In the digital era, personal data is continuously collected, processed, and shared by organizations across various sectors. Traditional consent management systems suffer from centralization, opacity, and insufficient user control, making it difficult for individuals to track and enforce their data-sharing preferences. This paper presents ConsentChain, a decentralized consent governance protocol built on the Polygon blockchain. The system leverages Ethereum-compatible smart contracts to implement purpose-bound, time-limited, and user-revocable consent records, backed by an immutable on-chain audit trail. The architecture employs two core Solidity smart contracts—ConsentManager and AccessController—supported by a React-based frontend and MetaMask wallet integration. Comprehensive end-to-end testing demonstrates 18 of 18 test cases passing, validating the correctness of consent lifecycle management, access validation, role-based access control, and event logging. ConsentChain demonstrates that blockchain technology can provide a transparent, tamper-proof, and user-sovereign alternative to conventional consent management systems, with clear pathways toward enterprise adoption, multi-chain deployment, and zero-knowledge privacy extensions. Index Terms—Blockchain, Consent Management, Smart Contracts, Data Privacy, GDPR, Decentralized Systems, Ethereum, Polygon, Access Control, Audit Trail.
Đức An Nguyễn, Minh Thành Phí, Duc Dung Pham, Thị Hương Thơm Hồ
Ngành logistics hiện đại đang đối mặt với các thách thức về sự thiếu minh bạch, chi phí trung gian cao và khó khăn trong việc giải quyết tranh chấp khi giao hàng trễ hạn. Nghiên cứu này đề xuất và triển khai một ứng dụng phi tập trung (Decentralized Application - DApp) quản lý hợp đồng logistics trên nền tảng Blockchain Ethereum (mạng thử nghiệm Sepolia). Hệ thống sử dụng kiến trúc lai (Hybrid Architecture) kết hợp giữa Blockchain để xử lý thanh toán hoặc logic nghiệp vụ và IPFS để lưu trữ hồ sơ phi tập trung. Điểm mới của nghiên cứu là việc tích hợp cơ chế "Ký quỹ thông minh" (Smart Escrow) và "Phạt vi phạm tự động" (Automated Penalty) ngay trong hợp đồng thông minh (Smart Contract). Kết quả thực nghiệm cho thấy hệ thống đảm bảo tính toàn vẹn dữ liệu, tự động hóa hoàn toàn quy trình thanh toán dựa trên trạng thái giao hàng thực tế và xử lý chính xác các trường hợp vi phạm thời gian cam kết mà không cần sự can thiệp của bên thứ ba.AbstractThe modern logistics industry faces challenges regarding lack of transparency, high intermediary costs, and difficulties in resolving disputes over late deliveries. This study proposes and implements a decentralized application (DApp) for logistics contract management on the Ethereum Blockchain (Sepolia testnet). The system employs a Hybrid Architecture combining Blockchain for payment or business logic processing and IPFS for decentralized record storage. A novel feature of this research is the integration of "Smart Escrow" and "Automated Penalty" mechanisms directly within the Smart Contract. Experimental results demonstrate that the system ensures data integrity, fully automates the payment process based on actual delivery status, and accurately handles delivery deadline violations without third-party intervention.Keywords: Blockchain, Ethereum, smart contract, IPFS, Logistics, automated Penalty.
Запропоновано середовище імітаційного моделювання явища максимально екстрактованої вигоди MEV (англ. Maximal Extractable Value), реалізоване мовою програмування Python із використанням бібліотеки Gymnasium, яке відтворює взаємодію сховища-мемпулу, конструювальника блоків, агента MEV-екстрактора та AMM-пулу децентралізованої біржі. Формально середовище описано як розширений та частково спостережуваний процес прийняття рішень, у межах якого агент взаємодіє з дискретно-часовою моделлю епізодів, що відображає послідовність надходження транзакцій, побудови блоків і виконання swap-операцій обміну на децентралізованій крипто-біржі. Для моделювання адаптивної поведінки агента використано методи навчання з підкріпленням, а для кількісного аналізу втрат користувачів застосовано контрфактичний підхід до оцінювання, що дає змогу порівнювати результати виконання транзакцій у різних режимах впорядкування за однакових вхідних умов. У дослідженні використано раніше описаний авторами метод зменшення негативних ефектів MEV-екстракції на основі логічних часових міток Лампорта, який реалізує локальне причинно-наслідкове впорядкування транзакцій у межах окремого смарт-контракту без модифікації глобального механізму консенсусу мережі блокчейн Ethereum. Для оцінювання практичної ефективності цього підходу сформовано три сценарії моделювання: базовий сценарій без систематичної MEV-атаки для визначення накладних витрат застосування механізму захисту, сценарій систематичної sandwich-атаки для аналізу та здатності методу зменшувати втрати користувачів та обмежувати можливості MEV-екстрактора, а також сценарій параметричного аналізу, спрямований на дослідження компромісу між рівнем захисту та "вартістю" його застосування. Отримані результати показали, що запропонований метод MEV-захищеного впорядкування може зменшувати цінові втрати користувачів від sandwich-атак і, водночас, впливати на частоту відхилення транзакцій та пов'язані комісійні витрати, що вказує на наявність керованого компромісу між ефективністю захисту та накладними витратами його використання. Практична цінність роботи полягає у створенні відтворюваного середовища імітаційного моделювання для дослідження стратегічної поведінки MEV-агентів і перевірки механізмів зменшення негативних наслідків MEV у контрольованих умовах, що може бути використано для подальшого аналізу безпеки протоколів децентралізованих фінансів та проєктування нових методів впорядкування транзакцій.
A decentralized system for academic credential verification using Ethereum blockchain and hybrid off-chain storage. The system replaces traditional manual verification by allowing institutions to issue digitally signed certificates whose cryptographic hashes are stored on-chain, ensuring immutability and tamper resistance. A dual-hashing approach (SHA-256 followed by Keccak-256) is used to enhance security and maintain compatibility with the Ethereum ecosystem. Credential files are stored off-chain (e.g., Supabase/IPFS) to reduce cost, while verification is performed by comparing hashes, achieving fast (under a few seconds) and reliable authentication. Overall, the paper demonstrates a scalable, secure, and efficient solution for real-world use cases such as academic admissions and recruitment.
Rongji Huang, Yifeng Ye, Gerui Wang, Mingchao Wan · 8 authors
Due to regulatory compliance and governance management, modern (permissioned) blockchains require flexible endorsement, which allows the endorsement policy for each contract or state object to be individually defined. To enable flexible endorsement, Hyperledger Fabric employs an execute-order-validate (EOV) paradigm, in which transactions first undergo speculative execution and endorsement, and are only then ordered and validated. Meanwhile, most blockchain systems, including the platform targeted in this work (i.e., ChainMaker), still follow a conflict-free order-execute framework. We argue that the EOV paradigm still faces several limitations, notably high abort rates in high-contention workloads such as those in Decentralized Finance (DeFi). To avoid refactoring our system and better suit DeFi applications, we try to integrate flexible endorsement into the classical order-execute architecture and accordingly propose a new framework. The key challenge is to deterministically remove problematic transactions from an ordered list, while preserving censorship resistance and decentralization for the remaining ones. We instantiate this framework on top of Tendermint, a seminal Byzantine fault-tolerant (BFT) protocol adopted in our system, and thereby propose FlexTender. By elegantly embedding endorsements into consensus, FlexTender incurs no additional messaging overhead in the normal case. Empirical evaluation using an Ethereum USDT workload demonstrates that FlexTender achieves up to $10.6\times$ speedup in throughput over an EOV simulation on the same platform.
Zhuoran Pan, Yue Li (102191), Zhi Guan, Jianbin Hu · 5 authors
The emergence of Large Language Models (LLMs) offers a transformative interface for Web3, yet existing benchmarks fail to capture the complexity of translating high-level user intents into functionally correct, state-dependent on-chain transactions. We present \textsc{Intent2Tx}, a high-fidelity benchmark featuring 29,921 single-step and 1,575 multi-step instances meticulously derived from 300 days of real-world Ethereum mainnet traces. Unlike prior works that rely on synthetic instructions, \textsc{Intent2Tx} grounds natural language intents in real-world protocol interactions across 11 categories, including diverse long-tail Decentralized Finance (DeFi) primitives. To enable rigorous evaluation, we propose an execution-aware framework that transcends surface-level text matching by employing differential state analysis on forked mainnet environments. Our extensive evaluation of 16 state-of-the-art LLMs reveals that while scaling and retrieval-augmentation enhance logical consistency and parameter precision, current models struggle with out-of-distribution generalization and multi-step planning. Crucially, our execution-based analysis demonstrates that syntactically valid outputs often fail to achieve intended state transitions, highlighting a significant gap in current "reasoning-to-execution" capabilities. \textsc{Intent2Tx} serves as a critical foundation for developing autonomous, reliable agents in intent-centric Web3 ecosystems. Code and data: https://anonymous.4open.science/r/Intent2Tx_Bench-97FF .
Ermanno Francesco Sannini, Francesco Salzano, Simone Scalabrino, Rocco Oliveto · 7 authors
Smart Contracts are essential blockchain components, mainly written in Solidity. The high availability of public Solidity code leads to frequent reuse and high clone ratios. Since cloning can propagate vulnerabilities and flaws, effective detection is crucial. Although existing techniques work well in detecting syntactic clones, the identification of semantic clones is an open problem. To address this challenge, in this paper, we present and empirically assess a scalable methodology, based on analyzing code and comments, to spot semantically equivalent Solidity functions. We first collected an up-to-date dataset of about 300,000 Ethereum smart contracts, 82.07% of which are compliant with modern Solidity version 0.8. Manual validation of a statistically significant sample comprising 1,155 function pairs confirms the effectiveness of our solution, achieving an overall precision of 59% (rising to 84% for homonymous functions) and a recall of 97%. Besides, we explore the structural differences occurring on semantically equivalent Solidity functions, demonstrating that they often represent design alternatives focused on security choices, modularization, and gas optimization. Finally, we investigate the use of Large Language Models (LLMs) as documentation engines in scenarios where code comments are poor or absent. Our results show that LLM-generated summaries, combined with sentence transformers like BERT, can bridge the documentation gap, enabling the identification of semantic clones in uncommented code with 75% precision. This work establishes a modern benchmark for Solidity clone detection and provides a foundation for the automated discovery of secure and efficient code alternatives.
Code-driven auditing fails when correctness depends on what the specification requires rather than how the code is written. Production blockchain networks expose this directly: byzantine consensus runs many independent clients of a shared specification, so a specification-divergence defect in one client can fork the network or halt finality. Existing tools reason one repository at a time, with no shared baseline held constant across implementations. We present SPECA, an LLM-driven audit framework that derives explicit, categorized security properties (invariants, pre/postconditions, trust assumptions) from natural-language specifications and reuses them across implementations. SPECA enables controlled cross-implementation comparison, detections grounded in specification invariants no code pattern encodes, and false positives traceable to a specific pipeline phase rather than opaque model errors. On the Sherlock Ethereum Fusaka Audit Contest (10 targets, 366 submissions), SPECA recovers all 15 in-scope H/M/L vulnerabilities expert-augmented (8/15 automated-only) and surfaces 4 fix-confirmed bugs, including a cryptographic-invariant violation missed by every adjudicated finding. On the RepoAudit C/C++ benchmark, SPECA reaches 88.9% precision at 100% recall (F1=0.94) and surfaces 12 author-validated bugs beyond ground truth, two externally validated. SPECA also flags 5 of RepoAudit's 40 published bugs as defensive-coding fixes with no reachable exploit path. False positives trace to three pipeline-pinned root causes; a multi-model study identifies property-generation quality as the binding constraint. End-to-end cost is ~$30 per H/M/L bug (~42 min wall-clock under parallel execution).
The distributional specification in Markov-switching GARCH models has historically been driven by empirical convention rather than statistical theory. This paper derives the two-regime MS-GARCH specification from the Maximum Entropy Principle, providing an information-theoretic motivation for Student-t regime-conditional innovations in cryptocurrency volatility modelling. The framework is applied to five major cryptocurrencies, Bitcoin, Ethereum, Ripple, Litecoin, and Bitcoin Cash, over the period January 2017 to March 2026, comprising 15,834 daily observations spanning six complete market cycles. Three principal findings emerge. First, a Calm-Phase Fragility pattern is identified: four of five assets exhibit calm-regime half-lives below one trading day (0.48 to 1.16 days), with turbulence the dominant long-run state (stationary turbulent probability in [0.451, 0.771] across all assets), establishing turbulence rather than calm as the structural baseline of the cryptocurrency ecosystem. Second, the Maximum Entropy derivation yields endogenous Student-t degrees of freedom, with heavy-tailed turbulent innovations (degrees of freedom approximately 4.5) confirmed across all assets, validating the MaxEnt constraint framework empirically. Third, near-unity turbulent GARCH persistence drives MS-GARCH point forecasts toward the persistence ceiling, consistent with an information-theoretic bound on predictability when the calm half-life collapses below one trading day; HAR-RV achieves the lowest QLIKE loss for three of five assets under these near-critical conditions. Cross-asset consistency is confirmed across seven statistical indicators including Hill tail exponents in [2.31, 3.26], Hurst exponents in [0.543, 0.577], and Wald tests rejecting parameter homogeneity at p < 0.001 for all assets. The framework is formalised as a deployable expert system for real-time regime monitoring and risk management.
Interoperability of patient files between hospitals continues to present significant obstacles. Health systems frequently utilize central EHR systems that could suffer malfunctions, data breaches, and unauthorized access by third parties. Not only does this jeopardize patient confidentiality, but it also hinders the efficient operations of hospital processes.Blockchain technology is viewed as a prospective remedy for the issue. Blockchain keeps its data differently, allowing users to store data securely and make changes difficult. In this study, we analyze research works published between 2016 and 2023 regarding blockchain-based hospital-to-hospital data exchange.The methodologies differ widely: there are cases where researchers use smart contracts in Ethereum, build a system on Hyperledger Fabric, and deploy IPFS. Moreover, certain studies incorporate encryption methods, machine learning algorithms, and more. In summary, the results show that blockchain allows for improved data protection and transparency while giving patients more control over their personal information. Still, some issues persist, such as scalability, expenses, integration with existing infrastructure, and adherence to GDPR and HIPAA requirements. For future work, more improvements are necessary. For instance, zero-knowledge proofs, cybersecurity measures for new technologies, and using artificial intelligence to audit and validate smart contracts may be promising solutions.
The agri-food sector is an essential pillar of economies worldwide, playing a vital role in food security and safety, as well as the livelihoods of millions. However, it continues to tackle recurrent issues such as supply chain inefficiencies, lack of transparency, and simple food fraud. Smart contracts, a form of blockchain technology capable of executing programmable agreements between parties, are a promising remedy for these challenges. A smart contract is basically software that has contractual rules written into it. This chapter lays the foundation for smart contracts operating on a blockchain-based architecture, explains how they differ from regular contracts, and outlines the features that make them unique. In agribusiness, platforms like Ethereum and Hyperledger serve a crucial role in the implementation of smart contracts. Key applications include improving traceability, automating supplier-buyer interactions, and strengthening food safety and quality control. Real-world implementations illustrate their effectiveness in preventing fraud and ensuring compliance with industry standards. Despite their potential, the adoption of smart contracts in agribusiness is influenced by various factors, as analysed through Rogers' diffusion of innovation framework. Comparative advantage, compatibility, and complexity play pivotal roles in determining adoption rates. Case studies showcase successful implementations while shedding light on adoption challenges. Barriers to widespread use include technological constraints, regulatory uncertainties, infrastructure costs, and knowledge gaps, particularly among small-scale farmers. Additionally, data security and privacy concerns remain significant obstacles. Addressing these challenges is essential for harnessing the full potential of smart contracts in agribusiness. This chapter provides insights into overcoming these hurdles and fostering a more transparent and efficient agri-food ecosystem.
This project introduces a blockchain-powered Blood Donation and Emergency Response System, which would provide blood donation, allocation, and emergency delivery secure, transparent and efficient manner for all donors, patients, blood banks, hospitals, ambulance drivers and system administrators involved. Without having to trust traditional records, the system uses blockchain-based digital identities so that every registration, every donation, request, and inventory update is permanent and easy to verify. Blood banks update their inventory on the blockchain for easy real-time tracking of blood supplies. When patients need blood, smart contracts verify availability and automatically execute the process or immediately notify hospitals and suitable donors of the extra units needed without requiring manual coordination. Hospitals can raise urgent requests, also logged onto the blockchain and smart contracts that notify eligible donors directly to their respective email address. System administrators monitor all activity through the admin portal, preventing data tampering and ensuring privacy and security are straightforward. In this manner, the decentralized storage of data, the creation of unchangeable records and the automatic execution of smart contracts reduce delay, fraud and complete traceability, thus dramatically improving the response to emergencies. Above all, this blockchain approach now helps in managing these very important operations of blood supply to make a reliable, tamperproof and location-based mechanism that saves lives where and when it matters.
Penelitian ini mengevaluasi efektivitas federated learning dalam mendeteksi alamat ilegal pada blockchain Ethereum untuk Anti Money Laundering (AML). Studi ini membandingkan XGBoost centralized dan federated dalam simulasi multi exchange, data dibagi secara horizontal menjadi 3 subset yang merepresentasikan entitas bursa independen. Untuk mengisolasi pengaruh pelatihan terdistribusi, pembagian data dilakukan secara homogen (IID), sehingga analisis difokuskan pada perbedaan mekanisme pembelajaran tanpa dipengaruhi heterogenitas data. Hasil menunjukkan bahwa model federated mencapai performa yang kompetitif dengan PR AUC 0,9962 dan akurasi 97,11%, dibandingkan model terpusat dengan PR AUC 0,9975 dan akurasi 97,75%. Namun, performa tersebut disertai peningkatan durasi pelatihan 4,01 detik dibandingkan 3,39 detik, yang disebabkan oleh beban komunikasi selama proses pembaruan model. Temuan ini menegaskan adanya trade off antara kinerja dan efisiensi dalam penerapan federated learning. Meskipun mampu mendekati performa pembelajaran centralized, pendekatan ini menimbulkan biaya operasional tambahan. Studi ini merupakan evaluasi dasar dalam kondisi terkontrol dan belum merepresentasikan skenario dunia nyata, sehingga diperlukan penelitian lanjutan pada data non IID dan skala sistem yang lebih besar.
Vabuk Pahari, B. Chandrasekaran, Johnnatan Messias, Krishna P. Gummadi · 5 authors
A decentralized autonomous organization (DAO) is a governing entity that empowers its stakeholders (i.e., users who hold one or more of its tokens) to manage blockchain-based protocols (i.e., smart contracts) collaboratively. The governance of a DAO is explicitly encoded in the DAO's governance contract, which defines how stakeholders participate in governance and how much influence (or voting power) they have in any decision. While decentralization and autonomy are the fundamental tenets of a DAO's design, empirical evidence suggests that in practice governance is often highly centralized. In this work, we study the designs and implementations of 48 public and actively used DAOs, with substantially large capital, deployed on Ethereum. We identify how three key governance mechanisms--token registration, staking, and delegation--originally introduced to improve security or participation, contribute to the concentration of voting power. Unlike prior work on centralization of voting power in specific DAOs, our findings reveal that these governance mechanisms of DAOs themselves systematically reinforce centralization. By elucidating the relationship between governance design and voting centralization, this work advances the understanding of DAO governance structures and highlights the inherent trade-offs between decentralization, security, and usability of DAOs.
Aman Chaudhary, Bhavy Singhal, Aryan Siwach, Priyanka Dhanraj
Abstract The rapid increase in digital data has led to heavy reliance on centralized cloud computing. Consequently, users are exposed to critical vulnerabilities, including unauthorized access, privacy invasion, and single points of failure. This study proposes a cloud storage system that is trustless to address these challenges that have persisted. The underlying methodology utilizes distributed data hosting based on the InterPlanetary File System (IPFS) and decentralized access control through Solidity smart contracts. Under this architecture, file metadata is stored safely on an unalterable blockchain registry, and the media files are stored off-chain. These contracts are automatically run by granular access controls like specific public and private visibility modes. At any point, no outside intervention of a third party is needed. The system was checked during the testing time in terms of a functional accuracy in regards to a secure storage, verifiable retrieval, and instant revocation of permissions. According to the key results, the elimination of intermediary control, prevention of unauthorized access to data attempts, and high data availability are achieved. In conclusion, this shows that a combination of programmable smart contracts and peer-to-peer storage will provide a potentially scalable and secure alternative to the traditional cloud architecture. This leads to a considerable improvement in user data sovereignty and systemic resilience as a whole. Keywords Ethereum, Solidity, IPFS, Smart Contracts, Decentralized Storage
The increasing reliance on centralized ride-sharing structures, and exposes users to risks such as system failures and privacy breaches. manipulation, single points of failure, and privacy violations. In addition, high commission fees imposed by such platforms reduce the net earnings of drivers and compromise fairness within the ecosystem. To address these inefficiencies, this project introduces a decentralized vehicle borrowing system and carpooling, based on Ethereum Compatible blockchain and smart contracts. The proposed platform eliminates intermediaries by allowing KYC-verified drivers, passengers, and vehicle owners to interact directly, thereby building trust and operational transparency. All ride postings, bookings, car borrowing transactions, and agreement verifications are recorded immutably through smart contracts. Identity proofs, vehicle documents are cryptographically signed through MetaMask and uploaded via a decentralized file system (IPFS), ensuring authenticity and wallet-to-user binding. For drivers who borrow cars, temporary verification is enabled after signing a smart-contract-based agreement linked to the vehicle's verified owner. To maintain decentralization without depending on an administrator, the system introduces a Global Dispute Center where only users who fulfill certain predefined conditions—having verified their identity—can participate in resolving concerns through a voting process. This decentralized decision-making process enhances fairness and trust. Additionally, a structured post-ride rating system builds mutual accountability and trust among participants, while integrated CO₂ tracking encourages environmentally conscious behavior. Together, these features help minimize traffic load, support conscious travel habits, and build a reliable, user-governed mobility system that is secure, transparent, and environmentally supportive—functioning entirely without any centralized authority or administrative oversight, thereby ensuring long-term sustainability. To address these limitations, this project proposes a blockchain-powered peer-to-peer carpooling and vehicle borrowing system that enables direct interaction between passengers, drivers, and vehicle owners without intermediaries. The platform utilizes smart contracts to automate agreements, MetaMask for secure authentication, and IPFS for decentralized storage of essential records. By shifting operational control to users, the system enhances transparency, fairness, and reliability in transactions. Conventional mobility services also face issues such as opaque processes, inefficient dispute handling, and limited mechanisms for conflict resolution. Drivers often lose a substantial portion of their income to service fees, while users lack trust in centralized decision-making systems. Furthermore, minimal emphasis is placed on promoting environmentally responsible travel practices. Motivated by the need for an open and community-driven mobility platform, this research aims to establish a distributed ecosystem that eliminates third-party dominance and ensures tamper-proof record keeping. The system incorporates KYC-based digital identity verification, smart contract-enforced agreements, decentralized dispute resolution through voting, and CO₂ emission tracking to encourage sustainable transportation. The scope of the project includes enabling secure ride booking, vehicle borrowing under verified ownership, and democratic dispute resolution among verified users. By leveraging distributed networks and digital wallets, the platform presents a scalable and sustainable alternative to centralized ride-sharing models.
We present a graph-theoretic and convex optimization framework for multi-path routing in decentralized exchange networks, together with its implementation and empirical evaluation on Ethereum mainnet. The framework models the market as a directed token multigraph whose arcs carry AMM exchange functions. Routing is decomposed into two implemented layers: candidate path generation via gas-aware marginal k-shortest-path enumeration, where edge scores embed expected execution cost directly into graph traversal with an explicit pool-simple constraint tracked during path construction, and continuous flow allocation over the selected candidates solved as a concave maximization over a simplex with a per-pool price-impact cap. Under standard concavity and monotonicity assumptions, the KKT conditions imply marginal-output equalization across active paths. The central technical contribution is an improving-path certificate: after solving the allocation on k=20 candidate paths, the KKT multiplier is used as a threshold to determine via a single shortest-path query whether any omitted pool-simple path could improve the current solution; in our implementation the certificate confirms sufficiency in the majority of epochs. Execution is protected by an on-chain slippage tolerance enforced at the smart-contract level. We evaluate the implemented engine against four production DEX aggregators on repeated WETH-USDT quote observations across six trade sizes on Ethereum mainnet: median shortfall is below 5 bps across all sizes and top-3 quote rank exceeds 57% of epochs.
Artjom Pugatsov, Can Umut Ileri, Jérémie Decouchant
The successive generations of consensus algorithms have progressively shifted the performance bottleneck of blockchains to the execution layer. While recent works address this by parallelizing transaction execution, they often overlook the critical role of transaction sequencing. Historically, transaction ordering was left to validator discretion, a practice prone to Maximal Extractable Value (MEV) attacks, or rigid fair-ordering protocols that limit validator revenue. In this work, we address the tension between validator revenue and order fairness using a dynamic optimization framework. We introduce a blockchain-independent model for transaction sequencing in a continuous setting where block executions can overlap. Within this framework, we propose an anytime genetic algorithm that utilizes gas prices, object sets, and predicted execution times to optimize schedules. We evaluate our approach with real-world datasets from Sui and Ethereum, and demonstrate that our algorithm increases validator profit by approximately 15% and accelerates congestion relief by up to 58%. Furthermore, we quantify the impact of fair-ordering constraints, showing they can reduce validator revenue by 50% to 60% during periods of high congestion. We provide the first evidence that enforcing strict fair ordering effectively nullifies the advantages of advanced sequencing.
Technical traders have long relied on visual analysis of candlestick charts to identify market patterns and predict price movements. While deep learning has achieved remarkable success in image classification, its application to financial chart images remains underexplored. This paper presents a systematic study comparing different visual representations for cryptocurrency regime prediction. We evaluate three image encoding methods (raw candlestick charts, Gramian Angular Fields, and multi-channel GAF), five chart component configurations, four neural network architectures (CNN, ResNet18, EfficientNet-B0, and Vision Transformer), and the impact of ImageNet transfer learning. Through eight controlled experiments on Bitcoin, Ethereum, and S&P 500 data spanning 2018-2024, we identify optimal configurations for visual regime classification. Our results show that a simple 4-layer CNN on raw candlestick charts achieves 0.892 AUC-ROC, outperforming larger pretrained models. Surprisingly, simpler representations (price-only charts, 128x128 resolution) consistently outperform more complex alternatives. We provide interpretability analysis using GradCAM and demonstrate that transfer learning improves performance by 4-16% despite the domain gap between natural images and financial charts.
ABSTRACT Forgery of academic and professional certificates remains a major concern across institutions. Traditional centralized systems are prone to manipulation and single points of failure. This work presents a blockchain-based certificate issuance and verification platform developed using Spring Boot and the Ethereum Sepolia test network. The system supports multiple organizations where issuers register and are approved by an administrator before generating certificates. Each certificate is assigned a unique identifier, and a SHA-256 hash of its data is stored on the blockchain through smart contracts. The platform also automates PDF certificate creation with embedded QR codes and sends them via email. Additional features include bulk certificate generation, revocation support, and public verification without requiring a blockchain wallet. Experimental observations indicate an average issuance time of around 4 seconds and verification within 1.5 seconds. Keywords: Blockchain, Ethereum, Smart Contracts, SHA-256, Certificate Verification, Spring Boot, Web3j, PDF Automation
Phishing attacks pose a significant security issue in Ethereum-based blockchain systems. Existing solutions, like TEGDetector, address these attacks by analysing how transactions evolve over time using Transaction Evolution Graphs (TEGs) constructed via time slicing, followed by a dynamic graph classifier that captures both spatial structure and temporal evolution with learned time coefficients. However, building and managing these graphs across multiple stages makes the overall approach complex and difficult to implement. In this work, we propose E2E-EmbedDetector, a lightweight end-to-end neural classification model that works directly with raw transaction data. The model learns embedding representations for important entities such as From, To, and ContractAddress, and also used two additional numeric features: transactional value and a derived input length. We train and evaluate the model on a balanced dataset of 50,000 Ethereum transaction using an 80/20 stratified split. The model achieves an accuracy of 95.63%, precision of 0.9265, recall of 0.9912, an F1 score of 0.9578, a ROC-AUC score of 0.9915 and a PR-AUC score of 0.9909. These results show that strong phishing can be achieved using a simpler and more practical tabular approach, without relying on complex temporal graph- based networks.
Leeladhar Chourasiya, Mr. Anand Jawdekar, Mr. Sanjay Patsariya, Ms. Aparajita Biswal · 7 authors
The blistering development of the decentralized technologies is transforming the conceptual and functional limits of the contemporary digital ecosystems. One of such innovations is blockchain, which is being presented as a core infrastructure of facilitating autonomous, trustless, and self-organizing systems, which has also been emphasized in recent academic conversations. The paper will examine how blockchain will be used to lay the foundations of autonomous societies where governance, economic dealings and social interactions will be implemented in the absence of a centralized force. The suggested framework is based on decentralized ledger technology, smart contracts, consensus mechanism, in order to promote transparency, security, and accountability on digital communities. The paper highlights the role of blockchain platforms (especially Ethereum-style architectures) in the development of decentralized autonomous organizations (DAOs) that serve as building blocks to bigger social organizations. Identity management, decentralized models of governance, token-based economies, and trustless interactions are some of the critical components that are analyzed. Moreover, the paper also looks at how emerging technologies such as artificial intelligence and distributed storage systems can be integrated to make autonomous environments more scalable, adaptable, and make decisions. Issues concerning scalability, regulatory limitations, interoperability, and ethical aspects are also presented and possible solutions and future research areas specified. The results indicate that blockchain infrastructure has the capacity to reinvent the social structure and provide decentralized, robust, and participative digital economies. The article is a contribution to the existing literature on next-generation sociotechnical systems and a strategic roadmap of fully autonomous digital societies development.