Volodymyr Valihura, Oleksandr Odaiskyi, Ivan Vakulich, Mykhailo Soroka
Valihura V.A., Odaiskyi O.B., Vakulich I.P., Soroka M.V. PRAGMATICS OF FISCAL DECENTRALIZATION IN THE CONTEXT OF FORMING LOCAL BUDGET REVENUES IN UKRAINE Purpose. The aim of the article is to identify the prerequisites for the reform of fiscal decentralization in Ukraine, highlight individual stages of its implementation and assess the impact on the formation of local budget revenues. Methodology of research. The research process involved the use of general scientific and special methods of cognition, in particular analysis and synthesis, induction and deduction, systemic, structural and functional approaches. To assess the fiscal effects of the reform, comparative and dynamic analysis methods were used, as well as statistical methods for processing official data from the Ministry of Finance of Ukraine and the state budget web portal for citizens. The phasing of fiscal decentralization was substantiate using an institutional approach and elements of the concept of fiscal federalism. Findings. The article proves that fiscal decentralization reform in Ukraine had an uneven fiscal effect in the short term, but in the long term contributed to the growth of financial independence of local budgets. It has been established that in 2011–2014, the revenue base of local budgets was characterized by high dependence on interbudgetary transfers and the limited role of local taxes. The introduction of the reform in 2015 was accompanied by a temporary decrease in the share of own revenues due to the revision of personal income tax (PIT) crediting standards, but in 2016–2019, there was a gradual strengthening of the tax capacity of communities. It has been substantiated that the sharp reduction in official transfers in 2020 and 2022 was due to the completion of the administrative and territorial reform, the COVID-19 pandemic and the transition of the budget system to functioning under martial law. Originality. A comprehensive analysis of fiscal decentralization was conducted from the perspective of its pragmatic impact on the formation of local budget revenues in the long term. The author proposes an approach to the periodization of fiscal decentralization reform in Ukraine, taking into account institutional changes and crisis factors, and substantiates the relationship between the transformation of interbudgetary relations and the dynamics of tax revenues at the subnational level. Practical value. The obtained results can be used by state authorities and local self-government bodies in the formation of tax and budget policy, the improvement of interbudgetary equalization mechanisms and the development of a post-war strategy for the development of fiscal decentralization. Key words: fiscal decentralization, fiscal federalism, fiscal policy, local budgets, taxes, tax revenues, interbudgetary transfers, territorial communities, personal income tax.
Blockchain technology offers an immutable record of verified information, which enables its participants to exchange data in a trustless environment. However, providing at once the properties of integrity, verifiability, availability raises challenges in scenarios where data confidentiality must be preserved. While techniques such as data anchoring, zero-knowledge proofs, or homomorphic encryption have been proposed to address these challenges, formalizing their uses in the context of blockchains, into accessible design patterns for non-expert audiences remains underexplored. This paper proposes a comprehensive collection of blockchain patterns addressing confidentiality-related use cases. The patterns are organized into three families: (1) patterns for confidential data sharing; (2) patterns for claim management—including proof issuance and verification—originally introduced in a previous paper and revised herein; and (3) patterns for secure computation over private inputs. This collection provides a conceptual framework that structures and unifies emerging approaches in this fast-evolving area, laying the groundwork for future standardization and implementation efforts. It offers actionable insights for practitioners, combining best practices with architectural guidance for safeguarding data within blockchain systems.
Abstract The reliability and security of Smart Contracts largely depend on the consistency of their rule design. This paper introduces a novel approach for analyzing and ensuring consistency in Smart Contracts prior to their implementation. By proposing a formal grammar that captures the concurrent primitives inherent in Smart Contracts, and an algorithm that generates finite state machines (FSMs) from these grammar-based rule sets, the study enables the identification of several problems at the design stage. The paper compares this methodology with related works that focus on post-implementation verification, highlighting the advantages of early-stage analysis. Through theoretical and real-world examples, including an analysis of the DAO attack, the paper demonstrates how inconsistencies can be detected systematically. Finally, a formal definition of consistency is presented, offering a foundation for future tools aimed at enhancing Smart Contract designs.
Applies the Evidence-Based Subjective Logic (EBSL) framework to zero-knowledge reputation systems and decentralised identity. Demonstrates how reputation opinions that are provably correct can be published without revealing the underlying evidence graph, using the EZKL zkML framework for proof generation.
Extends the EQBSL (Evidence-Quality Bayesian Subjective Logic) framework with zero-knowledge proof constraints. Allows a prover to demonstrate that their trust opinion was computed correctly from private evidence, without revealing the evidence itself. Bridges cryptographic privacy guarantees with the epistemic trust formalism of EQBSL.
Blockchain technology has fueled exponential growth across various industries, including finance, supply chain management, and healthcare, enabling greater transparency in transaction management and supporting decentralized implementations. This paper presents a comprehensive performance analysis of permissioned and permissionless blockchain platforms, specifically Hyperledger Fabric and Ethereum. The study evaluates these platforms with varying transaction workloads (100 to 1000 transactions) with a consistent network. Our objective is to measure key performance metrics such as send rate, throughput, latency, resource utilization, and transaction success rate using established benchmarking tools and methodologies. The findings offer valuable insights into the comparative strengths, limitations, and optimal use cases of these blockchain platforms across different performance parameters. The results indicate that Hyperledger Fabric achieves, on average, 3.5–4.5 times higher throughput and 10–12 times lower latency than Ethereum, while consuming 2.5–3 times less memory across tested workloads. In contrast, Ethereum demonstrates a higher send rate and lower CPU demand in some operations. Overall, the study suggests that Hyperledger Fabric is better suited for enterprise applications that demand high scalability and performance.
This study aims to examine the relationship between Bitcoin trading volume and key technical indicators using data-mining techniques to better understand how trading activity influences momentum and volatility in blockchain markets. The methodology involves analyzing a historical dataset of Bitcoin’s daily trading records from 2018 to 2023, which includes the Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), Simple and Exponential Moving Averages (SMA, EMA), and the Average True Range (ATR). Pearson correlation analysis was applied to identify linear associations between trading volume and these technical indicators. The results show significant positive correlations between trading volume and momentum or trend measures such as the 7-day RSI (r = 0.45, p < 0.05), SMA (r = 0.38, p < 0.05), EMA (r = 0.41, p < 0.05), and ATR (r = 0.48, p < 0.05), indicating that higher participation accompanies stronger market momentum and greater price variability. Conversely, the weak and non-significant correlation with MACD (r = –0.12, p = 0.15) suggests that volume has limited influence on lagging trend-reversal signals. The novelty of this study lies in integrating volume-based behavior into technical indicator analysis, extending the traditional volume–price–volatility framework to cryptocurrency markets and providing practical insights for momentum-driven trading strategies and volatility-aware risk management.
Andrei Brînzea, Răzvan-Andrei Leancă, Iulian Aciobăniţei, Florin Pop
Traditional Time-Stamping Authorities provide reliable temporal evidence. However, they operate as single points of trust and do not supply a tamper-evident record of event ordering. This paper presents a standards-compliant extension that anchors each issued timestamp token to a blockchain ledger while preserving full compatibility with existing TSA clients. Our proposal is compliant with RFC 3161. The implementation uses an identifier in the token that is also included in the distributed ledger. Experiments were conducted on the Ethereum and Hyperledger Fabric networks. Our design allows for external verification of the existence and relative ordering of tokens without modifying the RFC-defined validation process. Experimental evaluation compares issuance latency, anchoring time, and transaction cost across both networks. Our work presents a practical and viable approach to enhancing trust in digital signature infrastructures by combining the regulatory reliability of qualified TSAs with the auditability and persistence of distributed ledgers.
Access control in P2P network is extremely challenging due to several reasons such as decentrailized network, anonymous connectivity, high churn, resource constraints and large attack surface. The number of attributes to be tracked grows exponentially as P2P networks scale, exacerbating issues in traditional approaches such as RBAC, ABAC, RuBAC. This paper presents a novel blockchain-based access control framework for P2P networks using Ethereum smart contracts to offer an adaptable, decentralized, and scalable solution. Our token economy framework incentivizes nodes that demonstrate consistent compliance and penalize malicious actions using fungible ERC-20 tokens. Hierarchical static role-based structure consisting of regular members, primary and secondary group is the foundational layer of role management. Dynamic state management through a finite state machine classifies peers as benign, suspicious, or malicious mapping their behavior to the STRIDE threat model. We have used standard blockchain tool stack consisting of OpenZeppelin (contract library), Mocha (testing), and Web3.js (deployment). Our framework is tested with eight test cases, such as privilege escalation and denial-of-service attacks, and built using three types of Ethereum smart contracts. Our focus is on collecting four metrics gas cost, latency, execution time, simultaneous requests that provides a comprehensive view of realistic network activity. This study goes beyond conceptual nature of state-of-art architecture to showcase the realistic mapping between resources, peer roles and standard threats in a P2P network. The experimental results shows low gas costs (upto 1.4 million gas) and fast execution times (80–550 ms), confirming the design efficiency. The framework handles up to five concurrent requests with minimal performance impact, demonstrating strong scalability across roles and threat conditions. This framework enables scalable, behavior-driven governance in P2P networks, enhancing IoT and Web3 security.
This research explores the application of data mining techniques, specifically XGBoost, to predict game pricing trends and optimize discount strategies within the digital gaming market. Game prices are influenced by various factors, including production costs, market demand, and promotional strategies. This study analyzes historical pricing data from multiple online stores to identify key pricing patterns and factors that influence price changes over time. The model developed in this study predicts game prices by incorporating features such as retail price, discount percentages, past price trends (lags), and other time-based features. The findings reveal that retail price and recent price trends (e.g., 7-day rolling averages) are the most influential features in predicting future prices. Additionally, discount strategies significantly impact game sales, with certain discount ranges showing higher effectiveness in driving consumer purchases. The model also demonstrates variability in prediction accuracy, particularly at higher price points, highlighting the challenges of capturing complex price fluctuations in a dynamic digital marketplace. The significance of this study extends to the Metaverse market, where pricing and the use of digital assets like non-fungible tokens (NFTs) play a critical role. The model's application could aid in optimizing pricing strategies within virtual economies, enhancing both the consumer experience and retailer profitability. Future work includes integrating additional features such as user reviews and exploring its application to Metaverse game platforms. The practical implications of this research are significant for online game retailers looking to leverage data-driven insights for more effective pricing and promotional strategies.
J Dinesh Kumar, P Dhayanithi, C Suresh, J Angeljulie
Due to the fast emergence of deepfake technologies, the authenticity of digital media is under serious threat, and issues such as misinformation, identity fraud, and reputation damage, are encountered. Older deepfake detection techniques (based on pattern recognition or supervised learning) have poor capabilities to identify very state-of-the-art fake content and do not have a way to confirm the origin of content. This research introduces a new Blockchain-Integrated Generative AI model, which is the solution to these drawbacks, integrating Non-Fungible Token (NFT)-based media provenance with Generative AI-powered content analysis. Within this system, original digital media is initially stored on a blockchain and given a unique NFT that establishes an indelible and traceable record of origin of content. Then, a Generative Adversarial Network (GAN) evaluates the media to reveal minor anomalies, including pixel-level anomalies, unnatural facial expressions, or lighting anomalies, to show that the media is manipulated. The proposed system guarantees the authenticity and integrity of digital information by combining the NFT-based verification with artificial intelligence-based anomaly detection. Initial analyses indicate that this two-layered solution can greatly increase the detection efficiency relative to the traditional solutions besides offering safe provenance verification. The suggested framework provides a scalable, real-time system to verify digital media, which reduces the volume of threats posed by deepfakes and inspires trust in the Internet ecosystem. The study helps to build a strong basis of safe digital content handling and prevent the increasing issues of the fake media.
Digital Media Forensic Detection
Generative Adversarial Networks and Image Synthesis
The rapid changes under modern technology have had a significant impact on the business and investment sector, with the emergence of new businesses and investments in these businesses beginning to be leveraged by investors and specialized companies to generate significant profits.Therefore, virtual assets have emerged and spread, representing new, modern, and highly advanced digital tools. Specialized and appropriate legislation has been developed by a number of countries, with millions of dollars allocated to them. Individuals and investors have begun trading in them through specialized digital platforms, which are characterized by high security and guarantees.Virtual assets vary in several types, including cryptocurrencies, non-fungible tokens, and virtual land.From the above, the emergence of virtual assets has provided a distinct qualitative leap, thus necessitating the establishment of a specialized authority to monitor virtual assets traded and invested in across various platforms. This will achieve significant security for those engaged in these activities. Furthermore, there is a clear and significant legislative deficiency in the treatment of virtual assets at the Arab level in many countries, which constitutes a significant challenge in this regard. Furthermore, there is the problem of providing guarantees for the emerging technologies in various financial and commercial transactions. It is noteworthy that the Emirate of Dubai in the United Arab Emirates was the first to do so, under the Virtual Assets Law issued in 2022, the Yemeni Virtual Assets Draft Law of 1445 AH, and the Jordanian Virtual Assets Law, which was passed in May 2025.
Mustafa M. Abd Zaid, Husam I. Shaheen, Nigora Abduraimova, Jasim Gshayyish Zwaid · 7 authors
Smart contracts are self-executing computer-based agreements that are implemented on blockchain systems and that their security is of paramount importance because they are not subject to change. The knowledge of token-level semantics can be helpful in determining the areas that may pose a weakness in these contracts. Nevertheless, current techniques tend to be based on rule based analysis, or syntax level analysis, which find it difficult to reflect the richer semantic structures that result in complex vulnerabilities. In order to overcome these limitations, this paper presents a framework that combines the pretrained transformer functionality of CodeBERT with task-specific fine-tuning and, as such, auto-detects and highlights vulnerabilities in smart contract Integrated Development Environment (IDEs). The method is an examination of token-level semantics, making it possible to identify vulnerabilities and understand them correctly in context. This framework can be applied directly in real-time to IDEs by developers to get vulnerability notifications and recommendations. The experimental outcomes prove that FTC-BERT is much more effective in detecting vulnerabilities and remembering experiments than traditional, and it is a semantic-sensitive, efficient, and automated method to detect vulnerabilities in smart contracts.
I Made Candra Girinata, Budi Styawan, Arwin Wahyu Saputra, M Aidil Arif · 5 authors
ABSTRAK Perkembangan aset kripto yang pesat, khususnya Ethereum, menuntut adanya model prediksi harga yang akurat untuk mendukung strategi investasi dan manajemen risiko. Penelitian ini bertujuan untuk menganalisis dan membandingkan kinerja dua algoritma machine learning ensemble, yaitu Random Forest (RF) dan XGBoost, dalam memprediksi harga harian Ethereum. Dataset historis ETH/USD sebanyak 3.423 observasi dari periode September 2016 hingga Juli 2025 diperoleh dari platform Bitfinex. Setelah melalui tahap pra-pemrosesan data dan rekayasa fitur temporal, dataset dibagi dengan rasio 80:20 untuk pelatihan dan pengujian. Model dievaluasi menggunakan metrik Root Mean Square Error (RMSE) dan Koefisien Determinasi (R²). Hasil eksperimen menunjukkan bahwa XGBoost secara signifikan mengungguli Random Forest, dengan nilai RMSE 134.63 dan R² 0.958. Sebagai perbandingan, Random Forest menghasilkan RMSE 208.45 dan R² 0.899. Temuan ini mengindikasikan bahwa mekanisme boosting pada XGBoost lebih efektif dalam menangkap kompleksitas dan volatilitas data pasar kripto. Kata kunci: Prediksi Harga, Ethereum, Machine Learning, XGBoost, Random Forest.
The advancement of digital technology has transformed access to information and creativity, enabling widespread distribution of digital works. However, this ease of access has led to significant challenges in enforcing Intellectual Property Rights (IPR), particularly in Indonesia, where legal frameworks like Law Number 28 of 2014 on Copyright and Law Number 1 of 2024 concerning the Second Amendment to Law Number 11 of 2008 concerning Electronic Information and Transactions are tested by rampant digital infringements. This study examines the adequacy of existing Indonesian legal frameworks in protecting IPR in the digital era, focusing on gaps in enforcement and regulatory adaptation to emerging technologies such as streaming platforms and Non-Fungible Tokens (NFTs). Using a normative juridical method, this research analyzes relevant statutes, case law, and legal doctrines to assess their effectiveness in addressing digital IPR violations. The findings reveal that while Indonesia has a robust legal foundation, enforcement remains weak due to limited public awareness and inadequate mechanisms for addressing digital-specific infringements. This study emphasizes the need for regulatory reform, enhanced enforcement mechanisms, and targeted education to strengthen IPR protection, fostering innovation and economic competitiveness in Indonesia’s digital landscape.
Decentralized finance (DeFi) represents a novel financial ecosystem built on open blockchain networks and smart contracts, enabling the provision of financial services without traditional intermediaries. This article examines the conceptual foundations of DeFi, its legal nature, associated risks, and regulatory challenges through a comparative analysis of international practice and the emerging legal framework of Uzbekistan. Particular attention is paid to the composability of DeFi protocols, the legal uncertainty surrounding smart contracts, and the difficulty of identifying responsible parties in decentralized systems. The study analyzes scholarly perspectives, including those of Schär and Zetzsche, and reviews regulatory responses in the United States and the European Union, with a focus on enforcement actions and AML/CFT concerns. It further evaluates risks such as cyberattacks, fraud, money laundering, and consumer harm, highlighting the systemic vulnerabilities of DeFi infrastructures. The article argues that while DeFi offers transparency and innovation, effective regulation requires balancing technological neutrality with robust consumer protection and compliance mechanisms. The findings suggest that Uzbekistan may adopt a cautious, technology-integrated regulatory approach to harness DeFi’s potential while mitigating legal and financial risks.
The decentralized and anonymous nature of Ethereum makes it a prime target for phishing scams. These scams account for nearly 50% of all blockchain-related fraud, thereby causing a substantial financial loss and eroding user trust. Unlike conventional phishing, Ethereum phishing users exploit user anonymity, lack of awareness, and market-driven dynamics to deceive normal users. Despite of a plethora of research in this direction, there is a lack of a rigorous and comprehensive survey which can fortify an insightful comparison of the existing works and provide a concrete future research guidance. To this end, this paper presents a systematic review of 90 studies published between 2020 and 2024, offering the following novel contributions, (1) Structured Taxonomy: We introduce a structured three-fold taxonomy that classifies existing methods into feature engineering-based, representation learning-based, and fusion-based frameworks. (2) Theoretical Analysis: Through theoretical analysis, we evaluate these approaches against the critical research challenges, such as rapid network dynamism, data leakage, and network sparsity and provide a comparative mapping of novel techniques adopted across the studies. (3) Empirical Evaluation: We conduct an extensive empirical evaluation of 14 representative models over multiple public datasets to assess their robustness under varying data conditions. The findings indicate that while feature-based models are more interpretable, they struggle with temporal adaptability; representation learning approaches, particularly GNN-based models, capture complex behavioral patterns but are computationally demanding and less explainable. Fusion methods demonstrate the most balanced trade-off between accuracy, scalability, and interpretability. (4) Future Research Guidance: Finally, we identify still persisting issues such as network sparsity, behavioral volatility, and scalability, and outline future research directions emphasizing temporal graph reasoning, self-supervised fusion, and explainable AI for developing transparent and deployable phishing detection frameworks on Ethereum.
Ho Yeol Yu, Kyu-soo Chung, Anthony D. Pizzo, Sangwon Na · 5 authors
Digital assets have garnered widespread attention for their potential to generate revenues. Grounded in innovation diffusion theory, this study investigated the adoption behavior of esports consumers as it pertains to the application of digital assets, especially non-fungible tokens (NFT) in-game items (i.e., virtual skins and items). The purpose of this study was to explore the relationships among innovation adoption, esports identification, and purchase intentions. With a sample of 309 esports gamers, confirmatory factor analysis and structural equation modeling were performed to test the measurement and hypothesized paths using R-Studio. The results revealed that the innovation adoption of digital assets had a significant impact on purchase intentions. In addition, esports identification was positively associated with purchase intentions, and the moderating effect of esports identification was identified. This novelty of digital assets such as NFTs and their increasing popularity in digital culture will continue to shift public perceptions of digital assets in esports industries. This study has originality and value in that it sheds light on the impact of the adoption behavior of esports consumers in relation to NFT-based in-game items.
Open access
Digital Games and Media
Consumer Behavior in Brand Consumption and Identification
A proof-carrying trust framework where every EQBSL trust claim ships with a zero-knowledge validity certificate verifiable by any third party without re-running the computation. Establishes the formal link between evidence-based subjective logic opinions and zero-knowledge proof systems, enabling trustless trust attestation in decentralised networks.