Blockchain Papers

Liuyu Yang, Xinxuan Zhang, Yi Deng, Zhuo Wu · 5 authors

No abstract is available for this record.

Brian R. Cook, Nicholas Harrigan, Van Touch, Kirt Hainzer · 7 authors

ABSTRACT The arts are envisioned as able to help address the longstanding ‘implementation gap’ between research and realisation of the Sustainable Development Goals (SDGs). For SDG2 (Zero Hunger), Forum Theatre offers a participatory alternative to top‐down interventions, yet its impacts have not been evaluated using rigorous, mixed‐methods that are attentive to both quantitative and qualitative data, to spillover effects, or to diffusion over time. This study analyses 13 performances in Northwest Cambodia, each attended by 50–150 people, with follow‐up interviews conducted with 66 attendees 1 year later. Results identify a replicable impact pathway: learning correlates with on‐farm behaviour change, which is predictive of knowledge‐sharing with non‐attendees, whereas recollection alone does not. By evidencing this process, the analysis provides rare empirical proof of theatre's effectiveness as a catalyst for change. More broadly, it evidences a replicable pathway for achieving the SDGs, but one that requires moving beyond information‐transfer models toward participatory interventions that foster dialogue, critical reflection, forum, and the collective diffusion of new practices. A short documentary and accompanying video of performances are available to illustrate the process and support others seeking to replicate or adapt the approach in different contexts.

Revista, Zen, MATH, 10

Reverse Mathematics is a program in mathematical logic that investigates the minimal axiomatic subsystems of second-order arithmetic required to prove theorems of ordinary mathematics. Developed primarily by Harvey Friedman and Stephen Simpson, this field seeks to "go backwards" from established mathematical theorems to determine the precise set-existence principles necessary for their proofs. The central framework for this analysis is second-order arithmetic ($Z_2$), which formalizes natural numbers and sets of natural numbers. By working within weak base theories, typically Recursive Comprehension Axiom Zero (RCA$_0$), researchers classify a vast array of mathematical theorems into a hierarchy of five main subsystems: RCA$_0$, Weak König's Lemma (WKL$_0$), Arithmetical Comprehension Axiom Zero (ACA$_0$), Arithmetical Transfinite Recursion Zero (ATR$_0$), and $Pi^1_1$-Comprehension Axiom Zero ($Pi^1_1$-CA$_0$). This paper provides a comprehensive overview of Reverse Mathematics, detailing its historical development, core methodology, the characteristics of the "Big Five" subsystems, and representative mathematical theorems classified within each. It explores the philosophical implications of this program, highlighting how it unveils the precise logical and foundational microstructure underlying seemingly diverse mathematical results, thereby contributing to a deeper understanding of the inherent strengths and dependencies of mathematical knowledge.

Yibin Yang

No abstract is available for this record.

Nicholas Tio, Octara Pribadi, Robet Robet

The increasing need for trustworthy digital document verification presents challenges in ensuring authenticity, transparency, and tamper resistance without relying on centralized authorities. This study aims to develop and evaluate a decentralized document notarization system using Ethereum and IPFS that offers secure, transparent, and cost-efficient verification. The system employs modular smart contracts deployed through a factory pattern to create user-specific verifier instances, enabling document submission, revocation, and verification using keccak-256 hashes, ECDSA signatures, and IPFS content identifiers. Methods include contract development, deployment on a local Hardhat network, performance benchmarking, and front-end integration for user interaction. Results show that verifier deployment consumes approximately 1.19 million gas (≈$85 at 20 gwei), document submission around 85 thousand gas (≈$6), and revocation about 50 thousand gas (≈$3.50). Client-side operations such as hashing and IPFS pinning occur in under 50 milliseconds, while real-world blockchain confirmations take 10–30 seconds. The findings demonstrate that decentralized notarization using Ethereum and IPFS is both technically feasible and economically viable. Future enhancements, including Layer 2 rollups, batch notarization, and privacy-preserving features such as encrypted IPFS pinning or zero-knowledge proofs, are proposed to further improve scalability, cost-efficiency, and data confidentiality

pavan

In India’s rapidly expanding digital finance ecosystem, Jaimax Coin has emerged as one of the most reliable, transparent, and growth-oriented blockchain assets. As the demand for secure crypto coin options increases, Jaimax is positioned as one of the best presale crypto coin in India, offering visionary investors unmatched opportunities in the evolving world of decentralized finance, NFTs, and digital currency innovation. Backed by advanced blockchain architecture, transparent tokenomics, and a strong mission toward accessible financial technology, Jaimax stands apart from the countless new crypto coins in India.

Monika Malik

The artificial intelligence (AI) infrastructures have been centralized leading to limited accessibility, monopoly of computational resources, and an uneven distribution of services. CloudChain is a decentralized AI compute market that is made out of blockchain and can solve these challenges with a transparent, trustless, and fair system. It brings together decentralized storage, smart contracts, as well as token incentives to allow fairness, privacy, and auditing. Privacy is ensured through encryption and zero-knowledge proofs, task allocation, distribution of rewards and enforcement of SLA is automated through smart contracts. The performance metrics measured in a 30-day simulation of the major cloud providers (AWS, Google Cloud, Azure, Hetzner, Lambda Labs) and the community nodes included the performance measures of latency, throughput, and SLA compliance, as well as token allocation and resource utilization. Findings indicate that CloudChain does provide the necessary balance in the workloads, high quality in the service delivery, and equitable rewards among heterogeneous members. The suggested framework envisioned will create a democratized, secure, and sustainable platform of decentralized AI, enabling innovation, openness, and diversity of global AI ecosystems.

Zhaoyu Wang, Pingchuan Ma, Zhantong Xue, Yanbo Dai · 6 authors

Prescriptive analytics seeks to identify optimal interventions for achieving desired outcomes, with causal inference playing a pivotal role in assessing intervention impacts on complex systems. However, existing approaches frequently neglect critical data privacy considerations and provide no means to verify the integrity of their recommendations. These limitations hinder its adoption in high-stakes domains such as healthcare and finance. In this paper, we introduce, zkCLEAR, a zero-knowledge proof (ZKP)-based C ausal Inference ( LEA rning and R easoning) framework for privacy-preserving and verifiable prescriptive analytics. Our solution allows data owners or service providers to cryptographically prove the validity of prescriptive conclusions derived from causal analysis without disclosing sensitive source data or proprietary causal models. We develop a suite of ZKP-friendly causal operators to build efficient causal modules, including structure learning, parameter learning, probabilistic inference, and counterfactual reasoning. To optimize performance, we also introduce a workflow decomposition strategy to facilitate efficient proof generation for complex workloads. We demonstrate the utility of zkCLEAR through three real-world applications. The framework faithfully follows the behavior of non-ZKP counterparts, with moderate overheads for privacy and verifiability. Additionally, we evaluate its efficiency and scalability using real-world datasets. It shows up to a 35.1× speedup in proof generation time and a 214.5× reduction in proof size compared to current general-purpose ZKP systems.

Dheerendra Mishra, Rohit Raj Sharma

No abstract is available for this record.

Fatemeh Tabe, Ali Mansourabady, Amir Hossein Rasekh, Betsabeh Tanoori

No abstract is available for this record.

Revista, Zen, IA, 10

This paper addresses the critical need for accountability in artificial intelligence (AI) systems, particularly in domains where decisions have significant societal and ethical implications. We propose a novel framework leveraging auditable attestations to ensure provable compliance with predefined standards and regulations. The core of our approach involves generating verifiable proofs about the behavior and characteristics of machine learning models, allowing for independent audits and assessments. We explore the theoretical foundations of such attestations, focusing on cryptographic techniques like zero-knowledge proofs and secure multi-party computation, which enable the verification of model properties without revealing sensitive information. Furthermore, we discuss the practical implementation of our framework, including the design of attestation protocols, the selection of relevant model properties to verify, and the development of tools for generating and validating attestations. We illustrate the effectiveness of our approach through case studies in areas such as fairness in lending, transparency in healthcare, and safety in autonomous driving. Our results demonstrate the potential of auditable attestations to enhance trust and accountability in AI systems, fostering responsible innovation and deployment.

Mazari, Ilyes Tarik, Mazari, Yanis, Mazari, Ilyan

We introduce the Y.I.N. Mazari Ordering, a fundamental primitive for achieving verifiable differential privacy in federated learning systems. The ordering (noise → proof → encrypt → aggregate) is proven to be necessary—no efficient alternative exists—and universal across all encryption schemes, proof systems, and aggregation topologies. Patent pending: US 63/923,348, US 19/399,646, US 19/403,244 Keywords: Verifiable Differential Privacy, Federated Learning, Zero-Knowledge Proofs, Homomorphic Encryption, Privacy-Preserving Machine Learning

Mazari, Ilyes Tarik, Mazari, Yanis, Mazari, Ilyan

We introduce the Y.I.N. Mazari Ordering, a fundamental primitive for achieving verifiable differential privacy in federated learning systems. The ordering (noise → proof → encrypt → aggregate) is proven to be necessary—no efficient alternative exists—and universal across all encryption schemes, proof systems, and aggregation topologies. Patent pending: US 63/923,348, US 19/399,646, US 19/403,244 Keywords: Verifiable Differential Privacy, Federated Learning, Zero-Knowledge Proofs, Homomorphic Encryption, Privacy-Preserving Machine Learning

Sarim Zia, Saleha Qureshi, Muhammad Zulfiqar, Arfa Ijaz

The paper discusses the economic and infrastructural challenges preventing the adoption of Electric Vehicles (EVs) in Pakistan.It focuses on key factors such as affordability, consumer preferences, and the overall readiness of the market.Based on a segment-wise comparison, the analysis reveals that four-wheeler EVs carry an initial price premium of 20 to 64 percent over internal combustion engine (ICE) vehicles, with payback periods ranging from 11 to 25 years, placing them out of reach for most middle-income consumers.In contrast, electric two-and three-wheelers-comprising more than 90 percent of registered vehicles-offer a significantly more practical and affordable pathway for mass adoption.These vehicles exhibit minimal upfront cost differences, annual operational savings exceeding PKR 62,000, and short payback periods of just 4 to 6 months, making them highly feasible in the local context.The study adopts a mixed-methods approach using national price data, vehicle registration records, and international case studies from India, Kenya, and Norway.It evaluates financing innovations such as battery leasing, concessional green loans, and carbon-credit-linked microfinance, and outlines a consumer-focused policy framework that emphasizes financial inclusion, decentralized infrastructure development, and phased implementation strategies.By aligning global lessons with Pakistan's socioeconomic and infrastructural realities, the paper offers a scalable and inclusive roadmap for accelerating EV adoption through targeted, consumer-driven solutions.

Zhao Zhang, Chunxiang Xu, Changsong Jiang

No abstract is available for this record.

Helger Lipmaa

No abstract is available for this record.

Sara Abossedgh, Ali Yeganeh, Arne Johannssen

Crypto analysts have to deal with a variety of challenges, with the most important area being the price volatility of cryptocurrencies. Due to uncertain market trends, many studies have been conducted on forecasting techniques, and some of these techniques have been integrated with advanced analytical tools, including machine learning (ML) techniques. Making reliable predictions of the speculative behavior of financial assets, especially in non-stationary and highly volatile environments such as the cryptocurrency market, is a challenging task. In this study, ML techniques are used to identify influential features that affect the prices of cryptocurrencies, especially for Bitcoins. In addition, multivariate control charts are utilized for signal detection, allowing for a structured approach to develop trading strategies for seasonal market conditions. Unlike other studies that do not take seasonality into serious consideration when analyzing market fluctuations, the proposed approach explicitly accounts for it. The developed strategy is tested across various market conditions, including the final days of each year from 2019 to 2024, and demonstrates strong and consistent performance in all cases. By systematically identifying key on-chain features and analyzing them by means of control charts, this study develops a structured approach to anomaly-based trading strategies in Bitcoins. These discoveries address an extensive discussion on automated trading systems, demonstrating that feature selection, technical indicators, market seasonality, and halving impacts are important components in hinting at successful cryptocurrency exchange strategies.

Antonio Martínez Raya, Alejandro Segura de la Cal, Javier Espina Hellín

Since its launch in 2009, Bitcoin has become a market disruptor due to its primary function as a virtual currency supported by blockchain technology and the high volume of economic transactions it facilitates. This article examines the key theoretical principles that have contributed to Bitcoin’s recognition as a cryptocurrency. It assesses whether Bitcoin meets the criteria for being considered a form of money and evaluates its importance as a financial asset. This analysis of Bitcoin from 2014 to 2025 reveals that it does not sufficiently fulfill all the typical functions of money, such as serving as an internationally accepted means of payment, a unit of account, a securities depository, and a standard for deferred payments. Despite its usual close correlation with stock indices in financial markets, a decentralized digital currency like this still does not meet the requirements of fundamental analysis. In practice, this leads to its exclusion as a currency, since it does not fulfill the functions of money nor fully qualify as a crypto asset, as its value is primarily based on investors’ expectations of high returns. Apart from a lack of foundation in tangible goods or services that justifies their value and dependence on new investors, the findings do not indicate conditions typical of a developed pyramidal model. Nevertheless, this does not prevent future technological innovations from responding positively to the functions of money or from offering real money services, especially those related to service innovation and the digital economy.

Hae Sun Jung, Haein Lee

This study conducts a bibliometric review of Bitcoin research in the Business and Economics domains, using VOSviewer to visualize network structures and Bidirectional Encoder Representations from Transformers Topic (BERTopic) to derive semantically coherent topic clusters. The analysis identifies five major research themes: (1) Diversification, hedging, and safe-haven properties; (2) Market dynamics, efficiency, and investor behavior; (3) Bitcoin price and volatility prediction attempts; (4) Environmental impact of Bitcoin; and (5) Financial impact of Central Bank Digital Currency (CBDC). Based on these themes, the study recommends further investigation into the influence of Exchange-Traded Fund (ETF) approvals, regulatory frameworks, and institutional investor participation on Bitcoin’s safe-haven potential; the role of market dynamics and regulatory interventions; early detection of herding behavior and price bubbles; the integration of machine learning and deep-learning models for price prediction; the environmental costs associated with mining; and the evolving regulatory and implementation challenges of CBDCs. Overall, this review synthesizes existing scholarship and outlines future research directions for the rapidly evolving cryptocurrency ecosystem.

Farhan Rachmawan, Astika Nurul Hidayah

Digital assets such as cryptocurrency, social media accounts, NFTs (Non-Fungible Tokens), and other types of virtual ownership have emerged as a result of rapid technological advances. This phenomenon raises new issues in Islamic inheritance law in Indonesia, particularly regarding the status, inheritance procedures, and security of assets. This research method uses a normative juridical approach with a legislative and conceptual approach. The study utilizes primary legal sources from legislation, hadith, and the Quran, as well as secondary legal sources from literature reviews. The objective of this research is to evaluate the status of digital assets as inheritable property from an Islamic legal perspective and to determine their legal certainty within the Islamic inheritance system in Indonesia. The results of the study indicate that digital assets, which have economic value, can be inherited according to Islamic law. Highlighting the absence of regulations, technical challenges in access and security, and the need for harmonization between Sharia law and technological developments, despite the detailed regulation of principles of justice and transparency in Islamic inheritance law, the implementation of digital asset distribution is still hindered by the absence of clear legal mechanisms and operational standards for the inheritance of digital assets. To achieve justice and avoid disputes among heirs, there is a need to strengthen regulations, promote Sharia digital literacy education, and foster collaboration between the government, religious scholars, and technology practitioners.

Abdessamad Snoussi Amouri

This comprehensive review examines the evolutionary trajectory of financial information systems from the 1670s to the present day, analyzing how technological innovations have fundamentally transformed financial reporting, auditing practices, and information accessibility. Through a bibliometric and conceptual analysis of seminal literature, this study identifies key technological inflection points including the emergence of structured bookkeeping systems, the institutionalization of financial publicity through the 1867 law, the development of sophisticated financial communication tools, and the recent integration of blockchain technology and data analysis capabilities. The review demonstrates that each technological wave has progressively enhanced data accuracy, real-time reporting capabilities, and audit efficiency while simultaneously introducing new challenges related to data security, regulatory compliance, and technological adoption barriers. Contemporary developments in distributed ledger technology and advanced analytics represent a paradigm shift toward autonomous financial reporting systems with unprecedented transparency and verification capabilities. The findings suggest that future financial information systems will be characterized by increased automation, enhanced predictive analytics, and seamless integration of blockchain-based audit trails. This evolution has profound implications for accounting professionals, regulatory frameworks, and corporate governance structures, necessitating adaptive strategies for stakeholder education and regulatory modernization.

Zulian Wahid, Suryo Adhi Wibowo, Andry Alamsyah

The growth of Decentralized Finance (DeFi) demands advanced fraud detection, yet current methods face a trade-off: tabular models lack semantic understanding, while language models like RoBERTa struggle with structured data and high computational costs. This paper introduces a novel pipeline that transforms structured Ethereum transaction data into natural language sentences, enabling a standard RoBERTa model to analyze financial behavior efficiently using Gradient Accumulation. A stratified 5-fold cross-validation on a public dataset revealed a key performance trade-off: while Random Forest achieved the highest F1-Score (0.796), our RoBERTa GA model proved superior in the critical metric of Recall (0.769). This finding validates our semantic approach not merely as a competitive alternative, but as a strategically advantageous method when the primary goal is minimizing missed fraudulent transactions. Our work confirms the viability of applying NLP to blockchain security and provides a foundation for future language-model-driven monitoring systems.

Revista, Zen, IA, 10

The traditional paradigm of centralized artificial intelligence systems often faces significant challenges when confronted with complex, dynamic, and uncertain real-world environments. These challenges include issues of scalability, resilience to partial failures, and adaptability to unforeseen circumstances. This paper introduces the concept of "Emergent Ensembles," a transformative approach rooted in self-organizing collective intelligence, designed to address these limitations for adaptive AI systems. Drawing inspiration from natural collective behaviors such as ant colonies and bird flocks, Emergent Ensembles propose a decentralized architecture where numerous autonomous agents collaborate, adapt, and self-organize through local interactions to achieve complex global objectives. The framework emphasizes key attributes such as task generalization, collective resilience, scalability, and self-assembly, enabling systems to dynamically reconfigure their structure, behavior, and scale during inference. We explore the underlying principles of self-organization, decentralized decision-making, adaptive learning, and context-rich communication protocols, such as gossip mechanisms, that facilitate the emergence of intelligent global behavior from simple local rules. By integrating AI-driven adaptive nodes capable of autonomous power adjustment and leveraging multi-layer perceptron models for local decision-making, these ensembles demonstrate enhanced connectivity, robustness, and energy efficiency. This work outlines a conceptual framework for designing, analyzing, and engineering resilient, scalable, and adaptive AI systems, paving the way for innovative applications in fields ranging from robotics and optimization to environmental monitoring and smart cities. The ultimate goal is to foster AI systems that can exhibit robust performance and self-sustainment in highly dynamic and unpredictable real-world scenarios, addressing computational bottlenecks and ethical considerations inherent in decentralized AI.

Xue Ma, ShuoShuo Lv, Wenbao Hu, Cunqiang Huang · 5 authors

Inter-provincial electricity transactions within China’s unified power market are complicated by spatial heterogeneity, asynchronous dispatch timelines, and strategic deviations in bilateral commitments. Existing mechanisms often struggle with ex-post contestability, temporal inconsistencies, and poor alignment between real-time system conditions and deviation pricing, undermining the market’s fairness and reliability. To address these challenges, this paper proposes a novel Tri-Ledger Coordinated Settlement (TCS) framework with built-in temporal consistency. The tri-ledger design consists of (1) a Contract Ledger capturing day-ahead bilateral schedules, (2) a Dispatch Ledger reflecting system-level nodal redispatch outcomes, and (3) a Deviation Ledger reconciling discrepancies across provinces through an enforceable and tamper-resistant protocol. Central to this framework is a Distributionally Robust Deviation Pricing (DRDP) model, which penalizes deviation behaviors not based on deterministic thresholds but through ambiguity-aware dual pricing anchored in Wasserstein-ball uncertainty sets. This allows the pricing system to anticipate manipulative strategies while offering probabilistic fairness to genuine imbalances caused by renewables or congestion. Furthermore, a Non-Contestable Decoupled Execution Mechanism (NCDEM) is developed to isolate provincial profit zones during redispatch operations, ensuring that a province cannot benefit by manipulating its declared bilateral trades or influencing others’ deviation compensations. The proposed approach guarantees strategy-proofness under minimal information assumptions and supports distributed execution by provincial grid companies without centralized re-optimization. The effectiveness of the framework is demonstrated on a stylized multi-province testbed derived from China’s Eastern and Central grid clusters. Numerical experiments show that the DRDP-based settlement leads to over 18.4% improvement in fairness-adjusted social welfare and reduces strategic deviation incentives by up to 73% compared to deterministic baseline models. Sensitivity analyses validate robustness under multiple load and RES penetration scenarios. The proposed TCS framework offers policy-relevant insights for implementing transparent and resilient provincial electricity market settlements under China’s “dual-track” trading architecture.