Zero-knowledge proof systems are now deployed widely in production cryptographic protocols, yet many rely on assumptions (discrete logarithms, pairings, or structured reference strings) that a fault-tolerant quantum computer would break via Shor's algorithm. This systematization of knowledge (SoK) presents a four-layer decomposition (L1-L4) that separates where quantum risk enters a proof system: arithmetization, polynomial commitment, protocol logic, and non-interactive compilation. Using a two-axis taxonomy that crosses cryptographic impact (structural break, modularly replaceable break, or quantitative degradation) with deployment migration feasibility, we classify the major proof-system families, derive a modularity test for evaluating upgrade paths, and introduce "collect now, forge later" (CNFL) as the proof-system analogue of harvest-now-decrypt-later. Published resource estimates place the cost of breaking 256-bit elliptic-curve discrete logs at 1,200-1,450 logical qubits, with the pairing-friendly curves underlying KZG (BN254, BLS12-381) of the same order of magnitude but somewhat larger; under these estimates, such L2 constructions would face structural breaks once fault-tolerant hardware reaches that regime. Hash-based transparent systems, by contrast, degrade quantitatively under Grover-type speedups and QROM reduction losses rather than collapse. Case studies of Zcash, zkSync Era, and StarkNet show that practical post-quantum outcomes depend on deployment governance and upgrade architecture as much as on cryptographic primitives. The scope covers IOP/PCS-based and algebraic proof families; MPC-in-the-Head constructions are excluded. This is a self-published technical report. It has not been peer reviewed.
This study examines the transformation of artistic ownership in fine arts through blockchain-based non-fungible tokens (NFTs). It explores how NFTs reshape traditional systems of provenance, authenticity, and value creation by decentralising ownership verification and embedding transaction records within blockchain infrastructure. Using a qualitative research design based on secondary data analysis, the study synthesises findings from academic literature, industry reports, and case studies of NFT marketplaces and institutional adoption. The analysis is grounded in Actor-Network Theory, Institutional Theory, and Cultural Economics, enabling a multidimensional interpretation of technological, institutional, and economic change. Findings indicate that NFTs reconfigure artistic ownership through programmable smart contracts, disrupt traditional intermediary roles in the art world, and introduce new forms of digital scarcity that drive speculative valuation. However, challenges such as regulatory ambiguity, environmental concerns, and market volatility remain significant. The study concludes that NFTs represent not merely a technological innovation but a structural transformation of ownership systems in contemporary fine arts. Keywords: Blockchain, NFTs, Artistic Ownership, Fine Arts, Digital Art Markets, Cultural Economics, Institutional Change
The complete codebase and supplementary materials for this study have been archived on Figshare to ensure full reproducibility and to facilitate adoption by other researchers and practitioners. The archive includes all Python scripts used for data preprocessing, model training, hyperparameter tuning, threshold optimisation, and SHAP explainability analysis. Also included are the processed CSV files used for the analysis, along with all figures and tables presented in this paper. The repository is organised to enable straightforward replication of the experiments and adaptation of the framework to other datasets or blockchain platforms.
Sovereign entities – states, international organizations, and autonomous infrastructure networks – face a governance paradox: centralized systems become brittle under stress, while decentralized systems fragment into incoherence. This paper proposes the Constitutional Lattice v2.0, a mathematically structured frame-work for coordinating sovereign autonomy within a constitutional corridor, built on a three-term agent-interaction force law (oscillatory coupling, linear restoring, inverse-square repulsion) with a Lennard-Jones-style short-range hardening term. This paper is offered, in the spirit of a companion theoretical proposal in the psychotherapy and Human–AGI relational-dynamics literature [1], as a theoretical contribution with an explicitly preliminary empirical status. The framework’s central structural conjecture – that the coupling ratio ρ= kg /km has a privileged value at Φ−1 ≈ 0.618 – was tested computationally in a simplified two-dimensional setting (Section 6). The test did not find evidence supporting this conjecture: the measured stability metric varied smoothly and monotonically across the tested range of ρ, with no distinguishing feature at Φ−1. This result, its scope, and its limitations are reported in full, following the disclosure standard set out in [1]. The paper’s remaining contributions – the federated lattice architecture, the quarantine and cold-boot recovery mechanisms, and the Constitutional Drift Index as a transparency instrument – are presented as an architecture and a research programme, not as validated engineering.
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Opinion Dynamics and Social Influence
Advanced Research in Systems and Signal Processing
The complete codebase and supplementary materials for this study have been archived on Figshare to ensure full reproducibility and to facilitate adoption by other researchers and practitioners. The archive includes all Python scripts used for data preprocessing, model training, hyperparameter tuning, threshold optimisation, and SHAP explainability analysis. Also included are the processed CSV files used for the analysis, along with all figures and tables presented in this paper. The repository is organised to enable straightforward replication of the experiments and adaptation of the framework to other datasets or blockchain platforms.
Bitcoin (BTC) wealth distribution is often studied with macro indicators like wallet balances, prices, network activity, fees, and hashrate. This letter proposes a "Crypto-Microeconomic Observability Framework" to examine micro-level Bitcoin wealth disparities across five labeled agent classes: Service, Abuse, Malware, Individuals, and Benign. Using descriptive, inequality, and longitudinal concentration metrics, we show that Bitcoin wealth is highly concentrated across major classes, consistent with a persistent "Whale-Effect". Service entities hold the largest share of observed BTC (75.15%), while Abuse controls a disproportionately large share relative to its entity count (24.26% of BTC vs. 3.53% of entities). Individuals, Abuse, and Service show near-maximal within-class inequality (e.g., Gini = 0.9993 for Individuals), and time-series analysis indicates these patterns persist. Overall, Bitcoin wealth among labeled economic agents remains structurally uneven and concentrated in a small subset of entities.
Bitcoin research increasingly relies on on-chain indicators to study network activity, monetary issuance, transaction demand, miner incentives, coin-age behavior, and long-run monetary dynamics. However, many commonly used Bitcoin metrics are dispersed across commercial platforms, subject to heterogeneous definitions, or not fully reproducible from primary blockchain data. This manuscript introduces Open Bitcoin Metrics (OBM), a reproducible, full-node-derived dataset and reference guide for Bitcoin on-chain time series designed for economic and econometric research. The dataset provides documented daily series covering block production, block-space usage, transaction counts, supply, issuance, fees, miner revenue, mining difficulty, estimated hashrate, Bitcoin Days Destroyed, dormancy, liveliness, UTXO counts, spent output value, and related UTXO-age indicators. Metrics are reconstructed from a locally maintained Bitcoin Core full node, a persistent spent-output indexer, or deterministic transformations of previously generated OBM series. Each series is accompanied by open-source Python code, stable identifiers, explicit definitions, metadata, validation procedures, interpretive caveats, and comparisons with the closest publicly available metrics. The dataset is intended to support transparent empirical research, replication, teaching, and comparative analysis across monetary economics, financial economics, and blockchain studies.
Rahman Najia, Mahbub Md. Seratul, Amin Md. Ruhul, Kazi Abdul Mannan
The rapid expansion of non-fungible tokens (NFTs) has transformed the digital art ecosystem by enabling decentralised ownership, new economic models, and global market access for artists and collectors. However, the sustainability of NFT-based art remains highly contested, particularly in relation to environmental, economic, and social dimensions. This study investigates stakeholder perceptions of sustainability in NFT-based art through a qualitative analysis of secondary data, including academic literature, industry reports, and documented narratives of artists and collectors. Grounded in socio-technical systems theory and sustainability transition theory, the study explores how technological developments, market dynamics, and social discourses shape sustainability perceptions. The findings reveal a complex and often contradictory landscape: while NFTs are perceived as empowering tools that enhance artistic autonomy and financial opportunities, they are also criticised for their environmental impact, speculative nature, and ethical challenges. Technological innovations such as proof-of-stake mechanisms and green NFTs are gradually reshaping perceptions, though scepticism persists. The study concludes that sustainability in NFT-based art is a socially constructed and evolving concept requiring integrated technological, economic, and social approaches. Keywords NFT art; sustainability perception; blockchain technology; digital art economy; green NFTs; socio-technical systems; sustainability transition
This paper establishes a rigorous, intrinsic mathematical axiomatic framework for the Information-Emergent Spacetime Theory (IEST), largely alleviating phenomenological presuppositions that rely on smooth classical backgrounds or continuous manifold embeddings. Utilizing Discrete Exterior Calculus (DEC), we characterize the microscopic reality as a directed 1-cell complex, where localized information flux is defined as a discrete 1-form. The underlying dynamics of the system are driven by the Principle of Minimum Entropy Action (PLEA) under coordinate-free constraints. The topological resistance is established as the matrix element of a positive-definite discrete Hodge star operator, providing a rigorous resolution to the mathematical vulnerability of non-positive definiteness in un-embedded networks. We demonstrate that Einstein's field equations spontaneously emerge as the topological self-balancing condition of decentralized network flow self-organization. Finally, we derive the explicit dynamical evolution equation of Stochastic Topological Noise (STN), yielding deterministic falsifiable phenomenological pathways across macroscopic astrophysical observations.
A blockchain is a chain for a cryptographic reason: each block must hash to its predecessor to anchor proof-of-work. Remove that requirement and the linear structure has no geometric necessity. This paper proposes crystal-currency: a distributed ledger whose validity condition is a geometric consistency constraint rather than a computational puzzle, capital stake, or authority signature. The constraint derives from the Fano plane $\mathrm{PG}(2,2)$ — the unique projective plane on seven points — whose automorphism group $\mathrm{PSL}(2,7)$ of order 168 acts rigidly on the seven orbit types of the three-qubit Pauli group under the Clifford group. The natural data structure is not a chain but a block complex: a growing simplicial complex in which each confirmed block adds a tetrahedron (3-simplex) subject to a Fano consistency condition. We define Proof of Volume (PoV), a consensus primitive requiring all seven orbit types to be simultaneously attested, and prove three results: (i) PoV cannot be satisfied by fewer than seven geometrically distinct parties, since $\mathrm{PG}(2,2)$ admits no proper sub-plane; (ii) combined with Proof of Stake, orbit-aware slashing graded by Fano distance $d_F(\mathcal{O}_i, \mathcal{O}_j)$ makes even two-party cross-orbit collusion detectable on-chain; (iii) for permissioned central-bank digital currency (CBDC), the circle orbit $\mathcal{O}_0$ intersects every Fano line, giving the central bank geometric sovereignty that cannot be outvoted or out-staked. The open cryptographic problem is reduction of orbit-type unforgeability to the Hidden Subgroup Problem over $\mathrm{PSL}(2,7)$. Keywords crystal currency, blockchain, block complex, simplicial complex, Fano plane, $\mathrm{PG}(2,2)$, $\mathrm{PSL}(2,7)$, proof of volume, proof of stake, orbit-aware slashing, Fano distance, CBDC, central bank digital currency, geometric sovereignty, hidden subgroup problem, distributed ledger, Clifford group, Pauli group
The literature on blockchain-based databases is divided into permissioned blockchains and permissionless account-based blockchains. However, the former is not fully decentralized, and the latter suffers from challenges in scalability and practicality. We propose SpendableStore, a hybrid on/off-chain database that operates on top of permissionless UTXO-based blockchains as a novel approach to the problem of data decentralization. Our design integrates atomic data units into individual UTXOs to create a new blockchain concept called Spendable Data Objects that perform traditional CRUD operations. The integrity, immutability, and ownership of these Spendable Data Objects are safeguarded directly by the blockchain peer nodes, thus constraining the power of database administrators to achieve true data decentralization. We further support database transactions and propose an isolation mechanism called Future Now Snapshot Isolation to reason about transactional correctness in SpendableStore. We performed experiments on a major blockchain's Mainnet and observed up to 16x better throughput compared to a state-of-the-art blockchain-based database.
Shah Nawaz Haider, Steve Austin, Arnab Barua, Sarowar Morshed Shawon · 5 authors
Time-series forecasting supports decisions in finance, en-ergy, transportation, public health, and industrial monitoring. Recent foundation models improve transfer across forecast-ing tasks, but many depend on centralized data and Trans-former attention, which restricts their use for long, high-di-mensional, and privacy-sensitive signals. This paper presents QuantFlow, a probabilistic forecasting framework that com-bines inverted sequence embedding, bidirectional Mamba state-space decoders, quantile regression, and federated learning. Each variable is embedded over the complete ob-servation window, processed in forward and reverse direc-tions, and projected to five conditional quantiles. TSMixup expands temporal diversity through Dirichlet-weighted inter-polation while preserving sequence structure. Experiments cover cryptocurrency, traffic, electricity, Electricity Trans-former Temperature, influenza, and weather data. QuantFlow obtains mean squared errors of 0.2834 on ETTm1 and 0.2218 on Weather, and a 20-client non-IID deployment retains use-ful accuracy after three communication rounds without cen-tralizing raw records. The results indicate that selective state-space modelling is a promising basis for scalable, uncer-tainty-aware, and privacy-conscious time-series prediction, while also revealing limitations on irregular epidemiological signals and long-horizon generalization.
Dr. Archana Bendale, Prof. Pawan Malani, Sakshi Shirole, Samiya Shaikh
Abstract: The widespread adoption of Electronic Health Record (EHR) systems has improved clinical documentation and provided easier access to patient information in modern healthcare environments. However, a large number of healthcare information systems are in a fragmented state, creating barriers for information exchange. Blockchain technology has been identified as a secure and distributed method for managing patient information. Despite its benefits, incorporating blockchain technology into healthcare systems is confronted by interoperability challenges, including technical, semantic, and organizational aspects that limit information sharing among heterogeneous platforms. This study identifies interoperability challenges in blockchain-based healthcare architectures. A three-layer evaluation framework is developed, including performance metrics. A systematic review of recent blockchain-based healthcare architectures is conducted to guide the framework's development. This study aims to provide a valuable methodology for healthcare system architects to evaluate interoperability readiness before deployment, including potential research areas. Keywords: Block chain Technology, Electronic Health Records, Healthcare Interoperability, Distributed Ledger Technology, Health Information Exchange.
Blockchain technology has moved from the fringes of cryptographic research into the center of serious conversations about how industries govern data, verify transactions, and establish trust between parties who have no prior relationship and no shared authority to appeal to. Yet for most professionals working in management, finance, healthcare, and logistics, the technology remains opaque — described in either overly technical language that assumes a computer science background, or in breathless promotional terms that obscure more than they reveal. This paper is an attempt to close that gap honestly. Drawing on a progressive self-directed engagement with blockchain fundamentals, this work develops a conceptual framework covering four interconnected dimensions: its foundational governance philosophy of decentralization and equal network rights; its cryptographic security architecture, encompassing public and private key pairs, symmetric and asymmetric encryption, and hash-based data integrity; its distributed node network, comprising full nodes, lightweight nodes, and mining nodes and their respective governance roles; and its real-world application domains across supply chain management, healthcare information systems, financial services, human resources verification, and artificial intelligence data integrity. The paper adopts a conceptual analysis methodology, synthesizing foundational and applied blockchain literature to construct an integrated framework accessible to management researchers and practitioners. The central argument is that blockchain's significance is not primarily technological but institutional: it represents a structural alternative to the centralized authority model that has governed data ownership and transactional trust for centuries.
Aletheia is a knowledge substrate organized around a write-time admission gate: a fact is accepted only if it does not structurally contradict what the base already holds. The gate inherits a Lean 4 soundness proof, so the admitted store stays acyclic, asymmetric, type-disjoint, and temporally consistent under any stream of typed edges. We bind the proof to the implementation by differential testing over 104 adversarial inputs, zero divergences. We first tried to build a partial-truth disinformation detector on this gate. Measurement refused. On real political claims almost nothing decomposes into the gate’s six relations: 0 of 155 atoms were gate-testable, and where it did fire it lost to a cold language model, 0 of 21 against 17. Most real disinformation violates truth, not structure, so a structural gate is the wrong instrument. We retract the detector claim. What remains is a guarantee rather than a rate. Each catch names the axiom it violated; the verdict is bit-exact and carries a machine-checked admission proof; and a safety property whose core is now machine-checked in Lean holds that no finite feed of self-asserted credibility can mint a false endorsement, conditional on authority granted upstream (0 of 210 adversarial sequences, against 140 of 210 for a credibility-naive baseline). A frontier model matches our hit-rate on constructed distortions, and a reasoning model matches even our one structural edge, so we claim no detection advantage. We claim instead that the jurisdiction of a structural guarantee can be measured, and we measure it across two regimes: where the base lets it adjudicate, and where it abstains.
Zhengqing Liu, Alberto Sonnino, Igor Zablotchi, Eleftherios Kokoris-Kogias · 5 authors
Modern blockchains rely on a modular architecture that decouples consensus from execution. Recent advances in consensus algorithms have shifted the bottleneck to the execution layer, which must deterministically follow the consensus order and handle increasingly complex, compute-intensive smart contracts. We identify that single-node validators cannot keep up, motivating the need for a scale-out design. We design Remora, a scale-out smart contract execution engine. Remora adopts an efficient asymmetric architecture with centralized transaction dispatching and distributed execution, and depends on an object versioning scheme with a strict ownership model to guarantee deterministic scale-out execution. Remora achieves up to 3x throughput improvement compared to state-of-the-art deterministic execution schemes, scales up to 250k TPS, matching modern consensus performance, and reduces latency by up to 5ms. We also show that Remora elastically adapts to bursty workloads and dynamic access patterns using real-world traces. Remora's main performance benefits come from a novel stateless-stateful separation during smart contract execution, which overlaps the execution of state-independent tasks with consensus, and a new locality-aware and load-balanced scheduling scheme.
Smart contract vulnerabilities are predominantly logic bugs whose detection requires structured, step-by-step procedural knowledge of attack patterns and contract semantics. Existing LLM-based methods struggle to generate this knowledge automatically: prompt-based methods rely on manually crafted detection rules, while fine-tuning requires massive labeled datasets that are inherently scarce in this domain. We present EvoVuln, an automated framework that reformulates vulnerability detection as a procedural knowledge evolution problem, synthesizing and refining detection logic using only a minimal number of labeled samples. To achieve this, EvoVuln introduces two key mechanisms. First, a Runtime with an Inversion of Control (IoC) architecture compiles detection rules into Executable Policies. This strictly decouples deterministic control flow from LLM semantic reasoning, ensuring faithful logical adherence and producing dense diagnostic telemetry for precise error localization. Second, a two-phase evolution pipeline refines the rule via abductive semantic debugging without any parameter updates: Cold Start bootstraps and stress-tests an initial rule using auto-synthesized corner cases; Few-Shot Evolving then grounds the policy in real-world semantics using only five vulnerable and five safe examples per vulnerability type. Evaluated across five real-world vulnerability types, EvoVuln achieves a 71% macro-average F1-score, outperforming all baselines. The evolved procedural knowledge is portable across models: it enables a lightweight, low-cost model to surpass a much larger zero-shot model by 19 percentage points, and transfers to other LLMs without retraining, at a one-time evolution cost under $50.
Our study examines the impact of climate policy uncertainty on the volatility of Bitcoin, Ethereum and Litecoin. Using monthly Climate Policy Uncertainty Index data from 2010 to 2024, we forecast daily cryptocurrency volatility with a GARCH-MIDAS model. The results show that higher climate policy uncertainty significantly increases volatility across all three cryptocurrencies over the full sample period. Out-of-sample analysis, which captures structural changes in energy consumption, reveals stronger effects for Bitcoin. Ethereum shows insignificant responses following its transition to a proof-of-stake mechanism, while Litecoin exhibits a significant positive relationship with uncertainty. Overall, climate policy uncertainty proves to be a strong predictor of cryptocurrency volatility, particularly for energy-intensive assets. The findings highlight the importance of policy-related information in shaping investor behaviour in crypto markets and provide useful implications for cryptocurrency issuers, retail investors and portfolio managers seeking to manage risk under changing regulatory and environmental conditions.
Zero-knowledge proofs (ZKPs) are emerging as a core technology for privacy-preserving computation. Despite steady progress in protocol and algorithm design, generating these proofs remains computationally intensive, driving growing interest in hardware acceleration for kernels such as number-theoretic transform (NTT) and multi-scalar multiplication (MSM). Among them, the sumcheck protocol offers a compelling alternative with O(n) prover complexity compared to O(nlog n) for NTT-based approaches, yet our analysis reveals its execution is fundamentally memory-bound, with severely underutilized compute resources. This characteristic demands a memory-centric acceleration strategy, in contrast to compute-centric approaches of prior work.
Zero-Knowledge Proof (ZKP) is a cornerstone in privacy-preserving computing, addressing critical challenges in domains such as finance and healthcare by ensuring data confidentiality during computation. However, the high computational overhead of ZKP, particularly in proof generation and verification, limits its scalability and usability in real-world applications. Existing efforts to accelerate ZKP primarily focus on specific components, such as polynomial commitment schemes or elliptic curve operations, but fail to deliver an integrated, flexible, and efficient end-to-end solution that includes witness generation on commercial computing platforms.
ASEGUNLOLUWA E. BABALOLA, DAVID O. ILESANMI, PREYE ADEOLA
Electronic voting can improve the speed of ballot processing and result generation, but conventional systems often depend on centrally controlled infrastructure that may create concerns relating to record alteration, transparency and administrative control. This study presents the development of a blockchain based electronic voting prototype that integrates election creation, candidate management, voter address authorization, ballot submission and result retrieval within a web application. The system adopts an Ethereum based architecture comprising a Next.js user interface, Web3 communication, MetaMask wallet connection, Solidity smart contracts and a local blockchain environment provided by Ganache. A factory smart contract is used to create separate election contracts, enabling each election to maintain its own candidates, authorized voter addresses, election status and vote totals. Before a ballot is accepted, the relevant election contract verifies that the election is active, that the submitting address is authorized and that the address has not previously voted. The developed prototype provides interfaces for election creation, voting and result presentation, demonstrating the integration of the web application with the smart contract and blockchain components. The study provides a basis for the independent management of multiple elections through separate smart contract instances.
Classical Block Withholding (BWH) attacks have been extensively studied in block-dependent reward schemes, where pool members are compensated upon a block discovery within the pool. However, most contemporary mining pools operate under share-based schemes, wherein participants are paid immediately upon submission of valid shares. In this paper, we analyze BWH under Pay-Per-Share (PPS) and Full-PPS (FPPS) schemes for Nakamoto-style blockchains and prove that these mechanisms are not incentive compatible -- contrary to claims in prior literature. Under PPS/FPPS, the optimal strategy for a BWH attacker is the All-out Attack (AoA): the adversary allocates its entire hashpower toward the victim pool, submitting only partial Proof-of-Work shares (pPoW) while withholding all valid blocks, i.e., full Proof-of-Work (fPoW). Prior to the first difficulty adjustment, the adversary incurs negligible loss from withheld fPoWs. After the adjustment reduces block difficulty, the adversary either generates more pPoWs per unit time or, if pPoW difficulty is held fixed, earns a higher reward per share, in both cases achieving a relative gain of $\fracα{1-α}$ over pre-adjustment rates, where $α$ is the adversarial hashpower fraction. Honest miners benefit at the same rate as the adversary per unit hashpower, while the victim pool operator bears all losses, paying out-of-pocket for pPoW submissions without receiving fPoW compensation in return. Finally, advanced BWH variants such as Fork After Withholding (FAW) yield no additional profit under PPS/FPPS.
Sushila Dhaka, Jane-Hwa Huang, Chin-Min Yu, Li-Chun Wang
This paper proposes an SNR-adaptive optimal threshold design framework for energy detection in Dynamic Spectrum Access (DSA). Unlike conventional constant false-alarm rate (CFAR)-based schemes that determine the sensing threshold solely from a predefined false-alarm constraint, the proposed method directly minimizes the total probability of error by deriving a closed-form analytical solution. The threshold optimization problem is formulated as a quadratic expression whose coefficients explicitly characterize the effects of signal-to-noise ratio (SNR) and number of samples. This analytical structure enables adaptive threshold selection under heterogeneous SNR conditions without exhaustive numerical search. Simulation results demonstrate that the proposed approach reduces the error probability compared with fixed-threshold and detection-constrained schemes, particularly in low-SNR regimes. Furthermore, the impact of SNR and number of samples on detection performance is systematically analyzed, providing deeper insight into the trade-off between false alarm and missed detection. The proposed framework improves sensing reliability and practical adaptability in dynamic spectrum access systems. It also establishes a foundation for secure cooperative spectrum sensing, including blockchain-assisted aggregation mechanisms.