Suggested Citation: Jo, Gwangsik. (2026). DLT-DSA: Design and Verification of a Local Autonomous Integrity Verification Model Using Adaptive Verification Intensity. Zenodo. AbstractIn some distributed ledger systems, transaction verification requires consensus procedures and network synchronization, and previous studies have reported that verification overhead tends to scale with increasing request frequency. This paper proposes a lightweight architecture, DLT-DSA (Distributed Ledger Technology – Decentralized Sovereign Access), designed to reduce dependence on global consensus and to pre-determine request integrity at the node level. The model adopts a multi-stage local verification structure using a context-aware mechanism: normal requests undergo lightweight verification based on ROA (Reduced Overhead Access), while anomalous conditions trigger autonomous integrity verification based on SHV (Self-Hash Verification). Proof-of-Concept (PoC) experiments show that the execution time of the verification logic remains within a stable range independent of variations in network round-trip time (RTT) and exhibits deterministic resource utilization under varying load conditions. These observations suggest that partial relocation of verification procedures to local processing can serve as a viable design alternative in real-time response environments. For more information about the author's professional background and ongoing projects, please visit: LinkedIn: https://www.linkedin.com/in/gwangsik-jo-3295a43b5 ORCID: https://orcid.org/0009-0008-5702-8940
Hybrid Finance (HyFi) is a research and implementation initiative focused on establishing an operational framework that enables legally interpretable financial relationships to be settled using decentralized execution mechanisms. Historically, Traditional Finance (TradFi) and Decentralized Finance (DeFi) developed as mutually incompatible systems. TradFi ensures regulatory compliance, identity accountability, and institutional trust but suffers from latency and geographic constraints. DeFi enables transparent, borderless, and automated settlement but lacks enforceable responsibility mapping in real-world contractual contexts. The HyFi framework introduces a translation architecture that separates relationship governance from value execution. Institutional structures define responsibility and legal context, while decentralized networks perform settlement. A certification layer binds cryptographic execution to real-world intent, producing auditable and compliance-compatible financial records. This community archives research papers, technical disclosures, implementation references, diagrams, and supporting documentation related to: Hybrid financial operational models Compliance-aware blockchain settlement Certified digital asset transactions Programmable accountability frameworks Institutional adoption methodologies Educational and operational standards for blockchain integration The objective of this repository is to document the emergence of a third financial paradigm — not a replacement of TradFi or DeFi, but a structured convergence enabling borderless yet compliant financial activity.
This paper formalizes a mathematical physics theory for the verification of inherited scientific knowledge through a Diffeomorphic Manifold and the Successive Controlled Collapse (SCC) protocol. We define the history of science and technology as a three-tiered manifold—comprising Modern, Contemporary, and Old (Inherited) tiers—where information is transported by the "Common Language" of a lingual locale. By admitting three classes of knowledge agents—Intelligence-Human (IH), Intelligence-Artificial (IA), and Intelligence-Metaphysical (IM )—we demonstrate how high-entropy Informational Inheritance (Sacred Texts) can be distilled into zero-entropy Epistemological Truth. Using the Hala-Operator (Hˆ) as a non-adiabatic spectral regulator, we provide a proof-by-construction using the Hala-Lewis Gaseous Gate as a physical case study. Experimental results from a 23 Factorial Design quantify the Reality Gap (ϵ) at 0.124 and a Hala-Operator Efficiency (η) of 80.9%, proving that the transition from abstract nonlinear dynamics to physical prototyping is a predictable outcome of managed collapse. This framework establishes an Epistemological Barrier that protects historical context while ensuring the verifiability of technical exits in Internet 3.0 and deep-tech RD.
Purpose : This study examined year-over-year (YoY) structural growth dynamics across four major cryptocurrency classes—Bitcoin (BTC), Ethereum (ETH), stablecoins, and altcoins, for the period of 2020–2024. Research Methodology : A quantitative approach was employed to analyze YoY market capitalization trends across BTC, ETH, stablecoins, and altcoins, from 2020 to 2024, by using data from CoinMarketCap, by analyzing growth patterns through percentage and absolute market capitalization changes, supported by trend visualizations. A multiple linear regression model assessed the effect of time and asset type, with BTC as the reference category. The analyses were conducted using SPSS version 27. Findings : The findings revealed that 2023 was the period in which none of the cryptocurrency variants performed well due to factors such as regulatory pressure and a global economic slowdown. In contrast, 2024 marked a period of market correction, during which BTC and altcoins experienced a strong resurgence, followed by stablecoins. ETH remained robust throughout the period, supported by decentralized finance (DeFi) applications. Practical Implications : The results indicated that the cryptocurrency market functioned as a network of fragmented yet interconnected components, and continued to develop under a highly volatile and competitive environment. These findings provided important implications for investors, regulators, and scholars interested in the cryptocurrency market structure, risk behavior, and long-run predictability of cryptocurrencies. Originality/Value : This study presented a new application for a venue-specific market cap analysis in cryptocurrency spanning over five years. By leveraging YoY analysis, it provided insights into growth variances, market recovery, resilience, and increasing maturity of the crypto market in response to evolving rules and regulations.
Large Language Models (LLMs) are accelerating the shift from an Internet of information to an Internet of Agents (IoA), where autonomous entities discover services, negotiate, execute tasks, and exchange value. Yet today's agents are still confined to platform silos and proprietary interfaces, lacking a common stack for interoperability, trust, and pay-per-use settlement. This article proposes \textit{Agent-OSI}, a functional interoperability architecture for a decentralized IoA, whose core contribution is agent-to-agent (A2A) communication and a Web-compatible, backend-agnostic settlement protocol built on HTTP 402 (Payment Required); identity, verifiable execution, and semantic orchestration are treated as boundary layers with interfaces to existing standards. We treat HTTP 402 as an application-layer challenge-response primitive -- analogous to HTTP 401 for authentication -- whose settlement backend (escrow contract, payment channel, or signed off-chain receipt) is a pluggable choice, instantiated via a blockchain escrow in our prototype. We implement a prototype and evaluate its communication and settlement performance. Results show that, for generative workloads, end-to-end latency is dominated by task execution rather than settlement confirmation, and that keeping negotiation and delivery off the settlement backend reduces per-session settlement cost by approximately 51\% relative to a more on-chain baseline.
Blockchains are widely used for secure transaction processing, but their scalability remains limited, and existing multichain designs are typically static even as demand and capacity shift. We cast blockchain configuration as a multiagent resource-allocation problem: applications and operators declare demand, capacity, and price bounds; an optimizer groups them into ephemeral chains each epoch and sets a chain-level clearing price. The objective maximizes a governance-weighted combination of normalized utilities for applications, operators, and the system. The model is modular -- accommodating capability compatibility, application-type diversity, and epoch-to-epoch stability -- and can be solved off-chain with outcomes verifiable on-chain. We analyze fairness and incentive issues and present simulations that highlight trade-offs among throughput, decentralization, operator yield, and service stability.
Real-time payment architectures are the latest wave, eased by the convergence of cloud-native technologies, continuous transaction processing, and demand from regulators for instant settlement. Batch architectures fall short of consumer and business expectations for immediacy‚ transparency‚ and the always-on availability needed to support the digital economy and new digital use cases. For real-time systems, advanced distributed architectures, messaging, and interoperability frameworks may allow for the execution of transactions across multiple institutions and geographies. These may be supported by cloud infrastructures (e.g., cloud platforms), providing scalability and fault tolerance via microservices, multi-region deployments, and zero-trust security principles to support the execution of transactions in real-time. Additional technical solutions such as distributed ledger technology, artificial intelligence-based fraud prevention, and API-based ecosystem architecture, as well as operational intelligence, are evolving. However, ultra-low latency, global interoperability, demand-based capacity scalability, and distributed consistency guarantees are some of the challenges for the continued evolution of a real-time financial system.
Bastien Buchwalter, Jean-Michel Maeso, Vincent Milhau
We develop a reproducible three-step protocol to clean daily cryptocurrency data from CoinMarketCap, one of the most used data providers in academic research. The procedure targets three recurring anomalies that distort market-level indicators: (1) extreme market-cap spikes, (2) one-day and multiday dips in Bitcoin dominance, and (3) abnormal trading volumes. Using more than 28,000 cryptocurrencies from 2014 to 2024, we show that the method modifies only a small subset of data while improving the reliability of key market indicators. We do not adjust prices or returns, preserving actual trading conditions. As an application, we construct dynamic investable universes using cleaned data and realistic constraints based on market capitalization and volume. This exercise shows that cleaning and filtering jointly produce more reliable universes, reducing spurious extremes and making them suitable for empirical asset pricing research and portfolio construction.
В статье анализируются современные подходы к регулированию криптовалют, включая определение их правового статуса, надзор за криптовалютными посредниками и проблемы регулирования децентрализованных финансов, с учетом стандартов FATF и национальной практики. На основе выявленных ограничений предлагается модель международной организации по регулированию криптовалют и оценивается ее влияние на повышение мировой финансовой стабильности. The article analyzes contemporary approaches to cryptocurrency regulation, including the determination of their legal status, oversight of cryptocurrency intermediaries, and the challenges of regulating decentralized finance, taking into account FATF standards and national practices. Based on the identified limitations, the paper proposes a model of an international organization for cryptocurrency regulation and assesses its potential impact on improving global financial stability is being assessed.
John Chinemerem Ogbete, AbuYusuf Aminu-Ibrahim, Obinna Chima Iwuanyanwu
Scaling molecular diagnostic facilities is essential for meeting growing demands for infectious disease surveillance, oncology, genetic screening, and precision medicine, particularly across resource-constrained and rapidly expanding health systems. This study examines how standardized infrastructure and operational design models can enable scalable, high-quality molecular diagnostics while ensuring biosafety, regulatory compliance, and cost efficiency. It synthesizes insights from laboratory engineering, health systems planning, and diagnostic network design to propose an integrated approach to molecular facility expansion. Standardized infrastructure models emphasize modular laboratory layouts, flexible cleanroom zoning, validated airflow and contamination control systems, and harmonized utilities for power, water, and waste management. These design principles enable rapid replication, phased expansion, and adaptability to evolving assay technologies without compromising analytical integrity. Operational design models complement physical standardization through optimized workflow sequencing, sample logistics, equipment utilization, and quality management systems aligned with international laboratory standards. Together, these models reduce setup time, minimize variability, and support consistent performance across decentralized molecular testing sites. The study further highlights the role of digital enablement in scaling molecular diagnostics, including laboratory information management systems, remote monitoring platforms, and standardized data architectures that support traceability, quality assurance, and network-level oversight. Workforce-aligned operational models, incorporating task differentiation, competency-based training, and remote supervision, are identified as critical to sustaining performance in settings with limited specialist capacity. Financing and governance mechanisms, including pooled procurement, regional laboratory networks, and public–private partnerships, are discussed as enablers of affordability and long-term sustainability. The study concludes that scalable molecular diagnostic capacity depends on the integration of standardized infrastructure with adaptive operational design. By embedding flexibility, quality assurance, and interoperability into facility and workflow models, health systems can rapidly expand molecular testing while maintaining safety, reliability, and regulatory alignment. Such approaches strengthen outbreak preparedness, support routine disease management, and advance equitable access to advanced diagnostics across diverse healthcare contexts. Importantly, standardization does not constrain innovation but provides a stable platform for continuous technological evolution, network optimization, and resilient diagnostic system growth in low-, middle-, and high-income settings globally. These frameworks also facilitate benchmarking, performance comparison, regulatory audits, and coordinated scale-up across national, regional, and cross-border diagnostic ecosystems worldwide.
Ahmad Khalifah Zamrud, Usman Jafar, Abdul Wahid Haddade
IntroductionThe rapid expansion of cryptocurrency has generated significant debate within Islamic economic discourse. Bitcoin, as the first decentralized digital currency, offers technological advantages such as transparency, efficiency, and global accessibility. However, it also raises concerns regarding price volatility, speculative trading behavior, and the absence of intrinsic value. These issues have prompted Islamic scholars and regulatory institutions to evaluate cryptocurrency from the perspective of Islamic law and financial ethics. In Indonesia, the Indonesian Ulema Council issued a religious ruling declaring Bitcoin impermissible due to elements of uncertainty, speculation, and potential economic harm. This ruling has stimulated ongoing discussion about the compatibility of cryptocurrency innovation with Islamic economic principles.ObjectivesThis study aims to critically analyze the religious ruling on Bitcoin issued by the Indonesian Ulema Council by examining its legal reasoning, its relationship with Islamic economic principles, and its implications for the governance of digital financial innovation. The research also seeks to explore whether cryptocurrency can be accommodated within an Islamic economic framework under certain regulatory and ethical conditions.MethodThe study employs a qualitative research design using a transdisciplinary analytical approach that integrates perspectives from Islamic jurisprudence, Islamic economics, financial regulation, and digital financial technology. Data were collected through documentation of religious rulings, regulatory policies, and scholarly literature related to cryptocurrency and Islamic finance. The data were analyzed through thematic and comparative analysis to identify the legal reasoning underlying the prohibition of Bitcoin and to evaluate alternative scholarly interpretations regarding the status of digital assets in Islamic economics.ResultsThe findings indicate that the prohibition of Bitcoin is primarily based on concerns about excessive uncertainty, speculative trading behavior, and potential economic harm associated with cryptocurrency markets. Nevertheless, the analysis also reveals that cryptocurrency may be considered permissible when these elements are mitigated through transparent governance, regulatory oversight, and the development of asset-backed digital financial instruments.ImplicationsThe study highlights the importance of developing regulatory and institutional frameworks that reconcile financial innovation with Islamic ethical principles. Such frameworks can provide clearer guidance for Muslim investors while supporting responsible digital financial development.Originality or NoveltyThis research contributes to the growing literature on cryptocurrency in Islamic economics by offering a critical analysis of religious rulings within the broader context of digital financial transformation and regulatory governance.
Amro Saleem Alamaren, Korhan K. Gökmenoğlu, Nigar Taşpınar
Abstract This study investigates the volatility spillover and connectedness networks among renewable energy sources (Biofuel, Fuel cell, Geothermal, Solar), green bonds, and cryptocurrencies (Bitcoin, Ethereum, Tether, and BNB coin) in the U.S. market. To accomplish this objective, we analyzed data from November 15, 2017, to May 31, 2024, via the methods introduced by Diebold and Yilmaz (Int J Forecast 28:57–66, 2012) and Baruník and Křehlík (J Financ Econometr 16:271 296, 2018). Our findings reveal that major global disruptions—including the COVID-19 pandemic, the Russia–Ukraine war, the collapse of Silicon Valley Bank, and the Credit Suisse crisis—have intensified volatility spillovers and financial contagion across markets, exacerbating their outcomes. The findings suggest that the effectiveness of green finance depends on its allocation across these sectors, highlighting the importance of examining each sector to understand the success of these financial initiatives. The influence of COVID-19 on the U.S. economy has increased transmission risk across markets. Renewable energy is less volatile than green bonds and cryptocurrencies are, with these indices reacting more quickly to short-term shocks. Investors should focus on short-term impacts to manage market risk effectively. By providing insights into how financial shocks propagate across sectors, emphasizing the need for a sector-specific approach to assessing financial sustainability, and underscoring the importance of short-term risk management strategies, this research offers valuable contributions to decision-makers and investors.
Engram Commitments introduce a cryptographically verifiable, substrate-rooted identity primitive for large language models. The method extracts engrams from differential execution behavior, aggregates them into an engram vector, compresses this representation using locality-sensitive hashing, and seals it inside a binding-and-hiding cryptographic commitment. Zero-knowledge proofs enable verification of identity continuity and lineage without revealing model parameters. The construction remains stable under non-destructive transformations and degrades predictably under destructive ones, supporting collapse-aware auditing, tamper-evident provenance, and regulator-verifiable attestation. This work unifies the engram calculus, identity ontology, collapse taxonomy, and cryptographic commitments into a single framework for AI provenance, governance, and safety.
Zero-knowledge (ZK) proofs can be formally correct while their deployment pipelines remain fragile. The practical failure modes often arise not at the proof layer, but at the layers where trust is injected: setup, key custody, entropy sourcing, implementation, governance, and deployment interfaces. This paper models ZK pipelines as trust-graphs and proposes an audit-first separation between (i) proof correctness and (ii) pipeline integrity. The core claim is structural: for any non-trivial ZK pipeline, there exists at least one responsibility binding layer R where trust is required and accountability must be assigned. Removing a ceremony does not remove responsibility; it relocates it. We provide minimal definitions, a traceable audit interface, and compact structural examples intended to support reproducible security reviews without overclaiming. Keywords: zero-knowledge; trusted setup; CRS; SNARK; STARK; trust graph; audit; governance; pipeline integrity; responsibility relocation
M. Ángeles López-Cabarcos, Isaac González López, Aurora Pérez-Pérez, Juan Piñeiro Chousa
Purpose The classification of cryptocurrencies remains an open challenge to make valid decisions due to their diverse technical structures, financial applications and evolving use cases. The scientific literature does not provide a simple technical categorization that facilitates asset comparison, enhances risk measurement, provides a structured approach to understanding the dependencies between different crypto-assets and facilitates decision-making processes among a wide range of stakeholders. This study proposes a technical categorization framework that classifies cryptocurrencies based on their underlying blockchain infrastructure or smart contract functionalities. Design/methodology/approach The authors designed the categories and classify the top 100 market cap cryptocurrencies with them. To validate the proposal, the same task was executed by using multiple large language models (LLMs), including ChatGPT, Perplexity, Claude and Gemini; with zero-shot classification approach. Findings The results indicate that, when prompted with predefined categories, LLMs achieve substantial agreement with human classification, with ChatGPT demonstrating the best results. Moreover, categorization without any guidance is inconsistent across models, often defaulting to use-case- based groupings. Notably, providing additional information about cryptocurrencies or detailed definitions of categories does not significantly alter classification outcomes, suggesting that LLMs rely predominantly on their internal knowledge base. Research limitations/implications Future research should focus on refining empirical measures for decentralization, expanding classification testing with human participants and leveraging advancements in LLMs for improved categorization accuracy. Originality/value This study highlights the potential of LLMs as tools for the systematic classification of cryptocurrencies, a key part of an important organizational decision-making process. It is remarked that the importance of having structured categories of cryptocurrencies is for all kinds of decision-makers, including investors, industry stakeholders, fund managers and regulators. Future research should focus on refining empirical measures for decentralization, expanding classification testing with human participants and leveraging advancements in LLMs for improved categorization accuracy. Highlights
José-María Oliet-Villalba, Jose-Amelio Medina-Merodio, Mikel Ferrer-Oliva, José-Javier Martínez-Herráiz
The rapid growth of cryptocurrencies and non-fungible tokens (NFTs) has expanded technological opportunities, but it has also increased the exposure surface to cyber threats, creating a need for a more precise understanding of the field’s scientific evolution. This study aims to systematically analyse academic output related to cybersecurity and cyber threats within cryptocurrency and NFT ecosystems, identifying central themes, the most influential authors, and emerging trends. A bibliometric methodology was employed, based on the PRISMA 2020 protocol and scientific mapping tools such as SciMAT (v1.1.06) and VOSviewer (v1.6.20), using a corpus of 337 articles published between 2014 and 2025. The findings indicate sustained growth in the literature, a marked geographical and editorial concentration, and the presence of motor themes such as blockchain, cybersecurity, emerging technologies and illegal mining, alongside emerging areas such as intrusion detection. The results also reveal a progressive integration of artificial intelligence techniques in the detection and prevention of attacks. In conclusion, this study provides a comprehensive overview of the state of the art, identifies critical gaps, and underscores the need for interdisciplinary approaches to strengthen security in decentralised environments.
This study explores the current landscape of fiscal decentralization in India, with particular attention tothe financial structure and functioning of rural and urban local government bodies. It investigates thecomposition and trends of own-source revenues versus intergovernmental transfers, the extent of fiscalautonomy enjoyed by local institutions, and the institutional and policy challenges that hinder effectivedevolution of financial powers. Drawing upon secondary data, government reports, and existing scholarlyresearch, the paper analyses persistent vertical and horizontal fiscal imbalances, variations across states, andthe implications of limited fiscal capacity on local governance and service delivery. Furthermore, the studyidentifies critical policy gaps, administrative bottlenecks, and capacity constraints that undermine the objectivesof decentralized governance. It concludes by proposing strategic reforms to strengthen fiscal empowerment,improve transparency and accountability, and enhance the overall effectiveness of India’s multi-tiered fiscalframework
We present an end-to-end framework for systematic evaluation of LLM-generated smart contracts from natural-language specifications. The system parses contractual text into structured schemas, generates Solidity code, and performs automated quality assessment through compilation and security checks. Using CrewAI-style agent teams with iterative refinement, the pipeline produces structured artifacts with full provenance metadata. Quality is measured across five dimensions, including functional completeness, variable fidelity, state-machine correctness, business-logic fidelity, and code quality aggregated into composite scores. The framework supports paired evaluation against ground-truth implementations, quantifying alignment and identifying systematic error modes such as logic omissions and state transition inconsistencies. This provides a reproducible benchmark for empirical research on smart contract synthesis quality and supports extensions to formal verification and compliance checking.
The past decade has witnessed the burgeoning and continuous development of blockchain and its applications. Besides various cryptocurrencies, an industry that has quickly embraced this trend is gaming. Thanks to the support of blockchain, games have started to incorporate non-fungible tokens (NFTs) that can enable a new gaming model, play-to-earn (P2E), which incentivizes users to participate and play. While recent studies looked at several NFT games qualitatively and individually, an in-depth understanding is still missing, particularly on how the P2E model has transformed traditional games. In this work, we set to conduct a measurement study of NFT games, aiming to gain a comprehensive understanding of the effectiveness of P2E in practice. For this purpose, we collect and analyze relevant NFT transaction data from the underlying blockchain (e.g., Ethereum) of 12 games, supplemented with various data scraped from their websites. Our study shows that (1) a few top wallets control unproportionally high percentage of NFTs, and the majority of wallets own only one or two NFTs and do not actively trade; (2) promotion events do boost the trade amount and the NFT price for some games, but their effect does not sustain; and (3) few players actually earned a profit, and players in 9 out of 12 games who traded NFTs have a negative profit on average. Motivated by these findings, we further investigate effective incentive mechanisms based on game theory to improve the trading profits that players can earn from these NFT games. Both modeling and simulation results confirm the effectiveness of the proposed incentive mechanism.
Secure and private sharing of electronic health records (EHRs) among multiple parties remains a significant challenge in digital healthcare. Although Blockchain technology can ensure data integrity through security and transparency, protecting patient privacy and enabling secure collaborative analysis continue to be difficult problems. To address these challenges, differential privacy (DP) and Zero-Knowledge Proofs (ZKPs) are integrated into a Blockchain-based solution in the innovative design of this multi-institutional EHR-sharing system architecture. ZKPs enable the verification of user identities and access requests without revealing sensitive information, while DP ensures that analytical results are statistically valid and that underlying data are protected against re-identification attacks. A permissioned Blockchain system is developed to support verifiable and privacy-preserving federated analytics over distributed data. Experimental results demonstrate that the proposed framework successfully achieves privacy protection, secure access, and interoperable data-sharing objectives.
A Software Bill of Materials (SBOM) is a key component for the transparency of software supply chain; it is a structured inventory of the components, dependencies, and associated metadata of a software artifact. However, an SBOM often contain sensitive information that organizations are unwilling to disclose in full to anyone, for two main concerns: technological risks deriving from exposing proprietary dependencies or unpatched vulnerabilities, and business risks, deriving from exposing architectural strategies. Therefore, delivering a plaintext SBOM may result in the disruption of the intellectual property of a company. To address this, we present VeriSBOM, a trustless, selectively disclosed SBOM framework that provides cryptographic verifiability of SBOMs using zero-knowledge proofs. Within VeriSBOM, third parties can validate specific statements about a delivered software. Respectively, VeriSBOM allows independent third parties to verify if a software contains authentic dependencies distributed by official package managers and that the same dependencies satisfy rigorous policy constraints such as the absence of vulnerable dependencies or the adherence with specific licenses models. VeriSBOM leverages a scalable vector commitment scheme together with folding-based proof aggregation to produce succinct zero-knowledge proofs that attest to security and compliance properties while preserving confidentiality. Crucially, the verification process requires no trust in the SBOM publisher beyond the soundness of the underlying primitives, and third parties can independently check proofs against the public cryptographic commitments. We implement VeriSBOM, analyze its security, and evaluate its performance on real-world package registries. The results show that our method enables scalable, privacy-preserving, and verifiable SBOM sharing and validation.
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Physical Unclonable Functions (PUFs) and Hardware Security
Komang Irvan Tri Permadi, Si Ngurah Ardhya, Ratna Artha Windari
Skripsi ini membahas mengenai analisis yuridis perlindungan hak cipta atas gambar Non Fungible Token (NFT) ditinjau dari Undang-Undang Hak Cipta. Penelitian ini bertujuan untuk memahami pengaturan, dan perlindungan hak cipta atas gambar yang diperuntukkan sebagai Non-Fungible Token yang berada di indonesia dengan menggunakan perbandingan negara amerika dan UniEropa. Jenis penelitian hukum normatif penelitian ini terfokus kepada doktrin ataupun peraturan perundang-undangan (law in books) dipandang dari hukum positif atau das sollen. Bahan hukum primer, sekunder, dan tersier adalah sumber bahan hukum yang akan digunakan sebagai acuan dalam merancang penelitian normatif ini. penelitian normatif terutama berkaitan dengan data sekunder, yang mencakup undang-undang, putusan pengadilan, teori hukum, konsep hukum, dan hasil penelitian ilmiah akademisi (doktrin). Hasil dari penelitian ini menunjukan bahwa Pengaturan Hak Cipta di Indonesia seperti Undang-Undang Nomor 28 Tahun 2014 Tentang Hak Cipta. Belum secara spesifik mengatur keberadaan hak cipta melalui NFT. Selain itu, bentuk sistem pengawasan hak cipta belum sepenuhnya adaptif terhadap teknologi baru seperti blockchain. Di Amerika Serikat, undang-undang hak cipta, terutama Digital Millennium Copyright Act (DMCA) dan Undang-Undang Hak Cipta 1976, mengatur perlindungan karya yang dicetak sebagai NFT. Uni Eropa belum memiliki UU khusus yang mengatur hak cipta NFT secara spesifik, Pengaturan Directive on Copyright In The Digital Single Market (EU 2019/790) dalam pasal 17 yang memberikan hak eksklusif kepada pemegang hak cipta atas karya digital.
This paper presents a framework that integrates blockchain-enabled Federated Learning (FL) with consensus mechanisms to mitigate poisoning attacks in healthcare environments. The framework incorporates blockchain consensus mechanisms, with Proof-of-Work (PoW) used as a baseline and Proof-of-Stake (PoS) adopted as the proposed approach; both are evaluated independently within the same Secure Multiparty Computation (SMPC)-enabled federated learning architecture for privacy preservation. The proposed system is evaluated on the OCTMNIST and TissueMNIST datasets under both centralized and federated settings, including poisoning scenarios with 10% and 50% malicious clients. Results show that consensus-aware aggregation reduces the influence of unreliable client updates and improves the robustness of the global model under poisoning conditions. In addition, the framework prioritizes trustworthy client contributions during aggregation, supporting reliable model sharing in collaborative healthcare learning environments. Unlike prior blockchain-based federated learning defenses that introduce heavy cryptographic overhead, the proposed PoS-based aggregation explicitly balances robustness and computational efficiency, enabling practical deployment under high poisoning ratios.
Sihao Hu, Selim Furkan Tekin, Yichang Xu, Ling Liu
Launchpads have become the dominant mechanism for issuing memecoins, exposing investors to a new class of high-risk launches that existing rug-pull detection methods cannot capture. We argue that detecting these threats requires structured behavioral traces that underlie raw heterogeneous blockchain data, i.e., how insiders accumulate, coordinate, and unwind positions. To enable such analysis, we introduce MELT (MEmecoin Launch Trace, the first behavioral trace dataset for analyzing and detecting high-risk memecoin launches on Solana. MELT covers 41k+ memecoin launches with 200M+ transactions parsed into typed behavioral records that distinguish swaps, wash trades, transfers, and mints. Beyond per-account behaviors, MELT contributes bundle-trace data that links accounts controlled by the same entity, revealing that, on average, 36.5% of token supply is held by coordinated accounts, a concealment strategy that disguises the true ownership concentration from unsuspecting buyers. On top of these traces, MELT provides 122 behavioral features and risk-level annotations, enabling supervised learning at a population scale. We benchmark representative ML models on the high-risk launch detection task. Integrating their predictions into a simple memecoin selection strategy reduces investment loss significantly, demonstrating that behavioral traces can be translated into risk mitigation. Our dataset and code is available at https://github.com/git-disl/MELT.