We provide a large-scale empirical audit of DEX routing using 2.98 million WETH-USDC swaps on Ethereum. Comparing realized routes with optimized benchmarks, we measure an average shortfall of 2.02 bps per trade or \$24 million. To attribute losses, we introduce three reproducible optimal benchmarks: a Support-Constrained Optimum (SCO) that evaluates split quality conditional on the pools actually used; a Full-Venue Optimum (FVO) that considers all available pools to quantify the value of broader pool access; and a Gas-Aware FVO (G-FVO) that augments FVO with gas costs to capture the trade-off between additional pool usage and gas expenditure. Computing these benchmarks at scale is enabled by a bisection-based algorithm for optimal routing across multiple pools for the same token pair. Two regularities emerge. First, information timeliness is crucial: moving from execution-time state to one-block lagged state optimization significantly raises mean shortfall and additional delays further degrade performance, albeit with diminishing increments; evaluated on the same stale snapshots, realized routes lie closer to optimal, indicating timing-mismatch as a key component. Second, inefficiency is heterogeneous and heavy-tailed: small trades suffer higher percentage losses, while a few extreme outliers dominate the aggregate dollar shortfalls. Finally, we demonstrate that sandwiching attacks drive a significant fraction of routing sub-optimality. Our benchmark protocol and algorithm offer a rigorous, reproducible basis for evaluating and improving information-timely, gas-aware routing.
Bruno Ramos-Cruz, Javier Andreu-Perez, David Richerby, Luis Martínez
The operation of blockchain is governed by consensus algorithms (CA). Several consensus mechanisms require significant computational power, while others necessitate high amounts of stakes to select the participant to validate and verify the transactions in the block, leading to centralisation of power and participant exclusion. This paper proposes a novel methodology to address these issues in reputation-based consensus algorithms by studying the reputation behaviour of the validator using intuitionistic fuzzy sets (IFSs) and uninorm aggregation operations (UAOs). Our approach uses IFSs to express the "reputation" because the reputation values in a consensus algorithm eventually imply uncertainty, and IFSs facilitate the representation of a lack of precise knowledge about reputation. Moreover, this methodology utilises uninorm aggregation operations to monitor reputation over time and reinforces the importance of negative and positive reputation. Consequently, this solution allows validators to rectify past failures in subsequent verification processes and foster an equitable consensus algorithm design. The proposed framework maintains linear computational complexity and does not introduce additional communication overhead beyond the underlying consensus protocol. Supported by experimental results, our methodology demonstrates improved performance and evaluation, promising advancements in blockchain network fairness and inclusivity.
K.E. Otebaliyeva, Zh. T. Shaimukhanova, Z. A. Erzhanova, A. .K. Adibayeva
A smart contract is more than a technical phenomenon; it raises legal questions about intent, transaction form, and obligation performance in a digital environment. Kazakhstani law, including the Civil Code and the Law on Electronic Documents, provides a basis for digital tools in contracts, recognizing electronic forms and the principles of freedom of contract. AIFC law further validates automated systems. However, the lack of a conceptual definition in civil legislation creates challenges for public law. This article argues that smart contracts should not be viewed as standalone contract types but through a functional approach: as either a form of expressing intent or an automated performance mechanism. Special focus is placed on criminal proceedings. The authors demonstrate that the absence of a clear definition complicates distinguishing civil torts from cybercrimes and hinders the use of code as evidence or the seizure of digital assets. The core issue is the discrepancy between traditional civil law constructs, blockchain logic, and current procedural evidentiary standards in Kazakhstan.
The pseudonymous nature of blockchain transactions, combined with the rise of encrypted DNS protocols such as DNS-over-HTTPS (DoH) and DNS-over-TLS (DoT), has created a new frontier for sophisticated tax evasion. Malicious actors can now exfiltrate transaction details and coordinate transfers by encoding data within the payloads of encrypted DNS queries, effectively bypassing traditional network monitoring and forensic analysis. This paper proposes a novel detection framework that leverages a hybrid deep learning architecture to identify such covert, tax-evading activities. Our system integrates a Convolutional Neural Network (CNN) for its superior ability to extract spatial and sequential patterns from raw network flow data and encrypted payload characteristics, with a Long Short-Term Memory (LSTM) network to model the temporal dynamics of blockchain interactions and DNS query sequences. By fusing these two paradigms, the hybrid model can distinguish between benign encrypted DNS traffic and malicious payloads used for illicit financial coordination. We evaluate our framework using a synthetically generated dataset that simulates realistic tax-evasion strategies, including micro-transaction splitting and delayed transaction relaying. Preliminary results indicate that our approach achieves a significantly higher detection rate and lower false-positive rate compared to conventional signature-based or single-model machine learning methods. This research demonstrates the efficacy of hybrid neural networks in preserving financial integrity and provides a critical tool for regulatory agencies to enforce tax compliance in the age of encrypted communications and decentralized finance.
Abstract We study comovement among major cryptocurrencies from a portfolio management perspective. To this end, we develop two new statistical tools. First, we propose a new measure called the portfolio-conditional correlation defined as the correlation conditional on the portfolio return being below or above a given threshold. Second, we develop a new multivariate model named the Common Autoregressive Jump Intensity Score-based (ComARJIS) model in which the time-varying intensity of a common jump in cryptocurrency returns is formulated under the Generalized Autoregressive Score (GAS) framework. Our main findings are as follows: First, we find an adverse downside correlation: the downside correlation is higher than the upside correlation. Second, the ComARJIS model successfully shows the correlation asymmetry of cryptocurrencies. Third, and most importantly, a market-timing strategy with the common jump intensity improves the Sharpe ratio. This result suggests that time diversification could be helpful for cryptocurrency investors even if asset diversification is impossible. Fourth, the meltdown risk represented by the common jump intensity is associated with the financial market stress in the U.S.
Madi Gali, Aray Kassenkhan, Y. Chinibayev, A. M. Abshukirova · 5 authors
Static, one-time authentication mechanisms such as passwords and PINs are increasingly inadequate for protecting mobile devices throughout an active session. Behavioral biometric continuous authentication (BBCA) addresses this gap by passively monitoring user-specific interaction patterns—keystroke dynamics, touch and swipe gestures, gait, and motion—to verify identity on an ongoing basis. This systematic review synthesizes 80 studies selected via a PRISMA-compliant protocol from IEEE Xplore, ACM Digital Library, Scopus, ScienceDirect, Web of Science, and SpringerLink (2017–2025). We examine behavioral and multimodal biometric modalities, machine learning approaches ranging from classical classifiers to deep sequence and transformer architectures, and their integration with intelligent personal agents, wearable devices, and IoT/edge infrastructures. Security analyses cover spoofing, adversarial and generative attacks, mimicry, and model-level threats including membership inference and reconstruction. Privacy-preserving mechanisms—cancelable biometrics, Bloom filter encodings, zero-knowledge proof protocols, federated learning, and blockchain-based identity management—are evaluated against practical trade-offs in energy consumption and latency on resource-constrained devices. Key research gaps are identified: the absence of standardized adversarial benchmarks, lack of end-to-end pipeline evaluations under simultaneous adversarial and privacy threat models, and limited user-centered studies on consent and acceptance of privacy-preserving mechanisms under frameworks such as GDPR. Recommended future directions combine adaptive multimodal fusion, privacy-preserving cryptography, energy-aware modality selection, and interdisciplinary human-centered evaluation to advance practical, resilient continuous authentication for mobile and assistant-enriched environments.
The convergence of agricultural digitalization and decentralized finance presents critical opportunities for mitigating carbon-related financial risks in emerging markets. However, the integrity of environmental, social, and governance reporting is frequently undermined by information asymmetries and inadequate audit trust. This paper introduces a cloud-native architectural framework utilizing Amazon Web Services to construct a real-time, blockchain-verified carbon disclosure pipeline. By deploying distributed Python middleware integrated with serverless computational nodes, the system programmatically extracts agricultural carbon intensity metrics and cross-references them against immutable blockchain ledgers. This methodology structurally eliminates manual reporting friction, providing rural credit institutions and multinational enterprises with deterministic, verifiable environmental data. Preliminary architectural evaluations confirm that integrating high-velocity Application Programming Interfaces with decentralized ledgers significantly reduces information asymmetry, establishing a highly scalable foundation for green finance and rural revitalization.
The article provides a comprehensive scientific analysis of the procedural status and jurisdictional powers of the European Public Prosecutor’s Office (EPPO) as the first autonomous supranational body of criminal justice. The long history of the institute’s formation is researched, starting from the 1995 initiatives and the 1997 «Corpus Juris» academic project, which laid the foundation for the codification of EU criminal law, to the publication of the 2001 Green Paper as a key consultative document for stimulating pan-European debates. Particular attention is paid to the role of the 2007 Lisbon Treaty, which, through the implementation of Article 86 TFEU, created a direct legal basis for the establishment of the body under the enhanced cooperation procedure using «accelerator clauses». The material competence of the EPPO in the field of PIF crimes is determined in accordance with Directive 2017/1371, in particular regarding large-scale VAT fraud, corruption, and money laundering, and doctrinal proposals for expanding the mandate to environmental crimes and terrorism are considered. The study emphasizes the unique nature of the office as a body sui generis with direct enforcement powers that distinguish it from traditional agencies. The organizational structure of the body, which combines central (College, Permanent Chambers) and decentralized (delegated prosecutors) levels, ensuring institutional independence from the governments of Member States, is analyzed. Through the prism of the case law of the EU Court of Justice (Taricco, G. K. and Others, Stan v EPPO cases), the mechanisms of cross-border interaction between the handling and assisting delegated prosecutors are revealed, and the priority of national courts in exercising judicial control over the acts of the prosecutor’s office is confirmed. Systemic risks of implementing the right of evocation caused by shortcomings of Article 25 of Regulation 2017/1939 are outlined, illustrated by conflicts of competence in Spain and Croatia. The dynamics of Ukraine’s integration into the supranational financial security system of the EU are investigated: from the 2022 Working Arrangement and memorandums with NABU and SAPO to the ratification of the Framework Agreement on the Ukraine Facility in 2024. The paper examines the legal implications of the 2024 Agreement which serves as a mandatory legal guarantee for the effective protection of the Union’s financial interests during the reconstruction of Ukraine.
The article provides a comprehensive scientific analysis of the procedural status and jurisdictional powers of the European Public Prosecutor’s Office (EPPO) as the first autonomous supranational body of criminal justice. The long history of the institute’s formation is researched, starting from the 1995 initiatives and the 1997 «Corpus Juris» academic project, which laid the foundation for the codification of EU criminal law, to the publication of the 2001 Green Paper as a key consultative document for stimulating pan-European debates. Particular attention is paid to the role of the 2007 Lisbon Treaty, which, through the implementation of Article 86 TFEU, created a direct legal basis for the establishment of the body under the enhanced cooperation procedure using «accelerator clauses». The material competence of the EPPO in the field of PIF crimes is determined in accordance with Directive 2017/1371, in particular regarding large-scale VAT fraud, corruption, and money laundering, and doctrinal proposals for expanding the mandate to environmental crimes and terrorism are considered. The study emphasizes the unique nature of the office as a body sui generis with direct enforcement powers that distinguish it from traditional agencies. The organizational structure of the body, which combines central (College, Permanent Chambers) and decentralized (delegated prosecutors) levels, ensuring institutional independence from the governments of Member States, is analyzed. Through the prism of the case law of the EU Court of Justice (Taricco, G. K. and Others, Stan v EPPO cases), the mechanisms of cross-border interaction between the handling and assisting delegated prosecutors are revealed, and the priority of national courts in exercising judicial control over the acts of the prosecutor’s office is confirmed. Systemic risks of implementing the right of evocation caused by shortcomings of Article 25 of Regulation 2017/1939 are outlined, illustrated by conflicts of competence in Spain and Croatia. The dynamics of Ukraine’s integration into the supranational financial security system of the EU are investigated: from the 2022 Working Arrangement and memorandums with NABU and SAPO to the ratification of the Framework Agreement on the Ukraine Facility in 2024. The paper examines the legal implications of the 2024 Agreement which serves as a mandatory legal guarantee for the effective protection of the Union’s financial interests during the reconstruction of Ukraine.
The rapid advancement of artificial intelligence, particularly the breakthroughs in large language models and AI agents, is driving a fundamental paradigm shift in the fintech sector. This paper proposes a theoretical framework to characterize the transition of fintech from a "tool empowerment" phase, where technology serves as an efficiency-enhancing instrument within existing financial structures, to an "ecological reconstruction" phase, where AI agents, embedded finance, and decentralized technologies fundamentally reshape the organizational forms, value creation mechanisms, and competitive dynamics of the financial industry. We develop a three-dimensional analytical framework encompassing technological architecture, institutional logic, and value network to systematically examine this transformation. Through a mixed-methods approach combining comparative case studies of 12 representative financial institutions and quantitative analysis of patent data from 2015 to 2025, we find that: (1) the paradigm shift follows a non-linear S-curve trajectory, with a critical inflection point occurring around 2023-2024; (2) AI agent-driven autonomous workflows can reduce operational costs by 35-48% while improving risk assessment accuracy by 22-31%; (3) the ecological reconstruction phase exhibits distinct network effects where platform-based financial ecosystems achieve 2.3-3.7 times higher customer lifetime value compared to traditional linear models; (4) the transition presents significant regulatory challenges, particularly regarding algorithmic accountability, data sovereignty, and systemic risk aggregation in interconnected AI-financial networks. Our findings contribute to the theoretical understanding of technology-induced institutional change in financial systems and offer practical implications for financial institutions, technology firms, and policymakers navigating this transformative period.
Zero-Knowledge Ethereum Virtual Machines (zkEVMs) secure Ethereum rollups by generating zero-knowledge proofs that guarantee off-chain execution correctness. However, subtle implementation bugs (e.g., incorrect gas accounting) can lead to valid proofs certifying semantically faulty states, thereby silently defeating cryptographic guarantees. Formal verification via SMT solvers can prevent this, but is bottlenecked by specification: current zkEVM development practice lacks automated methods to translate Rust opcode handlers into verification models. Current practices rely on unsustainable manual specifications, while LLM-based approaches suffer from hallucination and lack formal guarantees. To address this, we propose VeriSynth, a framework that synthesizes executable Python/Z3 verification models from Rust zkEVM code. VeriSynth enforces a hybrid paradigm: an LLM acts strictly as a formalization frontend to translate code into symbolic constraints, while an SMT solver serves as the correctness arbiter. To handle complex multi-component state transitions, VeriSynth integrates semantic decomposition, retrieval-grounded prompting, and verification-guided auto-repair into a closed-loop pipeline. We evaluate VeriSynth on the first source-level zkEVM verification benchmark, encompassing both correct and faulty opcode implementations. VeriSynth achieves a bug detection rate of over 90%, substantially outperforming direct and conversational LLM baselines, as well as a production-grade handwritten mutation-testing suite. Ablation studies confirm that each pipeline component is critical to the framework's overall effectiveness.
One of these financial crimes, which seem to sound like a concept straight out of a dream until you get a sense of the magnitude of the issue, is money laundering. According to the United Nations, Between $800 billion and $2 trillion in illicit money is transacted through the world financial system each and every year. The problem with this approach is that the criminals seldom use only one bank. They thread their way across five, ten, and sometimes dozens of institutions, all seeing merely a harmless nugget. In isolation, looking at his or her own transaction logs, no single bank will easily know that there is a problem. This paper is about a system, called AMLNet, which tackles this blind spot. Unlike the traditional approach, which would allow banks to share their customers' data with each other,AMLNet trains a detection model on customers' data within each bank, and shares only what the detection model learned from the data, not the data itself. All collaborative training is documented in a blockchain ledger, making it transparent and tamper-proof. With a Zero-Knowledge Proof, each bank is able to prove cryptographically that it is acting honestly, but not disclose anything private. A graph of transaction data (accounts as nodes, transfers as edges) is used to extract structural features, which are compressed by PCA before being input to a Multi-Layer Perceptron (MLP) risk-scoring classifier of each account. Together they increase fraud recall by approximately 20% over any single institution operating alone, while maintaining a low false positive rate, and that the overall computation time is less than 10 minutes on an average laptop.
The rapid growth of cybercrime, ransomware attacks, digital fraud, and large-scale cyber threats has significantly increased the need for secure and collaborative cyber forensic investigations. Traditional machine learning approaches often require organizations to share or centralize sensitive forensic datasets, creating challenges related to privacy, confidentiality, data ownership, and security. To address these limitations, this project proposes a PrivacyPreserving Distributed Training Architecture for Cyber Forensics using Blockchain and Homomorphic Encryption. The proposed framework integrates Federated Learning, Distributed Learning, CKKS-based Homomorphic Encryption, Blockchain Technology, and a Secure Model Exchange Space to enable multiple agencies to collaboratively train machine learning models without exposing their raw forensic data. Federated Learning allows organizations to train models locally and securely aggregate encrypted model updates, while Distributed Learning enables encrypted dataset partitions to be processed collaboratively by helper nodes without revealing the original data. CKKS Homomorphic Encryption protects sensitive information during computation, and blockchain technology provides decentralized trust through secure node authentication, transparent validation, immutable audit trails, and trusted model exchange among participating agencies. The framework is implemented using Python, Flask, Scikit-learn, TenSEAL, Ganache, Solidity, and Web3.py, providing a web-based platform for collaborative project management, encrypted training, blockchain monitoring, secure model sharing, performance evaluation, and cyber forensic prediction. Experimental results demonstrate that the proposed architecture successfully supports secure collaborative learning, encrypted computation, blockchain-based validation, and trusted model sharing while maintaining effective prediction performance. By integrating distributed learning, federated learning, homomorphic encryption, and blockchain into a unified framework, the proposed system provides a scalable, secure, and privacy-preserving solution for next-generation cyber forensic intelligence, enabling organizations to collaboratively strengthen cybersecurity without compromising the privacy, confidentiality, or ownership of sensitive forensic data
Facial recognition has become an essential technology in modern surveillance and law enforcement for the automatic identification of individuals from images and video streams. Conventional facial recognition techniques often experience reduced accuracy due to variations in illumination, facial pose, occlusion, low-quality images, and aging effects. To address these challenges, this paper proposes a Blockchain-Based Criminal Recognition and Evidence Management System that integrates advanced deep learning models with secure blockchain technology. The proposed system employs Multi-task Cascaded Convolutional Networks (MTCNN) for accurate face detection and facial alignment, followed by StyleGAN for age progression and age transformation to generate age-invariant facial representations while preserving the individual's identity. The transformed facial images are then processed by a Convolutional Neural Network (CNN)-based facial recognition model to extract discriminative facial features and accurately identify suspects by comparing them with a criminal database. Upon successful recognition, the system automatically generates real-time alerts for authorized personnel and securely stores recognition results, timestamps, confidence scores, and evidence metadata on a blockchain using Web3.py and Ganache, ensuring data integrity, transparency, traceability, and protection against unauthorized modification. By combining robust face detection, ageinvariant facial recognition, and tamper-proof evidence management, the proposed system provides an accurate, secure, and reliable solution for modern criminal identification and digital forensic investigations.
Das Forschungs- und Entwicklungsprojekt LCBIT (Low-Code Blockchain Integration Toolkit) wurde mit großem Erfolg durchgeführt. Das übergeordnete Ziel, hochkomplexe Blockchain-Technologien so zu abstrahieren, dass auch Nicht-Softwareentwickler in die Lage versetzt werden, dezentralisierte Anwendungen (dApps) eigenständig zu entwickeln und bereitzustellen, konnte im Berichtszeitraum vollständig erreicht werden. Im Mittelpunkt des Erfolgs stand die methodische und technologische Weiterentwicklung der bestehenden Low-Code/No-Code-Plattform (LCNC) der Heisenware GmbH zu einem umfassenden und intuitiven Werkzeugkasten für Web3-Anwendungen. Durch die exzellente und interdisziplinäre Zusammenarbeit im Konsortium - bestehend aus der Heisenware GmbH, der Hochschule Mittweida (Blockchain Competence Center Mittweida - BCCM), der TU Chemnitz (Professur Fabrikplanung und Intralogistik - FPIL) und dem assoziierten Partner in.hub GmbH - wurde ein modulares System geschaffen. Dieses vereint die Integration von Blockchain, IoT-Sensorik und klassischen Datenquellen nahtlos in einem Low-Code-Umfeld. Das Projekt hat bewiesen, dass sich durch die konsequente Abstraktion technischer Komplexität die Entwicklung dezentraler Anwendungen drastisch vereinfachen lässt. Insbesondere kleine und mittlere Unternehmen (KMU) erhalten dadurch einen niedrigschwelligen Zugang zu Web3-Technologien, ohne kostenintensive, eigene Blockchain-Expertise aufbauen zu müssen.
Decentralized autonomous organizations (DAOs) represent one of the most consequential experiments in organizational design to emerge from blockchain technology. By encoding governance rules into smart contracts and recording every vote, proposal, and treasury decision immutably on-chain, DAOs offer globally distributed communities a high degree of transparency and accountability in collective decision-making. This study examines governance design and participatory innovation across three DAOs: RARI DAO, Arbitrum DAO, and Optimism DAO. Each has taken a distinct structural approach to the problem of collective decision-making at scale. Using a qualitative comparative case study method, the research draws on governance forum discussions, proposal records, and official documentation, analyzed through thematic coding and cross-case comparison. The theoretical frame draws primarily from Ostrom’s (1990) commons governance principles, with Scott’s (1995, 2014) institutional theory and Donaldson’s (2001) contingency theory applied as supplementary analytical lenses. Across all three cases, the findings indicate the emergence of increasingly formalized governance architectures designed to balance decentralization, coordination efficiency, and operational security. Communities building governance infrastructure from scratch, iterating rapidly in response to community feedback, and developing structural solutions: delegate incentive programs, participation incentive mechanisms, bicameral legitimacy systems, constitutional frameworks, and dedicated legal entities that represent an emerging configuration of governance mechanisms. Two cross-case findings are particularly notable. First, all three DAOs independently converged on a three-body governance architecture comprising a legal foundation, a security council, and token-holder governance — suggesting that similar governance problems, encountered in similar technical and legal environments, tend to produce similar structural solutions. Second, while these architectures are structurally similar, they differ significantly in how governance processes are implemented in practice, reflecting differences in scale, formalization, and community context. These findings contribute to the literature by providing a structured cross-case analysis of DAO governance design and offering practical insights into programmable institutional design and blockchain-enabled coordination systems.
Learn more about Theta Network and its impact on the development of decentralized infrastructure via blockchain-enabled media distribution, edge computing, AI integration, and Web3 innovation. With this in-depth overview, you will gain valuable information about its technology, features, practical applications, and future perspectives, emphasizing the need for thorough research before making an investment decision. If you are interested in blockchain, then this article is for you!
Many administrative processes, such as internship agreement processes, often rely on manual approval workflows and centralized record-keeping. This makes the process susceptible to delays and unauthorized modification while introducing limited traceability. This study presents StajChain, a permissioned blockchain-based multi-party internship management system developed using Hyperledger Fabric. The proposed system implements the complete internship agreement lifecycle through smart contracts and enforces role-based authorization using Hyperledger Fabric CA. The architecture consists of a React frontend, a NodeJS backend, an off-chain SQLite database, and the on-chain Fabric ledger. Users such as students, companies, faculty internship committee members, and the central internship unit can perform specified operations according to their role and identity. The agreement lifecycle follows predefined sequential steps, and at each step, the ledger status is updated and recorded securely. Furthermore, the system was evaluated using functional and performance tests, indicating acceptable throughput and latency for verifiable administrative workflows. This implementation demonstrates how permissioned blockchain technology can improve transparency, integrity, and accountability while preserving controlled access to institutional data and providing a working prototype that can be used in various future systems.
x402 is an emerging payment protocol for Web APIs and autonomous AI agents. x402 extends HTTP 402 with a payment negotiation flow and delegates payment proof verification and on-chain settlement to third-party facilitators. As a result, facilitators serve as a shared payment infrastructure for many independent merchants. This centralizes trust and validation in one component, so a single flaw can affect many services. Despite rapid adoption by major vendors and economically meaningful mainnet activity, the security posture of real-world x402 deployments remains poorly characterized. We present the first systematic study of authorization correctness and execution safety in current facilitator-mediated x402 deployments in the wild, identifying eight security rules for facilitators as critical payment infrastructure. Based on our analysis of rule violations, we derive four new attack vectors, including Free Shopping, Asset Theft, Service Denial, and Gas Abuse. These attacks exploit weaknesses in the real-world facilitator and server implementations and cause severe harm, including direct financial loss to merchants, theft of facilitator-held assets, unbounded sponsor-paid gas/fees, and disruption of payment services. To assess the security of x402 deployments at scale, we propose a semi-automated black-box tool and apply it to 15 major x402 facilitators collectively used by over 60K sellers and 360K buyers. Alarmingly, we find violations in all evaluated facilitators. We responsibly disclosed our findings to the affected parties, who acknowledged the issues and adopted mitigations, including changes by Coinbase. Finally, we complement our controlled testing with an empirical measurement of over 119 million recent Base and Solana transactions, quantifying x402 adoption, facilitator centralization, and ecosystem-level risk indicators.
We audit whether candle-based machine-learning models can turn predictions of cryptocurrency extrema or short-horizon outcomes into positive Binance Spot paper policies after assumed costs. Numerical results come from scripted fixed-seed model runs and deterministic simulators; human-supervised AI agents supported the July 20 evidence-integrity revision through literature retrieval, separately tasked critique, artifact reconciliation, documentation, and source packaging, not trading decisions. The strongest later-period evidence, conditional on extensive predecessor search, is negative: an unchanged ten-pair mandatory-daily selector lost 6.72\% over 19 July cycles at an assumed 31-bps completed-cycle cost, with 3 wins and 16 losses. In short model-specific July evaluations, the validation-selected local-minimum policy returned -1.79\%, while the local-maximum sell-to-cash/re-entry policy underperformed continuous holding by 2.80\%; their gross mean advantages of 11.11 and 12.21 bps were below even the 21-bps stress. A Gurgul-inspired, OHLCV-only daily adaptation attained minimum/maximum ROC AUC of 0.874/0.896 but average precision of only 0.134/0.116 and lost 44.30\% over seven cycles, versus -41.20\% for buy-and-hold. A forensic audit also downgraded an earlier One4All "30-day holdout": its dates had influenced prior architecture work, its four-hour outcome horizon was not purged at split boundaries, it used same-close entry, and its raw result directories were absent. Across the tested, mostly exploratory protocols, event-ranking performance did not establish positive executable policy value. Every operational decision remains NO\_TRADE.
Agentic commerce protocols such as AP2 and ACP define mechanisms for secure agent-initiated transactions but do not provide interoperable, tamper-evident auditability or verifiable temporal ordering of events across heterogeneous domains. This paper addresses these gaps by proposing a verifiable global event timeline for agentic commerce, constructed from four core components: canonical event schemas that enforce deterministic serialization, deterministic batch formation ensuring reproducible ordering without reliance on synchronized clocks, Merkle-based append-only commitments providing logarithmic-cost inclusion proofs, and blockchain anchoring establishing a tamper-evident temporal backbone. Building on this infrastructure, we introduce a cryptographically signed fraud marker that binds risk labels to anchored evidence through an unforgeable provenance chain, and a dataset lineage model enabling reproducible, tamper-evident AI training pipelines. Empirical results from a prototype implementation demonstrate: Merkle tree construction processes 50,000 events in 47 milliseconds; end-to-end verification completes in under 0.013 milliseconds regardless of batch size; inclusion proof sizes grow logarithmically from 320 bytes at 1,000 events to 512 bytes at 50,000 events; and Merkle-based verification outperforms linear scan by 14.4x at 50,000 events.
Gouher Ahmed, Hamza Naim, Aqila Rafiuddin, Mohammed Nizamuddin · 5 authors
This study deals with the performance analysis and volatility estimation of conventional indices including Dow Jones, S&P 500, Brent Oil, Crude Oil and Gold and cryptocurrencies including Bitcoin and Ethereum for the period January 3, 2011 to November 26, 2021 for all of the indices except Ethereum for which the period chosen was from March 10, 2016 to November 26, 2021 due to late incorporation of the cryptocurrency. The stationarity, heteroscedasticity, and serial correlation of the data were considered. Time series regression using the GARCH model is applied for performance analysis and volatility estimation. GARCH (1, 1) estimates show the high performance of cryptocurrencies over the conventional indices, except Gold, which was insignificant, with Ethereum followed by Bitcoin being the most volatile among the different indices. However, Gold remains inert in response to the different indices. However, although the cryptocurrencies add to the country’s revenue, thus minimizing the deficits, there should still be proactive policies and practices to prevent the exploitation of stakeholders, especially for the sake of minority ones.
Edison Andres Arteaga Lopez, Gustavo Ramírez-González, Andrea Sabbioni, Carlos A. Astudillo
Abstract Distributed ledger technologies (DLT) can enhance trust and auditability in the Internet of Things (IoT). Among them, IOTA has been specifically designed to support machine-to-machine interactions and IoT data anchoring through scalable DLT architectures. However, their integration with Low-Power Wide-Area Networks (LPWANs) remains limited due to device constraints, strict timing requirements, and the operational costs of on-chain transactions. The transition from the fee-less Stardust to the fee-based IOTA Rebased model introduces explicit transaction costs, questioning the viability of continuous IoT data anchoring. IOTA provides a suitable platform to examine the challenges of integrating distributed ledger technologies with LPWAN-based IoT systems. Its transition to a fee-based execution model raises important questions regarding cost predictability and performance in continuous data anchoring scenarios, particularly under the constraints of resource-limited and latency-sensitive environments. This article investigates the practicality of the execution and payment model introduced by IOTA Rebased for IoT scenarios requiring continuous data notarization. We provide an empirical evaluation of continuous IoT data notarization on the public IOTA Rebased Mainnet and characterize the performance implications on edge-oriented deployments, including resource-constrained and resource-rich devices. We implement a notarization oracle that ingests LoRaWAN uplinks from The Things Network (TTN), canonicalizes payloads, generates SHA-256 commitments, and records them on-chain through reusable notarization objects. The oracle enables continuous anchoring of IoT telemetry while minimizing transaction overhead through object reuse. Two 24-h experimental campaigns compare a notarization oracle on resource-constrained and resource-rich hardware under periodic workloads. Results show consistent steady-state gas consumption for UPDATE operations, indicating that object reuse enables stable on-chain cost behavior in IOTA Rebased regardless of the deployment platform. From a performance perspective, both environments achieve stable execution; however, the resource-constrained edge deployment exhibits higher median and tail latency, alongside tighter memory margins compared to the resource-rich centralized baseline. These findings confirm the feasibility of deploying notarization services on constrained edge infrastructure under the new fee-based model.
Diky Paramitha, Etik Ipda Riyani, Nadhira Hardiana, Kan Wen Huey
Bitcoin has a tendency of price volatility that is much higher than other cryptocurrency assets, this makes a very significant difference from other financial assets that can go beyond conventional market logic thus creating a major obstacle in risk management. This study aims to dissect the extreme anomalies of bitcoin trading volume against the volatility of Bitcoin returns. Using a quantitative time series approach, the study analyzed monthly data on bitcoin price and trading volume using Bitcoin prices in the period February 2015 to December 2025. We assess volatility using the GARCH-X model to introduce trading volume as an exogenous variable. The basic GARCH shows significant volatility persistence, indicating a clustering of high volatility in Bitcoin's returns. This finding results that trading volume is not just a static transaction number but reflects a very crucial information proxy. Every movement of trading activity generates new signals in which aggressive price react. Trading volume is also highly correlated with the volatility of returns, although the volatility of the model indicates the need for careful interpretation. Bitcoin's volatility is not solely due to historical volatility dynamics, but also the impetus from trading activity, highlighting the need to consider accurate volatility modeling in the digital asset market. This research adds value by embedding trading volumes into the GARCH model to evaluate its contribution in explaining Bitcoin's volatility through empirical insights for investment decisions and risk management in the cryptocurrency market