Cross-border trade finance supports liquidity and global supply chain growth, especially for small and medium enterprises (SMEs). Traditional documentation and invoice factoring systems face delays, huge documentation, fraud risks, poor interoperability, and limited transparency which results in unsustainable action. Blockchain-based trade finance improves immutability, decentralized validation, and automation through smart contracts. However, it suffers from scalability limitations, fragmented data management, and regulatory integration issues. Academic literature lacks a unified framework combining blockchain and cloud to address these operational gaps. This study fills the gap by evaluating traditional, blockchain, and hybrid models in trade finance systems. The paper reviews major use cases to understand realworld blockchain implementations in trade finance. These include HSBC and ING's blockchain letter of credit, and Bank of Canada's Project Jasper. UBS's blockchain payment initiative is also analyzed for its real-time settlement and auditability benefits. The study compares traditional and blockchain systems across speed, security, transparency, and integration parameters. It identifies that blockchain alone is insufficient due to its lack of scalability and limited system compatibility. To address this, the paper proposes a Blockchain-Cloud Integrated Trade Finance (TF) Framework. This includes hybrid data storage, Application Programming Interface (API) compliance, digital identity verification, and stakeholder dashboards. Using a design science approach, the framework improves auditability, transparency, and regulatory alignment. Findings benefit regulators, banks, and FinTech's. The framework not only enhances efficiency and compliance but also contributes to sustainable trade finance
Privacy in blockchain-based cryptocurrencies has become a critical area of research and development. This is driven by the transparent nature of public ledgers and the growing demand for confidential transactions. This study provides a comprehensive exposition of a prominent privacyenhancing protocol, Zcash. We begin by elucidating the design and operation of Tornado Cash, a smart contract-based mixer on Ethereum that leverages zero-knowledge proofs to enable unlinkable transactions. Building on this foundation, we explore the Zcash protocol, tracking its evolution from Bitcoin and its innovative use of zk-SNARKs. We also examine the intricate mechanisms that highlight its privacy guarantees, including circuit design, Merkle tree structures and transaction relations. By systematically analyzing and comparing these protocols, this study aims to clarify their inner workings, highlight their strengths and limitations, and provide information on the future of privacy in decentralized finance.
The Absolute Smart Contract (ASC) presents a universal conceptual framework that unifies the spiritual, natural, and scientific dimensions of existence under one governing intelligence. It views reality — from atomic order to human morality — as operating within intrinsic laws of balance, reciprocity, and consequence. Whether expressed as divine will, natural order, or logical computation, each represents the same intelligent structure sustaining creation. The ASC is not a religion or ideology; it is a neutral interpretive model that reconciles seemingly divided worldviews through recognition of one absolute principle — the self-enforcing intelligence of existence itself.
Hansa Vaghela, Vivek Khirasaria, Rachit Adhvaryu, Krupali Gosai
Blockchain technology has transformed commercial transactions with its decentralized platform, transparency, and security. Self-executing contracts with coded terms are at the foundation of smart contracts, resulting in increased efficiency, lower operating costs, and greater transparency. Smart contracts offer tremendous benefits across businesses, but their immutability raises serious security risks. Contract code faults and vulnerabilities remain unmodifiable or erasable once published. This can pose irreparable problems, especially with financial or sensitive data. Transparency makes these contracts more vulnerable to manipulation by malicious actors. This study examines smart contract vulnerabilities, including reentrancy attacks, overflow and underflow issues, and unauthorized access. Smart contract creation involves specific risks and a full security framework. This framework reduces vulnerabilities with formal verification, automated analysis, and optimal coding practices. This research aims to improve smart contract security, reliability, and integrity. The study improves blockchain technology security and promotes its adoption across businesses, making smart contracts secure, efficient, and trustworthy.
Cryptocurrency markets are characterized by high volatility and complex patterns, creating both challenges and opportunities for traders and investors. This study introduces a machine learning framework for cryptocurrency trading optimization that leverages advanced analytical techniques to enhance trading decisions. We extracted historical data for 30 cryptocurrencies over a four-year period from Yahoo Finance. After preprocessing, we applied Principal Component Analysis (PCA) and K-means clustering to select representative coins. Four machine learning models (Gradient Boosting, XGBoost, Support Vector Regression, and Long Short-Term Memory networks) were trained to predict cryptocurrency price movements. Model performance was evaluated using multiple metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R 2 ). Gradient Boosting and XGBoost consistently outperformed SVR and LSTM models across all cryptocurrencies, with R 2 values of approximately 0.98 for most coins. The framework successfully identified trading signals through both moving average strategies and machine learning predictions, providing actionable insights for cryptocurrency traders. Our analysis demonstrates that ensemble-based models offer superior performance for cryptocurrency price prediction compared to neural network approaches. The integration of advanced visualization tools and trading signal generation creates a comprehensive system for data-driven cryptocurrency trading decisions.
Pedro Baptista, Bernardo Pacheco, Filipe Apolinário, João Silveira · 6 authors
Having the ability to prove your knowledge is essential for obtaining a job. In the programming field, applicants make claims about the programming languages they master, and it is up to the interviewer to check the veracity of those claims. The goal of this work is to facilitate this process by extracting important information from GitHub, such as the number of bytes programmed in each programming language. The user is then able to ask for a Zero Knowledge Proof which can be downloaded and sent to any entity which places trust on out platform. The proof is verifiable without the entity interacting with our platform and does not leak information about the users' GitHub. The obtained results are promising, even though proofs can take several minutes to generate, they can be verified in many devices, such as laptops and smartphones, which greatly increases the number of users who can use our platform.
AI-based Problem Solving and Planning
Logic, Reasoning, and Knowledge
Intelligent Tutoring Systems and Adaptive Learning
This article explores the economic trade-offs between centralized and decentralized financial systems. Centralized finance (CeFi) relies on regulated intermediaries such as banks and custodians, offering stability, regulatory oversight, and support for monetary policy. Decentralized finance (DeFi), based on smart contracts and cryptographic protocols, reduces barriers to entry and increases flexibility but introduces technical and operational risks. The paper examines efficiency, risk allocation, financial inclusion, innovation, and international implications, supported by quantitative evidence such as global account ownership, cryptocurrency market capitalization, and total value locked (TVL) in DeFi. The analysis highlights that neither system is categorically superior; effective policy should balance innovation and stability through coordinated, technically informed, and proportionate regulations.
This research introduces a next-generation cryptographic framework aimed at securing payment systems and IoT-enabled financial ecosystems against both contemporary cyber threats and the anticipated risks posed by quantum computing. The proposed architecture supports intelligent payment cards, contactless transactions, and IoT-based banking infrastructures by integrating classical cryptographic methods—such as Advanced Encryption Standard (AES), Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC), Secure Hash Algorithm 3 (SHA-3), and Hash-based Message Authentication Code (HMAC)—with post-quantum algorithms, including CRYSTALS-Kyber and Dilithium. In addition, the framework aligns with widely adopted industry standards, such as the Payment Card Industry Data Security Standard (PCI DSS), Europay-MasterCard-Visa (EMV), ISO 27001, and National Institute of Standards and Technology (NIST) guidelines, thereby ensuring compliance and regulatory resilience. To address the evolving cyber threat landscape, the system integrates blockchain-based decentralized identity management with zero-knowledge proofs (ZKPs) for trustless authentication. Furthermore, privacy-preserving techniques such as Secure Multi-Party Computation (MPC) and Fully Homomorphic Encryption (FHE) are employed to enable secure processing of encrypted data. The security framework is further strengthened through AI-driven fraud detection, which leverages deep learning and federated learning models to detect anomalies in real time without compromising customer privacy. Additionally, confidential computing enclaves and hardware trust anchors—including Hardware Security Modules (HSMs), Trusted Platform Modules (TPMs), and Physically Unclonable Functions (PUFs)—are utilized to enhance system integrity and reliability. By combining quantum-resistant cryptography, privacy-preserving computation, and intelligent anomaly detection, this work presents an adaptive and future-proof security roadmap for the financial sector. The proposed framework is designed not only to counteract today’s sophisticated cyberattacks but also to maintain resilience against the emerging challenges of the quantum era.
Physical Unclonable Functions (PUFs) and Hardware Security
Public Key Infrastructure (PKI) is foundational to secure digital communication, yet its traditional reliance on centralized Certificate Authorities (CAs) introduces significant risks, costs, and operational bottlenecks. This paper presents a novel, fully decentralized approach to PKI by leveraging the XRP Ledger (XRPL) blockchain for the issuance, storage, retrieval, and revocation of X. 509 certificates. The core methodology stores certificates directly within XRPL transactions using the native Memos field, offering a lightweight, immutable, and cost-effective solution without requiring modifications to the underlying protocol. To enhance certificate lifecycle management and enable native on-chain revocation, we also introduce a complementary approach that uses Non-Fungible Tokens (NFTs) to store certificate data and metadata. NFT-based storage supports features such as instant revocation through burning or flagging, and enables additional transparency and auditability. Certificate retrieval involves querying transaction history or owned NFTs, decoding and reassembling the data if chunked, and validating the reconstructed X. 509 certificate using standard tools. We implement a Python-based web application that exposes RESTful APIs for certificate lifecycle management, secure messaging, and encrypted email, demonstrating seamless integration with existing PKI-aware applications. Our evaluation shows that this architecture drastically reduces operational costs compared to traditional CAs while enhancing transparency, auditability, and resilience. This work demonstrates the feasibility and advantages of blockchain-native PKI, paving the way for scalable, trustminimized, and user-centric digital identity management.
The emergence of 6G-connected smart cities introduces unprecedented challenges in ensuring security, privacy, and scalability for billions of heterogeneous devices and mission-critical services. Traditional Zero Trust Architectures (ZTA) provide continuous verification but rely on centralized control, making them vulnerable to insider threats and single points of failure. Conversely, blockchain-based frameworks ensure immutability and decentralized trust but suffer from high latency and limited scalability. This paper proposes a novel Blockchain-Enabled Zero Trust Architecture (BZTA) that integrates blockchain’s distributed trust management with Zero Trust’s continuous authentication and micro-segmentation, optimized for 6G urban infrastructures.The contributions of this work are fourfold. First, we design a four-layer BZTA model incorporating decentralized identity management, Zero Trust Gateways (ZTGs), blockchain-based ledgers, and smart contracts for adaptive access control. Second, we formalize the methodological foundations of the framework, including trust computation, Zero-Knowledge Proof (ZKP)-based authentication, authorization logic, and Proof-of-Authority consensus mechanisms. Third, we present a comprehensive evaluation using analytical models and simulations. Results show that BZTA achieves 95% trust classification accuracy, sub-20 ms authentication latency compliant with 6G URLLC, revocation within 3 s, and throughput up to 50,000 transactions per second, while reducing authentication energy costs by 35–40% compared to blockchain-only systems. Fourth, we demonstrate that BZTA provides robust defense against spoofing, replay, insider, lateral, and location spoofing attacks, significantly outperforming both ZTA-only and blockchain-only approaches.The findings highlight BZTA as a scalable, resilient, and privacy-preserving security paradigm for 6G smart cities. By merging blockchain immutability with Zero Trust’s dynamic verification, BZTA enables secure and transparent deployment of critical urban services such as healthcare, transportation, and energy management. This work positions BZTA as a foundational step toward building resilient, citizen-centric, and quantum-ready smart city infrastructures.
Blockchain technology is evolving rapidly, bringing significant opportunities as well as complex challenges, particularly in the area of smart contract security This paper addresses these challenges by presenting an enhanced version of Slither, a static analysis framework, specifically designed to improve vulnerability detection in Ethereum-based smart contracts. Our contributions target critical vulnerabilities of reentrancy, unchecked call return values, and access control issues, which pose serious threat to decentralized apps (dApps). The enhanced Slither tool incorporates advanced detection methodologies, including refined control-flow analysis, sophisticated pattern recognition, and comprehensive visibility checks. Experimental evaluation demonstrates that this proposed tool significantly improves accuracy, precision, recall, and F1-score across various metrics, while also effectively reducing false positives. This advanced model not only strengthens the security posture of smart contracts but also contributes to fostering a more secure and reliable blockchain ecosystem.
There is a lack of standardized datasets for NFT (Non-Fungible Token) recommendation systems. This study presents a comprehensive dataset designed for NFT recommendation systems, incorporating both NFT-related data (e.g., images, textual descriptions, rarity and transaction data) and user-related data (e.g., purchase price, transaction duration, and NFT holding period). To create the dataset, a Data Collection Tool was developed to gather raw data via the OpenSea API, and a Data Preparation Tool was implemented for preprocessing and filtering. All data used in this study are publicly available and anonymized, ensuring that user privacy is fully preserved. The dataset is evaluated using NFT-NCFAE, a deep learningbased NFT recommendation model, with performance measured by Recall and NDCG evaluation metrics. The evaluation results demonstrate the suitability and value of the proposed dataset for NFT recommendation systems. By making the dataset and its associated tools publicly available, this work aims to establish a benchmark for future research and enable comparability across different models.
K. Ramesh Babu, M. Ramesh, M Gnana Prasuna, G. Ganesh Kumar · 6 authors
This article presents the Adaptive Multi-Modal Federated Optimization (AMMFO) framework which was developed to tackle important challenges in data privacy, fairness, and accountability in learning in the context of federated learning. The AMMFO framework addresses the need to train models with data from multiple modalities available at the edge in a secure and efficient manner while building trust and respecting privacy in a decentralized system. AMMFO applies differential privacy to protect sensitive model updates from adversarial inference and it leverages a blockchain-based trust mechanism to allow for transparency, immutability and decentralized accountability. The framework optimally adapts learning across multiple data modalities enhancing communication efficiency and stability of model convergence. Results from experiments indicate that AMMFO achieves 8-10% higher accuracy compared to FedAvg and FedProx, and 7-12% greater privacy resistance compared to FedDP while exploring different privacy budgets. Additionally, AMMFO improves convergence time by 15–20 percent and achieves less than 8% blockchain overhead. Overall, these results demonstrate the AMMFO framework's balance of performance, privacy, and scalability which enables next generation AI systems that are situated within privacy and trust-worthy frameworks in domains such as healthcare, finance, autonomous systems, and smart cities.
Francisco Resende, Daniel Costa, Pedro Granate, Armando Teixeira · 8 authors
Non-Fungible Tokens (NFTs) are unique digital assets whose valuation presents a significant challenge due to their non-fungibility, low liquidity, and subjective features. This paper presents a machine learning-based approach to intra-collection NFT valuation using LightGBM, a gradient boosting model. The model was trained on historical sales, metadata, floor prices, and temporal dynamics across six prominent NFT collections. Our approach outperforms traditional valuation baselines, including floor price heuristics, rarity scores, and trait valuation models, achieving significantly lower prediction error (MAPE). The study demonstrates the potential of advanced ML models in enhancing valuation accuracy at a token-level for non-floor assets, with applications in NFT marketplace pricing, portfolio/NAV marking or NFT specialised lending.
Nurhajar Anugraha, Muhammad Riswanto, Lindawati Lindawati, Asrul Asrul
Penelitian ini bertujuan untuk mengembangkan sistem e-voting berbasis blockchain dengan autentikasi biometrik sidik jari serta penerapan protokol zero-knowledge proofs sebagai pengamanan tambahan terhadap data pemilih dan hasil suara. Permasalahan utama yang dihadapi dalam sistem pemungutan suara elektronik konvensional adalah rendahnya kepercayaan terhadap keamanan data dan potensi manipulasi hasil. Metode penelitian yang digunakan mencakup perancangan sistem dengan arsitektur client-server, implementasi teknologi blockchain untuk pencatatan suara yang terenkripsi, serta integrasi biometrik sidik jari menggunakan BiometricPrompt API pada Android. Selain itu, sistem diverifikasi dengan kode OTP melalui email institusional sebagai bentuk validasi ganda pengguna. Hasil pengujian menunjukkan bahwa sistem dapat berjalan dengan baik dan memberikan keamanan yang tinggi karena setiap data suara tersimpan secara permanen dan tidak dapat diubah di jaringan blockchain. Autentikasi biometrik juga memastikan bahwa setiap pemilih terverifikasi secara unik sehingga tidak terjadi pemungutan suara ganda. Dengan demikian, sistem e-voting ini dinilai layak diterapkan untuk lingkungan akademik dan dapat dikembangkan lebih lanjut untuk pemilihan umum berskala lebih besar.
Blockchain technology offers a mechanism for storing data with cryptographic links between blocks, creating a tamper-resistant ledger. Although this immutability ensures data integrity, it complicates recovery in cases of errors or intrusions. This work proposes Bonsai, an error and intrusion recovery system designed for token exchanges on Ethereum based applications. The system includes a custom ERC-20 token (BON) that maintains a one to one peg with ETH tokens while enabling transaction reversals through arbitration trials and an insurance mechanism to protect users against losses. Our experimental evaluation on Ethereum Sepolia and ZKsync Sepolia demonstrates that Bonsai can successfully trace and reverse token flows through up to five wallets in under 20 seconds, at an average cost of approximately ${\$}$0.30 on ZKsync. Existing blockchain recovery approaches are slow and costly, with reversal operations taking up to 126 seconds, and some may not be able to complete the reversal. The system provides a practical solution for blockchain applications requiring error and intrusion correction capabilities while preserving the authentication, integrity, immutability, and non-repudiation properties of Blockchain.
As public services become increasingly digitized, governments worldwide are exploring innovative technologies to enhance operational efficiency, transparency, and citizen engagement. Blockchain technology, particularly through smart contracts, digital identity systems, and decentralized autonomous organizations (DAOs), presents unprecedented opportunities for creating more transparent, accountable, and participatory governance systems. This paper provides a comprehensive analysis of blockchain applications in digital governance, examining real-world implementations, regulatory challenges, and legal frameworks. We propose an adaptive four-layer framework that enables governments to systematically integrate blockchain technologies while addressing privacy, security, and compliance requirements. Through case studies from Estonia, India, Dubai, and emerging DAO implementations, this research demonstrates both the transformative potential and practical challenges of blockchain-based governance systems.
Kassem Danach, Abbas Tarhini, Wael Hosny Fouad Aly, Hussin Hejase
Blockchain technology relies on cryptographic mechanisms for transaction security and data integrity. However, the growing computational complexity, high transaction costs, and scalability issues pose significant challenges to blockchain adoption. Traditional cryptographic methods—such as hashing, key generation, encryption, and decryption—introduce excessive computational overhead, leading to energy inefficiencies and increased latency. This research proposes an optimization-driven crypto analysis framework that integrates metaheuristic algorithms, combinatorial optimization, reinforcement learning, and game theory to enhance the efficiency and security of blockchain cryptographic processes. The framework focuses on optimized cryptographic computation, gas fee reduction in smart contracts, security enhancement against cryptanalysis, and improved scalability of consensus mechanisms. Experimental evaluations demonstrate up to 39.4\% reduction in cryptographic execution time, 29.4\% savings in smart contract gas fees, and 33.3\% improvement in decentralization of Proof-of-Stake validators. These results validate the effectiveness of the proposed framework in achieving secure, scalable, and cost-efficient blockchain operations.
As the "agentic web" takes shape-billions of AI agents (often LLM-powered) autonomously transacting and collaborating-trust shifts from human oversight to protocol design. In 2025, several inter-agent protocols crystallized this shift, including Google's Agent-to-Agent (A2A), Agent Payments Protocol (AP2), and Ethereum's ERC-8004 "Trustless Agents," yet their underlying trust assumptions remain under-examined. This paper presents a comparative study of trust models in inter-agent protocol design: Brief (self- or third-party verifiable claims), Claim (self-proclaimed capabilities and identity, e.g. AgentCard), Proof (cryptographic verification, including zero-knowledge proofs and trusted execution environment attestations), Stake (bonded collateral with slashing and insurance), Reputation (crowd feedback and graph-based trust signals), and Constraint (sandboxing and capability bounding). For each, we analyze assumptions, attack surfaces, and design trade-offs, with particular emphasis on LLM-specific fragilities-prompt injection, sycophancy/nudge-susceptibility, hallucination, deception, and misalignment-that render purely reputational or claim-only approaches brittle. Our findings indicate no single mechanism suffices. We argue for trustless-by-default architectures anchored in Proof and Stake to gate high-impact actions, augmented by Brief for identity and discovery and Reputation overlays for flexibility and social signals. We comparatively evaluate A2A, AP2, ERC-8004 and related historical variations in academic research under metrics spanning security, privacy, latency/cost, and social robustness (Sybil/collusion/whitewashing resistance). We conclude with hybrid trust model recommendations that mitigate reputation gaming and misinformed LLM behavior, and we distill actionable design guidelines for safer, interoperable, and scalable agent economies.