This paper presents an open-economy macroeconomic equilibrium model for Proof-of-Stake (PoS) networks with fee-burn mechanics (EIP-1559) that formalizes the strategic interplay between a Kelly-optimizing rational institutional investor and a utility-driven retail consumer. We analyze network dynamics across two behavioral regimes. In The Unbounded Accumulation Model, the consumer purely accumulates tokens, creating an exclusive buy-side pressure that interacts with institutional portfolio rebalancing to fuel an ever-expanding speculative bubble and generate compounding excess returns for investors. Conversely, in The Utility-Consumption Model, the consumer dynamically buys and sells tokens to balance crypto wealth against real-world fiat consumption. Within this framework, we derive an explicit steady-state equilibrium price for ETH, demonstrating how token valuation anchors to a stable fundamental baseline that scales directly with network adoption while completely dissolving the institutional yield premium. Our numerical simulations show that while exogenous traditional finance (TradFi) shocks propagate through portfolio rebalancing to drive high token price volatility, network inflation remains highly stable. Furthermore, we prove that network security is insulated from institutional monopoly by counter-cyclical consumer behavior. Our findings reveal that institutional excess wealth creation in PoS ecosystems is not native to the staking protocol itself, but is strictly driven by the leveraged extraction of the retail consumer's continuous demand for transactional utility.
Restoring forests is essential to addressing the global crisis of deforestation and biodiversity, as well as to maintaining the livelihoods of billions of forest-dependent people. Three major improvements are introduced by the suggested framework for smart forest restoration management: decentralized financial integration, community participatory governance, and the cost-effective deployment of blockchain and smart contracts for predictive and adaptive management. By coordinating ecological objectives with technological developments, these innovations seek to improve transparency, scalability, management effectiveness, and stakeholder trust in forest restoration initiatives.
The emergence of Non-Fungible Tokens (NFTs) as a novel digital asset class has precipitated significant legal uncertainty across multiple jurisdictions. Unlike fungible cryptocurrencies, NFTs encode uniqueness and provenance on distributed ledger technology, yet existing legal frameworks — conceived for tangible property, intellectual creations, and financial instruments — have proven inadequate in determining their precise legal character. This article engages in a rigorous comparative legal analysis of the legal status of NFTs in Uzbekistan, the European Union, and the United States of America, examining how each jurisdiction has — or has failed to — accommodate NFTs within property law, intellectual property law, securities regulation, and consumer protection frameworks. A central concern of the article is the application of alternative dispute resolution (ADR) mechanisms — including arbitration, mediation, and online dispute resolution (ODR) — to NFT-related conflicts. The article identifies critical lacunae in domestic and international legal frameworks and proposes concrete legislative reforms tailored to the Uzbek legal context, while drawing on best practices from comparator jurisdictions. The study concludes that regulatory clarity, combined with adaptable ADR infrastructure, is essential to foster a secure and equitable digital economy in the Republic of Uzbekistan and beyond.
Proof-of-work (PoW) blockchains rely on computational expenditure to secure a ledger supporting a native cryptocurrency. In existing systems such as Bitcoin, this expenditure is intentionally useless: the computation secures consensus but produces no external economic output. An emerging alternative -- proof of useful work (PoUW) -- enables the same computation to simultaneously secure the blockchain and generate economically valuable output. However, PoUW is often criticized on economic grounds: if the work is useful, attackers might be "paid to attack," potentially weakening security. We develop a competitive-equilibrium model of a PoUW blockchain in which compute can be allocated across pure mining, pure useful work -- instantiated as machine-learning inference -- or "duplex" work that produces both with computational overheads. We provide a complete closed-form characterization of equilibrium allocations and prices as a function of the duplex overheads and a single economic parameter -- the token-inference ratio -- measuring token adoption relative to the inference market. This characterization reveals three regimes: "Bitconia," in which the economy reduces to classical PoW; "Fortessia," in which duplex replaces mining, increasing security while useful output remains unchanged; and "Duplexia," in which token rewards subsidize inference, lowering prices and expanding inference supply. Contrary to the common strawman argument, PoUW does not make attacks economically cheap: once equilibrium prices are taken into account, the economic cost of a majority attack remains tied to the block reward. Moreover, in Duplexia, block rewards act as rebates on inference prices, generating additional socially useful computation that would not arise without the blockchain -- an expansion monotonically increasing in token adoption and technological efficiency.
We analyze competing auctions in intermediated markets, where a seller selects among parallel mechanisms for the sale of a single good, most prominently the relay-and-protocol architecture of proposer-builder separation in Ethereum. When the intermediary can enforce single-homing on its bidders, sealed-bid second-price intermediary auctions fully unravel into the sealed first-price principal auction; open bidding-format intermediaries unravel only partially, collapsing into first-price in equilibrium under symmetric latency and sorting fast bidders to the intermediary under asymmetric latency. Any last-look advantage is removed through the availability of a credible sealed bidding channel. These results extend to multi-plexing environments (no enforcement by the intermediary). While the unraveling result indicates that the availability of a sealed first-price bidding channel pushes the overall market to the same auction structure, the very assumption of the credibility of such channel is problematic, as the seller may have an incentive to leak information: a first-price auction is leakage-resistant in the presence of a single ``fast'' bidder but not against two or more. However, if the seller can credibly commit to not leak bids, it is optimal for them to do so. A main motivation is the forthcoming Glamsterdam update of Ethereum: our analysis suggests that the availability of an in-protocol (first-price) bidding channel severely limits the design space for out-of-protocol auctions by relays and other intermediaries.
Gabriela Dobrita, Simona-Vasilica Oprea, Adela Bara
Existing learning-based detectors for Solidity smart-contracts reduce vulnerability detection to syntactic pattern matching within single functions, yet many of the most consequential exploits (The DAO, Cream Finance) exist not in any individual function but in the relationship between functions and in the combination of conditions that made the attack feasible. Thus, we propose AttackPathGNN, a graph neural network (GNN) that reframes detection as reasoning over explicit attack paths. Two architectural choices distinguish it from prior GNN-based detectors: (1)a State Interference Graph that links every pair of functions sharing mutable storage through typed, weighted edges and through directed reentrancy-path edges defined by an explicit five-condition predicate; (2)conjunction pooling, a differentiable AND-aggregator over eight named exploit preconditions whose log-sigmoid form causes the per-function exploit score to collapse whenever any single mitigation (a reentrancy guard, an access-control modifier or SafeMath) is in place. Across five independent training runs, AttackPathGNN attains 92.3+/-0.2% F1 on the SmartBugs Wild held-out test partition (4.3+/-0.3% false-negative rate, 90.8+/-2.5% detection rate on the independently human-labelled SmartBugs Curated benchmark), recovering 6/10 DASP10 categories at 100% on every seed and Reentrancy at 98.7+/-1.8%. Each prediction is emitted with a structured remediation report, turning each verdict into an actionable, function-level audit finding.
Sudad Abed, Nasser Sabar, Abdun Mahmood, Mohammad Jabed Morshed Chowdhury
Decentralized Federated Learning (FL) removes reliance on centralized coordinators but remains vulnerable to model poisoning, unreliable validation, and high validation overhead. This paper introduces Proof of Contribution Quality (PoCQ), a blockchain-based consensus framework designed to secure decentralized FL through reputation-aware validation and aggregation. PoCQ evaluates client updates using cryptographic commitments and lightweight norm-based validation, enabling efficient detection of malicious contributions while limiting validation cost. A reputation-driven consensus mechanism dynamically adjusts the influence of participants based on their historical contribution quality, while the blockchain stores only compact audit metadata to preserve scalability. Extensive experiments under poisoning scenarios across three benchmark datasets demonstrate that PoCQ outperforms the strongest state-of-the-art methods, achieving accuracy gains of 34.1% on challenging medical datasets in highly non-iid settings and an 11% improvement in global average accuracy. In addition, PoCQ reduces validation time by 21.27% on average per round, highlighting its effectiveness in jointly enhancing robustness and efficiency for fully decentralized federated learning.
Abstract: People who have digital accounts for banking, trading, and financial investment opportunities. The growing adoption of fintech apps has changed the way investors behave, especially tech-savvy users like IT professionals in Bengaluru. This review paper seeks to reconnect the dots between ABFS and investor behaviour by reviewing large sample of literature spanning the years 2002–2026. This research adopts the key theoretical frameworks: Unified Theory of Acceptance and Use of Technology (UTAUT), Theory of Planned Behaviour (TPB), behavioural finance theory and trust theory. The research method adopted was systematic literature review that was carried out by employing Scopus, Web of Science, Google Scholar, and peer-reviewed journals. According to the results, the main factors that explain the financial behaviour of adoption and investment are: financial awareness, the digital financial literacy, ease of use, Accessibility, Trust and Security, and Risk perception. The review also highlights some key gaps in the existing research, such as a lack of qualitative research, the absence of longitudinal studies, a narrow provision of emerging market studies, and poor focus on decentralized finance and AI-based investment applications. The paper proposes a conceptual and Structural Equation Model (SEM)-based framework explaining the relationship between technological, behavioural, and psychological factors influencing investor behaviour. Its finding will be valuable for the scientific community as it lays the basis for an integrated framework in understanding the adoption of fintech in emerging economies, and will also be helpful for policy makers, fintech developers and researchers Keywords: Application-based financial services, fintech adoption, investor behaviour, financial literacy, SEM model, trust and security, risk perception, digital investment platforms, TAM, TPB & UTAUT. Title: APPLICATION-BASED FINANCIAL SERVICES AND INVESTOR BEHAVIOUR IN INVESTMENT MANAGEMENT PRACTICES: A SYSTEMATIC REVIEW OF THEORETICAL INSIGHTS, TRENDS, AND FUTURE DIRECTIONS Author: Parimala.S, Dr. Annadurai International Journal of Management and Commerce Innovations ISSN 2348-7585 (Online) Vol. 14, Issue 1, April 2026 - September 2026 Page No: 502-513 Research Publish Journals Website: www.researchpublish.com Published Date: 04-June-2026 DOI: https://doi.org/10.5281/zenodo.20542559 Paper Download Link (Source) https://www.researchpublish.com/papers/application-based-financial-services-and-investor-behaviour-in-investment-management-practices-a-systematic-review-of-theoretical-insights-trends-and-future-directions
Mathematics has been an important part of human civilization since ancient times and has developed continuously with human progress. Early mathematical ideas emerged from practical needs such as counting, trade, land measurement, construction, and astronomy. Over time, these simple methods evolved into organized mathematical systems. Ancient civilizations such as Egypt, Mesopotamia, India, Greece, and China made significant contributions to mathematics. Egyptians used geometry in architecture and land surveying, while Mesopotamians developed numerical systems and astronomical calculations. Indian mathematicians introduced the decimal system and zero, which greatly advanced mathematical studies. Greek scholars transformed mathematics into a logical and theoretical subject through proofs and geometrical reasoning. During the medieval period, Arab and Islamic scholars preserved and expanded mathematical knowledge. They translated earlier works, developed algebraic methods, and promoted the exchange of scientific ideas across cultures. Their contributions strongly influenced European mathematics. The Renaissance period brought major developments such as analytical geometry and calculus, leading to rapid scientific and technological progress. In the modern era, mathematics has become essential in engineering, medicine, economics, computer science, artificial intelligence, and space research. It supports scientific discoveries, technological innovation, and problem-solving in everyday life. The historical development of mathematics shows how civilizations and scholars contributed to its growth over centuries. Understanding this evolution helps us appreciate the importance of mathematics in shaping modern society and future advancements.
Traditional auditing processes are inefficient and produce low-quality audit reports due to human intervention. This research project constructs a novel automated auditing architecture based on smart contracts, comprising four functional modules: (i) data acquisition, (ii) rule encoding, (iii) execution verification, and (iv) report output. This paper demonstrates how to achieve a high-throughput, low-latency, and verifiable automated auditing system by utilizing technologies such as multi-source data cross-validation, formal encoding of audit rules, privacy protection based on zero-knowledge proofs, and cross-chain communication. The developed novel auditing process can shorten the traditional audit cycle to 8 to 15 days, reduce manual operation costs by 37.5% to 44.4%, reduce the error rate to 0.2% to 0.5%, and exhibit high fault tolerance during disaster recovery, making it an effective approach to achieve digital transformation of auditing processes.
In response to the difficulty of balancing privacy protection and system efficiency in energy data trading, this article analyzes the limitations of existing methods: static pseudonym mechanisms can easily lead to long-term identity link risks, traditional zk-SNARKs schemes have high computational overhead, and Raft consensus mechanisms lack robustness in adversarial environments. To address the above challenges, an integrated privacy protection scheme based on dynamic pseudonyms and lightweight zk-SNARKs is proposed. This scheme breaks the temporal correlation of transactions through a dynamic pseudonym generation mechanism, uses blockchain level batch processing proofs to reduce the computational and storage overhead of zero knowledge proofs, and introduces an LSTM based node health assessment model and incremental log synchronization mechanism to enhance the error tolerance and synchronization efficiency of the Raft consensus algorithm. The experimental results show that the proposed scheme outperforms traditional methods in terms of privacy, transaction processing performance, and system availability, effectively achieving a balance between privacy protection and operational efficiency, and providing a feasible technical path for energy data trading.
This study addresses the growing importance of cryptocurrency (CC) as a financial asset and its increasing popularity as an investment option. Given the rapid expansion of research in this field, the main objective is to systematically synthesize the existing literature on cryptocurrency investment and decision-making, focusing on its intellectual structure, dominant themes, theoretical foundations, and emerging research trends. To achieve this, a hybrid review framework is employed, combining a theory-based systematic literature review with bibliometric analysis, following PRISMA guidelines. The analysis covers 1,184 articles indexed in Scopus and published between 2015 and 2025. Additionally, the study integrates the TCCMR, ADO, and PICO frameworks to provide a comprehensive, multidimensional evaluation of the selected body of literature. The findings reveal that cryptocurrency research is predominantly focused on volatility, market connectedness, portfolio diversification, and behavioral aspects of investment. The results also indicate a strong reliance on econometric and predictive modeling approaches. Emerging research directions highlight increasing attention to sustainability concerns, regulatory challenges, and the application of artificial intelligence in investment analytics. Based on these insights, the study proposes a future research agenda emphasizing theoretical integration, methodological diversification, sustainability perspectives, and decision-oriented modeling. The implications of the research are relevant for investors, regulators, and financial institutions, as they provide a deeper understanding of risks, governance, and decision-making processes in cryptocurrency markets. This study contributes to the literature by offering a comprehensive knowledge structure and research roadmap, representing one of the first attempts to combine bibliometric mapping with TCCMR, ADO, and PICO frameworks in the context of cryptocurrency investment research.
Marzanna Poniatowicz, Paweł Konopka, Agnieszka Piekutowska
The paper refers to the twelve principles of fiscal decentralization, proposed by Roy Bahl (Bahl’s Rules for Fiscal Decentralization – BRfd), with special emphasis on the first Bahl’s rule (regarding the comprehensiveness of the fiscal decentralization, CSfd). The purpose of the research was to estimate the level of CSfd in OECD countries. Thus, the objective was to construct a synthetic measure of the first Bahl’s rule (CSfd) which constitutes the value added of this research. The goal was also to rank countries as the main idea was to advance empirical assessment in the field of fiscal federalism and to provide practical guidance for shaping public finance policy including policies on the collection of public revenues and the allocation of public expenditure. This directly corresponds to the research problem of the very limited number of tools available for measuring fiscal decentralization. Based on Hellwig’s method of linear ordering two rankings were developed using the Euclidean metric and the Mahalanobis metric. The results indicate that the most comprehensive fiscal decentralisation systems in 2022 were found in the Slovak Republic, Switzerland and Canada (ranking based on the Mahalanobis metric). When the Euclidean metric is used, Switzerland emerges as the leader, followed by Spain and Austria. First published online 4 June 2026
О. М. Литвинов, О. В. Чуприна, В. О. Гребеніков, М. В. Чуприна
The article explores the concept of containerized mobile hubs as an innovative solution for the infrastructural support of autonomous operations integrating civil aviation and civil unmanned aerial vehicles (UAVs). The relevance of transitioning to flexible, decentralized, and highly automated solutions is substantiated in the context of the development of Advanced Air Mobility (AAM) and Urban Air Mobility (UAM), which require new approaches to ground infrastructure organization. The key problem is identified as the infrastructure gap between the rapid advancement of UAV technologies and the limited capabilities of traditional aeronautical systems.A concept of a mobile hub based on a standardized ISO container (in particular, High Cube or refrigerated type) is proposed. The hub performs the functions of power supply, dispatching, communication, maintenance, and charging of exclusively civil and commercial UAVs. The hub is considered as an intelligent node integrated with civil UTM/U-space systems, ensuring the coordination of manned and unmanned flights within a unified digital airspace. The scope of practical application of the complex is outlined, encompassing environmental monitoring, support for humanitarian missions, and civil protection operations. Existing commercial "drone-in-a-box" solutions are analyzed, and their limitations are identified, including narrow functionality and insufficient mobility.Special attention is paid to the technical aspects of hub implementation, including container design selection, climate control, energy efficiency, and rapid deployment capabilities in environments where stationary infrastructure is absent or damaged. It is demonstrated that the proposed approach significantly reduces operational costs and enables continuous 24/7 operations of civil UAVs.It is concluded that containerized mobile hubs can become a key element in forming a new decentralized aeronautical infrastructure for civil aviation, particularly in the context of the peaceful recovery of Ukraine's transport sector, providing rapid deployment, versatility, and a high level of autonomy.
Open access
UAV Applications and Optimization
Military Technology and Strategies
Advanced Control and Stabilization in Aerospace Systems
In response to problems such as a lack of trust, low resource utilization rates, conflicts due to multiple constraints, and security risks associated with sharing 5G wireless access network resources, this study proposes an efficient, trustworthy, and secure distributed resource sharing system and optimizes the resource allocation strategy. First, it performs virtual decoupling and atomic modeling for the three core computing resources: spectrum, security, and computing power. It also designs a five-layer distributed resource-sharing framework that integrates blockchain and software-defined networks. Additionally, it proposes an improved delegated proof-of-stake consensus mechanism, as well as an asymmetric encryption transaction authentication and resource status traceability mechanism. Second, for the multi-constraint conflict issue, it designs a multi-agent deep deterministic strategy gradient secure resource allocation algorithm integrating long-term and short-term memory state prediction. The verification experiments were carried out based on the 5G-RAN public resource scheduling dataset in accordance with the 3GPP TR38.901 protocol specification. The experimental hardware was equipped with Intel Core i9-13900K processor, NVIDIA RTX 4090 graphics card, etc. The simulation platform was built on the Ubuntu 22.04 LTS system using the PyTorch 2.1.0 deep learning framework and the NS-3 3.36 simulation tool. The comparison benchmarks were mainstream centralized resource allocation schemes, blockchain, federated deep reinforcement learning schemes, and consortium chain hierarchical cross-slice schemes. The experimental results showed that the resource utilization rate of this framework reached 89.3%, the transaction delay was only 21.8 ms, the service quality satisfaction and security compliance rate were 96.7% and 98.2% respectively, the double-spend attack resistance rate and resource status traceability accuracy rate both reached 99.9%, and all related indicators were significantly superior to the existing comparison schemes. This study provided technical support for 5G resource collaboration in scenarios such as industrial internet and vehicle networking, effectively solving the trust bottleneck and scheduling problems in distributed environments. However, the research has not fully considered the adaptability of resource scheduling in extreme network environments. The computational power consumption of the algorithm in large-scale node deployment scenarios must be optimized further. The computational cost of the blockchain and multi-agent deep reinforcement learning components is high. Additionally, the system’s scalability in ultra-dense 5G scenarios must be improved. To a certain extent, this framework’s immediate large-scale practical application in complex 5G network environments is limited.
The emergence of Agentic Artificial Intelligence (AI) marks a significant shift from passive generative systemsto autonomous agents capable of reasoning, planning, and executing actions with minimal human intervention.These agents increasingly interact with external tools, APIs, cloud resources, and software environments, enablingadvanced automation across domains such as software engineering, cybersecurity, business operations, andscientific research. However, the ability of AI agents to generate and execute code autonomously introducessubstantial security challenges, including unauthorized resource access, privilege escalation, prompt injectionattacks, malicious code execution, data leakage, and supply chain vulnerabilities. Traditional security mechanismsdesigned for human-operated applications are often insufficient to address the dynamic and autonomous nature ofagent-driven execution environments.This study proposes a distributed and sandboxed execution architecture for securing autonomous AI agentsthrough the integration of WebAssembly (Wasm), container runtimes, and Kubernetes-based orchestration. Theproposed framework adopts a defense-in-depth approach that isolates AI-generated actions within lightweightWasm sandboxes while leveraging Kubernetes for scalable workload management, policy enforcement, resourcegovernance, and runtime monitoring. By combining cloud-native technologies with secure execution principles,the architecture aims to minimize attack surfaces, contain potentially harmful agent behaviors, and provideauditable execution pathways for autonomous operations.A design science research methodology is employed to develop and evaluate the conceptual framework. Thearchitecture is analyzed against common threat scenarios associated with Agentic AI, including code injection,unauthorized system interactions, and compromised execution modules. The findings indicate that WebAssemblybased sandboxing offers stronger isolation and reduced overhead compared to traditional virtualizedenvironments, while Kubernetes enhances scalability and operational resilience. The study contributes a vendorneutral security model for autonomous AI systems and provides practical guidance for organizations seeking todeploy trustworthy, secure, and scalable Agentic AI infrastructures. Future research directions includeconfidential computing integration, adaptive policy engines, and decentralized security frameworks for multiagent ecosystems.
The Indus Valley Script (IVS) has long resisted definitive decipherment due to its extreme brevity (averaging 4.6 glyphs per inscription), the absence of a bilingual parallel text, and lingering uncertainty regarding its underlying linguistic family. This study presents a mathematically validated, globally optimized decipherment of the Harappan corpus by deploying our Unified Discovery and Inference Architecture (UDIA)—a five-layer recursive reasoning framework that decouples model generation, contextual probability, and structural self-critique. Treating the script as a high-density administrative parameter space, our findings reveal that the script did not record narrative prose, but instead functioned as a decentralized, physical Distributed Ledger System governing an algorithmic framework of Astro-Jurisprudence. Indus inscriptions served as time-locked legal contracts—Astro-Temporal Permits—valid only when economic transactions aligned with precise celestial windows. Global optimization and spectral analysis validate this model against a high-status administrative dialect of Proto-Dravidian, demonstrating exceptional structural, phonetic, and morphosyllabic alignment with the South Dravidian branch. Statistical validation yields a Zipf’s Law correlation of $r = 0.98$ (slope of $-1.02$) and an organic token-distribution entropy of 3.41 bits/token. By resolving the "Universal Solvent Paradox" through a strict Dual-Track Shuffled Permutation Audit under zero-guidance parameters ($\gamma = 0, \beta \to \text{Static}$), this framework establishes an unassailable mathematical standard that isolates genuine historical convergence from engineered statistical alignments, fundamentally transforming our understanding of Bronze Age legal systems.
LoisID proposes a portable trust and reputation infrastructure designed to enable individuals and organizations to accumulate, verify, and transport trust across digital ecosystems. The framework extends beyond identity verification and introduces a reusable trust layer for finance, education, employment, governance, and Web3 environments. By transforming trust into a portable and interoperable digital asset, LoisID seeks to address reputation fragmentation and establish a foundation for the next generation digital economy.
Recent innovation theories on economics remain largely grounded in assumptions of hierarchical firms and closed organizational boundaries, offering limited insight into how innovation unfolds within decentralized, digitally native organizations. Decentralized Autonomous Organizations (DAOs) represent an emerging form of innovation ecosystem characterized by blockchain-based transparency, open participation, and token-driven governance, in which sustainability can be embedded directly into organizational design. This study compares two standards, ERC-8004 and Google A2A, who address the same agent interoperability question, while the former is governed by DAO and the latter by corporation consortium. They are examined through an LLM-powered comparative pipeline for large-scale governance discourse analysis, integrating automated annotation, neural topic modeling, and multi-layer network analysis to study socio-technical power structures. The study provides evidence-based insights for scholars, policymakers, and designers seeking to align innovation, technological governance, and sustainability in future organizational forms.
Sequential trust detection in rating networks relies on continuous observation models that fail on real data. On Bitcoin-OTC, 56\% of ratings take a single value under standard mapping, breaking the distributional assumptions that parametric detectors require. This paper makes three contributions. It derives a Bayes-optimal F1 detection ceiling for per-node sequential detectors using empirically measured observation parameters. At Bitcoin-OTC's median in-degree of 2, this ceiling falls to 0.451 for strategic attacks, explaining why unsupervised methods cluster near $F1 \approx 0.4$. The analysis shows that detector-model matching, not information content, determines performance: binary models retain 86\% of mutual information while enabling exact parametric fit. A dual-regime architecture is presented where Bernoulli CUSUM detects behavioral shifts and triggers asymmetric scoring. Ablation reveals a co-design constraint: the modulation mechanism improves AUC by 0.030 on binary observations but degrades it by 0.094 on continuous observations. The combined system achieves AUC 0.749 on Bitcoin-OTC and 0.796 on Bitcoin-Alpha, beating GaaSTrust on all 8 attacks ($p < 0.003$), with founder-label AUC of 0.999.
Ju Yang, Weili Wang, Jianyu Niu, Jianzong Wang · 5 authors
Confidential blockchains leveraging Trusted Execution Environments (TEEs) have garnered extensive attention for transaction confidentiality. In this paper, we first taxonomize two classes of attacks against confidential blockchains, i.e., execution-inference and execution-replay attacks, which exploit TEEs' long-lasting side-channel and state-continuity issues to compromise the confidentiality of existing consortium blockchains. Then, we present ODYSSEY, a confidential blockchain that efficiently mitigates these attacks. The core innovations of ODYSSEY are the following: (1) Its delegation model: clients delegate transaction execution to their designated trustees, while other participants synchronize only the execution results, which significantly reduces the attack surface while preserving confidentiality and system performance. (2) Two novel techniques to improve ODYSSEY's efficiency and security: location-aware concurrent execution and delegation failure handler. Finally, we develop a prototype of ODYSSEY on FISCO BCOS, an enterprise-grade consortium blockchain platform. We have conducted various experiments, and our evaluation results show that in a WAN environment with 3 nodes, ODYSSEY can achieve about 4k throughput while keeping latency as low as 0.4-0.5s.
Pearl, a Layer-1 blockchain with high-profile AI industry endorsements, markets its Proof-of-Useful-Work (PoUW) protocol as simultaneously securing the network and performing AI inference. We present the first systematic empirical measurement of a deployed PoUW system, finding that Pearl's 24 EH/s network -- representing approximately 320,000 GPU-equivalents consuming an estimated 112 MW -- produces zero useful AI computation. Budget GPU rental prices rose 38% and utilization surged from 57% to 94% following the mining software's public release, displacing legitimate research workloads. Our measurements span five dimensions: (1) network composition analysis of 8,012 workers shows all have inference-capable hardware, yet the dominant mining software contains no inference code; (2) the verification protocol accepts random matrices by design, confirmed by 44 pool-accepted shares from our open-source miner across NVIDIA, AMD, CPU, and Apple Silicon hardware; (3) statistical distribution checks are trivially defeated by adversarial Gaussian sampling; (4) mining economics are marginal at current PRL prices ($0.76), with ROI ranging from -1% to +67% depending on GPU tier -- near breakeven for most hardware; and (5) the mining computation is commodity integer arithmetic portable to any hardware platform, offering no vendor lock-in. These findings quantify the verifiability-usefulness tension identified theoretically by Leinweber et al., providing concrete measurements of its magnitude and economic consequences in a deployed system.
This study aims to determine whether the application of Deep Reinforcement Learning (DRL) as a specialized execution overlay can enhance pair trading in highly volatile cryptocurrency markets. Although classical implementations of the strategy have proven successful in traditional equities, they frequently exhibit rigidity and suffer from severe divergence risks when applied to high-variance environments. To address this need, this research introduces novel concepts. To construct a robust system, we developed a hierarchical "Filter-then-Rank" pair selection methodology and a proprietary "Fixed Risk, Adaptive Mean" execution model. The system employs a Proximal Policy Optimization (PPO) agent with a Long Short-Term Memory (LSTM) layer to govern execution decisions within strict deterministic risk management boundaries. Evaluated on 1-hour interval data from the Binance USD-M Futures market, the optimized RL policy achieved an out-of-sample performance that substantially outperformed the heuristic baseline. A stationary circular block bootstrap robustness check confirms that the agent's risk-adjusted outperformance is statistically significant at the 10 percent level. Although falling marginally short of the stricter 5 percent threshold, this result highlights the extreme idiosyncratic variance characteristic of digital assets. Ultimately, this thesis contributes to the quantitative finance literature by introducing a hybrid architecture that combines statistical arbitrage with DRL execution policies. Furthermore, it delivers a novel framework for safe reinforcement learning via deterministic shielding, proving that anchoring a neural policy to statistically robust boundaries successfully mitigates severe divergence risks.
Muhammad Hadi, Muhammad Jahangir, Talha Shafique, Muhammad Khuram Shahzad
Federated Learning (FL) has emerged as an effective paradigm for collaborative intelligence while preserving data privacy. However, data heterogeneity arising from non-IID distributions and decentralized security threats remain significant challenges, particularly in resource-constrained enterprise environments. This paper presents TITAN-FedAnil+, a Trust-Based Adaptive Network for blockchain-enabled federated learning in intelligent enterprises. The proposed framework introduces affinity propagation-based adaptive clustered aggregation to identify and filter malicious updates without requiring prior knowledge of the number of attackers. In addition, GPU-accelerated vectorization is employed to improve computational efficiency, while a signed state jump mechanism enables lightweight blockchain resynchronization. Experimental results demonstrate substantial reductions in memory overhead, achieving up to 81% savings across 50 communication rounds on constrained 8 GB edge devices compared with the baseline framework. The results indicate that TITAN-FedAnil+ effectively improves robustness, scalability, and resource efficiency for secure federated learning deployments in intelligent enterprise environments.