For the last decade the rapid movement of the cryptocurrency and Non-Fungible Tokens (NFT) has expanded stratospherically into our conscious, yet marketing has been slow in response to its marketability.Much of this has to do with failing to have a conceptual understanding of the value that cryptocurrencies and NFTs convey to consumers.The purpose of this study is to properly understand the value constructs of these recent additions to commerce and their theoretical underpinnings.With an understanding of the constructs that make up its value, we can hopefully predict its use in future transactions.Cryptocurrencies began as a white paper by a programmer known as Satoshi Nakamoto (Chohan, 2022) who proposed a way of storing information on and off the Internet as a format that is not linked to a centralized data file, but decentralized across multiple platform.He proposed that programmers incentivized to promote the security of the system will provide a structure for data to be stored that is virtually tamper-proof.
Purpose The purpose of this study is to examine the applicability of conventional criminological theories to white-collar offenders involved in cryptocurrency-related market manipulation, specifically pump-and-dump schemes. Using Sutherland’s differential association (DA) framework as a theoretical foundation, this research tests whether demographic and theoretical factors – such as self-control, DA, anomie and strain – predict illegal financial behavior in emerging digital markets. Design/methodology/approach Survey data from a national sample of US adults on the promotion of cryptocurrencies for financial gain were analyzed using t-tests and regression models. Findings The findings of this study suggest that traditional theories of crime, including DA, anomie and strain, lose predictive significance when demographic variables are considered. High-income, male and younger individuals were most likely to engage in cryptocrime in general. Overall, the results of this study highlight the complexity of white-collar criminality in digital spaces and suggest that financial and demographic factors outweigh conventional criminological theories when predicting involvement in cryptocrime. Originality/value This paper considers early notions of white-collar crime against modern online financial crimes. The authors addressed the intersection of criminological theory and modern cryptocurrency crime.
Over the last few years, debates regarding cryptocurrency have become extremely popular among both scholars and ethicists, but most notably, in the area of Islamic Finance. This research will examine how these conversations are evolving, by combining the perspectives developed from a Multi-Expert Forum held by the International Open University (IOU) with responses received from academics and practitioners who work in the area of cryptocurrency, through an open-ended questionnaire. It also provides evidence of a diverse set of views, as opposed to a singular position. There are a number of respondents who see great promise in blockchain and other distributed ledger technologies, primarily because of the potential for decentralized systems, and in developing asset-based crypto models. In contrast, a large number of participants felt a sense of instability from the nature of speculative gharar (speculation) in digital currencies, in addition to the absence of tangible or intrinsic value in digital currency. Additionally, an ethical dilemma arose when discussing digital currency usage in unregulated marketplaces or when engaged in illegal activities. Regardless of their viewpoint on the issue, all of the participants agreed that engaging in digital currencies should be done with extreme care and with caution; and that digital currency engagement should be regulated. A large number of the participants suggested the need for increased regulation of digital currency and greater collaboration among scholars so that the Islamic ruling process regarding digital currency will be both credible and proactive rather than reactive. This research has also demonstrated the need for educational opportunities to enable communities to be aware of the digital economy and have the knowledge and confidence to engage with it while maintaining the ethical and moral principles of Islamic finance.
With the proliferation of mobile devices such as smart phones, tablets, and laptops, consumers are creating and sharing large amounts of multimedia information such as images, videos, and documents. Traditional centralized systems have significant risks on data privacy and often share an unequal share of revenue with the intermediaries, which requires the creation of a secure, decentralized data trading and storage system. This paper presents a blockchain-based edge computing platform that leverages high capacity relay nodes to store encrypted and tamper-proof data to enable quick processing and maximize revenue to the data producers. A Rounding-based Data Placement Algorithm (RDPA) is a useful tool to locate cost-effective relay nodes to minimize storage and transmission costs, and a sustainable consensus mechanism, Proof-of-Data-Trading (PODT), is a synthesis of Proof-of-Work and Proof-of-Stake to safely publish blocks with the minimum use of energy. A set of encrypted data is stored on relay nodes and kept within a certain timeframe, and the metadata and location are recorded on the blockchain, which enables safe and open transactions between producers and consumers. Smart contracts make it possible to automate the data transaction, access, and verification process. A compression method is used to reduce the size of the large data items to make the most out of the relay nodes. The proposed solution will ensure decentralized storage of data in a way that is scalable, secure, and cost-effective which will enable the producers to support massive revenue potential and consumers to reliably retrieve data within a blockchain-based edge architecture.
Este artigo apresenta um esboço estruturado sobre “Eficiência de Capital em AMMs de Faixa Concentrada (Concentrated Liquidity).”. O objetivo é analisar os fundamentos técnicos e econômicos da liquidez concentrada, tomando Uniswap v3 e outros <i>concentrated liquidity market makers</i> (CLMMs) como referência, e discutir suas implicações para o ecossistema Web3. A metodologia baseia‑se em revisão bibliográfica e análise de casos práticos, com foco na comparação entre AMMs de faixa infinita (como Uniswap v2) e AMMs com faixas de preço configuráveis pelos provedores de liquidez. Documentação oficial e materiais de lançamento do Uniswap v3 destacam que a ideia definidora do protocolo é permitir que LPs aloquem liquidez em faixas de preço customizadas, alcançando até cerca de 4.000× mais eficiência de capital em relação ao modelo v2 quando a liquidez é concentrada em uma faixa de 0,10%, com capacidade técnica de ranges tão granulares quanto 0,02%, o que elevaria a eficiência para até 20.000×, ainda que com custos maiores de gas por swap. Análises de segurança e guias educacionais sintetizam que, ao invés de espalhar capital em toda a curva de preços teórica, a liquidez concentrada permite que o capital atue apenas nos intervalos em que as negociações são mais prováveis, aproximando o comportamento do AMM a um <i>order book</i> tradicional e oferecendo melhor <i>price impact</i> com o mesmo capital. Ao mesmo tempo, estudos acadêmicos sobre CLMMs mostram que, embora a eficiência de capital aumente, também se intensificam riscos como <i>impermanent loss</i> (uma vez que a posição pode sair da faixa e ficar 100% em um único ativo) e estratégias adversariais como <i>just‑in‑time liquidity</i>, nas quais LPs estratégicos entram e saem em micro‑janelas para capturar taxas de forma desproporcional. Pesquisas mais amplas em design de AMMs sugerem ainda que abordagens multi‑token e mecanismos de compartilhamento de reservas podem aumentar a liquidez efetivamente ativa em CLMMs em 2,6–5,9×, mitigando efeitos de fragmentação de faixa. Conclui‑se que AMMs de faixa concentrada são um avanço significativo em eficiência de capital e qualidade de execução, mas exigem modelos de risco mais sofisticados, tanto para LPs quanto para protocolos, em comparação com AMMs de faixa infinita.<br>
Este artigo apresenta um esboço estruturado sobre “Dinâmica de Arbitragem entre DEXs e CEXs: Velocidade e Lucratibilidade.”. O objetivo é analisar os fundamentos técnicos e econômicos da arbitragem entre <i>centralized exchanges</i> (CEXs) e <i>decentralized exchanges</i> (DEXs), com foco em como velocidade, latência e estrutura de taxas condicionam a lucratividade dessas estratégias no ecossistema Web3 contemporâneo. Estudos empíricos recentes medem, em detalhe, a economia por trás da arbitragem CEX‑DEX e do MEV associado, mostrando que a maioria dos lucros é capturada por poucos <i>searchers</i> profissionais e que as oportunidades de arbitragem desaparecem em janelas de tempo de frações de segundo. Análises de mercado indicam que, em média, operações bem‑sucedidas de arbitragem CEX‑DEX podem exibir margens brutas em torno de 30–40% sobre o capital efetivamente arriscado por trade, mas que a competição e os pagamentos a <i>block builders</i> comprimem esses retornos ao longo do tempo, caracterizando um mercado altamente monopolizado. Pesquisas teóricas sobre <i>latency arbitrage</i> e sobre o <i>timing</i> ótimo de arbitragem entre CEXs e DEXs modelam explicitamente o efeito da latência de blockchain, da ordem “first‑come, first‑served” e da vantagem de co‑location em data centers, demonstrando que a maior parte do <i>excess return</i> se concentra em janelas de 0,5 a 2 segundos após o surgimento de um desvio de preço entre venues. Trabalhos que estudam a dinâmica de preços em AMMs mostram, ainda, que taxas de swap introduzem uma banda de não‑arbitragem em torno do preço de referência em CEXs, restringindo as oportunidades de arbitragem a desvios acima de um certo limiar e conectando diretamente estrutura de taxas, liquidez e frequência de arbitragem. Conclui‑se que a arbitragem CEX‑DEX é hoje um jogo de alta frequência e forte competição, em que velocidade de execução, acesso a canais privados (MEV‑Boost) e otimização de custos determinam quem captura a maior parte das oportunidades de lucro.<br>
This paper analyses MicroMix, a noncustodial Ethereum mixer that unlinks deposits from withdrawals using browser-side zkSNARKs, a centralised relayer, and on-chain enforcement via Semaphore and Mixer contracts. The study formalises core acceptance conditions—value conservation, nullifier uniqueness, external‑nullifier scoping, and signal binding—and evaluates risks that persist despite sound cryptography, including timing correlation in small anonymity sets, Sybil pool distortion, single‑relayer censorship, ETH payout liveness under gas‑stipend limits, ERC‑20 heterogeneity, circuit–verifier input/order mismatches, and cross‑chain replay. The work proposes concrete mitigations: randomised scheduling and probabilistic batching, multi‑denomination support, decentralised relayer participation with user-paid fallbacks, guarded call patterns with reentrancy protection, SafeERC20 enforcement and token whitelisting, strict public‑input ordering and signal‑to‑field mapping, a fixed mixer-scoped external nullifier, and chain-bound proofs. With these measures, MicroMix can preserve unlinkability while improving liveness and correctness in adversarial environments, advancing practical, privacy‑preserving withdrawals on Ethereum.
Este artigo discute como padrões ERC e mecanismos de comunicação cross‑contract sustentam a interoperabilidade entre contratos inteligentes em Ethereum e EVM‑chains. Padrões de tokens como ERC‑20, ERC‑721, ERC‑777 e ERC‑1155 definem interfaces mínimas para transferência, consulta de saldo e eventos, permitindo que carteiras, DEXs, marketplaces e outras aplicações interajam de forma uniforme com ativos fungíveis e não fungíveis, enquanto extensões multi‑token como o ERC‑1155 combinam características de ERC‑20 e ERC‑721 e suportam transferências em lote. A interoperabilidade é reforçada pelo padrão ERC‑165, que introduz um mecanismo padronizado de detecção de interfaces via supportsInterface(bytes4), permitindo que contratos verifiquem, on‑chain, se outros contratos implementam interfaces específicas antes de interagir com eles. Na prática, a comunicação cross‑contract em Ethereum é implementada via opcodes de chamada (CALL, DELEGATECALL, STATICCALL), que permitem compor funcionalidades entre contratos, mas introduzem riscos de segurança como reentrância cross‑contract, em que contratos mal projetados são reentrados por outros antes de atualizar seu estado, abrindo espaço para exploração. Estudos e guias de segurança catalogam padrões de ataques de reentrância, incluindo reentrância entre funções e entre contratos, e recomendam padrões como checks‑effects‑interactions, uso de mutexes e desenho criterioso de callbacks em tokens com hooks (como ERC‑777) para mitigar esses riscos. Conclui‑se que a interoperabilidade de contratos na Web3 depende tanto de padrões de interface bem definidos (ERCs, ERC‑165) quanto de práticas seguras de comunicação cross‑contract, com impacto direto na liquidez, composabilidade DeFi e governança de protocolos.<br>
Este artigo apresenta um esboço estruturado sobre “Análise de Custo de Deploy em Diferentes EVMs.”. O objetivo é analisar os fundamentos técnicos e econômicos do custo de implantação de contratos inteligentes em Ethereum L1, diversas Layer‑2 (rollups) e outras chains EVM‑compatíveis, discutindo implicações para o ecossistema Web3 e tendências de mercado. A metodologia baseia‑se em revisão bibliográfica e análise de casos práticos, com foco na decomposição do custo de deploy em componentes de gas (execução, armazenamento de código, dados de transação) e em como upgrades recentes – como Cancun/Deneb e a introdução de blobs de dados – alteram a estrutura de custos, especialmente para rollups que publicam dados em L1. Estudos mostram que, enquanto o gas é uma unidade abstrata consistente, o custo econômico por byte de código e por transação varia significativamente entre L1 (onde picos históricos chegaram a dezenas de dólares por transação) e L2s, onde taxas médias frequentemente ficam abaixo de centavos, especialmente após a redução em até 94% do custo por byte de dados com blobs. Ao mesmo tempo, análises de mercado indicam que L2 fees são estruturalmente compostas por uma parcela L1 (custo de dados e liquidação) mais uma parcela L2 (execução local), de modo que mudanças na economia de gas da L1 impactam indiretamente o custo de deploy e operação nas L2s. Conclui‑se que decisões de arquitetura e de escolha de EVM para deploy devem considerar não apenas o custo imediato de gas, mas também a herança de segurança, a volatilidade das taxas e a dependência em upgrades de protocolo que alteram a economia de dados e execução.<br>
Este artigo apresenta um esboço estruturado sobre “Migração de Lógica de Negócio para Layer‑2: Desafios de Compatibilidade.”. O objetivo é analisar os fundamentos técnicos e econômicos relacionados à migração de contratos e aplicações da camada base (L1) para soluções de escalabilidade em Layer‑2, discutindo implicações para o ecossistema Web3 e tendências de mercado. A metodologia baseia‑se em revisão bibliográfica e análise de casos práticos, com foco em diferenças entre soluções L2 EVM‑equivalentes e apenas EVM‑compatíveis, modelos de segurança de bridges, padrões de liquidez multi‑chain e impactos em tooling, UX e governança. Argumenta‑se que a migração não é apenas um “lift‑and‑shift” de bytecode, mas um processo que envolve reavaliação de suposições de segurança, dependências de infraestrutura (oráculos, indexadores, sequencers) e design de incentivos em ambientes com finalização e custos distintos da L1. Casos práticos de migração de protocolos de DeFi e indexação evidenciam trade‑offs entre custo por transação, fragmentação de liquidez e complexidade operacional, bem como a importância de padrões de bridging, mensageria cross‑chain e governança multi‑domínio para manter coerência de lógica e de risco entre instâncias L1/L2. Conclui‑se que migrar lógica de negócio para Layer‑2 exige abordagem incremental e consciente de compatibilidade, com atenção especial à equivalência de EVM, à segurança de rollups e bridges, e à coordenação de liquidez e governança em um ecossistema crescentemente modular e multi‑chain.<br>
Este artigo analisa o design de contratos inteligentes orientados a eventos como estratégia para maximizar a eficiência de indexação off‑chain em protocolos Web3. Em plataformas como Ethereum, eventos são registrados como logs com topics indexados, permitindo que indexadores filtrem rapidamente transações relevantes sem precisar reexecutar o estado completo do contrato, o que torna a indexação por eventos uma abordagem muito mais eficiente do que consultas frequentes ao estado on‑chain via eth_call. Frameworks de indexação como The Graph organizam esses eventos em subgraphs, definidos por manifests que especificam quais contratos e eventos acompanhar, e por esquemas que mapeiam logs para entidades armazenadas em bancos de dados otimizados para consulta via GraphQL. Boas práticas de desenvolvimento orientado a eventos recomendam projetar contratos de modo que toda transição de estado relevante para a aplicação gere um evento completo (com dados suficientes para reconstruir o contexto off‑chain), reduzindo a necessidade de chamadas adicionais à cadeia, o que melhora desempenho de indexing e diminui custos de infraestrutura. Técnicas como uso de entidades imutáveis em subgraphs, cache inteligente de eth_call e cálculo de derivadas off‑chain a partir de eventos (em vez de leitura repetida de storage) podem acelerar indexação e consultas em ordens de grandeza, especialmente em cenários de histórico extenso e baixa densidade de eventos relevantes. Conclui‑se que contratos orientados a eventos, aliados a arquiteturas de indexação especializadas, são fundamentais para tornar dados on‑chain consultáveis em escala, suportando experiências de usuário ricas e composabilidade DeFi com menor custo operacional.<br>
Este artigo analisa o “Uso de Oráculos Computacionais para Execução Off‑Chain.”, com foco em como redes descentralizadas de oráculos (DONs) ampliam as capacidades de smart contracts ao executar lógica complexa fora da blockchain com garantias verificáveis. Oráculos computacionais utilizam redes de nós para realizar qualquer tipo de cálculo fora da cadeia, ancorando o resultado on‑chain por meio de provas criptográficas, assinaturas e acordos de serviço que minimizam a necessidade de confiança em um operador único. Plataformas como Chainlink introduziram capacidades de computação off‑chain generalizada (Functions, Automation 2.0), nas quais nós orquestram execuções off‑chain, geram calldata para apenas a parte necessária da lógica on‑chain e assinam respostas, permitindo automação e processamento intensivo com economia de até 90% de gas em alguns casos. A literatura também explora arquiteturas híbridas que dividem contratos em componentes on‑chain e off‑chain para melhorar escalabilidade e privacidade, bem como mecanismos criptográficos (MPC, provas de conhecimento zero, fraud proofs, reexecução on‑chain) que permitem verificar a correção da computação off‑chain. Estudos recentes sobre redes de oráculos destacam ainda a importância de mecanismos de reputação, testes encobertos de nós e incentivos econômicos para garantir acurácia dos resultados e resiliência da rede. Conclui‑se que oráculos computacionais são um pilar para contratos inteligentes híbridos, permitindo que a Web3 incorpore cálculos intensivos, dados externos e lógica condicional complexa sem perder as garantias de auditabilidade e minimização de confiança da blockchain subjacente.<br>
Bobba Pavan Santosh, Bommeneni Pavan Madhav, Dr. J. R. Jayavelu, Dr. P. Dhivya
Cryptocurrencies have found their way into contemporary financial systems as a significant component of modern-day financial systems because of their decentralized nature, their ease of adoption and uptake. Ether is considered to be one of the most actively traded currencies and its value tends to be highly volatile. It is not easy to forecast the market price trend of Ethereum due to the influence that technical trends, investor behavior, and external factors have over the market. In this project, the researcher will use machine learning to assess the future price direction of Ethereum the following day through the use of Python. The past trends of prices are analyzed and augmented with various technical indicators in order to reflect the market trends and momentum. The best potential machine learning model was selected after training and evaluating many models using Logistic Regression. The results demonstrate that machine learning may be used to provide rational insights into the price movement of Ethereum and to aid in decision-making using these insights.
Artistic content, especially in the digital era, is increasingly susceptible to unauthorized appropriation, imitation, and misuse, often with little to no attribution to original creators. In this paper, an unified AI-assisted visual similarity-detecting system with a blockchain-based provenance-checking base is introduced to prevent art appropriation and securely and in interpretable and real-time apply digital copyright. With the help of a hybrid deep learning framework that consists of Vision Transformer (ViT) and ResNet-based architectures, the system compares the stylistic and structural characteristics of the artworks with those of cosine similarity, Euclidean distance, and style loss measurements. The triplet loss-trained visual encoder allows the model to be useful in distinguishing between direct copies, derivative works, and inspired originals. In an image-based art collection (designed as a WikiArt replica, though not a real WikiArt collection) of more than 10,000 examples, the framework obtained a high classification performance of 94.3, and exact copy performance (precision) of 96.4 and inspired work performance (precision) of 88.6. Artwork metadata and visual fingerprints are hashed with SHA-256 and stored on a permissioned blockchain (Hyperledger Fabric) to provide provenance records and immutable and to enforce copyright permissioned via smart contracts. The proposed system is more accurate in detection and explainable than the available tools like Google Reverse Search and the TinEye. It is also supplemented with Grad-CAM-based visual heatmaps and real-time smart contract execution as the dispute resolution. Further development of the model will focus on extending it to video art and 3D designs, as well as generative AI art (e.g., GANs, Diffusion), and the global copyright databases will be integrated to be universal. This system is a strong move towards the protection of the integrity and rights of Web3 digital artists.
The contemporary education landscape is often marred by escalating costs and centralized pedagogical structures, which collectively create significant barriers to entry for millions of potential learners worldwide. This paper presents \textbf{Skill Link}, a sophisticated decentralized platform designed to democratize skill acquisition through a specialized credit-based barter system. Unlike conventional e-learning platforms that rely on traditional currency transactions, Skill Link enables a frictionless exchange of knowledge by utilizing a virtual credit economy where participants earn and spend "learning credits." To address the critical issue of credential fraud in decentralized environments, the platform integrates Ethereum-based blockchain technology to ensure the absolute immutability and verifiable authenticity of all earned certificates. Key innovations include a multi-tiered course classification system, an automated mock assessment framework with negative marking capabilities, an intelligent context-aware AI assistant powered by advanced language models, and a rigorous verification mechanism for professional social links (LinkedIn, GitHub, Indeed). Developed using the robust Django framework, Python-based Web3 utilities, and a secure PostgreSQL/SQLite back-end, Skill Link provides a highly secure, transparent, and scalable ecosystem for peer-to-peer knowledge sharing, ultimately fostering a global community of experts and lifelong learners. The system's architecture emphasizes data integrity through atomic transactions and cryptographic verification, ensuring a trustless environment for global skill exchange.
Technology has evolved and rapidly changed the way we do most things in our lives: work, go to school, engage in commerce and socialize.The term metaverse has been coined to describe and facilitate the integration of our virtual and physical lives.This "new" merger of social and ecommerce presents a robust market opportunity for brands.While the Web3 space is not yet fully developed, brands must prepare now-or risk being left behind.The fashion industry has been quick to take notice of the opportunities that exist and embrace change.The purpose of this paper is to examine the current role of the fashion industry within the metaverse as well as look ahead to the future.
Diana Moses, Mannem Sirivalli, Mohammed Zaid, Afeefa Aref
Abstract Crowdfunding is a proven way to support startups and social projects; nevertheless, conventional crowdfunding platforms are based on centralized architecture, which brings problems like poor transparency, excessive transaction costs, and low level of control over the usage of money. In this paper, a decentralized crowdfunding solution that will solve the described problems will be presented. Blockchain technology is applied to implement the platform through the usage of Ethereum smart contracts, thus providing safe and transparent transactions. The users will be able to launch a campaign, make payments, and control their transactions via MetaMask extension. Milestone-based funding will be used as well to monitor the spending of money properly. Frontend of the project will be designed using React.js and Web3.js. Experimental results show significant advantages of the proposed solution such as improved transparency, reduced dependence on third parties, and increased trust between people. Keywords:Blockchain,Crowdfunding, Ethereum, Smart Contracts, Web3.js, React,js
Automated matching engines execute millions of orders per session, yet systematic asymmetries in latency, order size, and market access compound into persistent execution disparities that erode participant trust. We formulate provably fair order matching as a Constrained Markov Decision Process and propose CPO-FOAM (Constrained Policy Optimization with Feedback-Optimized Adaptive Margins). An inner loop computes an analytic trust-region step on the Fisher information manifold; a PID-controlled outer loop dynamically tightens safety margins, suppressing the sawtooth oscillations endemic to Lagrangian methods under non-stationary dynamics. Group fairness (demographic parity, equalized odds) enters the CMDP cost vector while individual Lipschitz fairness is enforced deterministically via spectral normalization. We prove BIBO stability and that the integral term drives steady-state violations to zero. On LOBSTER NASDAQ data across six market regimes, CPO-FOAM recovers 95.9% of unconstrained throughput at 2.5% constraint violation frequency; on crypto-asset LOB data under MEV injection it captures 98.4% of the reward envelope at 3.2% CVF. The method scales sub-linearly to M=8 constraints, settles on-chain within one Ethereum block, and yields a 2.1X reward improvement on Safety-Gymnasium, confirming domain-agnostic generalization.
Blockchain and artificial intelligence (AI) are increasingly proposed together for securing intelligent networks, but the literature remains fragmented across ledger design, AI-driven detection, cyber-physical applications, and emerging agentic workflows. This paper synthesizes the area through three reusable contributions: (i) a taxonomy of blockchain-AI security for intelligent networks, (ii) integration patterns for verifiable and adaptive security workflows, and (iii) the Blockchain-AI Security Evaluation Blueprint (BASE), a reporting checklist spanning AI quality, ledger behavior, end-to-end service levels, privacy, energy, and reproducibility. The paper also maps the evidence landscape across IoT, critical infrastructure, smart grids, transportation, and healthcare, showing that the conceptual fit is strong but real-world evidence remains uneven and often prototype-heavy. The synthesis clarifies where blockchain contributes provenance, trust, and auditability, where AI contributes detection, adaptation, and orchestration, and where future work should focus on interoperable interfaces, privacy-preserving analytics, bounded agentic automation, and open cross-domain benchmarks. The paper is intended as a reference for researchers and practitioners designing secure, transparent, and resilient intelligent networks.
This volume contains the proceedings of PLACES 2026, the 17th edition of the Workshop on Programming Language Approaches to Concurrency and Communication-cEntric Software. The workshop is scheduled to take place in Turin, Italy, on April 11, 2026, as a satellite event of ETAPS, the European Joint Conferences on Theory and Practice of Software. PLACES offers a forum for exchanging new ideas on how to address the challenges of concurrent and distributed programming and how to improve the foundations of modern and future computer applications. PLACES welcomes researchers from various fields, and its topics include the design of new programming languages, models for concurrent and distributed systems, type systems, program verification, and applications in various areas (e.g., microservices, sensor networks, blockchains, event processing, business process management).
Fahd Alhaidari, Sarah AlQahtani, Noura AlDossary, Rachid Zagrouba
As the Internet of Things (IoT) continues to expand across various domains, the number of connected devices is rapidly increasing, exposing IoT environments to large-scale Distributed Denial-of-Service (DDoS) botnet attacks. Due to limited computational and memory resources, IoT devices remain particularly vulnerable to traffic flooding and coordinated malicious behavior. This paper presents a layered security framework that integrates machine learning, protected gateway servers, and blockchain-based smart contracts to detect and mitigate DDoS botnet attacks in IoT environments. The proposed model performs behavioral anomaly detection off-chain using a two-stage machine learning process, while leveraging smart contracts on the blockchain for tamper-resistant logging, automated policy enforcement, and controlled economic penalties. A bounded spending mechanism and quarantine policy are introduced to discourage large-scale malicious traffic while limiting the impact on compromised legitimate devices. The system architecture and enforcement algorithms are presented to demonstrate the feasibility, scalability, and security advantages of the proposed framework.
Abstract - The healthcare supply chain faces challenges such as inefficient procurement, lack of transparency, counterfeit medicines, and poor tracking mechanisms. This paper proposes a blockchain-based solution integrating smart contracts and decentralized storage systems to enhance traceability, security, and efficiency. The system connects stakeholders including manufacturers, distributors, retailers, and healthcare providers through the Ethereum blockchain. Smart contracts automate transactions, while IPFS and Hyperledger Fabric ensure secure and decentralized storage. The proposed framework improves transparency, reduces fraud, and enhances communication across the supply chain. Experimental results demonstrate improved security, cost efficiency, and system reliability. Key Words: Blockchain Technology, Healthcare Supply Chain, Smart Contracts, Ethereum, Decentralized Storage, IPFS (InterPlanetary File System), Hyperledger Fabric, Supply Chain Management, Data Security, Traceability, Transparency, Counterfeit Drug Prevention, Distributed Ledger Technology
Stock markets in emerging economies are shaped by a combination of global integration and domestic financial drivers. In recent years, modern variables such as cryptocurrencies have drawn attention as potential new determinants of equity performance. This study evaluates the comparative influence of traditional variables-Foreign Institutional Investor (FII) flows, USD/INR exchange rate, and NIFVIX-and a modern variable, Bitcoin returns, on the Nifty50 index. Monthly data spanning January 2015 to January 2025 were collected from Investing.com and Moneycontrol. Nifty50, Bitcoin, and USD/INR series were converted into log returns, while FII flows and NIFVIX were used in their original form. Correlation analysis and simple linear regression were done by using Microsoft Excel to measure associations and explanatory power. The results indicate a clear hierarchy of explanatory strength. USD/INR log returns emerged as the most influential determinant, explaining 26% of Nifty50 return variation with a strong negative relationship. NIFVIX explained 14% of the variation, also with a negative and highly significant effect. Bitcoin returns exhibited a modest but statistically significant positive effect, explaining around 8% of the variance. In contrast, both FII equity and total flows were statistically insignificant. The findings suggest that traditional variables-particularly exchange rates and volatility indices-remain dominant drivers of Indian equity returns, while modern variables such as Bitcoin are new but not yet central. The study contributes by showing one of the first systematic comparisons between traditional and modern variables in the Indian equity market context. Keywords: Nifty50, Bitcoin Returns, Foreign Institutional Investors (FII), USD/INR Exchange Rate, NIFVIX, Traditional vs. Modern Variables, Indian Stock Market
A hybrid analytical framework is developed for the forensic investigation of Bitcoin transaction networks, addressing the inherent challenges posed by the decentralized and pseudo-anonymous characteristics of blockchain systems. While Bitcoin transactions are publicly accessible, detecting illicit activities within complex transaction graphs remains a significant challenge. Existing approaches typically depend on isolated techniques, such as rule-based methods or standalone machine learning models, which often lack sufficient effectiveness.The proposed framework combines graph-based network analysis, statistical modeling, and machine learning to enhance detection capability. Transactions are represented as a directed graph, where wallet addresses function as nodes and transactions as edges. From this representation, structural, behavioral, and temporal features are systematically extracted and integrated into a unified dataset. A Random Forest classifier is subsequently employed to categorize wallet addresses as either normal or suspicious.This integrated approach improves accuracy, scalability, and robustness, facilitating efficient analysis of large-scale blockchain data and enabling more reliable identification of fraudulent activities in real-world forensic investigations.