Zero-knowledge virtual machine (zkVM) is a powerful infrastructure for proving the correctness of a program execution with a succinct proof, attracting significant interest from researchers, developers, and users. It has been widely used in applications such as blockchain rollups, privacy-preserving machine learning, and off-chain computation. As the field grows, a wide range of zkVMs have been proposed. However, they adopt different choices in instruction formats, trace layouts, and proving backends, which results in a highly heterogeneous design landscape and makes it difficult to understand the relations among these systems.To bridge this gap, we provide a comprehensive study of zkVMs that covers both their theoretical foundations and practical implementations. We decompose zkVMs into three layers: (1) the ISA layer, which defines instruction semantics and determines the structure of the execution trace, (2) the VM layer, which captures program execution and organizes constraints through modular circuit components, and (3) the proving layer, which converts execution traces into algebraic constraints and generates the final proofs. This decomposition allows us to isolate the role of each layer while also examining how they interact in real systems. To give readers a more direct understanding of how these design choices affect performance, scalability, and usability, we conduct a comprehensive experimental evaluation of representative zkVMs following this layered framework. Finally, we conclude the paper by summarizing the main observations from our analysis and outlining several potential directions for zkVM design and implementation.
Este Trabajo Fin de Grado presenta un análisis criptográfico y matemático de la arquitectura de Monero, una criptomoneda diseñada con la privacidad como propiedad fundamental de su protocolo. El estudio comienza con la formalización de los fundamentos algebraicos que conforman el sistema, como las curvas de Edwards retorcidas y la completitud de su ley de grupo, característica que contribuye a mitigar vulnerabilidades asociadas a ataques de canal lateral. Sobre esta base se estudia el protocolo Ring Confidential Transactions (RingCT), núcleo de los mecanismos de privacidad de la red. En particular, se analizan las direcciones sigilosas (stealth addresses), que garantizan la no vinculabilidad de los receptores mediante intercambios Diffie–Hellman sobre curvas elípticas; las firmas de anillo CLSAG y las imágenes de clave, que proporcionan anonimato al emisor y previenen el doble gasto; y los compromisos de Pedersen, utilizados para ocultar las cantidades transferidas. Asimismo, se estudian las pruebas de rango Bulletproofs+, destacando su función en la reducción del tamaño de las transacciones mediante argumentos de producto interno. Finalmente, se examinan diversas vulnerabilidades históricas y técnicas de análisis de trazabilidad aplicadas a Monero, evaluando el grado de resistencia que ofrece el protocolo frente a distintos ataques. Los resultados ponen de manifiesto cómo la integración de herramientas avanzadas de criptografía de clave pública, pruebas de conocimiento cero y estructuras algebraicas sobre curvas elípticas permite construir un sistema financiero con garantías de privacidad, seguridad y fungibilidad. ABSTRACT This Bachelor’s Thesis presents a cryptographic and mathematical analysis of the architecture of Monero, a cryptocurrency designed with privacy as a fundamental property of its protocol. The study begins with the formalization of the algebraic foundations underlying the system, including twisted Edwards curves and the completeness of their group law, a feature that helps mitigate vulnerabilities associated with side-channel attacks. Building upon this mathematical framework, the Ring Confidential Transactions (RingCT) protocol, which forms the core of Monero’s privacy mechanisms, is examined. In particular, the thesis analyzes stealth addresses, which ensure receiver unlinkability through Diffie–Hellman key exchanges over elliptic curves; CLSAG ring signatures and key images, which provide sender anonymity and prevent double-spending; and Pedersen commitments, which are used to conceal transferred amounts. Furthermore, Bulletproofs+ range proofs are studied, highlighting their role in reducing transaction size through efficient inner-product arguments. Finally, several historical vulnerabilities and traceability analysis techniques applied to Monero are reviewed in order to evaluate the protocol’s resistance against different types of attacks. The results demonstrate how the integration of advanced public-key cryptography, zero-knowledge proofs, and algebraic structures based on elliptic curves makes it possible to build a financial system with strong guarantees of privacy, security, and fungibility.
The pair, in its most absolute sense, is not just two objects but a minimal structure—a dyad—in which two poles define each other through a single opposition. This article builds a self‑contained formal model of the dyad: a set equipped with an operation that exchanges the two poles without leaving any pole unchanged, together with a sign that distinguishes them. A proof by contradiction shows that any perfect binary distinction necessarily obeys this model. The proof uses only the notions of exhaustivity, exclusivity, and symmetry, making the law scale‑invariant and independent of any particular scientific domain. We then tour the natural and human sciences, tracing the dyadic law from the spin of an electron and the positive‑negative charge pair, through left‑ and right‑handed molecules, the complementary strands of DNA, male and female gametes, the opponent colours of vision, mirror neurons, the day–night cycle, the Earth’s magnetic reversals, binary stars, the warm and cool hemispheres of the cosmic microwave background, to Boolean logic, error‑correcting codes, mathematical dualities, zero‑sum games, and the I–Thou relation. Each example is explained in plain terms to make the article self‑contained. The dyad emerges as a universal structural law—a law of form that prescribes the necessary architecture of otherness across all scales of reality and all branches of knowledge.
We present ENI6MA and Rosario Cypher as a proof-based identity and authorization architecture for emerging cybersecurity threats involving shadow AI, deepfakes, prompt injection, autonomous agents, credential theft, privacy exposure, and post-quantum risk. The paper responds to major 2026 cybersecurity forecasts by identifying a common root cause across many attack surfaces: conventional systems depend on reusable, stealable artifacts such as credentials, tokens, private keys, sessions, API keys, and stored personal data. ENI6MA replaces possession-based authentication with per-event proof of knowledge, policy-bound authorization, privacy-clean auditability, and contract enforcement behind cryptographically secure proof. Special attention is given to autonomous-agent security. The paper explains how ENI6MA constrains agents through per-action proof, verifier allowlists, policy identifiers, scoped pass credentials, and immutable validation records, reducing the risk of hijacked agents, excessive privilege, non-human identity sprawl, and zero-click prompt-injection exfiltration. The white paper also describes ENI6MA’s flexible deployment and capability model, including passwordless single sign-on, PII validation without disclosure, agent-to-agent authentication, proof-gated signing and custody, post-quantum sealing, sovereign/offline operation, and public verifier anchoring. This document is intended for cybersecurity leaders, AI governance teams, identity architects, privacy and compliance stakeholders, investors, technology partners, and researchers evaluating post-credential identity systems for human and autonomous-agent workflows.
Prof. Suvarna A. Bahir, Tejas Vaidya, Ranjeet Waghmode, , Abhishek Gavand, · 5 authors
Electronic voting systems have gained significant attention due to their ability to improve the efficiency and accessibility of elections. However, traditional voting methods and centralized electronic voting systems face challenges such as vote tampering, lack of transparency, unauthorized access, and delayed result generation. Blockchain technology offers a decentralized and secure solution to address these limitations. This paper presents a Secure Blockchain-Based E-Voting System Using Smart Contracts that leverages Ethereum blockchain technology to provide transparent, secure, and tamper-resistant elections. The proposed system integrates voter authentication, election management, candidate registration, vote recording, and real-time result monitoring within a single platform. Smart contracts developed using Solidity are used to automate election operations and ensure the integrity of voting transactions. The system is implemented using HTML, CSS, JavaScript, FastAPI, MySQL, Ethereum, Ganache, and MetaMask. Votes are securely recorded on the blockchain, preventing unauthorized modifications and improving election transparency. The proposed framework enhances voter trust, reduces dependency on centralized authorities, and simplifies election management. This solution can be effectively used for academic institutions, organizations, and small-scale election environments requiring secure and reliable voting processes. Keywords: Blockchain, Electronic Voting, Ethereum, Smart Contracts, Solidity, Decentralized Voting.
Strategic alliances have long required their participants to combine the certainty of formal contracts with the adaptive flexibility of relational mechanisms, and the substitutes-complements debate in alliance governance has spent decades trying to clarify how these two qualities can be combined.The recent emergence of blockchain-enabled smart contracts complicates this picture in interesting ways.This article asks how smart contracts interact with the contractual and relational governance mechanisms documented in the strategic alliance literature, what conditions shape this interaction, and what the implications are for alliance theory.Drawing on the alliance governance literature and the blockchain governance literature in roughly equal measure, the paper develops a framework that positions smart contracts as a third governance mechanism alongside contractual and relational forms, producing a hybrid arrangement termed algorithmic-relational governance.Three propositions are derived and illustrated through a case study of Walmart Canada's DL Freight platform, one of the larger production-grade smart contract deployments in a multi-party alliance setting.The findings suggest that smart contracts function primarily as governance complements rather than substitutes, that they alter alliance dynamics in ways transaction cost economics alone cannot predict, and that their effectiveness depends on deliberate architectural design choices that are themselves products of relational negotiation between alliance partners.
Nazneen Fatema, Abdullah Mohammed Ibrahim, Jesmin Sabnam, Abdullah Mohammad Ismail
This bibliometric study maps research trends in cybersecurity and data breaches within the financial sector from 2020 to 2024, analyzing 7355 documents from the Web of Science. The findings reveal a rapidly expanding and interdisciplinary field, driven by the digital transformation of finance, heightened cyber threats, and the impact of global events such as the COVID-19 pandemic. The research landscape has evolved from descriptive, technical studies to sophisticated analyses incorporating network theory, econometrics, and risk management. Most prolific authors and sources, such as IEEE, demonstrate strong international collaboration and significant citation impact, with China, the USA, and the UK leading in citations. Co-citation network analysis identifies three major intellectual clusters: economic modeling of cyber risk, network-based risk propagation, and systemic macro-financial implications of cyberattacks. The study highlights an increasing focus on quantifying the financial and reputational impacts of cyber incidents, making research directly relevant to business and regulatory stakeholders. Limitations include reliance on a single database and quantitative methods. Future research directions emphasize the security implications of emerging technologies (e.g., quantum computing, decentralized finance, artificial intelligence), behavioral and cultural aspects of cybersecurity, and systemic regulatory challenges. The field is dynamic, reflecting the financial sector’s evolving risk landscape.
Blockchain-based electronic voting systems have been identified as a solution to enhance the transparency, security, and efficiency of modern electoral processes. However, the existing system has three major problems, which include scalability, privacy issues, and cybersecurity attacks. The researcher proposed an innovative solution to develop an electronic voting system with enhanced security, scalability, and transparency of voters' information. This paper introduced the Hybrid Cryptographic and Enforced Blockchain (HCE VoteChain) framework, which combines Hyperledger Fabric with various sophisticated forms of cryptography, including SHA256 hashing, Advanced Encryption Standard (AES256) encryption, Elliptic Curve Digital Signature Algorithm (ECDSA) and digital signatures, Paillier Homomorphic Encryption, and Zero Knowledge Proof (ZKP) auditing. The experimental evaluation demonstrated that the system achieved a throughput of 288 Transactions Per Second (TPS) while maintaining an average latency of 2.521 seconds, a transaction speed of 0.13 votes per second, and a data immutability score of 0.999 and security resilience of 10000 and fault tolerance of 0.96, which proved its high reliability and robustness across different operational conditions. The results indicate that the framework suggested is a big step up in terms of security, scalability, and transparency over the existing solutions. Besides, it does not compromise the voter's privacy and auditability. The innovation of this work is the combination of multi-layer cryptographic mechanisms with the permission blockchain architecture to not only come up with a balanced compromise between performance and security but also to make the system capable of handling large-scale real-world digital elections.
Federated Learning (FL) is increasingly deployed in healthcare to enable collaborative intelligence while keeping sensitive data privately at local institutions. However, existing healthcare-oriented FL frameworks still suffer from several limitations: they are vulnerable to adversarial model updates, provide limited transparency and verifiable auditability, and often lack predictable performance under constrained resources. We present IIN-Health, a blockchain-enhanced intelligent fusion network tailored for dependable healthcare FL. IIN-Health adopts a dual-chain architecture with policy-aware access control and auditable provenance tracking to integrate learning, security, and governance in a unified framework. Evidence-Carrying Access Tokens (ECATs), combined with zero-knowledge proofs, are introduced to enforce patient-defined policies and validate access decisions without disclosing sensitive information. In addition, we design MedBFT-Δ, a domain-specific Byzantine fault-tolerant protocol that ensures reliable system behavior in the presence of faulty or malicious participants. We conduct several experiments to validate its feasibility and accuracy on the MNIST dataset. The results demonstrate that IIN-Health achieves smooth and stable convergence, exhibits strong resilience against poisoning attacks, and maintains graceful performance degradation under resource constraints, while preserving verifiable auditability of model updates and data flows. These observations indicate that IIN-Health can provide a practical balance among performance, security, and regulatory compliance, and thus offers a promising foundation for trustworthy and scalable FL deployments in healthcare.
Imran Hasan, Abdullah All Ahhad, Md Zamilur Rahman, Bikash Chandra Singh
Smart contracts enable decentralized applications across domains such as finance, logistics, and healthcare, but their immutable nature and complex execution logic make them highly susceptible to vulnerabilities, including reentrancy, integer overflows, and access control flaws. These weaknesses can lead to severe financial and operational losses. Traditional static or rule-based detection tools lack scalability and adaptability, while existing deep learning models often struggle with limited data, poor generalization, and the absence of actionable mitigation guidance. This paper proposes a hybrid multi-task learning framework that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks for smart contract vulnerability detection, coupled with a transformer-based Large Language Model (LLM) for root cause analysis and dynamic mitigation generation. The framework extracts spatial opcode features using CNNs and captures temporal execution patterns via LSTMs, supported by preprocessing steps that include opcode extraction, positional encoding, static and dynamic analysis features, and data augmentation. A feature fusion module consolidates spatial and temporal information, while SHAP and LIME provide interpretability by identifying features driving model predictions. The mitigation layer employs an encoder–decoder transformer to map detected vulnerabilities to their underlying causes and generate context-aware remediation strategies. Experimental results show strong performance, achieving 93% accuracy, 90% precision, and an AUC-ROC of up to 90% across multiple vulnerability categories. Beyond accurate detection, the framework delivers explainable root cause insights and tailored mitigations, offering a scalable and adaptive solution for enhancing smart contract security in modern blockchain ecosystems.
Financial technology (FinTech) has emerged as a key driver of financial inclusion, transforming access to payments, credit, savings, and insurance for households, small businesses, and underserved populations worldwide. This study synthesizes a decade of Scopus-indexed bibliometric and systematic-review research on FinTech and financial inclusion published between 2015 and 2025. Rather than conducting a new bibliometric extraction, it provides a comparative synthesis of major peer-reviewed review studies, consolidating evidence on publication trends, intellectual structure, geographic distribution, and emerging research themes. The findings reveal rapid growth in scholarly output since 2016, led by China, India, the United States, and the United Kingdom. Dominant themes include digital payments, mobile money, regulatory technology, artificial intelligence, decentralized finance, financial literacy, SME finance, and sustainability-oriented digital finance. The review identifies persistent gaps in low-income regions and limited integration of AI and ESG perspectives. It offers a consolidated evidence base and proposes directions for future research, policy formulation, and practice.
Jyotiplaban Talukdar, Ferdous Ahmed Barbhuiya, Shouraya Mishra, Shubhashish Shukla
Asset ownership tracking is fundamental to economic activity, yet it remains fragmented across paper-based deeds, centralised registries, and siloed databases. Public blockchains provide immutability but suffer from scalability limitations and inflexible consensus mechanisms unsuitable for enterprise deployment. We present Keychain Signet (KCS), a decentralised architecture designed as a notarised asset ledger that strictly separates application logic, consensus, and persistence. KCS distributes responsibilities: service providers manage application workflows, notaries enforce validity and uniqueness via BLS signatures, and storage nodes execute BFT-Raft consensus to order blocks. This architecture enforces a "notarised append" model where authorised writes are persisted and ordered by the storage cluster. Unlike standard blockchains, our architecture resolves the latest asset state in O(1) time via a disc-backed Distributed Hash Table while maintaining an immutable, cryptographically linked history for auditability. Experimental evaluation demonstrates 401.1 TPS throughput, notary signing latency of 1.6–10.2 ms scaling linearly with committee size, signature verification under 0.81 ms, and storage persistence under 700μs. The system tolerates Byzantine faults, storage corruption, and node crashes while preserving safety and liveness guarantees.
Energy demand in urban and metropolitan regions has been growing rapidly, often exceeding production capacity, leading to imbalances in energy distribution. Existing peer-to-peer (P2P) energy trading models, along with classical algorithms like FCFS and best-fit frameworks in smart cities, address some of these issues. However, they often face challenges such as limited transaction success percentage, inefficiencies in price matching, and privacy concerns during trades. To overcome these limitations, a framework is proposed that integrates game-theoretic pricing-based collaborative trading with Nash equilibrium and an additional pricing mechanism (CoGap) to enhance fairness and transaction success percentage in decentralized energy markets. The proposed framework is implemented on an Ethereum-based blockchain using Solidity smart contracts, incorporating cryptographic security through the Keccak-256 hash function and privacy-preserving zero-knowledge proofs (ZKPs). Moreover, it ensures security and price negotiations while maximizing transaction efficiency. Simulation results demonstrate that CoGap consistently achieves higher transaction success rates compared to four state-of-the-art collaborative energy trading schemes.
The rapid expansion of cryptocurrency markets has fundamentally transformed the global financial system and challenged traditional approaches to financial regulation. Cryptocurrency exchanges have emerged as key intermediaries facilitating the purchase, sale, transfer, and storage of digital assets across jurisdictions. However, the borderless and decentralized nature of cryptocurrencies has generated significant legal concerns relating to anti-money laundering compliance, counter-terrorist financing measures, consumer protection, taxation, cybersecurity, market manipulation, and regulatory enforcement. This article examines international legal frameworks governing cryptocurrency exchanges, analyzes regulatory approaches adopted by leading jurisdictions, including the European Union and the United States, and evaluates major enforcement actions involving Binance and FTX. The study further explores emerging challenges associated with decentralized finance (DeFi) and proposes recommendations aimed at strengthening international cooperation and harmonizing legal standards for digital asset regulation.
Transaction monitoring and efficient audit management have become increasingly importantin modern financial systems. Traditional centralized databases and auditing methods often face challengesrelated to security vulnerabilities, fraudulent activities, and data manipulation. A blockchain-based financialtransaction monitoring system integrates smart contracts, decentralized ledgers, and audit trails to automatefinancial operations, enhance transparency, and reduce fraud risks. The proposed architecture is implementedon Ethereum and Hyperledger Fabric platforms, enabling automated transaction validation and executionthrough smart contracts. All transactions are stored in an immutable decentralized ledger, while audit trailsare generated and maintained automatically. Simulation results demonstrate a 40–60% reduction in fraudulentactivities and up to a 70% decrease in audit processing time compared with conventional approaches. Theapplication of cryptographic algorithms and Zero-Knowledge Proofs further strengthens data security andprivacy protection. The proposed solution contributes to the improvement of financial control and auditingsystems within the framework of the digital economy.
The growing demand for secure, transparent, and efficient financial systems has accelerated interest in blockchain technology within the financial sector. This study investigates the determinants and outcomes of financial blockchain adoption by proposing an integrated research model that links core blockchain characteristics security and transparency, smart contracts, and decentralization to financial blockchain adoption and its subsequent effects on fraud reduction, risk management, and cost efficiency. Using data collected from financial institutions operating in Jordan, the study applies Partial Least Squares Structural Equation Modeling (PLS-SEM) to empirically test the proposed hypotheses. The findings demonstrate that security and transparency, smart contracts, and decentralization significantly and positively influence financial blockchain adoption. Moreover, the results confirm that financial blockchain adoption has a strong positive impact on fraud reduction, risk management effectiveness, and operational cost efficiency. By conceptualizing blockchain adoption as a strategic organizational capability rather than a purely technological choice, this study extends existing blockchain and fintech literature. The findings provide valuable theoretical insights into how blockchain characteristics translate into tangible financial outcomes and offer practical guidance for financial institutions and policymakers seeking to enhance trust, governance, and efficiency through blockchain-based financial systems.
Shahinaz Hanem Abdellatif, Marwan Kobtan, Mostafa Zeinelabdein, Ramina Pashaee
The study examines the role of development finance theory, stakeholders’ theory, creating shared value (CSV), and the triple bottom line (TBL) framework to advance the Sustainable Development Goals (SDGs). Most studies focus on sustainable Business Models (SBMs) from the perspective of developed countries and often overlook the interdisciplinary nature and peculiarities of emerging economies, in terms of technology opportunities, financing constraints, and governance challenges, in the Global South. This study proposes a multi-layered framework to advance economic sustainability in emerging economies by integrating blended green finance, public-private partnerships (PPP), and fintech-enabled SBMs. The multi-layered framework redefines traditional PPP as a collaborative delivery and governance mechanism involving public, private, and development multilateral organizations to implement Economic, Social, Governance (ESG), and climate-aligned infrastructure. It also reshapes the role of blended finance strategies and emphasizes the often-overlooked role of non-banking financial institutions (NBFIs); particularly leasing companies, small and microfinance institutions, alongside banks in enabling inclusive green finance, and incorporates financial technology (FinTech) innovations, including decentralized finance (DeFi), blockchain, and digital crowdfunding, to improve access to capital, and financial inclusion. Tailored to the context of MENA economies, like Egypt's Vision 2030, the framework offers policy insights and a smooth transition toward sustainable development. Keywords: Economic Sustainability, Green Finance, Blended Finance, Public-Private Partnerships (PPP), Fintech-Enabled Sustainable Business Models, Economic, Social, Governance (ESG), Egypt Vision 2030
Abstract Food security and the stakeholders’ trust are essential to ensure that agricultural supply chains are transparent and secure. This research presents a Queueing-Assisted Blockchain Smart Contract (QABSC) framework to enhance end-to-end traceability in the millet supply chain. The framework incorporates fog computing into real-time data processing to reduce latency and optimizes transaction flow using queueing techniques, thereby ensuring an efficient and scalable blockchain supply chain platform. The Internet of Vehicles and Things (IoVT) connects cars, sensors, roadside infrastructure, and cloud and edge technologies to make transportation and mobility smarter. By integrating fog-layer intelligence with blockchain-based immutable record-keeping, Internet of Vehicles and Things enabled sensing and vehicular logistics, and end-to-end visibility, the proposed system may ensure tamper-resistant monitoring of millet products from farms to customers. Internet of Things (IoT) sensors collect real-time information about millet quality and storage conditions. This data is securely stored using the InterPlanetary File System (IPFS) and verified by smart contracts on a distributed ledger. This approach ensures automated compliance verification for auditors and regulators, immutable data storage, and conditional privacy. The proposed model reduces bottlenecks in blockchain transaction processing and enhances efficiency, privacy, data integrity, and trust among producers, distributors, retailers, farmers, and buyers. The proposed model is evaluated based on key performance metrics. The experimental evaluations of the proposed framework demonstrate enhanced throughput, improved transparency, reduced computational overhead, and robust security. This research focuses on a unique integration of smart contracts, queueing theory, IPFS, Fog Computing, IoT devices, and blockchain technology to promote sustainable and transparent millet supply chain management.
Execution Tickets (ET) have emerged as a leading proposal for mitigating MEV-related centralization risks by internalizing MEV through a protocol-level lottery system. This paper provides an empirical game-theoretic analysis (EGTA) of the ET mechanism under an infinite-supply design, modeled as a Tullock contest. We evaluate a 2-slot lookahead window as a minimal temporal design that limits multi-slot MEV while preserving support for user pre-confirmations. By introducing a forfeiture parameter, we parameterize a continuum between All-Pay and Winner-Pay regimes. We then map the fairness-revenue frontier, revealing a fundamental design tension: higher contest decisiveness and forfeiture rates can improve protocol revenue, but may reduce allocation fairness by entrenching dominant builders. We identify a quantitative Goldilocks zone that balances MEV-capture efficiency with market diversity.
Azkia (IAI Darussalam Martapura), Annisa Nur Aulia Purnama (IAI Darussalam Martapura), Muhammad Sauqi (IAI Darussalam Martapura)
Penelitian ini bertujuan untuk mengkaji konsep kepemilikan aset digital melalui perspektif m?l (harta) dalam fikih kontemporer. Perkembangan teknologi digital telah melahirkan berbagai bentuk aset baru, seperti cryptocurrency, token digital, non-fungible token (NFT), serta aset berbasis blockchain lainnya yang memiliki nilai ekonomi dan diperdagangkan dalam sistem ekonomi modern. Fenomena ini menimbulkan pertanyaan mengenai status hukum dan kedudukan aset digital dalam perspektif hukum Islam. Penelitian ini menggunakan pendekatan kualitatif dengan metode studi kepustakaan (library research), melalui penelaahan terhadap literatur fikih klasik dan kontemporer, buku ekonomi Islam, serta artikel ilmiah yang relevan dengan perkembangan aset digital. Analisis dilakukan secara deskriptif-analitis untuk mengkaji kesesuaian karakteristik aset digital dengan konsep m?l dalam fikih Islam. Hasil penelitian menunjukkan bahwa aset digital pada dasarnya dapat dikategorikan sebagai m?l, karena memenuhi kriteria utama harta dalam hukum Islam, yaitu memiliki nilai ekonomi, dapat dimiliki secara sah, serta memberikan manfaat bagi pemiliknya. Namun demikian, status hukum beberapa jenis aset digital masih menjadi perdebatan di kalangan ulama, terutama yang memiliki tingkat volatilitas tinggi dan mengandung unsur spekulatif. Dengan demikian, fikih kontemporer memiliki peran penting dalam memberikan landasan ijtihad terhadap fenomena ekonomi digital agar tetap selaras dengan prinsip-prinsip syariah. Penelitian ini diharapkan dapat memberikan kontribusi akademik dalam pengembangan kajian fikih muamalah, khususnya terkait kepemilikan aset digital dalam konteks ekonomi modern.
This report synthesises findings from 9 peer-reviewed papers addressing the following research question: How does the robustness of Fed-DPRoC scale with the number of Byzantine clients compared to baseline federated averaging in cross-domain settings such as federated natural language processing tasks. Federated learning (FL) is a machine learning setting where many clients (e.g., mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g., service provider), while keeping the training data decentralized. FL embodies. 10 claims were extracted from source literature; 10 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.5/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: How does the robustness of Fed-DPRoC scale with the number of Byzantine clients compared to baseline federated averaging in cross-domain settings such as federated natural language processing tasks (e.g., GLUE benchmark)? Autonomous literature synthesis. Automated review score: 8.5/10. Full text and citation available at Assignee Research.
Major sociopolitical events can reshape public attention toward identity-related issues, potentially influencing valuation patterns in digital markets where identity-related characteristics are embedded in digital assets. Using the overturning of Roe v. Wade as an exogenous policy shock, this paper examines how gender attributes represented in non-fungible token (NFT) avatars affect market outcomes. Using transaction data from six major avatar-based NFT collections traded on Etherscan in 2022, we apply a quasi-experimental design combining propensity score matching and a difference-in-differences model. The results indicate that the policy shock significantly increased the resale prices of NFTs representing female avatars. These findings suggest that sociopolitical shocks can change investor attention and pricing dynamics in decentralized digital markets by highlighting identity-related signals embedded in digital assets.
Samukeliso Mabarani, Mohammad Saidur Rahman, Iqbal Gondal, H. M. N. Dilum Bandara
The tokenization of real-world assets (RWAs) through non-fungible tokens (NFTs) has introduced new opportunities for liquidity, enabling fractional ownership of traditionally illiquid assets. Yet, current NFT fractionalization models remain static, lacking adaptive governance and real-time responsiveness required for managing the dynamic nature of RWAs. This paper presents an Adaptive NFT Fractionalization Framework with Rights Segregation that integrates modular smart contracts, oracle data, and machine learning (ML) insights to enable dynamic rights management. The framework segregates and defines distinct rights, governed through cross-layer decision-making and adaptive rights management that updates allocations based on market data and predictive analytics. Experimental results demonstrate accurate, real-time adjustments of fractional rights, consistent governance execution, and efficient gas utilization across stress and concurrency tests. The findings validate the framework's scalability, responsiveness, and cost-effectiveness, establishing it as a viable approach for adaptive, data-driven management of fractionalized RWAs.