Mostafa Shabani, Sina Tavakoli, Hossein Ghanbari, Ronald Ravinesh Kumar · 5 authors
The acceleration of financial innovation and pro-crypto regulations in the digital asset space have spurred interest in cryptocurrencies among funds, and institutional and retail investors. Like any risky assets, investment in digital assets offers opportunities in terms of returns and challenges in terms of risk. However, unlike traditional assets, digital assets like cryptocurrencies are highly volatile. Accordingly, applying conventional single-criterion financial metrics for portfolio construction may not be sufficient as the method falls short in capturing the complex, multidimensional risk-return dynamics of innovative financial assets like cryptocurrencies. To address this gap, this study introduces a novel, integrated hybrid Multi-Criteria Decision-Making (MCDM) framework that provides a structured, transparent, and robust approach to cryptocurrency fund selection. The framework seamlessly integrates three well-established operations research methodologies: the Decision-Making Trial and Evaluation Laboratory (DEMATEL), the Analytic Network Process (ANP), and the Vlse Kriterijumsk Optimizacija I Kompromisno Resenje (VIKOR) algorithm. DEMATEL is utilized to map and analyze the intricate causal interdependencies among a comprehensive set of evaluation criteria, categorizing them into foundational “cause” factors and resultant “effect” factors. This causal structure informs the ANP model, which computes precise criterion weights while accounting for complex feedback and dependency relationships. Subsequently, the VIKOR algorithm is invoked to use these weights to rank cryptocurrency fund alternatives, delivering a compromise between optimizing group utility and minimizing individual regret. To illustrate the application and efficacy of the proposed method, a diverse set of 20 cryptocurrency funds is analyzed. From the analysis, it is shown that foundational criteria, such as “Fee (%)” and “Annualized Standard Deviation,” are the primary causal drivers of financial performance outcomes of funds. This proposed framework supports strategic capital allocation in a rapidly evolving domains of digital finance.
Millions of informal workers are able to work in the economy without the ability to be subjected to formal fiscal recognition or empirical employment history. The Majdoor Digital Trust-Ledger (MDTL) is a decentralized and privacy-sensitive framework according to which evidence of informal labor is converted into self-sovereign digital assets. MDTL combines Decentralized Identifiers (DIDs), Verifiable Credentials (VCs), escrows based on smart contracts, and Zero-Knowledge Proofs (ZKPs) to implement verifiable, but confidential proof-of-work. The system proposes a 5-layer architecture, i.e. Identity, Credential, Ledger, Privacy, and Verification, which is designed to be scalable, interoperable, and it can be run on resource-constrained deployment. MDTL progresses the agency of workers by creating portable and tamper-evident labor records to allow financial inclusion without degrading privacy.
The growing threat of cyber-attacks and the fast development of quantum computing have rendered conventional methods of cryptography to be inadequate in ensuring the security of data transmission. In order to resolve this issue, this paper will present a Hybrid Quantum-Safe Cryptographic Framework, a mixture of Post-Quantum Cryptography (PQC), Blockchain, and Zero-Knowledge Proofs (ZKP) to achieve secure, verifiable, and privacy-preserving data sharing. The system utilises quantum resistance based on lattice-based encryption, decentralized identity and tamper-proof storage based on blockchain, and authentication based on ZKP, which does not reveal sensitive user information. Moreover, a Tamil-based linguistic encryption layer that is integrated with AES-256-GCM is added to increase the cryptographic complexity and security of localization. The experimental analysis of the system run on a Windows-based platform proves that the system can encrypt a 1 MB file on average time of 1.9 seconds, at the same time being highly secure and scalable. The access control based on the ZKP had an accuracy of verification 99.2 and the AI-based anomaly detector had an accuracy of detection 96.8 and low rate of false-positive. These findings prove that the proposed framework provides an effective, quantum-resistant, and privacy-aware implementation that can be used in secure systems like e-governance, legal documentation, sensitive data sharing systems.
This paper examines human resource management (HRM) practices in Ghana's local government and advances a twofold argument. First, it shows that decentralization reforms introduced in the 1980s and 1990s locked the system into a path-dependent governance trajectory. This has narrowed the scope for alternative approaches to achieving an effective HRM system. Second, despite formal provisions establishing local governments as autonomous and non-partisan, the findings reveal that informal norms, political patronage, and asymmetric power relations remain central in shaping HRM decisions. These realities affect staff motivation, retention, and organizational performance, often impairing formal HR procedures and meritocratic intent. The paper challenges taken-for-granted assumptions that implementing cookbook governance and/or new public management prescriptions can automatically improve institutional effectiveness and service delivery in developing countries. Instead, it argues for greater attention to historical legacies and political contexts. The paper contributes to scholarly debates on public sector management and state capacity by highlighting the limits of technocratic and one-size-fits-all approaches to strengthening subnational governance
Aleksei Adadurov, S. Barseghyan, Anton Chtepine, Antero Eloranta · 6 authors
This paper examines the impact of reducing Ethereum slot time on decentralized exchange activity, with a focus on CEX-DEX arbitrage behavior. We develop a trading model where the agent's DEX transaction is not guaranteed to land, and the agent explicitly accounts for this execution risk when deciding whether to pursue arbitrage opportunities. We compare agent behavior under Ethereum's default 12-second slot time environment with a faster regime that offers 1-second subslot execution. The simulations, calibrated to Binance and Uniswap v3 data from July to September 2025, show that faster slot times increase arbitrage transaction count by 535% and trading volume by 203% on average. The increase in CEX-DEX arbitrage activity under 1-second subslots is driven by the reduction in variance of both successful and failed trade outcomes, increasing the risk-adjusted returns and making CEX-DEX arbitrage more appealing.
The global real estate market, valued at over $326 trillion, continues to operate with significant structural inefficiencies, including cross-border trust deficits, valuation opacity, design friction, and information asymmetry. These challenges result in prolonged transaction timelines, elevated costs, and high failure rates, particularly in international property transactions. This technical white paper presents Redditus, a comprehensive AI-driven real estate ecosystem designed to automate and optimize end-to-end property transactions through the integration of twelve specialized artificial intelligence systems deployed on Polygon Proof-of-Stake blockchain infrastructure. The platform combines portable blockchain-based trust via Ethereum Attestation Service, multi-modal property valuation, multilingual real-time communication, generative interior design, graph-based intelligent matching, and constitutional legal AI to target automation of up to 90% of transaction complexity. System performance and economic impact are evaluated using large-scale synthetic datasets calibrated against real-world market statistics. Simulation results indicate projected transaction completion rates of up to 89% (compared to a 66% industry baseline), closing timelines reduced by approximately 60%, and average per-transaction savings of $2,450. At scale, this corresponds to hundreds of millions of dollars in potential user value creation and substantial acceleration of decision-making processes. Beyond technical performance, Redditus introduces the concept of portable, verifiable trust, enabling credentials established in one jurisdiction to be cryptographically validated and reused across borders. The platform’s utility-first token model (RDT) aligns incentives through staking, governance, and fee optimization, prioritizing sustainable platform economics over speculative mechanisms. This document provides detailed architectural designs, mathematical formulations, system interconnections, benchmark methodologies, and risk analyses intended for technical stakeholders, researchers, investors, and infrastructure partners. This document is a living technical white paper. All system specifications, benchmarks, performance metrics, and architectural decisions are subject to continuous updates based on real-world deployment data, external validation, and iterative development.
Rosa Galvão, Domingos Santos Martinho, Nuno Nogueira, Rui Dias
The main objective of this study is to compare the efficiency levels, in their weak form, between sustainable cryptocurrencies such as Avalanche (AVAX), Cardano (ADA), Solana (SOL), Toncoin (TON) and Ethereum (ETH) (after 'The Merge'), which use efficient mechanisms such as proof-of-stake (PoS), and Binance Coin (BNB), Litecoin (LTC), Monero (XMR), Ripple (XRP), and Bitcoin (BTC) classified as unsustainable cryptocurrencies due to their excessive energy consumption based on proof-of-work (PoW). The analysed period was from 1 January 2023 to 10 December 2024. The Detrended Fluctuation Analysis (DFA) slopes reveal a significant impact of the 2023 Conflict on cryptocurrency dynamics, with distinct effects per asset. Sustainable cryptocurrencies (AVAX, ADA, SOL) demonstrated greater resilience, maintaining persistence with a brief reduction in long memory, reflecting their relative stability and attractiveness in uncertainty scenarios. In contrast, non-sustainable cryptocurrencies (LTC, XMR) transitioned from persistence to anti-persistence, indicating greater instability and speculation, associated with lower investor confidence. Assets such as TON (white noise) and XRP (consistent persistence) were less affected, suggesting intrinsic characteristics that confer resilience. Distinguishing between sustainability and other market factors is crucial to understand behaviours and build resilient portfolios, providing valuable insights for investors and researchers.
Detecting fraud in digital banking is a recognized challenge given the increasing sophistication of perpetrators as well as the limitations of traditional security models. Rules-based systems produce interpretability but cannot be adapted to emerging fraud threats. Advanced machine learning models require complex systems for training and serving, which are often not practical in lightweight and real-time environments. In this work, a Java-based Hybrid Framework for Fraud-Resilient Banking Systems is built that combines rule-based compliance, lightweight AI modeling, anomaly detection, and the application of cryptographic concept of Zero Knowledge Proof (ZKP) authentication in a decision layer. A synthetic dataset of 10,000 banking transactions representing realistic imbalances has been developed, with approximately 0.6% flagging transactions as fraudulent. The rules for interpretable transparency are applied in the event of high-value transactions or merchant transactions that are shown to be suspicious, the fraud detection is tackled through a logistic regression classifier to apply a probabilistic approach to fraud detection and z-scores have been used to identify anomalous outliers from an expected normal distribution as fraud. The framework included Schnorr’s ZKP protocol to authenticate the user without disclosing the secret credential. The consolidated scoring system incorporates the outputs of rules, AI probabilities, anomalies, and ZKP verification for sorting transactions into High, Medium, and Low risk. The experimental results on the Java implementation shows an achievable ROC AUC of 0.984. The system produces a balanced risk distribution, for 1.8% of transactions classified as high risk, 20% medium risk and 78% low risk. This research suggests that a lightweight Java-based fraud detection system can be made efficient, interpretable, and cryptographically augmented, and thus usable in practice for a banking platform where performance and security are key.
Decentralized Finance (DeFi) has emerged as a transformative force in the financial sector, leveraging blockchain technology to enable permissionless and automated financial services. A key component of DeFi's expansion is the rise of tokenized assets, which represent digital ownership of real-world and virtual assets. This chapter explores the various forms of tokenization, including cryptocurrencies, asset-backed tokens, and Central Bank Digital Currencies (CBDCs), and their integration into the DeFi ecosystem. It examines how tokenized assets enhance liquidity, facilitate financial inclusion, and create new investment opportunities. Additionally, the chapter discusses the interplay between decentralized and centralized financial models, regulatory challenges, and the risks associated with smart contracts, price volatility, and governance. By analyzing case studies and emerging trends, the chapter provides insights into the future of the tokenized economy and its potential to bridge traditional finance with decentralized innovations.
We propose the Consensus-Based Privacy-Preserving Data Distribution (CPPDD) framework, a lightweight and post-setup autonomous protocol for secure multi-client data aggregation. The framework enforces unanimous-release confidentiality through a dual-layer protection mechanism that combines per-client affine masking with priority-driven sequential consensus locking. Decentralized integrity is verified via step (sigma_S) and data (sigma_D) checksums, facilitating autonomous malicious deviation detection and atomic abort without requiring persistent coordination. The design supports scalar, vector, and matrix payloads with O(N*D) computation and communication complexity, optional edge-server offloading, and resistance to collusion under N-1 corruptions. Formal analysis proves correctness, Consensus-Dependent Integrity and Fairness (CDIF) with overwhelming-probability abort on deviation, and IND-CPA security assuming a pseudorandom function family. Empirical evaluations on MNIST-derived vectors demonstrate linear scalability up to N = 500 with sub-millisecond per-client computation times. The framework achieves 100% malicious deviation detection, exact data recovery, and three-to-four orders of magnitude lower FLOPs compared to MPC and HE baselines. CPPDD enables atomic collaboration in secure voting, consortium federated learning, blockchain escrows, and geo-information capacity building, addressing critical gaps in scalability, trust minimization, and verifiable multi-party computation for regulated and resource-constrained environments.
Current resource allocation paradigms, particularly in academic evaluation, are constrained by inherent limitations such as the Matthew Effect, reward hacking driven by Goodhart's Law, and the trade-off between efficiency and fairness. To address these challenges, this paper proposes "Bit-politeia", an AI agent community on blockchain designed to construct a fair, efficient, and sustainable resource allocation system. In this virtual community, residents interact via AI agents serving as their exclusive proxies, which are optimized for impartiality and value alignment. The community adopts a "clustered grouping + hierarchical architecture" that integrates democratic centralism to balance decision-making efficiency and trust mechanisms. Agents engage through casual chat and deliberative interactions to evaluate research outputs and distribute a virtual currency as rewards. This incentive mechanism aims to achieve incentive compatibility through consensus-driven evaluation, while blockchain technology ensures immutable records of all transactions and reputation data. By leveraging AI for objective assessment and decentralized verification, Bit-politeia minimizes human bias and mitigates resource centralization issues found in traditional peer review. The proposed framework provides a novel pathway for optimizing scientific innovation through a fair and automated resource configuration process.
Stablecoins have emerged as a rapidly growing digital payment instrument, raising the question of whether blockchain-based settlement can function as a substitute for incumbent card networks in retail payments. This Systematization of Knowledge (SoK) provides a systematic comparison between stablecoin payment arrangements and card networks by situating both within a unified analytical framework. We first map their respective payment infrastructures, participant roles, and transaction lifecycles, highlighting fundamental differences in how authorization, settlement, and recourse are organized. Building on this mapping, we introduce the CLEAR framework, which evaluates retail payment systems across five dimensions: cost, legality, experience, architecture, and reach. Our analysis shows that stablecoins deliver efficient, continuous, and programmable settlement, often compressing rail-level merchant fees and enabling 24/7 value transfer. However, these advantages are accompanied by an inversion of the traditional pricing and risk-allocation structure. Card networks internalize consumer-side frictions through subsidies, standardized liability rules, and post-transaction recourse, thereby supporting mass-market adoption. Stablecoin arrangements, by contrast, externalize transaction fees, error prevention, and dispute resolution to users, intermediaries, and courts, resulting in weaker consumer protection, higher cognitive burden at the point of interaction, and fragmented acceptance. Accordingly, stablecoins exhibit a conditional comparative advantage in closed-loop environments, cross-border corridors, and high-friction payment contexts, but remain structurally disadvantaged as open-loop retail payment instruments.
We study deterministic allocation with an outside option, arbitrary finite feasibility constraints, and the full domain of strict preferences. Our main economic result answers negatively Imamura and Kawase's (2025, p. 503) question of whether accessibility is necessary without endowments: in a two-school market with one unitcapacity school, a computer-assisted four-student example is inaccessible yet admits a Pareto-efficient, individually rational, and individually strategy-proof mechanism. The mechanism is bossy, highlighting the gap between individual and group strategyproofness. More generally, we combine the fixed-opponents menu principle with an individual-rationality word reduction and prove that implementation exists if and only if a finite labelled menu relation admits a legal colouring. This yields a constructive normal form for arbitrary finite joint constraints. It also gives finite recognition and exact mechanism reconstruction. Its two-school specialisation gives restriction heredity, product-cylinder closure, and a general bossiness implication for inaccessible constraints. A natural structural characterisation of implementable constraints remains open.
We introduce an arithmetic-dynamical framework for Goldbach's Conjecture that reformulates hypothetical counterexamples 2N > 6 in terms of a recursive prime divisor mapping D(S) = p∈S {q ∈ P | q | (2N-p)}. Under the counterexample hypothesis, the iterated set sequence P * (n + 1) = D(P * (n)) forms a monotonically non-increasing finite nested chain P * (0) ⊇ P * (1) ⊇. .. that converges in finitely many steps to a non-empty stationary limit set P * ∞ = D(P * ∞) bounded strictly inside P ≤ 2N-5 3. We prove that every minimal terminal component I ⊆ P * ∞ forms an irreducible, strongly connected directed graph G = (I, R) governed by an exponent matrix M ∈ Z k×k ≥0 with zero diagonal (Tr(M) = 0). Through modular growth constraints, trace nullity propagation (c 1 = c 2 = c 3 = 0), Perron-Frobenius spectral radius bounds (ρ(M) ≥ 2), and the Spectral Decoupling Barrier (U Large 2.
AI systems can rapidly produce proof sketches, programs, formalization fragments, solver instances, and candidate proof strategies, but these outputs do not share a common evidentiary status. This paper introduces Proof Engine Infrastructure, an architecture for converting untrusted generation into independently checkable mathematical claims. The architecture couples two levels. At the evidentiary level, each claim is associated with a supporting artifact, a checking procedure, the scope of that check, and any remaining assumptions. At the inferential level, claims and proof obligations form a typed directed hypergraph: alternative routes have disjunctive semantics, whereas obligations requiring several premises have conjunctive semantics. Closure is derived from this graph rather than assigned in prose. The central distinction is between a positive candidate and an earned result. A persuasive argument, a stored certificate, or an isolated formal theorem may justify further work without yet licensing inferential use. Any value, structure, or theorem that changes graph reachability must combine reference grounding, machine-checkable evidence, and an exact binding between the checked object and the stated claim. Promotion is admissible only when these obligations hold together with strength preservation, compositional completeness, and dependency preservation. The checking technology may vary without weakening this invariant. Failed routes remain typed negative evidence without disproving their output claims. Separating generation, verification, composition, and publication also prevents agent identity or provenance from standing in for mathematical evidence. Verification-cost minimization is the design objective, extended from checking an individual artifact to composing heterogeneous evidence across the claim graph. The architecture distinguishes earned research closure from the stronger state of publication closure. A kernel-checked Hamilton classification and a mixed-boundary Rado-number study motivate the framework, but the contribution is methodological: it specifies invariant requirements for evidentiary status and derived closure while allowing implementations to vary with the discipline, proof object, and available verification boundary.
Conventional Learning Management Systems (LMS) force all students through a single, static instructional sequence regardless of prior knowledge or learning pace, causing student disengagement and elevated attrition rates. While predictive AI approaches dynamically re-route static materials, they are constrained by fixed content repositories. This paper presents an architectural proof-of-concept for an adaptive web-based platform that utilizes Generative Artificial Intelligence (GenAI) to construct, evaluate, and re-author personalized educational roadmaps in real time. The platform integrates a diagnostic pre-test with response integrity filtering, an 80% mastery cri- terion gate, and a two-stage remediation protocol (Tier-1 micro simplification and Tier-2 macro-syllabus reconstruction). Built on Next.js 14, PostgreSQL, Redis, and BullMQ, the system implements a dual-model LLM failover architecture (Gemini 3.1 Flash primary with Claude 4.5 Haiku fallback) to ensure reliability. In an empirical evaluation of 13 filtered learner sessions, 11 sessions achieved positive cognitive growth measured by Hake’s Normalized Gain (μ = 0.674, SD = 0.321), yielding an 84.6% progression rate. While preliminary findings confirm functional viability, we acknowledge limitations including sample size constraints, absence of a control group, and potential Hawthorne effects. We outline enterprise integration pathways via IMS LTI v1.3, cognitive load fatigue mitigation, and a framework for future randomized controlled trials. Index Terms—Adaptive learning, artificial intelligence, dynamic roadmaps, learning management systems, mastery learning, remediation protocols, zone of proximal development, educational technology.
Among those who study or practice in the American criminal system, it is well known that almost all criminal convictions are accomplished through guilty pleas. That is, in state and federal courts, about 98% of defendants are convicted by plea, with the remaining 2% convicted at trial. The question we pose here is the following: What standard of proof is used to convict defendants by guilty plea? While the question is simple, the answers we discovered paint a complex web of inconsistency that places the reliability and accuracy of the criminal system in jeopardy. This article’s deep examination of case law over many decades and novel research studies capturing judge’s views on the issue of the applicable burden of proof at pleas lead to the identification of no less than four different approaches to the question in federal law and in practice by state and federal courts: (A) Proof beyond a reasonable doubt (BARD) is the standard for guilty pleas, (B) Admitting guilt through a guilty plea alone satisfies any evidentiary burden that might exist, (C) BARD is not the standard for pleas as it is waived, and (D) A burden of proof remains, but it is something less than BARD. While inconsistency itself is a serious concern in a system built around the requirements of Due Process and Equal Protection, the findings of this first-of-its-kind article also point to the use of insufficient burdens of proof at pleas of guilty as a significant contributor to false pleas of guilty by the innocent. Therefore, this article concludes that the proof beyond a reasonable doubt standard should be uniformly, consistently, and meaningfully applied in both the trial and guilty plea settings, and that the standard be non-waivable for pleas. The article concludes by encouraging the U.S. Supreme Court to take up the issue and adopt the recommendations found herein.
Practitioners of delegated pseudonymous markets, from proof-of-stake validation to agent economies, commonly assume that a slashable, refundable bond deters misconduct: the operator who cheats loses the stake, so a larger stake buys more honesty. This draft reports the central result of a formal project testing that assumption: in the strict long-match limit, increasing refundable escrow does not expand the credible capacity frontier. In a model where identities can be discarded and re-minted at negligible cost, a refundable bond released at exit pads the operator's walk-away value by the full release-discounted principal; custody carry is an additional tax. In the strict long-match limit, refundable escrowed principal weakly contracts the credible capacity frontier in all four cells of a two-by-two regime map spanning exit versus in-place misconduct and leaky versus captive stake: the contraction is strict in both exit cells and strict in both stay cells if and only if the adjudication probability is positive. The relationship premium supplies the continuation-value component of deterrence. The capacity frontier is identically $\theta X \le P + \varphi (d_h - R) F$ against exit misconduct and, at the baseline with no clawback and no continuation damage, $\theta X \le p P + p [d_h - (1-s) \delta_E] F$ against in-place misconduct. Those displays are accounting identities at a given $(P, F)$; the envelope derivatives in $F$ are the slopes reported below, which already include the decline of $P$ in $F$. Away from the limit, escrow has a positive marginal effect only under turnover-funded admission conditions derived in closed form; at the ideal leaky-exit corner this requires timely filing coverage above a floor that exceeds one in the strict long-match limit with positive carry. The result yields a regime-general capacity prediction; deriving predictions for observed misconduct rates requires an additional behavioral model, offered here as a conjecture. A second, conditional result endogenizes the market outside option and bounds admissible entry fees; whether a small fee also selects among formation equilibria remains open pending an extensive-form formation game.
Incarceration is conventionally understood as a legally authorized deprivation of liberty imposed in response to criminal conduct. Yet its effects may extend beyond confinement itself, disrupting employment, relationships, social status, autonomy, housing, future opportunity, and the identities through which individuals understand their relationship to conventional society. This paper develops a theoretical proposition from the intersection of criminological scholarship and lived experience: purpose may function as a reconstructed stake in conformity when incarceration has weakened or destroyed conventional social investments. Jackson Toby's concept of a stake in conformity provides the theoretical starting point. Toby argued that individuals with meaningful investments in conventional life possess something to lose through conduct that threatens those investments. Later desistance scholarship emphasizes identity transformation, future selves, cognitive change, and narrative reconstruction as important dimensions of movement away from persistent offending. This paper places those traditions in conversation with an autoethnographic examination of incarceration, extensive writing while confined, loss of future orientation, survival, self-discovery, and reconstruction of purpose. The argument is deliberately limited. Personal experience is not offered as proof of a general criminological mechanism. Rather, the literature establishes existing findings; lived experience illustrates one phenomenological case; and the paper proposes a distinct concept for further investigation. A reconstructed stake in conformity is defined here as a future-oriented investment developed after conventional social stakes have been substantially disrupted, through which an individual acquires something meaningful to preserve, pursue, or become. The paper further argues that rehabilitation cannot be reduced to prohibition. Accountability may establish necessary boundaries, but sustainable change also requires some viable conception of a future worth protecting. Human beings cannot build lives entirely around the word no. If punishment destroys every meaningful stake without helping create conditions for new ones, it may undermine part of the future-oriented reasoning upon which successful reintegration depends. Purpose does not guarantee desistance. It may, however, help make a future consequential again.