This study compares classical, deep learning, and hybrid approaches for cryptocurrency price forecasting across Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), and Cardano (ADA) using daily data from 15 September 2022 to 15 September 2025. We implement an ARIMA baseline, a stacked LSTM network, and an inverse-error-weighted ARIMA- LSTM ensemble. Feature engineering includes trend, momentum, volatility, and volume indicators; models are evaluated with expanding walk-forward validation and multiple metrics (R2, RMSE, MAE, MAPE, sMAPE, MASE). Statistical significance is assessed via Diebold-Mariano tests. Results indicate that ARIMA consistently outperforms LSTM across all assets, with average performance of R2= 0.924 ± 0.051, MAPE = 2.19 ± 0.81%, and MASE = 0.95 ± 0.30, compared with LSTM’s R2= 0.527 ± 0.709, MAPE = 4.36 ± 0.26%, and MASE = 1.91 ± 1.25. The ensemble attains R2= 0.902 ± 0.084 and MAPE = 2.36 ± 0.58%, retaining ~97% of ARIMA’s explanatory power while reducing volatility relative to LSTM. Asset-specific analyses show strong ARIMA performance for ETH (R2= 0.972 ± 0.038) and BNB (R2= 0.964 ± 0.045), and LSTM failure on BTC (R2= -0.534 ± 0.214). Cross-asset dispersion in R2is markedly lower for ARIMA than for LSTM, indicating superior generalization. DM tests confirm ARIMA’s advantage over LSTM (p < 0.001) across assets, with ARIMA versus ensemble differences nonsignificant for BNB. Under rigorous out-of-sample evaluation, the parsimonious ARIMA model provides the most accurate and stable forecasts, while the ensemble offers a robust alternative when cross-asset stability is prioritized.
Brahma Sakthieswari G, Abisha Kamal D, Jesintha Sharon S, Priyanka S
The widespread adoption of e-learning platforms has increased the issuance of digital certificates; however, conventional certificate management systems are centralized and vulnerable to forgery, unauthorized alteration, and data loss. These limitations reduce trust and make verification dependent on intermediaries. This paper proposes a Blockchain and IPFS-based framework that ensures secure issuance, tamper-proof storage, and automated verification of digital certificates. The system deploys an Ethereum smart contract to register certificate metadata on-chain while the actual certificate file and associated JSON metadata are stored on IPFS, enabling decentralized storage and verifiable ownership. A dual-hash mechanism binds the certificate and its metadata to prevent hash spoofing. Additionally, Optical Character Recognition (OCR) and Natural Language Processing (NLP) are integrated for semantic integrity verification, allowing detection of textual manipulation even when the certificate's appearance remains unchanged. A dataset of 400 certificates was used to evaluate the system. The OCR/NLP module achieved 95% precision in name-matching, and smart contract operations incurred less than $0.05 per certificate, with verification completed in under 3.5 seconds. Results demonstrate that combining blockchain immutability, decentralized storage, and NLP-assisted verification provides a scalable and tamper-evident solution suitable for academic and professional certificate management.
Muhammad Sannan Khaliq, Sium Bin Noor, Subroto Kumar Ghosh, Love Allen Chijioke Ahakonye · 6 authors
Smart contracts are integral to blockchain applications; yet, their immutability creates security vulnerabilities, such as reentrancy and overflow, which can be critically damaging. While detection tools exist, many rely on symbolic execution, graph preprocessing, or binary classification, limiting their efficiency and practicality. This study presents an optimized DeBERTa V3-based transformer model for multilabel detection of smart contract vulnerabilities. The proposed approach operates directly on tokenized Solidity code, leveraging disentangled attention and position embeddings to model semantic patterns. Evaluation on three public datasets achieves up to 100% F1-scores on key vulnerabilities and maintains an average inference latency below 58 ms per smart contract. These results demonstrate the feasibility of integrating high-accuracy, low-latency vulnerability detection into real-time auditing tools, thereby enhancing contract security before deployment.
Carl P. Lipo, Terry L. Hunt, Gina Pakarati, Thomas J. Pingel · 9 authors
Ethnohistoric and recent archaeological evidence suggest that Rapa Nui (Easter Island, Chile) was a politically decentralized society organized into small, relatively autonomous kin-based communities across the island. The more than 1,000 monumental statues (moai) of Rapa Nui thus raise a critical question: was production at Rano Raraku-the primary moai quarry-centrally controlled or did it mirror the decentralized pattern found elsewhere on the island? Using Structure-from-Motion (SfM) photogrammetry with over 11,000 UAV images, we created the first comprehensive three-dimensional model of the quarry to test these competing hypotheses. Our analysis reveals 30 distinct quarrying foci distributed across the crater, each containing redundant production features and employing varied carving techniques. This spatial organization, combined with evidence for multiple simultaneous workshops constrained by natural boundaries, indicates that moai production followed the same decentralized, clan-based pattern documented for other aspects of Rapa Nui society. These findings challenge assumptions that monumentality requires hierarchical control, instead supporting emerging frameworks that recognize how complex cooperative behaviors can emerge through horizontal social networks. The high-resolution 3D model also establishes a crucial baseline for the cultural heritage management of this UNESCO World Heritage site, while advancing methodological approaches for testing sociopolitical hypotheses through the spatial analysis of archaeological landscapes.
NFT (Non-Fungible Token) has emerged as a trending topic in the digital world. This article focuses on the working principle of NFTs and their practical applications in real-world scenarios. Ethereum blockchain serves as the foundational technology that powers NFTs. This document provides a comprehensive technical overview of Ethereum blockchain technology applied in the textile industry for maintaining product ownership verification and authenticity
Open access
Blockchain Technology Applications and Security
Physical Unclonable Functions (PUFs) and Hardware Security
Legacy, profit-driven organizational structures often lack the adaptability, equity, and innovation required for the evolving complexities of contemporary patient-centered care. They can limit access, constrain options, impose financial burdens, and hinder optimized delivery. Here, we propose a new architecture for metaversal integrative healthcare using decentralized autonomous organizations (DAOs). Blockchain technology and smart contracts underpin equitable, transparent, and resilient health ecosystems beyond institutional gatekeeping and entrenched hierarchies. Central to this innovation is the introduction of Ecosystem Value Networks (EVNs), which quantify how diverse contributors, including patients, function as “wellness stewards” within decentralized, interconnected networks of value. EVNs represent a paradigm shift from transactional, top-down authority structures toward relational, emergent, and self-perpetuating interconnected systems. By programming prosocial principles into the architecture of DAOs via governance tokens, digitally encoded collective voting, autonomous smart contract enforcement, and transparency-by-design; these dynamics intrinsically reinforce collaborative over competitive behaviors and outcomes. Traditional institutions often reward dominance, prestige, and hierarchy, that represent conditioned patterns rooted in ancestral scarcity-based modes of fear and control; ill-suited for equitable healthcare. DAOs and EVNs, in contrast, condition new modes of operation grounded in abundance, interdependence, and shared stewardship. Over time, these digital frameworks can transform how health knowledge is generated, distributed, and enacted, establishing a “new normal” from the inside out, in which interconnectivity defines leadership and value. Moving from vision to strategy, this work maps a pathway toward truly participatory (opposed to nominal or symbolic), adaptive, and value-responsive healthcare systems for the new digital age.
Open access
Blockchain Technology Applications and Security
Legal, Health, Environmental and COVID-19 Challenges
<p>Existing financial systems are bloated with inefficiencies in their operation, lack of transparency and are characterized by and fallible and fragile accumulation points, whereas emerging decentralized finance (DeFi) platforms lack intelligent risk management, self-adaptive governance and provable security assurances. This paper proposes the Intelligent, Verifiable Financial Ledger (IVFL), a novel framework that harmoniously converts both Artificial Intelligence (AI) and blockchain to counteract their core drawbacks. AI-based smart contracts of a formally verifiable character that allows the intelligent, secure and auditable automated execution of complex financial transactions an agile and informed governance system, which is represented by the use of AI enhancements to the Decentralized Autonomous Organization (DAO). Simulation analysis shows that the IVFL framework enables substantial enhancements compared to baseline models, such as detecting anomalies with over 95% accuracy, decreasing operational overhead by 40 percent and becoming less vulnerable to coordinated network attacks. Coming back to provable security and adaptive intelligence, the IVFL framework represents a credible way of creating financial systems.</p>
Ke Zhang, Xiaoning Zhao, Chaocheng Zheng, Jiahong Ning · 8 authors
This study proposes Tool-RoCo, a novel benchmark for evaluating large language models (LLMs) in long-term multi-agent cooperation based on RoCo, a multi-robot cooperative benchmark. Recent research on LLM-based multi-agent systems has relied on predefined orchestration, while ignoring agent autonomy. Tool-RoCo treats other agents as tools and introduces cooperative tools, leveraging tool usage to evaluate multi-agent cooperation and self-organization. Tool usage means that each agent (LLM) selects a tool from a candidate set based on the current state, receives feedback, and adjusts its selection in subsequent rounds. To evaluate different autonomy levels, we propose four LLM paradigms: (1) centralized cooperation, where a single LLM allocates tools to all agents; (2) centralized self-organization, where a central LLM autonomously activates agents while keeping others inactive; (3) decentralized cooperation, where each agent has its own LLM and calls tools based on local information; and (4) self-organization, where a randomly chosen initial agent can request collaboration, activating additional agents via tool calls. Tool-RoCo includes three multi-robot tasks, SORT, PACK, and CABINET, to measure format and parameter accuracy and agent coordination through tool usage. The results using several LLMs showed that cooperative tools accounted for only 7.09% of all tools, indicating that LLM-based agents rarely invoked others as assistants. Moreover, activation tools accounted for 96.42%, suggesting that current LLMs tend to maintain active agents while seldom deactivating them for adaptive coordination. Tool-RoCo provides a systematic benchmark to evaluate LLM autonomy and cooperation in multi-agent tasks. Code and Demo: https://github.com/ColaZhang22/Tool-Roco
Zubaida Rehman, Mark A. Gregory, Iqbal Gondal, Hai Dong · 5 authors
Ethereum has emerged as one of the most widely used blockchain platforms, underpinning decentralized finance, smart contracts, and distributed applications. With its growing adoption, the Ethereum peer-to-peer network is susceptible to networklayer attacks including eclipse (node-isolation) attacks. To study the threats to Ethereum and to develop effective detection and mitigation strategies, researchers require controlled, reproducible, and labeled network datasets. However, datasets are scarce due to the complexity of capturing live blockchain traffic and the difficulty of confidently labeling malicious activity on public networks. In this paper, we present the design and deployment of a private Ethereum testbed for dataset collection. Our testbed consists of five virtual machines running Geth clients interconnected via a controlled gateway: four nodes act as benign Ethereum peers and one node acts as a malicious entity that performs eclipse attacks. The testbed emulates normal blockchain operations (block propagation, transaction exchanges, and peer discovery) and adversarial scenarios focused on node isolation. Wireshark is deployed on the gateway to capture the network traffic, enabling us to record raw packet traces for benign and attack scenarios. The resulting dataset provides a comprehensive view of Ethereum network-layer behavior, with traffic labeled according to ground truth (node role and attack phase). We describe the testbed, the attack procedure for generating eclipse conditions, the capture and labeling pipeline, and potential uses of the dataset for intrusion detection and resilience analysis.
Istiaque Ahmed, Tadashi Nakano, Kentaroh Toyoda, Thi Hong Tran
Digital identity verification has become crucial to every service in daily life. The privacy concerns associated with traditional Know Your Customer (KYC) systems have come to the forefront. These systems often require the sharing of personal information, which is stored in centralized databases, making them vulnerable to unauthorized access. To address these challenges, this work implements an electronic KYC system with selective disclosure using Merkle Tree and Zero-Knowledge Proofs (ZKP). Selective disclosure enables users to share only the necessary information, thereby reducing the exposure of sensitive data. ZKP enables the verification of this information without revealing the actual data, ensuring that privacy is preserved. The combination of selective disclosure and zkSNARKs in the proposed framework provides a solution for generating a single proof compared to multiple market proofs. This work demonstrates significant improvements in privacy protection compared to traditional identification systems. The implementation process and performance evaluation explore its potential impact on eKYC.
Cardano, a major Proof-of-Stake (PoS) blockchain, aims to achieve decentralization through its reward distribution mechanism governed by the protocol parameter k, which defines the ideal number of reward-receiving stake pools. While k plays a critical role in shaping network dynamics, its current value is statically determined and lacks a formal theoretical basis.In this study, we conduct an empirical analysis of Cardano’s on-chain data to evaluate the effects of changing k on decentralization metrics such as the number of active pools, stake distribution, and the Nakamoto coefficient. Our findings show that while increasing k initially enhances decentralization, the long-term effects are limited due to dynamic shifts in pool performance and stake concentration.Based on these insights, we propose the foundation for a dynamic optimization framework that determines the appropriate value of k by integrating decentralization, operational cost, and behavioral factors. This work contributes to the development of sustainable and fair PoS governance through data-driven parameter tuning.
Distributed Ledger Technology (DLT) as a principle of corporate governance represents an institutional shift of the law of the firm. Once relegated to academic theorizing and cryptocurrency, DLT now forms institutional infrastructure with a nascent market of tokenized real-world assets (RWAs) surpassing $33B at the close of Q4 2025. This paper analyzes how DLT intersectors three pillars of management - Strategic, Operational and Financial - in conjunction with Transaction Cost Economics (TCE) and Agency Theory that also coincide with inextricably lower baseline costs of trust and coordination. Strategically, Decentralized Autonomous Organizations (DAOs) and Intellectual Property Non-Fungible Tokens (IP-NFTs) are increasingly at the forefront of governance and R&D-related compensation structure. Operationally, smart contracts govern supply chains at near-real time with the Global Shipping Business Network (GSBN) going live with container tracking implementations and the FDA implementing pilot programs for near-instant visibility into temperature-controlled shipping needs. Financially, treasuries and debt instruments are increasingly tokenized to allow firms to harness an illiquidity premium while equitizing their working capital. Ultimately, this research concludes that the international financial architecture is bifurcated as high-stable assets transition to permissioned DLTS while high-velocity assets remain in public programmable spaces.
There has been an exponential rise of Internet of Things (IoT) devices and autonomous systems, which have thrown light on the weaknesses of centralized cloud computing, especially in latency, bandwidth, and security. This paper will solve such problems by suggesting an integrated blockchain-edge architecture, which uses distributed trusting mechanisms to protect and optimize edge networks. The process of the methodology consists of four steps: architectural modeling, lightweight consensus design, performance-security trade-off analysis, and real-life validation. Experiments with iFogSim and BlockSim showed that edge networks enhanced with blockchain cuts latency and bandwidth consumption by 37 and 36 percent respectively compared to cloud-centric models. Consensus protocols such as Practical Byzantine Fault Tolerance (pBFT), Proof-of-Elaboration (PoE) and Leased Proof-of-Stake (LPoS) were designed and tested, using much less energy and having much faster transaction finality compared to Proof-of-Work. High resilience to Sybil, tampering, and 51% attacks was proven with Raspberry Pi clusters, and an 8% latency trade-off was observed, when smart contracts were used to enforce automated access control. Lastly, experimental validation with healthcare and industrial IoT datasets demonstrated that blockchain decreased attempts to access information unauthorized to nearly zero in the healthcare industry and minimized manipulations with machine logs by 70 percent in the industrial IoT. These results highlight blockchain-edge convergence as a potential direction towards the construction of scalable, secure and trustful decentralized systems.
The convergence of artificial intelligence (AI) and decentralized web technologies represents a pivotal shift in digital infrastructure, giving rise to the concept of AI-native protocols. These protocols integrate AI capabilities directly into their fundamental design, moving beyond mere application-level AI to create intelligent, adaptive, and autonomous decentralized systems. This paper explores the transformative potential of AI-native protocols in reshaping the decentralized web, often referred to as Web3. We delve into the architectural implications, key benefits such as enhanced security, efficiency, and scalability, and the profound societal impact of such a paradigm shift. Through a comprehensive literature review, we identify existing challenges in both AI and blockchain domains that AI-native protocols are uniquely positioned to address, including algorithmic bias, data privacy, and consensus mechanism inefficiencies. We propose a conceptual framework for designing these protocols, emphasizing core components like intelligent consensus, autonomous agents, and AI-powered smart contracts. Furthermore, the paper discusses the ethical considerations inherent in embedding AI within decentralized governance structures and outlines future research directions for fostering responsible innovation. Our findings suggest that AI-native protocols are not merely an incremental improvement but a foundational evolution that promises to unlock unprecedented levels of intelligence and autonomy across the decentralized digital landscape, fostering a more robust, equitable, and resilient internet.
Omar Otoniel Flores-Cortez, Ernesto Rivas Valdez, Vladimir A. Polanco-Zepeda, Carlos Pocasangre Jimenez · 5 authors
This paper presents the design and implementation of thesocks.net, a decentralized social finance network inspired by the CAW protocol manifesto. thesocks.net leverages Web3 technologies and the Polygon blockchain to provide a communitygoverned, anonymous, and censorship-resistant platform that integrates traditional features of social networks with decentralized financial services. The architecture is built around two core elements: the NFTs-Username utility cryptocurrency deployed on Polygon and NFT_Username identities, which function as user accounts represented by unique and tradable NFTs. Smart contracts written in Solidity govern key features such as content publication, user interactions (likes, shares, follows), stake, token burning, and reward distribution, ensuring transparency and autonomy. The platform enables users to create profiles, send encrypted messages, transfer tokens, and participate in staking and games, all without centralized control or personal data collection. This paper outlines the technical framework, the development process, and the deployment phases of thesocks.net, demonstrating how the blockchain-based infrastructure can support secure, scalable and inclusive social ecosystems. The implementation highlights the potential of decentralized architectures to foster freedom of expression, digital ownership, and financial participation on a global scale.
Behavioral finance explores the psychological influences and cognitive biases that affect investor behavior and financial decision-making, including herding, the disposition effect, overconfidence, and others. Algorithmic trading is a method that uses computer programs to automatically execute buy and sell orders based on predefined mathematical models and trading strategies. With the continuous development of modern technology, the advent of the Web3 era, and the gradual evolution of artificial intelligence, algorithmic trading is becoming increasingly prevalent and garnering significant attention. While algorithmic trading is automated and may seem immune to human cognitive biases, the opposite is often true. This study aims to review the main findings of existing research from the perspective of the stock market, exploring the interactive relationship between behavioral finance and algorithmic trading and how cognitive biases such as herding and the disposition effect can influence algorithm performance. The results emphasize the importance of behavioral finance in both the research and practice of algorithmic trading, while also proposing the potential for using machine learning techniques to advance the field of behavioral finance. By integrating existing theories, this study contributes to a deeper understanding of the relationship between behavioral finance and algorithmic trading and offers new perspectives for its future development.
Matteo Bjornsson, Taylor Hardin, Taylor Heinecke, Marcin Furtak · 6 authors
Distributed ledger technologies (DLTs) rely on distributed consensus mechanisms to reach agreement over the order of transactions and to provide immutability and availability of transaction data. Distributed consensus suffers from performance limitations of network communication between participating nodes. BLOCKY ZipperChain guarantees immutability, agreement, and availability of transaction data, but without relying on distributed consensus. Instead, its construction process transfers trust from widely-used, third-party services onto ZipperChains's correctness guarantees. ZipperChain blocks are built by a pipeline of specialized services deployed on a small number of nodes connected by a fast data center network. As a result, ZipperChain transaction throughput approaches network line speeds and block finality is on the order of 500 ms. Finally, ZipperChain infrastructure creates blocks centrally and so does not need a native token to incentivize a community of verifiers.
Mohammad Alvian Dharma Nararya, Shuri Mariasih Gietty, Himawan Aditya Pratama
Tulisan ini mengkaji secara kritis kemunculan gim Play-to-Earn (P2E) dalam kerangka teknologi Web3, dengan berargumen bahwa janji desentralisasi yang dibawa oleh blockchain dan Non-Fungible Token (NFT) justru mereproduksi, bahkan memperkuat, pola-pola eksploitasi kapitalisme tradisional. Model P2E merujuk pada sistem permainan digital yang memungkinkan pemain memperoleh keuntungan finansial dari aktivitas bermain melalui mekanisme ekonomi berbasis token kripto, di mana aset dalam gim memiliki nilai tukar di pasar digital. Sementara itu, blockchain merupakan teknologi pencatatan terdistribusi yang menyimpan data transaksi di banyak komputer (nodes) dan sering diklaim sebagai fondasi desentralisasi digital karena tidak bergantung pada otoritas tunggal. Melalui analisis terhadap infrastruktur Web3 dan studi kasus gim Axie Infinity (2018), tulisan ini menunjukkan bahwa sistem digital yang diklaim membebaskan pengguna dari kontrol terpusat justru memusatkan kekuasaan ekonomi dalam bentuk yang lebih terselubung. Dengan kerangka teori kapitalisme digital dan konsep false needs dari Herbert Marcuse, penelitian ini memperlihatkan bahwa ekonomi P2E mengubah aktivitas bermain menjadi bentuk kerja (playbor) dan menundukkan pemain pada pasar spekulatif yang menguntungkan pengembang dan pemilik modal. Di Asia Tenggara, tempat basis pemain P2E tetap besar meskipun gelembung pasarnya telah pecah, sistem ini mengeksploitasi kondisi sosial-ekonomi yang rentan dengan membingkai ketidakstabilan finansial sebagai peluang. Tulisan ini berargumen bahwa “desentralisasi” dalam Web3 merupakan bentuk sentralisasi terselubung melalui kontrol algoritmik, opasitas infrastruktur, dan privatisasi platform, menunjukkan bahwa Web3 dan gim P2E bukanlah alternatif pasca-kapitalis, melainkan fase baru dari kapitalisme digital yang mengomodifikasi permainan dan mendistribusikan risiko ke bawah sambil mengonsolidasikan keuntungan di atas.
Web3 technologies, notably Non-Fungible Tokens (NFTs) and Decentralized Finance (DeFi), have generated extensive social media discourse. This study integrates Social Network Analysis (SNA) and BERTopic to examine spreader roles in shaping Web3 conversations on platform X. We collected 12,925 NFT and 7,087 DeFi posts from August 23 to September 23, 2025, using domain-specific keywords. Data preprocessing removed duplicates, spam, and irrelevant content through URL stripping, hashtag filtering, and manual verification of top spreaders to exclude automated accounts. All collection adhered to X's Terms of Service using publicly available English-language posts without personal identifying information. In-degree centrality analysis identified top spreaders. @GiveRep in NFT achieved a time reached of 288 hours with an average propagation speed of two hours, while @BioProtocol in DeFi demonstrated a uniform persistence of 192 hours uniform persistence across top actors. BERTopic analysis revealed thematic differences. NFT discussions centered on community engagement and speculation, whereas DeFi discourse concentrated on protocol infrastructure and yield mechanisms. NFT spreaders exhibited varied influence duration aligned with thematic diversity, while DeFi spreaders showed uniform persistence constrained by content overlap. This integrated framework advances computational social research methodologies and offers practical insights for Web3 stakeholders to identify key influencers and optimize community engagement strategies. Limitations include single-platform focus and one-month observation period.
The Paris Journal on AI & Digital Ethics Bootstrapping Trust across Web2 and Web3 Domains Using Publicly Verifiable Web Data Yuan Lu¹, Qiang Tang² Corresponding authors:luyuan@iscas.ac.cn • qiang.tang@sydney.edu.au Abstract Through […]