Enterprise identity systems can authenticate workloads, credentials, and attested platforms, but they do not close the composition layer where runtime model identity enters authorization. A token can verify that a service is running in a trusted environment, that its credentials are valid, and that its actions are authorized — without ever establishing which neural network is actually computing. When model identity evidence is inserted into standard authorization flows, new security properties emerge that are not inherited from the underlying protocols and must be formally established rather than presumed. This paper presents a live integration architecture for model-identity attestations in JWT and SPIFFE-style token flows, grounded in real measurements from six neural networks executed inside an NVIDIA H100 Confidential Computing enclave. It formally verifies four composition properties — non-separability, temporal binding, issuer authenticity, and reference integrity — across three Coq proof files with zero unfinished proof obligations. Every remaining trust dependency is explicitly named, traced to an integration control, and paired with a concrete falsification witness. The result is a formally hardened composition layer where no security property is left implicit and no assumption is left silent. Supplementary Material This paper is accompanied by three Coq proof files — ComposableIdentity.v, IssuerAuthenticity.v, and ReferenceIntegrity.v — that formally verify the four composition properties described in §§4–6: non-separability, temporal binding necessity, issuer authenticity, and reference integrity. Together the files prove thirteen theorems from eleven named axioms, each paired with a concrete falsification witness and an integration control. No file contains unresolved obligations (Admitted), and all three compile cleanly under the Rocq Prover 9.1.1 (the current release of the Coq proof assistant, compiled with OCaml 5.4.0). They are available for download as supplementary files attached to this record. The Neural Network Identity Series — Mathematical foundations, empirical validation, and governance frameworks for verifying which model is running Newest addition: Technical Note: The Disappearing Window — AI Logprob Access Withdrawal and the Structural Verifiability of Frontier Model Contracts (DOI: 10.5281/zenodo.20362098) Paper 1: The δ-Gene: Inference-Time Physical Unclonable Functions from Architecture-Invariant Output Geometry (DOI: 10.5281/zenodo.18704275) Paper 2: Template-Based Endpoint Verification via Logprob Order-Statistic Geometry (DOI: 10.5281/zenodo.18776711) Paper 3: The Geometry of Model Theft: Distillation Forensics, Adversarial Erasure, and the Illusion of Spoofing (DOI: 10.5281/zenodo.18818608) Paper 4: Provenance Generalization and Verification Scaling for Neural Network Forensics (DOI: 10.5281/zenodo.18872071) Paper 5: Beneath the Character: The Structural Identity of Neural Networks — Mathematical Evidence for a Non-Narrative Layer of AI Identity (DOI: 10.5281/zenodo.18907292) Paper 6: Which Model Is Running?: Structural Identity as a Prerequisite for Trustworthy Zero-Knowledge Machine Learning (DOI: 10.5281/zenodo.19008116) Paper 7: The Deformation Laws of Neural Identity (DOI: 10.5281/zenodo.19055966) Paper 8: What Counts as Proof? — Admissible Evidence for Neural Network Identity Claims (DOI: 10.5281/zenodo.19058540) Paper 9: Composable Model Identity — Formal Hardening of Structural Attestations in the Enterprise Identity Stack (DOI: 10.5281/zenodo.19099911) Paper 10:Where Identity Comes From: Path Sensitivity and Endpoint Underdetermination in Neural Network Training (DOI: 10.5281/zenodo.19118807) Paper 11: Post-Hoc Disclosure Is Not Runtime Proof: Model Identity at Frontier Scale (DOI: 10.5281/zenodo.19216634) Paper 12: Family-Dependent Response to Reasoning Distillation Across Structural and Functional Identity Layers (DOI: 10.5281/zenodo.19298857) Paper 13: Safety-Alignment Removal as a Model-Identity Failure — Structural Evidence from Published Weight-Level Mutation Checkpoints (DOI: 10.5281/zenodo.19383019) Technical Note: Agent Identity Is Not Model Identity (DOI: 10.5281/zenodo.19240883) Technical Note: Gap Invariance: Why PPP Measurements Are Domain-Independent by Construction (DOI: 10.5281/zenodo.19275524) Technical Note: Measured Model Substitution Under Valid Agent Credentials (DOI: 10.5281/zenodo.19342848) Technical Note: Artifact Identity Is Not Runtime Identity — Trustfall Lite and the Boundary of File-Level Model Verification (DOI: 10.5281/zenodo.20019127) Formal Verification Stack for Neural Network Structural Identity (IT-PUF Coq Proofs) (DOI: 10.5281/zenodo.18930621) Copyright (c) 2026 Anthony Ray Coslett / Fall Risk AI, LLC. All Rights Reserved. Confidential and Proprietary. Patent Pending (Applications 63/982,893, 63/990,487, 63/996,680, 64/003,244).
Annotatsiya.Ushbu maqolada blockchain texnologiyasining moliyaviy tranzaksiyalarni nazorat qilishdagi arxitekturaviy imkoniyatlari IMRAD metodologiyasi doirasida tahlil qilinadi. Distributed Ledger Technology (DLT), smart-kontraktlar va kriptografik audit izlarining moliyaviy nazorat tizimiga integratsiyasi nazariy va empirik jihatdan asoslantirilgan. Yettita konsensus algoritmi (PoW, PoS, PBFT, DPoS, PoA, Raft) moliyaviy sektor uchun qiyosiy baholangan. An’anaviy va blockchain asosidagi nazorat tizimlarining olti o‘lchamda qiyosiy tahlili o‘tkazildi. ISO 31000:2018 asosida yettita asosiy risk (R₁–R₇) baholandi. Estoniya, Dubay, Singapur, JPMorgan, Braziliya va O‘zbekiston misolida xalqaro tajriba sintezi amalga oshirildi. Samaradorlik tahlili shuni ko‘rsatadiki, blockchain asosidagi nazorat tizimi tranzaksiya narxini 40–60%, operatsiya vaqtini 60–70%, xatoliklar sonini 80–90% va audit jarayonini 50–60% qisqartirishi mumkin. O‘zbekiston uchun Hyperledger Fabric + PBFT/PoA konsensus asosidagi ruxsatli blockchain modeli tavsiya etiladi. Kalit so‘zlar: blockchain, distributed ledger technology (DLT), smart-kontrakt, konsensus algoritmi, kriptografik audit izi, moliyaviy nazorat, zero-knowledge proof, CBDC, Hyperledger Fabric, O‘zbekiston raqamli moliyasi. Аннотация. В данной статье в рамках методологии IMRAD анализируются архитектурные возможности технологии блокчейн в сфере контроля финансовых транзакций. Теоретически и эмпирически обоснована интеграция технологии распределенного реестра (DLT), смарт-контрактов и криптографических аудиторских следов в систему финансового контроля. Проведена сравнительная оценка семи алгоритмов консенсуса (PoW, PoS, PBFT, DPoS, PoA, Raft) для финансового сектора. Осуществлен сравнительный анализ традиционных и основанных на блокчейне систем контроля по шести параметрам. На основе стандарта ISO 31000:2018 оценены семь ключевых рисков (R1-R7). Выполнен синтез международного опыта на примере Эстонии, Дубая, Сингапура, JPMorgan, Бразилии и Узбекистана. Анализ эффективности показывает, что система контроля на основе блокчейна может сократить стоимость транзакций на 40-60%, время операций - на 60-70%, количество ошибок - на 80-90% и длительность аудита - на 50-60%. Для Узбекистана рекомендуется модель разрешенного блокчейна на базе Hyperledger Fabric с консенсусом PBFT/PoA. Ключевые слова: блокчейн, технология распределенного реестра (DLT), смарт-контракт, алгоритм консенсуса, криптографический аудиторский след, финансовый контроль, доказательство с нулевым разглашением, CBDC, Hyperledger Fabric, цифровые финансы Узбекистана. Abstract. This article analyzes the architectural capabilities of blockchain technology in monitoring financial transactions, utilizing the IMRAD methodology. It provides theoretical and empirical justification for integrating Distributed Ledger Technology (DLT), smart contracts, and cryptographic audit trails into financial monitoring systems. Seven consensus algorithms (PoW, PoS, PBFT, DPoS, PoA, Raft) are comparatively evaluated for their suitability in the financial sector. A comparative analysis of traditional and blockchain-based monitoring systems is conducted across six dimensions. Seven key risks (R1-R7) are assessed in accordance with ISO 31000:2018. A synthesis of international experience is presented, drawing on case studies from Estonia, Dubai, Singapore, JPMorgan, Brazil, and Uzbekistan. The efficiency analysis indicates that a blockchain-based monitoring system can reduce transaction costs by 40-60%, operational time by 60-70%, error rates by 80-90%, and audit process duration by 50-60%. For Uzbekistan, a permissioned blockchain model based on Hyperledger Fabric, utilizing a PBFT or PoA consensus algorithm, is recommended. Keywords: blockchain, distributed ledger technology (DLT), smart contract, consensus algorithm, cryptographic audit trail, financial monitoring, zero-knowledge proof, CBDC, Hyperledger Fabric, Uzbekistan digital finance.
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Advanced Computational Techniques in Science and Engineering
Smart contract vulnerability benchmarking lacks standardized, reproducible datasets that enable fair and consistent evaluation of static analysis tools. This paper presents SARMF-Bench, a compact, deterministic, and fully reproducible benchmark dataset comprising five SWC-aligned Solidity smart contracts (SC01–SC05) covering reentrancy (SWC-107), integer overflow/underflow (SWC-101), access-control weakness (SWC-105), unchecked external calls (SWC-104), and denial-of-service via unbounded loops (SWC-113). Each contract is intentionally minimal to isolate a single structural vulnerability pattern and is paired with machine-readable JSON outputs generated using Slither v0.11.5 in a version-locked environment, preserving detector identifiers, impact levels, and confidence metadata. SARMF-Bench is archived across multiple open repositories with permanent DOIs to enable fully reproducible smart contract security tool evaluation experiments. Baseline static analysis results are reported for each vulnerability class. All artifacts are publicly released under open licenses.
Abdul-Wahab Tahiru, Silas Uwumborge Takal, Samuel Jerry Cobbina, Wilhemina Asare · 5 authors
Northern Ghana faces acute climate vulnerabilities, yet adaptation pathways remain fragmented and poorly synthesized. This study systematically reviewed 15 peer-reviewed literature covering the Savannah, Upper East, Upper West, North East, and Northern regions to assess climate impacts on agro-pastoral communities, evaluate existing adaptation strategies, and explore policy implications. Findings reveal that erratic rainfall, prolonged droughts, rising temperatures, and land degradation have undermined food security and livestock systems, while increasing pest outbreaks and intensifying farmer–pastoralist conflicts. Communities have responded through water harvesting, livelihood diversification (including agroforestry, shea processing, small livestock rearing, and seasonal migration), and reliance on indigenous knowledge systems. However, these strategies remain constrained by inadequate finance, weak infrastructure such as faulty hand pumps, gender inequalities, and limited integration with formal climate services. The review underscores the need for coherent policies that expand decentralized water infrastructure, scale climate-smart financing, institutionalize conflict-resolution platforms, and embed gender-responsive and indigenous approaches into national adaptation planning. Strengthening the interface between local innovation and formal governance is critical for building inclusive and scalable resilience across Northern Ghana’s agro-pastoral systems.
This paper presents an analysis of five years (2021 - 2025) of conference discourse across six digital art conferences, aiming to trace thematic shifts associated with the rapid development of emerging technologies, namely artificial intelligence (AI), immersive technologies (including XR and the metaverse), and blockchain technologies and non-fungible tokens (NFTs). The results indicate a marked increase in AI-related contributions, while immersive technologies maintain a relatively stable share of the discourse, and blockchain- and NFT-based works remain marginal. Overall, whereas immersive technologies and blockchain-related topics exhibit relative stability, AI shows a significant rise after 2022, emerging as a dominant theme within digital art conference discourse.
Ethereum Inscriptions (Ethscriptions) repurpose Ethereum calldata into a persistent inscription channel by embedding \texttt{data:}~URI payloads. These transactions typically target externally owned accounts, allowing the payload to bypass EVM execution while remaining permanently replicated across full nodes. Although calldata was originally designed for compact smart-contract parameters, this repurposing enables structured data embedding with long-term storage consequences. We present the first large-scale empirical study of Ethscriptions, treating them as a distinct \emph{calldata-resident workload} rather than merely a subset of general calldata usage. Our analysis focuses on the \textit{Ethscription} operational subset, which consists of payloads that decode to JSON and conform to a token-operation grammar (e.g., \texttt{p}, \texttt{op}, \texttt{tick}, \texttt{amt}). From $6.27$ million Ethscription candidates (\Uone), we extract $4.75$ million Ethscription operations (\Utwo, $75.8\%$ of \Uone). This result shows that structured token-like activity dominates the ecosystem. Our measurements further reveal (i) a complete workload lifecycle compressed into nine months (bootstrap, expansion, saturation), (ii) proliferation of $30$+ competing protocols without convergence toward a dominant standard, (iii) a lifecycle funnel exhibiting $201\times$ deploy-to-mint amplification and a $57.6{:}1$ mint-to-transfer collapse indicative of speculative minting, (iv) extreme participation inequality (Gini~$0.86$), and (v) a measurable permanent data footprint imposed on the Ethereum network.
Climate change, driven by global warming and associated greenhouse gas (GHG) emissions, poses a significant global challenge. International organizations and governments are actively pursuing emission reduction strategies, yet these efforts are often constrained by the direct relationship between emissions and national economic activity. This paper proposes a blockchain-based carbon footprint (CF) management system named P u r e C a r b o P r i n t , that leverages data from IoT devices, and security is ensured by zero-knowledge proofs (ZKP) to track and reduce CF at the individual level. Individual data is collected and converted into carbon coin, a hybrid (online-offline) crypto coin operating on the Pure Chain network. A smart contract, deployed on the Pure Chain network using the Pure Chain coin, governs the proposed system. Additionally, zk-SNARK is applied to implement ZKP for the validity and integrity of the information verification without revealing private information. Based on theoretical models and previous studies on behavior-based carbon reductions, it is expected that the system can achieve up to a 30% reduction in CF per user within the first year.
We present an experience report on YouTube-Synch, a production system that replicates content from 10,000+ creator-authorized YouTube channels to a blockchain-based decentralized platform. Although replication is authorized, the system must still defeat YouTube's anti-automation defenses - API quota restrictions (10,000 units/day), IP-based rate limiting, behavioral bot detection, and OAuth token lifecycle policies - which do not distinguish authorized bulk access from abuse. Our central observation is a coupled-defense phenomenon: these protection layers are not independent, so circumventing one (e.g., API quotas) silently activates another (e.g., OAuth token expiration), producing cascading, delayed failures. We ground this in three production incidents with concrete impact - 28 duplicate on-chain objects from a database throughput failure, 10,000+ channels lost to a single OAuth mass-expiration, and 719 daily errors from queue pollution - observed over 15 releases and 3.5 years of operation. We further argue, from the system's own concurrency and rate parameters alone, that detection-driven anti-bot measures impose a download-bound throughput ceiling on the order of 10^3 videos/day per instance - at or below the steady-state demand of 10,000 channels. This analysis shows why priority-based triage and horizontal scaling are structural necessities rather than optimizations, and explains the survivability-over-throughput tradeoff that drove a 25x reduction in download concurrency. We detail the resulting architecture - a four-stage DAG pipeline, a Write-Ahead-Log fault-tolerance model with cross-system state reconciliation, and a trust-minimized ownership-verification protocol that eliminates OAuth - and distill design principles that generalize to extraction against other heavily-defended centralized platforms.
Privacy protection mechanisms are a fundamental aspect of security in cryptocurrency systems, particularly in decentralized networks such as Bitcoin. Although Bitcoin addresses are not directly associated with real-world identities, this does not fully guarantee user privacy. Various deanonymization solutions have been proposed, with network layer deanonymization attacks being especially prominent. However, existing approaches often exhibit limitations such as low precision. In this paper, we propose \textit{NTSSL}, a novel and efficient transaction deanonymization method that integrates network traffic analysis with semi-supervised learning. We use unsupervised learning algorithms to generate pseudo-labels to achieve comparable performance with lower costs. Then, we introduce \textit{NTSSL+}, a cross-layer collaborative analysis integrating transaction clustering results to further improve accuracy. Experimental results demonstrate a substantial performance improvement, 1.6 times better than the existing approach using machining learning.
Language isn't just a rigid system of symbols. Instead, it's a living, embodied phenomenon, deeply intertwined with our physical experience and shaped by our interaction with the environment (Wang, 2019;Zhou & Luo, 2024). However, the dynamic nature of language brings a significant challenge to cognitive science: the well-known "stability-plasticity dilemma" (Grossberg, 1980). On one hand, for clear communication, meanings of words need to be stable and widely recognized, so everyone can understand them, no matter when or who speaks them. On the other hand, these meanings must also be flexible and adaptable in varying contexts. While traditional computational models, from early generative grammar to standard Bayesian approaches, have excelled at modeling these stable meanings, they often treat semantic ambiguity as "noise" that needs to be eliminated, rather than a valuable resource (Gärdenfors, 2014).Even with the significant "probabilistic turn" in cognitive science, which brought Bayesian models to handle uncertainty, most of these models still rely on classical probability theory. They assume the meaning of a concept is a pre-defined distribution over a set of fixed features. As Bruza and Cole (2005) pointed out, this dependence on classical set theory creates a major epistemological barrier because it treats semantic ambiguity as "noise" rather than a fundamental part of meaning construction. Though scholars have recently developed more complex tools, like Gradient Symbolic Representations (GSR), to model meanings as weighted mixtures (Smolensky et al., 2014;Mondal, 2024), these approaches are still limited by Kolmogorovian probability. They still follow the Law of Total Probability, which forces conflicting meanings to be simply added together and mixed. We argue that this basic "mixture" method isn't enough to describe or handle complex situations where meanings are incompatible or interfere with each other in context. Therefore, much empirical evidence suggests that capturing these dynamic features requires a non-classical, quantum probability framework (Surov et al., 2021).The importance of this paradigm shift becomes most clear when we analyze how everyday language works and how we interpret deep meanings in complex literary works. A classic example of such "semantic superposition" is the iconic "big fish" in Ernest Hemingway's The Old Man and the Sea.Within the novel's narrative structure, this phrase isn't a static label. Instead, it operates simultaneously on multiple, even mutually exclusive, semantic levels. Here, it serves as a biological marlin, a worthy adversary, and a transcendent symbol of life's ultimate tragedy. A classical probabilistic model fails to capture the dynamic tension that a reader feels, because it forces these meanings to compete for probability mass, implying only one can be dominant. In contrast, human reading suggests that meaning exists in a "superposition" state. It stays that way until a specific context makes it "collapse" into a concrete interpretation. Crucially, these overlapping meanings aren't simple probabilistic blends. They are coherent "quantum states" within a complex adaptive system.This study proposes that Quantum Cognition offers the necessary mathematical formalism to resolve the "stability-plasticity dilemma". This is supported by its proven success in solving decision-making paradoxes in psychology (Busemeyer & Bruza, 2012;Widdows et al., 2023;Huang et al., 2025). We introduce an integrated quantum theoretical model. In this model, the interaction between embodied experience and linguistic context is characterized as a genuine quantum interference phenomenon.This framework reinterprets the tension between stability and plasticity through the lens of Wave-Particle Duality. In our model, the "particle" corresponds to the stable, discrete symbols used for communication. The "wave" captures the fluid, context-sensitive potential that allows for creative interpretation.Next, by employing the mathematical formalism of Hilbert space, we will mathematically demonstrate how semantic ambiguity can be maintained as a useful resource, rather than mere noise.This approach effectively overcomes the limitations inherent in traditional methods like static vectors and gradient symbolic mixtures. To ground these abstract formalizations, we focus on the "Big Fish" motif in Hemingway's The Old Man and the Sea. Through this case analysis, we will reveal how meaning dynamically evolves, similar to "state vector collapse". Our study also extends to address the fundamental limitations of current Artificial Intelligence, particularly Large Language Models (LLMs). We argue that current LLMs, relying heavily on static statistical correlations, lack the "grounding" for true understanding. Therefore, we propose a pathway toward Quantum-Embodied AI and photonic intelligent systems by incorporating quantum-semantic principles. These systems could mimic the non-algorithmic fluidity of the human mind. Quantum probability is not an exotic addition to linguistics but a fundamental requirement for describing dynamic meaning. The research will first analyze the evolution from gradient representations to quantum interference, then formally express the wave-particle duality of meaning using mathematical methods. We will then validate this theoretical framework through the "big fish" case study and neurophysiological evidence, concluding with an exploration of its practical implications for Generative AI and Photonic we need the "quantum we the evolution of semantic theory. We will focus on mathematical models often to capture the nature of meaning. 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In the contemporary landscape of artificial intelligence (AI) and machine learning (ML), the integrity, diversity and quality of training datasets are critical for ensuring the accuracy and reliability of predictive models. However, the phenomenon of big-data pollution, manifested through AI-generated synthetic data, inconsistencies, biases, and data poisoning within datasets, undermines model performance by diminishing the Shannon Entropy of the system. This study proposes a novel framework that integrates the Dataset Core approach with tokenized data, triple-entry accounting (TEA), and distributed ledger technology (DLT) to address these challenges. Our Dataset Core method preserves essential information value while filtering out potentially harmful elements, providing mathematically grounded protection against data pollution. Combined with blockchain-based verification, this approach establishes a foundation for enhanced transparency and trustworthiness in AI applications, with significant implications for sectors such as finance, healthcare, and beyond.
The accelerating digitization of financial services has transformed global economic ecosystems while simultaneously amplifying the scale, speed, and structural complexity of financial fraud. Real-time payments, open banking infrastructures, fintech platforms, and decentralized finance environments have expanded transactional connectivity, creating highly dynamic and interconnected risk landscapes. Conventional rule-based and standalone machine learning systems demonstrate limited effectiveness against adaptive adversaries, coordinated fraud rings, synthetic identity schemes, and cross-platform laundering networks. Advanced detection strategies require intelligent architectures capable of modeling temporal behavior, relational dependencies, and large-scale streaming data within production-grade environments. This chapter presents a comprehensive framework for next-generation financial fraud detection integrating Artificial Intelligence, Deep Learning, and Graph Analytics. The discussion synthesizes supervised, unsupervised, and semi-supervised learning approaches with sequential deep learning architectures, transformer-based models, and graph neural networks for network-aware inference. Emphasis is placed on hierarchical multi-stage detection systems, cloud-native deployment strategies, adversarial robustness, privacy-preserving computation, and real-world validation methodologies. Critical challenges such as extreme class imbalance, concept drift, scalability of graph processing, explainability under regulatory constraints, and cross-institution collaboration are systematically examined. A unified hybrid AI–graph intelligence architecture is articulated to address both transactional anomalies and coordinated fraud ecosystems. The chapter contributes a structured taxonomy of modern financial fraud, an integrated modeling perspective combining temporal and structural intelligence, and a deployment-oriented evaluation framework aligned with real-world financial operations. By bridging theoretical advancements with production-grade implementation considerations, this work establishes a rigorous foundation for scalable, interpretable, and resilient fraud detection systems within evolving digital financial infrastructures.
Jose Almarcha-Sanchez, Maria-Jesus Alba-Baena, Volodymyr Dubetskyy, Maria‐Dolores Cano
Abstract Open-source simulators let engineers stress-test blockchain ideas long before field deployment, yet few studies compare tools side-by-side. This tutorial article benchmarks two research-grade simulators, namely, BlockSim and Simewu, and the production-grade IOTA Hornet node under an identical traffic harness that runs on laptop-class hardware. Results show that consensus style dominates capacity. A DAG ledger that finalizes one milestone per second (≈ 6 tx s⁻¹) surpasses the 10 Transactions Per Second (TPS) ceiling of a six-node Bitcoin simulation, while Ethereum-style 12 s blocks lift the same mesh to approximately ~ 20TPS.BlockSim reproduces proof-of-work fairness within ± 3% of theoretical expectations, and a ten-fold increase in propagation delay cuts a miner’s reward roughly in half despite equal hash power. Hornet delivers protocol-truth execution, but at noticeably higher CPU, memory and bandwidth cost than the simulators. All scripts, Docker files and raw logs are released under an open license, providing a one-click baseline for future benchmarking of new distributed-ledger technologies.
The rapid expansion of digital banking, mobile payments, decentralized finance, and cross-border electronic transactions has fundamentally transformed global financial ecosystems while intensifying exposure to sophisticated cyber threats, fraud networks, synthetic identity schemes, and money laundering operations. Conventional rule-based security infrastructures lack the adaptability required to counter dynamic and large-scale financial crimes. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative enablers of intelligent financial security, supporting real-time fraud detection, behavioral authentication, transaction risk scoring, and regulatory compliance automation. This chapter presents a comprehensive examination of advanced machine learning techniques—including deep learning, graph neural networks, anomaly detection models, and reinforcement learning—for securing digital transactions and identifying coordinated fraud rings within complex financial networks. Integration of AI with blockchain consensus mechanisms, cryptographic infrastructures, and Regulatory Technology (RegTech) platforms is analyzed to demonstrate how adaptive intelligence enhances network resilience, transparency, and operational efficiency. Emphasis is placed on explainable and fairness-aware AI frameworks to ensure ethical accountability, regulatory alignment, and bias mitigation in automated financial decision systems. Privacy-preserving approaches such as federated learning and secure multi-party computation are also explored to address data governance constraints in cross-institutional collaboration. The chapter consolidates emerging research directions, identifies persistent technical and ethical challenges, and proposes an integrated AI-driven security architecture for scalable and trustworthy digital financial ecosystems.
The rapid digitalization of financial ecosystems has transformed online payments, transaction processing, and investment management into highly interconnected, data-intensive infrastructures. This transformation has simultaneously expanded exposure to cyber fraud, money laundering, identity theft, and market volatility, necessitating intelligent and adaptive security mechanisms. Advanced artificial intelligence techniques, including machine learning, deep learning, reinforcement learning, and graph-based analytics, have emerged as critical enablers of secure payment processing, real-time fraud detection, and predictive financial forecasting. Intelligent architectures embedded within online payment systems facilitate dynamic risk scoring, anomaly detection, behavioral profiling, and automated decision-making under strict latency constraints. This chapter presents a comprehensive examination of intelligent system frameworks for digital finance, integrating scalable cloud-based deployment, blockchain-enabled transaction integrity, explainable AI for regulatory compliance, and synthetic data generation for fraud simulation. Reinforcement learning approaches for portfolio optimization and risk-aware forecasting are analyzed to highlight adaptive investment strategies in volatile markets. Emphasis is placed on addressing class imbalance, adversarial threats, model interpretability, privacy preservation, and governance challenges within automated financial infrastructures. Emerging research directions such as federated learning, decentralized finance intelligence, and AI-driven anti-money laundering systems are also discussed to outline future technological trajectories. The presented synthesis establishes a structured foundation for developing secure, transparent, and scalable intelligent financial ecosystems aligned with regulatory and operational requirements of modern digital economies.
This study examines the determinants of transparency and accountability in village financial management, focusing on the roles of village facilitator competence, village government commitment, and oversight by the Village Consultative Body (BPD). Grounded in good governance and principal–agent theory, the study addresses persistent governance challenges at the village level, where substantial public funds are managed amid limited administrative capacity and uneven institutional oversight. Using a quantitative design, primary data were collected through a structured survey administered to 510 respondents from 85 villages in Donggala and Sigi Regencies, Indonesia. The data were analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS), enabling simultaneous testing of direct and mediating relationships among governance variables. The results show that village facilitator competence, village government commitment, and BPD oversight each have significant positive effects on financial transparency and accountability. Importantly, transparency plays a central mediating role, indicating that the effects of competence, commitment, and oversight on accountability are largely transmitted through improved information disclosure. These findings confirm that accountability in village financial management is difficult to achieve without adequate transparency mechanisms that reduce information asymmetry between village governments and the community. Theoretically, this study extends the application of good governance and principal–agent theory to the village governance context by empirically validating transparency as a key governance mechanism linking institutional factors to accountability outcomes. Practically, the findings highlight the importance of strengthening facilitator capacity, fostering integrity-driven leadership commitment, and enhancing the effectiveness of BPD supervision as integrated strategies to improve village financial governance. By providing evidence from a large-sample empirical study, this research offers insights for policymakers and practitioners seeking to promote transparent and accountable village finance management in decentralized governance systems.
Moses Arthur Baidoo, Wang ZhiCheng, Liu Qi, Zhou ShuMin · 6 authors
Access to reliable electricity remains a pressing challenge in Sub-Saharan Africa, particularly in rural areas where over 600 million people live without power. This paper explores the potential of decentralized solar energy systems; such as solar home systems, mini-grids, and solar-powered appliances in addressing energy access challenges across rural Sub-Saharan Africa. While these systems offer clean, reliable, and scalable alternatives to conventional grid expansion, their adoption is constrained by regulatory uncertainty, limited financing options, and local capacity gaps. Drawing on case studies from five countries, the paper examines how recent innovations -including mobile-based Pay-as-you-go (PAYG) financing, hybrid renewable systems, and improved energy storage technologies are reshaping energy access models. It also outlines policy recommendations aimed at strengthening regulatory coherence, promoting regional cooperation, and enhancing sustainability. Ultimately, the study highlights how decentralized solar solutions can contribute to long-term environmental, financial, and social resilience, with direct implications for poverty alleviation and inclusive rural development in the region.
The global digital identity landscape is undergoing an unprecedented crisis. Approximately 1.1 billion individuals worldwide lack any verifiable form of digital identity, while existing identity systems face existential threats from the industrialization of deepfake technology with injection attacks targeting biometric verification surging 900% since 2022 and occurring at a rate of once every five minutes in 2024. Simultaneously, conventional blockchain-based identity proposals that store biometric templates on-chain introduce critical privacy vulnerabilities incompatible with emerging regulatory frameworks including the EU AI Act (2024) and GDPR. This paper presents ZKP-GDIS (Zero-Knowledge Proof Global Decentralized Identity System), a novel, privacy-by-design identity architecture that fundamentally departs from prior work in three key dimensions. First, ZKP-GDIS never stores raw biometric data on-chain; instead, it employs zk-SNARK (Zero-Knowledge Succinct Non-Interactive Argument of Knowledge) cryptographic commitments that allow identity verification without any disclosure of underlying biometric features. Second, we introduce a Hybrid Deepfake-Resistant Liveness Pipeline (HDRLP) — a multi-modal anti-spoofing layer that fuses passive CNN-based texture analysis, photoplethysmography (PPG) heart-rate detection, and hardware-attested device fingerprinting to defeat both presentation and injection attack vectors. Third, the system adopts W3C Decentralized Identifier (DID) standards and implements a federated governance model, enabling cross-jurisdictional interoperability while respecting national digital sovereignty. We provide formal security proofs under the computational Diffie-Hellman hardness assumption, evaluate the system against the ISO/IEC 30107-3 Presentation Attack Detection benchmark, and report experimental results demonstrating 99.87% genuine acceptance rate, 0.004% false acceptance rate under deepfake attack, and 94% reduction in on-chain gas costs versus Ethereum mainnet through zkEVM Polygon deployment. ZKP-GDIS establishes a reproducible, standards- compliant, and audit-ready framework for the next generation of global digital identity infrastructure.
This study aims to comparatively examine the relationships between Bitcoin and Ethereum's energy consumption and price dynamics. Using daily frequency data, Augmented Dickey-Fuller (ADF), Phillips-Perron (PP), ARDL cointegration tests, and Toda–Yamamoto causality analysis were applied to evaluate the effects of cryptocurrency markets on energy demand from both short-term and long-term perspectives. The analysis results indicate that there is a long-term cointegration relationship between energy consumption and prices for Bitcoin and a unidirectional causality from prices to energy consumption. In contrast, ARDL boundary test results for Ethereum revealed no long-term relationship, and causality analysis also failed to detect any directional causality between price and energy consumption. This indicates that with Ethereum's transition to a Proof-of-Stake mechanism, energy consumption has become independent of price movements. The findings reveal that the effects of cryptocurrency markets on the energy economy vary according to technology-specific structural characteristics.
The rapid expansion of algorithmic decision systems, blockchain infrastructures, and autonomous operational technologies has begun to transform the structural organization of corporations. Increasingly, firms rely on automated governance mechanisms, decentralized ledgers, and smart contract frameworks that allow economic transactions and corporate decisions to occur with minimal direct human intervention. This transformation raises serious ethical and professional questions for accounting, a discipline historically grounded in human judgment, fiduciary responsibility, and professional oversight. Ethical accounting within autonomous corporate structures therefore emerges as a critical field of inquiry. The study examines how accounting ethics must evolve when financial reporting, asset transfers, contractual obligations, and performance measurement are executed through autonomous computational systems rather than traditional managerial decision chains. Particular attention is given to the implications for accountability, transparency, auditability, and stakeholder trust when algorithmic processes replace or supplement human managerial authority. Drawing upon ethical theories, accounting governance principles, and recent technological developments such as blockchain based corporate systems and algorithmic management, the study explores how ethical safeguards may be preserved in environments where corporate operations become partially or fully self executing. The analysis also considers the responsibilities of accountants, auditors, regulators, and system designers in ensuring that autonomous corporate structures remain aligned with principles of fairness, transparency, and societal responsibility. Ultimately, the discussion highlights the need for expanded ethical frameworks capable of addressing emerging technological realities within modern corporate governance systems.
This study identifies major approaches in token design for founders in the cryptocurrency/web3/blockchain space. The high failure rate of blockchain companies means that successful long-term performance will depend greatly on well-designed tokens. This study will integrate all prior research to highlight the most important aspects of structured tokenomics, including token utility, governance, and security. The study also contributes to the literature by introducing the Business Model Canvas as a conceptual framework that enables the integration of best practices for token design, drawing on both academic and industry literature. The results indicate significant gaps in the literature. This study offers new and practical insights for founders to enhance stakeholders’ engagement, improve regulatory compliance, and ensure project viability in the volatile cryptocurrency market. Furthermore, this research generates new knowledge that bridges the gap between the theory and practice of tokenomics, laying the groundwork for future research to develop and refine token design strategies.
Severin Bonnet, Jan-Gero Alexander Hannemann, Frank Teuteberg
Abstract In this paper, we report on the initial stage of a design science research (“DSR”) project aimed at establishing design principles for DeFAI (the intersection of DeFi and AI) generative AI-based chatbot assistants tailored to decentralized finance (“DeFi”). Addressing challenges such as user trust, data privacy and security, and regulatory compliance, we conducted a targeted literature review, expert interviews, as well as iterative prototype ideation and evaluation to derive three design principles: (1) Human-Centered Design, (2) Resilience and Interoperability, and (3) DeFi-Native User Experience Together, these principles operationalize general chatbot design guidance for DeFi contexts characterized by self-custody, irreversible transactions, and adversarial risk environments. We demonstrate these principles through DeFAIGuide, a mockup that illustrates how technical barriers in DeFi can be abstracted to enhance accessibility for novice users while also offering advanced features for expert users. Our study contributes actionable design knowledge to support the future development of DeFAI solutions that advance inclusivity, security, privacy, and self-sovereignty, paving the way for a more transparent and participatory financial future.