There have been various attempts at token standards on numerous blockchain platforms today to fundamentally change the way assets are traded in the traditional capital markets, but there is a lack of research and resolution on regulatory issues that become the common foundation for interoperability and reusable standards. Our proposal, Regulatory Compliance Protocol (RCP), is based on the regulations and reports of 15 global financial institutions and standardizes recommendations and guidelines involving the overall asset tokenization of TradFi and DeFi into five regulatory groups: Traceability, Privacy, Enforceability, Finality and Tokenizability, compiling them into 31 items and presenting a benchmark for technology and standards as an underlying protocol. To review the legality and effectiveness of RCP, it was validated based on three tokenization and trading scenarios, and by benchmarking existing asset-tokenization standards (ERC-20, ERC-7943, ERC-1400, and ERC-3643) against RCP, it makes explicit which regulatory requirements each standard addresses at the token level and which remain inherently off-chain.
Real-world asset (RWA) tokenization has emerged as a prominent application of blockchain technology, enabling off-chain financial and non-financial assets to be represented through blockchain-based instruments. However, deployed RWA systems remain difficult to compare because legal claims, custody arrangements, token mechanics, verification processes, and on-chain integrations are often described separately. This paper develops a systems-level taxonomy of RWA tokenization to classify how off-chain assets are legally, economically, and technically represented on-chain. Following an iterative taxonomy-development method, we organize twenty-three dimensions into five components: governance, asset structure, token properties, distributed ledger technology, and economy. We apply the taxonomy to twenty major RWA systems selected by market capitalization and compare their design choices across asset classes and implementation models. The classification shows that current RWA tokenization is predominantly implemented through hybrid architectures: blockchain tokens support representation, transfer control, redemption workflows, pricing, and composability, while core legal guarantees remain anchored in off-chain legal wrappers, custodial arrangements, compliance processes, and verification mechanisms. The analysis also reveals recurring documentation gaps concerning voting rights, dispute forums, burn mechanics, supply constraints, and reserve verification. Overall, the taxonomy provides a structured basis for comparing RWA systems, identifying design patterns and limitations, and supporting future research on blockchain-based financial infrastructure.
Generative search engines are reshaping information access by replacing traditional ranked lists with synthesized answers and references. In parallel, with the growth of Web3 platforms, incentive-driven creator ecosystems have become an essential part of how enterprises build visibility and community by rewarding creators for contributing to shared narratives. However, the extent to which exposure in generative search engine citations is shaped by external attention markets remains uncertain. In this study, we audit the exposure for 44 Web3 enterprises. First, we show that the creator community around each enterprise is persistent over time. Second, enterprise-specific queries reveal that more popular voices systematically receive greater citation exposure than others. Third, we find that larger follower bases and enterprises with more concentrated creator cores are associated with higher-ranked exposure. Together, these results show that generative search engine citations exhibit exposure bias toward already prominent voices, which risks entrenching incumbents and narrowing viewpoint diversity.
While traditional AI and data-driven facilities management approaches have improved building operational efficiency, they remain constrained by centralized organizational structures that are vulnerable to cyber attacks, limited contextual understanding, and decision-making processes that exclude key stakeholders from governance. This paper introduces a novel AI- and data-driven distributed governance framework for smart building management that integrates decentralized autonomous organizations (DAOs), digital twins, large language models (LLMs), and blockchain technology. The framework enables transparent collective decision-making through a DAO governance platform, implements data-driven management using IoT and digital twins, incorporates LLM-based virtual assistants for enhanced decision support, and utilizes blockchain for secure building automation. A full-stack decentralized application was developed to facilitate user interaction with these integrated components. The system was evaluated for cost efficiency, scalability, data security, and usability using the System Usability Scale (SUS). Expert interviews were also conducted to assess its practical benefits and implementation challenges.
Traditional facility management often relies on centralized decision-making structures that limit stakeholder participation, leading to misalignment with occupant needs and reduced satisfaction. This paper proposes a novel blockchain- and Decentralized Autonomous Organization (DAO)-based framework for community-based facilities management in smart buildings. The framework comprises two key components: a decentralized governance platform that facilitates transparent collective decision-making through blockchain-based voting, and a maintenance management platform with an incentivization mechanism that encourages building occupants to actively contribute to facility upkeep through tokenized rewards. System evaluation includes cost analysis, scalability, data security considerations, usability testing, and semi-structured interviews with facility managers and researchers to assess the platform's usefulness, challenges, and adoption potential. The findings demonstrate the framework's potential as a viable incentivization solution for engaging stakeholders in the collective upkeep and improvement of building infrastructure.
Stefano Balietti, Pietro Saggese, Markus Strohmaier
Decentralized Autonomous Organizations (DAOs) use token-weighted voting to allocate resources, set protocol rules, and legitimate collective decisions. Yet, support in DAO voting is strikingly concentrated. What happens inside the ballot that produces this concentration? We study DAOs' governance at the proposal-choice level, linking each choice's voting-power share to three observable features: whether it expresses an approval-oriented stance, where it appears in the choice list, and whether it is selected by the proposal author. We find that (i) author-selected choices show the strongest and most robust association with voting-power share, with a 58.8% increase relative to non-author choices; (ii) approval-oriented choices retain a positive but slightly less consistent advantage (27.1%); and (iii) first-listed choices also attract systematically higher shares, consistent with position and order effects (7.7%). Results are robust across several specifications, which include subtracting an author's own voting power from computations. We use bias descriptively, to denote systematic associations rather than proven causal distortion. The results shift attention from proposal outcomes alone to the interface and social signals through which choices are presented. In DAO governance, ordering, author signals, and vote visibility should be treated as institutional design choices, not neutral implementation details.
In this paper, we introduce Sark, a reference architecture for transferring unforgeable, stateful, oblivious (USO) assets. We describe the motivation, design, and implementation of the core subsystems of Sark, Porters, which accumulate and roll-up commitments from Clients, and Sloop, a permissioned, crash fault-tolerant (CFT) blockchain system. We analyse the operation of the system using the `CIA Triad': Confidentiality, Availability, and Integrity. We then introduce the concept of \textit{local centrality} and use it to address design trade-offs related to decentralization. Finally, we point to future work on Byzantine fault-tolerance (BFT), and mitigating the local centrality of Porters.
Sensor data in IoT (Internet of Things) systems is vulnerable to tampering or falsification when transmitted through untrusted services. This is critical because such data increasingly underpins real-world decisions in domains such as logistics, healthcare, and other critical infrastructure. We propose a general method for secure sensor-data logging in which tamper-evident devices periodically sign readouts, link data using redundant hash chains, and submit cryptographic evidence to a blockchain-based service via Merkle trees to ensure verifiability even under data loss. Our approach enables reliable and cost-effective validation of sensor data across diverse IoT systems, including disaster response and other humanitarian applications, without relying on the integrity of intermediate systems.
Romance-baiting scams have become a major source of financial and emotional harm worldwide. These operations are run by organized crime syndicates that traffic thousands of people into forced labor, requiring them to build emotional intimacy with victims over weeks of text conversations before pressuring them into fraudulent cryptocurrency investments. Because the scams are inherently text-based, they raise urgent questions about the role of Large Language Models (LLMs) in both current and future automation. We investigate this intersection by interviewing 145 insiders and 5 scam victims, performing a blinded long-term conversation study comparing LLM scam agents to human operators, and executing an evaluation of commercial safety filters. Our findings show that LLMs are already widely deployed within scam organizations, with 87% of scam labor consisting of systematized conversational tasks readily susceptible to automation. In a week-long study, an LLM agent not only elicited greater trust from study participants (p=0.007) but also achieved higher compliance with requests than human operators (46% vs. 18% for humans). Meanwhile, popular safety filters detected 0.0% of romance baiting dialogues. Together, these results suggest that romance-baiting scams may be amenable to full-scale LLM automation, while existing defenses remain inadequate to prevent their expansion.
David Arroyo, Rafael Mata Milla, Marc Almeida Ros, Nikolaos Lykousas · 7 authors
Crime as a Service (CaaS) has evolved from isolated criminal incidents to a broad spectrum of illicit activities, including social media manipulation, foreign information manipulation and interference (FIMI), and the sale of disinformation toolkits. This article analyses how threat actors exploit specialised infrastructures ranging from proxy and VPN services to AI-driven generative models to orchestrate large-scale opinion manipulation. Moreover, it discusses how these malicious operations monetise the virality of social networks, weaponise dual-use technologies, and leverage user biases to amplify polarising narratives. In parallel, it examines key strategies for detecting, attributing, and mitigating such campaigns by highlighting the roles of blockchain-based content verification, advanced cryptographic proofs, and cross-disciplinary collaboration. Finally, the article highlights that countering disinformation demands an integrated framework that combines legal, technological, and societal efforts to address a rapidly adapting and borderless threat
Maternal mortality in Sub-Saharan Africa remains critically high, accounting for 70% of global deaths despite representing only 17% of the world population. Current digital health interventions typically deploy artificial intelligence (AI), Internet of Things (IoT), and blockchain technologies in isolation, missing synergistic opportunities for transformative healthcare delivery. This paper presents IyaCare, a proof-of-concept integrated platform that combines predictive risk assessment, continuous vital sign monitoring, and secure health records management specifically designed for resource-constrained settings. We developed a web-based system with Next.js frontend, Firebase backend, Ethereum blockchain architecture, and XGBoost AI models trained on maternal health datasets. Our feasibility study demonstrates 85.2% accuracy in high-risk pregnancy prediction and validates blockchain data integrity, with key innovations including offline-first functionality and SMS-based communication for community health workers. While limitations include reliance on synthetic validation data and simulated healthcare environments, results confirm the technical feasibility and potential impact of converged digital health solutions. This work contributes a replicable architectural model for integrated maternal health platforms in low-resource settings, advancing progress toward SDG 3.1 targets.
This paper analyzes the emerging memecoin phenomenon on the Solana blockchain, focusing on the Pump$.$fun platform during Q4 2024. Using on-chain data, it is explored how retail-focused token creation platforms are reshaping blockchain ecosystems and influencing market participation. This study finds that Pump$.$fun accounted for up to 71.1% of all tokens minted on Solana and contributed 40-67.4% of total DEX transactions. Despite this activity, fewer than 2% of tokens successfully transitioned to major decentralized exchanges, highlighting a highly speculative market structure. The platform experienced rapid growth, with daily active users rising from 60,000 to peaks of 260,000, underscoring strong retail adoption. This reflects a broader shift towards accessible, socially-driven market participation enabled by memecoins. However, while memecoins lower entry barriers and encourage retail engagement, they introduce significant risks. The volatile and speculative nature of these platforms raises concerns about long-term sustainability and the resilience of the blockchain ecosystem. These findings reveal the dual impact of memecoins: they democratize token creation and alter market dynamics but may jeopardize market efficiency and stability. This paper highlights the need to critically assess the implications of retail-driven speculative trading and its potential to disrupt emerging blockchain economies.
The telecommunications and financial services industries face substantial challenges in inter-operator settlement processes, characterized by extended reconciliation cycles, high transaction costs, and limited real-time transparency. Traditional settlement mechanisms rely on multiple intermediaries and manual procedures, resulting in settlement periods exceeding 120 days with operational costs consuming approximately 5 percent of total revenue. This research presents a blockchain-anchored audit trail model enabling transparent, immutable, and automated inter-operator settlement. The framework leverages distributed ledger technology, smart contract automation, and cryptographic verification to establish a unified, tamper-proof transaction record. Empirical evaluation demonstrates 87 percent reduction in transaction fees, settlement cycle compression from 120 days to 3 minutes, and 100 percent audit trail integrity. Smart contract automation reduces manual intervention by 92 percent and eliminates 88 percent of settlement disputes. Market analysis indicates institutional adoption accelerated from 8 percent in 2020 to 52 percent by April 2024, with projected industry investment reaching 9.2 billion USD annually. The framework addresses scalability (12,000 transactions per second), interoperability, and regulatory compliance across multiple jurisdictions.
As data is an essential asset for any DeFi application, selecting an oracle is a critical decision for its success. To date, academic research has mainly focused on improving oracle technology and internal economics, while the drivers of oracle choice on the client side remain largely unexplored. This study addresses this gap by gathering insights from leading DeFi protocols, uncovering their rationale for oracle selection and their preferences regarding whether to outsource or internalize data-request mechanisms. Data are collected from founders, C-level executives, and oracle engineers of 32 DeFi protocols, whose combined total value locked (TVL) exceeds 55% of the oracle-using DeFi segment. The study leverages a one-time mixed-method survey, using tailored question paths for in-house versus third-party oracle users. Quantitative answers are summarized, compared across groups, and examined through Spearman rank-order correlations to explore pairwise associations among evaluation dimensions, while open-ended responses are inductively coded into keywords and broader themes to triangulate common selection motives and switching challenges. Insights support the view that protocol choices are tied to technological dependencies, in which the immutability of smart contracts amplifies lock-in, hindering agile switching among data providers. Furthermore, when viable third-party solutions exist, protocols generally prefer to outsource rather than build and maintain internal oracle mechanisms.
Memecoins, emerging from internet culture and community-driven narratives, have rapidly evolved into a unique class of crypto assets. Unlike technology-driven cryptocurrencies, their market dynamics are primarily shaped by viral social media diffusion, celebrity influence, and speculative capital inflows. To capture the distinctive vulnerabilities of these ecosystems, we present the first Memecoin Ecosystem Fragility Framework (ME2F). ME2F formalizes memecoin risks in three dimensions: i) Volatility Dynamics Score capturing persistent and extreme price swings together with spillover from base chains; ii) Whale Dominance Score quantifying ownership concentration among top holders; and iii) Sentiment Amplification Score measuring the impact of attention-driven shocks on market stability. We apply ME2F to representative tokens (over 65% market share) and show that fragility is not evenly distributed across the ecosystem. Politically themed tokens such as TRUMP, MELANIA, and LIBRA concentrate the highest risks, combining volatility, ownership concentration, and sensitivity to sentiment shocks. Established memecoins such as DOGE, SHIB, and PEPE fall into an intermediate range. Benchmark tokens ETH and SOL remain consistently resilient due to deeper liquidity and institutional participation. Our findings provide the first ecosystem-level evidence of memecoin fragility and highlight governance implications for enhancing market resilience in the Web3 era.
High-stakes decision domains are increasingly exploring the potential of Large Language Models (LLMs) for complex decision-making tasks. However, LLM deployment in real-world settings presents challenges in data security, evaluation of its capabilities outside controlled environments, and accountability attribution in the event of adversarial decisions. This paper proposes a framework for responsible deployment of LLM-based decision-support systems through active human involvement. It integrates interactive collaboration between human experts and developers through multiple iterations at the pre-deployment stage to assess the uncertain samples and judge the stability of the explanation provided by post-hoc XAI techniques. Local LLM deployment within organizations and decentralized technologies, such as Blockchain and IPFS, are proposed to create immutable records of LLM activities for automated auditing to enhance security and trace back accountability. It was tested on Bert-large-uncased, Mistral, and LLaMA 2 and 3 models to assess the capability to support responsible financial decisions on business lending.
This study explores Bitcoin's value formation through the Granular Interaction Thinking Theory-Value Theory (GITT-VT). Rather than stemming from material utility or cash flows, Bitcoin's value arises from informational attributes and interactions of multiple factors, including cryptographic order, decentralization-enabled autonomy, trust embedded in the consensus mechanism, and socio-narrative coherence that reduce entropy within decentralized value-exchange processes. To empirically assess this perspective, a Bayesian linear model was estimated using daily data from 2022 to 2025, operationalizing four informational value dimensions: Store-of-Value (SOV), Autonomy (AUT), Social-Signal Value (SSV), and Hedonic-Sentiment Value (HSV). Results indicate that only SSV exerts a highly credible positive effect on next-day returns, highlighting the dominant role of high-entropy social information in short-term pricing dynamics. In contrast, SOV and AUT show moderately reliable positive associations, reflecting their roles as low-entropy structural anchors of long-term value. HSV displays no credible predictive effect. The study advances interdisciplinary value theory and demonstrates Bitcoin as a dual-layer entropy-regulating socio-technological ecosystem. The findings offer implications for digital asset valuation, investment education, and future research on entropy dynamics across non-cash-flow digital assets.
Regulatory technology (RegTech) is transforming financial compliance by integrating advanced information technologies to strengthen anti money laundering and countering the financing of terrorism (AML CFT) frameworks. Recent literature suggests that such technologies represent more than just an efficiency tool; they mark a paradigm shift in regulation and the evolution of financial oversight (Kurum, 2023). This paper aims to provide a narrative review of recent RegTech applications in financial crime prevention, with a focus on key compliance domains. A structured literature review was conducted to examine publications between 2020 and 2024 with a thematic synthesis of findings related to customer due diligence (CDD) and know your customer (KYC), transaction monitoring, regulatory reporting and compliance automation, information sharing and cross border cooperation, as well as cost efficiency. Findings reveal that RegTech solutions give financial institutions more responsibility for detecting and managing financial crime risks, making them more active players in compliance processes traditionally overseen by regulators. The combined use of technologies such as artificial intelligence (AI), blockchain, and big data also generates synergistic effects that improve compliance outcomes beyond what these technologies achieve individually. This demonstrates the strategic relevance of integrated RegTech approaches.
This paper presents a comprehensive comparative analysis of two dominant blockchain consensus mechanisms, Proof of Work (PoW) and Proof of Stake (PoS), evaluated across seven critical metrics: energy use, security, transaction speed, scalability, centralization risk, environmental impact, and transaction fees. Utilizing recent academic research and real-world blockchain data, the study highlights that PoW offers robust, time-tested security but suffers from high energy consumption, slower throughput, and centralization through mining pools. In contrast, PoS demonstrates improved scalability and efficiency, significantly reduced environmental impact, and more stable transaction fees, however it raises concerns over validator centralization and long-term security maturity. The findings underscore the trade-offs inherent in each mechanism and suggest hybrid designs may combine PoW's security with PoS's efficiency and sustainability. The study aims to inform future blockchain infrastructure development by striking a balance between decentralization, performance, and ecological responsibility.
Reddit is in the minority of mainstream social platforms that permit posting content that may be considered to be at the edge of what is permissible, including so-called Not Safe For Work (NSFW) content. However, NSFW is becoming more common on mainstream platforms, with X now allowing such material. We examine the top 15 NSFW-restricted subreddits by size to explore the complexities of responsibly sharing adult content, aiming to balance ethical and legal considerations with monetization opportunities. We find that users often use NSFW subreddits as a social springboard, redirecting readers to private or specialized adult social platforms such as Telegram, Kik or OnlyFans for further interactions. They also directly negotiate image "trades" through credit cards or payment platforms such as PayPal, Bitcoin or Venmo. Disturbingly, we also find linguistic cues linked to non-consensual content sharing. To help platforms moderate such behavior, we trained a RoBERTa-based classification model, which outperforms GPT-4 and traditional classifiers such as logistic regression and random forest in identifying non-consensual content sharing, showing better performance in this specific task. The source code and model weights are publicly available at https://github.com/socsys/15NSFWsubreddits.
This paper introduces a structured approach to improving decision making in Decentralized Autonomous Organizations (DAO) through the integration of the Question-Option-Criteria (QOC) model and AI agents. We outline a stepwise governance framework that evolves from human led evaluations to fully autonomous, AI-driven processes. By decomposing decisions into weighted, criterion based evaluations, the QOC model enhances transparency, fairness, and explainability in DAO voting. We demonstrate how large language models (LLMs) and stakeholder aligned AI agents can support or automate evaluations, while statistical safeguards help detect manipulation. The proposed framework lays the foundation for scalable and trustworthy governance in the Web3 ecosystem.
The oracle problem refers to the inability of an agent to know if the information coming from an oracle is authentic and unbiased. In ancient times, philosophers and historians debated on how to evaluate, increase, and secure the reliability of oracle predictions, particularly those from Delphi, which pertained to matters of state. Today, we refer to data carriers for automatic machines as oracles, but establishing a secure channel between these oracles and the real world still represents a challenge. Despite numerous efforts, this problem remains mostly unsolved, and the recent advent of blockchain oracles has added a layer of complexity because of the decentralization of blockchains. This paper conceptually connects Delphic and modern blockchain oracles, developing a comparative framework. Leveraging blockchain oracle taxonomy, lexical analysis is also performed on 167 Delphic queries to shed light on the relationship between oracle answer quality and question type. The presented framework aims first at revealing commonalities between classical and computational oracles and then at enriching the oracle analysis within each field. This study contributes to the computer science literature by proposing strategies to improve the reliability of blockchain oracles based on insights from Delphi and to classical literature by introducing a framework that can also be applied to interpret and classify other ancient oracular mechanisms.
The potential of agricultural data (AgData) to drive efficiency and sustainability is stifled by the "AgData Paradox": a pervasive lack of trust and interoperability that locks data in silos, despite its recognized value. This paper introduces AgriTrust, a federated semantic governance framework designed to resolve this paradox. AgriTrust integrates a multi-stakeholder governance model, built on pillars of Data Sovereignty, Transparent Data Contracts, Equitable Value Sharing, and Regulatory Compliance, with a semantic digital layer. This layer is realized through the AgriTrust Core Ontology, a formal OWL ontology that provides a shared vocabulary for tokenization, traceability, and certification, enabling true semantic interoperability across independent platforms. A key innovation is a blockchain-agnostic, multi-provider architecture that prevents vendor lock-in. The framework's viability is demonstrated through case studies across three critical Brazilian supply chains: coffee (for EUDR compliance), soy (for mass balance), and beef (for animal tracking). The results show that AgriTrust successfully enables verifiable provenance, automates compliance, and creates new revenue streams for data producers, thereby transforming data sharing from a trust-based dilemma into a governed, automated operation. This work provides a foundational blueprint for a more transparent, efficient, and equitable agricultural data economy.
Student retention is one of the rising problems seen in educational institutions. With the rising cost of education and issues in the education sector, such as curriculum relevance, student engagement, and rapidly changing technological advancements, ensuring the relevance of academic programs in a fast-evolving job market has created a significant concern for educational institutions. With the intent to adapt to such challenges, educational institutions are dealing with alternative solutions for education, in which micro-credentials are at the very center of this, which are short-term academic programs or standalone courses. However, one of the challenges of micro-credentials is a lack of credit transfer among institutions. With the lack of standardization of assessments among educational institutions, it is difficult to transfer micro-credentials to larger qualifications. Regarding such challenges, micro-credentials with blockchain technology can bring significant benefits. Blockchain technology offers a decentralized and immutable platform for securely storing and verifying credentials. This paper presents a prototype model for micro-credential verification. With the policies decided by the educational institution, the learner provides a micro-credential certificate to the system. Upon validation of the certificate by the verifying body, the educational institution will review the assessment criteria and provide exemptions based on the provided criteria. The prototype uses the Hyper-ledger Fabric platform and utilizes off-chain technology, which acts as a middle-man storage platform. With the combination of off-chain and on-chain technologies, congestion on the blockchain is reduced, and transaction speed is improved. In summary, this research proposes a prototype for secure micro-credential verification and a more efficient course exemption process.