On-demand authentication is critical for scalable quantum systems, yet many existing quantum signature and message-authentication schemes are signer-initiated, requiring advance distribution of authentication material even when no verification occurs. We introduce verifier-initiated quantum digital signatures (VIQDS), in which the verifier requests authentication only when needed and the signer responds once; after issuance, verification proceeds without further interaction. Practically, shifting authentication to a verifier-driven, on-demand workflow reduces avoidable communication and storage overhead and aligns with deployments where verification is sporadic, such as distributed services and audit-oriented infrastructures. Our approach leverages quantum zero-knowledge techniques so that verification reveals nothing about the signerâs secret key beyond the fact that the signature is valid. We present a general conversion principle from suitable quantum proof protocols to VIQDS, together with a concrete realization based on elementary qubit platforms. Here, we show information-theoretic security against forgery and privacy against curious verifiers without computational hardness assumptions. The authors introduce a verifier-initiated quantum message-authentication method, in which authentication is requested only when needed. Their approach uses quantum zero knowledge techniques to protect information about the signerâs secret key while providing information-theoretic security against forgery
Bitcoin mining hardware acquisition requires strategic timing due to volatile markets, rapid technological obsolescence, and protocol-driven revenue cycles. Despite mining's evolution into a capital-intensive industry, there is little guidance on when to purchase new Application-Specific Integrated Circuit (ASIC) hardware, and no prior computational frameworks address this decision problem. We address this gap by formulating hardware acquisition as a time series classification task, predicting whether purchasing ASIC machines yields profitable (Return on Investment (ROI) >= 1), marginal (0 < ROI < 1), or unprofitable (ROI <= 0) returns within one year. We propose MineROI-Net, an open-source Transformer-based architecture designed to capture multi-scale temporal patterns in mining profitability. Evaluated on data from 20 ASIC miners released between 2015 and 2024 across diverse market regimes, MineROI-Net outperforms recurrent, convolutional, and attention-based baselines, achieving 83.2% accuracy and 83.5% macro F1-score. The model demonstrates strong economic relevance, achieving 97.8% precision in detecting unprofitable periods and 81.5% precision in detecting profitable ones, while avoiding misclassifying profitable scenarios as unprofitable and vice versa. These results indicate that MineROI-Net offers a practical, data-driven tool for timing mining hardware acquisitions, potentially reducing financial risk in capital-intensive mining operations.
Background Global supply chains are increasingly challenged by disruptions, environmental pressures, and evolving market demands, necessitating a strong digital transformation. This study explores how the integration of Artificial Intelligence (AI), Blockchain, and the Internet of Things (IoT) is revolutionizing supply chain management (SCM) by improving operational efficiency, transparency, resilience, and sustainability. Methods Adhering to the PRISMA framework, a systematic review of literature published between 2010 and 2024 was undertaken. Comprehensive searches were conducted in Scopus database. The collected literature was rigorously screened and analyzed using Atlas-ti software to identify recurring themes and assess the synergistic impact of AI, Blockchain, and IoT on supply chain operations. Results The review reveals that digital transformation significantly improves SCM through improved demand forecasting, optimized inventory management, and real-time decision-making capabilities. AI provides predictive insights that mitigate risks and streamline processes, Blockchain offers secure, transparent, and immutable records that improve trust and traceability, and IoT enables real-time monitoring and connectivity across the supply chain network. Despite these benefits, challenges remain, including cybersecurity vulnerabilities, interoperability with legacy systems, and the need for workforce upskilling. Conclusion The integration of AI, Blockchain, and IoT into SCM presents a compelling pathway toward creating more resilient and sustainable supply chains. The paper offers a comprehensive analysis of the benefits and challenges associated with these digital technologies and provides strategic recommendations for practitioners and policymakers to encourage a balanced, technology-driven, and sustainable supply chain ecosystem. JEL codes O33, M11, M15
The aim of the study is to focus on the marketing communication strategies in the banking and finance sector from past to present, and to detail the concepts of phygital banking and metaverse banking in terms of both usage and the advantages and disadvantages it brings from the perspective of industry professionals. In-depth interviews were conducted with a total of 6 expert bankers from 3 different banks, which constitute the universe of the research while providing sample criteria. The data transcripts created with participant statements were divided into six themes and forty-three sub-codes and presented to expert opinion to ensure the external control of the research. The data were subjected to content analysis using the MAXQDA 2022 qualitative analysis program. Based on the findings, answers were sought to the following questions: (1) What are the definition, scope, and application areas of digital marketing communication in the banking and finance sector? (2) What are the elements of digital marketing communication used in the banking and finance sector? (3) What are the advantages and disadvantages of the digital marketing communication era in the banking and finance sector compared to the traditional marketing communication era shopping experience? According to the data analysis results, participants define digital marketing as a new marketing strategy that enhances consumer experience by combining traditional financial services with digital technologies. In addition, digital applications in the banking and finance sector are concentrated in areas such as application processes, marketing activities, payment systems, and smart voice systems. While the most commonly used digital elements are artificial intelligence (AI) and QR code, augmented reality (AR), virtual reality (VR), and blockchain are following these digital elements. According to the research results, the prominent advantage of digital marketing is experience-orientedness, while it is observed that digital spaces such as metaverse, with their decentralized and anonymous structure, also bring some privacy and security disadvantages. Concepts such as digital and metaverse are important innovative concepts that shape the future understanding of marketing communication. In the study, focusing on the digital marketing strategies used in the banking and finance sector, their characteristic features and technological components were evaluated from the perspective of industry professionals, and recommendations were made to the banking and finance sector based on the findings.
Keybyte Systems, Intentix Lab , Melbourne, Australiapronab@keybytesystems.com.au Supported by AusIndustry Grant IR2405165 __________________________________AbstractModern cloud-native applications distribute business logic across multiple layers: application code, orchestration frameworks, service meshes, and infrastructure configurations. This distribution creates âhidden logicââexecution rules embedded in infrastructure that are invisible during design and difficult to trace at runtime. We present Intention Space , a computing model built on the CPUX (Common Path of Understanding and Execution) paradigm that consolidates all business logic into explicit, design-time declarations using plain-language state pulses. In our model, Design Nodes (DNs) contain computation while Gatekeepers declare execution conditions as named pulses (e.g., âpayment validatedâ: Y). The infrastructure provides only mechanical enforcement through an Intention Loop that matches runtime state to Gatekeepers without adding decision logic. We demonstrate that complex workflowsâtraditionally requiring nested if-then branching and explicit loopsâcan be expressed as linear CPUX sequences where execution paths emerge from data state rather than code branching. Our Golang implementation shows complete elimination of orchestration code while maintaining full cognitive traceability. Beyond technical innovation, CPUX addresses a critical social computing crisis: the lack of accountability in distributed social platforms. By creating unique, device-level CPUX footprints for every interaction, our model enables verifiable traceability from device identity through user intention to executed actionârestoring accountability to social computing while preserving privacy. We argue this separation of intent (CPUX) from enforcement (infrastructure) is essential for building LLM-integrated, auditable, and socially responsible distributed systems.Keywords: CPUX, Intention Space, Design Nodes, Cognitive Computing, Data-Driven Execution, Microservices Architecture, Cloud Computing, LLM Integration, Social Computing Accountability__________________________________1. Introduction1.1 The Hidden Logic ProblemConsider a typical e-commerce order processing system deployed on Kubernetes with Istio service mesh:// order-service/main.go (Business Logic Layer) func ProcessOrder(order Order) error { if order.Amount > 1000 { if err := premiumValidator.Validate(order); err != nil { return retry(premiumValidator.Validate, 3, order) } } else { standardValidator.Validate(order) } // ⊠more branching logic }# k8s/hpa.yaml (Infrastructure Layer) spec: metrics: - type: Resource resource: name: cpu target: type: Utilization averageUtilization: 80 # Hidden rule: Scale when CPU > 80%# istio/retry-policy.yaml (Service Mesh Layer) spec: http: - retries: attempts: 3 perTryTimeout: 2s # Hidden rule: Retry 3 times on failureQuestion: What is the complete execution flow for a $1500 order that fails validation on first attempt?Answer: One must read and correlate:Application code (branching logic)Kubernetes manifests (scaling rules)Istio configurations (retry policies)Service mesh observability logs (runtime behavior)This hidden logic distribution creates fundamental problems:Traceability : No single artifact shows complete flowTestability : Must test infrastructure + code interactionsAuditability : Business stakeholders cannot validate logicMaintainability : Changes require coordinating multiple layersLLM Integration : No structured representation for AI reasoningSocial Accountability : Cannot trace interactions to source devices/users1.2 The Core InsightWe observe that traditional computing conflates two distinct concerns:What should happen(business intent)How to make it happen(mechanical execution)Current architectures intertwine these concerns across code, configuration, and infrastructure, making systems cognitively opaque.Our Contribution: We introduce CPUX (Common Path of Understanding and Execution) , a paradigm that separates business intent from infrastructure enforcement:CPUX Structure : Declares all possible execution paths as sequences of Design Nodes (DNs) with plain-language Gatekeeper conditionsInfrastructure : Provides mechanical execution (Intention Loop) that enforces CPUX declarations without adding decision logicDevice-Level Identity : Each CPUX execution tied to unique device fingerprint + user intention, enabling social computing accountabilityResult : Complete business logic is visible in CPUX; infrastructure remains purely mechanical; every social interaction is traceable1.3 Key ContributionsFormal Model : CPUX as cognitive execution contract with Design Nodes, Intentions, Objects, and Pulses as primitive componentsElimination of Hidden Logic : All business decisions visible in design-time CPUX declarations; infrastructure adds zero decision logicPlain-Language State Declarations : Execution conditions expressed as named pulses (e.g., âinventory confirmedâ: Y) enabling business stakeholder review and LLM integrationData-Driven Execution : Runtime branching eliminated from code; execution paths emerge from pulse state matching via SyncTestSocial Computing Accountability : Device-level CPUX fingerprints create unique, traceable identity for every social interaction, addressing the accountability crisis in platforms like Facebook, Twitter, TikTokImplementation & Evaluation : Golang framework code sample with concrete use case demonstrating zero orchestration code while maintaining full traceability1.4 Paper OrganizationSection 2 examines related work. Section 3 presents the PnR computing model and CPUX formalism. Section 4 details the architecture and implementation. Section 5 evaluates our approach through metrics and case studies. Section 6 discusses LLM integration. Section 7 introduces CPUX for social computing accountabilityâthe urgent global need. Section 8 concludes with future directions.__________________________________2. Related Work2.1 Workflow Orchestration SystemsAWS Step Functions [1] and Azure Logic Apps [2] provide visual workflow definition with explicit state machines. However, they:Use proprietary JSON/XML DSLs (not plain language)Embed conditional logic in workflow definitions (still branching)Remain platform-specific (vendor lock-in)Require reading workflow definitions to understand flowCannot trace to device/user identityApache Airflow [3] and Temporal [4] define workflows as code with DAG structures. They improve on step functions ,recoverability but:Business logic still in code (if-then branches)Workflow orchestration separate from executionNo plain-language condition declarationsNo device-level traceabilityCPUX Advantage : All logic in plain-language pulses, platform-agnostic, no explicit branching in declarations, device-level identity for every execution, recoverability built into platform. 2.2 Service Mesh & OrchestrationIstio [5] and Linkerd [6] provide traffic management, retries, circuit breaking. Kubernetes Operators [7] encode reconciliation logic. These systems:Hide business rules in YAML configurationsDistribute logic across mesh config + operator codeFocus on infrastructure concerns (not business flow)Lack unified view of complete execution pathNo user/device attributionCPUX Advantage : Consolidates all execution logic in CPUX; infrastructure config aligned with business intent; device identity integral.2.3 Event-Driven ArchitecturesApache Kafka [8], AWS EventBridge [9] enable event-driven systems with loose coupling. Reactive systems [10] promote message-passing. However:Event flows implicit (must trace message paths)Conditional logic in event handlers (code-level branching)No design-time declaration of all possible flowsNo provenance tracking to source deviceCPUX Advantage : Explicit declaration of all event-driven paths as DN sequences with visible Gatekeepers; device identity in event provenance.2.4 Intent-Based SystemsIntent-Based Networking [11] translates high-level intents to network configurations. Policy-based management [12] separates policy from mechanism. Closest to our work, but:Focus on infrastructure (not application logic)Policies often domain-specific (not general computing)Limited plain-language expressivenessNo user accountabilityCPUX Advantage : General-purpose computing model with full plain-language pulse declarations applicable to any domain; device-level user accountability.2.5 Formal Methods & Model CheckingTLA+ [13], Alloy [14], and Petri Nets [15] enable formal specification and verification. These are powerful but:Require specialized formal notation (high learning curve)Specification separate from implementation (sync problems)Not designed for runtime executionNo social computing traceabilityCPUX Advantage : Declarations are executable; CPUX structure IS the implementation contract; device identity embedded.2.6 Social Computing & AccountabilityBlockchain-based identity [16] and zero-knowledge proofs [17] address digital identity but:Focus on cryptographic primitives (not execution tracing)Donât integrate with application logicNo cognitive representation of intentFederated social networks [18] (Mastodon, ActivityPub) improve decentralization but:Still lack device-level traceabilityNo structured intent representationCannot prove user intended specific actionCPUX Advantage : First system to integrate device identity, user intention, and execution trace in single cognitive framework.2.7 PositioningCPUX is the first system to combine:Plain-language execution conditions (like Intent-Based Networking)Executable specifications (unlike formal methods)Complete flow visibility (unlike distributed orchestration)Zero hidden infrastructure logic (unique contribution)Device-level social accountability (unique contribution)__________________________________3. The PnR Computing Model3.1 Core Abstractions3.1.1 Pulse: Atomic State UnitA Pulse is the fundamental data unit representing a named state with optional response and trivalence:Pulse = (Name: String, Response: Value, Trivalence: {Y, N, U})Name : Plain-language identifier (e.g., âpayment validatedâ)Response : Optional value (e.g., transaction ID)Trivalence : Y (yes/true), N (no/false), U (undecided)
The fast evolution of the cryptocurrency has brought new dynamics to the trading setting at the international level which has altered the manner in which value is created, captured and retained across border. This paper will examine the impacts of cryptocurrency in the international trade by examining its impact on efficiency in transactions, financial accessibility, regulation, and market stability. The cryptocurrencies are based on decentralized blockchain networks, and they allow almost instant international payments, that do not involve the use of intermediaries and less expenditure on transactions by traders. The features are very beneficial to the firms in a region where the conventional banking systems have limited scope, which can spread to international trade. The paper also discusses the role of cryptocurrency in enhancing transparency through unchanging digital records of transactions to increase confidence between the trading partners and reduce the risks of fraud and late payments. At the same time, the insecurity of digital currencies, changing regulations, and suspicion of unlawful financial operations are not the last challenges that may limit mass adoption. The reactions of governments and international institutions to such uncertainties through new rules, cross-border banking digital currency (CBDC) programs and cross-border regulatory collaboration are discussed. With the compilation of the case studies in the various markets, the research finds out the effective implementations of cryptocurrency in trade finance, and the limitations faced by businesses, as they seek to trade volatile markets in digital currencies. According to the findings, one can state that although the role of cryptocurrency is hardly going to replace traditional financial systems wholesale, it is becoming a more and more prevalent complementary opportunity that will be able to facilitate faster payments, increased financial accessibility, and better flexible trading opportunities. Lastly, the study finds that the participation of cryptocurrency in global commerce will depend on upcoming tendencies in the harmonisation of the regulations, technological security and maturity of the industry. These will determine how the digital assets will see the dawn of day as either a trusted and stable component of the world trading system or a niche provision that is put to selective use in special industries.
Abdul Aziz Usman Balarabe, Gilbert I.O. Aimufua, Kene Tochukwu Anyachebelu, Umar Abdullahi
The management of judicial workflow through traditional paper-based and centralized systems presents a wide range of challenges. This is particularly evident in processes such as: case filing, evidence submission, and verdict execution, where inefficiencies, bureaucratic delays, and security vulnerabilities often undermine the credibility of the judiciary. While electronic systems like Case Management Systems (CMS) and Electronic Court Filing (ECF) have been introduced to digitize court operations, they remain largely centralized and therefore susceptible to unauthorized access, data manipulation, and cyberattacks. Numerous scholars have proposed digital and automated solutions for judicial processes; however, existing models often fail to adequately address key challenges of transparency, immutability, and interoperability across jurisdictions. The main objective of this paper is to provide a blockchain-enabled smart contract framework for judicial workflow management that integrates automation, security, and transparency into legal processes. Data flow and use case diagrams were developed to illustrate the core functionalities of the framework, including case registration, evidence management, judicial rulings, and court scheduling. Access to records within the framework is governed by blockchainâs decentralized consensus mechanism, while smart contracts automate routine judicial tasks based on predefined rules. The adoption of this approach is expected to significantly reduce administrative inefficiencies, minimize fraudulent practices, and enhance trust in judicial outcomes.
Rutuja Chirwatkar, Beemkumar Nagappan, L. P. Singh, Anoop Dev · 6 authors
Another recent paradigm for enabling ubiquitous smart cities to be efficient, resilient, and innovative is Distributed Resource Management (DRM), in which heterogeneous devices, infrastructures, and services operate autonomously and continuously. This paper discusses more advanced concepts of edge-cloud synergy, decentralized coordination, cyber-physical integration, and context-aware optimization to address the increasing burden on urban energy, transportation, communication, and environmental systems. These are the main objectives: (1) to create scalable DRM frameworks with the features of real-time decision, (2) to enhance the interoperability of distributed heterogeneous resources, and (3) to enhance sustainability and service quality as a result of flexible allocation schemes. The proposed solutions will be the multi-agent systems, distributed ledger technologies (DLT), machine-learning-based prediction systems, and dynamic resource-orchestration algorithms. A hybrid simulation-prototype was applied to test the performance based on the metrics of latency, reliability, load balancing, and energy efficiency. Results suggest that significant improvements (up to a 35 percent reduction in resource contention, a 28 percent reduction in response time, and a 20 percent increase in system robustness under high-density urban workloads) have been achieved. A qualitative measure also fosters greater transparency and trust in cross-domain operations. In totality, the paper identifies that the disruptive potential of decentralized management systems can make smart-city ecologies adaptive, secure, and sustainable.
This study examines the legal validity of contracts generated by AI-based smart contract generators from the perspective of Indonesian civil law, in comparison with French law. The background lies in the development of Society 5.0, the use of blockchain and AI in contract drafting, and the absence of specific regulations on smart contracts and generators in Indonesia. This research analyses contracts produced by smart contract generators, the legal framework governing the use of smart contract generators in contract formation in Indonesia and France, and the implications of this comparative analysis for strengthening legal certainty regarding smart contract generators in Indonesia. The study is a normative juridical research using statutory and comparative approaches, relying on primary legal materials, namely the Indonesian Civil Code, the Electronic Information and Transactions Act, the French Civil Code and AI regulations, as well as secondary materials on smart contracts, blockchain, and AI. The results show that contracts generated by such tools have the characteristics of automation, immutability, transparency, security, and decentralisation, but from a legal standpoint must still satisfy the general requirements for a valid agreement. In Indonesia, smart contracts can in principle be classified as electronic contracts insofar as they comply with Article 1320 of the French Civil Code, while the generator is viewed merely as a tool owned by the parties, with legal responsibility placed on users and a remaining regulatory gap on technical aspects. In France, smart contracts generated by AI are assessed under the general rules of contract law but are reinforced by the explicit recognition of DLT in financial law and a more developed AI supervision framework. Based on these findings, the study concludes that Indonesia needs to strengthen its national framework through explicit recognition of the contractual function of smart contracts, the adoption of technical standards and liability rules for generator providers, and the development of judicial and sectoral guidelines to ensure legal certainty and protect weaker parties.
Sample alignment performs a crucial role in vertical federated learning, aiming to identify shared user samples among multiple parties without exposing their private identifier data. However, most existing alignment protocols are designed for two-party scenarios, while those developed for multi-party settings suffer from limited anti-collusion capability and inefficient verification mechanisms. To address these issues, we propose an efficient and secure protocol for sample alignment in multi-party vertical federated learning (MESA). The protocol leverages a threshold oblivious pseudo-random function (T-OPRF) combined with a distributed key generation scheme to defend against collusion attacks. Moreover, an oblivious keyâvalue store encoding (OKVS) mechanism is introduced to enable secure and efficient keyâvalue mapping and decoding, thereby reducing communication overhead. Under the malicious security model, MESA further incorporates non-interactive zero-knowledge proof (NIZKP) to verify the consistency and validity of results submitted by clients, effectively preventing data forgery and disruption attacks. Experimental results and analysis demonstrate that MESA provides strong privacy guarantees while achieving high computation and communication efficiency in deployments involving multiple untrusted clients.
AI systems rely heavily on high-quality training data, yet provenance tracking remains fragmented and vulnerable to manipulation. This study presents a blockchain-enabled data provenance framework designed to bring transparency and verifiability to AI training pipelines. The architecture records dataset lineage, preprocessing steps, annotation events and model updates using immutable smart contract transactions. A lightweight off-chain storage mechanism reduces blockchain overhead while maintaining audit guarantees. The system was tested with three machine learning pipelines involving image classification, text processing and sensor analytics. Results show a 95 percent reduction in provenance disputes and full traceability across all data contributors. Smart contracts automate compliance checks and access permissions, ensuring that only validated datasets feed into the training process. The framework improves accountability for AI ethics, model bias evaluation and regulatory reporting. Experiments confirm that blockchain latency does not significantly affect pipeline throughput due to parallelized validation nodes. The work demonstrates how decentralized technologies can support trustworthy AI development. Future research will explore integration with zero-knowledge proofs to further enhance confidentiality.
ExecMesh introduces cryptographically verifiable computation as a foundational primitive for regulatory compliance and audit trail requirements in AI/ML systems [1â3]. By combining commitmentbased verification with secure multi-party oracles and a two-tier regulatory architecture, ExecMesh enables enterprises to meet FDA, SEC, and EU AI Act requirements while maintaining the benefits of decentralized infrastructure. Immediate Value Proposition: ExecMesh provides immediate value as an audit trail and provenance layer for regulated AI systems, independent of advances in zero-knowledge proof technology. Even without full verification of large neural networks, the system delivers cryptographic guarantees for data integrity, execution timestamps, and pipeline reproducibilityâmeeting core regulatory requirements today.
This publication introduces Zero-Knowledge Behavioral Proof (ZKBP) as a post-biometric authentication primitive designed for the QADMON canonical security framework. ZKBP replaces traditional biometric and password-based identity with cryptographically verifiable behavioral continuity. The protocol proves liveness, integrity and continuity of behavior without revealing biometric templates, raw behavioral signals, or any permanent human identifier. The package includes: - Formal cryptographic definition of ZKBP - Security proofs under LWE-based post-quantum assumptions - Comprehensive threat model (AI imitation, replay, side-channels, insider threats) - Protocol specification in JSON - Comparative security tables (CSV) - Multilingual human-readable documentation (EN, RU, HE, ZH, AR) - Implementation notes for PQC + TEE environments This module follows the canonical QADMON axiom: FSIG â Cryptographic Key FSIG = Zero-Knowledge Behavioral Proof The only cryptographic secret is a post-quantum key stored inside a Trusted Execution Environment (TEE). This work is published as Module 02 of the QADMON Canonical Security Framework.
In the rapidly evolving landscape of cryptocurrency in India, a powerful new contender has emergedâJaimax Coin, often hailed as the best presale crypto coin in India. As blockchain technology reshapes finance, digital ownership, and decentralized ecosystems, we stand at the forefront of innovation, introducing Jaimax, the next-generation crypto pre-sale coin designed for long-term scalability, high utility, and mass adoption. As a revolutionary new crypto coin India trusts for transparent and secure financial growth, Jaimax is strategically engineered to transform both the DeFi (Decentralized Finance) and NFT (Non-Fungible Token) markets. With advanced tokenomics, unmatched security standards, and a visionary roadmap, Jaimax is positioned to become one of the most influential digital assets in Indiaâs expanding blockchain environment.
The complexity of modern supply chain networks requires sophisticated approaches to inventory management that can effectively handle demand uncertainty and coordinate decisions across multiple organizational levels. This paper proposes a novel hierarchical multi-agent reinforcement learning framework for dynamic inventory allocation in multi-echelon supply chains facing stochastic demand patterns. The hierarchical architecture decomposes the inventory control problem into strategic and operational decision layers, where high-level agents coordinate allocation policies across distribution networks while low-level agents optimize local replenishment decisions. The framework integrates Centralized Training with Decentralized Execution paradigm, enabling autonomous agents to learn coordinated policies through shared experience while maintaining operational independence during deployment. Experimental results demonstrate that the proposed approach achieves significant reductions in total system costs compared to traditional base-stock policies and single-agent reinforcement learning methods, while effectively mitigating the bullwhip effect in supply chains with high demand variability.
Remigio Santiago Siguencia Montero, Miriam Carmita Mora Urdiales
With the significant advancement of technology and networks at the global level, it can currently be observed that there is a wide digital access gap between urban and rural areas, a situation that creates a major obstacle to guaranteeing the right to education, especially for children and adolescents who live in areas where there are no internet connectivity towers and who, in addition, face extreme poverty that prevents them from acquiring an electronic device for their academic activities. Various statistical data on connectivity in rural areas identify the main factors that perpetuate inequality in access: infrastructure, affordability, and digital skills, situations that affect educational equity. According to evidence from studies carried out by international and regional organizations, several aspects that generate this problem are discussed, such as regulatory implications and, mainly, the poor implementation of public policies by the Central Government and the Autonomous Decentralized Governments. Based on this, recommendations are proposed for state institutions and multilateral actors aimed at ensuring meaningful connectivity and, in this way, guaranteeing the right to education in rural areas.
Concentrated-liquidity automated market makers (CLAMMs), as exemplified by Uniswap v3, are now a common primitive in decentralized finance frameworks. Their design combines continuous trading on constant-function curves with discrete tick boundaries at which liquidity positions change and rounding effects accumulate. While there is a body of economic and game-theoretic analysis of CLAMMs, there is negligible work that treats Uniswap v3 at the level of formal state machines amenable to model checking or theorem proving. In this paper we propose a formal modeling approach for Uniswap v3-style CLAMMs using (i) networks of priced timed automata (PTA), and (ii) finite-state transducers (FST) over discrete ticks. Positions are treated as stateful objects that transition only when the pool price crosses the ticks that bound their active range. We show how to encode the piecewise constant-product invariant, fee-growth variables, and tick-crossing rules in a PTA suitable for tools such as UPPAAL, and how to derive a tick-level FST abstraction for specification in TLA+. We define an explicit tick-wise invariant for a discretized, single-tick CLAMM model and prove that it is preserved up to a tight additive rounding bound under fee-free swaps. This provides a formal justification for the "$Δ$-slack" used in invariance properties and shows how rounding enters as a controlled perturbation. We then instantiate these models in TLA+ and use TLC to exhaustively check the resulting invariants on structurally faithful instances, including a three-tick concentrated-liquidity configuration and a bounded no-rounding-only-arbitrage property in a bidirectional single-tick model. We discuss how these constructions lift to the tick-wise structure of Uniswap v3 via virtual reserves, and how the resulting properties can be phrased as PTA/TLA+ invariants about cross-tick behaviour and rounding safety.
Rollups are central to blockchain scalability, but their governance is still evolving. Existing voting models risk capture by large stakeholders or inactive delegates. This paper introduces RepRoll, a governance model that uses delegated reputation scores backed by verifiable activity logs. Reputation grows through provable contributions: fraud-proof submissions, code audits, uptime guarantees, and community moderation. These contributions are recorded through a decentralized attestation layer similar to optimistic verification. Votes in protocol upgrades weigh both token stake and reputation, reducing plutocratic influence. A simulation of 10,000 participants demonstrates that RepRoll improves proposal quality and reduces governance attacks. We deploy a prototype on an Ethereum Layer-2 testnet, showing low on-chain overhead. The paper discusses vulnerabilities such as collusion, reputation laundering, and sybil amplification, and proposes cryptographic mitigations.
The Proof of Stake (PoS) consensus mechanism is increasingly used in blockchain systems; however, resource allocation for PoS-based mobile blockchain networks remains underexplored, particularly given the constraints of mobile devices. This work introduces MEC-Chain, a new framework that integrates Mobile Edge Computing (MEC) with mobile blockchain to support efficient validator-node execution under PoS. MEC-Chain formalizes a multi-objective resource-allocation problem that jointly considers latency, reliability, and cost from both the validator and MEC-provider perspectives. To address this challenge, we develop a deep reinforcement learning-based allocation agent using the Proximal Policy Optimization (PPO) algorithm. Experimental results show that PPO achieves a 30â40% reduction in total execution time, 25â35% lower transmission latency, and 10â15% higher reliability compared to A2C (Advantage ActorâCritic) and DQN (Deep Q-Network), while offering comparable cost savings across all methods. These results demonstrate the effectiveness of MEC-Chain in enabling low-latency, reliable, and resource-efficient PoS validation within mobile blockchain environments.
The exponential growth of sophisticated cyber threats in Internet of Things (IoT) environments has exposed fundamental weaknesses in existing Cyber Threat Intelligence (CTI) platforms, including centralized architectures, trust deficits, privacy vulnerabilities, and single points of failure. To overcome these limitations, this paper proposes BlockIntelChain, a blockchain-based framework for secure, scalable, and collaborative CTI sharing across distributed IoT networks. The system integrates a hybrid consensus mechanism that combines Proof-of-Stake with reputation-based validator selection, supported by a multi-layered privacy framework employing Differential Privacy (DP), Zero-Knowledge Proofs (ZKP), Homomorphic Encryption, and Secure Multi-Party Computation. BlockIntelChain further embeds Federated Learning (FL) to enable distributed model training directly on IoT edge nodes without exposing raw threat telemetry. Comprehensive evaluations on real-world Malware Information Sharing Platform (MISP) datasets show that BlockIntelChain achieves 923 Transactions per Second at 500 nodes with 99.6% consensus success, while maintaining resilience against 51% and Byzantine attacks tolerating up to 33% malicious validators. Privacy analysis confirms an optimized utility-privacy trade-off, with DP (Δ = 0.1) preserving 92% data utility and ZKP achieving 94% verification accuracy. The FL-based models outperform centralized baselines, reaching 96.4% accuracy for IoT malware classification, 94.7% for phishing detection, and 95.2% for network anomaly identification. Economic modeling validates sustainability through contributor growth (156 â 1,245 in 12 months) and improved contribution quality (0.73 â 0.92). The proposed framework directly benefits Security Operation Centers and edge-deployed IoT systems by enabling real-time threat intelligence exchange with strong security, privacy, and efficiency. Comparative benchmarking demonstrates BlockIntelChain's superiority over MISP, ThreatConnect, and IBM X-Force in decentralization, privacy, and cost efficiency, positioning it as a transformative solution for next-generation privacy-aware CTI ecosystems.
With the rapid evolution of the Decentralized Web (DWeb), decentralized technologies have paved new avenues for Web3 applications and the authentication of digital assets. Among them, Non-Fungible Tokens (NFTs) have gained significant popularity due to their immutability and uniqueness, reshaping the landscape of artistic creation, marketing, and intellectual property protection. However, current blockchain-based NFT implementations still face core challenges within decentralized architecture: how to maintain decentralization while ensuring the visual uniqueness of digital assets and reducing storage costs. The rampant issue of duplication undermines the scarcity of digital art and erodes market confidence in copyright authenticity. Moreover, high gas fees and energy consumption further hinder the widespread adoption of NFTs, while reliance on external storage solutions like InterPlanetary File System (IPFS) introduces risks of data instability and loss. To address these challenges, this article presents the UniqueNFT framework, a novel architecture that deeply integrates blockchain oracles with decentralized storage verification mechanisms. The framework achieves three key technological breakthroughs: Using image inversion and generation techniques based on Encoder for Editing (E4E) and StyleGAN3, it extracts compact and expressive semantic features from NFT images, enabling efficient data compression and significantly reducing on-chain storage volume; The Crypto-Mask algorithm, by utilizing the hash value of blockchain user information (user-controlled SHA-256 digest of Ethereum address, user nickname, and registration time), ensures the visual uniqueness of NFTs; A smart contract extension compatible with the ERC721 standard, demonstrating UniqueNFTâs seamless integration within the blockchain ecosystem. By leveraging the technologies of the Decentralized Web, our framework represents an important step forward in enhancing the security and uniqueness of digital assets. It not only innovatively resolves the issues of NFT duplication and homogenization but also injects new vitality and long-term momentum into the creation of a trusted, sustainable blockchain-based digital asset ecosystem.
This paper reviews the innovative applications of AI and Web3 in metaverse social platforms. It first analyzes the foundational roles of AI (e.g., virtual avatar generation, intelligent interaction, personalized recommendation) and Web3 (e.g., blockchain, NFTs, decentralized identity) in enabling immersive, secure, and user-centric social interactions. It then examines their synergies, with case studies of Decentraland and The Sandbox illustrating practical integrations. The research identifies key challenges, including technical bottlenecks (e.g., AI realism, blockchain scalability), user-related issues (e.g., awareness, privacy concerns), and industry-level hurdles (e.g., regulatory ambiguities, homogenization). Finally, it proposes future directions: advancing AI/Web3 technologies, expanding application scenarios across education and entertainment, and implementing strategic recommendations to foster inclusive and sustainable metaverse social ecosystems.
This paper analyzes the emerging memecoin phenomenon on the Solana blockchain, focusing on the Pump$.$fun platform during Q4 2024. Using on-chain data, it is explored how retail-focused token creation platforms are reshaping blockchain ecosystems and influencing market participation. This study finds that Pump$.$fun accounted for up to 71.1% of all tokens minted on Solana and contributed 40-67.4% of total DEX transactions. Despite this activity, fewer than 2% of tokens successfully transitioned to major decentralized exchanges, highlighting a highly speculative market structure. The platform experienced rapid growth, with daily active users rising from 60,000 to peaks of 260,000, underscoring strong retail adoption. This reflects a broader shift towards accessible, socially-driven market participation enabled by memecoins. However, while memecoins lower entry barriers and encourage retail engagement, they introduce significant risks. The volatile and speculative nature of these platforms raises concerns about long-term sustainability and the resilience of the blockchain ecosystem. These findings reveal the dual impact of memecoins: they democratize token creation and alter market dynamics but may jeopardize market efficiency and stability. This paper highlights the need to critically assess the implications of retail-driven speculative trading and its potential to disrupt emerging blockchain economies.