Alexandra Vultureanu‐Albişi, Costin Bădică, Mirjana Ivanović
The Internet of Things (IoT) paradigm is evolving and the Next-Generation IoT (NG-IoT) ecosystem will incorporate distributed ledger and blockchain technology, AI-adapted components, and intelligent edge solutions that take advantage of edge computing, Artificial Intelligence (AI), networks, and communications. In addition to the low integration of eXplainable Artificial Intelligence (XAI) in the IoT or NG-IoT contexts, the explainability of these systems is rarely evaluated. Due to these limitations, we thoroughly examined the current state of XAI integration with IoT services. We propose a new conceptual framework called eXING-IoT (eXplainability Integrated in the Next Generation IoT) for better NG-IoT systems' explainability integration and evaluation. This includes a list of qualities that future NG-IoT environments should have, thus paving the way for the advancement of NG-IoT beyond the state of the art.
Open access
Explainable Artificial Intelligence (XAI)
Scientific Computing and Data Management
Artificial Intelligence in Healthcare and Education
Sung-eun Heo, Manho Kim, Wijin Kim, Jongseok Choi · 12 authors
Biometric data has the potential to revolutionize health analytics and pharmacology by providing personalized insights into drug efficacy and health trajectories. However, its governance presents significant ethical challenges, particularly around individual ownership and privacy. This study addresses these challenges by proposing a sustainable and ethical framework that integrates biometric data with non-fungible tokens (NFTs). We developed a customized NFT framework with advanced smart contract functionalities that enhance privacy protection and decentralized authentication of biometric data ownership. This approach ensures the secure and ethical management of digital health data while reinforcing individuals' control over their biometric information. By leveraging cryptographic techniques for privacy protection, this framework enhances both the security and efficiency of personal health data management, offering a new perspective on ownership in digital health. Furthermore, a sustainable economic model is proposed to facilitate ethical transactions of tokenized biometric data within the NFT marketplace. The implications of this study extend beyond technology and commerce, offering valuable insights into human behavior in emerging digital economies and contributing to the creation of a more sustainable and equitable digital health ecosystem.
We initiate the study of transaction fee mechanism design for blockchain protocols in which multiple block producers contribute to the production of each block. Our contributions include: - We propose an extensive-form (multi-stage) game model to reason about the game theory of multi-proposer transaction fee mechanisms. - We define the strongly BPIC property to capture the idea that all block producers should be motivated to behave as intended: for every user bid profile, following the intended allocation rule is a Nash equilibrium for block producers that Pareto dominates all other Nash equilibria. - We propose the first-price auction with equal sharing (FPA-EQ) mechanism as an attractive solution to the multi-proposer transaction fee mechanism design problem. We prove that the mechanism is strongly BPIC and guarantees at least a 63.2% fraction of the maximum-possible expected welfare at equilibrium. - We prove that the compromises made by the FPA-EQ mechanism are qualitatively necessary: no strongly BPIC mechanism with non-trivial welfare guarantees can be DSIC, and no strongly BPIC mechanism can guarantee optimal welfare at equilibrium.
Ali Rahimi, Babak H. Khalaj, Mohammad Ali Maddah-Ali
Verifiable computing (VC) has gained prominence in decentralized machine learning systems, where resource-intensive tasks like deep neural network (DNN) inference are offloaded to external participants due to blockchain limitations. This creates a need to verify the correctness of outsourced computations without re-execution. We propose \texttt{Range-Arithmetic}, a novel framework for efficient and verifiable DNN inference that transforms non-arithmetic operations, such as rounding after fixed-point matrix multiplication and ReLU, into arithmetic steps verifiable using sum-check protocols and concatenated range proofs. Our approach avoids the complexity of Boolean encoding, high-degree polynomials, and large lookup tables while remaining compatible with finite-field-based proof systems. Experimental results show that our method not only matches the performance of existing approaches, but also reduces the computational cost of verifying the results, the computational effort required from the untrusted party performing the DNN inference, and the communication overhead between the two sides.
Recent years have witnessed a rapid development of platform economy, as it effectively addresses the trust dilemma between untrusted online buyers and merchants. However, malicious platforms can misuse users' funds and information, causing severe security concerns. Previous research efforts aimed at enhancing security in platform payment systems often sacrificed processing performance, while those focusing on processing efficiency struggled to completely prevent fund and information misuse. In this paper, we introduce SecurePay, a secure, yet performant payment processing system for platform economy. SecurePay is the first payment system that combines permissioned blockchain with central bank digital currency (CBDC) to ensure fund security, information security, and resistance to collusion by intermediaries; it also facilitates counter-party auditing, closed-loop regulation, and enhances operational efficiency for transaction settlement. We develop a full implementation of the proposed SecurePay system, and our experiments conducted on personal devices demonstrate a throughput of 256.4 transactions per second and an average latency of 4.29 seconds, demonstrating a comparable processing efficiency with a centralized system, with a significantly improved security level.
Smart contracts are a key component of the Web 3.0 ecosystem, widely applied in blockchain services and decentralized applications. However, the automated execution feature of smart contracts makes them vulnerable to potential attacks due to inherent flaws, which can lead to severe security risks and financial losses, even threatening the integrity of the entire decentralized finance system. Currently, research on smart contract vulnerabilities has evolved from traditional program analysis methods to deep learning techniques, with the gradual introduction of Large Language Models. However, existing studies mainly focus on vulnerability detection, lacking systematic cause analysis and Vulnerability Repair. To address this gap, we propose LLM-BSCVM, a Large Language Model-based smart contract vulnerability management framework, designed to provide end-to-end vulnerability detection, analysis, repair, and evaluation capabilities for Web 3.0 ecosystem. LLM-BSCVM combines retrieval-augmented generation technology and multi-agent collaboration, introducing a three-stage method of Decompose-Retrieve-Generate. This approach enables smart contract vulnerability management through the collaborative efforts of six intelligent agents, specifically: vulnerability detection, cause analysis, repair suggestion generation, risk assessment, vulnerability repair, and patch evaluation. Experimental results demonstrate that LLM-BSCVM achieves a vulnerability detection accuracy and F1 score exceeding 91\% on benchmark datasets, comparable to the performance of state-of-the-art (SOTA) methods, while reducing the false positive rate from 7.2\% in SOTA methods to 5.1\%, thus enhancing the reliability of vulnerability management. Furthermore, LLM-BSCVM supports continuous security monitoring and governance of smart contracts through a knowledge base hot-swapping dynamic update mechanism.
To meet latency constraints, fog computing takes computational assets to the network edge. Blockchain and reinforcement learning are increasingly being integrated into the Industrial Internet of Things (IIoT) to enhance security and efficiency. This study introduces a Reinforcement Learning-based Resource Scheduling Approach for Blockchain Networks in IIoT. Unlike previous studies, which mainly focus on either blockchain security or resource allocation, our approach integrates reinforcement learning for dynamic resource scheduling, improving efficiency while minimizing latency. The methodology is illustrated through a flowchart. Simulation results validate the effectiveness in multiple scenarios. Future work includes enhancing inter-node communication reliability.
Ameer Ahmed, Asjad Shahzad, Afshan Naseem, Shujaat Ali · 5 authors
Blockchain technology is widely used in almost every domain of life nowadays including healthcare sector. Although there are existing frameworks to govern healthcare data but they have certain limitations in effectiveness of data governance to ensure security and privacy. This study aimed to evaluate effectiveness of health care data governance frameworks, examining security and privacy concerns and limitations within the existing frameworks of ISO Standards, GDPR, and HIPAA. In this study quantitative research approach was followed. A sample of 250 participants from Islamabad, Lahore and Karachi based healthcare experts, IT specialist, blockchain research and developer, administrator was selected. The collected data was analyzed though frequencies and descriptive statistical tests with the help of SPSS. The results revealed un-satisfaction for data governance frameworks, i.e., ISO standards, GDPR, and HIPAA in terms of security concerns, i.e., data encryption, access controls, audit trails, interoperability and standards, smart contracts for compliance, data integrity, regulatory compliance monitoring and privacy concerns, i.e., consent management, anonymization and pseudonymization, data minimization. The participants agreed that there is a need of integration of reliable data governance framework in health care data management. Various personalized governance techniques, targeted security upgrades, and continuous improvement in the specific customized data governance framework has been presented based on the findings of the study. An implementation of blockchain-based systems is recommended in order to ensure and expand the security and privacy of healthcare data management.
Abstract— This research introduces a Blockchain-based Decentralized Application designed to address these issues. Leveraging Ethereum smart contracts and decentralized storage via IPFS, the application ensures secure peer-to-peer communication, immutable data storage, and enhanced transparency. By eliminating the need for centralized intermediaries, the Blockchain-based Decentralized Application empowers users, prioritizes data privacy, and fosters trust. This research introduces a Blockchain-based Decentralized Application designed to address these issues. Leveraging Ethereum smart contracts and decentralized storage via IPFS, the application ensures secure peer-to-peer communication, immutable data storage, and enhanced transparency. By eliminating the need for centralized intermediaries, the Blockchain-based Decentralized Application empowers users, prioritizes data privacy, and fosters trust. Index Terms—Distributed Ledger Technology(DLT), Smart Contracts, InterPlanetary File System(IPFS), Cryptographic Security.
The Internet of Things (IoT) and the development of smart cities provide significant opportunities to enhance security and public safety. However, the increasing complexity of smart city technologies makes them more vulnerable to cyber threats. To address this challenge, this study proposes a Hybrid Detection and Prevention IoT Framework (HDPIoTF) based on blockchain technology. The framework integrates IoT and blockchain to ensure a secure and efficient smart city system through six key stages: IoT sensor development, IoT gateways, blockchain networks, smart contracts, security analytics, and real-time notifications. Using design science as the analytical approach, this study aims to enable real-time monitoring, enhance security, automate responses, and improve interoperability. By leveraging blockchain and IoT, the proposed framework strengthens the protection of critical infrastructure while promoting public well-being.
ABSTRACT The article argues that the European Central Bank's (ECB) regulatory stance toward cryptocurrencies was underpinned by efforts to preserve legitimacy and monetary sovereignty. Triangulating a content analysis on the ECB's policy statements on cryptocurrencies, examination of European macroeconomic data, and price dynamic analysis of Bitcoin from 2014 to 2025, this article traces an evolution in the ECB's regulatory stance toward cryptocurrencies through two phases that inadvertently abetted cryptocurrency adoption: neutralization (2018–2019) and cooptation (2020‐present). From 2018 to 2019, the ECB assumed a hostile stance toward cryptocurrencies, attempting to neutralize its influence. However, its market‐oriented approach to regulation created a lack of controls over cryptocurrencies and a deregulation of payment processing that enabled their expansion. By 2020, the ECB shifted toward tolerance and even cooptation when unsuccessful policy attempts to contain economic precarity amid the pandemic subsequently incentivized household adoption of cryptocurrencies which, still unregulated, gained notoriety as a prospective alternative source of income. During this period, the shift to digital payments, global isomorphic pressures from the SEC's history with cryptocurrencies, and global currency competition against the Euro energized the ECB's aspirations for a digital Euro, for which it sought to coopt cryptocurrency stablecoin designs and popularity to secure public legitimacy.
Cryptocurrencies are decentralized digital currencies secured by blockchain technology. Their growing popularity has a significant impact on traditional financial markets. The purpose of this paper is to examine the impact of cryptocurrency investment on stock financial development. Our empirical evidence is conducted on (30) Canadian firms during the period August 2017- May 2023. The firms are the most important companies in the financial sector. The results of the VECM estimation show a positive and significative impact of Bitcoin Value on each variable assessing stock market development in long term as Market Liquidity, Market Size, Market Capitalization. In short term, this same relationship is observed with Market Size and Market Liquidity. Bitcoin value has a negative impact on Market Capitalization. The Exchange Rate and Unemployment Rate provide a negative and significant relationship towards stock market development in the long-term. In contrast, the short-term relationship results show that Exchange Rate acts positively only on the Market Capitalization. In contrast, Market Liquidity has a positive impact on the Exchange Rate. Moreover, we find the absence of the impact of Unemployment Rate on Stock Financial Development in short term. But, there is a significant and negative incidence of Market Liquidity and Market Size on Unemployment Rate. Our results demonstrate also the positive and significant impact of Unemployment Rate on Bitcoin Value.
A novel electronic voting system (EVS) was developed by integrating blockchain technology and advanced facial recognition to enhance electoral security, transparency, and accessibility.The system integrates a public, permissionless blockchain-specifically the Ethereum platform-to ensure end-to-end transparency and immutability throughout the voting lifecycle.To reinforce identity verification while preserving voter privacy, a facial recognition technology based on the ArcFace algorithm was employed.This biometric approach enables secure, contactless voter authentication, mitigating risks associated with identity fraud and multiple voting attempts.The confluence of blockchain technology and facial recognition in a unified architecture was shown to improve system robustness against tampering, data breaches, and unauthorized access.The proposed system was designed within a rigorous research framework, and its technical implementation was critically assessed in terms of security performance, scalability, user accessibility, and system latency.Furthermore, potential ethical implications and privacy considerations were addressed through the use of decentralized identity management and encrypted biometric data storage.The integration strategy not only enhances the verifiability and auditability of election outcomes but also promotes greater inclusivity by enabling remote participation without compromising system integrity.This study contributes to the evolving field of electronic voting by demonstrating how advanced biometric verification and distributed ledger technologies can be synchronously leveraged to support democratic processes.The findings are expected to inform future deployments of secure, accessible, and transparent electoral platforms, offering practical insights for governments, policymakers, and technology developers aiming to modernize electoral systems in a post-digital era.
Financial market efficiency is significantly influenced by the availability and quality of information, with information asymmetry posing a major barrier to optimal market functioning. This article reviews the role of data science in mitigating information asymmetry and enhancing market efficiency, comparing traditional approaches with modern data-driven methods (e.g., machine learning, NLP, and blockchain). It systematically evaluates traditional approaches used to measure and mitigate information asymmetry and highlights their limitations in accurately capturing complex market dynamics. Traditional approaches such as statistical testing, price behavior analysis, and asset pricing models provide fundamental insights but often fail to capture complex, non-linear market dynamics, such as adverse selection, moral hazard, and asset mispricing, due to their reliance on historical data and linear assumptions. In contrast, data science has revolutionized financial market analysis by combining machine learning, natural language processing (NLP), big data analytics, and blockchain technology to solve information imbalances. It enables real-time analysis of unstructured data, improves predictive modeling, and enhances transparency through sentiment analysis, algorithmic trading, and decentralized ledgers. It concludes that integrating data science with traditional finance theory significantly reduces information gaps, offering policymakers and investors tools to foster fairer, more efficient markets. This bridges theoretical finance with computational innovations, demonstrating how data science addresses longstanding limitations in measuring and improving market efficiency.
This study aims to understand the effect of bitcoin and macroeconomic fundamentals (inflation, exchange rate, BI Rate, money supply, and IDX) on the LQ45 index. The type of data used in this study is secondary data in the form of time series with a research period of January 2018 to December 2022. The data in this study consisted of quantitative data. The data analysis technique applies multiple linear regression which is processed using SPSS version 25. The results state that bitcoin has a negative effect on the LQ45 index, inflation has a positive effect on the LQ45 index, exchange rate has a negative effect on the LQ45 index, BI Rate has a negative effect on the LQ45 index, money supply has a negative effect on the LQ45 index, IDX has a positive effect on the LQ45 index.
By bringing digital artworks to one’s everyday shopping list, NFTs have revolutionized a large group’s understanding of art. In this article, I challenge the stance presented by authors such as Taylor and Sloane that NFTs shift the consumption of objects to the liquid consumption of the non-material by drawing on the aesthetic identity of NFTs as images. Firstly, I analyse the evolution of blockchain products’ function from that of strictly fungible tokens to non-fungible goods. NFTs rely on art’s specificity and acquire their new identity by virtue of entering the art market, which does not happen with ordinary cryptocurrencies. NFTs thus benefit from the contextualization of art as a socially and technologically mediated immaterial process: they emerge as instances of the artification of a non-artistic immaterial experience and become art-like. However, an NFT as a commodity is not pure code; it does have an image attached. This distinguishes NFTs from particular fungible tokens that hold added value due to their unique historical context. I further point out that the fact that NFTs have, as commodities, taken the form of digital images is an expression of a pervasive need for interaction with objects in today’s everyday digital experience. Also, the shift of NFTs from being just “specific” phenomena as art objects to “singular” phenomena construed as authors of the ways in which they will be perceived and understood is discussed. NFTs create their own impact, on the one hand, independently of their often poor artistic or aesthetic quality, and on the other hand, as entirely dependent on their aura as mathematically and ontologically unique, visually perceptible entities.
Blokzincir teknolojisindeki gelişmelerle birlikte, NFT'ler (Non-Fungible Tokens) özellikle sanat, medya, oyun ve koleksiyon gibi alanlarda alternatif birer yatırım aracı olarak giderek daha fazla tanınan özgün dijital varlıklar haline gelmiştir. Kripto para varlıklar ile ile ilgili geniş bir literatür ve düzenleme altyapısı olmasına rağmen NFT konusu yeterince araştırılmış değildir. Bu çalışma, NFT'lerin muhasebeleştirilmesi, değerlemesi ve finansal raporlama süreçlerini hem ulusal hem de uluslararası muhasebe standartları çerçevesinde incelemektedir. Çalışmada, Kamu Gözetimi Kurumu (KGK), IASB, FASB, IAASB, AICPA ve SEC dahil olmak üzere standart yapıcı ve düzenleyici kurumların yaklaşımlarını incelemek amacıyla karşılaştırmalı bir döküman analizi yapılmıştır. Araştırmada, NFT varlıklarını finansal tablolarında raporlayan şirketlerin halka açık finansal raporları döküman analizi kullanılarak incelenmiştir. Bu belgeler, NFT varlıklarının sınıflandırılması, değerlemesi ve dipnot açıklamaları gibi temalara göre incelenmiştir. Alanında önde gelen şirketleri seçmek için amaçsal örnekleme kullanılmış, bu da mevcut raporlama uygulamalarının derinlemesine incelenmesini sağlamıştır. Bulgular, özellikle değerleme yöntemleri, sınıflandırma tercihleri ve muhasebeleştirme gerekliliklerinde önemli uygulama farklılıkları olduğunu ve NFT varlıklara özgü düzenleme eksikliklerini ortaya koymaktadır. Bu sonuçlar, NFT'lerin finansal raporlama sistemlerine entegrasyonuna rehberlik edecek daha net, tutarlı ve kapsamlı muhasebe standartlarına duyulan ihtiyacın altını çizmektedir. Çalışma, muhasebe profesyonelleri ve politika yapıcılar için hem teorik söyleme hem de pratik rehberliğe katkıda bulunmayı amaçlamaktadır.
This study explores the macroeconomic factors driving cryptocurrency price fluctuations, focusing on Bitcoin and Ethereum using the Random Forest machine learning model. It analyzes daily data from 2015 to 2025, incorporating key economic and financial indicators such as the S&P 500, NASDAQ, Treasury yield spreads, Federal Funds Rate, long-term Treasury rates, inflation, Brent oil prices, major exchange rates, and the Volatility Index. Separate predictive models for Bitcoin and Ethereum achieved high accuracy (R² = 0.999 and R² = 0.995, respectively), demonstrating the model's strong forecasting capability. The findings reveal that cryptocurrencies are increasingly influenced by traditional financial markets, particularly US stock indices, highlighting their integration into the global economic system. Bitcoin emerged as relatively stable and less sensitive to monetary policy shifts, functioning as a speculative hedge. In contrast, Ethereum showed greater sensitivity to liquidity and interest rate variables due to its linkage with decentralized finance applications. The study concludes that machine learning methods, combined with traditional macroeconomic indicators, can effectively explain and predict cryptocurrency market behavior, offering a foundation for developing new hybrid economic models tailored to the unique nature of digital assets.
Termin crypto art pojawił się około 2017 roku w środowiskach, które, podążając za przykładem Rare Pepes (niszowych memów przekształconych w kolekcjonerskie karty cyfrowe) oraz projektów takich jak CryptoPunks i CryptoKitties, zaczęły kojarzyć cyfrowe dzieła sztuki z zarejestrowanymi w blockchainie i kontrolowanymi przez inteligentne kontrakty NFT (non-fungible tokens). Termin ten, sformułowany przez analityków danych, artystów, kolekcjonerów, właścicieli platform i badaczy sztuki w „zdecentralizowanym artykule” opublikowanym na łamach czasopisma „Leonardo”, został nieodpowiedzialnie spopularyzowany przez masowe media i czasopisma artystyczne w trakcie mody na NFT, która na początku 2021 roku zawładnęła częścią świata. Posługiwanie się nim jako nazwą gatunku sztuki budzi jednak zastrzeżenia na wielu poziomach. Niniejszy artykuł oferuje pogłębioną analizę samego określenia oraz praktyk nim opisywanych aby wykazać jego nieadekwatność oraz zwrócić uwagę na potencjalne zagrożenia, które może przynieść stosowanie go.
This paper offers an overview of the African art scene, with a special focus on the Kenyan art scene. It will also examine non-fungible tokens (NFTs) as a new marketing tool and platform for artists from Africa, exploring how this technology has diversified artistic practices through experimentation beyond traditional painting and sculpture to video, performance art, installations, photography, and digital media. The paper does not delve into the technicalities of creating NFTs but presents an overview and experiences based on conversations with artists and a gallery owner based in Kenya. It also highlights key marketplaces available for minting and marketing NFTs online, noting those most popularly used by artists from Africa.
We introduce EtherBee, a global dataset integrating detailed Ethereum node metrics, network traffic metadata, and honeypot interaction logs collected from ten geographically diverse vantage points over three months. By correlating node data with granular network sessions and security events, EtherBee provides unique insights into benign and malicious activity, node stability, and network-level threats in the Ethereum peer-to-peer network. A case study shows how client-based optimizations can unintentionally concentrate the network geographically, impacting resilience and censorship resistance. We publicly release EtherBee to promote further investigations into performance, reliability, and security in decentralized networks.
The article discusses certain aspects of blockchain-based art, including the ecology and economy of non-fungible tokens (NFTs), applying projects curated by Ruangrupa for documenta 15, such as Jalar, Dayra, Cheesecoin, and BeeCoin, created within the lumbung Economy. In light of earlier investigations into the ecology of NFTs, the main areas of concern now are the ways in which information about the environmental effects of NFTs presents itself, the monopolization of particular technological mechanisms, the perpetuation of cultural biases, and, from this angle, the belief of artists in the potential for change brought about by the new technology. Given the economic and ecological implications arising from the blockchain projects discussed in this article, I propose to use the notion of ecosophy defined by Félix Guattari as an approach for lumbung Economy interpretation.
Kazokutchi is an artistic project created by So Kanno, Akihiro Kato, and Takemi Watanuki in 2022. The installation combines robot-based digital artificial life forms, NFTs, and a blockchain-based community. Its origins can be traced back to the ideas underlying cellular automata and issues raised by evolutionary robotics. Combined with the ideas of Web3, blockchain, and NFTs, this project unfolds a vision of forthcoming social constructs created by fluid, yet well-organized, communities.
Munir Hussain, Amjad Mehmood, Muhammad Altaf Khan, Jaime Lloret · 5 authors
The recent developments in telecommunication technologies and monitoring devices have brought many changes in modern electronic healthcare systems (EHSs) by improving quality and decreasing healthcare expenses. Despite the benefits, they have privacy and security issues because the communication between patients and service providers takes place generally over public channels. Several user authentication protocols using distributed ledger technology (DLT) have recently been proposed to address these issues in EHSs. However, many are still vulnerable to a single point of failure (SPoF), privacy, and security attacks. Besides, they suffered from high communication and computational costs. Therefore, in this paper, we proposed a user authentication protocol using DLT to avoid these issues. A Burrows-Abadi-Needham (BAN) logic proof method has been used to check the security of the proposed protocol and ensure it achieves the desired security goals. In addition, an informal security analysis has been conducted to verify its important security requirements. A formal security analysis has been performed via the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool and Real-or-Random (ROR) model for further security strength. The results demonstrate that the proposed user authentication protocol is SAFE against all types of Man-in-the-Middle (MitM) attacks, impersonation, replay, and forgery attacks . Finally, performance analysis has been performed and results show that it achieves better performance by consuming 29.63 % and 13.21 % less communication and computational overheads as compared to existing related user authentication protocols. The security and performance analysis make it a more appropriate choice for the EHSs.