We introduce the Overlap-Weighted Hierarchical Normalized Persistence Velocity (OW-HNPV), a novel topological data analysis method for detecting anomalies in time-varying networks. Unlike existing methods that measure cumulative topological presence, we introduce the first velocity-based perspective on persistence diagrams, measuring the rate at which features appear and disappear, automatically downweighting noise through overlap-based weighting. We also prove that OW-HNPV is mathematically stable. It behaves in a controlled, predictable way, even when comparing persistence diagrams from networks with different feature types. Applied to Ethereum transaction networks (May 2017-May 2018), OW-HNPV demonstrates superior performance for cryptocurrency anomaly detection, achieving up to 10.4% AUC gain over baseline models for 7-day price movement predictions. Compared with established methods, including Vector of Averaged Bettis (VAB), persistence landscapes, and persistence images, velocity-based summaries excel at medium- to long-range forecasting (4-7 days), with OW-HNPV providing the most consistent and stable performance across prediction horizons. Our results show that modeling topological velocity is crucial for detecting structural anomalies in dynamic networks.
The rapid expansion of AI-driven applications powered by large language models has led to a surge in AI interaction data, raising urgent challenges in security, accountability, and risk traceability. This paper presents AiAuditTrack (AAT), a blockchain-based framework for AI usage traffic recording and governance. AAT leverages decentralized identity (DID) and verifiable credentials (VC) to establish trusted and identifiable AI entities, and records inter-entity interaction trajectories on-chain to enable cross-system supervision and auditing. AI entities are modeled as nodes in a dynamic interaction graph, where edges represent time-specific behavioral trajectories. Based on this model, a risk diffusion algorithm is proposed to trace the origin of risky behaviors and propagate early warnings across involved entities. System performance is evaluated using blockchain Transactions Per Second (TPS) metrics, demonstrating the feasibility and stability of AAT under large-scale interaction recording. AAT provides a scalable and verifiable solution for AI auditing, risk management, and responsibility attribution in complex multi-agent environments.
The accurate and timely classification of toddlers' nutritional status is critical for early intervention, particularly in remote or underserved communities with limited access to healthcare professionals. However, data security, especially for children's health data, is equally essential to ensure safe storage and access. To address these challenges, this study proposes a hybrid AI-powered chatbot that integrates ensemble learning, blockchain, and decentralized storage to support both nutritional status classification and educational interaction. The system combines a random forest model for classification with GPT-3.5 Turbo for bilingual (IndonesianâEnglish) stunting education deployed via Telegram. Preprocessing includes standardizing, normalizing, and encoding Indonesian-language nutrition data to ensure machine learning readiness. Six ensemble algorithms are evaluated using stratified five-fold cross-validation, with classification results hashed using SHA-256 and immutably stored on the Interplanetary File System (IPFS) and a local Ethereum blockchain. The chatbot effectively manages both structured inputs and natural language queries, ensuring secure, transparent, and real-time nutritional assessments. Results demonstrate high classification performance, with the random forest model achieving the highest mean F1-score (0.9987) and the lowest deviation. Its robustness was validated by a 20% hold-out test set and stratified five-fold cross-validation, which obtained excellent balanced performance across nutritional status categories (F1-macro, precision, recall, accuracy â 0.99; ROC AUC = 1.00). External validation also yielded robust and consistent results (F1-macro = 0.97, precision = 0.97, recall = 0.96, ROC AUC = 0.98, and accuracy = 0.97), demonstrating the model's generalization ability and mitigating concerns regarding overfitting. Blockchain evaluation confirmed stable and linear CID transaction throughput (blocks 29â46) with no observed latency, ensuring reliable and continuous data recording. Furthermore, gas prices decreased by ~87.5%, highlighting significant improvements in cost efficiency and scalability, which reinforces blockchain's feasibility for decentralized, AI-driven health data management. Received: 9 June 2025 | Revised: 29 September 2025 | Accepted: 31 October 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/rendiputra/stunting-balita-detection-121k-rows and https://www.kaggle.com/datasets/jabirmuktabir/stunting-wasting-dataset. Author Contribution Statement Wa Ode Siti Nur Alam: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing â original draft, Writing â review & editing, Visualization, Project administration. Riri Fitri Sari: Conceptualization, Writing â review & editing, Supervision, Funding acquisition.
Abstract This paper considers option valuation under finite mixture models in a discrete-time economy. Specifically, the Esscher transform is employed to select a pricing kernel. Novel finite mixture models with negative-shifted Gamma and negative-shifted inverse Gaussian distributions are developed. A hybrid finite mixture model that allows different parametric forms for component distributions is introduced to incorporate model uncertainty. An empirical characteristic function estimation method is employed to estimate the finite mixture models. Closed-form pricing formulas for a European call option are obtained for some finite mixture models. Empirical examples using data on the Bitcoin-USD prices are provided to illustrate an application of the proposed models to value Bitcoin options.
Mohammed Alhayani, Wisam Hazım Gwad, Shahab Wahhab Kareem, Moustafa Fayad
The increasing use of blockchain smart contracts has introduced new security challenges, as small coding errors can lead to major financial losses. While rule-based static analyzers remain the most common detection tools, their limited adaptability often results in false positives and outdated vulnerability patterns. This study presents a comprehensive comparative analysis of machine learning (ML) and deep learning (DL) methods for smart contract vulnerability detection using the BCCC-SCsVuls-2024 benchmark dataset. Six models (Random Forest, k-Nearest Neighbors, Simple and Deep Multilayer Perceptron, and Simple and Deep one-dimensional Convolutional Neural Networks) were evaluated under a unified experimental framework combining RobustScaler normalization and Principal Component Analysis (PCA) for dimensionality reduction. Our experimental results from a five-fold cross-validation show that the Random Forest classifier achieved the best overall performance with an accuracy of 89.44% and an F1-score of 93.20%, outperforming both traditional and neural models in stability and generalization. PCA-based feature analysis revealed that opcode-level features, particularly stack and memory manipulation instructions (PUSH, DUP, SWAP, and RETURNDATASIZE), were the most influential in defining contract behavior.
This paper analyzes the role, structure, and challenges of central budget transfers namely block grants and earmarked transfers in financing primary and secondary education at the municipal level in the Republic of North Macedonia. In light of the country's ongoing fiscal decentralization, municipalities have assumed core responsibilities for delivering public education services. To fulfill these obligations, the central government supports local budgets primarily through block grants, which offer limited flexibility, and earmarked transfers designated for specific projects. Through a mixed-method approach combining legal analysis, comparative municipal data, and case-based evidence, the study evaluates the effectiveness, equity, and efficiency of the current intergovernmental transfer mechanisms. Special attention is given to disparities in per-student funding, differences in administrative capacity, and the extent to which formula-based allocations reflect local socio-economic conditions. Findings reveal substantial variation in municipalitiesâ capacity to ensure quality education, driven in part by weaknesses in funding formulas, late disbursements, and institutional constraints. The paper concludes with policy recommendations aimed at improving allocation methods, enhancing transparency, and building local administrative capacity to ensure fair, efficient, and sustainable education financing under a decentralized governance framework.
Africa's monetary and financial system, which is currently developing dynamically after a prolonged period of neocolonial dependence on transnational financial groups and international capital, is following its own unique path of development, one that differs significantly from the financial trajectories of other global regions. Sub-Saharan Africa, in particular, showcases a unique experience in the evolution of its monetary system, characterized by the world's highest growth dynamics in fintech and electronic non-bank payments. The active, extensive development of Pan-African financial groups and the trend of them displacing foreign and transnational financial corporations from the African continent are intensifying each year. Africa is one of the global leaders in the development of decentralized finance and cryptocurrency mining. At the same time, the continent remains burdened with high levels of external public debt, accumulated primarily in the post-colonial period of its history. This debt hinders the economic development of African nations and their achievement of the Sustainable Development Goals. Concurrently, the scientific and political discourse on reforming the global financial architecture is becoming increasingly acute, as the current system in its present form is incapable of effectively countering new challenges, which have further exacerbated the problems accumulated over recent decades. Africa stands to be a primary beneficiary of a reform of the global monetary and financial system, within which it must assume an adequate position commensurate with its growing international role that reflects its deserving status in geopolitical and geo-economic terms. Sub-Saharan Africa has the potential to become the trigger that accelerates the reform of the global financial architecture and to serve as a unique testing ground for corresponding pilot projects.
In 2008, the idea of Bitcoin, a peer-to-peer electronic cash system, was proposed by Satoshi Nakamoto. It describes a distributed system for managing digital transactions. Based on this idea, the blockchain concept has evolved. Blockchain is a distributed ledger. The ledger is immutable and shareable among all the user nodes. The ledger/blockchain contains several blocks chained by hash values. If we try to modify a block, its hash value will change; the hash value is already stored in the neighbor node, so the neighbor node will not allow it to change. Thus, immutability is achieved. The blockchain is worthy because of its good characteristics, such as data decentralization and a high level of trust. This chapter represents a detailed study on blockchain architecture, consensus mechanisms, and their application in digital image watermarking. Digital image watermarking is used for copyright protection, ownership claim, and image tamper detection. If we use blockchain with watermarking, the technique becomes more secure and robust. Though the applications of blockchain technology in image watermarking are in a nascent stage at present, the disruptive and revolutionary nature of the blockchain will make it a significant force shortly.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
With the rapid development of geological blockchains and Internet of Things-based data acquisition technologies, massive amounts of heterogeneous data are constantly emerging. However, this data is stored in a distributed manner across different organizational or business blockchains. Data sharing among multiple geological blockchains faces numerous challenges, either exposing sensitive data during verification or lacking effective authorization mechanisms. Therefore, how to achieve fine-grained access control and privacy protection across multiple blockchains has become a critical issue that must be addressed in geological data sharing. In this paper, we propose GeoCross, a cross-chain geological data sharing framework that enables fine-grained authorization management and privacy protection. First, GeoCross provides a hierarchical hybrid encryption mechanism that uses symmetric encryption for geological data protection and ciphertext-policy attribute-based encryption to enable flexible cross-chain access policies. Second, we integrate a Groth16-based zero-knowledge proof mechanism, which allows a chain to verify the existence, integrity, and accessibility of off-chain data without revealing the content. Furthermore, we introduce a Reputation-based Non-interactive Relay node Selection protocol (RNRS), which enhances the trustworthiness and fairness of cross-chain routing. Finally, we implement GeoCross in a multi-chain Hyperledger Fabric environment and evaluate its performance under real-world workloads. Results show that Groth16 verification requires only three bilinear pairings, achieving a throughput of up to 390 tps on a single chain and 1550 tps in a concurrent multi-chain environment. Even with 50% malicious nodes, the RNRS protocol still maintains a success rate of over 91%. These results demonstrate that GeoCross provides an efficient and practical solution for secure and privacy-preserving cross-chain geological data sharing.
Abstract Fair exchange is a cryptographic protocol that enables two parties to exchange their electronic data fairly, i.e., it ensures that no one can steal the other partyâs item. FairSwap and OptiSwap are well-known fair exchange protocols for files and coins based on smart contracts. Although the design principles for the two protocols are similar, there is an extreme difference in round complexity and communication overhead between the two protocols. This gap can be a barrier to users since they must choose one of them. In order to remove the barrier, this work generalizes these two protocols. It proposes a fair exchange protocol that allows users to adjust the communication overhead and the number of rounds by a newly introduced parameter. Our protocol contains FairSwap, OptiSwap, and protocols with intermediate efficiency between them. Moreover, to evaluate the gas costs of our protocol, we present a performance evaluation of the proposed protocol by Solidity implementation over Ethereum.
Secure electronic voting (e-voting) systems have become an essential component of modern democratic processes, demanding strong guarantees of privacy, integrity, verifiability, and resistance to coercion. Homomorphic commitment schemes, which integrate the properties of commitment schemes with homomorphic encryption, provide a promising approach to meeting these requirements by enabling computations on encrypted or committed data without revealing the underlying information. This capability allows secure vote tallying while preserving voter anonymity. This paper presents a comprehensive review of homomorphic commitment schemes within secure voting infrastructures, focusing on key security models such as privacy, verifiability, coercion resistance, and robustness against malicious adversaries. It also examines optimization techniques, including batching, threshold cryptography, and blockchain integration, which enhance system efficiency and scalability. Furthermore, emerging paradigms such as post-quantum cryptography and decentralized systems are discussed for their potential impact on voting protocols. The study highlights advancements in cryptographic primitives, zero-knowledge proofs, and distributed ledger technologies, while providing a comparative analysis of multiple research contributions. The findings indicate that although homomorphic commitment schemes significantly enhance transparency and privacy, challenges related to computational complexity, scalability, and real-world implementation persist, suggesting the need for lightweight, quantum-resistant, and hybrid secure voting solutions. , , , ,
This study investigates the long-run relationship between the net assets of Bitcoin spot exchange-traded funds (ETFs) and Bitcoinâs price. Using daily data from 11 January 2024 to 16 May 2025, we employ cointegration techniquesâFully Modified OLS, Dynamic OLS, and Canonical Cointegrating Regressionâto test for a stable equilibrium linking these series. The empirical results indicate a strong positive association in the long run: periods of expanding Bitcoin ETF assets correspond to higher Bitcoin price levels. Cointegration is confirmed at the 10% significance level, suggesting that the ETF assets under management and the Bitcoin market price move together in a persistent equilibrium. These findings support the hypothesis that ETF-driven demand exerts a lasting influence on Bitcoinâs valuation. By highlighting a structural connection between regulated Bitcoin investment vehicles and the underlying cryptocurrency, the study provides timely evidence of how financial innovation can shape asset pricing in the digital asset market.
Smart contracts are high-stakes software: their immutable, publicly accessible code may govern assets worth millions. This means that even minor defects can have severe consequences. Unit tests often miss edge cases. Although formal verification is the only route to full correctness, it demands substantial time and expertise. Property-based testing bridges this gap by exploring large input spaces and shrinking failures to minimal counterexamples. Used early, it filters defects and prioritises verification effort on code already validated by tests. Although Solidity benefits from a mature testing ecosystem, comparable support for other languages such as Daml remains limited. This dissertation addresses this gap by introducing Hypothesis2Daml, an open-source library that brings property-based testing to Damlby connecting Hypothesis withthe Daml JSON API. Hypothesis2Daml enables developers to specify invariants, pre/postconditions, and stateful workflows over realistic ledger interactions. It provides automatic input generation, shrinking to minimal counterexamples, party isolation per example, and request helpers that keep tests focused on properties rather than HTTP/JSON wiring. The approach is validated on a benchmark of eight contracts and twenty-seven properties that range from simple invariants to multi-step, role-sensitive workflows. All properties held under testing, with runs completing within practical time bounds, demonstrating that property-based testing is feasible and effective for Daml applications. A comparative evaluation situates Hypothesis2Daml among established tools and methods, highlighting strengths in usability and expressiveness, and acknowledging a throughput trade-off due to JSON-API transport overhead. Contributions include the Hypothesis2Daml library and a reusable benchmark with representative properties. Limitations concern transport overhead, ecosystem specificity, and dependence on developer-authored properties and generators. Future work targets automation, performance, andscope expansion. Together, these results establish a practical path for making property-based testing a routine part of building Daml applications.
Despite the growing emphasis on the nexus between growth and macroeconomic indicatorsÂ, research on the influence of cryptocurrencies on economic performance remains limited. This study compares the impact of two leading cryptocurrencies, Bitcoin and Ethereum, on economic growth, alongside inflation, market uncertainty, and oil and gold prices, using panel data from 14 countries between Q3 2015 and Q3 2023. The results demonstrate robust cross-sectional dependence, indicating that economic shocks in one country affect the entire group. Therefore, second-generation tests are employed to confirm the presence of stationarity in the variables. Except for Bitcoinâs trading volume, panel fully modified ordinary least squares estimations reveal a significantly positive impact of cryptocurrencies on growth. Cointegration is present in the long run, while in the short run, strong bi- and unidirectional causality is found for all cryptocurrency proxies. The study provides insights that can help policymakers develop strategies to align economic growth with the crypto market, benefiting the broader economy.
Yogesh, Saniya Khurana, Sourav Rampal, Pastor R. Arguelles · 7 authors
Data analytics implementation into the modern art market has changed the way the stakeholders analyze, invest, and interact with art pieces. The art market, traditionally opaque and subjectively valued, is currently adopting data-driven approaches to increase transparency and efficacy, as well as, decision-making. This essay examines the primary importance of the data analytics in transforming the art ecosystem with an emphasis on its uses, advantages, and difficulties. It starts with defining the key elements and classes of analytics: descriptive, predictive, and prescriptive and the technological tools used: artificial intelligence, big data platforms, machine learning algorithms. The tools are then placed in the framework of the art market and discussed on how they can solve the inefficiencies of pricing, valuation, and demand forecasting. Case study examples show how analytics can be used to identify the rising artists, identify the market trends, and prevent fraud risks and manipulation. Alongside these benefits, the paper also mentions such limitations as the lack of data, ethical concerns, and algorithmic bias. Lastly, it also looks into the future opportunities which include blockchain integration, value of digital art and analytics of non-fungible tokens (NFTs). In general, this paper highlights the fact that data analytics is not just democratizing the art investment, but also reshaping cultural and economic value in the ever more digital marketplace.
Rosario Violeta Grijalva Salazar, Jose Antonio Caicedo-Mendoza, Arturo Jaime Zuñiga Castillo, Erikson Olivas-Valencia · 5 authors
Taxation on cryptocurrency is becoming critical in global fiscal governance as digital assets adapt to the modern reality of existing outside of traditional regulatory constructs. Theoretical and practical understanding of cryptocurrency taxation is quite new, and so a systematic review was designed to present the most recent empirical research evidence on the legal, fiscal and behavioral aspects of cryptocurrency taxation from across the globe. Using the PRISMA-2020 guidelines, a structured search was applied to the Scopus database on 21 May 2025, with the search terms âcrypto-currencyâ, âcryptoassetâ and âtaxation.â The inclusion criteria consisted of original research articles published between the years of 2020 and 2025 in English or Spanish, that could be accessed via institutional library support, and that were related to taxation, legal regulation and/or compliance. Out of the original identified 224 records, 36 met the eligibility criteria after screening and verification through seven different stages of review. Socially, five themes were produced by the findings: legal ambiguity surrounding fiscal treatment, limited tax literacy and compliance issues, macroeconomic and monetary issues, application of digital technologies for fiscal tracking, and environmental repercussions from crypto mining. Many countries do not have any coherent tax frameworks to govern the risk that emerges from cryptocurrency taxation, creating uncertainty for both regulators and investors. The findings outlined in this systematic review point to the urgent need for creating a coherent approach to cryptocurrency taxation based on definitions, digital approaches to traceability, and tax literacy compliance strategies. In order to create effective cryptocurrency taxation, there must be a base balance between ensuring innovation, fiscal responsibility, transparency, equity and sustainability in the developing digital economy.
Jiayong Chai, Jun Guo, Muhua Wei, Mo Chen · 5 authors
Blockchain systems have been widely adopted in todayâs society, with consensus algorithms serving as their core component to ensure all participants in the network agree on a specific data state. Existing consensus algorithms such as Proof of Work (PoW), Proof of Stake (PoS), and the Practical Byzantine Fault-Tolerant Algorithm (PBFT) exhibit certain limitations in terms of scalability, security, and efficiency. To address these limitations, this paper proposes a novel Network-based Reputation Consensus (NRC) algorithm. The main research contributions of this work include the following: (1) An intelligent grouping mechanism that dynamically groups nodes based on network awareness, forming consensus groups with low internal latency and high bandwidth utilization, significantly reducing intra-group communication overhead. (2) A dynamic reputation system incorporating a âdiminishing returnsâ reward function and a âmultiplicative penaltyâ mechanism, effectively incentivizing honest node participation while preventing power monopoly. (3) A two-phase model of âintra-group BFT consensus + global communication committee orderingâ that decomposes complex global consensus into parallel intra-group processing and coordination among a small set of elite nodes, thereby drastically improving efficiency. (4) Comprehensive simulations comparing the NRC algorithm with mainstream consensus algorithms, demonstrating its superior performance in communication overhead, throughput, latency, and tolerance to malicious nodes, thereby laying the foundation for large-scale applications.
Recently, the need for unified orchestration frameworks that can manage extremely heterogeneous, distributed, and resource-constrained environments has emerged due to the rapid development of cloud, edge, and IoT computing. Kubernetes and other traditional cloud-native orchestration systems are not built to facilitate autonomous, decentralized decision-making across the computing continuum or to seamlessly integrate non-container-native devices. This paper presents the Distributed Adaptive Cloud Continuum Architecture (DACCA), a Kubernetes-native architecture that extends orchestration beyond the data center to encompass edge and Internet of Things infrastructures. Decentralized self-awareness and swarm formation are supported for adaptive and resilient operation, a resource and application abstraction layer is established for uniform resource representation, and a Distributed and Adaptive Resource Optimization (DARO) framework based on multi-agent reinforcement learning is integrated for intelligent scheduling in the proposed architecture. Verifiable identity, access control, and tamper-proof data exchange across heterogeneous domains are further ensured by a zero-trust security framework based on distributed ledger technology. When combined, these elements enable increasingly autonomous workload orchestration, trading centralized control for adaptive, decentralized operation with enhanced interoperability, scalability, and trust. Thus, the proposed architecture enables self-managing and context-aware orchestration systems that support next-generation AI-driven distributed applications across the entire computing continuum.
Blockchain consensus mechanisms form the backbone of decentralized systems by ensuring agreement among distributed nodes without a central authority. At the core of these mechanisms lie number-theoretic foundations, including cryptographic primitives such as modular arithmetic, hash functions, elliptic curve cryptography, and zero-knowledge proofs. These mathematical constructs enable secure transaction validation, identity verification, and resistance against adversarial attacks. This paper presents a systematic review of number-theoretic foundations underpinning blockchain consensus mechanisms, focusing on methods, architectural implementations, and emerging research directions. The study analyses widely adopted consensus algorithms such as Proof of Work (PoW), Proof of Stake (PoS), and Byzantine Fault Tolerant (BFT) protocols, highlighting their dependence on number theory for ensuring security, randomness, and fairness. A comprehensive review of 30 studies published between 2018 and 2023 is conducted to examine advancements in cryptographic techniques such as verifiable random functions (VRFs), homomorphic encryption, and zero-knowledge proofs. These techniques play a crucial role in improving scalability, privacy, and efficiency of blockchain systems. The findings reveal that while number-theoretic approaches provide strong security guarantees, challenges such as computational overhead, scalability, and energy consumption persist. The paper concludes by identifying future research directions, including post-quantum cryptography, lightweight cryptographic protocols, and AI-assisted consensus optimization.
Theoretical background: In general, authors claim that the business model for any human-beings organisation defines who and how creates values in a socio-economic context. Taking into account the organisational theories presented in literature, authors notice a variety of definitions and components of business models. In addition, values in the business models have different interpretations. By definition, decentralised autonomous organisation (DAO) is using the Blockchain 2.0 technology, which strongly supports its internal operational management, change of attitude towards organisation membersâ identification, and controlling internal activities. Purpose of the article: Construction of the Decentralised Autonomous Organisation (DAO) business model for determining DAO strategic development is the main purpose of this study. The authors aim to provide their own proposal of business model, as well as the identification of DAO business model components. The authors expand the DAO business model canvas, and beyond variables included in Osterwalderâs model, and consider some other important DAO features by example of TalentDAO case study. Research methods: The authors have focused on surveys of the management science literature in some popular repositories. Beyond that, they have added a DAO case study. They have done descriptive analysis of publications on business models and DAO business models. The authors applied the case study approach, because they argue that each DAO is different and taking into account suggestions provided by practitioners, the exploratory case study method is the best method to reveal idiosyncrasy of business organisation as well as applicability of theoretical business models for practice of DAO management. Main findings: Through the literature surveys, authors concluded that selected theories in science of management are fundamental for DAO construction and applicable for development of business models. Although the reviewed models are various, they have many common features and allow constructing the authorsâ model of DAO business, which is an extension of Osterwalder Business Model Canvas. The authors characterised DAO partners, customers, values, resources, and activities. The authors discussed constraints and risks of DAO activities as well as the applied methods of coordination and control. The authors claim that DAO supports decentralized decision-making and intra-organizational trust intensification. They argue that the case study on DAO business model is an exemplification, which can be useful for development of other similar DAOs.
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 tokenâs 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.
Amid the rapid evolution of digital currencies and the decentralized finance (DeFi) ecosystem, technology-driven, anonymous, and cross-border financial crimes pose systemic challenges to traditional regulatory frameworks. Grounded in three core theories of criminal psychologyâRational Choice Theory, Routine Activity Theory, and Techniques of Neutralizationâand integrating the âtechnologyâsociety co-constructionâ perspective from the sociology of technology, this study constructs a three-dimensional analytical framework encompassing âtechnological ecology, social cognition, and individual psychology.â It systematically elucidates the psychological formation logic and evolutionary pathways of financial crimes within the DeFi domain. The research reveals that the technical features of DeFiâanonymity, decentralization, and code autonomyâcollectively create a âstructural opportunity spaceâ characterized by low accountability costs and weakened moral constraints. Subcultural communities further supply âmorally neutralizing scriptsâ through narratives of crypto-libertarianism and the myth of âcode as law.â Under these dual influences, individual psychology undergoes transformation, manifesting as complex motivations, distorted risk perceptions, and heightened moral disengagement, ultimately leading to a rationalization mechanism for criminal acts veiled behind âtechnological neutrality.â