos de autor em conteúdos musicais reutilizados e na criação de mixes, através da integração de tecnologias blockchain e contratos inteligentes. O sistema permite o registo de obras originais, reutilizações parciais, a combinação de múltiplas faixas em novas composições (mixes) e a atribuição automática de compensações aos titulares de direitos, assegurando transparência e rastreabilidade em todo o processo. A solução combina uma infraestrutura centralizada, suportada por uma base de dados MongoDB e armazenamento de ficheiros GridFS, com uma camada descentralizada baseada em Ethereum, responsável pela execução dos contratos inteligentes que formalizam a partilha de royalties. A nível prático, foi implementado um frontend web que permite o upload, análise, reutilização e criação de mixes musicais, bem como um backend Node.js que gere a lógica de negócio e a comunicação com a blockchain. O desenvolvimento da plataforma baseou-se nos conceitos teóricos e modelos de integração propostos pelos padrões Smart Contracts for Media (SC4M) e Interactive Music Application Format (IMAF), os quais orientaram a estruturação de metadados, a modelação de contratos e o desenho da arquitetura da plataforma. Por fim, foram conduzidos testes funcionais, de desempenho e de usabilidade que demonstraram o correto funcionamento da plataforma, a eficiência na execução de transações e a aceitação positiva por parte dos utilizadores, validando a viabilidade e o contributo da abordagem proposta.