Setchain has been proposed to increase blockchain scalability by relaxing the strict total order requirement among transactions. Setchain organizes elements into a sequence of sets, referred to as epochs, so that elements within each epoch are unordered. In this paper, we propose and evaluate three distinct Setchain algorithms, that leverage an underlying block-based ledger. Vanilla is a basic implementation that serves as a reference point. Compresschain aggregates elements into batches, and compresses these batches before appending them as epochs in the ledger. Hashchain converts batches into fixed-length hashes which are appended as epochs in the ledger. This requires Hashchain to use a distributed service to obtain the batch contents from its hash. To allow light clients to safely interact with only one server, the proposed algorithms maintain, as part of the Setchain, proofs for the epochs. An epoch-proof is the hash of the epoch, cryptographically signed by a server. A client can verify the correctness of an epoch with $f+1$ epoch-proofs (where $f$ is the maximum number of Byzantine servers assumed). All three Setchain algorithms are implemented on top of the CometBFT blockchain application platform. We conducted performance evaluations across various configurations, using clusters of four, seven, and ten servers. Our results show that the Setchain algorithms reach orders of magnitude higher throughput than the underlying blockchain, and achieve finality with latency below 4 seconds.
With the widespread adoption of cryptocurrencies, cryptojacking has become a significant security threat to crypto wallet users. This paper presents a front-end prototype of an AI-powered security dashboard, namely, CryptoGuard. Developed through a user-centered design process, the prototype was constructed as a high-fidelity, click-through model from Figma mockups to simulate key user interactions. It is designed to assist users in monitoring their login and transaction activity, identifying any suspicious behavior, and enabling them to take action directly within the wallet interface. The dashboard is designed for a general audience, prioritizing an intuitive user experience for non-technical individuals. Although its AI functionality is conceptual, the prototype demonstrates features like visual alerts and reporting. This work is positioned explicitly as a design concept, bridging cryptojacking detection research with human-centered interface design. This paper also demonstrates how usability heuristics can directly inform a tool's ability to support rapid and confident decision-making under real-world threats. This paper argues that practical security tools require not only robust backend functionality but also a user-centric design that communicates risk and empowers users to take meaningful action.
Predicting cryptocurrency returns is notoriously difficult: price movements are driven by a fast-shifting blend of on-chain activity, news flow, and social sentiment, while labeled training data are scarce and expensive. In this paper, we present Meta-RL-Crypto, a unified transformer-based architecture that unifies meta-learning and reinforcement learning (RL) to create a fully self-improving trading agent. Starting from a vanilla instruction-tuned LLM, the agent iteratively alternates between three roles-actor, judge, and meta-judge-in a closed-loop architecture. This learning process requires no additional human supervision. It can leverage multimodal market inputs and internal preference feedback. The agent in the system continuously refines both the trading policy and evaluation criteria. Experiments across diverse market regimes demonstrate that Meta-RL-Crypto shows good performance on the technical indicators of the real market and outperforming other LLM-based baselines.
Large language models (LLMs)-empowered autonomous agents are transforming both digital and physical environments by enabling adaptive, multi-agent collaboration. While these agents offer significant opportunities across domains such as finance, healthcare, and smart manufacturing, their unpredictable behaviors and heterogeneous capabilities pose substantial governance and accountability challenges. In this paper, we propose a blockchain-enabled layered architecture for regulatory agent collaboration, comprising an agent layer, a blockchain data layer, and a regulatory application layer. Within this framework, we design three key modules: (i) an agent behavior tracing and arbitration module for automated accountability, (ii) a dynamic reputation evaluation module for trust assessment in collaborative scenarios, and (iii) a malicious behavior forecasting module for early detection of adversarial activities. Our approach establishes a systematic foundation for trustworthy, resilient, and scalable regulatory mechanisms in large-scale agent ecosystems. Finally, we discuss the future research directions for blockchain-enabled regulatory frameworks in multi-agent systems.
This study proposes a hybrid deep learning model for forecasting the price of Bitcoin, as the digital currency is known to exhibit frequent fluctuations. The models used are the Variational Mode Decomposition (VMD) and the Long Short-Term Memory (LSTM) network. First, VMD is used to decompose the original Bitcoin price series into Intrinsic Mode Functions (IMFs). Each IMF is then modeled using an LSTM network to capture temporal patterns more effectively. The individual forecasts from the IMFs are aggregated to produce the final prediction of the original Bitcoin Price Series. To determine the prediction power of the proposed hybrid model, a comparative analysis was conducted against the standard LSTM. The results confirmed that the hybrid VMD+LSTM model outperforms the standard LSTM across all the evaluation metrics, including RMSE, MAE and R2 and also provides a reliable 30-day forecast.
The major challenges faced by confectionery supply chain are lack of information, traceability, managing the ownership of goods across supply chains, inability to track vendors in real-time. Blockchain and Internet of Things (IoT) in Industry 4.0 era help organisations to overcome these challenges by guaranteeing authentic information, real-time visibility, and transparency across the supply chain management. The extant literature has revealed that blockchain and IoT technologies are in their early stages of information management for the supply chain. This study explores the potential opportunities available with Blockchain and IoT in the confectionery supply chain. Utilising the inputs from the survey and interviews, the article identifies to present the gaps in the supply chain and proposes a new blockchain and IoT-based architecture of the food safety system for the confectionery supply chain. Typical blockchain architecture is designed for a food safety system, and the technical specifications required to implement blockchain are evaluated. Further, blockchain-assisted distribution information management is proposed to bring more visibility to the shipment of goods. Implications of deploying blockchain and IoT in the supply chain from a management perspective are discussed. The study distinguishes itself from existing literature in two ways: first, by introducing a tailored blockchain and IoT architecture specifically designed for food safety in the confectionery supply chain; and second, by demonstrating its practical implications in addressing real-time traceability, transparency, and operational efficiency in line with Industry 4.0 goals. This integrated approach helps organisations make informed decisions, reduce supply chain risks, and improve regulatory compliance across the confectionery value chain.
Blockchain has been increasingly adopted in various sectors to support transparent and tamper-proof data management. In the academic world, however, reputation systems remain centralized and often fail to represent the true contributions of researchers. Previous innovations such as Dynamic Smart Contracts (DSC) have enabled more flexible interaction models on blockchain, allowing logic and reward schemes to be updated without redeploying contracts. Building on this foundation, this paper introduces a blockchain-based reputation and incentive system tailored for the Decentralized Science (DeSci) ecosystem. The system records academic contributions such as publications, peer reviews, and experimental data into smart contracts that dynamically compute and update reputation scores. Each interaction is validated and permanently stored on-chain, enabling traceable, contribution-based recognition independent of centralized academic institutions. A series of tests conducted on the Ethereum testnet demonstrate that the system operates reliably, supports dynamic rule updates, and effectively tracks contribution-based reputation. This approach enhances transparency and fairness in scientific evaluation, strengthens community-driven validation, and supports the broader vision of DeSci by aligning incentives with openness, accountability, and verifiability.
The article reveals the role and importance of investments in the socio-economic development of the country. The role and main aspects of investments in the modernization and development of the economy in the context of modern globalization and technological changes are described. The volumes and growth rates of investments in fixed assets for 2020-2024 are analyzed. The share of investments in fixed assets from centralized and decentralized sources of financing in 2020-2024 is studied. The main problems that hinder the attraction and effective use of investments in the economy are identified. The impact of investments in fixed assets on the volume of gross domestic product was determined using correlation methods. Based on the results of the study, important proposals and recommendations were developed to increase the attractiveness of the investment environment in Uzbekistan and actively attract investments.
R. Uma Mageswari, K. Nallarasu, L. Remegius Praveen Sahayaraj, A. A. Abd El-Aziz
Blockchain-driven Federated Learning (BFL) represents an intriguing intersection of two cutting-edge technologies: blockchain and federated learning. A form of distributed machine learning technique known as Federated Learning (FL) aims to preserve the privacy of user data. FL supports privacy preservation, decentralization, and collaborative learning by the means of retaining user data on local devices, training the models without sharing raw data, minimizing the danger of leakage of user data, and avoiding the need for centralized data storage. Beyond these attractive features held by FL, arduous challenges like ensuring secure model aggregation and communication, failure of single points, vulnerability faced by centralized parameter servers, minimal client participation due to lack of motivation, and incentives lacking are encountered. To provide a solution for these obstructions, an innovative idea is to integrate FL with blockchain, which is another decentralized cutting-edge technology. This collaboration leads to a much more robust BFL. FL can be enhanced through blockchain via data provenance where blockchain records data origins as well as model updates by using consensus mechanisms. The consensus mechanisms here ensure the decentralized model integrity, and then the Smart Contracts ensure the automated reward distribution to incentivize participation. FL and blockchain technology use cases are mostly involved in sectors like healthcare, finance, transportation, smart cities, etc. independently. These two core technologies, FL and blockchain, are constructively combined to achieve inviolable higher-end applications, which promise minimized data leakage risk in collaborative data sharing.
The article examines the economic and organizational efficiency of implementing smart contracts based on blockchain technology in the public procurement system of the construction sector of the Russian Federation and St. Petersburg. Relevance research conditioned by the need to increase transparency, reduce transaction and administrative costs, and speed up procurement procedures in the context of large-scale public investment and limited budget resources. The paper develops a methodology for quantitatively assessing the economic effect of using smart contracts, including an analysis of direct savings in budget funds, reduced procurement processing time, and increased capital turnover. Based on official statistics and economic and mathematical modeling, it is shown that the introduction of smart contracts can reduce costs by 10% of the total volume of purchases, which is equivalent to savings of about 550 billion rubles for the Russian Federation and 68.2 billion rubles for St. Petersburg. Additional savings are achieved by reducing the average procurement processing time from 15 to 10 days, which leads to a decrease in administrative costs by 8.15 billion rubles and 1.13 billion rubles, respectively. A comprehensive assessment of the total economic effect confirms the high feasibility of digitalizing procurement procedures using smart contracts, which can become the basis for further transformation of the public finance management system and increasing the efficiency of using budget funds in the construction industry.
With the gradual shift towards the use of electric vehicles (EV), electricity demand is expected to increase especially in energy communities. Therefore, it is important to investigate how energy is generated as the provenance of electricity supply is directly linked to climate change. There are only a few studies that investigated the internet of energy and energy provenance, but this area of research is important to prevent the rebound effect of CO2 emission due to the lack of a transparent approach that verifies the source of electricity consumed for charging EVs. The energy system is a complex network, which results in difficulty verifying the source of electricity as related to the generation of energy. Identifying the provenance of electricity is challenging since electricity is a non-physical element. Moreover, the volatility of a Renewable Energy Source (RES), such as solar and wind power farms, in relation to the complex electricity distribution system makes tracking and tracing challenging. Disruptive technologies, such as Distributed Ledger Technologies (DLT), have been previously adopted to trace the end-to-end stages of products. Likewise, artificial intelligence (AI) can be adopted for the optimization, control, dispatching, and management of energy systems. Therefore, this study develops a decentralized intelligent framework enabled by AI-based DLT and smart contracts deployed to accelerate the development of the internet of energy towards energy provenance in energy communities. The framework supports the tracing and tracking of RES type and source consumed for charging EVs. Findings from this study will help to accelerate the production, trading, distribution, sharing, and consumption of RES in energy communities.
Siti Khadijah Abdullah Sanek, Irma Kamarudin, Arina Kamarudin
This article examines the evolving relationship between digital assets, intellectual property (IP), and emerging technologies, with a particular focus on legal implications under European Union (EU) law. Innovations such as digital assets, such as cryptocurrencies, non-fungible tokens (NFTs), and artificial intelligence (AI) generated works, are reshaping concepts of ownership and intellectual property (IP). The article adopts a threefold methodological approach. To assess the adequacy of current legal frameworks, a systematic review highlights key limitations in applying traditional property law to intangible assets like cryptocurrencies and NFTs. The second element analyses the effects of emerging technologies on IP rights and regulatory compliance through an interdisciplinary synthesis of recent research. Lastly, a comparative legal analysis draws on EU and international case studies to identify regulatory gaps and propose policy responses. The findings suggest that while digital assets promote innovation, their decentralised and intangible nature poses challenges to core legal concepts such as exclusivity, attribution, and enforceability. Despite progress in EU digital regulation, inconsistencies persist across jurisdictions. The article concludes that a more harmonised legal framework supported by clearer definitions, the integration of smart contracts, and effective cross-border dispute mechanisms is necessary to ensure that IP law remains effective in the digital economy.
Smart contracts empower many blockchain applications but are exposed to code-level defects. Existing methods do not scale to the evolving code, do not represent complex control and data flows, and lack granular and calibrated evidence. To address the above concerns, we present an across-graph corresponding contract-graph method for vulnerability detection: abstract syntax, control flow, and data flow are fused into a typed, directed contract-graph whose nodes are enriched with pre-code embeddings (GraphCodeBERT or CodeT5+). A Graph Matching Network (GMN) with cross-graph attention compares contract-graphs, aligns homologous sub-graphs associated with vulnerabilities, and supports the interpretation of statements at the level of balance between a broad structural coverage and a discriminative pairwise alignment. The evaluation follows a deployment-oriented protocol with thresholds fixed for validation, multi-seed averaging, and a conservative estimate of sensitivity under low-false-positive budgets. On SmartBugs Wild, the method consistently and markedly exceeds strong rule-based and learning baselines and maintains a higher sensitivity to matching false-positive rates; ablations track the gains to multi-graph fusion, pre-trained encoders, and cross-graph matching, stable through seeds.
Split Learning (SL) is a distributed learning approach that enables resource-constrained clients to collaboratively train deep neural networks (DNNs) by offloading most layers to a central server while keeping in- and output layers on the client-side. This setup enables SL to leverage server computation capacities without sharing data, making it highly effective in resource-constrained environments dealing with sensitive data. However, the distributed nature enables malicious clients to manipulate the training process. By sending poisoned intermediate gradients, they can inject backdoors into the shared DNN. Existing defenses are limited by often focusing on server-side protection and introducing additional overhead for the server. A significant challenge for client-side defenses is enforcing malicious clients to correctly execute the defense algorithm. We present ZORRO, a private, verifiable, and robust SL defense scheme. Through our novel design and application of interactive zero-knowledge proofs (ZKPs), clients prove their correct execution of a client-located defense algorithm, resulting in proofs of computational integrity attesting to the benign nature of locally trained DNN portions. Leveraging the frequency representation of model partitions enables ZORRO to conduct an in-depth inspection of the locally trained models in an untrusted environment, ensuring that each client forwards a benign checkpoint to its succeeding client. In our extensive evaluation, covering different model architectures as well as various attack strategies and data scenarios, we show ZORRO's effectiveness, as it reduces the attack success rate to less than 6\% while causing even for models storing \numprint{1000000} parameters on the client-side an overhead of less than 10 seconds.
Purpose Many tourism organizations are exploring non-fungible tokens (NFTs) for destination branding amid the metaverse and transformative technologies. However, limited literature explores the specific influence mechanisms of NFTs on tourism marketing, particularly about the development of destination brand love from a consumer perspective. Design/methodology/approach This research introduces a conceptual model integrating four gratifications (informativeness, entertainment, interpersonal utility and incentives), attitude towards NFTs, cultural contact, destination image and destination brand love. The model was tested using partial least squares structural equation modelling (PLS-SEM) with data collected from 383 Chinese NFT users, taking Dunhuang, China, as the research context. Findings The study finds that the four gratifications positively influence consumers’ attitude towards NFTs. Moreover, a positive attitude towards NFTs enhances both the destination image and cultural contact, which, in turn, fosters the development of destination brand love. Originality/value This research contributes to the literature on NFT marketing and destination branding by offering empirical evidence of how NFTs can be leveraged to strengthen emotional ties between consumers and destinations. The findings provide practical insights for tourism marketers seeking to use NFTs to build sustainable relationships with tourists.
Diverse Aspects of Tourism Research
Digital Marketing and Social Media
Consumer Behavior in Brand Consumption and Identification
Blockchain technology has driven the development of Decentralized Applications (DApps) in areas such as decentralized finance. However, as application scenarios become more complex, the limitations of computational resources and costs gradually lead to insufficient performance. Large Language Models (LLMs), as a promising technology, have the potential to enhance blockchain’s capabilities in complex task governance. However, due to factors such as consensus mechanisms, it is challenging to directly integrate them with blockchain. To address this issue, this article proposes and implements a general framework for integrating LLMs with blockchain data, C-LLM, which successfully overcomes interoperability barriers between the two. By combining semantic relevance evaluation and truth discovery techniques, this article presents an innovative data aggregation method, SenteTruth, which effectively improves the correctness and credibility of data generated by LLMs. To validate the framework’s effectiveness, we construct a dataset containing three types of questions, covering Q&A records between 10 oracle nodes and 5 LLM models. Experimental results show that, in the presence of 40% malicious nodes, the proposed method improves data correctness by an average of 17.74% compared with the optimal baseline. This research not only provides an innovative solution for the intelligent application of smart contracts but also demonstrates the potential for deep integration of LLMs and blockchain, driving the development of smarter and more complex application scenarios for smart contracts.
Decentralized networks have transcended the confines of Blockchain technology, permeating various spheres of our digital landscape. This chapter explores the profound impact and evolutionary trajectory of decentralized networks beyond Blockchain, elucidating their transformative potential and emerging trends. In addition to monetary exchanges, distributed systems are disrupting workflows of collecting and sharing data, while promoting openness, safeguards, and ownership in the age of data credibility and sovereignty crises. These networks allow Distributed File Systems that store data encrypted and in pieces in numerous nodes. Starting from ride-sharing services to peer-to-peer accommodation, such networks build reliability, effectiveness, and fair distribution of values among the nodes. In the sphere of management and cooperation, distributed systems provide a clear process of decision-making and allow to work together. Through the use of Blockchain and smart contracts, Decentralized autonomous organizations (DAOs) facilitate organisations that are self-governed through collective participation and control of assets and other organisational resources and activities. Also, decentralised networks are creating intrinsic dynamics in identity management where people own their digital identities. By means of self-sovereign identity solutions, the management and exchange of personal data are protected from third-party service providers, thus eliminating such unfavorable consequences as identity theft and surveillance. Some of the general problems being faced by decentralized networks include scale, how different parts of a network work together, and legal concerns. Though the progression of the centralization point can allow monopolization, creation of trust, and stimulation of the demand across various market sectors in terms of networks, the promise of decentralized networks in terms of democratization of access, creation of trust, and stimulation of various domains is unparalleled. Thus, it will be crucial to foster cooperation, prototyping, and better definition of regulatory requirements, which will be the defining decentralised future of the Blockchain technology era.
Abstract This article focuses on the Network State movement as embodying the venture capital (VC) logic of exit. Exit constitutes both a strategy for lucrative returns and an ideology seeking out new territories for financial and technological speculation. This movement has emerged around Balaji Srinivasan and the technologies of Web3 that encode the imperatives of exit. In the construction of liberated zones for the Network State, VC operates through a territorial logic, under the leadership of the founder‐philosopher and with the affordances of the American state. These logics evince the discursive power at the heart of the political economy of VC. The desires of the VC class shape “future social necessity” (Howard 2024; Finance and Society 10) and are “imprinted” (Cooiman 2024; Environment and Planning A 56) upon the social and technological networks of the Network State. The valorisation through exit seeks to produce “hyperstitious” (Lynch and Muñoz‐Viso 2023; Progress in Human Geography 48) value creation in which VC is the fount of civilisation.
Privacy-preserving blockchain systems are essential for protecting transaction data, yet they must also provide auditability that enables auditors to recover participant identities and transaction amounts when warranted. Existing designs often compromise the independence of auditing and transactions, introducing extra interactions that undermine usability and scalability. Moreover, many auditable solutions depend on auditors serving as validators or recording nodes, which introduces risks to both data security and system reliability. To overcome these challenges, we propose SilentLedger, a privacy-preserving transaction system with auditing and complete non-interactivity. To support public verification of authorization, we introduce a renewable anonymous certificate scheme with formal semantics and a rigorous security model. SilentLedger further employs traceable transaction mechanisms constructed from established cryptographic primitives, enabling users to transact without interaction while allowing auditors to audit solely from on-chain data. We formally prove security properties including authenticity, anonymity, confidentiality, and soundness, provide a concrete instantiation, and evaluate performance under a standard 2-2 transaction model. Our implementation and benchmarks demonstrate that SilentLedger achieves superior performance compared with state-of-the-art solutions.
Scammers are increasingly harnessing generative AI(GenAI) technologies to produce convincing phishing content at scale, amplifying financial fraud and undermining public trust. While conventional defenses, such as detection algorithms, user training, and reactive takedown efforts remain important, they often fall short in dismantling the infrastructure scammers depend on, including mule bank accounts and cryptocurrency wallets. To bridge this gap, a proactive and emerging strategy involves using conversational honeypots to engage scammers and extract actionable threat intelligence. This paper presents the first large-scale, real-world evaluation of a scambaiting system powered by large language models (LLMs). Over a five-month deployment, the system initiated over 2,600 engagements with actual scammers, resulting in a dataset of more than 18,700 messages. It achieved an Information Disclosure Rate (IDR) of approximately 32%, successfully extracting sensitive financial information such as mule accounts. Additionally, the system maintained a Human Acceptance Rate (HAR) of around 70%, indicating strong alignment between LLM-generated responses and human operator preferences. Alongside these successes, our analysis reveals key operational challenges. In particular, the system struggled with engagement takeoff: only 48.7% of scammers responded to the initial seed message sent by defenders. These findings highlight the need for further refinement and provide actionable insights for advancing the design of automated scambaiting systems.
An Pham Ngoc Nguyen, Marija Bezbradica, Martin Crane
As global financial markets become increasingly interconnected, financial contagion has developed into a major influencer of asset price dynamics. Motivated by this context, our study explores financial contagion both within and between asset communities. We contribute to the literature by examining the contagion phenomenon at the community level rather than among individual assets. Our experiments rely on high-frequency data comprising cryptocurrencies, stocks and US ETFs over the 4-year period from April 2019 to May 2023. Using the Louvain community detection algorithm, Vector Autoregression contagion detection model and Tracy-Widom random matrix theory for noise removal from financial assets, we present three main findings. Firstly, while the magnitude of contagion remains relatively stable over time, contagion density (the percentage of asset pairs exhibiting contagion within a financial system) increases. This suggests that market uncertainty is better characterized by the transmission of shocks more broadly than by the strength of any single spillover. Secondly, there is no significant difference between intra- and inter-community contagion, indicating that contagion is a system-wide phenomenon rather than being confined to specific asset groups. Lastly, certain communities themselves, especially those dominated by Information Technology assets, tend to act as major contagion transmitters in the financial network over the examined period, spreading shocks with high densities to many other communities. Our findings suggest that traditional risk management strategies such as portfolio diversification through investing in low-correlated assets or different types of investment vehicle might be insufficient due to widespread contagion.