Podcasts are a useful educational resource for improving student success, yet traditional methods of podcasting remain inefficient, vulnerable to censorship and deletion, and access-restricted. One approach to addressing these constraints is utilitarian digital pedagogy, which focuses on the use of digital tools to advance education for the greater good. Framed as such, this article outlines the conceptual and theoretical issues underlying how generative artificial intelligence (genAI), Web3, and open access (OA) improve podcasting’s utility relative to the alternatives: manual creation, Web2, and closed access. The article concludes by looking ahead to the major problems—hallucination, technical complexity, and rights management—to overcome in practice.
Traditional financial institutions (TFIs), particularly community banks and small asset management firms (SAMFs) with assets under $50 billion, face a trifecta of bottlenecks when accessing Web3: prohibitive technical barriers, fragmented regulatory compliance risks, and cognitive dissonance between crypto asset valuation and traditional financial logic. In the U.S. market, constrained by multi-agency oversight (SEC, OFAC, FinCEN), the adoption rate of Web3 access among these small TFIs remains merely 5.2% (SIFMA, 2025), far below the 37.8% penetration among large institutions with assets exceeding$500 billion. Leveraging my dual expertise in quantitative finance (CFA Level III) and Web3 multi-chain development (Uniswap V3/V4 protocol experience, daos.world multi-chain DAO incubation), this study constructs a three-dimensional synergistic theoretical framework integrating regulatory adaptation, technical simplification, and valuation migration. A low-barrier access pathway is proposed, centered on the “TradFi-Web3 Connector” system—featuring compliant wallet custody based on EIP-4337 account abstraction and a traditional finance-derived Web3 asset valuation model. Empirical validation across 8 U.S. small TFIs (4 community banks, 4 SAMFs) over an 8-month period (March–October 2025) demonstrates that this pathway reduces the average onboarding cycle from 2.8 months to 9.7 days (82.5% improvement), cuts compliance costs by 61.3% (from $95,400 to$37,300 per annum), achieves a 92.4% investment decision accuracy rate, and maintains a 100% pass rate in SEC compliance reviews with zero regulatory incidents. This research fills a critical gap in low-barrier Web3 access for resource-constrained TFIs, provides a replicable paradigm for the digital transformation of U.S. traditional finance, and empirically validates the synergy between regulatory compliance and technical innovation in cross-ecosystem integration.
Decentralized Finance (DeFi) enables financial services to operate without centralized intermediaries, using smart contracts and blockchain consensus to ensure transparency and trust minimization. While DeFi protocols like Aave and MakerDAO use overcollateralization to mitigate credit risk, this approach creates capital inefficiencies and limits access to borrowers lacking on-chain assets. This paper introduces Inverum, a novel DeFi lending protocol designed to support undercollateralized loans for Web3 businesses and Decentralized Autonomous Organizations (DAOs). Inverum integrates on-chain credit scoring via soulbound tokens, decentralized liquidity pools, and governance-driven incentives to enable trustless, reputation-based lending. The protocol offers a fully composable framework for exploring undercollateralized lending without relying on traditional identity or off-chain reputation systems, contributing a research-ready model for future experimentation and protocol design.
. This study aims to explore the transformation of human resource management in the Web3 era through a bibliometric analysis of global research trends. The research investigates how decentralized technologies, such as blockchain, smart contracts, tokenization, and Decentralized Autonomous Organizations (DAO) reshape human resource management practices toward transparency, autonomy, and efficiency. Using a descriptive qualitative approach combined with bibliometric analysis, data were collected from the Scopus database (2020–2025) and analyzed using VOSviewer to map keyword networks, identify clusters, and determine research evolution. The findings reveal four major research clusters focusing on blockchain applications, human resource analytics, organizational transformation, and smart contract implementation. Results indicate a paradigm shift in human resource management from administrative functions to strategic, technology-driven roles emphasizing digital competence and data transparency. Moreover, the study highlights challenges in privacy, data regulation, and digital literacy as critical barriers to Web3 adoption in human resource systems. The research provides conceptual insights and a framework for understanding human resource management digital evolution, offering implications for policymakers and organizations to design adaptive, decentralized, and human-centered human resource management strategies.
Secure interoperability across heterogeneous blockchains remains one of the most pressing challenges in Web3 with existing bridge protocols vulnerable to both classical exploits and emerging quantum threats. This paper introduces QLink a quantum-safe Layer 3 interoperability protocol that integrates postquantum cryptography (PQC) quantum key distribution (QKD) and hardware security modules (HSMs) into a unified validator architecture. To our knowledge, QLink is the first interoperability framework to combine these mechanisms to secure validator communication proof aggregation and key management. Validators exchange encryption keys through QKD channels, achieving information-theoretic security against interception, while cross-chain proofs are generated and aggregated with NIST-standardized PQC algorithms. Private keys remain sealed inside HSM enclaves mitigating the risk of theft or leakage. Deployed as a dedicated Layer 3 protocol QLink operates independently of Layer 1 and Layer 2 chains providing a scalable decentralized foundation for secure cross-chain messaging and asset transfer. Experimental evaluation using network simulations demonstrates that validator communication overhead remains sub-second while security guarantees extend beyond current bridge architectures to resist both classical and quantum adversaries. By addressing today vulnerabilities and anticipating future quantum threats QLink establishes a practical and future-proof pathway for blockchain interoperability.
Ridwan Yusuf, Andreas Perdana, Febri Sugandi, Untoro Apsiswanto
Smart contract pada platform Ethereum mengelola aset finansial bernilai besar, namun sifat immutable membuat kerentanan kode berdampak permanen sebagaimana ditunjukkan kasus The DAO, Parity Wallet, dan Ronin Bridge. Dua pendekatan pendeteksian kerentanan telah berkembang luas: static analysis yang memeriksa kode sumber atau bytecode tanpa eksekusi, dan dynamic analysis yang menjalankan kontrak pada lingkungan simulasi dengan masukan terstruktur. Keduanya memiliki trade-off kecepatan, cakupan, dan tingkat false positive yang berbeda, namun perbandingan sistematis pada lingkungan pengembangan lokal Indonesia menggunakan alat versi terbaru masih jarang dilakukan. Artikel ini memaparkan rancangan penelitian eksperimental kuantitatif yang akan membandingkan Slither (static) dengan Foundry dan Echidna (dynamic) pada dataset 25 smart contract Solidity yang mencakup lima kategori kerentanan utama: reentrancy, integer overflow, access control, unchecked return values, dan unbounded loop. Metrik perbandingan meliputi precision, recall, F1-score, dan waktu komputasi. Kontribusi yang diharapkan adalah kerangka komparatif yang dapat menjadi rujukan praktis bagi pengembang Web3 Indonesia dalam memilih alat keamanan dan strategi pengujian hibrid yang sesuai dengan tingkat risiko proyek serta sumber daya tim.
Traditional health data infrastructure fragments longitudinal health status into isolated clinical encounters, introduces significant self-reporting bias, and concentrates data ownership among centralized custodians.This paper proposes an institutional research lab architecture-Proof of Health-that treats verified health status as a cryptographically attestable primitive suitable for decentralized trials, data marketplaces, and risk-adjusted health contracts.The architecture integrates three core components: (1) multi-modal longitudinal data collection via remote patient monitoring (RPM), wearable sensors, and structured clinical assessments; (2) privacy-preserving verification using off-chain encrypted storage paired with on-chain attestations and zero-knowledge proofs; and (3) decentralized trial infrastructure supporting hybrid recruitment, telemedicine visits, and electronic patient-reported outcomes (ePROs).We define a standardized "Proof of Health" metric derived from biomarker trajectories, behavioral adherence logs, and imaging-derived phenotypes, versioned using FHIR interoperability standards and blockchain-based metadata provenance.The lab architecture incorporates HL7 FHIR compliance, GDPR/HIPAA-aligned consent automation via smart contracts, and risk-based remote monitoring (RBM) protocols aligned with ICH-GCP guidelines.Initial pilot studies (N = 20-50 participants per cohort) will validate the Proof of Health signal across three use cases: (1) insurance risk stratification, (2) employment wellness contracts, and (3) participation in decentralized science (DeSci) research data marketplaces.Participants retain cryptographic custody of raw data while institutions gain provably valid, tamper-evident health intelligence.We present the system architecture, methodology, preliminary endpoint definitions, and regulatory pathways for pilot and confirmatory trials.This framework aims to resolve the central tension in modern health research: enabling rigorous longitudinal science while strengthening individual data sovereignty and consent transparency.
Probate stands as a bastion of legal formalism, seemingly resistant to the transformative currents of digital innovation that have swept through other domains of American law. While financial transactions, real property conveyances, and contract execution have increasingly begun exploring the use of Web3 technologies such as blockchain and smart contracts, estate and probate law remain tethered to paper-based procedures and rigid execution requirements. Nevada was the first state to provide legal support for Web3 technology, amending its Uniform Electronic Transactions Act statutes in 2017 to recognize blockchain-based transactions as valid and judicially enforceable. Yet despite this progressive legislative framework, the state’s estate and probate laws remain unchanged. What reforms are required to extend this legal recognition of blockchain to testamentary instruments and probate administration? To explore this, I begin in Part I by examining Nevada’s existing statutory framework for traditional paper wills, electronic wills, and probate administration, identifying where these laws diverge from the state’s more progressive legislation governing blockchain-based transactions. In Part II, I introduce the concept of a blockchain will, explain its technical functionality, and discuss how such instruments can be amended, revoked, or rendered obsolete. I then propose specific legislative reforms that could allow blockchain wills to serve as legally recognized alternatives to traditional paper wills, including the creation of a state-managed blockchain will registry that would provide the procedural infrastructure for securely filing, validating, and preserving blockchain wills. To illustrate how these proposals might operate in practice, hypothetical examples modeling blockchain-based testamentary execution and probate are included. Finally, I analyze the policy considerations both for and against reform, examining the legal barriers that must be addressed and the potential benefits this technology could bring to probate courts.
Historical Context and Problem Statement The digital revolution has created two parallel challenges that have resisted comprehensive solutions: Internet Data Transfer Limitations: Despite decades of progress, internet download speeds remain constrained by inefficient protocols that don't adapt to network topology dynamics. Traditional download managers like IDM operate with static segmentation strategies that ignore the quantum-inspired probabilistic nature of network paths. Web3 Liquidity Fragmentation: Decentralized finance (DeFi) suffers from fragmented liquidity across multiple venues, resulting in significant MEV exploitation. As documented by Qin et al. (2021), MEV extraction has cost users over $680 million in 2021 alone, with no comprehensive solution addressing the root cause. These seemingly disconnected problems share a common underlying structure: both involve the transfer of "value" (data or financial assets) across complex networks where efficiency is hampered by non-resonant transmission strategies.
The web3 applications have recently been growing, especially on the Ethereum platform, starting to become the target of scammers. The web3 scams, imitating the services provided by legitimate platforms, mimic regular activity to deceive users. However, previous studies have primarily concentrated on de-anonymization and phishing nodes, neglecting the distinctive features of web3 scams. Moreover, the current phishing account detection tools utilize graph learning or sampling algorithms to obtain graph features. However, large-scale transaction networks with temporal attributes conform to a power-law distribution, posing challenges in detecting web3 scams. To overcome these challenges, we present ScamSweeper, a novel framework that emphasizes the dynamic evolution of transaction graphs, to identify web3 scams on Ethereum. ScamSweeper samples the network with a structure temporal random walk, which is an optimized sample walking method that considers both temporal attributes and structural information. Then, the directed graph encoder generates the features of each subgraph during different temporal intervals, sorting as a sequence. Moreover, a variational Transformer is utilized to extract the dynamic evolution in the subgraph sequence. Furthermore, we collect a large-scale transaction dataset consisting of web3 scams, phishing, and normal accounts, which are from the first 18 million block heights on Ethereum. Subsequently, we comprehensively analyze the distinctions in various attributes, including nodes, edges, and degree distribution. Our experiments indicate that ScamSweeper outperforms SIEGE, Ethident, and PDTGA in detecting web3 scams, achieving a weighted F1-score improvement of at least 17.29% with the base value of 0.59. In addition, ScamSweeper in phishing node detection achieves at least a 17.5% improvement over DGTSG and BERT4ETH in F1-score from 0.80.
One of the main Web3 applications is Non-Fungible Tokens, blockchain-based certificates to keep track of the ownership of unique digital or physical assets. Nowadays, there is no standard method to evaluate an NFT, and only for a trait-based collection can we rely on the rarity score, which estimates the scarcity of the traits of the NFT. However, rarity is unsuitable for describing the price of a token in a volatile market, and it is not a good price indicator because a token’s price is strictly related to external unpredictable events and the interest people have in specific assets. In this paper, we propose an evaluation model called The Popularity Model , that aims to evaluate NFTs based on marketability The Popularity Model is based on a set of indices which define a dynamic, socioeconomic indicator, with an antifraud system. We formalised and compared our popularity model and the rarity score to show their differences. Finally, we propose two applicable use cases in which the popularity index can be applied. The experiments show and confirm the utility and efficacy of the proposed evaluation model.
Digital forensic investigation in 2025 faces unprecedented challenges posed by the convergence of decentralized web technologies (Web3), adversarial generative AI systems, and darknet infrastructure. Traditional attribution and evidence preservation methodologies prove in-sufficient when adversaries exploit blockchain immutability, synthetic media generation, and privacy-enhancing technologies to obscure malicious intent. This paper in-traduces SHARD (Shadowed and Silicon Hybrid Attribution and Reconstruction Diagnostic), a multi-modal forensic framework designed to recover, correlate, and at-tribute malicious artifacts across distributed ledger systems, synthetic content generators, and anonymized net-works. Through systematic analysis of 47 real-world cybercriminal cases and forensic evaluation against 12 at-tack vectors, SHARD achieves 89.2% attribution accuracy while reducing investigative timelines by 64% com-pared to conventional methods. We present novel techniques for blockchain temporal analysis, deepfake prove-nance tracking, and Tor-exit node correlation. The frame-work integrates machine learning-based anomaly detection with cryptographic verification to distinguish legitimate decentralized activity from adversarial manipulation. Our contributions include: (1) a formal threat model encompassing Web3 forensics; (2) a hybrid architecture combining on-chain and off-chain analysis; (3) algorithmic innovations for synthetic media fingerprinting; and (4) extensive empirical validation against contemporary attack scenarios. This work addresses a critical gap in digital forensics as investigative techniques must evolve alongside the technological infrastructure that criminals exploit.
The user-ownership model of Web3 commerce is widely viewed as a potential paradigm shift for the digital economy, yet its macroeconomic implications remain under-quantified within a unified, dynamic, and parameterized framework. This paper develops a tractable dynamic macroeconomic model of a “wealth flywheel” featuring two feedback channels. The income loop operates through profit-backed user rebates that raise income-equivalent purchasing capacity and stimulate consumption. The asset loop operates through consumption-driven profit and valuation growth, which expands household wealth under user ownership and feeds back into consumption via wealth effects. In a static setting, the paper derives a closed-form consumption multiplier and a corresponding stability condition. Aggregate consumption responds proportionally to an exogenous income impulse, and the system is stable if the combined strength of rebate-induced consumption feedback and wealth-effect amplification remains below unity. The static mechanism is then embedded into a global multi-period simulation framework with time-varying Web3 penetration, finite-horizon household deposit reallocation into consumption, and endogenous valuation paths. Using illustrative parameterizations, the paper simulates trajectories for global real GDP, equity market capitalization, household wealth, and inflation under neutral and aggressive adoption scenarios. The analysis further examines distributional implications when capitalization gains are directed toward user cohorts with higher marginal propensities to consume. The framework provides a parsimonious diagnostic for stability in mechanism design and contributes to macro-prudential discussions of self-reinforcing growth dynamics. Importantly, the analysis abstracts from collateralized borrowing, leverage, rehypothecation, and other financial intermediation channels. All amplification effects in the model arise from ownership structure and wealth effects rather than from credit-driven financial accelerators.
How may digital platforms be redesigned to better serve the interests of the artists whose creative work gives them value? An artist- and user-owned streaming platform is proposed that would decentralize control and redistribute revenue from corporations to creators. Using Web3 infrastructure, the model enables direct artist payment through blockchain-based transactions that scale based on user consumption, minimizing fees and ensuring transparency. The design also emphasizes community governance and localized music discovery to encourage the regrowth of music culture. By reducing reliance on profit-driven intermediaries, the system aims to create a sustainable environment where independent artists can thrive. Spotify exemplifies how a platform’s designed-in incentives can perpetuate exploitation. The social construction of technology framework suggests that Spotify’s ownership model, pro- rata payment system, and algorithmic design prioritize shareholder value over fairness. Spotify’s supposed mission to “unlock the potential of human creativity” is undermined by its own architecture, which locks artists into dependency. Together, these projects show that achieving fairness in a digital music economy requires not only reforming compensation models but rethinking the infrastructures that define creative labor itself.
Even in this day and age, when digital technologies are becoming more and more prevalent, it is still extremely important for democratic systems to maintain the honesty and openness of their voting procedures. This article introduces NextGenVote, a decentralised online voting platform developed to address the security, transparency, and confidence issues traditional electronic voting systems face. Automation of election operations, including voter registration, candidate administration, ballot casting, and result computation, is achieved through smart contracts written in the Solidity programming language. The system is built on the Ethereum blockchain. MetaMask is a React-based frontend that uses Web3.js to connect to the blockchain. MetaMask is responsible for ensuring that user authentication and transaction signatures are secure. Therefore, to prevent unauthorised manipulation, the platform utilises a role-based access control approach that clearly distinguishes between administrative capabilities and voter credentials. NextGenVote assures that election results are tamper-proof, traceable, and auditable. It was deployed and tested in a local blockchain environment powered by Ganache. The system provides a solid foundation for scalable, secure, and transparent digital elections by eliminating centralised intermediaries and relying solely on processes executed on the blockchain.
The increasing use of decentralized finance (DeFi) accelerates the demand for trustless, secure mechanisms for crosschain token exchange. This paper outlines a complete model for atomic token swaps based on the Hashed Timelock Contract (HTLC) scheme, allowing for intermediary-free token exchanges across disparate blockchain systems. The system makes use of the local blockchain simulation framework, Ganache, to design and test cross-chain interactions in a sandbox environment. To improve the decision-making capabilities for users, a real-time cryptocurrency price forecasting subsystem is added which utilizes machine learning models to analyze and predict the market and its volatility. Additionally, the system harnesses Generative AI capabilities through prompt engineering to tailor investment advice for individual users by analyzing the market, their preferred risk level, expected returns, and provide investment strategies aligned with users' preferences. Apart from sophisticated trading algorithms, the solution also offers a simple dashboard for market price monitoring and performs rapid token swaps at the user's command. Smart contracts are implemented using Solidity, token and price feeds are ports to Web3.js, predictive analytics is done in Python, while the frontend and backend are structured in Next.js alongside Node.js. System testing validates hypotheses on the provision of secure cross-chain swaps within one transaction without compromising.
ABSTRACT In the contemporary digital landscape, high-profile individuals including celebrities, executives, political leaders, and public officials face unprecedented threats from online impersonation, sophisticated misinformation campaigns, AI-generated deepfakes, and fraudulent social media profiles. The convergence of generative artificial intelligence technologies and social media platforms has dramatically expanded the attack surface, enabling malicious actors to create synthetic identities, manipulate multimedia content, and spread false narratives with alarming ease and speed. Existing security solutions remain fragmented, requiring extensive manual intervention and lacking the capability for real-time monitoring and automated threat response, thereby leaving critical gaps in digital protection for vulnerable public figures. This research paper presents GuardIQ, an integrated, fully automated, end-to-end VIP Threat Detection and Monitoring Platform that combines post-quantum cryptography, multi-factor biometric authentication, artificial intelligence-powered threat detection, and blockchain-based evidence preservation. The platform architecture is built upon seven core pillars: quantum-secure biometric registration utilizing Kyber Key Encapsulation Mechanism (KEM), real-time threat detection engine monitoring multiple social media platforms, AI-powered content verification distinguishing authentic media from AI-generated deepfakes, automated fake profile detection comparing discovered accounts against registered handles, live analyzer for instant authenticity verification, immutable evidence collection using Web3 technologies, and unified dashboard providing comprehensive threat intelligence visualization. GuardIQ employs CRYSTALS-Kyber post-quantum cryptographic algorithms (Kyber512 for lightweight mobile endpoints and Kyber768/1024 for enterprise deployments) combined with AES-256-GCM symmetric encryption to ensure quantum-resistant data protection. The biometric registration module captures facial recognition data, voice patterns, gesture signatures, and official social media handles, all protected through quantum-safe encryption. Large Language Models (LLMs) integrated within the threat detection engine perform real-time classification of suspicious content, achieving 92-97% accuracy in identifying impersonation attempts, misinformation campaigns, and image misuse across platforms including Twitter, Facebook, Instagram, and LinkedIn. The AI content detection module leverages advanced deep learning architectures including Convolutional Neural Networks (CNNs) for image analysis, Recurrent Neural Networks (RNNs) for sequential pattern detection, and transformer-based models for multimedia authenticity verification. Experimental results demonstrate the system's capability to distinguish AI-generated content from authentic material with confidence scores exceeding 94%, providing early detection of deepfakes and synthetic media targeting VIP credibility. The fake profile detection algorithm analyzes multiple parameters including account creation timestamps, username patterns, biographical information, follower-to-following ratios, engagement metrics, and posting behavior patterns to identify fraudulent accounts with 89% precision. Evidence collection is facilitated through Web3-based blockchain infrastructure ensuring tamper-proof, immutable storage of all flagged incidents, suspicious posts, and detected impersonations. This cryptographically verifiable evidence chain supports legal proceedings and investigative actions by providing irrefutable proof of malicious activities. The unified dashboard aggregates threat intelligence from all modules, presenting real-time alerts, authenticity scores, risk assessments, and recommended remediation actions through intuitive visualizations requiring minimal manual oversight. Performance evaluation reveals that post-quantum TLS handshakes introduce only 5-10 milliseconds additional latency compared to classical TLS implementations, demonstrating practical feasibility for production deployment. The automated threat detection pipeline reduces incident response time by 72% compared to manual monitoring approaches, while the quantum-resistant encryption framework ensures long-term security against emerging quantum computing threats. System architecture supports horizontal scalability through microservices deployment, containerization using Docker and Kubernetes orchestration, and cloud-native infrastructure compatible with AWS, Azure, and Google Cloud Platform. This research addresses the urgent need for comprehensive digital protection solutions in an era where AI-generated content, quantum computing capabilities, and sophisticated social engineering attacks converge to create unprecedented risks for public figures. GuardIQ represents a paradigm shift from reactive security measures to proactive, automated threat intelligence platforms capable of defending high-profile individuals against modern digital adversaries while maintaining usability, scalability, and legal compliance. Keywords : VIP Protection, Post-Quantum Cryptography, Kyber KEM, Deepfake Detection, AI Content Verification, Biometric Authentication, Threat Intelligence, Social Media Monitoring, Blockchain Evidence, Web3 Security, Impersonation Detection, Misinformation Prevention, Large Language Models, Zero- Trust Architecture, Quantum-Safe Encryption, Identity Verification, Automated Security Response, Digital Reputation Management
Quang Huy Duong, Carlos F.A. Arranz, Mao Xu, Li Zhou · 5 authors
The rapid transition to electric vehicles has intensified challenges in electric vehicle battery (EVB) closed-loop supply chains (CLSC), particularly regarding material traceability, supply chain transparency, and recycling efficiency. While decentralised technologies, particularly Web3 and Metaverse, offer promising solutions, their integration into EVB CLSC remains fragmented and insufficiently examined. We introduce an Operational Decentralisation Framework enabling a systematic analysis of centralised operations and a critical evaluation of decentralised alternatives as transformational forces. By adopting a holistic perspective, the framework equips firms with strategic guidance for transitioning from centralised structures to decentralised ecosystems. We analyse 588 academic articles and 1,168 industry documents through two advanced text mining techniques – Dynamic Latent Dirichlet Allocation and Burst Detection. Web3 and metaverse can potentially reconfigure the design, manufacturing, end-of-life diagnostics, procurement, waste management, load balancing, capacity planning, inventory management and service operations of two key areas: (1) EVB CLSC operations and (2) EVB circular energy/grid operations. We also found that while blockchain and digital twins show established applications, Web3 and Metaverse applications face significant barriers, including scalability, technology complexity, and expertise gaps, despite their great potentials. Therefore, we propose four visionary models integrating Web3, Metaverse, and AI technologies that have the potential to overcome existing barriers and enable transformative decentralisation. Extending the TOE framework, the study contributes to the theory by developing an integrated framework for evaluating decentralised technology adoption in EVB CLSCs. For practitioners, we provide actionable insights and pathways for technology implementation across different CLSC stages and guidance for addressing key adoption barriers.
This study proposes a structural model for understanding digital trust in smart-market environments by comparing the market-based trust architecture of Korea and the state-based trust architecture of China. Although both countries rely on similar technological foundations—blockchain, data infrastructure, AI systems, and CBDC—their institutional path dependencies and regulatory philosophies have produced divergent trust mechanisms. To explain these differences, the study introduces the 4-Layer Trust Architecture (4LTA–Seo), comprising incentives, rule enforcement, verification (data/AI), and institutional linkage. This framework conceptualizes tokens as digital institutions that integrate these layers to automate trust formation and oversight.Methodologically, the research applies Qualitative Comparative Analysis (QCA) using policy documents, technical whitepapers, and regulatory texts from both countries. It incorporates Zhang & Wang’s DTI (Data–Algorithm–Risk–Privacy) framework to compare how information architectures shape verification dynamics and trust costs. The study analyzes how institutional configurations rearrange the weighting and function of each trust layer, producing different stability and cost outcomes.Findings are expected to show that Korea’s market-driven architecture emphasizes incentives and behavioral inducement, while China’s state-driven model prioritizes rule enforcement and systemic integration. The research clarifies how tokens function as "units of trust" only when embedded within institutionally coherent architectures. Ultimately, the study offers structural insights for reinstitutionalizing trust in digital systems, with implications for Web3 governance, CBDC design, and digital public administration.
Understanding the economic intent of Ethereum transactions is critical for user safety, yet current tools expose only raw on-chain data, leading to widespread "blind signing" (approving transactions without understanding them). Through interviews with 16 Web3 users, we find that effective explanations should be structured, risk-aware, and grounded at the token-flow level. Based on interviews, we propose TxSum, a new task and dataset of 100 complex Ethereum transactions annotated with natural-language summaries and step-wise semantic labels (intent, mechanism, etc.). We then introduce MATEX, a multi-agent system that emulates human experts' dual-process reasoning. MATEX achieves the highest faithfulness and intent clarity among strong baselines. It boosts user comprehension by 23.6% on complex transactions and doubles users' ability to find real attacks, significantly reducing blind signing.
Understanding the economic intent of Ethereum transactions is critical for user safety, yet current tools expose only raw on-chain data or surface-level intent, leading to widespread "blind signing" (approving transactions without understanding them). Through interviews with 16 Web3 users, we find that effective explanations should be structured, risk-aware, and grounded at the token-flow level. Motivated by these findings, we formulate TxSum, a new user-centered NLP task for Ethereum transaction understanding, and construct a dataset of 187 complex Ethereum transactions annotated with transaction-level summaries and token flow-level semantic labels. We further introduce MATEX, a grounded multi-agent framework for high-stakes transaction explanation. It selectively retrieves external knowledge under uncertainty and audits explanations against raw traces to improve token-flow-level factual consistency. MATEX achieves the strongest overall explanation quality, especially on micro-level factuality and intent quality. It improves user comprehension on complex transactions from 52.9% to 76.5% over the strongest baseline and raises malicious-transaction rejection from 36.0% to 88.0%, while maintaining a low false-rejection rate on benign transactions.
The study is grounded on the significant shifts, central values, and increased influence of Blockchain Technology on the industries. It addresses Blockchain Technology starting theoretically as a cryptographic concept of the beginning through to its contribution as a primarycomponent of decentralized computing (Web3). The discussion begins as the key issues are examined, namely, decentralized agreement, cryptographic hashing, and immutability. Such subjects enable the Blockchain Technology to gain trust in cases where mediators were being used in the past. Moreover, the research considers the impacts of Blockchain Technology on such critical industries as Decentralized Finance (DeFi), supply chain management, and decentralized governance (DAOs).
With the rapid evolution of the Decentralized Web (DWeb), decentralized technologies have paved new avenues for Web3 applications and the authentication of digital assets. Among them, Non-Fungible Tokens (NFTs) have gained significant popularity due to their immutability and uniqueness, reshaping the landscape of artistic creation, marketing, and intellectual property protection. However, current blockchain-based NFT implementations still face core challenges within decentralized architecture: how to maintain decentralization while ensuring the visual uniqueness of digital assets and reducing storage costs. The rampant issue of duplication undermines the scarcity of digital art and erodes market confidence in copyright authenticity. Moreover, high gas fees and energy consumption further hinder the widespread adoption of NFTs, while reliance on external storage solutions like InterPlanetary File System (IPFS) introduces risks of data instability and loss. To address these challenges, this article presents the UniqueNFT framework, a novel architecture that deeply integrates blockchain oracles with decentralized storage verification mechanisms. The framework achieves three key technological breakthroughs: Using image inversion and generation techniques based on Encoder for Editing (E4E) and StyleGAN3, it extracts compact and expressive semantic features from NFT images, enabling efficient data compression and significantly reducing on-chain storage volume; The Crypto-Mask algorithm, by utilizing the hash value of blockchain user information (user-controlled SHA-256 digest of Ethereum address, user nickname, and registration time), ensures the visual uniqueness of NFTs; A smart contract extension compatible with the ERC721 standard, demonstrating UniqueNFT’s seamless integration within the blockchain ecosystem. By leveraging the technologies of the Decentralized Web, our framework represents an important step forward in enhancing the security and uniqueness of digital assets. It not only innovatively resolves the issues of NFT duplication and homogenization but also injects new vitality and long-term momentum into the creation of a trusted, sustainable blockchain-based digital asset ecosystem.