P. Anupama, Akhilandeshwari, Shama Priyanka, Putta Srihari · 5 authors
The increasing digitization of administrative and personal records has created a strong demand for systems that guarantee secure storage, data integrity, and reliable verification of sensitive documents. Conventional document management solutions typically depend on centralized servers, where files are vulnerable to unauthorized modification, loss, or deletion without clear traceability. This centralized model reduces trust, increases exposure to cyber threats, and often requires time-consuming manual verification to confirm document ownership and authenticity. Consequently, individuals and organizations encounter challenges such as document forgery, inconsistent records, unauthorized access, and delays in retrieval, emphasizing the necessity for a more secure and tamper-resistant solution. In traditional vault systems, documents are usually stored as basic files with minimal metadata, lacking cryptographic protection and comprehensive audit mechanisms. Due to the absence of immutability, detecting alterations in stored documents becomes difficult. Additionally, reliance on manual validation processes introduces inefficiencies and a higher likelihood of errors. These drawbacks make centralized systems unsuitable for handling critical records such as legal documents, identity credentials, certificates, and criminal records, which require strict integrity and security measures. To overcome these limitations, the proposed solution combines blockchain technology with a Django-based web platform to establish a decentralized and tamper-proof digital vault. Key document metadata, including ownership information, descriptions, timestamps, and file references, is recorded on the blockchain using smart contracts, ensuring permanent and unalterable entries. The actual files are securely stored on the server, while Web3 enables seamless communication between the application and the blockchain network. Functionalities such as document upload, search, verification, and secure access support complete transparency and data integrity. This framework significantly strengthens trust by preventing unauthorized modifications and maintaining a permanent, verifiable history of all stored documents. By integrating blockchain immutability with an intuitive web interface, the system delivers a secure, scalable, and future-oriented solution suitable for government agencies, legal bodies, and organizations managing sensitive records.
KENOS — Kohenoor Operating System Official Description and Public Disclosure KENOS, the Kohenoor Operating System, is the unified digital operating environment of the Kohenoor ecosystem. It brings together artificial intelligence, Education 3.0, blockchain infrastructure, hybrid finance, business applications, development tools, governance controls and operational supervision within one coordinated ecosystem. <Explainer film added> The transition from KENHYFI Hub to the broader KENOS architecture reflects the continued expansion of the Kohenoor ecosystem. KENHYFI was originally developed as a hybrid-finance and ecosystem hub. However, the name and positioning of KENHYFI did not fully represent the wider capabilities that had developed around it, particularly: KAI — Kohenoor Artificial Intelligence, the ecosystem’s multilayered intelligence powerhouse and orchestration system. ProEdge, the Education 3.0, professional learning and workforce-development hub. Blockchain, development, commerce, security, governance and institutional-support applications extending beyond hybrid finance. For this reason, KENOS was established as the umbrella operating environment for the complete ecosystem. KENHYFI remains an important integrated hub within KENOS, but it no longer represents the entire ecosystem by itself. The relationship is therefore defined as follows: KENOS is the complete Kohenoor Operating System and umbrella ecosystem. KAI is the principal intelligence and orchestration powerhouse of KENOS. ProEdge is the principal Education 3.0 and professional-learning hub. KENHYFI is the integrated hybrid-finance and ecosystem-services hub within KENOS. Other applications and modules provide specialized capabilities in blockchain, commerce, development, security, finance and operational management. KENOS is built on three foundational pillars: Education 3.0 Artificial Intelligence Blockchain These pillars support the complete digital-economic journey: Learn → Plan → Build → Execute → Analyze → Supervise → Improve → Scale Artificial Intelligence Pillar KAI, Kohenoor Artificial Intelligence, serves as the principal intelligence powerhouse of KENOS. KAI is designed as a multilayered hybrid-intelligence and workflow-orchestration system rather than a conventional chatbot. It supports knowledge retrieval, document analysis, specialist-role activation, business intelligence, financial analysis, educational guidance, application planning, risk assessment, reporting, workflow coordination and Human-in-the-Loop escalation. Within KENOS, KAI connects users, knowledge, applications, workflows and authorized human decision-makers. Education 3.0 Pillar ProEdge serves as the principal learning and professional-development hub within KENOS. It supports practical education, workforce transformation, professional training, institutional capacity building and industry-linked learning in areas including: Artificial intelligence Blockchain and Web3 Business intelligence Cybersecurity Hybrid finance Digital transformation Communication and professional skills Software and application development Entrepreneurship and business execution ProEdge ensures that KENOS is not limited to providing technology. It also develops the human capability required to understand, manage and apply that technology effectively. Blockchain Pillar The blockchain pillar provides smart contracts, programmable assets, digital ownership, transparent records, settlement mechanisms, token utilities and verifiable ecosystem operations. Blockchain functions are designed to operate alongside KAI-supported intelligence, business rules, governance controls and authorized human supervision. Purpose of KENOS KENOS is designed to support individuals, professionals, businesses, educational institutions, developers, government entities and other organizations participating in the AI-powered digital economy. It connects learning with intelligence, intelligence with execution and execution with monitoring and supervision. KENOS may support: Education and professional development Artificial intelligence and business intelligence Financial and hybrid-finance services Blockchain and smart-contract development Digital commerce and procurement Application and software development Security and operational resilience Governance and institutional intelligence Reporting, monitoring and supervision Development Status At the time of this publication: KENOS is in the Early Beta phase. KENHYFI Hub is in the Alpha+ phase. Individual applications and modules may have different levels of development, testing and availability. The official public web host and disclosure gateway for KENOS is: https://www.kohenoor.net Within the KENOS architecture: KAI serves as the principal intelligence and orchestration layer. KENHYFI Hub operates as an integrated hybrid-finance and ecosystem services hub. Education 3.0 platforms support learning, reskilling and professional development. Blockchain applications provide smart-contract, digital-asset, settlement and verification capabilities. Business and development modules support planning, commerce, procurement, software development, financial intelligence, security, reporting and operational management. KENOS is intended to serve individuals, professionals, businesses, educational institutions, developers, government organizations and other entities participating in the AI-powered digital economy. The architecture is modular and may support public web access, controlled organizational deployments, private-cloud environments, local installations, sovereign infrastructure and integration with existing enterprise systems. Governance remains a core element of KENOS. High-stakes activities are intended to remain subject to authorized human review, role-based permissions, validation controls, risk classification, activity logging and Human-in-the-Loop approval. At the time of this publication, KENOS is in the Early Beta phase, while KENHYFI Hub is in the Alpha+ phase. Applications and modules within the ecosystem may therefore have different levels of development, testing, availability and production readiness. The official public web host and disclosure gateway for KENOS is: https://www.kohenoor.net This publication provides the official conceptual definition, ecosystem positioning, service scope, architectural relationships, development status, governance principles and public-disclosure framework of KENOS. Keywords: KENOS; Kohenoor Operating System; Kohenoor Technologies; KAI; Kohenoor Artificial Intelligence; KENHYFI; Education 3.0; artificial intelligence; blockchain; hybrid finance; digital economy; business intelligence; digital transformation; smart contracts; Human-in-the-Loop; ecosystem architecture; AI governance; Web3; enterprise AI; institutional intelligence Kohenoor Technologies remains committed to transparency, security, responsible disclosure, and continuous improvement of the KEN ecosystem. #kenhyfi #kai #hyfi #kohenoortechnologies #futureofeducation #futureoffinance #futureofai #kohenoorken #cryptocurrencies #kohenoorken #AI #actionai #agenticai #AGI #ArtificialGeneralIntelligenceAGI #AIAssistant #education3 #defi #hybridfinance #hyfi #cedefi #blockchain #innovation #settlements #auditreadycertificates #DASC #cybersecurity #web3 #businessintelligence #proedge #industrygradetrainings #quantumcomputing
Gossipsub is the primary peer-to-peer dissemination protocol used by large-scale Web3 systems such as Ethereum, Filecoin, and IPFS. Despite its widespread deployment, the choice of its key parameters—the eager mesh degree D (number of peers that receive messages eagerly) and the gossip degree Dlazy (number of peers periodically notified via gossip)—has largely relied on heuristics, with little quantitative guidance. Consequently, production networks lack a principled understanding of the delivery rate, bandwidth cost, and latency tradeoffs induced by these parameters.
In the context of developments in the field of financial technology, cryptocurrencies, emerging as a new asset class, have garnered significant attention in financial markets in recent years, attracting investors, researchers, and regulators, and leading to numerous publications. Bibliometric studies evaluate these publications based on criteria such as the number of publications, their quality, the countries of publication, authors, and journals. This study aims to perform a bibliometric analysis of the academic literature available in the Web of Science (WoS) database, focusing on the volatility of cryptocurrency prices. It analyzes the magnitude and development of academic interest in this field, along with key words, the most cited works, and research trends, in an effort to determine the density of studies, their impact areas, and the academic networks that have emerged in this field. Based on the general findings, it is observed that the number of studies has been on an increasing trend over the years, and that the publications are predominantly in the field of Business Economics. Moreover, it has been found that publications are mainly in finance journals. In terms of network maps, the findings suggest a moderate level of collaboration among authors, with the United Kingdom and the People's Republic of China occupying central positions in international collaboration. In terms of citations, authors such as Lucey, and Katsiampa, Paraskevi, have emerged as prominent figures in the fields of cryptocurrencies and volatility. Regarding key words, terms like 'cryptocurrency', 'cryptocurrencies', 'volatility', and 'bitcoin' are predominantly used in these studies." Keywords: cryptocurrencies, bitcoin, volatility, bibliometric analysis
A deployed AI system can be interrogated for its identity in several distinct ways, and the answers do not interchange. This note concerns one of them — which neural network is producing this output at inference time? — and a popular method for answering it: behavioral fingerprinting, which samples an endpoint under a fixed prompt battery and flags it when the output distribution shifts beyond a statistical threshold. The note argues that behavioral fingerprinting, while a legitimate and valuable instrument for one task, does not establish model identity. It develops two measured failure modes. First, a behavioral signature is not durable: ordinary continued training erases the behavioral provenance trace — more effectively, in fact, than an informed adversary trains directly to suppress it — so the same model after a benign fine-tune presents as behaviorally distinct and triggers a false alarm. Second, a behavioral signature is reproducible by a different model: knowledge distillation converges a substitute toward a target's behavioral template by construction, so a behavior-matched substitute passes the check and produces a false acceptance. Both failures follow from a single fact about the layering of neural identity — behavior is the transient layer, which transfers under distillation and washes out under benign training, while the structural layer (the geometry of internal computation during a forward pass) does neither. The two methods answer different questions and compose rather than compete: behavioral monitoring is a continuous, low-cost tripwire that flags something moved; structural verification is a deterministic resolver that answers is it still the enrolled model. A system that ships only the tripwire has shipped drift detection and labeled it identity. The note documents the structural layer's direct test against the failure mode that defeats behavioral methods — behavior-preserving substitution — and situates the argument alongside independent work on intrinsic parameter-level fingerprints and cryptographic verifiable inference, both of which bind identity to the model rather than infer it from outputs. This is a category statement, not a product comparison: no specific system or vendor is named, and the argument rests on published, reproducible measurements. The Neural Network Identity Series — Mathematical foundations, empirical validation, and governance frameworks for verifying which model is running 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) Technical Note: Artifact Identity Is Not Runtime Identity — Trustfall Lite and the Boundary of File-Level Model Verification (DOI: 10.5281/zenodo.20019127) Technical Note:: The Disappearing Window — AI Logprob Access Withdrawal and the Structural Verifiability of Frontier Model Contracts (DOI: 10.5281/zenodo.20362098) 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).
This study compares the forecasting performance of four deep learning architectures—GRU, LSTM, RNN, and CNN—for one-step-ahead Bitcoin price prediction. A grid search determined the optimal configuration, which was applied uniformly across models to ensure fair evaluation. Using daily BTC closing prices from January 2018 to July 2025, it is found that the GRU model achieved the lowest forecasting errors (MSE, RMSE, MAE, MAPE) and the highest R², with LSTM performing closely behind. Visual analyses confirmed that GRU and LSTM maintained stronger alignment with actual prices during volatile periods. To assess economic value, model forecasts were integrated into a rule-based trading strategy under realistic market frictions, including a 0.10% transaction cost and a 0.10% trading threshold, with both short-selling-enabled and long-only variants tested. The GRU strategy with short-selling generated the highest terminal wealth (approximately 24% higher than the Buy-and-Hold benchmark) and superior risk-adjusted returns, measured by CAGR, Maximum Drawdown, and Sharpe Ratio. The findings demonstrate that careful hyperparameter optimization, coupled with an architecture capable of capturing complex temporal dependencies, can significantly improve both predictive accuracy and trading profitability in cryptocurrency markets. These results provide practical implications for designing AI-driven trading systems.
A claim can be argued well and still be false. Standard verification across the disciplines certifies claims by the quality of a single line of support: a formal proof, a measured correlation, a replicated experiment, an expert consensus. Each of these is one axis of warrant, and each can be strong while the claim is wrong, because a single strong axis cannot detect that it is the only axis, nor that it secretly shares a source with the others. This paper presents a verification method, Trisduction, that certifies a claim by the geometry of its warrant rather than by the strength of any one line of it. A proposition is decomposed onto three structurally independent axes, a formal-structural axis, an empirical-material axis, and an epistemic-registrational axis, and the warrant is certified only when the three stand at mutual right angles and span a genuine three-dimensional volume. The test is closed-form and executable: three warrant vectors are composed through a quaternion product whose scalar part squares to a Gram determinant, and the determinant reads the volume the three axes enclose. A volume near its maximum is a seal. A collapsed volume is a structural break with a named cause. An ill-conditioned volume is an honest under-determination. A second register extends the method to formal and mathematical propositions, separating the part of a problem that is decidable and sealed from the part whose truth is genuinely open, and refusing to read a geometric lock as a proof. The method carries one discipline throughout: social consensus carries zero evidential weight, every verdict states its warrant grade, and the instrument audits itself with no exemption. We demonstrate the method on 360 propositions spanning logic, mathematics, physics, quantum foundations, cosmology, the mind, psychology, the social sciences, geopolitics, and metaphysics, from elementary facts that seal cleanly to celebrated open problems where the honest verdict is that the question remains open and the method says exactly why. 360 Audits.
Ayei E. Ibor, Denis U. Ashishie, John Adinya Odey, Bassey Ele · 5 authors
ABSTRACT Elliptic curve cryptography ( ECC ) underpins the security of most blockchain systems, yet its practical implementations face numerous vulnerabilities. In this systematic literature review ( SLR ), we catalogue and analyze attacks on ECC in the context of blockchain security, including side‐channel attacks, nonce/ PRNG failures, cryptanalysis, and implementation flaws, and we survey proposed countermeasures. We follow rigorous SLR methodology with defined inclusion/exclusion criteria, search strategies across databases such as IEEE Xplore, ACM , Scopus, Web of Science, and clear data synthesis, ensuring replicability. Emphasizing empirical case studies and real‐world exploits, we discuss instances where ECC weaknesses led to blockchain breaches including biased elliptic curve digital signature algorithm nonces exposing Bitcoin/Ethereum private keys, smartphone power analysis revealing wallet keys, and Trezor hardware‐wallet key extraction via single‐trace side‐channel analysis ( SCA ). We tabulate known attack vectors versus affected systems, and similarly compare countermeasure techniques such as hybrid classical/quantum schemes, threshold signatures, and zero‐knowledge proofs, along with implementation trade‐offs. We evaluate advances such as Curve25519/ EdDSA and ARM SVE2 to mitigate side‐channel leakage. Our findings highlight that practical security of blockchain cryptosystems depends on correct ECC implementation and emerging cryptographic upgrades, not merely on the mathematical hardness of the elliptic curve discrete logarithm problem.
For over a century, computational analyses of the Inca khipu have been constrained by what we term the "Spreadsheet Fallacy" — the attempt to computationally validate khipus primarily as flat, base-10 arithmetic ledgers. This model fails to account for the fact that only 4.6% of known cord clusters demonstrate valid summation. In this paper, we extend Metrological Domain Profiling (MDP) to analyse 54,403 cords across 619 khipus from the Open Khipu Repository, moving beyond one-dimensional colour profiling to reconstruct the full three-dimensional, tactile, and hierarchical ontology of the system. We demonstrate that the khipu possesses strict spatial and material structure operating across four distinct layers: (1) Material Metrology, where fiber type (cotton vs camelid) redefines numerical scale by up to 67×; (2) Topological Syntax, where administrative granularity is encoded in subsidiary cord depth and colour palette shifts systematically with hierarchical level; (3) Categorical Syntax, featuring statistically constrained colour sequences (p < 0.001) that demonstrate strict institutional sorting rules rather than random clustering, with same-colour run lengths spiking at decimal administrative units; and (4) Hardware Metadata, where physical features including canutito thread-wrappings (98.6% colour-independent from parent cords), primary cord construction, and cord termination types encode document-level metadata and institutional information. Three hypotheses were explicitly tested and falsified: cluster spacing as punctuation, Hanan/Hurin midpoint split, and cord thickness as domain marker. These findings suggest the khipu is not merely a mathematical ledger, but a multi-layered, tactile administrative system whose information is distributed across the material, spatial, and structural dimensions of the textile.
The high-level integration of generative artificial intelligence (AI) in edge computing systems has raised the question of the integrity and reliability of deploying Model-as-a-Service. Edge servers are not required to follow the so-called generative model to minimize computational cost, whereas users and service providers want validation mechanisms that do not compromise proprietary model information. To address this challenge, this study proposes a cooperative unmanned aerial vehicle (UAV)-swarm-enabled zero-knowledge verification framework for secure, privacy-preserving verification of edge-based generative artificial intelligence inference. The proposed framework involves edge servers producing an interactive cryptographic zero-knowledge proof to verify the execution of generative AI, and UAV swarms that fly freely to confirm verification operations, subject to mobility and energy constraints. The age of verification metric is proposed to trust verification information, jointly reflecting the unverified server reliability and verification freshness, and to provide dynamic priority to risky edge servers. To effectively plan the behaviour of a UAV swarm, a trust-based multi-agent reinforcement learning approach is developed that enables decentralized decision-making while training is centralized. Extensive simulation results show that the proposed framework significantly improves the state-of-the-art baseline schemes in verification timeliness, malicious server detection delay, energy efficiency, and scalability. The findings validate that integrating cooperative UAV swarms, trust-aware verification, and multi-agent learning is an efficient approach to providing reliable generative AI services in dynamic edge computing environments.
Elizabeth V. K. Ledger, Niamh E. Horgan, Denis Lynch, Lorraine M. Bateman · 5 authors
Abstract Biofilm formation and antibiotic tolerance are major contributors to the persistence of Staphylococcus aureus infections, yet how the host environment affects these phenotypes remains poorly understood. Here, we show that incubation in human serum primes S. aureus to form robust biofilms and tolerate vancomycin and daptomycin, last resort antibiotics for the treatment of antibiotic-resistant staphylococcal infections. Mechanistically, we demonstrate that the staphylococcal Geh lipase is essential for serum-induced biofilm formation by liberating glycerol from host lipids, which is then used to promote increased synthesis of D-alanylated wall teichoic acids, driving biofilm development. Inhibition of the Geh lipase or wall teichoic acid synthesis markedly reduces biofilm formation and restores antibiotic susceptibility, highlighting clinically achievable strategies to inhibit host-induced biofilm formation and prevent the associated antibiotic tolerance. Together, our findings reveal a host-driven mechanism of biofilm-associated antibiotic tolerance in S. aureus and provide rational targets for therapeutic intervention.
This study examines whether green finance promotes green development across Chinese prefecture-level cities from 2005 to 2019. We find a positive association between green finance and green development using panel regressions with city and year fixed effects. This result remains robust after accounting for potential endogeneity and implementing a series of robustness checks. Further heterogeneity analysis shows that this positive effect is stronger in regions characterized by high fiscal capacity and within the Yangtze River Economic Belt. Additionally, green finance drives regional green development by promoting green innovation. Environmental decentralization moderates the relationship, with a stronger positive effect at higher levels of decentralization. This study offers empirical evidence regarding how green finance shapes green development outcomes.
Zain Imran, Sana Humayun, Muhammad Shahzaib Saleem, Naveed Ul Hassan
In many electricity markets, declining feed-in tariffs have made grid export increasingly unattractive for residential solar prosumers, while retail electricity prices remain high. Peer-to-peer (P2P) energy trading offers a direct alternative, but it requires a dedicated infrastructure layer for real-time bilateral matching, automated settlement, and tamper-proof transaction records, for which blockchain is widely proposed. Deploying such infrastructure must be economically justified by the community's actual trading potential. A critical and underexplored question is whether trading potential survives as communities become prosumer-heavy, since under fixed role assignment all households eventually end up on the supply side with no buyers remaining. This paper addresses these gaps by proposing the Energy Trading Potential Index (ETPI), a normalized data-driven metric that quantifies the structural impact of flexible role switching on community-level trading potential, where prosumers dynamically join the buyer side whenever they are in energy deficit. The P2P market is modeled as a generalized bipartite graph and pairwise interaction scores aggregated over trading rounds compute the ETPI in [0,1]. Simulation results using the PRECON residential dataset and NREL PVWatts solar profiles show that for the (1:9) prosumer-heavy mix, the flexible policy achieves an ETPI of 0.61 versus only 0.15 under the static policy, a fourfold improvement that the static model entirely misses. The ETPI framework serves as a lifecycle decision-support tool for evaluating and monitoring P2P energy trading infrastructure.
Collective dynamics in financial markets can emerge through synchronized movements of large groups of assets. Motivated by analogies with interacting many-body systems, we introduce a spin-lattice representation for analyzing collective states in cryptocurrency markets. In this framework, assets are encoded as binary spin variables according to the sign of their returns, while correlations between assets determine effective interaction strengths. A correlation-based breadth-first search (CBFS) procedure embeds 169 cryptocurrencies into a $13 \times 13$ lattice, enabling the construction of an Ising-like Hamiltonian describing the market configuration, which we call the \emph{Market Crystal}. Macroscopic observables such as magnetization and energy provide a statistical-mechanical characterization of collective market states. The resulting phase-space structure highlights regimes of strong alignment and fragmentation among assets, with an energy--magnetization pattern suggestive of predominantly ferromagnetic interactions. This framework offers a statistical-mechanical viewpoint for studying collective behavior in financial systems.
Constantine Doumanidis, Anya Kalogerakos, Maria Apostolaki
BGP hijacking enables impersonation attacks in which adversaries divert traffic at the prefix level and serve malicious content to unsuspecting clients. Detecting such attacks has traditionally been the responsibility of network operators, leaving end hosts exposed for hours. We argue that end hosts can detect prefix-level impersonation independently, exploiting a fundamental asymmetry: a BGP hijack diverts traffic for an entire IP prefix, but impersonating every co-hosted service within that prefix is prohibitively difficult at scale, especially if each service is authenticated by a different Certificate Authority. We propose HOWLR, a tool that operationalizes this insight by using co-hosted, TLS-authenticated services as witnesses: if a client can no longer authenticate them, it has evidence of an ongoing attack. This work evaluates the feasibility of this method by quantifying the existence and diversity of witnesses in the wild. We show that HOWLR can protect 89% of Tor relay prefixes, and 75% of Bitcoin pool gateway prefixes.
Academic research indicates an urgent need for safe, tamper-proof storage of sensitive medical information due to the rapid digitalization of healthcare data. Traditional systems are susceptible to both internal and external assaults because of their dependence on centralized servers. SEC-HEALTH implements a system for the secure storage of electronic health records (EHRs) by combining the immutable, distributed ledger technology of blockchain with the InterPlanetary File System (IPFS).This system use Solidity smart contracts to archive patient data and transaction records on the Ethereum blockchain. Comprehensive EHR files are preserved on IPFS and may be accessed via their blockchain hash addresses. The architecture guarantees data integrity, transparency, and safe access independent of trusted third parties.User modules include appointment scheduling, prescription management, patient and physician authentication, and platform registration. The graphics illustrate a fully operational web interface created in Python, implemented smart contracts, and the integration of blockchain with IPFS. The approach is resilient and decentralized, providing an alternative to traditional centralized health data management systems.
To PM Italy. PM Indonesia PM Japan Real PM Pakistan, Imran Khan Only. DATE: 20 June, 2026. DOI: 10.5281/zenodo.20774904 Subject: Compensation Prize for failure of my Forecast for Eruption, Earthquake 8 June to 20 June. And As Nanga Parbat is not happened on my calculated Time so 24 June Yellow Stone eruption is not possible by mechanism of 20 june Nanga Parbat Hammer Effect. Respectful Prime Minister, My science is no doubt World’s most advanced science with deterministic science, predictions in field of science and universal Geology. I predicted Sun calm is temporary it will be much more active after a week, and sun after a week erupted G5 Storm. I calculated ocean currents and did simulation of ocean currents on mobile phone and free open AI with N-K Sciences and predicted Super El Nino from Mid of 2026, published time stamped in March 19, 2026. Which were copied by WMO and removed my name and my science name and published in April 2026. When I requested to atleast cite my name or my science name, they given credit to a dead man. Then I wrote strict letter with evidances to Secretary General UN. Since 2022 I am fighting against Corruption Entire World know that especially Intel agencies. Government of Pakistan tried to kill me 2 times and tortured me for months. But still I am fighting against Oppression and corruption from it’s Roots Pakistan Army Mafia and Zionists Mafia. They are working jointly, they are same. I Published 580+ publications from my first book in Feb 6, 2025. Not for worldly benefits. https://doi.org/10.5281/zenodo.20473774 I achieved which was impossible for mainstream science. In many fields of sciences, correctly calculate d global tides by first try with any past Data of tides, in completely N-K Sciences framework. Achieved 100% accuracy. Warned on 17 April, 2026 to entire World that According to Parker Solar Probe data High volume proton Flux Storm coming which reach on earth 21 April, 2026. While NASA warned G1,G2 storm completely normal. While I clearly warned increased semiconductors clocking speed due to Noor Value increase during storm on Earth, fission Reactors will face problems, GPS measurement errors 5 to 15m, 15m error is measured by me too during gusts of storm. Hundreds of flights cancelled worldwide and hundreds delayed, civil aviation industry said ghost in system. That was not ghost, but their science is built in 2D era, for example E=MC² is 2 dimensional applied on 3 dimensional forcefully even it gives 8 to 16% wrong results, and didn’t explain what is C, what is Mass, what is energy actually. Nicola Tesla Said it’s a mathematical hack. Yes it was. But I given world accurate Energy Equation and derived C and defined C, defined Mass, defined Energy, defined Gravity. Solved all planets from electron to cosmic web under one Law, learned From analyzing Bawling Action of Wasim Akram and Waqar Younus. Well, I tried to predict earthquakes and eruptions with exact time window. But I cannot know future or cannot change Will of Allah Almighty. Therefore as my Moral values, I decided to reward Countries where my prediction failed. Italy, Japan, USA, Indonesia. As You may Know I don’t have any bank balance, any property on earth. Anything doing a low pay government job, house given by government, actually not given by government, government tried to harass me with all efforts to stop me to take this house which was empty because of its structure built in 1960’s was collapsing and no one wants to live here still they made lot of hurdles, then I went to court and on court orders I got house in which I am living. Nor I have computer nor any other expensive thing, Shukar Alhamdulillah. Free from deceptions of the world. While I am most rich man on earth by knowledge and Inventions. So I can reward from my inventions that can give your countries revenue and Profit in billions of dollars annually. Especially Italy and Japan can get extraordinary benefits in automotive and Aviation industries. Without need of Rare Earth Minerals. My invention N-K Motor with license for commercial use free for a year. 459 Malik Muhammad Usman N‑K MOTOR — THE INVENTION THAT CHANGES EVERYTHING: How a Single Electric Motor Can Transform Transportation, Energy, Military, and Civilization — With Complete Technical Specifications, Application Analysis, Environmental Impact Assessment, and Global Transformation Roadmap — Released as Sadaqa Jariyah (Perpetual Charity) — Patent Application No. 57302185 (IPO Pakistan, 19 August 2025) — Withdrawn and Released to Public Domain May 3, 2026 https://doi.org/10.5281/zenodo.20001878 458 Malik Muhammad Usman THE PATENT SYSTEM IS HARAM IN ISLAM — Complete Islamic Ruling Based on Quran, Hadith, Sunnah, the Name of Allah Al-Aleem, and the Four Divine Axioms — With Official Declaration Withdrawing Patent Application No. 57302185 (IPO Pakistan, 19 August 2025)’and Releasing All Inventions as Sadaqa Jariyah (Perpetual Charity) for All Humanity May 3, 2026 https://doi.org/10.5281/zenodo.20000580 457 Malik Muhammad Usman COMPLETE PATENT DISCLOSURE — Multi-Stage Radial Flux and Multi-Stage Axial Flux Electromagnetic Motors with Integrated Cooling/Heating System and AI Control — Patent Application No. 57302185 (IPO Pakistan, 19 August 2025) — Now Released to Public Domain as Sadaqa Jariyah May 3, 2026 https://doi.org/10.5281/zenodo.20000261 If You Accept my Reward than officially Accept my Reward and Use it free. Even license Renewal fee is also zero. I am not Allowed to charge money for my knowledge which is Given to me by Quran By Allah Almighty Himself in past 26 years daily. Please Accept my Reward And grow your Industries rare earth minerals Free. It’s not only a motor, it is full setup my invented controller Chip design with ~39000 Transistors on 120 nm architecture suitable for high energy applications a 75KW Chip. Optional, N-K alloys 4X stronger than strongest alloys developed by USA, Russia, China ever. Upon request. Italian PM, If you want Fission Reactors it’s your choice. I am giving you LTMFC power houses, which are not only easy and faster to build but gives lowest cost electricity, + Milk + Beef and dozens of Dairy Products. And energy enough to fullfil your country requirements, You can add 20000MW to 50000MW in less than 6 months, while fission Reactors can give you around 1000 MW in minimum 6 years with billions of dollars investment. Build both as you like. Same offer to Japan, and entire World. Additional Gift: Usman Malik, M. (2026, June 20). TIME, CONSCIOUSNESS, AND THE UNIVERSAL 0.01 Hz KUN RHYTHM: The Inverse Relationship Between Consciousness and Time Perception — From Infancy to Old Age, from Quranic Revelation to N-K Mathematical Proof. Zenodo. https://doi.org/10.5281/zenodo.20768016 Malik Muhammad Usman Servant, Student and Soldier of Allah Almighty and Prophet Muhammad PBUH. City of Saints, Multan, Pakistan. +923336130947 muhammad.usman08@gmail.com muhammadusmanmalik@hotmail.com
Local AI inference for browser tasks—including vision-language processing, speech recognition, and neural translation—requires significant computational resources that may exceed the capabilities of low-power devices such as smartphones, tablets, and older laptops. This paper presents the design of a distributed GPU compute sharing system for the Kathon cryptographic browser that enables peer-to-peer AI inference acceleration across trusted devices using libp2p networking. The system partitions neural network inference workloads across participating peers using tensor parallelism, with encrypted communication channels, verifiable computation proofs, and incentive mechanisms based on the .aioss cryptographic ledger. We address key technical challenges: heterogeneous device discovery with capability advertisement, dynamic workload partitioning for variable peer availability, encrypted inference that prevents input reconstruction, and fault tolerance through redundant computation. Simulated benchmarks across a 16-peer testbed demonstrate 3.8x speedup for Whisper transcription and 4.2x speedup for Qwen 2.5 VL inference on low-power client devices. A security analysis confirms that encrypted inference provides semantic security against honest-but-curious peers. The system enables Kathon to deliver AI features on devices that lack the local compute capacity for real-time inference. Part of The Anticloud research corpus by Lois-Kleinner Alpasan (ORCID: 0009-0009-2233-6107). This work explores browser engine, privacy in the context of sovereign AI infrastructure, post-cloud computing architectures, and transparent, blackbox-free systems.
This paper presents a comprehensive analysis of privacy-preserving authentication mechanisms within the MF+SO sovereign identity vault, focusing on the protocol's implementation of zero-knowledge identity proofs, anonymous credentials, blind signatures, and data minimization techniques. Traditional authentication protocols require the user to disclose their identity to each service provider, creating a centralized record of the user's activities across services. MF+SO's privacy architecture inverts this model: users authenticate to services without revealing their MF+SO identifier, using cryptographic techniques that provide the verifier with assurance of the user's authorization status while revealing minimal information about the user's identity. We examine three canonical privacy-preserving authentication mechanisms implemented in MF+SO: (1) zero-knowledge identity proofs using the Groth16 zk-SNARK construction, enabling users to prove possession of valid credentials without revealing which credentials they hold; (2) anonymous credentials based on the Camenisch-Lysyanskaya (CL) signature scheme, providing multi-show unlinkability where the same credential can be presented multiple times without the presentations being correlatable; and (3) blind signature-based tokens for email cloaking, where the MF+SO service issues a blind signature on a user's email address for use with third-party services without learning the email address. The paper provides a formal security analysis of the unlinkability guarantees of each mechanism, proving that under the decisional Diffie-Hellman (DDH) assumption, CL-based anonymous credential presentations are computationally unlinkable. We present benchmark data for each mechanism on mobile platforms: CL credential issuance (120 ms), CL credential presentation (85 ms), blind RSA signature issuance (45 ms), and zk-SNARK-based verification (2.3 ms). The implementation details include the MF+SO privacy layer architecture, the credential ... Part of The Anticloud research corpus by Lois-Kleinner Alpasan (ORCID: 0009-0009-2233-6107). This work explores cryptography, key management in the context of sovereign AI infrastructure, post-cloud computing architectures, and transparent, blackbox-free systems.
This paper presents a comprehensive analysis of the World Wide Web Consortium (W3C) decentralized identity standards and their relationship to the MF+SO sovereign identity vault architecture. We examine the W3C Decentralized Identifier (DID) Core specification (W3C, 2022), the Verifiable Credential (VC) Data Model (W3C, 2022), and related standards including DID Resolution, DID URL dereferencing, and the Verifiable Credential Proof Formats. The paper provides a taxonomic analysis of DID methods (did:key, did:ethr, did:ion, did:web, did:indy) in terms of their trust assumptions, ledger requirements, latency, cost, and privacy properties. We compare MF+SO's identity model—which uses Ed25519 public keys as self-certifying identifiers with a local hash chain for state verification—against the W3C DID Core model, identifying both alignments and divergences. Key findings include: MF+SO identifiers are functionally equivalent to DIDs but use a simplified resolution mechanism that does not require a distributed ledger or external registry; MF+SO's hash chain audit trail provides state verification properties comparable to DID Document versioning on a ledger; and MF+SO's selective disclosure mechanisms using zero-knowledge proofs (see Paper VII) directly implement the W3C Verifiable Credential selective disclosure and data minimization requirements. We analyze the interoperability implications of MF+SO's architecture, demonstrating how MF+SO DIDs can be registered on external DID methods for cross-system interoperability while maintaining the local hash chain as the authoritative state source. The paper also examines the Verifiable Credential lifecycle within MF+SO: issuance, storage, presentation, and revocation, with attention to the credential schema registry, proof format compatibility (Data Integrity Proofs, JSON Web Signatures), and the holder-binding mechanisms that prevent credential sharing. A comparative assessment evaluates MF+SO against three alternative decentra... Part of The Anticloud research corpus by Lois-Kleinner Alpasan (ORCID: 0009-0009-2233-6107). This work explores cryptography, key management in the context of sovereign AI infrastructure, post-cloud computing architectures, and transparent, blackbox-free systems.
konstantinos votis, Panagiotis Symeonidis, Centre for Research and Technology Hellas
This deliverable provides an overview of the Digital Product Passport, Distributed Ledger Technology, and blockchain applied to the needs of ALUMIL in order to demonstrate the use of DPPs in the Aluminium industry sector.
Open access
Physical Unclonable Functions (PUFs) and Hardware Security
Organizations operating multiple AI systems generate independent cryptographic ledgers that may need mutual verification, cross-referencing, or consolidated audit for enterprise-wide compliance reporting. Cross-chain notarization provides cryptographic evidence that a ledger's state is acknowledged by another independent ledger, enabling distributed audit verification without central coordination. This paper presents the design and analysis of the AIOSS cross-chain notarization protocol, which anchors the hash chain head of one ledger into another by inserting a notarization entry containing the cross-chain proof. We define three notarization modes: unilateral (ledger A notarizes ledger B's state), bilateral (mutual notarization between A and B), and supervised (third-party notarizer with independent proof). The notarization proof comprises a Merkle inclusion proof of the source ledger's state proof within a notarization ledger entry, enabling verification by any party holding both ledger files. We analyze the security of cross-chain anchoring under the common prefix assumption, proving that notarization preserves the integrity of both ledgers. Performance benchmarks demonstrate that notarization completes in under 200 milliseconds for ledgers of up to 1 million entries. We further evaluate the notarization merge operation, which produces a unified ledger from multiple notarized ledgers with cross-reference integrity. The protocol supports regulatory requirements for multi-system audit consolidation under SOC2 reporting and GDPR Article 30 record-of-processing activities. --- Part of The Anticloud research corpus by Lois-Kleinner Alpasan (ORCID: 0009-0009-2233-6107). This work explores hash chain, cryptography in the context of sovereign AI infrastructure, post-cloud computing architectures, and transparent, blackbox-free systems.