Collaborative content generation (CCG) enables collective creation of artifacts like scientific articles. Quality is a paramount concern in CCG, and a multitude of methods have been proposed to evaluate the quality of artifacts. Nevertheless, the majority of these methods are reliant on centralized architectures, which present challenges pertaining to security, privacy, and availability. Blockchain technology proffers a potential resolution to these challenges, by furnishing a decentralized and immutable ledger of quality scores. In this manuscript, we introduce a blockchain-based quality control model for CCG that uses a semi-iterative algorithm to interdependently compute quality scores of artifacts and reputation of nodes. Our model addresses critical challenges in academic informetrics, such as citation manipulation, transparency in collaborative scholarship, and decentralized trust in metric computation. Our model also exhibits sensitivity to processing latency, rendering it more agile in the presence of delays. Our model's quality scores, evaluated against PageRank and HITS baselines, show comparable performance, with additional assessments of throughput, latency, and robustness against malicious nodes confirming its reliability. A theoretical comparison with recent studies validates its feasibility for real world informetric application.
Burak Arda Okutan, Stefan Schmid, Yvonne-Anne Pignolet
Decentralized autonomous organizations (DAOs) rely on governance mechanism without centralized leadership. This paper presents an empirical study of user behavior in governance for a variety of DAOs, ranging from DeFi to gaming, using the Internet Computer Protocol DAO framework called SNS (Service Nervous System). To analyse user engagement, we measure participation rates and frequency of proposals submission and voter approval rates. We evaluate decision duration times to determine DAO agility. To investigate dynamic aspects, we also measure metric shifts in time. We evaluate over 3,000 proposals submitted in a time frame of 20 months from 14 SNS DAOs. The selected DAO have been existing between 6 and 20 months and cover a wide spectrum of use cases, treasury sizes, and number of participants. We also compare our results for SNS DAOs with DAOs from other blockchain platforms. While approval rates are generally high for all DAOs studied, SNS DAOs show slightly more alignment. We observe that the SNS governance mechanisms and processes in ICP lead to higher activity, lower costs and faster decisions. Most importantly, in contrast to studies which report a decline in participation over time for other frameworks, SNS DAOs exhibit sustained or increasing engagement levels over time.
Ethereum, a leading blockchain platform, has revolutionized the digital economy by enabling decentralized transactions and the execution of smart contracts. Ethereum transactions form the backbone of its network, facilitating peer-to-peer exchanges and interactions with complex decentralized applications. Smart contracts extend Ethereum's capabilities by automating processes and enabling trustless execution of agreements. Hence, understanding how these smart contracts interact is important in order to facilitate various performance optimizations, such as warming objects before they are being accessed and enabling concurrent execution. Of particular interest to us are the development of the calling graph, as well as the read sets and write sets of invocations within the same block, and the properties of the associated conflict graph that is derived from them. The latter is important for understanding the parallelization potential of smart contracts on Ethereum. We traced upwards of 2 million recent Ethereum blocks using call tracer and prestate tracer, out of a total of 21.4 million blocks at the time of writing. We report on the transactions per block distribution, the structure of call trees in smart contract invocations, the ratio of value-transfer transactions to smart contract invocations, as well as provide a comprehensive study of the structure of blocks' conflict graphs. We find that conflict graphs predominantly show a star like configuration, as well as other noteworthy structural properties.
Hadis Rezaei, Mojtaba Eshghie, Karl Anderesson, Francesco Palmieri
While catastrophic attacks on Ethereum persist, vulnerability research remains fixated on implementation-level smart contract bugs, creating a gap between academic understanding of vulnerabilities and the root causes of high-impact, real-world incidents. To address this, we employ a two-pronged methodology: first, a systematic literature review of 71 academic papers to build a catalog of 24 active and 5 deprecated vulnerabilities. Second, we conduct an in-depth, empirical analysis of 50 of the most severe real-world attacks between 2022 and 2025, collectively incurring over $1.09B in losses, to identify their root causes. We introduce the concept of "exploit chains" by revealing that many incidents are not caused by isolated vulnerabilities but by combinations of human, operational, and economic design flaws that link with implementation bugs to enable an attack. Our analysis yields insights on how decentralized applications are exploited in practice, leading to a novel, four-tier root-cause framework that moves beyond code-level vulnerabilities. We find that real-world successful attacks on Ethereum (and related networks) trace back to one of the four tiers of (1) protocol logic design, (2) lifecycle and governance, (3) external dependencies, and (4) classic smart contract vulnerabilities. We investigate the suitability of this multi-tier incident root-cause framework via a case study.
We introduce the Graded Transformer framework, a new class of sequence models that embeds algebraic inductive biases through grading transformations on vector spaces. Extending Graded Neural Networks (GNNs), we propose two architectures: the Linearly Graded Transformer (LGT) and the Exponentially Graded Transformer (EGT). These models apply parameterized scaling operators, governed by fixed or learnable grading tuples and in the case of EGT exponential factors, to encode hierarchical structure in attention and representation layers and to improve efficiency for structured data. We establish rigorous guarantees, including universal approximation theorems for continuous and Sobolev functions, reduced sample complexity via effective VC dimension bounds, Lipschitz continuity of graded operations, and robustness to perturbations. A graded loss ensures gradient stability and alignment with domain priors during optimization. By treating grades as differentiable parameters, the framework enables adaptive feature prioritization, overcoming limitations of fixed grades in earlier models. The Graded Transformer provides a mathematically principled approach to hierarchical learning and neuro-symbolic reasoning. Applications include algebraic geometry (moduli spaces and zeta functions), physics (multiscale systems), natural language processing (syntactic parsing), biological sequence analysis (variant prediction), robotics and autonomous systems (safety-critical prioritization), the automotive industry (certifiable AI for ADAS), and blockchain and financial cryptography (secure coding and structured prediction).
Ningshuang Zeng, Xuling Ye, Shiqi Chen, Yan Liu · 6 authors
The integration of off-site manufacturing with onsite construction in the Modular Construction Supply Chain (MCSC) presents significant challenges due to the differences in production methods and geographical distances.While various technologies have transformed construction practices, substantial improvements are still needed in on-and off-site coordination to mitigate delays, ensure quality, and lower costs.Blockchain-enabled smart contracts offer a promising workflow engine for enhancing processoriented MCSC coordination.Additionally, Robotic Process Automation (RPA) holds the potential to further refine the modules within MCSC processes and automate tasks.This paper, therefore, aims to develop a new workflow engine that combines blockchain-enabled smart contracts, RPA, and related visualization technologies to address the coordination challenges inherent in MCSC.Demanddriven process modeling and smart contracts conversion with RPA actions are conducted with the MCSC coordination unit configuration, and the initial system is developed and validated in a realworld case study.Industrial requirements and feedback are collected and analyzed via questionnaires and interviews.Finally, practical implications are discussed, and the integration of RPA and AI is identified as a promising exploration for future work in MCSC coordination.
Fluktuasi harga Bitcoin telah menarik perhatian global, terutama terkait dengan pengaruh sentimen publik terhadap dinamika pasar. Penelitian ini menyajikan Systematic Literature Review (SLR) yang memeriksa analisis sentimen publik terkait Bitcoin, berdasarkan artikel yang diterbitkan antara 2020 hingga 2025. Artikel-artikel ini diambil dari database ilmiah terkemuka dan dianalisis menggunakan protokol PRISMA. Sebanyak 42 studi dipilih dan dikelompokkan berdasarkan teknik analisis sentimen dan sumber data. Hasil penelitian menunjukkan bahwa model deep learning, terutama LSTM dan GRU, merupakan pendekatan yang paling sering digunakan dalam penelitian terbaru mengenai analisis sentimen Bitcoin. Sumber data utama adalah Twitter, yang dipilih karena sifatnya yang real-time dan dapat diakses publik, meskipun beberapa studi juga memanfaatkan artikel berita dan platform media sosial lainnya untuk menangkap sentimen yang lebih luas. Selain itu, tinjauan ini menyoroti pentingnya teknik preprocessing data, seperti tokenisasi dan penyaringan noise, dalam meningkatkan akurasi klasifikasi sentimen. Penelitian ini memberikan sintesis komprehensif dari tren metodologis terkini, mengidentifikasi tantangan yang ada, serta menawarkan wawasan untuk penelitian masa depan di bidang analisis sentimen dalam cryptocurrency.
The global coffee supply chain is a complex multi-stakeholder ecosystem plagued by fragmented records, unverifiable origin claims, and limited real-time visibility. These limitations pose risks to ethical sourcing, product quality, and consumer trust. To address these issues, this paper proposes a blockchain and IoT-enabled framework for secure and transparent coffee supply chain management. The system integrates simulated IoT sensor data such as Radio-Frequency Identification (RFID) identity tags, Global Positioning System (GPS) logs, weight measurements, environmental readings, and mobile validations with Ethereum smart contracts to establish traceability and automate supply chain logic. A Solidity-based Ethereum smart contract is developed and deployed on the Sepolia testnet to register users and log batches and to handle ownership transfers. The Internet of Things (IoT) data stream is simulated using structured datasets to mimic real-world device behavior, ensuring that the system is tested under realistic conditions. Our performance evaluation on 1000 transactions shows that the model incurs low transaction costs and demonstrates predictable efficiency behavior of the smart contract in decentralized conditions. Over 95% of the 1000 simulated transactions incurred a gas fee of less than ETH 0.001. The proposed architecture is also scalable and modular, providing a foundation for future deployment with live IoT integrations and off-chain data storage. Overall, the results highlight the system’s ability to improve transparency and auditability, automate enforcement, and enhance consumer confidence in the origin and handling of coffee products.
Fintech provides technological services to increase operational efficiency in financial institutions, but traditional perimeter-based defense mechanisms are insufficient against evolving cyber threats like insider attacks, malware intrusions, and Advanced Persistent Threats (APTs). These vulnerabilities expose Fintech organizations to significant risks, including financial losses and data breaches. To address these challenges, this paper proposes a blockchain-integrated Zero Trust framework, adhering to the principle of "Never Trust, Always Verify." The framework uses Ethereum smart contracts to enforce Multi Factor Authentication (MFA), Role-Based Access Control (RBAC), and Just-In-Time (JIT) access privileges, effectively mitigating credential theft and insider threats, the effect of malware and APT attacks. The proposed solution transforms blockchain into a Policy Engine (PE) and Policy Enforcement Point (PEP), and policy storage, ensuring immutable access control and micro-segmentation. A decentralized application (DApp) prototype was developed and tested using STRIDE threat modeling, demonstrating resilience against spoofing, tampering, and privilege escalation. Comparative analysis with Perimeter-based systems revealed a trade-off: while the framework introduced a marginal latency increase (74.0 ms vs. 49.33 ms) and reduced throughput (30.77 vs. 50.0 requests/sec), it significantly enhanced security by eliminating single points of failure and enabling tamper-proof audit trails. Experimental validation on a 200-node simulated network confirmed the framework's robustness, with future optimizations targeting Layer-2 solutions for scalability. This work bridges the gap between Zero Trust theory and practical blockchain implementation, offering Fintech organizations a decentralized, cost-effective security model.
In this paper, we present the Tokenized Sovereign Debt Conversion Mechanism (TSDCM), a smart-contracted instrument that, upon meeting both debt-to-GDP and GDP-growth thresholds, automates the retirement of sovereign debt. TSDCM initiates the conversion of a portion of outstanding bonds into performance-linked tokens by integrating a two-state regime-switching jump-diffusion framework into decentralized protocols. We prove finite-time activation and expected debt reduction through new propositions, establish the existence and uniqueness of the underlying stochastic processes, and introduce a main theorem that ensures a strict decline in expected debt levels. With significant tail-risk mitigation, calibration using IMF data and MATLAB Monte Carlo simulations shows a 20-25% decrease in expected debt-to-GDP ratios over a ten-year period. A transparent and incentive-aligned route to sustainable sovereign debt management is provided by TSDCM.
This study develops a conceptual simulation model for a tokenized recycling incentive system that integrates blockchain infrastructure, market-driven pricing, behavioral economics, and carbon credit mechanisms. The model aims to address the limitations of traditional recycling systems, which often rely on static government subsidies and fail to generate sustained public participation. By introducing dynamic token values linked to real-world supply and demand conditions, as well as incorporating non-monetary behavioral drivers (e.g., social norms, reputational incentives), the framework creates a dual-incentive structure that can adapt over time. The model uses Monte Carlo simulations to estimate outcomes under a range of scenarios involving operational costs, carbon pricing, token volatility, and behavioral adoption rates. Due to the absence of real-world implementations of such integrated blockchain-based recycling systems, the paper remains theoretical and simulation-based. It is intended as a prototype framework for future policy experimentation and pilot projects. The model provides insights for policymakers, urban planners, and technology developers aiming to explore decentralized and market-responsive solutions to sustainable waste management. Future work should focus on validating the model through field trials or behavioral experiments.
Cryptocurrency markets are often volatile with no central authority as a decentralized system based on cryptography makes the records, making investors in this sector particularly susceptible to reliance on social cues rather than fundamentals. This paper uses a qualitative study design that collects 1000 text-rich entries in social media and forum posts to determine herding behaviour in cryptocurrency markets, particularly behavioural trading strategy design and decision risk quantification. This data is collected using a purposive sampling strategy that selects texts published during periods of extreme volatility, such as during Bitcoin crashes and meme coin surges between January 2021 and January 2025. Following a thematic analysis of this data, the results indicate that herding behaviour among investors is mostly caused by fear of missing out (FOMO), emotional contagion, influencer reliance, collective rationalisation of risk, and post-herd regret and justification. These social cues lead to investors relying on them to value cryptocurrency assets instead of conducting fundamental analysis, which would be beneficial.
Xinran Huang, Linzhi Tan, Haozhe Su, Jeremy Eng‐Tuck Cheah
ABSTRACT One of the critical risks associated with cryptocurrency assets is the so‐called downside risk, or tail risk. Conditional Value‐at‐Risk (CVaR) is a measure of tail risks that is not normally considered in the construction of a cryptocurrency portfolio. In this paper, we propose a new approach to portfolio construction based on a deep learning CVaR utility function. This approach is designed to address the issue of tail risk. We evaluate the performance of this approach in comparison to other portfolio construction techniques, including the naïve, minimum variance and mean‐variance portfolios. Our findings indicate that the proposed approach outperforms traditional optimisation models.
This study investigates Granger-causality relationships between crypto-assets (Bitcoin and Ethereum) and traditional financial assets (stock indices and exchange rates) in BRICS-T countries over the 2016–2024 period. The findings highlight significant interlinkages: bidirectional causality exists between Bitcoin and Russia's stock market, and between Ethereum and both Brazil's stock market and the USD/INR exchange rate. Unidirectional causality is observed from Bitcoin to the stock markets of Brazil, India, and China, while the USD/TRY exchange rate influences Bitcoin. Similarly, Ethereum affects the stock markets of Russia, India, and South Africa, while the USD/TRY exchange rate also Granger-causes Ethereum. These results indicate a growing synchronization between crypto-assets and conventional financial markets. The presence of both unidirectional and bidirectional causalities emphasizes the increasing integration of global financial systems and highlights the importance for investors to consider cross-market interactions when making decisions. Crypto-assets are no longer isolated but are embedded in broader financial dynamics.
Cryptocurrency markets are experiencing rapid growth, but this expansion comes with significant challenges, particularly in predicting cryptocurrency prices for traders in the U.S. In this study, we explore how deep learning and machine learning models can be used to forecast the closing prices of the XRP/USDT trading pair. While many existing cryptocurrency prediction models focus solely on price and volume patterns, they often overlook market liquidity, a crucial factor in price predictability. To address this, we introduce two important liquidity proxy metrics: the Volume-To-Volatility Ratio (VVR) and the Volume-Weighted Average Price (VWAP). These metrics provide a clearer understanding of market stability and liquidity, ultimately enhancing the accuracy of our price predictions. We developed four machine learning models, Linear Regression, Random Forest, XGBoost, and LSTM neural networks, using historical data without incorporating the liquidity proxy metrics, and evaluated their performance. We then retrained the models, including the liquidity proxy metrics, and reassessed their performance. In both cases (with and without the liquidity proxies), the LSTM model consistently outperformed the others. These results underscore the importance of considering market liquidity when predicting cryptocurrency closing prices. Therefore, incorporating these liquidity metrics is essential for more accurate forecasting models. Our findings offer valuable insights for traders and developers seeking to create smarter and more risk-aware strategies in the U.S. digital assets market.
We present Free-Delete, a censorship-resistant group-chat protocol whose membership is validated by non-fungible-token (NFT) ownership while user privacy ranges across five selectable modes. A single Groth16 circuit, anchored in a sparse Merkle tree of verifiable commitments, realises (i) Fully Anonymous messaging, (ii) Linkable Anonymous reputation building, (iii) Publicly Identified disclosure, (iv) Confidential end-to-end encryption, and (v) Rate-Limited Accountability that revokes keys on spam—all without moderator involvement or economic deposits. A black-paper prototype written in TypeScript, Circom 2, and Solidity achieves 0.35 s for register and 1.38 s for postMessage on consumer hardware; on-chain verification costs 3–5.5×105 gas per proof on Polygon. These results demonstrate that NFT-gated, stake-free, privacy-preserving communication can be deployed today on any EVM chain.
Ali RajabiNekoo, Laleh Rasoul, Amirfarhad Farhadi, Azadeh Zamanifar
Traditional methods for identifying impactful liquidity providers (LPs) in Concentrated Liquidity Market Makers (CLMMs) rely on broad measures, such as nominal capital size or surface-level activity, which often lead to inaccurate risk analysis. The SILS framework offers a significantly more detailed approach, characterizing LPs not just as capital holders but as dynamic systemic agents whose actions directly impact market stability. This represents a fundamental paradigm shift from the static, volume-based analysis to a dynamic, impact-focused understanding. This advanced approach uses on-chain event logs and smart contract execution traces to compute Exponential Time-Weighted Liquidity (ETWL) profiles and apply unsupervised anomaly detection. Most importantly, it defines an LP's functional importance through the Liquidity Stability Impact Score (LSIS), a counterfactual metric that measures the potential degradation of the market if the LP withdraws. This combined approach provides a more detailed and realistic characterization of an LP's impact, moving beyond the binary and often misleading classifications used by existing methods. This impact-focused and comprehensive approach enables SILS to accurately identify high-impact LPs-including those missed by traditional methods and supports essential applications like a protective oracle layer and actionable trader signals, thereby significantly enhancing DeFi ecosystem. The framework provides unprecedented transparency into the underlying liquidity structure and associated risks, effectively reducing the common false positives and uncovering critical false negatives found in traditional models. Therefore, SILS provides an effective mechanism for proactive risk management, transforming how DeFi protocols safeguard their ecosystems against asymmetric liquidity behavior.
Lunodzo J. Mwinuka, Massimo Cafaro, Lucas Pereira, Hugo Morais
Energy systems generate vast amounts of data in extremely short time intervals, creating challenges for efficient data management. Traditional data management methods often struggle with scalability and accessibility, limiting their usefulness. More advanced solutions, such as NoSQL databases and cloud-based platforms, have been adopted to address these issues. Still, even these advanced solutions can encounter bottlenecks, which can impact the efficiency of data storage, retrieval, and analysis. This review paper explores the research trends in big data management for energy systems, highlighting the practices, opportunities and challenges. Also, the data regulatory demands are highlighted using chosen reference architectures. The review, in particular, explores the limitations of current storage and data integration solutions and examines how new technologies are applied to the energy sector. Novel insights into emerging technologies, including data spaces, various data management architectures, peer-to-peer data management, and blockchains, are provided, along with practical recommendations for achieving enhanced data sharing and regulatory compliance.
Pallavi Zambare, Venkata Nikhil Thanikella, Ying Liu
Pedestrian intention prediction is essential for autonomous driving in complex urban environments. Conventional approaches depend on supervised learning over frame sequences and require extensive retraining to adapt to new scenarios. Here, we introduce BF-PIP (Beyond Frames Pedestrian Intention Prediction), a zero-shot approach built upon Gemini 2.5 Pro. It infers crossing intentions directly from short, continuous video clips enriched with structured JAAD metadata. In contrast to GPT-4V based methods that operate on discrete frames, BF-PIP processes uninterrupted temporal clips. It also incorporates bounding-box annotations and ego-vehicle speed via specialized multimodal prompts. Without any additional training, BF-PIP achieves 73% prediction accuracy, outperforming a GPT-4V baseline by 18 %. These findings illustrate that combining temporal video inputs with contextual cues enhances spatiotemporal perception and improves intent inference under ambiguous conditions. This approach paves the way for agile, retraining-free perception module in intelligent transportation system.
Yul is an intermediate language used in the compilation of the Solidity programming language for Ethereum smart contracts. The compiler applies customizable sequences of transformations to Yul code. To help ensure the correctness of these transformations and their sequencing, we used the ACL2 theorem prover to develop a formalization of the syntax and semantics of Yul, proofs relating static and dynamic semantics, a formalization of some Yul code transformations, and correctness proofs for these transformations.
Cryptographic-based digital currencies, such as bitcoin, function as a medium of exchange. Bitcoin is a digital currency that exists within the open source P2P payment system. P2P is a computer network model that consists of two or more computers, and each computer in that network has the ability to share with each other. Bitcoin is one of the most famous digital currencies. The benefits and drawbacks of using Bitcoin as a payment tool. This is because bitcoin has not met some criteria to be considered a currency, especially in terms of the principles of buying and selling transactions. While bitcoin does not have an underlying asset used as a basis for transactions. There are two conclusions that can be drawn in this regard. First and foremost, digital money is not a currency. Second, there is an element of ambiguity (Gharar). This research uses academic literature as the main source for information on the use of Bitcoin in online transactions. Other sources include scientific works, fiqh muamalah texts, journals, articles, online media, and more. This research is prescriptive, meaning that it assesses and establishes rules on whether using Bitcoin as a transaction tool in online sales is permissible. Material and pragmatic analysis derived from Islamic law is then applied. Research on normative Islamic law is an example of a study aimed at establishing clear rules on how to use Bitcoin currency. Research findings show that in the view of Islam, Bitcoin transactions as a payment tool do not meet the requirements of al-sharf because they violate Islamic rules stating that transactions must be conducted in cash, even if there are pending orders. This is based on verses in the Qur'an that declare Bitcoin as batil, gharar, and maysir.
In our research, we propose the first practically deployable construction of a multi-prover zero-knowledge succinct non-interactive argument of knowledge (zkSNARK) protocol specifically tailored for restricted multiplication straight-line (RMS) programs, a computation model widely applicable in evaluating polynomials. Our protocol ensures input privacy, zero-knowledge, and security against fully malicious provers, all while eliminating the need for any inter-prover communication, making it highly suitable for distributed cloud environments. At the core of our approach is the introduction of the Restricted Quadratic Arithmetic Program model, an algebraic structure aligned with RMS semantics that enables provers to independently generate local proofs. We instantiate our framework using the Pinocchio protocol, resulting in a system that requires only 9 group elements per proof and 10 pairings for verification, nearly matching the efficiency of its single-prover counterpart. By leveraging our multi-prover zkSNARK protocol within a multi-server verification computation framework, we enable secure outsourcing of computations to the cloud of fully untrusted cloud servers. Compared to existing works, our protocol uniquely eliminates the need for any inter-server communication while achieving security even against adversaries controlling all servers.
This paper proposes Temporal Convolutional Networks (TCNs) for cryptocurrency forecasting at minute-resolution. TCNs show better accuracy to XGBoost, LightGBM, and LSTM. However, TCNs are less robust than the tree models regarding volatility. While TCNs require much more computations at inference than LightGBM, they run faster than LSTMs. The performance of TCNs is best configured using a TCN with dilated convolutions to capture the temporal patterns, and with residual connections for the stability.