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95,526 results · page 255 of 3,981
Aryan Mendiratta, Ojasvee Kumar Singh, P. Chandrakanta Singh, Priyanshu Kumar · 6 authors
No abstract is available for this record.
Ethan Ho Yi Wong
This report studies EvoVuln, a smart contract vulnerability analysis pipeline that represents vulnerability knowledge as a natural-language detection method, refines that knowledge through iterative training, and converts the result into an executable detection plan for downstream contract analysis. The report focuses on the Stage 2 update-acceptance mechanism, which determines whether a candidate knowledge revision should replace the current knowledge state during labelled-data refinement. In the baseline implementation, candidate updates are accepted based only on improvement over the previously misclassified subset. While simple and efficient, this rule may accept revisions that improve local error cases while worsening behaviour on other labelled contracts. To address this limitation, this report investigates a modified acceptance rule that adds full-set validation before acceptance, requiring candidate knowledge to reduce total errors on the full labelled training set. The modified rule was implemented as a localised change at the Stage 2 acceptance point, while the broader pipeline structure was retained. Evaluation was conducted on two selected vulnerability types using matched starting snapshots and repeated full-pipeline runs under both policies. The modified rule consistently accepted fewer candidate updates than the baseline policy, indicating stricter filtering during refinement. It also improved average downstream F1 in both vulnerability types, although the form of improvement differed: for price manipulation, recall increased while precision decreased, whereas for access control, average precision, recall, and F1 all improved. These findings suggest that Stage 2 acceptance is a meaningful design choice in iterative LLM-based vulnerability analysis pipelines. Within the scope of the evaluated cases, adding full-set validation was associated with improved downstream detector performance, although the results should be interpreted cautiously given the limited experimental scope and the pipeline’s sensitivity to LLM-related non-determinism.
Elvis Konjoh Selabi, Maurizio Murgia, António Ravara, Emilio Tuosto
The companion paper proposes a formal approach for specifying and implementing decentralised coordination in distributed systems, with a focus on smart contracts. The model captures dynamic roles, data-driven transitions, and external coordination interfaces, enabling high-level reasoning about decentralised workflows. A toolchain supports formal model validation, Solidity code generation (extensible to other smart contract languages), and automated test synthesis. Although targeting blockchain platforms, the methodology is platform-agnostic and may generalise to other service-oriented and distributed architectures. The expressiveness and practicality of the approach are demonstrated through modelling and realising coordination patterns in smart contracts. This artifact accompanies our paper [Elvis Konjoh Selabi et al., 2026]. It provides a toolchain for generating smart contract code from EDAM (Extended Data-Aware Machines) specifications. The artifact includes the complete source code, a Docker image for easy deployment, pre-generated experiment data (generated code, automated tests, and mutation testing results), and reproduction scripts.
Yaroslav V. Zemlyachenko
Despite the apparent lack of legal regulation regarding the definition of the content and rules of civil circulation of cryptocurrencies, which is the basis for courts to refuse to consider civil cases involving cryptocurrency, binding relationships related to cryptocurrency certainly exist and are developing. The impossibility of judicial protection of this kind of obligations raises the question of their legal nature and on the basis of what factors it is possible to transform these obligations into civil obligations subject to judicial protection. The purpose of the article is to consider the features of cryptocurrency as an object of natural obligations, to identify facts that serve as grounds for refusing to recognize transactions with cryptocurrency and their judicial protection, to establish the possibility of converting transactions with cryptocurrency from natural obligations to civil ones. When conducting the research, the main methods were general scientific methods of analysis and synthesis. Special methods such as comparative law, historical law, and formal law were used as auxiliary methods. As a result of considering cryptocurrencies as natural obligations that are not subject to legal protection, the conclusion is drawn: transactions with cryptocurrencies have a property such as latency, which removes this type of transaction from the jurisdiction of the courts, giving them the property of naturalness. The facts that serve as grounds for the courts to refuse to protect transactions with cryptocurrency are the following: 1) the owners of cryptocurrencies are individuals or legal entities whose personal law is not Russian law; 2) there is no information about the subjects of the transaction and other interested parties; 3) there is no information about the objects of the transaction; 4) there is no information about the transaction itself.
Santiago Ventura, Steven Murphy
Many poor long-term financial decisions are not “choices” but symptoms of a psychological state called Learned Helplessness. This is the belief, often learned from past setbacks, that one has no control over outcomes, leading to passivity and avoidance. In finance, this manifests as a belief that “it doesn’t matter what I do, I’ll never get ahead.” This is academically defined as an External Locus of Control, the belief that one’s financial future is in the hands of luck or external forces, not personal effort. An External Locus of Control can be directly linked to saving significantly less for retirement and avoiding proactive financial planning. In this research, we attempt to predict cryptocurrency engagement, given it's attractiveness for people who feel that traditional, effort-based financial structures are futile, as a function of beliefs about people's locus of control of their finances, planning horizon, and self-efficacy.
Ubaydullo Khattobov, Radjabova Sarvinoz Alisherovna, Nabixanova Nigora Shuxratbekovna, Olimjon Xamrayev Yaxshiboyevich · 5 authors
This study focuses on cryptocurrencies. At the beginning it explains what cryptocurrency is, its main features and main areas of its significance for the economy. In this section it deals with the possibility of cryptocurrency one day replacing traditional money, trading opportunities cryptocurrencies offer, possibility to finance a business with digital coins and its availability to people without the access to banking services. A brief overview of cryptocurrency history and a definition of the technology of blockchain are also provided. The practical part of the thesis is analysing cryptocurrencies Bitcoin, Ethereum and Litecoin. Firstly, these are described in terms of their origin, emission, circulation, price development and process of mining. Secondly, the impact of selected factors on the price fluctuation of selected cryptocurrencies is evaluated using statistical methods and econometric models. The analysis showed the cryptocurrency prices are more dependent on the internal factors such as the transaction volume, transaction fee, total supply, demand and hashrate, than on the external factors such as interest rates, exchange rates, stock prices and the price of gold.
Nafise Aalipour, Seyed Mehdian, Rasoul Rezvanian
No abstract is available for this record.
Vera Larionova
No abstract is available for this record.
Jihene Khoualdi, Ilhem Abdelhedi Abdelmoula, Uwe Roth, Hella Kaffel Ben Ayed · 5 authors
In this article, we provide an in-depth analysis of the different causes of food fraud, food safety, and food waste, which are recognized as the main challenges in agri-food supply chains. Leveraging technologies like blockchain- and IoT-enabled traceability systems presents promising solutions to overcome these challenges. Using the PRISMA methodology, we conducted a comprehensive literature review of 41 selected articles to assess the effectiveness of such solutions. The findings reveal that only 48% target primarily improved food safety, while food waste (9%) and food fraud (21%) played a less important role. Only a few papers actively incorporate these attributes into their architectural design and many papers lack practical implementation details, leaving significant gaps in understanding their practical applicability. One finding was that 61% of the proposed solution were build on a public permissionless blockchain (Ethereum) and 39% where build on a permissioned private or consortium blockchain (mainly Hyperledger Fabric and Sawtooth). Critical aspects such as data privacy, confidentiality, final infrastructure governance, or legal frameworks are in most cases missing, e.g., 75% did not discuss the governance of the solution at all. To address the identified limitations, we propose a modular reference architecture that balances transparency and confidentiality through a hybrid blockchain approach. It incorporates a trusted platform layer with secure data storage, publicly verifiable summaries, and role-based access control. The architecture is illustrated through multiple use cases and qualitatively evaluated against characteristics of existing solutions, highlighting its conceptual suitability for regulated agri-food ecosystems.
Janesh Sami
No abstract is available for this record.
Subramanya V. Odeyar, P. K. Lolakshi, L. Swetha, K. M. Thejaswini · 6 authors
Abstract The Bitcoin has recently garnered significant media and public attention due to its dramatic price increases and declines. As a result, many researchers have examined the various factors influencing Bitcoin’s price and the patterns behind its fluctuations, often using machine learning techniques. This study explores several machine learning algorithms for Bitcoin price prediction, including logistic regression and long short-term memory (LSTM) models. While LSTM-based models have shown superior performance in predicting Bitcoin prices (regression), this research provides a detailed investigation into Bitcoin’s evolution and a comprehensive review of the machine learning methods used for price prediction. Additionally, the study includes a Bitcoin price prediction model, which is developed using specific algorithms to forecast Bitcoin’s price, along with insights into the factors affecting its price movements. The proposed LSTM model has achieved 98% accuracy.
Mizbah Syed, R. Sreedevi, Rethu Chrishel, M. Rajavel
No abstract is available for this record.
John Hill
No abstract is available for this record.
Марк Энтин, Екатерина Энтина
No abstract is available for this record.
Damaris Jepkurui Kangogo, László Gönczy, Imre Kocsis
No abstract is available for this record.
Dmitry Korobeynikov
The article examines existing and prospective models of credit relations in the agro-industrial complex within the framework of financial platforms and non-financial ecosystems. These models are based on fintech innovations. This type of innovation changes models by involving new types of intermediaries in the intermediation of financial and information flows between the lender and the borrower. The following models of corporate credit functioning within the framework of digital platforms and ecosystems are identified: 1) financial information aggregators that accumulate and transfer client traffic for bank lead generation; 2) financial platforms that translate the transactional model of financial marketplaces to the corporate sector; 3) financial marketplaces based on distributed ledger technology (DLT) within the framework of a centralized or decentralized financial model; 4) an infrastructural industry ecosystem that ensures the integration of credit into industry value chains in the agro-industrial complex.
Fernando Paredes Garcia
Autonomous software agents on blockchains coordinate complex behavior by reading shared ledger state instead of exchanging direct messages. Arbitrage bots, liquidation keepers, and MEV searchers all watch balances, contract storage, and event logs; when conditions change, they act. This form of indirect coordination mirrors what Grassé called stigmergy in 1959: organisms coordinating through traces left in a shared environment, with no central plan. Stigmergy has mature formalizations in swarm intelligence and multi-agent systems, and on-chain agents already behave stigmergically in practice, but no framework bridges the two. We propose Coordinación indirecta basada en el estado del registro contable (“Indirect coordination grounded in ledger state”) as an applied definition that maps Grassé’s mechanism onto distributed ledger technology. We operationalize this with a state-transition formalism, derive four on-chain coordination patterns (State-Flag, Event-Signal, Threshold-Trigger, Commit-Reveal Sequencing), and evaluate a simulated task board against off-chain messaging and centralized orchestration baselines. The stigmergic approach matches the baselines on task completion under benign conditions while outperforming both under Byzantine adversarial pressure, at a gas-cost premium of roughly 1.5×.
Guanghui Zhang
The introduction of Vehicle-to-Everything (V2X) communications is a fundamental requirement for the evolution of today’s Autonomous Driving, but it leads to a new set of vulnerabilities in network infrastructure. It is important to note that cyber-attacks, including the availability ones, such as DoS, represent a significant threat to the safety of Intelligent Transport Systems (ITS). Traditional signature-based Intrusion Detection Systems (IDS) have a disadvantage in security due to their inability to adapt and manage these new and evolving attacks: they can be blind to new or “zero-day” kinds of attacks. This project is to solve this problem by proposing and validating an unsupervised Intrusion Detection System using a Deep Autoencoder architecture. Unlike typical supervised models, where labelled attack data is needed, this system is trained on normal network traffic patterns only. It tracks anomalies by learning to compress and reconstruct legitimate traffic features, marking large reconstruction errors as malicious intrusions. The model was developed in TensorFlow and tested against the KDD Cup 99 benchmark dataset. Experimental results show the high performance of the system with a total Accuracy of 99.49% and a critical Recall of 99.86%, effectively suppressing almost all availability attacks. In addition, the model is consistent with a Matthews Correlation Coefficient (MCC) of 0.9530, confirming its robustness and reliability even for very asymmetric network traffic. This research establishes solid proof-of-concept for the concept that unsupervised deep learning can work as a powerful new mechanism of security architecture for V2X infrastructure without relying on prior knowledge about specific attack signatures.
Naveenbalaji Gowthaman, Manoj Shanmugam
No abstract is available for this record.
Matthew Newman
Traditional digital trust architectures suffer from the “Library Problem”: dependency on pre-compiled, static lookup tables or binaries that must be trusted blindly, creating supply-chain vulnerabilities. This paper proposes a paradigm shift to Intrinsic Trust, where encoding infrastructure is mathematically regenerated at runtime rather than distributed. We introduce the 0MXI Calculus, a deterministic lattice system anchored on universal transcendental constants:the golden ratio Φ ≈ 1.618033988749895 and π ≈ 3.141592653589793, with a contraction ratio λ ≈ 0.339949771344778. Operations on a quantized F15 lattice ensure cross-platform determinism, bounded by a Prime Boundary Horizon (N = 23) that guarantees injective reversibility (Theorems 1 and 2).This framework underpins TreeOS, an operating system that bootstraps from a “Math Root-of-Trust” via autogenesis, regenerating a bijective Tick Table for byte encoding without stored dependencies. TreeBABEL, the verifiable data transport protocol, packages data as JSON artifacts with mathematical manifests for independent receiver validation. Extending this, the VMEM Node Architecture transforms online repositories into externalized memory banks, enabling AI models to scrape and derive OS state on demand, eliminating internal weight bloat and static knowledge cutoffs.We demonstrate adaptability to constrained ledgers (e.g., 280-character limits) for efficient chunking. Through rigorous proofs and a Python reference implementation, we show that trust can be calculated, not stored, decoupling systems from physical hardware and fostering entropy-neutral, zero-trust computation.
Ali Sadhik Shaik
Contemporary governance theory confronts a tripartite crisis that existing frameworks address only in isolation. First, algorithmic systems are systematically eroding the cognitive, affective, and epistemic conditions for individual personhood - what this paper terms the Personhood Atrophy Model. Second, recommendation-engine-driven fragmentation has dissolved the shared cultural and epistemic spaces upon which collective purpose and democratic deliberation depend. Third, the structural asymmetry between the pace of technological change and the operational tempo of democratic institutions has produced a compounding legitimacy crisis for the sovereign nation-state, increasingly outflanked by corporate platforms exercising sovereign-equivalent power without democratic accountability. Political theory and science and technology studies have addressed each of these dimensions in isolation. No integrated analytical framework currently exists that connects the micro-level erosion of selfhood, the meso-level collapse of shared meaning, and the macro-level transformation of sovereignty into a unified theory of algorithmic governance. This paper introduces the Republic of Code framework, drawing on the monograph by Shaik (2026), and proposes three original theoretical constructs: (1) the Wet Code/Dry Code distinction as a governance epistemology tool, formalizing the fundamental incompatibility between human-interpretable and machine-enforced law; (2) the Personhood Atrophy Model mapping algorithmic erosion of agency across cognitive, affective, and epistemic vectors; and (3) the Five Futures Matrix, a two-axis typology of possible political arrangements under algorithmic conditions. The paper concludes by proposing a suite of constitutional innovations - including Proof of Humanity (whose mechanism design infrastructure is formally developed in Shaik, 2026b), Zero-Knowledge Justice, and High-Fidelity Democracy - necessary for the reconstruction of democratic legitimacy in what it terms the Republic of Code. The analysis carries implications for legal scholarship, platform governance policy (including industrial cyber-physical systems, examined in Shaik, 2026e), and the updating of social contract theory for an era in which digital exit costs approach zero.
Abhijeeth Ganji, Priyanka Velpula
No abstract is available for this record.
Zuodong Wu, Dawei Zhang, Mianxiong Dong, Kaoru Ota
In the data-driven era, the unchecked collection and processing of personal data has given rise to serious privacy concerns. In response, the General Data Protection Regulation (GDPR) was introduced to grant individuals stronger control over the use of their data. Privacy data retrieval methods show considerable promise in this context, but further improvements are required to balance the principles of lawfulness and data minimization. To address this problem, we propose PDRAA, an efficient privacy data retrieval protocol with anonymous authorization based on the verifiable credential (VC). Specifically, our designed VC achieves anonymous identification of data subjects and facilitates fine-grained access control by supporting selective disclosure of attributes. By combining VC with non-interactive zero-knowledge (NIZK) proofs, PDRAA enables data subjects to anonymously authenticate via VC presentation. This allows the data controller to verify the legitimacy of retrieval requests while ensuring compliance with the principle of data minimization. Besides, PDRAA introduces a re-randomization mechanism to prevent linkability attacks during the authorization process and provides lightweight, flexible authorization revocation. Moreover, we utilize Labeled Private Set Intersection (Labeled PSI) technology to meet the privacy requirements of participants and support batch retrieval. Our protocol takes a comprehensive security analysis within the Universal Composability framework. Experimental results demonstrate that PDRAA outperforms existing methods in terms of performance, which is significant for promoting compliance with GDPR.
Achim Struve
No abstract is available for this record.