Guang Yang, Peter Trinh, Alma Nkemla, Amuru Serikyaku · 6 authors
The current Domain Name System (DNS) infrastructure faces critical vulnerabilities including poisoning attacks, censorship mechanisms, and centralized points of failure that compromise internet freedom and security. Recent incidents such as DNS poisoning attacks on ISP customers highlight the urgent need for resilient alternatives. This paper presents a novel blockchain-based Decentralized Domain Name System (DDNS). We designed a specialized Proof-of-Work blockchain to maximize support for DNS-related protocols and achieve node decentralization. The system integrates our blockchain with IPFS for distributed storage, implements cryptographic primitives for end-to-end trust signatures, and achieves Never Trust, Always Verify zero-trust verification. Our implementation achieves 15-second domain record propagation times, supports 20 standard DNS record types, and provides perpetual free .ddns domains. The system has been deployed across distributed infrastructure in San Jose, Los Angeles, and Orange County, demonstrating practical scalability and resistance to traditional DNS manipulation techniques. Performance evaluation shows the system can handle up to Max Theor. TPS 1,111.1 tx/s (minimal transactions) and Max Theor. TPS 266.7 tx/s (regular transactions) for domain operations while maintaining sub-second query resolution through intelligent caching mechanisms.
André Davi Lopes, Tais Mello, Wesley dos Reis Bezerra
This paper presents the development of a distributed digital identity system utilizing modern technologies, including FastAPI, MongoDB, gRPC, Docker, and blockchain simulation with Ganache and Ethereum. The objective is to demonstrate the benefits of distributed systems and blockchain for the security, traceability, and decentralization of digital identities. The methodology included the development of a microservices architecture with JWT authentication, data persistence in MongoDB, simulation of blockchain operations using Ganache, and containerization with Docker. The results demonstrate the feasibility of the proposed approach, with a functional web interface, complete audit logs, and blockchain simulation with Ethereum. The theoretical foundations, technical implementation, results obtained, and prospects for integration with real blockchain networks are discussed.
Many industries, including banking, government, energy, healthcare, etc., have taken an interest in blockchain technology since its inception in the last decade. A comprehensive overview of blockchain's potential uses in the healthcare industry is provided in this article. Research in this field is indeed progressing at a breakneck pace. Thus, we have discovered several cutting-edge applications of blockchain technology, such as medicine supply chain management, electronic medical record sharing, remote patient monitoring, etc. We have also highlighted the shortcomings of the methods that have been examined, and we have wrapped off by delving into some unanswered questions and potential spots for more study. The new Internet of Things applications in Industry 4.0 include blockchain technology, which is immutable, cryptographically secure, distributed ledgers, and a component of decentralized systems. Various entities or parties maintain and distribute exact copies of a succession of transaction lists using this technology. Blockchain technology's capacity to link disparate systems and improve the accuracy of electronic health data, together with a patient-centric approach to healthcare, make it an area with enormous promise.
Purpose. The purpose of this article is to conduct a philosophical-legal analysis of the evolving capabilities and structural constraints within smart contracts, viewed through the lens of practical philosophy. This analysis seeks to comprehend the contradictory nature of the digital transformation of legal reality.Design / Method / Approach: This work employs an interdisciplinary approach, integrating philosophical-legal analysis with elements of the dialectical method and critical research on technological innovations. The methodological foundation comprises concepts of practical philosophy, the theory of legal autonomy, and approaches from the critical theory of technology.Findings. The study reveals the evolving nature of smart contracts, which manifests in the simultaneous expansion of opportunities through the elimination of institutional intermediaries and the creation of new structural limitations via algorithmic rigidity. It is established that technological liberation from traditional forms of dependence generates qualitatively new forms of technological dependence. Furthermore, it is shown that technical complexity creates new forms of legal inequality between individuals with varying levels of technical literacy.Theoretical implications. The research results contribute to the development of philosophical-legal theory in the digital age by conceptualizing the dialectical nature of technological transformations in law, thereby enriching the understanding of the contradictions between technological efficiency and legal flexibility.Practical implications. The findings provide a theoretical basis for developing balanced regulatory approaches to smart contracts that account for their contradictory nature and for formulating principles of responsible design of technological systems.Originality / Value. A dialectical approach is proposed for analyzing smart contracts as a unity of opposites, where the expansion of opportunities and structural limitations mutually condition each other within a single technological phenomenon. The conceptual framework is expanded for understanding the contradictory nature of the digital transformation of legal reality through the prism of practical philosophy.Research limitations / Future research. Further in-depth research is needed on the philosophical-legal aspects of determinism in the execution of smart contracts, including an analysis of the transformation of legal temporality and issues of unforeseen circumstances in algorithmic systems.Paper type. Theoretical.
Purpose:The rapid growth of cryptocurrencies has revealed a significant disconnect between speculative digital assets and the ethical principles of Islamic finance.Bitcoin's volatility, three to four times higher than traditional equity indices, along with its energy-intensive mining process, directly contradict Shariah principles emphasizing stability, asset-backing, and minimization of gharar (excessive uncertainty).This study addresses the gap between blockchain technology's potential and the requirements of Islamic financial systems by proposing and empirically testing a Shariah-compliant digital finance model.Design/Methodology/Approach: A mixed-method approach was employed, integrating a PRISMA-guided systematic literature review, panel data analysis of 100 fintech firms from 2018 to 2024, and Monte Carlo simulation.Fixed-effects regression was used to assess the impact of blockchain adoption on financial performance (ROA, ROE) in both Islamic and conventional fintech firms.The simulation evaluated the efficiency of a Hybrid Shariah Blockchain Model for tokenized waqf (Islamic endowment) operations.Findings: Results indicate that blockchain adoption significantly improves financial performance in Islamic fintech (ROA: = 0.023, t = 3.41; ROE: = 0.067, t = 2.79).Simulation results demonstrate a reduction in transaction latency from 3.2 days to 12.4 seconds (95% CI: 10.1-14.7),complete auditability, and transaction costs below $1.00 per operation.Practical Implications: Policy recommendations include regulatory sandboxing and institutional integration strategies to mainstream Shariah-compliant blockchain applications.Originality/Value: This study presents the first simulation-based validation of waqf blockchain governance grounded in Islamic jurisprudence, offering a scalable framework for ethical, decentralized financial services benefiting 1.8 billion Muslims worldwide.
Fake cheque scams remain a pressing financial concern, leading to substantial monetary losses and legal challenges. The absence of real-time authentication mechanisms often results in delayed scam detection by financial institutions. This report presents a mobilebased blockchain system using wireless communication and distributed ledger technology to authenticate cheques in real-time and prevent fraud. Our system integrates Namecoin, SHA-256 hashing, and elliptic curve digital signature algorithm (ECDSA) into a secure mobile computing environment to enable accessibility and scalability. The system has two significant operational phases: cheque issue and authentication. When issued, banks retain cheque information on the blockchain using Lagrange polynomials, and aggregation is achieved rapidly. Authentication at the point of withdrawal confirms the cheque as valid by verifying blockchain-stored data, preventing reuse and forgery. This framework helps achieve financial inclusion through the enabling of ubiquitous mobile access to secure cheque authentication services, resulting in cost-effective, real-world applications. By virtue of applying mobile technology infrastructures and safe wireless networks, the solution not only enhances transaction safety but also adheres to the changing trends in adaptive digital finance and industrial applications. Mobile apps facilitate users to scan and verify and get instant fraud alerts, highly promoting accessibility, especially for rural dwellers.
Adolf Z. D. Siahay, Entis Sutisna, Jana Siti Nor Khasanah, Agus Dwianto
This research focuses on the influence of legal, managerial, political, and social variables (in this case, legal clarity, management accounting practices, elite capture, and community involvement) on governance performance in the context of special fiscal autonomy in Papua. We also investigate how Digital Fiscal Monitoring Systems (DFMS) attenuate these relationships. Quantitative survey; SPSS for regression and moderation analysis. Legal clarity and accounting transparency, and popular planning, positively affect governance, whereas elite capture has a negative influence on them. DFMS strengthens the positive impact of good governance and reduces the adverse effects of elite control. The interaction model of both shows that governance performance maximally increases if all institutional elements are synergized under digital monitoring. This paper offers an integrated governance framework of asymmetrical decentralization and positions DFMS as a moderator and an underdeveloped point of view in Indonesia’s subnational governance context. For Papua, the lessons are that the political process of passing laws must be accompanied by participatory planning and digital monitoring to ensure that autonomy meets its promise. This study has global implications for other poor or post-conflict areas that are contemplating such differentiated patterns of decentralization supported by digital public finance instruments.
The applicability of the in-situ pyrolysis of oil-rich coal is highly dependent on regional geological conditions. In this study, six major geological factors and 19 key parameters influencing the in-situ pyrolysis of oil-rich coal were systematically identified. An analytic hierarchy process incorporating index classification and quantification was employed in combination with the geological features of the Tiaohu mining area to establish a feasibility evaluation index system suitable for in-situ development in the study region. Among these factors, coal quality parameters (e.g., coal type, moisture content, volatile matter, ash yield), coal seam occurrence characteristics (e.g., seam thickness, burial depth, interburden frequency), and hydrogeological conditions (e.g., relative water inflow) primarily govern pyrolysis process stability. Surrounding rock properties (e.g., roof/floor lithology) and structural features (e.g., fault proximity) directly impact pyrolysis furnace sealing integrity, while environmental geological factors (e.g., hazardous element content in coal) determine environmental risk control effectiveness. Based on actual geological data from the Tiaohu mining area, the comprehensive weight of each index was determined. After calculation, the southwestern, central, and southeastern subregions of the mining area were identified as favorable zones for pyrolysis development. A constraint condition analysis was then conducted, accompanied by a one-vote veto index system, in which the thresholds were defined for coal seam thickness (≥1.5 m), burial depth (≥500 m), thickness variation coefficient (≤15%), fault proximity (≥200 m), tar yield (≥7%), high-pressure permeability (≥10 mD), and high-pressure porosity (≥15%). Following the exclusion of unqualified boreholes, three target zones for pyrolysis furnace deployment were ultimately selected.
This paper explores the causal relationship between the U.S. trade policy uncertainty and cryptocurrency returns using the quantile Granger causality test. Unlike traditional approaches that focus on average effects, this method captures asymmetric causal dynamics across the entire conditional distribution. The analysis employs two established indices of trade policy uncertainty developed by Caldara et al. (2020) and by Baker et al. (2016), ensuring robustness and mitigating potential biases from relying on a single measure. The empirical results indicate that changes in cryptocurrency prices consistently Granger cause movements in trade policy uncertainty across most quantiles, suggesting that cryptocurrencies may serve as early indicators of shifts in economic policy sentiment. In contrast, the effect of trade policy uncertainty on cryptocurrency returns is most pronounced in the tails of the distribution, highlighting a stronger influence during periods of extreme market conditions. These findings highlight the importance of accounting for nonlinear and asymmetric effects in assessing the interaction between economic policy uncertainty and cryptocurrency markets.
The rapid development of generative artificial intelligence (GAI) has sparked worldwide debates on how copyright law should respond to the challenges it has raised. In Hong Kong (HK), this conversation has taken centre stage in the recently published Consultation Paper on Copyright and Artificial Intelligence.1 With the aim of providing the HK legislator with a complete picture of the global debate, the School of Law at City University of Hong Kong (CityUHK) held an international conference entitled ‘Comparative Perspectives on AI and Copyright Law: Evaluating HK’s Policy Responses in the AI Era’ on 12–13 December 2024. The conference gathered international legal scholars, practitioners and policymakers to examine how copyright law and policy can properly respond to the AI challenges. This special issue, ‘AI and Copyright upgrate’, arises from that conference and presents six selected papers that together illuminate how copyright regimes can be updated for the AI era. Each contribution addresses a distinct facet of the GAI-copyright interface: the overall impact, copyrightability, infringement, intermediary liability, automated copyright enforcement and remuneration and inequality. Together, they offer insights into doctrinal rethinking, policy innovation and the fundamental values at stake. The issue opens with Daryl Lim’s article, which sets an ambitious tone by examining the extractive dynamics of GAI and their impact on core copyright assumptions. Lim deploys a vivid metaphor—Maurizio Cattelan’s Comedian (the infamous banana duct-taped to a wall)—to illustrate how GAI’s rise exposes structural inequalities in the creative economy. Lim highlights the extractive practices by which AI developers leverage vast amounts of human-created work without due credit or compensation, thereby amplifying existing power disparities between tech companies and individual creators. Lim argues that these inequities call for a recalibration of copyright law: rather than viewing AI as a neutral tool, the law must recognize and address the imbalance it creates. His contribution sets an equity-focused agenda for copyright reform, suggesting that any legislative responses must account for fairness to human artists and authors in an AI-driven marketplace. By rethinking foundational assumptions, Lim’s piece compellingly frames the normative stakes of AI’s impact on copyright and sets the stage for the more targeted analyses that follow. Following this broad structural critique, Chen Yang’s article turns to the issue of copyrightability of AI-generated content (AIGC). The focus is on HK’s ‘computer-generated work’ (CGW) doctrine under the HK Copyright Ordinance (HKCO), casting a critical eye on its ability to properly cover AIGC. Chen analyses HKCO, which the government asserts already, provides a backbone of copyright protection for AIGCs. Chen challenges this optimistic view by unpacking the doctrine’s limitations and the questionable assumptions behind it. In particular, he questions whether traditional requirements like originality or the so-called ‘necessary arranger’ rule can seamlessly extend to AIGCs by comparing the UK experiences. His paper argues that, without careful reconsideration, simply relying on the existing CGW framework is insufficient. While an overhaul may not be imminent, Chen’s piece underscores the need for a more nuanced approach if HK’s copyright regime is to truly harness AI’s creative opportunities. In his paper, Jiawei Zhang focuses on the much-debated issue of the potential copyright infringement risk of training AI using copyrighted works. He advocates a fundamental shift in regulatory perspective from inputs to outputs in the context of AI and copyright. Zhang argues that current debates fixate too much on the input side—the masses of copyrighted works ingested to train AI models—instead of focusing on the output—the contents that AI systems generate. He argues that an output-oriented approach would better calibrate copyright law to the realities of GAI. By judging AIGC on its own merits (for instance, whether an output unlawfully reproduces copyrighted works), policymakers can move away from abstract concerns over training data and towards concrete criteria for copyright infringement determination. This shift, he suggests, would lead to more balanced outcomes: it preserves incentives for human creativity while still allowing AI technology to flourish under clearer rules. The next article by Taorui Guan and Yang Lin tackles the issue related to the safe-harbour regimes for internet intermediaries. Their paper examines whether the safe-harbour regimes can be upgraded to accommodate the challenges raised by GAI through role-specific obligations. They note that the traditional Digital Millennium Copyright Act (DMCA)-style safe harbour—where internet services avoid liability by promptly removing infringing user uploads—does not translate neatly to AI systems, which do not store content in discrete files that can simply be taken down. To resolve this, they envision a reconfigured framework assigning tailored responsibilities to different players in the AI ecosystem. For example, AI model developers, platform providers and end-users would each have defined duties (such as monitoring, transparency or responsiveness to complaints) commensurate with their role in generating or disseminating AI content. This differentiated safe-harbour regime aims to maintain the DMCA’s innovation-friendly spirit while strengthening accountability: it would continue to shield good-faith innovators from crippling liability, but only on the condition that they proactively mitigate copyright risks appropriate to their function. Their contribution thus sketches a blueprint for legal reform that balances the protection of rights with the realities of AI-driven services. Connected to the previous article about intermediaries, Jesse Lu’s article focuses on the issue of platform governance and enforcement, criticizing the emerging trend of automated copyright moderation. He observes that, as platforms increasingly deploy algorithmic tools (like content filters and copyright bots) to police infringement, these systems often operate with minimal transparency or oversight. Lu argues that such ‘black box’ enforcement can erode due process: users may find their content removed or accounts penalized without a clear explanation or meaningful opportunity to appeal. Moreover, vesting quasi-regulatory power in private algorithms, he suggests, creates an accountability gap—one where corporate interests and error-prone AI can trump lawful user activities (eg, parody) with little recourse. To counter this, Lu calls for stronger regulatory checks on automated enforcement, including requirements for transparency in how infringement decisions are made and avenues for users to challenge wrongful removals. His piece underscores that any upgrade of copyright law in the AI era must not unfairly sacrifice individual rights and freedoms; on the contrary, it should impose ‘algorithmic accountability’ so that efficiency in enforcement does not come at the expense of fundamental rights and public interests. His contribution thus injects a note of caution: even as we adapt laws to govern AI, we must also govern the use of AI in law enforcement itself, keeping fundamental rights and values in sight. Rounding out the special issue, Rostam Neuwirth offers a provocative reframing of the entire AI-and-IP debate by shifting our focus to the overarching issue of global inequality. He argues that current discussions about AI and copyright—from questions of AI authorship to liability for AI-induced infringement—are missing the forest for the trees. The more pressing concern, in Neuwirth’s view, is that GAI is contributing to a widening global gap between those who control technology and the creative labour force that fuels it.2 Interestingly, but not surprisingly, his view echoes Lim’s from a different angle. He calls for rediscovering IP law’s original purpose of rewarding creators: rather than merely tweaking doctrines at the margins, the law should be reoriented to ensure that human creativity is justly compensated when AI systems become increasingly dominant. This could entail new legal mechanisms or reforms that guarantee authors a share in the value derived from AI’s use of their works, thereby preventing what he describes as the ‘plenty’ of AI’s output from making human creators ‘poor’. Neuwirth’s contribution, broad in scope and principle, ties together the theme of this special issue by reminding us that the ultimate goal of any AI-related copyright upgrade should focus on building a more equitable creative ecosystem. Together, these six articles demonstrate the multi-dimensional effort required to ‘upgrade’ copyright for the AI era. They range from rethinking fundamental doctrines (authorship and originality), to proposing new legislative and regulatory frameworks (for copyright infringement and for intermediary liability), to cautioning against unintended consequences of enforcement technologies and finally to re-centring the discussion on fairness and societal impact. Several common threads emerge. One is the importance of balance—balancing incentives for innovators with protection for creators, balancing the benefits of AI’s openness with the rights of those whose works are used, and balancing enforcement of rights with preservation of user liberties and the public domain. Another recurring theme is adaptability: copyright law, often rooted in pre-digital assumptions, must evolve in light of AI’s unprecedented capabilities, whether by updating old rules or by devising novel policy tools. Crucially, the contributions also remind us that copyright does not operate in a vacuum. GAI’s challenges intersect with questions of technology governance, competition and social justice. An ‘AI and Copyright Upgrade,’ therefore, it is not simply about doctrinal analysis—it is about ensuring that the copyright system continues to encourage human creativity and innovation while promoting equity and the public good in this new technological landscape. We hope that the ideas presented in this special issue will inform and inspire policymakers, academics and industry leaders as they work towards a future-proof and fair copyright regime for the AI age. Acting as the guest editor of this special issue, I would like to extend my gratitude to all the authors for their insightful contributions and careful research that made this special issue possible. I also thank the Hong Kong Commercial and Maritime Law Centre under the CityUHK School of Law for supporting the conference, which provided the fertile ground for these wonderful discussions. My gratitude also goes to all the conference participants, including Peter Yu, Guobin Cui, Jyh-An Lee, Yahong Li and Orabhund Panuspatthna, who kindly presented their views and shared their valuable comments. Special thanks to my colleague Yang Chen, our centre secretary Claire Dibo Huang and my PhD students Lingjun Gao and Yiyan Zhang, who co-organized the conference with me, for their hard work in setting up all the details. We are additionally grateful to the editorial team of the Journal of Intellectual Property Law & Practice, especially editor-in-chief Prof. Eleonora Rosati and managing editor Ms. Sarah Harris, for providing the invaluable platform for us, and reviewers who provided valuable feedback and helped shape these papers into their final form. Finally, we acknowledge the support of our institutions and colleagues in fostering an environment where cutting-edge topics like AI and copyright can be rigorously explored. This collective effort has made the ‘AI and Copyright Upgrade’ special issue a reality, and we trust that it will provide useful suggestions for the HK legislators to consider and contribute meaningfully to the ongoing dialogue at the intersection of technology and copyright law.
Blockchain consensus algorithms face trade-offs between performance, security, decentralization, and energy efficiency. Proof-of-Work (PoW) ensures strong security but is energy-intensive. Proof-of-Stake (PoS) is efficient but may risk centralization. Byzantine Fault Tolerance (BFT) offers low latency but lacks scalability. This paper proposes an Adaptive Hybrid Consensus (AHC) algorithm that dynamically integrates PoW, PoS, and BFT elements. The AHC mechanism is designed for general-purpose blockchain environments and can adapt to real-time network conditions. AHC is a promising solution for next-generation blockchain systems as it has the potential to significantly improve latency, throughput, and energy consumption [13][14].
Urban areas in the Global South are at the forefront of the climate crisis, contributing over 70% of global CO2 emissions while lacking access to intelligent, transparent, and equitable carbon governance systems. Existing carbon markets, plagued by opacity, centralization, and static MRV (Monitoring, Reporting, and Verification) practices, are inadequate for dynamically managing decentralized, sectoral emissions in rapidly evolving megacities. This research proposes a novel, AI-powered carbon market intelligence framework that integrates cutting-edge technologies: Long Short-Term Memory (LSTM) networks, Graph Neural Networks (GNNs), Deep Reinforcement Learning (DRL), blockchain-enabled smart contracts, federated learning (FL), digital twins, and explainable AI (SHAP, LIME). The system is modular, privacy-preserving, and designed for real-time urban-scale decarbonization, adaptive policymaking, and citizen-level participation. Using Dhaka, Bangladesh, a climate-vulnerable megacity, as the primary use case, and Nairobi as a secondary scalability testbed, this study simulates a comprehensive pipeline: IoT sensors stream data to digital twins; AI models forecast emissions and carbon prices; smart contracts trigger transparent offset issuance; and federated models ensure localized learning without compromising data sovereignty. The system achieves high predictive accuracy (R2 > 0.92), 27.6% emission reductions in waste-energy sectors, and 12.3% gains in offset ROI over static baselines. Smart contract execution remains under 4.5 seconds, with negligible energy use under Proof-of-Stake blockchain. The explainability layer enhances stakeholder trust and policy interpretability, while gamified P2P carbon trading and participatory digital twins democratize climate action. The framework aligns with global instruments, including the UNFCCC Enhanced Transparency Framework, Article 6 mechanisms, Verra and Gold Standard protocols, and ICAO’s CORSIA, positioning it for integration into national and voluntary carbon markets. Ethical safeguards address algorithmic bias, data privacy, system resilience, and governance decentralization via DAOs. A full AI sustainability audit quantifies environmental trade-offs, demonstrating that avoided emissions exceed compute footprints by orders of magnitude. This paper delivers the first end-to-end, federated-AI and blockchain-driven carbon governance system for urban infrastructures in the Global South. It enables a paradigm shift toward real-time, transparent, and just carbon markets, offering a scalable blueprint for Net Zero-aligned smart cities worldwide. The proposed architecture not only advances scientific frontiers but also lays the groundwork for high-impact funding, policy integration, and global replication.
We introduce the first complete formal solution to corrigibility in the off-switch game, with provable guarantees in multi-step, partially observed environments. Our framework consists of five *structurally separate* utility heads -- deference, switch-access preservation, truthfulness, low-impact behavior via a belief-based extension of Attainable Utility Preservation, and bounded task reward -- combined lexicographically by strict weight gaps. Theorem 1 proves exact single-round corrigibility in the partially observable off-switch game; Theorem 3 extends the guarantee to multi-step, self-spawning agents, showing that even if each head is *learned* to mean-squared error $\varepsilon$ and the planner is $\varepsilon$-sub-optimal, the probability of violating *any* safety property is bounded while still ensuring net human benefit. In contrast to Constitutional AI or RLHF/RLAIF, which merge all norms into one learned scalar, our separation makes obedience and impact-limits provably dominate even when incentives conflict. For settings where adversaries can modify the agent, we prove that deciding whether an arbitrary post-hack agent will ever violate corrigibility is undecidable by reduction to the halting problem, then carve out a finite-horizon "decidable island" where safety can be certified in randomized polynomial time and verified with privacy-preserving, constant-round zero-knowledge proofs.
Fatou Ndiaye Mbodji, Mame Marieme C. Sougoufara, Wendkûuni A. M. Christian Ouedraogo, Alioune Diallo · 7 authors
Smart contract comment generation has gained traction as a means to improve code comprehension and maintainability in blockchain systems. However, evaluating the quality of generated comments remains a challenge. Traditional metrics such as BLEU and ROUGE fail to capture domain-specific nuances, while human evaluation is costly and unscalable. In this paper, we present evalSmarT, a modular and extensible framework that leverages large language models (LLMs) as evaluators. The system supports over 400 evaluator configurations by combining approximately 40 LLMs with 10 prompting strategies. We demonstrate its application in benchmarking comment generation tools and selecting the most informative outputs. Our results show that prompt design significantly impacts alignment with human judgment, and that LLM-based evaluation offers a scalable and semantically rich alternative to existing methods.ResourcesVideo Demo: https://youtu.be/HXS_Yiszoz4Code and Data: https://anonymous.4open.science/r/SC_code_summarization-4653
A widespread belief in the blockchain security community is that automated techniques are only good for detecting shallow bugs, typically of small value. In this paper, we present the techniques and insights that have led us to repeatable success in automatically discovering high-value smart contract vulnerabilities. Our vulnerability disclosures have yielded 10 bug bounties, for a total of over $3M, over high-profile deployed code, as well as hundreds of bugs detected in pre-deployment or under-audit code. We argue that the elements of this surprising success are a) a very high-completeness static analysis approach that manages to maintain acceptable precision; b) domain knowledge, provided by experts or captured via statistical inference. We present novel techniques for automatically inferring domain knowledge from statistical analysis of a large corpus of deployed contracts, as well as discuss insights on the ideal precision and warning rate of a promising vulnerability detector. In contrast to academic literature in program analysis, which routinely expects false-positive rates below 50% for publishable results, we posit that a useful analysis for high-value real-world vulnerabilities will likely flag very few programs (under 1%) and will do so with a high false-positive rate (e.g., 95%, meaning that only one-of-twenty human inspections will yield an exploitable vulnerability).
Due to the distributed nature of federated learning (FL), the vulnerability of the global model and the need for coordination among many client devices pose significant challenges. As a promising decentralized, scalable and secure solution, blockchain-based FL methods have attracted widespread attention in recent years. However, traditional consensus mechanisms designed for Proof of Work (PoW) similar to blockchain incur substantial resource consumption and compromise the efficiency of FL, particularly when participating devices are wireless and resource-limited. To address asynchronous client participation and data heterogeneity in FL, while limiting the additional resource overhead introduced by blockchain, we propose the Directed Acyclic Graph-based Asynchronous Federated Learning (DAG-AFL) framework. We develop a tip selection algorithm that considers temporal freshness, node reachability and model accuracy, with a DAG-based trusted verification strategy. Extensive experiments on 3 benchmarking datasets against eight state-of-the-art approaches demonstrate that DAG-AFL significantly improves training efficiency and model accuracy by 22.7% and 6.5% on average, respectively.
This paper presents MPC-EVM, the first blockchain prototype that extends the EVM to enable asynchronous MPC invocations by smart contracts during transaction executions without compromising consistency or throughput. MPC-EVM uses an asynchronous execution model to process MPC-invoking transactions in a non-blocking fashion, saving the transaction's progress when it enters an MPC and resuming its execution upon MPC's completion. Additionally, it employs an access control mechanism that prevents inconsistent state access and modifications as a result of asynchronous executions. Benchmarking MPC-EVM's throughput show that the transactions per second (TPS) decreased by less than 3% compared to the baseline when MPC-invoking transactions are executed alongside regular transactions.
As decentralized finance (DeFi) evolves, distinguishing between user behaviors - liquidity provision versus active trading - has become vital for risk modeling and on-chain reputation. We propose a behavioral scoring framework for Uniswap that assigns two complementary scores: a Liquidity Provision Score that assesses strategic liquidity contributions, and a Swap Behavior Score that reflects trading intent, volatility exposure, and discipline. The scores are constructed using rule-based blueprints that decompose behavior into volume, frequency, holding time, and withdrawal patterns. To handle edge cases and learn feature interactions, we introduce a deep residual neural network with densely connected skip blocks inspired by the U-Net architecture. We also incorporate pool-level context such as total value locked (TVL), fee tiers, and pool size, allowing the system to differentiate similar user behaviors across pools with varying characteristics. Our framework enables context-aware and scalable DeFi user scoring, supporting improved risk assessment and incentive design. Experiments on Uniswap v3 data show its usefulness for user segmentation and protocol-aligned reputation systems. Although we refer to our metric as zScore, it is independently developed and methodologically different from the cross-protocol system proposed by Udupi et al. Our focus is on role-specific behavioral modeling within Uniswap using blueprint logic and supervised learning.
Ahmad Mutahhar, Tariq Jamil Saifullah Khanzada, Muhammad Farrukh Shahid
Large-scale events, such as festivals and public gatherings, pose serious problems in terms of traffic congestion, slow transaction processing, and security risks to transportation planning. This study proposes a blockchain-based solution for enhancing the efficiency and security of intelligent transport systems (ITS) by utilizing state channels and rollups. Throughput is optimized, enabling transaction speeds of 800 to 3500 transactions per second (TPS) and delays of 5 to 1.5 s. Prevent data tampering, strengthen security, and enhance data integrity from 89% to 99.999%, as well as encryption efficacy from 90% to 98%. Furthermore, our system reduces congestion, optimizes vehicle movement, and shares real-time, secure data with stakeholders. Practical applications include fast and safe road toll payments, faster public transit ticketing, improved emergency response coordination, and enhanced urban mobility. The decentralized blockchain helps maintain trust among users, transportation authorities, and event organizers. Our approach extends beyond large-scale events and proposes a path toward ubiquitous, Artificial Intelligence (AI)-driven decision-making in a broader urban transit network, informing future operations in dynamic traffic optimization. This study demonstrates the potential of blockchain to create more intelligent, more secure, and scalable transportation systems, which will help reduce urban mobility inefficiencies and contribute to the development of resilient smart cities.
Carlos Beis-Penedo, Francisco Troncoso‐Pastoriza, Rebeca P. Dı́az Redondo, Ana Fernández Vilas · 6 authors
The rapid growth of Internet of Things (IoT) devices and applications has led to an increased demand for advanced analytics and machine learning techniques capable of handling the challenges associated with data privacy, security, and scalability. Federated learning (FL) and blockchain technologies have emerged as promising approaches to address these challenges by enabling decentralized, secure, and privacy-preserving model training on distributed data sources. In this paper, we present a novel IoT solution that combines the incremental learning vector quantization algorithm (XuILVQ) with Ethereum blockchain technology to facilitate secure and efficient data sharing, model training, and prototype storage in a distributed environment. Our proposed architecture addresses the shortcomings of existing blockchain-based FL solutions by reducing computational and communication overheads while maintaining data privacy and security. We assess the performance of our system through a series of experiments, showing its potential to enhance the accuracy and efficiency of machine learning tasks in IoT settings.
The global food waste crisis poses significant environmental and economic challenges, particularly within institutional settings such as university campuses. At NSBM Green University, South Asia’s first green university, traditional food waste management systems are hampered by opacity and unverifiable data, impeding effective reduction strategies. This paper proposes a blockchain-based Smart Campus Waste Management Platform to revolutionize food waste tracking in campus canteens. Utilizing blockchain’s immutability, smart contracts, and integrated authentication, the system ensures secure, tamper-proof data management. It captures real-time waste data through authenticated manual inputs, processes it via a secure gateway, and records it on an Ethereum ledger, enabling precise sustainability reporting and data-driven interventions. The study details the system’s architecture, implementation with Ethereum and Solidity, and alignment with NSBM’s sustainability goals. Key benefits include enhanced accountability, optimized waste reduction, and reinforced green leadership, with future enhancements such as AI integration, scalability, and incentive mechanisms addressing potential challenges.
Engin Zeydan, Josep Mangues‐Bafalluy, Şuayb S. Arslan, Yekta Türk · 5 authors
The growing demand for mobile data services has made it necessary to find efficient and cost-effective ways to share networks. Blockchain technology offers a promising solution to the challenges of network sharing, such as interoperability, trust, and accountability. This article provides a comprehensive classification and categorization of blockchain-based network–sharing scenarios, highlighting their advantages and limitations. Seven network sharing scenarios are identified, ranging from centralized network sharing to fully decentralized spectrum sharing. The suitability of some selected blockchain consensus algorithms (namely Proof-of-Work (PoW) with Ethereum, Proof-of-Authority (PoA) with Ethereum, Practical Byzantine Fault Tolerance (PBFT) with Tendermint and Proof-of-Stake (PoS) with Cosmos) is assessed for selected scenarios through extensive evaluations. This article also identifies gaps and opportunities in blockchain–based network sharing solutions and outlines future research directions.