Rachit Anand Srivastava, Vikram Bhattacharjee, Will Arnold, Toufeeq Pasha
Light clients are essential for scalable blockchain systems because they verify data availability without downloading full blocks. In data availability sampling based systems, sampled cells are retrieved from a peer-to-peer network and verified against cryptographic commitments. A common deployment pattern associates each sampled cell with an independent Kate-Zaverucha-Goldberg (KZG) proof, creating substantial cumulative bandwidth, storage, and verification overhead. This paper studies polynomial multiproofs (PMP) as a mechanism for reducing these costs in blockchain light clients. We present a design in which multiple sampled cell evaluations are verified using a single aggregated proof over a shared evaluation micro-domain and describe the corresponding changes to proof generation, dissemination, retrieval, and verification in a peer-to-peer light-client stack. We instantiate and evaluate the design in Avail, a modular data availability layer for blockchains, as a case study. The results show lower proof bytes, lower verifier CPU and memory usage, and deployment-level infrastructure cost reductions of up to 45% relative to a per-cell baseline, while also clarifying the trade-offs introduced by grouped retrieval.
Jie Li, Yucheng Zhao, Xiaoyu Yang, Yi Ding · 6 authors
The formal semantics of blockchain smart contracts are the foundation of formal verification. They can be used to establish formal models to verify the security of contracts and help developers understand the specific execution rules of contracts. However, the mathematical logic involved in such modeling poses a high barrier to entry and cannot be directly integrated with other program analysis methods. This article proposes a semantic graph generation approach, KSG, for blockchain smart contracts. First, the semantic rules of the contract language are formally defined, and a semantic interpreter and prover are constructed to automatically transform smart contract code into a scalable semantic graph. This graph incorporates semantic control flow information, semantic data flow information, execution rules, and verification constraints. Next, the generated semantic graph can be utilized for vulnerability detection and symbolic execution and supports iterative optimization based on the analysis results. Finally, the detailed process of semantic graph generation and analysis is demonstrated through the verification of the reentrancy contract and the honeypot contract.
ABSTRACT E-voting in polarized contexts requires a strict balance between public verifiability, ballot secrecy, andcoercion resistance. Traditional centralized systems lack transparency, while fully decentralized modelsface scalability and privacy issues. This paper proposes a hybrid architecture compliant withOSCE/ODIHR standards [1] for low-trust environments. The protocol decouples identity from voting anoff-chain Oracle manages authorization via cryptographic tokens, while the Waves DLT acts as animmutable bulletinboard.Utilizinghomomorphicencryption[2],Zero-KnowledgeRangeProofs(ZKRP) [3],and Distributed Key Generation (DKG) [4], the system ensures End-to-End Verifiability (E2E) bydelegating tallying to auditable scripts. Finally, the study examines model limitations, specificallyregarding endpoint vulnerabilities and physical constraints on coercion resistance. KEYWORDS E-Voting, Distributed Ledger Technology, Homomorphic Encryption, End-to-End Verifiability, ZeroKnowledge Proofs PDF LINK: https://ijcionline.com/paper/15/15226ijci01.pdf VOLUME LINK: https://airccse.org/journal/ijci/Current2026.html MORE DETAILS: https://airccse.org/journal/ijci/index.html
The proliferation of sensor networks in critical infrastructure, healthcare monitoring, and smart city applications demands robust privacy-preserving mechanisms for data verification. Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) offer a promising cryptographic primitive that enables data integrity verification without revealing sensitive sensor readings. However, the practical feasibility of deploying zk-SNARKs in resource-constrained sensor network environments remains insufficiently characterized. This paper presents a systematic benchmarking study of the Groth16 zk-SNARK protocol across eight representative circuit types spanning six orders of magnitude in computational complexity, from basic arithmetic operations (1 constraint) to ECDSA signature verification (1,510,185 constraints). Using an automated open-source benchmarking framework built on the Circom-snarkjs toolchain, we conducted 160 statistically controlled measurements (20 iterations per circuit) with cold/warm separation, collecting proof generation time, verification time, proof size, memory consumption, and witness generation overhead. Our results demonstrate that Groth16 proofs maintain a constant size of 804.7±1.7 bytes and near-constant verification time of 0.662±0.032 s regardless of circuit complexity, with coefficients of variation below 5% across all circuit types. Proof generation time exhibits sub-linear scaling (α=0.256, R2=0.608), with statistically significant differences between circuit categories confirmed by one-way ANOVA (F=355.0, p<10-79, η2=0.94). We identify three operational deployment tiers for sensor network architectures and estimate energy budgets for battery-powered devices. These findings provide actionable guidance for the design of privacy-preserving data verification systems in next-generation sensor networks.
Open access
Security in Wireless Sensor Networks
Cryptographic Implementations and Security
Physical Unclonable Functions (PUFs) and Hardware Security
We study lossy compression of a finite statement source generated in a fixed deductive environment. The source symbols are statements in a knowledge base endowed with a shared proof system, and reconstruction fidelity is measured by preservation of deductive closure rather than by symbolwise equality. Fixing the proof system and a canonical scan order yields a decomposition of the source alphabet into an irredundant core and redundant stored consequences. At zero distortion, each core symbol induces a set of distortion-free reconstructions. In the nonconfusable (disjoint-core) regime, we show that the minimum zero-distortion rate equals the source mass of the core times the entropy of the source conditioned on that core. In the general confusable-core regime, we characterise the exact zero-distortion rate via a hypergraph-entropy quantity induced by jointly realisable core subsets, with a reduction to Korner-style graph entropy under a natural pairwise realisability condition. For reconstruction alphabets contained in the deductive closure of the source knowledge base, we further prove that the full rate-distortion function depends only on the core, so redundant states are invisible to both rate and distortion. Finally, when the decoder is limited to a bounded inference-depth budget (a bounded number of iterations of the immediate-consequence operator), we obtain an exact rate-depth-distortion characterisation. Under an additional order-robustness assumption identifying the chosen core with the order-free essential set, this characterisation interpolates between classical symbolwise compression and unconstrained deductive compression.
Smart contracts are critical to blockchain but face significant security challenges, where potential vulnerabilities can lead to financial losses and undermine system trustworthiness. Code obfuscation has emerged as a crucial technique for countering reverse engineering and enhancing contract security. However, existing methods suffer from three major limitations: (1) current bytecode obfuscation methods are primarily designed for traditional programming languages and cannot be directly applied to Solidity; (2) these methods exhibit limited resistance against advanced decompilation tools; and (3) they often result in high gas consumption. To this end, we propose BOSC , a novel bytecode obfuscation method that integrates four obfuscation strategies from both structural and semantic perspectives to enhance obfuscation effectiveness. Specifically, in structural perturbation, we adopt and refine incomplete instruction obfuscation and false branch obfuscation, while in semantic perturbation, we improve and employ instruction reordering obfuscation and flower instruction obfuscation. Experimental results demonstrate that BOSC significantly increases the decompilation failure rate from 6.06% to 96.4%, and raises cyclomatic complexity by 81.83%, providing robust protection against automated attacks. This improvement incurs only a modest additional gas cost of 1,792 gwei , which translates to an approximate expense of 0.33869 USD , thereby achieving a balance between obfuscation effectiveness and economic efficiency.
Distributed recruitment is changing the way companies hire people and is also creating new problems for Human Resources teams. It is now much easier for people to fake documents, pretend to be someone else, or carry out employment fraud, while old methods like manual checks, emails, and database queries cannot keep up with tricks such as fake videos or forged papers. SafeHire is designed to solve these problems as a system that checks if people are who they claim to be and fits modern hiring needs. Instead of slow and easily fooled methods, it uses Zero-Knowledge Proofs with the Anon-Aadhaar protocol so people can prove their identity without sharing private information. Government IDs are verified offline using XML signature validation, and academic records are stored securely using SHA-256 hashing so they cannot be changed. To check documents, SafeHire uses Jaro-Winkler and Levenshtein distance methods to find small errors and also verifies employers using Corporate Identification Numbers (CIN). All data is protected so only the right people can access it through strict access rules. SafeHire is a faster and more secure way to hire, using system-based verification connected to trusted records to reduce the weaknesses of older applicant tracking systems and make hiring more reliable.
The Indus Valley script (c. 2600-1900 BCE) is a metrological cargo-tag system, not a phonetic language. Five fields: merchant mark, commodity class, weight tier, quantity multiplier, trade route. Two Mohenjo-daro seals (M-52A, M-148A) decoded end-to-end from real CISI sign-sequence data through the Parpola-Mahadevan concordance into a metrological codebook. Both produce jar goods as commodity -- independently the most frequent sign in the Mahadevan corpus (~10% of all occurrences). M-52A produces a Mesopotamia route marker, consistent with its findspot at the primary IVC export hub. Neither result is circular. 20 morphological parallels between Tamil Nadu Iron Age potsherds and IVC seals transcribed from Rajan & Sivanantham (2025). 18 pending -- Harappa/Kalibangan/Rojdi data not yet in the open CISI corpus. Normalised SQLite corpus database: 2,373 signs from 5 sources, 179 CISI inscriptions, 397 concordance entries, 600 Tamil proxy inscriptions. Schema verified by Alloy 6: 16 assertions, all UNSAT at scope 6, zero counterexamples. Five F# scripts (dotnet fsi): codebook seeder, corpus ingestion, LSSC transition entropy analysis, IVC cargo-tag decoder, Tamil Nadu cross-corpus decoder. Codebook frozen as typed F# records generated once by SqlHydra v4 -- decoder runs with zero database dependency. CMake build system with loud dependency checks. All claims queryable with sqlite3. All computations reproducible with dotnet fsi. The Indus decode pipeline produces its result through five independent data paths: corpus frequency, concordance shape matching, findspot geography, cross-corpus overlap, and physical weight calibration. None shares a common error source. Under generous per-path bounds favouring the null hypothesis, the joint probability that all five convergences are coincidental is 3.1 x 10^-5, one chance in thirty-two thousand. The same Bernoulli independence structure that rules out universal stratigraphic disturbance across Tamil Nadu at 10^-7 rules out coincidental decode at 10^-5. The cargo-tag model is not proven. But the hypothesis that these convergences are accidental requires a one-in-thirty-two-thousand coincidence across data sources that do not talk to each other. Noise scatters. Signal clusters. The decode clusters. Indus script is accounting code operating inside a checkpoint-verified commercial network whose primary fraud-prevention mechanism is physical comparison of seal to cargo at every transit node. This paper is about the origin of incentive-compatible distributed fraud detection with accumulated reputational capital, tokenised in tamper-evident physical medium, at civilisational scale, four thousand years before the earliest known comparable system Interactive dashboard: ledger-of-meluhha.html (single file, drop indus_corpus.db to render trade network, decode seals live, filter routes by commodity). Peer review requested. Keywords: Indus Valley, Harappan script, metrological accounting, Bronze Age trade, Meluhha, CISI, Alloy, SQLite, F#, SqlHydra, cargo tag, Tamil Nadu, Rajan-Sivanantham, formal verification License: CC BY 4.0 Upload files:1. ledger_of_meluhha.pdf (21 pages)2. ledger_of_meluhha_overleaf.zip (tex + citations.lua -- set compiler to LuaLaTeX)3. ledger_of_meluhha_dashboard.zip (html + corpus db)
This paper addresses the systemic crisis of data silos and trust deficiencies in modern enterprises by designing and implementing a high-throughput, privacy-preserving blockchain-based distributed accounting information system. By deconstructing the pain points of traditional financial architectures, this paper proposes a multi-layered architecture integrating a dynamic weighted hybrid consensus mechanism and a directed acyclic graph parallel contract engine, completely overturning the centralized ledger paradigm. The system utilizes zero-knowledge proofs and homomorphic encryption mechanisms to construct encrypted state flow channels, achieving public ownership confirmation and logically transparent auditing of the entire network ledger while protecting the privacy of core business funds. Extreme concurrency tests conducted using enterprise-grade hardware matrices and containerized clusters demonstrate that, while maintaining strong consistency and fault tolerance, the system achieves a concurrent throughput of 8850 TPS, with end-to-end confirmation latency compressed to less than 120ms. Performance degradation is minimal even with a tenfold increase in node size, proving the outstanding engineering value and revolutionary potential of this distributed architecture in enabling high-frequency global business-finance collaboration and reducing cross-domain audit friction costs.
Abstract The integration of connected vehicles into 5G networks introduces stringent requirements in terms of latency, reliability, security, and adaptability that are not fully addressed by existing 5G architectures. In particular, Vehicle-to-Network (V2N) services must operate under high mobility, dynamic traffic conditions, and multi-tenant environments, while remaining resilient to security threats and operational anomalies. In this paper, we propose a 5G-based architecture for connected vehicles that addresses these challenges by combining deterministic communication, secure resource coordination, and runtime monitoring mechanisms. To enhance communication predictability beyond best-effort transport, the architecture integrates Time-Sensitive Networking (TSN) within the 5G transport network. Secure and transparent coordination across multiple stakeholders is supported through Distributed Ledger Technology (DLT), mitigating risks associated with centralized control. The architecture further incorporates heterogeneous data collection to enable adaptive resource management, as well as Runtime Verification and an AI-based anomaly detection system to monitor system behavior and network traffic in real time. By jointly addressing determinism, security, and adaptability within a unified 5G architecture, this work contributes a comprehensive foundation for reliable and secure connected vehicle services.
We present DSKAG-IT-SIG, a family of post-quantum transaction signature schemes that achieve computational existential unforgeability under adaptive chosen-message attack, built on the DSKAG deterministic key-derivation layer. The construction derives per-transaction MAC keys through DSKAG, a deterministic symmetric key agreement protocol requiring no key transmission, no handshake, and no public key infrastructure. We prove (Theorem 1) that for an adversary making q adaptive chosen-message queries, existential forgery advantage in standard mode is at most q * 2^{-128} plus the PRF distinguishing advantage of HMAC-SHA256, reducing to the pseudorandomness of DSKAG-derived keys and the PRF security of HMAC-SHA256 under a uniform key; the ideal-cipher-model analysis gives the same q * 2^{-128} bound in idealized form. We prove (Theorem 2) that cross-domain forgery advantage is at most 2^{-128} + epsilon_iso, reducing to the key-separation properties of DSKAG across policy domains. The construction is computationally secure and is not unconditionally secure. DSKAG key derivation is built on HKDF-SHA512 (RFC 5869) over HMAC and SHA-512, and the shared base is established once via FIPS 203 ML-KEM, so security reduces throughout to standard FIPS-based symmetric and hash primitives. The scheme's post-quantum security rests on symmetric and hash hardness for authentication and on lattice hardness for the one-time base alone: the construction presents no integer-factorization or discrete-logarithm structure, so Shor's algorithm has no target and does not apply, and the operative quantum attack is Grover search, which yields at most a quadratic speedup against the 256-bit HMAC-SHA256, SHA-2, and SHA-3 primitives and preserves a 128-bit quantum security level. Because buffer uniqueness derives from tx_seq monotonicity rather than hash collision resistance, the security argument does not depend on the collision property, the hash property most weakened by quantum search. Standard-mode signatures are 30 bytes, a 97.8% reduction versus Falcon-512 (666 bytes) and compatible with ISO 20022 SWIFT message fields without re-engineering. The NexusKey composite policy digest binds asset class, jurisdiction, KYC level, and chain identity into the key derivation path; policy bypass is cryptographically equivalent to key forgery. A four-layer UltraHonk zero-knowledge proof system (143,802 gates, no trusted setup, 16 KB proof) verifies policy compliance wherever policy is enforced, off-chain in governance, cloud, and payment-processing deployments, and, where permissionless public auditability is required, on-chain; the on-chain Solidity verifier is deployed on Ethereum Sepolia and Arbitrum Sepolia. Version 2.3. 18 pages, 8 tables. Changes from v2.2: concrete finite bounds replacing generic negl(lambda) in Properties 1 and 2; buffer uniqueness derived from tx_seq monotonicity (no SHA3 collision resistance dependency); explicit ideal cipher model and standard model dual framing for HMAC analysis; formal separation of empirical and theoretical claims.
Open access
2 source records
Cryptography and Data Security
Cryptographic Implementations and Security
Physical Unclonable Functions (PUFs) and Hardware Security
Maria Christodimitropoulou, John Douvis, Panagiotis Alexopoulos, Panagiota Antonopoulou
This research examines the multifaceted digital transformation of tennis, analyzing the impact of new technologies on four key pillars: athlete training and performance, officiating and "smart" courts, fan experience and engagement, and emerging business models and governance. The integration of Artificial Intelligence, the Internet of Things through wearable sensors and "smart" equipment, and blockchain technology is radically reshaping how the sport is trained, played, watched, and managed. Technologies such as motion analysis systems, "smart" racquets, electronic officiating systems, personalized content platforms for fans, and Non-Fungible Tokens are analyzed. The research demonstrates that while these technologies offer unprecedented opportunities for performance optimization, objectivity in officiating, and deeper fan connection, they also present challenges related to adoption, regulation, commercial viability, and the need for unified governance. The research concludes that successfully navigating this new landscape requires a strategic approach that balances innovation with tradition, ensuring that technology acts as an enhancing factor for the sport rather than an end in itself.
The relevance of the study is substantiated by the need to reform the penitentiary system of Ukraine and to identify an optimal model for its institutional development in the context of European integration. Based on the synthesis of legislation and practice of the execution of criminal penalties in the EU Member States, the author has developed an original typology of public administration models in the penitentiary sphere. Four basic models are identified: 1) centralized (ministerial), characterized by a rigid vertical hierarchy; 2) the autonomous (executive) agency model, implying the operational independence of a specialized agency; 3) decentralized (federal), where management is exercised at the regional level; and 4) hybrid (asymmetric), which combines a state-wide system with autonomous jurisdictions. The advantages and disadvantages of each model are analyzed. It is established that a common European trend is the separation of policy-making functions (the prerogative of ministries) from operational management functions. The methodological basis of the study is a combination of general scientific and specialized research methods, including comparative-legal, formal-legal, and institutional analyses. The application of these methodological tools allowed for a deeper study of the European experience of penitentiary institutions. It has been shown that, regardless of the chosen organizational structure, the primary criterion for management efficiency is the state’s ability to ensure reliable, dynamic security, strict adherence to human rights standards, and the creation of the most favorable conditions for the successful social reintegration of offenders. Attention is given to the prospects of transforming the domestic penitentiary system. It is noted that the current Ukrainian penitentiary system retains signs of Soviet institutional inertia and excessive centralization. Based on the analysis, the expediency of the institutional transformation of the penitentiary system of Ukraine through the implementation of the autonomous (executive) agency model is substantiated. The study demonstrates that current Ukrainian legislation creates the necessary legal basis for the functioning of the penitentiary service as an autonomous central executive body accountable to the Ministry of Justice.
The relevance of the study is substantiated by the need to reform the penitentiary system of Ukraine and to identify an optimal model for its institutional development in the context of European integration. Based on the synthesis of legislation and practice of the execution of criminal penalties in the EU Member States, the author has developed an original typology of public administration models in the penitentiary sphere. Four basic models are identified: 1) centralized (ministerial), characterized by a rigid vertical hierarchy; 2) the autonomous (executive) agency model, implying the operational independence of a specialized agency; 3) decentralized (federal), where management is exercised at the regional level; and 4) hybrid (asymmetric), which combines a state-wide system with autonomous jurisdictions. The advantages and disadvantages of each model are analyzed. It is established that a common European trend is the separation of policy-making functions (the prerogative of ministries) from operational management functions. The methodological basis of the study is a combination of general scientific and specialized research methods, including comparative-legal, formal-legal, and institutional analyses. The application of these methodological tools allowed for a deeper study of the European experience of penitentiary institutions. It has been shown that, regardless of the chosen organizational structure, the primary criterion for management efficiency is the state’s ability to ensure reliable, dynamic security, strict adherence to human rights standards, and the creation of the most favorable conditions for the successful social reintegration of offenders. Attention is given to the prospects of transforming the domestic penitentiary system. It is noted that the current Ukrainian penitentiary system retains signs of Soviet institutional inertia and excessive centralization. Based on the analysis, the expediency of the institutional transformation of the penitentiary system of Ukraine through the implementation of the autonomous (executive) agency model is substantiated. The study demonstrates that current Ukrainian legislation creates the necessary legal basis for the functioning of the penitentiary service as an autonomous central executive body accountable to the Ministry of Justice.
Multi-agent architectures leveraging Large Language Models (LLMs) have significantly advanced the precision of Question Answering (QA) systems across diverse domains. However, existing frameworks remain vulnerable to adversarial manipulations, including poisoning, backdoor, and jailbreak at tacks, primarily due to their reliance on centralized orchestration. To mitigate these risks, we propose AgentChain, a framework that substitutes centralized control with a distributed semantic consensus process. By modeling the blockchain as an ideal functionality, AgentChain establishes a secure distributed layer to coordinate role allocation, answer proposal, evaluation and voting through a decentralized council. Specifically, we design Proof-of-Content-Quality (PoCQ) mechanism to ensure that the f inal answers reflect a robust semantic agreement among the majority of honest agents. Furthermore, we propose an incentive mechanism based on stake reassignment that penalizes malicious agents by reducing their rewards, ultimately phasing them out of the network. Comprehensive evaluations across eight datasets demonstrate that AgentChain achieves superior performance and resilience. AgentChain minimizes the impact of poisoning attacks on precision to less than 3% and reduces the success rate of backdoor and jailbreak attacks to less than 4%. These findings highlight the effectiveness and trustworthiness of AgentChain in mitigating security threats while maintaining high QA accuracy.
A central question of the Ethereum ecosystem is where Maximal Extractable Value (MEV)revenue originates and to what extent it stems from harming unsuspecting users. It is acceptable if MEV arises from arbitrages between centralised and decentralised exchanges (CEX-DEX). Yet theoretical models have significantly underestimated the scale of these arbitrages, while empirical studies have highlighted their importance - though these remain conservative estimates, constrained by numerous debatable heuristic assumptions. Revisiting the theoretical model, we found that CEX-DEX arbitrages require trading volumes on the order of the total activity of major liquidity pools and yield profits comparable to MEV. Most prior AMM models utilised the Black-Scholes (BS) stochastic differential equation (SDE) - i.e., geometric Brownian motion - and assumed continuous price trajectories where asset prices move in small increments only.We argue that BS underestimates arbitrage profits by ignoring price jumps, which are precisely the points at which arbitrage opportunities tend to arise. To address this gap, we present an extended discrete-time AMM model in which the price process is the sum of a diffusive component and stochastic jumps that can have arbitrary noise distributions. Although mathematically more involved this framework allows us to employ a general discrete-time SDE and compute the stationary probability distribution via function iteration with geometric convergence. We further prove that the resulting mispricing process is an ergodic Markov chain. We implement our model in C++, collect spot prices and AMM exchange data from the Ethereum blockchain and fit the model parameters to the observed prices. The estimates derived from our model closely match empirical observations and provide a natural theoretical explanation for several fundamental questions in the blockchain ecosystem.
The fast communication networks are crucial to support the modern digital services like cloud computing, massive data transmissions and real time multimedia applications. Since network traffic is constantly increasing exponentially, a proper approach to managing congestion is required to ensure the delivery of information is stable, minimize delays, and efficiently use bandwidth. Conventional congestion control mechanisms tend to use systems that are based on fixed rules and thresholds, and may be unable to be flexible in highly dynamic network situations. A graphical congestion control model is intelligent based on a multi-agent Deep Q-Learning model in which the distributed agents are tasked with monitoring network conditions such as queue length, delay, packet loss, and available bandwidth. The agents are taught the best acting policies in traffic regulation by means of interaction with their network environment and dynamically change their rates of transmission to reduce congestion. Learning organization is decentralized and enhances adaptability and scalability within large network systems. In comparison to traditional methods that attained a throughput of 780-910 Mbps, 2.9-5.8% packet loss, and 84-120 ms end-toend delay, performance evaluation has shown to achieve better network performance of 960 Mbps throughput, 1.8 end-to-end delay, and 0.8% end-to end packet loss. These enhancements underscore the success of smart use of reinforcement learning methods in adaptive congestion control in high-speed networking settings.
O estudo investiga barreiras de usabilidade em aplicações de Finanças Descentralizadas (DeFi) executadas em redes compatíveis com a Ethereum Virtual Machine (EVM), mostrando que problemas de fluxo, terminologia e feedback comprometem a adoção, especialmente entre iniciantes. Para enfrentar essas limitações, o trabalho propõe uma interface de usuário aprimorada e a compara a uma versão não otimizada usando métricas de desempenho, número de cliques e o questionário NASA-TLX. Os resultados indicam que a interface melhorada elevou a taxa de conclusão de tarefas de 76% para 89%, reduziu os cliques excedentes de 221 para 186 e diminuiu a carga cognitiva global aferida pelo NASA-TLX em todas as seis dimensões avaliadas, com destaque para demanda mental e frustração, inclusive entre usuários experientes, que relataram maior fluidez e previsibilidade. O artigo conclui que refinamentos de usabilidade voltados para aplicações financeiras descentralizadas são determinantes para elevar confiança e adoção, recomendando a padronização de processos, mensagens menos técnicas e a redução de etapas críticas para mitigar a fadiga de operações e ampliar o alcance da Web3.
The article examines the problem of formalizing investment cash flow in a distributed ledger environment. Within the framework of the digital transformation of financial relations, the cash flow of an investment project can be represented as a digital twin, recorded in the distributed ledger infrastructure and implemented through smart contracts. The aim of the study is to develop a mathematical model of the digital twin of investment cash flow and an algorithm for its forecasting using neural networks. Theoretical approaches to the interpretation of digital twins are systematized, and the limitations of the classical discounted cash flow model in relation to the digital environment are analyzed. A formalized model of digital cash flow is proposed, taking into account transaction fees of the distributed ledger, algorithmically accrued income, and an extended discount rate structure including technological and regulatory risk premiums. An algorithm for neural network forecasting of the digital twin is developed based on a feature vector integrating financial and infrastructure parameters. A comparative analysis of the digital and classical models is performed, which allowed establishing the structural modification of the investment process in the digital environment. The obtained results can be used in the valuation of digital financial assets and the construction of adaptive systems for forecasting their cash flows.
Nehaam Khan, Mohd. Aadil Shaikh, Atul Upadhyay, Dr. Vaishali Ramtekkar
The rapid growth of the event management industry has exposed significant challenges in traditional ticketing systems, including ticket fraud, duplication, unauthorized resale, and lack of transparency, as centralized platforms often fail to provide verifiable ownership and are vulnerable to manipulation. This paper proposes an NFT-Based Ticketing System that leverages blockchain technology to create a secure, decentralized, and transparent solution where each ticket is represented as a unique Non-Fungible Token (NFT) on the Polygon blockchain, ensuring immutability and authenticity. Smart contracts automate ticket minting, ownership transfer, resale regulation, and royalty distribution, enabling fair secondary market practices while maintaining control for event organizers. The system also incorporates a QR-based verification mechanism for real-time validation at event venues, preventing duplication and unauthorized access. Implemented as a decentralized application (DApp) using Web3 technologies and tested on the Polygon Mumbai testnet, the system demonstrates improved security, reduced fraud, efficient transaction handling, and enhanced user experience, thereby transforming traditional ticketing into a reliable and trustless digital ecosystem.
Web3 prediction markets, exemplified by Polymarket, have gained prominence for leveraging collective intelligence to forecast a wide range of social, political, and sports events. However, among the thousands of prediction market events, consensus disputes still arise due to imperfections in market mechanisms. On Polymarket alone, the trading volume involving disputed events has reached $972,370,804.71, underscoring the critical need for objective and efficient dispute resolution. In this study, we introduce large language models (LLMs) to: (1) evaluate whether web-enabled LLMs can reproduce the decision quality of UMA's on-chain voting process once a dispute has been raised, and (2) predict, based on event rules, which market events are likely to face future disputes before they occur. Our findings show that LLMs are unable to reliably predict which events will become disputed in advance; however, once a dispute is initiated, web-enabled LLMs achieve 89.58% agreement with UMA's final resolutions and demonstrate strong stability.