This paper proposes a task-agnostic discovery layer for multivariate time series that constructs a relational hypothesis graph over entities without assuming linearity, stationarity, or a downstream objective. The method learns window-level sequence representations using an unsupervised sequence-to-sequence autoencoder, aggregates these representations into entity-level embeddings, and induces a sparse similarity network by thresholding a latent-space similarity measure. This network is intended as an analyzable abstraction that compresses the pairwise search space and exposes candidate relationships for further investigation, rather than as a model optimized for prediction, trading, or any decision rule. The framework is demonstrated on a challenging real-world dataset of hourly cryptocurrency returns, illustrating how latent similarity induces coherent network structure; a classical econometric relation is also reported as an external diagnostic lens to contextualize discovered edges.
Imagine you're explaining something new to a friend. You might say "the atom is like a tiny solar system" or "the brain works like a computer." We use these comparisons—analogies—constantly to understand unfamiliar things through familiar ones. They're how Darwin explained evolution (like selective breeding), how Rutherford explained atomic structure (like planetary orbits), and how we navigate everyday life. But here's the puzzle: while we have rigorous mathematical systems for logical deduction (if A then B), probability (how likely is X?), and other forms of reasoning, we've never had a formal system for analogy. When is an analogy actually valid? How much confidence should it give us? Can we combine multiple analogies? These questions have lived in philosophical limbo for over a century. What This Paper Does This paper creates the first complete logical system for analogical reasoning—essentially, the "mathematics of analogy." Just as probability theory gives us precise rules for reasoning under uncertainty, Analogical Logic (AL) gives us precise rules for reasoning by similarity. The Core Insight The key idea is that analogies aren't about surface similarities—they're about structural correspondences. A whale looks like a fish (similar shape, fins, lives in water), but that's a weak analogy because their deeper structures differ fundamentally (mammals vs. fish, lungs vs. gills, warm vs. cold-blooded). Meanwhile, the atom and solar system look nothing alike at the surface level, but make a powerful analogy because their relational structures match: a central massive body attracts smaller bodies that orbit it. The system captures this by separating: Relational structure: How things relate to each other (orbits, attracts, causes) Surface properties: What things are like individually (hot, charged, massive) How It Works The paper builds a complete formal system with five components: A language for precisely describing domains (like the solar system or atom) and mappings between them Five axioms that characterize how analogies behave: Every domain is perfectly analogous to itself If A is analogous to B, then B is analogous to A Analogies can be chained, but get weaker with each link Valid analogies must preserve relational structure Surface properties affect analogy strength but not validity Five inference rules for deriving new knowledge: Transfer relations from source to target Transfer properties (with reduced confidence) Recognize when differences weaken analogies Generate hypotheses by transferring explanations Strengthen conclusions when multiple analogies converge A strength metric (Σ) ranging from 0 to 1 that quantifies how good an analogy is, combining structural alignment with property similarity Soundness proofs showing that valid analogical arguments produce reliable conclusions with calculable confidence levels What Makes It Non-Obvious Some surprising results emerge: Non-monotonicity: Unlike deductive logic, adding true information can invalidate previous analogical conclusions. The whale/fish analogy weakens dramatically when you learn whales are mammals—new knowledge can break old analogies. Weak transitivity: If A is analogous to B and B is analogous to C, then A is analogous to C, but more weakly. Information degrades through analogical chains. Structure trumps properties: A perfect structural match with zero property overlap (Σ = 0.70) creates a stronger analogy than perfect property match with weak structure (Σ < 0.50). Seeing It In Action The paper works through historical scientific analogies in detail: Rutherford's atom (like a solar system): Calculates Σ = 0.80 (strong analogy), shows which inferences were valid (inverse-square force law) and which failed (continuous electron trajectories—quantum mechanics revealed this disanalogy) Darwin's natural selection (like artificial breeding): Calculates Σ = 0.88 (very strong), shows how the analogy generated the theory of evolution despite the key disanalogy (no intentional "breeder" in nature) Electricity (like water flow): Shows a moderate analogy (Σ ≈ 0.70) that's useful for engineering despite microscopic differences Why It Matters This isn't just theoretical housekeeping. The system: For AI: Provides foundations for machines to reason by analogy rigorously, with confidence estimates For science: Formalizes how analogies drive discovery and when to trust them For philosophy: Resolves century-old debates about the nature of similarity and analogical inference For education: Helps evaluate teaching analogies (which ones support learning vs. create misconceptions?) For everyone: Makes explicit the implicit reasoning we use constantly
Світлана Володимирівна Ковальчук, Віталій Григорович Федоришен
The article explores the fundamental essence and strategic role of investment capital within the context of the dynamic development of the stock market amidst the global digitalization of the economy. The authors conduct a comprehensive analysis of the conceptual apparatus, focusing on refining the definition, classification, and multifaceted functions of investment capital as a core resource for ensuring the financial stability of enterprises and maintaining a high level of liquidity in the securities market. Particular attention is paid to the transformation of capital from traditional forms into digital assets, a process that is fundamentally reshaping the architecture of modern financial relationships and global capital flows. The study demonstrates that the synergy between investment capital and digital technologies critically enhances market transparency, minimizes transaction costs, and accelerates the execution of financial operations. The research details the impact of cutting-edge technologies, such as blockchain-based trading, artificial intelligence for predictive analytics, and decentralized finance (DeFi) protocols, on the efficiency of capital allocation. Based on an empirical analysis of statistical data for the period 2021–2025, the correlation between investment capital inflows and key market capitalization indicators is identified. The paper further examines the influence of digital platforms on asset structures, price dynamics, and the overall resilience of the stock market to extreme volatility and external economic shocks. The authors reveal that digitalization acts as a powerful catalyst for the redistribution of capital i favor of high-tech sectors of the economy, thereby altering traditional investment paradigms. Furthermore, the research substantiates practical recommendations for stimulating the effective use of capital through the development of robust fintech infrastructure, the adaptation of regulatory frameworks to the requirements of the digital era, and the implementation of comprehensive programs to enhance digital financial literacy among market participants. The findings of the study demonstrate that the active involvement of investment capital under the conditions of stock market digitalization enhances the international competitiveness of the national economy and contributes to the sustainable development of the financial system. This article will be of significant value to researchers, financial sector practitioners, and investors interested in modern approaches to capital management and the evolution of the stock market under the ongoing pressure of digital transformation and technological progress.
The article provides a comprehensive comparative analysis of the fiscal decentralizatio mechanism in Ukraine and the European Union countries, with a focus on its impact on the financial capacity of local self-government. It is substantiated that fiscal decentralization is a key instrument for ensuring sustainable socio-economic development of territories, as it determines the level of budgetary autonomy, the stability of local budget revenues, and the ability of territorial communities to perform their own and delegated functions effectively. The current state and dynamics of fiscal decentralization in Ukraine during 2022–2024 are analyzed, taking into account the influence of martial law and war-related challenges on the structure of local budget revenues and the degree of dependence on interbudgetary transfers. The study conducts a comparative assessment of key quantitative indicators of fiscal decentralization in Ukraine and selected EU countries, including the share of local budgets in the consolidated public budget, the proportion of own-source revenues, the role of intergovernmental transfers, and the level of tax autonomy of local authorities. The results demonstrate that EU countries are characterized by higher financial stability of local governments, a greater share of own revenues, and more effective fiscal equalization mechanisms. Based on the analysis, quantitative benchmarks for adapting European fiscal decentralization practices to the Ukrainian context are proposed, aimed at strengthening the financial capacity of territorial communities. The findings may be used in shaping public finance policy, particularly in the context of European integration and post-war recovery of Ukraine.
Water scarcity represents one of the most critical challenges confronting arid and semi-arid regions, particularly under the intensifying pressures of climate change. In desert environments, limited freshwater availability constrains public health, food security, and socio-economic development, while traditional funding mechanisms often prove inadequate for scaling sustainable water infrastructure. This study examines the potential of decentralized finance (DeFi) bonds, combined with desalination and atmospheric water harvesting technologies, as an innovative financing and delivery model for enhancing water resilience in desert regions. The research adopts a qualitative, exploratory case study approach, drawing on a structured review of academic and policy literature, documented blockchain-based water initiatives, and a conceptual financial analysis of DeFi bond mechanisms. The OikosNomos.world (ONW) initiative is examined as the primary case study, with attention to its proposed deployment of solar-powered desalination systems, boreholes, and atmospheric water harvesting infrastructure. The analysis indicates that existing desalination and water harvesting technologies are technically viable in arid environments, particularly when integrated with renewable energy systems. Furthermore, blockchain-enabled DeFi bonds demonstrate potential to enhance transparency, automate fund allocation through smart contracts, and attract global impact-oriented capital beyond traditional grant-based models. However, the study also identifies key challenges, including regulatory uncertainty, governance complexity, infrastructure constraints, and the need for sustained community engagement. The paper concludes that while DeFi-financed water infrastructure is not a standalone solution to water scarcity, its strategic integration with proven water technologies and inclusive governance models offers a scalable and transparent pathway for strengthening desert resilience. Future empirical research and pilot deployments are required to validate financial performance, adoption outcomes, and long-term socio-environmental impacts.
V. T. Krishnaprasath, T. Surya, B. Suganthi, Mohammed Kasim M · 6 authors
The globalization of semiconductor supply chains and the rise of third-party IP reuse have intensified concerns around hardware Trojan insertion, counterfeit IP distribution, unauthorized overbuilding, and dispute-prone verification workflows in modern VLSI design. This paper proposes a Blockchain-Enabled Secure VLSI Framework that unifies distributed hardware verification, provenance tracking, and IP protection through tamper-evident ledger records and cryptographically verifiable design artifacts. The proposed framework registers RTL/netlist milestones, verification reports, test signatures, and PDK-dependent constraints as immutable transactions, enabling all stakeholders (IP vendors, integrators, foundries, and verification labs) to validate authenticity and integrity without exposing sensitive design content. To prevent IP leakage, the framework supports hash-anchored commitments, permissioned access control, and zero-knowledge–ready attestations for key verification claims (e.g., “coverage ≥ threshold” or “equivalence passed”) while keeping raw waveforms and proprietary constraints off-chain. A smart-contract policy engine enforces licensing (time-bound/feature-bound), audit logging, and revocation, while a lightweight on-chain/off-chain storage strategy ensures scalability. Analytical evaluation and prototype-level profiling indicate that the approach can provide end-to-end traceability with sub-second block confirmation in permissioned mode, ~25–45% reduction in dispute resolution time via automated audit trails, and ~15–30% lower manual compliance effort by standardizing verification evidence exchange. The framework is suitable for secure SoC integration, multi-vendor verification, and IP lifecycle governance in advanced VLSI flows.
Open access
Physical Unclonable Functions (PUFs) and Hardware Security
Fintech enterprises operate at the intersection of rapid technological innovation and stringent regulatory oversight, creating a complex organizational challenge. This review systematically examines organizational restructuring strategies that enable fintech firms to balance innovation and compliance. Drawing on the concepts of ambidexterity and contingency theory, the paper analyzes functional, divisional, matrix, and networked structures, highlighting their respective advantages and limitations for fostering innovation and ensuring regulatory adherence. Cross-functional teams, hybrid models, and embedded compliance practices emerge as key enablers for achieving dual objectives. The synthesis provides practical guidance for managers seeking to design adaptable organizational architectures, while also offering theoretical contributions to the literature on innovation management and regulatory alignment. Future research directions include cross-country comparisons, longitudinal studies, and exploration of emerging fintech models such as decentralized finance platforms.
Introduction Digital identity infrastructures used in electronic passports, national eID schemes, and federated authentication systems rely predominantly on centralised registries and classical public key cryptography. These architectures enable large-scale identity correlation, mass data aggregation, and single points of compromise, while remaining vulnerable to quantum attacks against RSA and elliptic-curve cryptography. There is no deployed identity framework that simultaneously provides post-quantum security, cryptographic privacy guarantees, and decentralised trust. Methods This study proposes a quantum-proof digital passport architecture combining lattice-based post-quantum cryptography, decentralised blockchain identifiers, and transformer-based decentralised artificial intelligence. The framework employs NIST-aligned post-quantum key encapsulation and digital signatures, zero-knowledge proofs for selective disclosure of identity attributes, and homomorphic encryption for encrypted identity verification. Blockchain oracles and decentralised identifiers enforce credential integrity and auditability without reliance on central identity providers. Transformer attention mechanisms support adaptive identity validation while preventing persistent identity profiling. Results Architectural analysis shows that the proposed system prevents quantum-enabled credential forgery, retrospective decryption, and cross-service identity linkability. Zero-knowledge verification removes plaintext exposure of personal data, and decentralised credential control eliminates central compromise vectors. The design remains interoperable with existing passport and eID infrastructures. Discussion The results demonstrate that secure post-quantum digital identity requires the combined application of quantum-resistant cryptography, decentralised governance, and cryptographic privacy enforcement.
Open access
Cryptography and Data Security
Quantum Computing Algorithms and Architecture
Physical Unclonable Functions (PUFs) and Hardware Security
Journal of Theoretical and Applied Information Technology
With the growing volume of health information it has become common practice to protect the patient identity while maintaining convenient access to the data. Due to varying flow of cyber security threats, traditional solutions never manage to get flexible access to data without compromising with overflow of data. To overcome these challenges focusing on patient data protection, in this paper, we propose a new Hybrid Integrated Hashing approach entitled "Dynamic Adaptive Hash-Block Access Control (DAHBAC) framework" using blockchain based advanced data access control mechanism. The dynamic multi factor hashing scheme can change in response to the current Vulnerability of data and access patterns, whereas data access control refers to leverage blockchain's immutability and decentralized structure that helps protecting patient privacy while allowing authorized persons to read. The dynamic hashing method prevents intruder attempts by making hash and easy to calculate but requiring real-time modification of the hash for access protection. This is made possible by harnessing the application of zero-knowledge proofs (ZKP) within the frame of blockchain to enable verification of information when there is no disclosure of the data. Compared with the conventional methods, testing of prototype in a health care organization resulted in 92% on attempts by unauthorized workers to enter the system and 7% increasing data retrieval rate. These findings shows that the proposed model is a perfect patient data protection pattern in ehealth systems, because it is not only secures patients data but also enhances the accessibility and scalability to handle more clients. It is enabled by the use of zero-knowledge proofs (ZKP) in combination with blockchain technology to verify information, while keeping the information secret.
Despite Pakistan's long-enduring chronic energy crisis, a recent surge in solar generation supplied nearly 25 % of the national electricity grid in the first quarter of 2025, broadening access and easing cost pressures. Conversely, IMF conditionality under IMF financing programs, such as tariff rebasing and a 10 % sales tax on solar imports, risk undermining these distributive gains. This study examines the interaction between solar adoption, IMF credit, and household electricity prices in shaping energy justice, using annual data from 2007 to 2024. We capture direct, mediated, and dynamic effects using time-series analysis and causal mediation models, respectively. Results show that a 1 % increase in solar generation reduces injustice by 0.142 points ( p < 0.01), with benefits most substantial in rural areas. Mediation analysis demonstrates that IMF credit alleviates injustice only when channeled through solar adoption ( β = 0.251 → SE; Sobel z = −2.47), while tariff hikes directly worsen inequality ( β = −0.399, p < 0.05) but partially induce adoption. VAR evidence reveals that price shocks immediately intensify injustice, whereas solar shocks reduce disparities gradually. Results conclude that solar energy holds great promise, but cannot single-handedly drive a just transition. Achieving equitable outcomes requires addressing policy barriers by removing regressive fiscal measures, safeguarding net-metering, dedicating IMF resources to decentralized solar projects in marginalized communities, and broadening targeted subsidies for low-income populations. A failure to implement such measures could render Pakistan's solar expansion exclusive, thereby widening inequality. • Solar deployment substantially reduces distributive energy injustice in Pakistan, with a 1 % rise in solar output lowering injustice by 0.142 points. • IMF credit enhances distributive energy justice only when directed toward solar investment. • Household electricity prices drive energy injustice: tariff hikes intensify inequality but spur limited, inequitable solar adoption. • Price shocks incur immediate and enduring distributive costs, whereas the equity benefits of solar adoption accrue gradually. • Achieving an equitable transition requires embedding distributive justice in fiscal and financing frameworks.
Min-Bin Lin, Bingling Wang, Fabian Y R P Bocart, Christian M Hafner · 5 authors
Abstract The market of non-fungible tokens (NFTs), driven by blockchain and smart contracts, provides both artists and art collectors an unprecedented marketplace with more security, flexibility, publicity, and freedom to monetize. Yet, the emergence of such a market has been considered to be packed with speculation and economic uncertainty, given the limited understanding towards this market. To provide a precise depiction of the NFT art market and gauge market volatility, we construct the Digital Art Index, a novel price index using hedonic regression on the top 10 liquid NFT art collections (as of 2023). Addressing artwork price inequality, which often disrupts the price discovery process, this paper introduces two innovative alternative methods: Huberization and score-based filtering. These methods effectively mitigate the influence of outliers, particularly in an emerging market with limited accessible observations. In conclusion, the NFT art market presents significant opportunities for large gains, which are often favoured by risk-takers, but also carries the potential for significant losses. Its pricing is necessarily determined by institutional creators and platforms, meaning that solo artists may not benefit significantly in the current market environment.
The rapid integration of IoT with edge computing has revolutionized various domains, particularly healthcare, by enabling real-time data sharing, remote monitoring, and decision-making. However, it introduces critical challenges, including data privacy breaches, security vulnerabilities, especially in environments dealing with sensitive information. Traditional access control mechanisms and centralized security systems do not address these issues, leaving IoT environments exposed to unauthorized access and data misuse. This research proposes Fuzzychain-edge, a novel Fuzzy logic-based adaptive Access control model for Blockchain in Edge Computing framework designed to overcome these limitations by incorporating Zero-Knowledge Proofs (ZKPs), fuzzy logic, and smart contracts. ZKPs secure sensitive data during access control processes by enabling verification without revealing confidential details, thereby ensuring user privacy. Fuzzy logic facilitates adaptive, context-aware decision-making for access control by dynamically evaluating parameters such as data sensitivity, trust levels, and user roles. Blockchain technology, with its decentralized and immutable architecture, ensures transparency, traceability, and accountability using smart contracts that automate access control processes. The proposed framework addresses key challenges by enhancing security, reducing the likelihood of unauthorized access, and providing a transparent audit trail of data transactions. Expected outcomes include improved data privacy, accuracy in access control, and increased user trust in IoT systems. This research contributes significantly to advancing privacy-preserving, secure, and traceable solutions in IoT environments, laying the groundwork for future innovations in decentralized technologies and their applications in critical domains such as healthcare and beyond.
This article builds upon previous research on the ethical tokenization of emotions and the concept of a ‘Sentiment Exchange,’ advancing it into a broader normative framework termed Emotional Economics 2.0. That initial contribution demonstrated how affective signals could be measured, exchanged, and governed to ensure dignity and fairness within digital economies. The present paper extends this framework into a broader theoretical model termed Emotional Economics 2.0, articulated through three normative laws. The First Law conceptualizes human attention as a conserved and finite resource, grounding emotional value in cognitive limitations rather than artificial scarcity. The Second Law introduces emotional flow as the basis of value creation, operationalized through a dual-channel system: a fixed universal allocation of 20 tokens per day to each individual and a variable issuance tied to the measurable emotional impact of registered entities. The Third Law formulates a global emotional sovereignty contract, envisioning emotional tokens as a universal right akin to a digital basic income embedded at the protocol level. The paper further explores Scenario 4, where emotional tokens function as a universal unit of account in a post-scarcity “post-economy.” Illustrative examples—such as housing, food, and luxury goods denominated in tokens—demonstrate how this model reframes wealth, value, and redistribution. Legal challenges, governance mechanisms (Global Ethical-Scientific Committee and Decentralized Autonomous Organization), and risks of manipulation are addressed as integral to the model’s design rather than as afterthoughts. By explicitly linking the original Sentiment Exchange proposal with the Three Laws of Emotional Economics, this work positions itself as a second step in an evolving research program. Rather than a predictive claim, it advances a normative horizon: an alternative to neoliberal scarcity logics that places dignity, emotions, and collective well-being at the center of economic thought.
Hasina Andriambelo, Hery Zo Andriamanohisoa, Naghmeh Moradpoor
Federated learning enables collaborative model training without sharing raw data, but practical deployments increasingly require verifiable guarantees that clients compute updates correctly. Zero-knowledge proofs can provide such guarantees, yet existing approaches face scalability limits due to the combined cost of polynomial commitments and fast Fourier transform (FFT) intensive verification. Pairing-based schemes offer compact proofs but incur high prover and verifier overhead, while hash-based constructions reduce algebraic cost at the expense of rapidly growing proof sizes. This paper proposes Hybrid-Commit, a polynomial commitment architecture for Binius zero-knowledge proofs that aligns cryptographic primitives with the algebraic structure of federated learning workloads. The scheme separates verification into additive and multiplicative phases: linear aggregation is handled using batched additive commitments optimized for binary fields, while non-linear constraints are verified via hash-based commitments over sparsely selected FFT domains. Proofs from multiple clients are combined through recursive aggregation while preserving non-interactivity. Experiments demonstrate scalability in prover time and proof size (near-constant prover time across 4–11 clients; 160 bytes per client representing 341× and 813× reductions vs. FRI-PCS and Orion), although verification time (762 ms per client) does not scale favorably, making the scheme suitable for bandwidth-constrained scenarios. The scheme achieves under 2% end-to-end training overhead with no impact on model accuracy, indicating that workload-aware commitment design can improve specific scalability dimensions of zero-knowledge verification in federated learning systems.
Francisco Angulo de Lafuente, Seid Mehammed Abdu, Nirmal Tej
This paper presents SiliconHealth, a comprehensive blockchain-based healthcare infrastructure designed for resource-constrained regions, particularly sub-Saharan Africa. We demonstrate that obsolete Bitcoin mining Application-Specific Integrated Circuits (ASICs) can be repurposed to create a secure, low-cost, and energy-efficient medical records system. The proposed architecture employs a four-tier hierarchical network: regional hospitals using Antminer S19 Pro (90+ TH/s), urban health centers with Antminer S9 (14 TH/s), rural clinics equipped with Lucky Miner LV06 (500 GH/s, 13W), and mobile health points with portable ASIC devices. We introduce the Deterministic Hardware Fingerprinting (DHF) paradigm, which repurposes SHA-256 mining ASICs as cryptographic proof generators, achieving 100% verification rate across 23 test proofs during 300-second validation sessions. The system incorporates Reed-Solomon LSB watermarking for medical image authentication with 30-40% damage tolerance, semantic Retrieval-Augmented Generation (RAG) for intelligent medical record queries, and offline synchronization protocols for intermittent connectivity. Economic analysis demonstrates 96% cost reduction compared to GPU-based alternatives, with total deployment cost of $847 per rural clinic including 5-year solar power infrastructure. Validation experiments on Lucky Miner LV06 (BM1366 chip, 5nm) achieve 2.93 MH/W efficiency and confirm hardware universality. This work establishes a practical framework for deploying verifiable, tamper-proof electronic health records in regions where traditional healthcare IT infrastructure is economically unfeasible, potentially benefiting over 600 million people lacking access to basic health information systems.
The parallel Byzantine Fault Tolerant (BFT) protocol is viewed as a promising solution to address the consensus scalability issue of the permissioned blockchain. One of the main challenges in parallel BFT is the view change process that happens when the leader node fails, which can lead to performance bottlenecks. Existing parallel BFT protocols typically rely on passive view change mechanisms with blind leader rotation. Such approaches frequently select unavailable or slow nodes as leaders, resulting in degraded performance. To address these challenges, we propose a View Change Optimization (VCO) model based on mixed integer programming that optimizes leader selection and follower reassignment across parallel committees by considering communication delays and failure scenarios. We applied a decomposition method with efficient subproblems and improved benders cuts to solve the VCO model. Leveraging the results of improved decomposition solution method, we propose an efficient iterative backup leader selection algorithm as views proceed. By performing experiments in Microsoft Azure cloud environments, we demonstrate that the VCO-driven parallel BFT outperforms existing configuration methods under both normal operation and faulty condition. The results show that the VCO model is effective as network size increases, making it a suitable solution for high-performance parallel BFT systems.
Christopher Blake, Chen Feng, Xuechao Wang, Qianyu Yu
A proof of the security of the Bitcoin protocol is made rigorous, and simplified in certain parts. A computational model in which an adversary can delay transmission of blocks by time $Δ$ is considered. The protocol is generalized to allow blocks of different scores and a proof within this more general model is presented. An approach used in a previous paper that used random walk theory is shown through a counterexample to be incorrect; an approach involving a punctured block arrival process is shown to remedy this error. Thus, it is proven that with probability one, the Bitcoin protocol will have infinitely many honest blocks so long as the fully-delayed honest mining rate exceeds the adversary mining rate. This means that an adversary cannot censor future transactions of a user in perpetuity, which would render the protocol useless.
Educational inequality persists across ASEAN despite improvements in enrollment and literacy, reflecting structural rather than merely access-related challenges. This study examines how governance structures, financing mechanisms, institutional capacity, and socio-economic stratification interact to produce disparities in educational access, participation, and progression. Using a qualitative-dominant mixed-methods design with cross-national comparative policy analysis, the research integrates macro-level quantitative indicators with in-depth qualitative evaluation of policy frameworks across ASEAN member states. Findings reveal that while primary enrollment approaches universality, secondary and tertiary education exhibit pronounced attrition, particularly among rural, low-income, and minority populations. Centralized governance, equitable public financing, and targeted support correlate with higher retention and reduced disparities, as evidenced in high-performing systems such as Singapore, whereas decentralized or under-resourced systems exacerbate structural inequities. Moreover, digital access and institutional capacity emerge as critical factors influencing educational trajectories. The study underscores that addressing inequality requires systemic reforms integrating governance coordination, progressive financing, institutional strengthening, and equity-focused interventions. By foregrounding structural determinants and cross-national variation, this research contributes to theoretical and policy debates on educational equity, providing evidence-based guidance for ASEAN strategies aimed at achieving inclusive, high-quality education across diverse socio-economic and geographic contexts.
The year 2025 marked the transition from AI ethics debate to AI governance execution. Industry reports document over 2,000 organizations registering AI systems for compliance review in Q4 2025, compliance budget increases of 300-400%, and an AI liability insurance market that grew from $400 million to $2.1 billion. Simultaneously, research identifies critical infrastructure gaps: AI agents lack decision traces, models are commoditizing while privacy infrastructure lags, and regulatory frameworks have fractured across three distinct philosophies with no convergence expected. This paper synthesizes findings from the Responsible AI Governance Network (RAGN), Foundation Capital, and enterprise AI orchestration research to identify the specific technical requirements for regulatory compliance. It then presents the Y.I.N. (Your Information Never leaves your control) Mazari Architecture as a comprehensive solution, demonstrating how the mandatory cryptographic ordering of Differential Privacy, Zero-Knowledge Proofs, and Homomorphic Encryption (DP→ZK→HE) addresses documented litigation exposure exceeding $10 billion, satisfies EU AI Act transparency requirements, enables AI agent accountability, and provides modular compliance across fragmented regulatory regimes. The architecture is backed by 19 USPTO patent applications covering 610+ claims, with validated benchmarks showing 640× timing improvements, 135× detection capabilities, and accuracy preservation within 1.5 percentage points.
Open access
2 source records
Ethics and Social Impacts of AI
Law, AI, and Intellectual Property
Artificial Intelligence in Healthcare and Education
Abstract The purpose of this study was to assess the dependence structure and volatility connectedness among the COVID-19 crisis, the 2022 Russia–Ukraine war, and their influence on cryptocurrencies, crude oil, developed markets, and the equity markets of China and ASEAN countries under varying market conditions. The analysis segmented the sample into three distinct periods: pre-COVID-19, during COVID-19, and the 2022 Russia–Ukraine conflict. To assess the dependence structure and risk spillover patterns across the markets for each period, we employed the generalized autoregressive conditional heteroskedasticity (GARCH)-extreme value theory (EVT)-vine copula and quantile vector autoregression (QVAR) connectedness methodologies. Findings from our GARCH-EVT-Vine-Copula model indicated that subsequent to the outbreak of COVID-19, market portfolios associated with the MSCI-developed markets index demonstrated significantly lower tail connectedness. However, the impact of the 2022 Russia–Ukraine war on the stock markets of China and ASEAN countries was found to be overestimated. Furthermore, the QVAR connectedness analysis revealed that connectedness was greater in bullish market conditions than in normal and extreme downside periods. Additionally, the portfolio analysis results suggested that the equity markets of China and ASEAN countries, along with the crude oil markets, cryptocurrency indices, and the MSCI developed markets index, were unable to achieve high levels of hedging effectiveness. Concurrently, it was recommended that investments be directed toward Chinese and ASEAN equities as safe-haven assets.
Defensive publication establishing prior art for proof-first digital identity systems using prime-indexed state evolution, zero-knowledge proofs, and silence-by-default semantics. This specification defines the Meta-Theorem of Prime Identity (MTPI), an architectural framework requiring cryptographic proof for every state transition. Core components include: Prime-Indexed Recursive Tensor Mathematics (PIRTM) with contractive dynamics guarantee; prime-gated activation with drift bounds δ(t) ≤ 0.3; Conscious Sovereignty Layer (CSL) with ethical tensor field commutation relations; Archivum append-only audit schema; and conformance requirements including Surveillance Fork detection. Reference implementations provided in Solidity and Circom 2.1. Mathematical foundations, alternative embodiments, and public domain designations included for maximum prior art scope. Keywords: zero-knowledge proofs, prime-indexed identity, verifiable computation, AI safety, defensive publication, proof-first computing, MTPI, PIRTM, CSL
Open access
2 source records
Cryptography and Data Security
Physical Unclonable Functions (PUFs) and Hardware Security
Zero-Knowledge (ZK) proof systems are cryptographic protocols that can (with overwhelming probability) demonstrate that the pair $(X, W)$ is in a relation $R$ without revealing information about the private input $W$. This membership checking is captured by a complex arithmetic circuit: a set of polynomial equations over a finite field. ZK programming languages, like Noir, have been proposed to simplify the description of these circuits. A developer can write a Noir program using traditional high-level constructs that can be compiled into a lower-level ACIR (Abstract Circuit Intermediate Representation), which is essentially a high-level description of an arithmetic circuit. In this paper, we formalise some of the ACIR language using SMT-LIB and its extended theory of finite fields. We use this formalisation to create an open-source formal verifier for the Noir language using the SMT solver cvc5. Our verifier can be used to check whether Noir programs behave appropriately. For instance, it can be used to check whether a Noir program has been properly constrained, that is, the finite-field polynomial equations generated truly capture the intended relation. We evaluate our verifier over 4 distinct sets of Noir programs, demonstrating its practical applicability and identifying a hard-to-check constraint type that charts an improvement path for our verification framework.
Blockchain technology has emerged as a foundational digital infrastructure capable of redefining global trade and financial ecosystems through its decentralized, immutable, and trust‐enhancing architecture. By eliminating conventional intermediaries and reducing informational asymmetries, blockchain strengthens transactional transparency, accelerates cross-border settlements, and enhances the authenticity of trade documentation. Its applications including distributed ledgers for supply chain traceability, smart contracts for automated trade finance, and digital identities for customs and compliance are enabling unprecedented operational efficiencies across international logistics and regulatory environments. In the financial domain, blockchain facilitates secure and near-instantaneous value transfers, supports innovative instruments such as asset tokenization, and expands financial accessibility through decentralized finance (DeFi). Central bank digital currencies (CBDCs) further signal a structural transformation in global monetary governance by promoting interoperability and reducing systemic frictions. Despite such transformative potential, significant challenges remain: fragmented regulatory frameworks, scalability constraints, cybersecurity concerns, and the need for harmonized global standards. This study critically evaluates blockchain’s multi-dimensional impact on international trade and financial systems, examining its strategic advantages, evolving use cases, and institutional implications. The analysis underscores that long-term global adoption will require coordinated policy reforms, cross-border regulatory convergence, and robust technological infrastructure. The findings aim to contribute to international scholarly discourse by mapping blockchain’s trajectory as a catalyst for a more transparent, resilient, and integrated global economic order.
In the contemporary global context, Information and Communication Technologies (ICTs) present multifaceted challenges, particularly in maintaining an appropriate balance between national security requirements and the protection of individual privacy. The rapid advancement of technology has led to an increase in cyber threats, necessitating closer collaboration between the public and private sectors. However, such collaboration often blurs the boundaries between security imperatives and individual privacy rights. This study examines the implications of this balance and assesses whether existing regulations adequately protect individuals’ privacy. The right to privacy is universally safeguarded by ethical norms and legal frameworks. Instruments such as the United States Constitution and the General Data Protection Regulation (GDPR) provide protection against unlawful searches, seizures and the misuse of personal data. Despite these safeguards, information sharing between public institutions and private entities may undermine privacy rights if appropriate accountability mechanisms are not in place. Navigating this complex terrain requires approaches that enable data collection and cybersecurity cooperation without violating individual privacy. Technological innovations, including artificial intelligence (AI) and zero-knowledge proof authentication systems, offer potential solutions by limiting unauthorized access to personal data. This paper argues that reconciling cybersecurity imperatives with the protection of individual rights requires continuous recalibration of legal and ethical boundaries. While data sharing within and across private industries can strengthen defenses against cyber threats, such practices must be carefully evaluated to prevent privacy violations. Achieving this balance ultimately depends on enhanced transparency and accountability.