Sorina Geanina Stănescu, Constantin Aurelian Ionescu, Maria Cristina Ștefan, Luiza Ionescu · 6 authors
The agri-food sector is currently undergoing a significant digital transformation, driven by climate change, frequent supply chain disruptions, and increasing demand for transparency and food safety. This article, based on a systematic review of 113 recent studies (in line with the PRISMA guidelines), delves into how emerging digital technologies, particularly blockchain, are reshaping agri-food supply chains towards sustainability, a circular economy, and complete product traceability from production to the final consumer. The paper identifies the main enabling factors, barriers, and implementation models of blockchain and other technologies associated with Industry 4.0 (IoT, artificial intelligence, smart contracts), highlighting their role in increasing the resilience of supply chains, optimising quality control, and sustainable resource management. A key contribution of the study is the introduction of the CTSAF (Converging Technologies for Sustainable Agri-Food Chains) conceptual framework, which provides practical implications for policymakers and organisations, enabling them to make informed decisions. The results also provide valuable insights for future research, supporting the transition to a more transparent, resilient, and sustainable global food system.
I‐Fang Su, Shun-Ming Wang, Yu-Chi Chung, Yi-Hsien Tsai
Abstract In this research, we introduce an advanced approach for the detection of smart contract vulnerabilities leveraging Large Language Models (LLMs). Smart contracts are pivotal in the ecosystem of decentralized finance (DeFi), functioning as automated protocols for data management and transaction execution. The foundation of numerous blockchain-based applications lies in smart contract technology. Nevertheless, these contracts’ code vulnerabilities can become targets for malicious exploitation, leading to substantial financial damages, exemplified by the 2016 Dao smart contract incident which incurred a loss of 55 million USD. In response to such challenges, detection mechanisms for smart contract vulnerabilities have been devised, drawing upon conventional static analysis, fuzzy testing, and machine learning methodologies. Owing to the swift progression of LLMs, such as GPT, a broad spectrum of entities has adopted these models for routine operational management. By recognizing LLMs’ inherent capability to comprehend programming code, we investigate their aptitude for identifying smart contract vulnerabilities. We have integrated prompt engineering techniques, including the Chain of Thought (CoT), Plan-and-Solve, and few-shot learning, to augment the LLMs’ vulnerability detection efficacy. Furthermore, a sequence of empirical studies has been orchestrated to validate the effectiveness of our proposed prompt engineering strategies against diverse smart contract vulnerabilities.
Abstract In digital higher education, digital transformation is mandatory. Blockchain technology, with its unique features of distributed ledgers, consensus mechanisms, smart contracts, and traceability, provides a new perspective for digital educational resource platforms. In this study, a blockchain-based design was proposed for an open service platform for digital education resources in universities. The platform addresses challenges such as limited openness, complex resource copyright certification, and difficulty in effectively ensuring resource security and quality. The platform offers resource publishing, resource trading, operation management, and interface management ensuring data security using the distributed ledger of blockchain. Consensus mechanisms and smart contracts are used to ensure fairness and efficiency in platform operation and automate resource transactions. Traceability is utilized to ensure the certification and protection of resource copyrights.
The integrity, coupled with the transparency of electoral systems, is vital for the existence of a democratic society if that society is to function well. Often, conventional electronic voting mechanisms are criticized for their security vulnerabilities, with a lack of transparency, together with limited public trust. Blockchain technology has come about to be a possible enabler for trustless and immutable systems. However, such a standard, privacy-preserving, verifiable voting model remains elusive. This work seeks to fill this void with the use of a blockchain e-voting system that uses QR codes to validate voters, cryptographically ensures integrity with the EFFT-SWIFFT hash, and also handles ballots through smart contracts. A feature matrix together with a visual chart was used in a systematic literature review of 28 peer-reviewed papers to analyze and compare authentication methods, transparency techniques, consensus mechanisms, and scalability solutions. Though the analysis reveals that entities greatly underutilize advanced cryptographic primitives such as zero-knowledge proofs and post-quantum hashing, these primitives potentially improve privacy and also verifiability. Present in the proposed model is a multi-layered architecture. Also, the model can offer a secure as well as transparent solution for addressing these gaps. Blockchain-based e-voting can increase trust, reduce fraud, and broaden democratic participation, but it requires real-world validation through pilot projects and usability testing.
Open access
Internet Traffic Analysis and Secure E-voting
Advanced Steganography and Watermarking Techniques
The rapid digitization of financial services has resulted in a staggering increase in sophisticated fraud, endangering global economies and damaging public trust. The dynamic nature of current fraud is outpacing classic fraud detection systems, which frequently rely on static, rule-based methods. This study reveals a new hybrid framework that pairs distributed ledger technology for immutable transaction avoidance with Machine Learning (ML) for real-time fraud detection. The fundamental driving force is to address the inherent shortcomings of centralized systems, as well as the lack of an unchangeable audit trail in ML-only solutions. Using a range of classification algorithms, our methodology entails creating separate machine learning pathways for three important financial domains: credit card, UPI, and loan applications. A fraud verdict is subsequently produced using the top-performing model for each domain, which is determined by a thorough analysis of metrics. Through a smart contract, this decision is safely and irrevocably documented on a private blockchain. This study shows how a strong security architecture may be produced by fusing the decentralized trust and immutability of blockchain technology with the predictive performance of machine learning. The findings demonstrate that this integrated approach strengthens the integrity and dependability of digital financial transactions by achieving high performance in fraud detection as well as creating a transparent and impenetrable record.
Samuel Ejiro Uwhejevwe-Togbolo, Ajueyitse Martins Otuedon, Jacob Martins Sigah, Theresa Nkechi Ofor · 6 authors
The study examined optimized design of digital ledger posting based on virtual reality technology. The convention of Virtual Reality (VR) and Distributed Ledger Technology (DLT) is a revolutionary change in the design and interaction of digital systems. The study finds that there are several design principles and technological considerations that were critical to the implementation of VR-enhanced digital ledger systems to succeed by a thorough examination of the existing literature and case studies. This research design is a qualitative study and will involve an exploratory approach to research the topic of Virtual Reality (VR) implementation with digital ledger posting systems. The study mainly includes a literature review and case study analysis of the existing literature and case studies in order to draw best practice and practical information. Case studies are also used as one of the main methodological instruments to provide the real-life examples of VR in financial, accounting, and the sphere of supply chains. The research is aimed at gaining insight into the way VR would maximize digital ledger posting, and not the quantification of predetermined variables. It was revealed in the study that VR provides users with many chances to perceive multidimensional datasets in a way that is not possible in a traditional 2D interface. The study concluded that the ongoing development of the digital economy, these systems will be able to increase the levels of transparency, minimize errors, and promote more efficient and cooperative and resilient organizational processes.
Energy consumption in Federated Learning (FL) has emerged as a major challenge due to the growing deployment of intelligent edge devices and the increasing complexity of machine learning models. FL enables collaborative model training across decentralized data sources without transferring raw data, thereby reducing communication overhead and enhancing data privacy by design. These features make FL particularly suitable for applications in healthcare, finance, and industrial IoT, where data sensitivity and resource constraints are critical. This paper provides a comprehensive survey of energy-efficient techniques in FL, classifying them into four main categories: model compression (including pruning and quantization), communication optimization, client selection, and hardware-aware strategies. The paper presents a unified taxonomy and discusses the strengths, limitations, and trade-offs of each approach. A comparative evaluation framework is introduced to assess energy savings, model accuracy, communication cost, and deployment feasibility. By analyzing current trends and open challenges, this review offers valuable guidance for researchers and practitioners in the development of scalable, energy-aware, and privacy-preserving federated learning systems.
本文提出了面向 AI 与 Web3 时代的全新治理共识——“爱的证明(Proof of Love, PoL)”。作者认为,爱——作为共情、互惠与创造性合作的伦理力量——是当代技术文明所缺失的核心原则。论文融合区块链治理、去中心化激励机制与伦理哲学,探讨如何以“爱”作为文明共治的新基石,让人类与 AI 在共享价值创造中实现共生。该研究跨越哲学、经济学与系统设计,旨在重新定义智能、治理与文明的意义。 This paper proposes Proof of Love (PoL) as a new ethical and governance consensus for the age of AI and Web3. It argues that love—understood as empathy, reciprocity, and creative cooperation—is the missing principle in current technological civilization. By integrating blockchain governance, decentralized incentives, and ethical philosophy, the study outlines how PoL can serve as a foundation for a new “Love-based Civilization,” where human and AI co-govern through shared value creation. The work bridges philosophy, economics, and systems design, aiming to redefine the meaning of intelligence, governance, and civilization itself.
Hasan Akgul, Daniel Borg, Arta Berisha, Amina Rahimova · 6 authors
Large language models are often adapted through parameter efficient fine tuning, but current release practices provide weak assurances about what data were used and how updates were computed. We present Verifiable Fine Tuning, a protocol and system that produces succinct zero knowledge proofs that a released model was obtained from a public initialization under a declared training program and an auditable dataset commitment. The approach combines five elements. First, commitments that bind data sources, preprocessing, licenses, and per epoch quota counters to a manifest. Second, a verifiable sampler that supports public replayable and private index hiding batch selection. Third, update circuits restricted to parameter efficient fine tuning that enforce AdamW style optimizer semantics and proof friendly approximations with explicit error budgets. Fourth, recursive aggregation that folds per step proofs into per epoch and end to end certificates with millisecond verification. Fifth, provenance binding and optional trusted execution property cards that attest code identity and constants. On English and bilingual instruction mixtures, the method maintains utility within tight budgets while achieving practical proof performance. Policy quotas are enforced with zero violations, and private sampling windows show no measurable index leakage. Federated experiments demonstrate that the system composes with probabilistic audits and bandwidth constraints. These results indicate that end to end verifiable fine tuning is feasible today for real parameter efficient pipelines, closing a critical trust gap for regulated and decentralized deployments.
This paper presents DESTinE Block, a blockchain-based data storage framework designed for power systems and optimized for resource-constrained environments, including grid-edge devices such as single-board computers. The proposed architecture leverages the InterPlanetary File System (IPFS) for storing large files while maintaining secure and traceable metadata on a custom blockchain named DESTinE Block. The metadata, comprising the IPFS Content Identifier (CID), uploader identity, administrator verification, and timestamp; is immutably recorded on-chain to ensure authenticity and integrity. DESTinE Block adopts a dual-blockchain abstraction, where the blockchain remains unaware of the IPFS storage layer to enhance security and limit the exposure of sensitive file data. The consensus mechanism is based on Proof of Authority (PoA), where both an administrator and an uploader with distinct cryptographic key pairs are required to create a block collaboratively. Each block contains verified signatures of both parties and is designed to be computationally efficient, enabling deployment on devices like the Raspberry Pi 5. The framework was tested on both an x86-based device and an ARM64-based Raspberry Pi, demonstrating its potential for secure, decentralized logging and measurement storage in smart grid applications. Moreover, DESTinE Block is compared with a similar framework based on Multichain. The results indicate that DESTinE Block provides a promising solution for tamper-evident data retention in distributed power system infrastructure while maintaining minimal hardware requirements.
The convergence of federated learning (FL) and differential privacy (DP) presents a transformative approach to secure predictive modeling in healthcare finance, where safeguarding sensitive patient and financial data is paramount. Traditional centralized machine learning methods often raise significant privacy concerns due to the necessity of aggregating data from multiple institutions. Federated learning mitigates this by enabling decentralized model training across disparate data sources, such as hospitals, insurance firms, and financial institutions, without exposing raw data. However, FL alone remains vulnerable to inference and reconstruction attacks. To enhance security, differential privacy introduces mathematically rigorous noise mechanisms that obfuscate sensitive information while preserving data utility. This paper explores the synergistic integration of DP and FL for building robust, privacy-preserving predictive models tailored to healthcare finance applications, such as fraud detection, insurance risk scoring, billing optimization, and cost forecasting. We discuss the architectural design, privacy-utility trade-offs, and implementation challenges involved, including issues of scalability, model accuracy, regulatory compliance (e.g., HIPAA and GDPR), and communication overhead. Furthermore, real-world use cases and simulation results demonstrate the efficacy of DP-FL frameworks in delivering secure and accurate predictive insights without compromising individual or institutional privacy. The study concludes by highlighting open research directions and recommending best practices for deploying privacy-enhanced federated learning systems in complex, multi-stakeholder healthcare financial ecosystems.
Zero-Knowledge Proofs (ZKP) and Homomorphic Encryption (HE) are crucial for data privacy in applications like cloud, blockchain, and analytics. However, the real-world adoption often faces performance challenges, particularly in the execution of the Number Theoretic Transform (NTT) required for polynomial multiplication involving sizes beyond \(2^{20}\) and large integer widths (e.g., 256 bits). FPGAs offer a promising platform for acceleration, but efficiently implementing large-size NTTs remains difficult due to the limited on-chip resources. The widely adopted four-step NTT method, used to relieve the need for large on-chip memory, introduces performance bottlenecks. Initially, the traditional dataflow NTT architecture may not fully exploit available compute capability, which hinders achieving peak performance. Furthermore, during the matrix transpose phase, the non-sequential access to external High-Bandwidth Memory (HBM) causes inefficiency. To address these challenges, we introduce HiFA, an FPGA-based automatic accelerator framework designed for high-performance and flexible large-size NTT computations. HiFA utilizes a stacked NTT architecture for high parallelism, maximizing HBM throughput. It supports various decomposed polynomial sizes via a novel reordering module. Additionally, a specialized cyclic shuffle module is integrated to optimize data movement during the matrix transpose step, alleviating random memory access delay. HiFA also provides an automatic Design Space Exploration (DSE) framework that identifies optimal four-step decomposition parameters and generates corresponding hardware configurations. Our experiments show that the FPGA implementation of HiFA achieves an average speedup of 2.97× and up to 7.25× improvement in latency over prior state-of-the-art FPGA solutions. Compared to prior GPU-based methods, HiFA achieves an average energy efficiency gain of 2.24×.
This paper introduces a methodology for software vulnerability detection that combines structural and semantic analysis through software metrics and topic modelling. We evaluate the approach using smart contracts as a case study, focusing on their structural properties and the presence of known security vulnerabilities. We identify the most relevant metrics for vulnerability detection, evaluate multiple machine learning classifiers for both binary and multi-label classification, and improve classification performance by integrating topic modelling techniques. Our analysis shows that metrics such as cyclomatic complexity, nesting depth, and function calls are strongly associated with vulnerability presence. Using these metrics, the Random Forest classifier achieved strong performance in binary classification (AUC: 0.982, accuracy: 0.977, F1-score: 0.808) and multi-label classification (AUC: 0.951, accuracy: 0.729, F1-score: 0.839). The addition of topic modelling using Non-Negative Matrix Factorization further improved results, increasing the F1-score to 0.881. The evaluation is conducted on Ethereum smart contracts written in Solidity.
Abstract This study explores the intersection between cryptocurrencies and criminal organizations through a systematic literature review. By examining peer-reviewed academic publications, the research identifies the mechanisms by which criminal groups leverage digital currencies for illicit activities such as money laundering, fraud, and extortion. A structured PRISMA-based methodology was adopted, employing well-defined inclusion, and exclusion criteria to ensure transparency and replicability. The study reveals critical trends in the misuse of cryptocurrencies, the technological challenges faced by regulators, and the limitations of current enforcement frameworks. The originality of this research lies in synthesizing existing academic insights while identifying key gaps in the literature. The findings highlight the urgent need for tailored regulatory responses and greater cross-border cooperation, contributing to both academic understanding and policymaking. The practical implications include recommendations for policy adaptation and future research pathways to mitigate cryptocurrency-enabled criminal behavior.
Nazia Azim, Khair Ul Burria, Muhammad Zain Asghar, Zeeshan Raza · 6 authors
Ethereum blockchain is the market leading platform for decentralized applications and smart contracts that have powered the new age of financial ecosystem. In order to improve security and performance, identify influential nodes, and understand network dynamics on Ethereum it is critical to identify influential nodes in Ethereum. This study explore machine learning techniques for discovery of these nodes using graph based algorithms, centrality measures and clustering methods. It studies the impact of a node in terms of frequency of usage, connectivity and computational power for a node. Finally, this study compare performance of proposed methodology combining supervised learning and graph neural networks to their traditional counterparts and demonstrate approach outperforms existing methods. The study demonstrate that highly influential nodes engage in unique patterns of behavior, which are detectable and categorizable. This study contribute to understanding of the network structure of Ethereum, along with a scalable approach to monitoring and optimising blockchain ecosystems. Moreover the study discuss the implications for network robustness, fraud detection and protocol enhancements, and demonstrate the promise of machine learning for blockchain analytics.
Rodrigo Dutra Garcia, Gowri Ramachandran, Christian Esteve Rothenberg, Daniel Macêdo Batista · 5 authors
The transition to 6G networks is expected to support a broader range of user-centric applications. As these applications expand, Quality of Experience (QoE) has emerged as a key metric for evaluating user satisfaction. However, the use of centralized systems lacks transparency and limits users’ ability to govern their data usage. It also introduces challenges in managing QoE data while preserving privacy. At the same time, verification mechanisms are needed that allow regulators to evaluate compliance without exposing confidential business information. To address this, we propose a decentralized, privacy-preserving QoE system that integrates blockchain with fully homomorphic encryption (FHE). This design enables transparent evaluations between users and service providers by supporting computations directly on encrypted data, ensuring that all information remains protected throughout the process. Users contribute QoE metrics through a decentralized infrastructure and retain control over their data. Regulators can monitor compliance without accessing raw data, and service providers can use encrypted QoE data to perform privacy-preserving computations via smart contracts without relying on a central authority. We developed a proof-of-concept integrating FHE smart contracts compatible with Ethereum Virtual Machine (EVM) blockchains and evaluated their performance using a video streaming dataset. Our results show that encryption and FHE operations consistently occur within milliseconds when tested in a local environment. We also evaluated these operations on a public blockchain testnet. In this setting, our system adds a millisecond-scale delay while supporting privacy-preserving computations directly on encrypted user data.
Tri Gunawan, Muchimah Muchimah, Dewi Nita Utami, Panggih Abdiguno · 5 authors
The digital era is transforming the institution of marriage, as online ceremonies without official registration create an urgent legal crisis involving fasād al-nasab (distortion of lineage), violations of women’s rights, and a lack of legal certainty. This paper aims to reconstruct Islamic marriage law by integrating blockchain-based smart contracts, assessing the validity of al-‘aqd al-raqmī (digital contract) through the lenses of fiqh al-mu‘āmalāt, legal pluralism, and smart legal contract theory. Employing a qualitative-descriptive methodology, primary data were drawn from Islamic legal texts, fatwas, state regulations, and blockchain technical documentation—collected via literature review and document analysis from authoritative sources such as Tarjih Muhammadiyah fatwas, Patricia Pixie (2024), and Blockchain Council reports (2024). Data were analyzed using a tri-theoretical framework—Wahbah al-Zuhaili’s transactional jurisprudence, Sally Falk Moore’s theory of legal pluralism, and Kevin Werbach’s smart legal contract theory—synthesized within the concept of tadākhul al-anṣāṭ al-qānūniyyah (overlapping legal systems). Findings indicate that digital marriage can be ṣaḥīḥ (valid) under Islamic law if it fulfills shurūṭ al-ṣiḥḥah (conditions of validity), and that blockchain can serve as a sijill al-‘adl (register of justice) due to its immutability (lā yuqbal al-ta‘dīl); however, without state recognition, such unions remain ghayr saḥīḥ qānūniyyan (legally invalid). Limitations include reliance on secondary data and the absence of standardized sharī‘ah-compliant digital identity protocols. Future research should pilot-test this model and examine al-mas’ūliyyah al-shar‘iyyah (Islamic legal liability) in cases of coding errors.
This paper investigates performance bottlenecks of consortium blockchains under high-throughput and low-latency requirements, focusing on excessive storage burden on full nodes and redundant computation in transaction validation. Based on consortium blockchain, a novel architecture named Server-Side Core Chain (SSC) is proposed. In this architecture, the core functions of blockchain ledger data storage and smart contract execution are delegated from decentralized consensus nodes to a server cluster jointly managed and trusted by consortium members. The consensus node layer is restructured into a lightweight ``Consensus and Audit Network,” dedicated to transaction ordering and state commitment verification. This paper elaborates on the design principles, operational workflow, and security model of the SSC architecture. Theoretical analysis and prototype experiments demonstrate that the architecture significantly enhances the transaction processing capacity of consortium blockchains (experimental results show a throughput improvement of more than 18 times), greatly reduces the entry barriers and operational costs for member nodes (storage overhead reduced by over 99%), and ensures the verifiability of off-chain computations and data privacy through cryptographic commitments and zero-knowledge proofs [1]. The SSC architecture offers a new solution for deploying consortium blockchains in large-scale applications, including finance, supply chain management, and e-government.
The Blockchain is an emerging technology that is used in various applications for data security and trustworthiness. In the case of a public Blockchain, the data cannot be edited or deleted. In the case of a consortium and private Blockchain, the data can be edited or deleted based on the assigned permission, and the data privacy can be maintained. Blockchain's smart contract provides security to stored data, but it is vulnerable to various security threats. Smart contracts still suffer from different variabilities like distributed denial of service attacks (DDoS), 51% vulnerability attacks, double-spending problems, and mining Pool attacks. The smart contract, run on a Blockchain framework, is the logical contract between two or more anonymous people without involving a third party. Hyperledger and Ethereum are two important frameworks that support the development of smart contracts using Blockchain technology. This paper has tried to analyze the security issues of smart contracts developed on the Ethereum framework. An application of class scheduling management and student attendance management has been designed to validate and generate a smart contract. Received: 31 May 2025 | Revised: 4 August 2025 | Accepted: 29 August 2025 Conflicts of Interest The author declares that he has no conflicts of interest to this work. Data Availability Statement The data used in this article are virtual data to implement and to establish the algorithm. It is available in GitHub at https://github.com/ashisgitup/e-learning-Blockchain.git. Author Contribution Statement Ashis Kumar Samanta: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing — original draft, Writing — review & editing, Visualization, Project administration.
The Blockchain-Based Medical Insurance Transaction System (BMIT) developed in this study addresses key issues in traditional medical insurance—information silos, data tampering, and privacy breaches—through innovative blockchain architectural design and technical infrastructure reconstruction. Built on a consortium blockchain architecture with FISCO BCOS (Financial Blockchain Shenzhen Consortium Blockchain Open Source Platform) as the underlying platform, the system leverages FISCO BCOS’s distributed ledger, granular access control, and efficient consensus algorithms to enable multi-stakeholder on-chain collaboration. Four node roles and data protocols are defined: hospitals (on-chain data providers) generate 3D coordinate hashes of medical data via an algorithmically enhanced Bloom Filter for on-chain certification; patients control data access via blockchain private keys and unique parameters; insurance companies verify eligibility/claims using on-chain Bloom filters; the blockchain network stores encrypted key data (public keys, Bloom filter coordinates, and timestamps) to ensure immutability and traceability. A 3D-enhanced Bloom filter—tailored for on-chain use with user-specific hash functions and key control—stores only 3D coordinates (not raw data), cutting storage costs for 100 records to 1.27 KB and reducing the error rate to near zero (1.77% lower than traditional schemes for 10,000 entries). Three core smart contracts (identity registration, medical information certification, and automated verification) enable the automation of on-chain processes. Performance tests conducted on a 4-node consortium chain indicate a transaction throughput of 736 TPS (Transactions Per Second) and a per-operation latency of 181.7 ms, which meets the requirements of large-scale commercial applications. BMIT’s three-layer design (“underlying blockchain + enhanced Bloom filter + smart contracts”) delivers a balanced, efficient blockchain medical insurance prototype, offering a reusable technical framework for industry digital transformation.
Alexandr Kuznetsov, Олексій Костенко, K.O. Klymenko, Zoriana Hbur · 5 authors
Blockchain-based cryptocurrency markets present unique analytical challenges due to their decentralized nature, continuous operation, and extreme volatility. Traditional price prediction models often struggle with the binary trade execution problem in these markets. This study introduces a confidence-based classification framework that separates directional prediction from execution decisions in cryptocurrency trading. We develop a neural network system that processes multi-scale market data, combining daily macroeconomic indicators with a high-frequency order book microstructure. The model trains exclusively on directional movements (up versus down) and uses prediction confidence levels to determine trade execution. We evaluate the framework across 11 major cryptocurrency pairs over 12 months. Experimental results demonstrate 82.68% direction accuracy on executed trades with 151.11-basis point average net profit per trade at 11.99% market coverage. Order book features dominate predictive importance (81.3% of selected features), validating the critical role of blockchain microstructure data for short-term price prediction. The confidence-based execution strategy achieves superior risk-adjusted returns compared to traditional classification approaches while providing natural risk management capabilities through selective trade execution. These findings contribute to blockchain technology applications in financial markets by demonstrating how a decentralized market microstructure can be leveraged for systematic trading strategies. The methodology offers practical implementation guidelines for cryptocurrency algorithmic trading while advancing the understanding of machine learning applications in blockchain-based financial systems.
Abstract New technologies, such as 3D digital rendering, immersive platforms, non-fungible tokens (NFTs) and AI, are creating new opportunities within the fashion industry, including virtual fashion. This chapter discusses how fashion brands communicate virtual products, by analysing the Neo-Ex campaign from Carlings. The study takes its point of departure in the key questions: What arguments are employed to market the garments, and how do fashion brands create a demand for these novel products, as well as attempting to construct a consumer understanding of what this new phenomenon is. Using a multimodal method and a document study, the study analyses campaign material, films, images, and articles, through the lens of brands as cultural intermediaries. Three themes emerged: virtual fashion as a concept for identity construction that advocates creativity , attitude , futurism , and early adopters , as well as communicating that virtual fashion is sustainability conscious. The main contribution is identifying the following three tactics for communicating virtual fashion: utilising emerging technologies for identity construction; educating the consumer; and using sustainability to validating its existence . While the first two tactics focus on building consumer awareness and acceptance, the third highlights sustainability as a rationale to legitimise virtual products.
Rashed Alnuman, Tayyab Sajid, Wesam Almobaideen, Qusai Hasan
Abstract Blockchain technology has revolutionized digital financial transactions and asset ownership by enabling decentralized and automated operations through smart contracts. Solidity smart contracts, used in the Ethereum blockchain network, facilitate secure and trustless execution of agreements. However, like any code, smart contracts are prone to vulnerabilities. Considering the assets and value of currency these smart contracts handle, their exploitation leads to severe financial losses and loss of operations. Such exploits have resulted in billions of dollars in stolen or locked assets. In this paper, we present an ensemble multilabel classifier model approach for the automated detection of vulnerabilities in Solidity smart contracts using a real smart contract dataset, with a detailed methodological process that includes processing the dataset. The proposed model stack achieves excellent results with F1 scores ranging from 82.0% to 99.9% for each vulnerability dataset. The proposed model is also compared with common static analyzer tools and models proposed in the literature following a similar approach. Moreover, we package the models into a web application, demonstrating deployment and functionality.
Machine Learning (ML) models are increasingly deployed to detect fraudulent activities in Ethereum, where phishing and scamming attacks pose serious security risks. Despite their promise, these models remain susceptible to adversarial manipulations. In this article, we present a comprehensive evaluation of ML-based Ethereum phishing detectors under a spectrum of adversarial perturbations. Our study examines multiple classifiers, including Random Forest, Decision Tree, K-Nearest Neighbors, Graph Neural Networks, and XGBoost, against rule-based, gradient-based, and black-box adversarial attacks. We conduct detailed feature-level analyses to identify transaction attributes most vulnerable to manipulation, and we evaluate the comparative robustness of classifiers under both targeted and untargeted attack scenarios. To strengthen model resilience, we assess mitigation techniques such as adversarial training and randomized smoothing, demonstrating their effectiveness in improving robustness without significant performance degradation.