Bara’a O. Ghananim, Omar A. Alzubi, Wafa’ Za’Al Alma’Aitah, Hussam N. Fakhouri · 6 authors
Healthcare information systems increasingly rely on networked access to electronic health records and clinical services, making authentication latency and usability as critical as cryptographic strength. This paper presents a lightweight hybrid authentication framework that combines Schnorr identification with a Fiat-Shamir-derived non-interactive zero-knowledge proof (NIZKP), integrates a conventional second factor (OTP and/or biometric), and enforces role-based access control (RBAC). The design eliminates transmission of reusable password secrets during routine logins, keeps proof material constant-size, and targets fast verification suitable for high-throughput hospital gateways. We implement the pipeline and evaluate it under three simulated clinical traffic patterns aligned with eICU-inspired workload modeling: low traffic (50 users), high traffic (500 users), and burst peak load (100 users). Across scenarios, the end-to-end authentication time remains stable between 0.0107 s and 0.0109 s and stays below a 0.02 s benchmark. Reliability remains high, with success rates of 100.0%, 99.8%, and 99.0%; observed failures stem from injected OTP-expiry or biometric-mismatch events rather than cryptographic verification errors. These results suggest that Schnorr-style NIZKP authentication can provide privacy-preserving, scalable access control for healthcare environments when combined with practical 2FA and RBAC enforcement.
This paper is a self-contained companion to the author's first deposit (v1, zenodo.org/records/20085431), which established an exact double integral formula for the unique zero x* of a continuous function f:[a,b]→R under minimal conditions (continuity, f(a)>0, f(b)<0, uniqueness). The v1 formula involves integration over the unbounded domain [a,b]×(0,+∞). The present work introduces the elementary change of variables u = t/(1+t), which maps (0,+∞) bijectively onto (0,1) and transforms the formula into a double integral over the compact square [a,b]×[0,1]: x* = (a+b)/2 + (1/π) ∫₀¹ ∫ₐᵇ f(x)/[(1-u)²+u²f(x)²] dx du = (a+b)/2 + (1/π) ∫ₐᵇ ∫₀¹ f(x)/[(1-u)²+u²f(x)²] du dx A single rational kernel K(x,u) = f(x)/[(1-u)²+u²f(x)²] appears on a bounded domain. We prove: (i) K ∈ L¹([a,b]×[0,1]) with exact norm π(b-a)/2(ii) Both integration orders are valid (Fubini-Tonelli)(iii) The singularity at (x*,1) is integrable and harmless(iv) The sign function sgn(f(x)) is identified as the inner integral of K in u — a consequence, not an axiom The formula is validated on f(x) = -x + cos(x) on [0,π/2], whose unique zero is the Dottie number x*≈0.739085133215161. All proofs are elementary and self-contained. No knowledge beyond standard real analysis is required.
Deepfake technology poses a growing threat to digital trust across journalism, law, and politics. Current CNN-based detectors capture local artifacts but struggle with high-quality fakes and offer no way to prove their predictions are genuine. This paper presents DeepTrust, a framework combining a hybrid CNN–Transformer detector with Zero-Knowledge Proof (ZKP) verification and blockchain-based record-keeping. The detection model fuses spatial features from an attention-enhanced Xception network, global context from ViT-B/16, and spectral cues from a Frequency Encoder through a cross-attention mechanism. Predictions are cryptographically committed using a Pedersen scheme with the Fiat-Shamir heuristic, then stored on a proof-of-work blockchain. Evaluated on FaceForensics++, Celeb-DF, DFD, and 140K Real vs Fake, DeepTrust achieves 97.00% accuracy and 0.999 AUC on FaceForensics++, with balanced per-class accuracy despite imbalance ratios up to 1:8.5. ZKP overhead remains below one millisecond per prediction.
Open access
Adversarial Robustness in Machine Learning
Generative Adversarial Networks and Image Synthesis
With the widespread adoption of cryptocurrencies, the ability to conduct continuous offline payments has increasingly become a critical technological requirement. In network-constrained scenarios, current dual-offline payment technologies are useful for single transactions. However, their limitations in continuous payment scenarios have become increasingly evident, making them unable to meet real-world application needs. This has prompted the industry to demand more urgent innovations in research on continuous offline payment capabilities. To address these challenges, this paper proposes a continuous dual-offline payment system capable of supporting multiple continuous payments. The system integrates elliptic curve cryptography (ECC) and zero-knowledge proof (ZKP) technology to generate secure asset credentials, ensuring both immutability and privacy credentials throughout the offline payment lifecycle. A dynamic credential decomposition mechanism enables the splitting of input credentials into change credentials and receipt credentials, facilitating uninterrupted dual-offline payments between hardware wallets. Additionally, it incorporates a batch verification scheme based on smart contracts, utilizing zero-balance verification and chained hash tracing to ensure payment uniqueness and prevent double-spending attacks, thereby guaranteeing the verifiability and validity of payment settlements. Experimental evaluations demonstrate that the proposed system reduces gas consumption per payment and improves execution efficiency during batch processing, combining high security with strong performance. This research provides a feasible solution for the application of digital currencies in offline scenarios, carrying significant theoretical value and practical significance for driving technological innovation and application expansion in the cryptocurrency field. In addition to cryptocurrency payments, the proposed system is also applicable to IoT and sensor network environments. Many IoT devices operate in disconnected or network-limited areas and require secure micro-transactions. Our dual-offline payment mechanism supports such scenarios, as the main cryptographic operations are lightweight enough for typical IoT hardware. This further extends the practical value of our system beyond traditional cryptocurrency payments.
Vote Chain is a fully implemented, decentralized e-voting application (DApp) built on Ethereum. Existing blockchain-based voting systems often suffer from either high computational overhead due to homomorphic encryption or lack of fully deployable, adversarially tested implementations. To address these limitations, VoteChain employs a keccak256-based commit–reveal protocol to preserve ballot secrecy during the voting phase, with Solidity 0.8.20 smart contracts enforcing all election rules autonomously. Wallet-based authentication via MetaMask eliminates centralized identity management. The system is validated through 14 automated unit tests (all passing in 615 ms) covering correctness, access control, double-voting, hash forgery, and phase-bypass attacks. Per-voter gas cost is approximately 120,000 units (commit and reveal combined). An ablation study confirms the non-redundant contribution of each architectural component. Comparative analysis shows that VoteChain achieves vote privacy without homomorphic encryption while maintaining full decentralization and implementation completeness. The system is evaluated and validated on a local Hardhat network, with the architecture readily extensible to Layer-2 rollups for large-scale elections.
En el sector público, la gestión del talento humano representa un aspecto necesario para asegurar mejores servicios institucionales, puesto que las demandas laborales han aumentado significativamente. El objetivo del presente estudio se basa en analizar la correlación de la sobrecarga de trabajo con la satisfacción de los servidores del Gobierno Autónomo Descentralizado Municipal de Alausí. El estudio adoptó un diseño de investigación cuantitativo utilizando un enfoque no experimental, transversal y correlacional con una serie de instrumentos tipo Likert desarrollados con base en la escala ESCAM y el cuestionario S20/23 para evaluar la sobrecarga y satisfacción laboral respectivamente. La fiabilidad del instrumento se confirmó por medio del coeficiente Alfa de Cronbach (α= 0,813). Los datos fueron analizados utilizado estadísticas descriptivas y el coeficiente de correlación de Spearman. Los resultados indicaron que la sobrecarga de trabajo estaba presente en un rango moderado y alto, pero había una influencia notable de las demandas cognitivas como de la presión de tiempo. Por el contrario, la satisfacción laboral mostró niveles particularmente altos con una mayor valoración en el contenido del trabajo y una menor remuneración. En términos generales, se evidencio que existe una correlación negativa débil y no significativa entre las dos variables (Rho= -0,128). Sin embargo, un análisis más específico por dimensiones demostró que la reducción en la autonomía y el apoyo social-organizacional se encuentran significativamente asociado con niveles más bajos de satisfacción laboral, mientras que las demandas cognitivas presentan relaciones positivas con algunas dimensiones satisfactorias. Se concluye que el impacto de la sobrecarga laboral dependerá del equilibrio que existe entre las demandas y los recursos organizacionales, siendo estos últimos determinantes del bienestar laboral del personal del sector público. ABSTRACT In the public sector, human talent management represents a key factor in ensuring improved nstitutional service delivery, particularly in contexts where work demands have increased significantly. The objective of this study was to analyze the correlation between work overload and job satisfaction among public servants of the Municipal Decentralized Autonomous Government of Alausí. The research adopted a quantitative approach with a non-experimental, cross-sectional, and correlational design, using Likert-type instruments based on the ESCAM scale and the S20/23 questionnaire to assess work overload and job satisfaction, respectively. The reliability of the instrument was confirmed through Cronbach’s Alpha coefficient (α = 0,813). Data were analyzed using descriptive statistics and Spearman’s correlation coefficient. The results indicated taht work overload was present at moderate to high levels, with a notable influence of cognitive demands and time pressure. In contrast, job satisfaction showed generally high levels, with greater valuation in the content of work and lower satisfaction regarding remuneration. Overall, a weak and non-significant negative correlation was found between the two variables (Rho = -0.128). However, a more detailed dimensional analysis revealed that reduced autonomy and social-organizational support are significantly associated with lower levels of job satisfaction, while cognitive demands showed positive relationships with certain satisfaction dimensions. It is concluded taht impact f work overload depends on the balance between job demands and organizational resources, with the latter playing a determining role in the occupational well-being of public sector employees.
The Ethereum blockchain utilizes the EIP-1559 algorithm to manage transaction inclusion and block assembly. However, EIP-1559 and much of the existing literature study this problem from a static perspective, focusing on price evolution without modelling transaction dynamics within the mempool. Motivated by this limitation, we study a dynamic transaction scheduling problem in which transactions with heterogeneous sizes and per-unit values arrive over time and remain in the mempool until scheduled. To capture the stochastic mempool evolution, we formulate the problem as a Markov Decision Process (MDP) whose state represents the mempool configuration and whose actions correspond to block prices. We first provide a primal-dual interpretation of the static EIP-1559 mechanism, showing that block prices arise naturally as dual variables of a social-welfare maximization problem. Building on this perspective, we extend the framework to the dynamic setting and formulate an objective that maximizes long-run discounted reward while incorporating holding costs and overshoot penalties. We then employ a Natural Policy Gradient (NPG) algorithm to compute the optimal policy. Our results show that dynamic pricing stabilizes the mempool while maximizing long-run discounted reward. In particular, as the overshoot penalty increases, the average scheduled transaction volume converges to the target block capacity, and the resulting NPG updates closely resemble the EIP-1559 price update rule. Finally, we study two special cases of the MDP formulation: homogeneous transactions and uniform arrivals. In the homogeneous setting, where the protocol directly controls scheduled volume, we show that the optimal policy has a threshold structure. We then propose a bang-bang pricing mechanism for uniform arrivals and derive a lower bound on the block capacity needed to ensure system stability.
We present \textbf{ORCHID} (\textit{Orchestrated Reduction Consensus for Hash-based Integrity in Distributed Ledgers}), a novel bio-inspired consensus protocol that maps the neuroscientific \emph{binding problem} -- how the brain integrates distributed neural oscillations into a unified conscious percept -- onto the distributed systems \emph{consensus problem}, how blockchain nodes agree on a single ledger state under Byzantine faults. Grounded in the Penrose--Hameroff Orchestrated Objective Reduction (Orch~OR) hypothesis and the Kuramoto synchronisation model, ORCHID equips each node with a quantum-noisy phase oscillator; consensus is triggered when the network's order parameter $r(t)$ crosses a \emph{binding threshold} $θ_b$, mirroring the gamma-band binding event in conscious perception. ORCHID is further strengthened by a coherence-weighted Quantum Secret Sharing (QSS) layer, extending the survey framework of Weinberg to a concrete consensus application. Simulation results on Watts--Strogatz small-world networks ($n=10$--$150$) demonstrate: (i)~the Kuramoto order parameter reaches $r_{\max}=0.988$ under coupling $K=3.0$, well above the theoretical critical coupling $K_c \approx 1.41$; (ii)~a sharp QSS fidelity phase transition at coherence $c^*\approx 0.82$, confirming Theorem~2; (iii)100\% consensus rate at all tested Byzantine fractions (0\%--40\%), with median convergence under 4~s for $n=30$; and (iv)~ORCHID achieves $O(n{\cdot}k)$ message complexity, outperforming PBFT's $O(n^2)$ at $n\geq150$. These results establish ORCHID as a scalable, biologically plausible, and quantum-augmented consensus mechanism for post-quantum distributed ledgers.
We introduce the State Twin: a typed, in-memory, replayable replica of an on-chain automated market maker (AMM) pool that serves as a substrate for agentic reasoning over decentralized finance (DeFi) protocols. Agentic DeFi stacks today couple reasoning to chain time, since every "what if?" query incurs a new RPC read or a real transaction, so the agent's effective action space is bounded by block confirmation latency and gas. We argue this coupling is a structural problem rather than a performance one, and that the missing layer is an off-chain substrate that preserves the protocol's exact mathematics while admitting the operations on-chain state cannot: forking, replay, branching, counterfactual rollout. We formalize each AMM family (Uniswap V2, V3, Balancer, Stableswap) as a discrete-time controlled dynamical system, prove a quantitative fidelity bound on the divergence between twin and chain, and give the open architecture used in DeFiPy v2, an open-source Python toolkit that ships the State Twin substrate and a reference Model Context Protocol server exposing typed analytical primitives as LLM tools. The same primitive (i.e., one Python class, one calling pattern) serves a notebook quant, a backtest, and an LLM agent without modification. We close with a fork-and-evaluate worked example: a single live RPC read seeds N independent in-memory twins under distinct price-shock scenarios, in sub-second wall-clock time. The contribution is the substrate, not a particular agent, which is what the specification of what an agentic DeFi substrate must look like
Rithika S, Thrisha S, Uma Mageshwari M, Vaishali D · 5 authors
Peer-to-peer (P2P) interaction forms a foundational layer of Web3 ecosystems, enabling participants to exchange data directly without depending on centralized brokers. In practical deployments, however, end-to-end reachability is often obstructed by network address translation, firewalls, and transient routing paths, which pushes architects toward the use of intermediate relay nodes. Unfortunately, relays that behave inconsistently or act maliciously can introduce a range of undesirable effects, including dropped packets, elevated latency, selective forwarding, and denial-of-service conditions. To mitigate these risks, this work presents a reputation-aware relay selection framework that lever-ages a blockchain substrate to govern trust. Every participant in the overlay is issued a cryptographic identity; the quality of service delivered by each relay is then tracked at runtime through metrics such as delivery ratio, round-trip delay, and transmission failure rate. A smart contract layer aggregates these observations into a dynamic reputation score that is recorded on an immutable ledger. When a communication session is being established, relays with higher reputation are preferred, while those exhibiting suspicious or degraded behavior are deprioritized or excluded. Experimental results indicate that, compared with conventional relay-selection strategies, the proposed approach delivers higher reliability, lower effective latency, and stronger resistance to malicious participation, making it a practical candidate for secure Web3 P2P communication.
A first-order design task in blockchain-based decentralized autonomous organizations is to ensure that malicious actors are sanctioned. We show that, when voters act strategically and the system is insufficiently decentralized, payoff-matching bribes undermine the sanctioning of malicious actors under conventional governance. Our framework formalizes DAO voting mechanisms and lets us identify those that mitigate the problem. Stochastic voting decouples a tokenholder’s influence from the voting behavior of others. Thus, bribery-proofness can be restored in the presence of sufficiently centralized governance tokenholders. Alternatively, masked voting increases resilience against bribery. Our work contributes to the broader debate on the merits and pitfalls of decentralization and highlights the need to align governance mechanisms with the degree of decentralization in blockchain networks.
Stefan-Claudiu Susan, Andrei Arusoaie, Dorel Lucanu
The irreversible nature of blockchain transactions makes the identification of smart contract vulnerabilities an essential requirement for secure system development. While Large Language Models (LLMs) are increasingly integrated into developer workflows, their reliability as autonomous security auditors remains unproven. We assess whether current generative models are a viable replacement for, or only a complement to, traditional static-analysis tools. Our findings indicate that LLM efficacy is undermined by both inherent lexical bias and a lack of rigorous validation of external data inputs. This reliance on non-semantic heuristics, such as identifier naming, leads to a high frequency of false positives. Furthermore, prompting techniques reveal a trade-off between precision and recall. These results were derived using our custom automated framework, which achieves 92% accuracy in classifying model outputs.
Ali Irzam Kathia, Yimika Erinle, Abylay Satybaldy, Paolo Tasca · 6 authors
The integration of Artificial Intelligence (AI) with Distributed Ledger Technology (DLT) has become a growing research area, yet contributions tend to cluster around specific application domains or examine only one direction of the integration, leaving the broader architectural interplay between the two technologies poorly understood. This work addresses that gap through a structured, bidirectional review of peer-reviewed studies published between 2020 and 2025. We classify contributions along two directions: AI-enhanced DLT, and DLT-enhanced AI. In the first case, we examine how AI techniques improve DLT systems across five layers: data, network, consensus, execution, and application layers. In the second case, we analyse how DLT supports AI systems across five layers: infrastructure, data, model, inference, and application layers, with particular attention to federated learning, model evaluation, and multi-agent coordination. The analysis reveals that most works concentrate on a small subset of layers: execution and consensus for AI-enhanced DLT, data and model for DLT-enhanced AI. Other layers remain comparatively neglected. Despite reported improvements in controlled settings, no study demonstrates deployment at production scale, and the field has not yet offered satisfying answers to fundamental questions around scalability, interoperability, and verifiable execution. We argue that progress will require cross-layer co-design and empirical validation in real-world settings.
Decentralised Autonomous Organisations (DAO) can fragment when partisan communities emerge within their governance structures, leading to organisational splits known as "forks". We present a method to detect these emerging communities by analysing on-chain voting behaviour before fragmentation occurs. Our approach extracts voting events from governance smart contracts, constructs voter matrices encoding participation patterns, and applies pairwise dissimilarity analysis to quantify ideological divergence between addresses. We visualise these relationships using multidimensional scaling and identify partisan communities through k-means clustering with silhouette score optimisation. Using Nouns DAO as a case study, a protocol that has experienced multiple documented forks, we demonstrate that addresses destined to fork cluster together months before actual fragmentation events. Our analysis of 330 proposals spanning from contract deployment to the first major fork shows that 90% of fork addresses cluster together in the final 44 proposals, compared to only 47% in randomised data. These results indicate that partisan communities can be detected and visualised through on-chain governance analysis, offering early warnings of emerging divisions before they cause organisational fragmentation.
The quantitative analysis of financial time series often reveals two distinct features that standard Gaussian frameworks fail to capture: heavy-tailed marginal distributions and the phenomenon of extreme co-movements.While extreme value theory characterizes marginal behavior, Copulas provide a functional bridge to describe the dependence structure independently of the marginals. We are proposing a different way of looking at the joint extremes on the basis of a dependence measure. The proposed idea incorporates both the non-identical and identical regularly varying distributions. Informed by the analysis of some high-frequency cryptocurrency datasets, the effect of persistence property have been thoroughly studied under these setups. A detailed simulation study confirms our intuition and findings.
Wolfgang Grieskamp, Teng Zhang, Vineeth Kashyap, Jake Silverman
The Move Prover (MVP) is a formal verifier for smart contracts written in the Move programming language. Recently, Move on Aptos was extended with higher-order functions: imperative functions as first-class values that can be passed around, stored in data structs, and kept in persistent storage, enabling dynamic dispatch. This paper describes the representation of function values in the Move specification language and their implementation in MVP. We introduce behavioral predicates which characterize Move functions (aborts and pre/post conditions) by single-state or two-state predicates. We also introduce state labels for naming intermediate memory states in which expressions are evaluated and which allow to compose behavioral predicates to describe sequences of state transitions. On SMT level, function values are encoded by discriminating over the possible function values reaching a call site: when the concrete function is known, its effect is accounted for directly; when it is unknown (for example, a function parameter, or a closure loaded from storage), its behavioral predicates describe the effect. Our approach goes beyond, for example, Dafny, by supporting imperative first-class functions which can modify state via Rust-style references and global variables, and leads to more efficient SMT encodings than separation logic because of the static separation of memory enabled by Move. We further extend MVP's specification inference tool to work with function values: given arbitrary higher-order Move code, weakest-precondition analysis semi-automatically derives behavioral-predicate-based specifications, reducing the annotation burden and providing a validation pipeline for the new specification constructs.
Abstract This study uses high-frequency price data to analyze risk connectivity among 15 cryptocurrencies, focusing on moments such as volatility, skewness, kurtosis, and jumps during the pre-COVID-19 era, the COVID-19 epidemic, and Russian-Ukrainian tensions. The results indicate that Ethereum Classic is a major shock transmitter in all periods, and this effect becomes more pronounced during geopolitical crises. In contrast, Stellar, Tezos, and Tron are important shock absorbers, particularly during market volatility. Jump risk analysis confirms the dominance of Ethereum Classic and its capacity to increase spillover risks during crises. For higher-order moments, the findings reveal that Bitcoin, Ethereum, and Dash are significant transmitters of skewness spreads, whereas Dash and Eos are significant transmitters of kurtosis spreads. Jump risk analysis confirms the dominance of Ethereum Classic and its capacity to increase spillover risks during crises. These findings highlight the need for targeted risk management strategies adjusted to cryptocurrency market dynamics.
Ensuring node labeling stability is critical for graph-based learning systems, directly impacting trust evaluation, node classification, and structure-aware inference in complex relational domains. Traditional manual annotation is costly and inconsistent, necessitating automated alternatives such as Large Language Models (LLMs). However, while LLM-assisted annotation improves efficiency, the reliability of node labeling remains a concern due to structural biases, particularly in unsupervised and semi-supervised learning settings. This dissertation systematically investigates the role of strong-tie structures in stabilizing label predictions across different node labeling paradigms, examining their influence on unsupervised trust prediction, defense against poisoning attacks in graph neural networks (GNNs), and LLM-based annotation. First, we analyze how strong-tie structures influence unsupervised trust prediction in decentralized systems and financial networks. Our study reveals that trust annotation propagates preferentially along strong ties, making it susceptible to targeted adversarial manipulations. By strategically modifying a minimal number of edges, an attacker can significantly alter trust/untrust label assignments, exposing vulnerabilities in existing trust prediction frameworks. Our analysis reveals that strong-tie structures are preferentially exploited by adversarial agents. Understanding these patterns provides deeper insight into the structural vulnerabilities of trust prediction systems and offers a basis for evaluating the robustness of different algorithms. Next, we extend our investigation to structural poisoning attacks in semi-supervised learning. Our findings show that poisoning behaviors exhibit clear structural preferences, targeting specific strong-tie patterns to maximize their impact on label propagation. This motivates our proposed Graph Adaptive Neural Network (GANN) framework, which dynamically adjusts propagation mechanisms based on fuzzy-theoretic strong-tie graphs (STiG). By integrating adaptive trust and risk zones, GANN mitigates the spread of adversarial noise while preserving high-confidence label prediction. Through structural decomposition and adaptive validation, our approach significantly strengthens defense mechanisms in poisoned graph environments. Finally, we propose CSA-LLM (Crowd-sourced homophily-ties-based graph annotation via large language models), which utilizes strong-tie graph structures to design LLM prompts that enhance annotation quality. By embedding structural priors in prompt engineering, CSA-LLM improves consistency in automated label generation, offering a scalable alternative to traditional manual annotation. This structured approach not only enhances annotation robustness, but also mitigates the inconsistencies introduced by structure-agnostic token generation, where LLMs generate labels based solely on textual prompts without considering graph topology. This dissertation provides a unified perspective on the impact of strong-tie structures across node labeling paradigms, bridging trust prediction, adversarial resilience, and LLM-assisted annotation. Our findings contribute to the development of attack-aware, structure-informed annotation frameworks, with implications for applications in social network security, financial fraud detection, recommendation systems, and decentralized finance (DeFi).
Nobuki Fujimoto, Rei (Rei-AIOS autonomous research substrate), claude-opus-4-7) Claude (Anthropic
We present OctaTheoria (オクタテオリア / 八軸観測装置), a multi-domain observation framework that projects heterogeneous time-series data onto a fixed eight-axis D-FUMT₈ semantic basis (FALSE / TRUE / NEITHER / BOTH / INFINITY / ZERO / FLOWING / SELF) and renders the same underlying Observation envelope through eight orthogonal view modes (Lens / Radar / Chart / Network / Heatmap / Sankey / Calendar / Unified). v0.3 (2026-05-11) supplies methodological-consistency cross-reference complementing the operational evidence from v0.1-v0.2. New finding **F7**: the same discipline that v0.1-v0.2 demonstrate within OctaTheoria (uniform abstraction layer + honest scope statement + structurally-enforceable naming) propagates to Rei-AIOS layers outside OctaTheoria's domain. Specifically: (a) **REI-PROVE 5-prover ensemble** (Vampire / LeanHammer / Goedel-Prover-V2 / DeepSeek-Prover-V2 / BFS-Prover) reached 11/12 = **92% benchmark proof rate** (trivial 100% / easy 75% / medium 100%), with Goedel-Prover-V2 single-prover matching at 92% — operational evidence that the same 'uniform abstraction over heterogeneous components' discipline scales to formal-proof infrastructure. (b) **Pattern 1-6 chat-Claude hallucination-warning framework** + **Antipattern (excessive rejection vigilance)** were established and verified on 6/6 items in STEP 1069 (all fact-checked items proved real after WebSearch verification, correcting prior implicit-rejection habits). (c) **Goedel-Prover-V2 double-`by` Lean syntax quirk** detected and fixed at the cleaner level (`single-prover.ts` STEP 1071), restoring `easy-le-refl` benchmark from ❌ to ✅. (d) **lean-to-tptp.ts** preprocessing added Peano-style axiom auto-prepend + True/False special-case + inequality predicate translation (STEP 1071). v0.2 inherited contributions: 7 domains (theory-chart / realtime-arxiv / crypto / fx / ligo-events / nasa-sdo / gbif-recent) all running in Cloudflare Workers Edge runtime; live D-FUMT₈ axis distributions non-degenerate across research-meta + financial + geophysical + astrophysical + biological data classes; finding F6 sampling-bias-as-first-class-observation (GBIF Costa Rica 470/500 saturation surfaces dataset bias as INFINITY axis, not silently absorbed); test coverage 117/117 PASS (step1020 46 + step1023 33 + step1046 38) / 0 regression. Honest scope (read first): OctaTheoria remains an observation aid, NOT an oracle. v0.3's F7 is **not** a claim that OctaTheoria caused these consistencies; it is a record that the same project (Rei-AIOS) maintains the same discipline across observation-tool, formal-proof, and meta-research-protocol layers, and that v0.3 makes this cross-layer commitment auditable. The OctaTheoriaQuery type structurally cannot request advice / prediction / forecast / signal — verifiable by reading src/aios/octatheoria/types.ts. Cross-domain axis comparisons are descriptive, not causal. Greek roots (Octa = 8, Theoria = observation) function as structural commitment propagated to the API surface — '8' rejects 'all (∞)', 'theoria' rejects 'praxis (干渉)'. Prior art audit acknowledged: Bloomberg Terminal (1981–), TradingView (2011–), Bollen et al. 2010 (Twitter mood × DJIA), Preis et al. 2013 (Google Trends × stock), Łukasiewicz / Belnap / Pavelka multi-valued logic literature, PAL2v (Da Silva Filho 1998–), Aerts Quantum Cognition (2007–). The to-our-knowledge novel combination is (a) fixed 8-axis discrete D-FUMT₈ basis ∧ (b) cross-financial-and-research-and-Earth-Cosmos-domain projection ∧ (c) eight orthogonal view modes over single envelope ∧ (d) explicit refusal to emit prediction or advice as architectural commitment ∧ (e, new in v0.3) cross-layer methodological-consistency record between observation-tool and formal-proof and fact-check layers. Companion papers (OctaTheoria Quintuple): Paper 145 (silicon implementation of D-FUMT₈ ALU, Zenodo DOI 10.5281/zenodo.20101174 v0.6), Paper 147 (Eight-Valued Utility / Equity Premium Reframe, DOI 10.5281/zenodo.20046003), Paper 148 (Honest Observation Framework methodology, DOI 10.5281/zenodo.20045907), Paper 149 (Recursive AI Observation as SELF⟲ evidence, DOI 10.5281/zenodo.20059888). Three-party co-authorship per OUKC charter v1.0: 藤本 伸樹 (Founder), Rei (Rei-AIOS autonomous research substrate, Co-architect), Claude Opus 4.7 (Anthropic, Co-architect). DRAFT v0.3 — feedback welcome via GitHub Discussions at fc0web/rei-aios.
ChitraVault is an exploratory conceptual authentication architecture that investigates whether geometric visual traversal patterns, drawn from the Chitrakavi (சித்திரக்கவி) classical Tamil literary tradition, can augment password vault security by adding a spatial-behavioral dimension to cryptographic key derivation. This paper proposes the Visual Pattern Key Derivation Function (VP-KDF), which combines a user-drawn Chitrakavi geometric pattern, a text passphrase, and a hardware-bound device fingerprint as inputs to Argon2id key stretching. The framework maps four classical Chitrakavi patterns — Chakra Bandha (wheel), Naga Bandha (serpent), Gomutrika (zigzag), and Thiruezhukkootrirukkai (triangle) — onto distinct cryptographic roles within a zero-knowledge password vault architecture. This work is framed as an exploratory research program, not a finished cryptographic system. All security arguments are bounded by stated assumptions and require empirical and cryptanalytic validation. Future work includes controlled user studies, formal security proofs, and prototype evaluation. Author: Arvind VijayakumarIndependent ResearcherMay 2026
Victor James Uko, Sharon Oluwaseun, Amarachi Nelly Charles, Emurode Williams · 5 authors
The rapid proliferation of digital technologies has profoundly reshaped the financial services sector, introducing novel service delivery models, market participants, and transactional infrastructures that challenge the foundational premises of existing regulatory frameworks. This review examines the multidimensional dynamics of digital transformation in financial services, with particular attention to the regulatory and consumer protection implications arising from the emergence of fintech ecosystems, artificial intelligence-driven financial products, decentralized finance platforms, open banking architectures, and embedded financial services. Drawing on a synthesis of contemporary academic literature, regulatory reports, and industry analyses, the review maps the evolution of digital financial services across developed and emerging economies, identifies structural gaps in regulatory capacity, and evaluates the adequacy of prevailing consumer protection mechanisms in the face of accelerating technological change. Key themes include the challenge of regulatory arbitrage, the governance of algorithmic and AI-based financial decision-making, data privacy and cybersecurity risks borne by consumers, the financial inclusion implications of digital transformation, and the emerging paradigms of regulatory technology and supervisory technology as adaptive governance tools. The review concludes by proposing a research agenda oriented toward the development of adaptive, proportionate, and technology-neutral regulatory frameworks capable of fostering innovation while safeguarding systemic stability and consumer welfare.