Proof-of-Stake (PoS) is a prominent Sybil control mechanism for blockchain-based systems. In "e-PoS: Making PoS Decentralized and Fair," Saad et al. (TPDS'21) introduced a new Proof-of-Stake protocol, e-PoS, to enhance PoS applications' decentralization and fairness. In this comment paper, we address a misunderstanding in the work of Saad et al. The conventional Proof-of-Stake model that causes the fairness problem does not align with the general concept of Proof-of-Stake nor the Proof-of-Stake cryptocurrencies mentioned in their paper.
Denis Shakhov, Руслан Вахаевич Баташев, Ilyоs Abdullayev
This study investigates the transformative impact of artificial intelligence on corporate communications, focusing on AI-powered personalization systems in business environments. Through a systematic literature review (2019-2024), the research establishes an empirical framework for evaluating these systems’ technological infrastructure. The findings reveal distinct sector-specific performance variations: the retail sector showing 58% enhanced engagement metrics, while B2B segments demonstrated 28% improvement in key performance indicators. The technological foundation comprises machine learning algorithms, natural language processing frameworks, and high-performance computing systems enabling real-time personalization. The methodology integrates the adaptive personalization framework (APF) with the multidimensional personalization model (MPM) to elucidate machine learning mechanisms. This framework supports user profiling, navigation optimization, and behavioral pattern modification, secured through distributed ledger technologies. Empirical analysis reveals the complementarity between AI and human capabilities. While AI systems excel in response velocity (mean: 4.92), human interactions demonstrate superior responsiveness (5.27) and professional competency metrics (5.32 vs. 4.87), suggesting the optimality of a hybrid model. The study culminates in a conceptual framework balancing communication scalability with personalized relevance while adhering to ethical imperatives of data protection, algorithmic fairness, and transparency protocol.
Edgar Roberto Dulce Villarreal, Giovanni Hernández, Jesús Insuasti, Julio Ariel Hurtado Alegría · 5 authors
The exchange of medical information significantly benefits people's quality of life, improving their care and treatment. The interoperability of the entire healthcare ecosystem is a constant challenge. Blockchain technology is an alternative to find a balance in the healthcare ecosystem. Smart contracts (SC) are decentralized and self-executing programs that allow the automation of agreements without intermediaries to improve operational efficiency. However, the constant development of new Blockchain technologies and programming languages for smart contracts is a growing problem. This work presents the validation by expert judgment of the MUISCA (Mechanism for UnIversal SmartContrAct) tool, which uses Model Driven Engineering (MDE). MUISCA uses transformations of models and models to text to generate smart contracts in healthcare environments and specific to Blockchain platforms. The validation is conducted by smart contracts development experts, who show positivity in the perceived usefulness.
Lukas Kirchdorfer, Robert Blümel, Timotheus Kampik, Han van der Aa · 5 authors
Abstract Business process simulation (BPS) is a powerful tool for estimating process performance across different scenarios, offering critical support for organizational process redesign and optimization. Traditional BPS approaches predominantly rely on a control-flow-first perspective by enriching a process model with simulation parameters. While these approaches seem suitable for capturing centrally orchestrated processes, such as those managed by workflow systems, they fall short of accurately reflecting real-world processes characterized by decentralized decision-making and distinct resource behaviors. To overcome this limitation, we propose AgentSimulator , a resource-first BPS approach that discovers a multi-agent system from an event log. By modeling the distinct behaviors and interaction patterns of individual resources, AgentSimulator effectively simulates the underlying process. Our approach automatically identifies whether resource behavior is rather orchestrated or autonomous, adapting to the specific decision-making structure of the process. Experimental results reveal that AgentSimulator achieves state-of-the-art simulation accuracy while ensuring high adaptability to various process types.
This article examines the transformative impact of Agentic Process Automation (APA) on modern business workflows, highlighting the evolution from traditional Robotic Process Automation to autonomous intelligent systems. The article establishes APA as a paradigm shift that transcends the limitations of conventional automation approaches through self-governing agent models capable of adaptive decision-making. Through comprehensive analysis spanning architectural foundations, comparative capabilities, multi-agent collaboration frameworks, and real-world implementations, this article demonstrates how APA systems deliver superior performance in dynamic business environments. Key aspects explored include decentralized intelligence, machine learning integration, ethical governance frameworks, and strategic implementation methodologies. Case studies across financial services, healthcare, and manufacturing sectors provide empirical evidence of APA's operational benefits, while also highlighting implementation challenges and mitigation strategies. The article reveals that organizations implementing agentic systems achieve significant improvements in process efficiency, adaptability, and cost optimization compared to traditional automation approaches, particularly for complex workflows requiring judgment and contextual understanding. This article provides valuable insights for organizations navigating the transition toward intelligent automation and offers a structured framework for evaluating APA readiness, implementation priorities, and governance considerations within enterprise environments
This article examines key emerging technologies transforming financial platform engineering. Platform engineering plays a pivotal role in building these systems by leveraging microservices architecture, event-driven systems, and cloud-native technologies. This article explores how modern platform engineering practices ensure low latency, high throughput, security, and regulatory compliance while integrating cutting-edge technologies like machine learning and blockchain. Machine learning has revolutionized fraud detection by enabling the analysis of vast transactional datasets to identify patterns invisible to human observers. Blockchain technology has gained adoption for transaction verification, providing distributed ledger systems that ensure security and immutability while enabling smart contracts that automate complex financial agreements. Real-time analytics capabilities allow financial institutions to process streaming data for immediate insights on market trends, customer behavior, and risk factors, supporting data-driven decision-making at market speed. Finally, API ecosystems have created interconnected networks of services that facilitate innovation through standardized interfaces, transforming how financial services are developed and consumed across core banking, partner integration, and public marketplace contexts.
Decentralized Autonomous Organizations (DAOs) are a type of Decentralized Applications (DApps) that utilize smart contracts to support governance processes. To achieve a high degree of utility of the system, stakeholders need to identify a suitable organizational structure in the early stages of design. While Model-Driven Development (MDD) methods are established for DApp and smart contract design, they lack specialization for modeling the organizational structures of DAOs. To address this gap, we propose a modeling language and a method which support crucial DAO design and development phases. The method is evaluated through an in vivo case study. Unlike existing solutions, comprehensive stepwise guidance is provided by our method for both technical and non-technical stakeholders involved in DAO development from the initial stages of the project.
Distributed ledger technology (DLT) enable the immutable, transparent, and tamper-proof storage of transaction records in a shared electronic register. Due to these intrinsic properties, DLT has the potential to be highly disruptive to businesses. However, the heterogeneity and intricacy of the underlying technologies impede their more widespread adoption. A primary challenge lies in comprehending the interrelationships between the organizational, economical, and technical dimensions of DLT systems. An integrated perspective on the fundamental concepts of DLT within each dimension can be accomplished through the implementation of domain-specific conceptual modeling languages that explicitly account for intrinsic properties. Conceptual models adhere to well-defined elements and rules for the utilization of these elements. This enables the algorithmic processing of models to generate derivations and new insights.The focus of prior research on modeling support for DLT has been on the technical dimension. However, research on conceptual modeling approaches enabling the joint consideration of organizational, economical, and technical factors is lacking. This situation is undesirable both from a scientific standpoint and from a practical one. To address this gap, this thesis advances the current state-of-the-art in the design of DLT applications by introducing several domain-specific approaches addressing concerns across dimensions. This includes support for the decision-making in organizations, for the design of their business model, its alignment with the underlying DLT systems, and for the development of smart contracts and subsequent generation of functional code. The artifacts are consolidated into a domain-specific conceptual modeling method, facilitating the design of DLT applications and business cases.
This paper presents a comprehensive Model-Driven Engineering (MDE) methodology for automatically transforming Business Process Model and Notation (BPMN) diagrams into executable blockchain-based smart contracts. The proposed approach defines a set of Atlas Transformation Language (ATL) rules that systematically map BPMN elements to Solidity con-structs, ensuring semantic consistency and traceability through-out the transformation process. The framework integrates several stages, including process modeling, model validation, code generation, and deployment, supported by tools such as Camunda, Eclipse ATL, Remix IDE, and MetaMask. Experimental vali-dation on the Ethereum Sepolia test network demonstrates the approach’s ability to enhance automation, reduce manual coding errors, and improve synchronization between business work-flows and their on-chain implementations. Compared to existing BPMN-to-blockchain frameworks, the proposed solution offers a unified and reusable transformation pipeline that bridges the gap between business process modeling and blockchain execution. The study concludes that MDE provides a scalable, traceable, and standardized foundation for developing decentralized business process applications.
Background: Ontologies and knowledge graphs have become critical for structuring data into machine-interpretable knowledge, especially in dynamic domains like IT service management (ITSM). Traditional ontology engineering relies heavily on domain experts, making it costly and slow. This study investigates whether a domain-specific ontology can be extended from a top-level ontology without expert involvement, using the IT service management ontology (ITSMO) and the descriptive ontology for linguistic and cognitive engineering (DOLCE-lite) as a test case used in this study. Methodology: We propose an automated mapping approach integrating lexical approaches, embeddings, graph neural networks (GNN), and large language models (LLMs). Two primary mapping methods were developed: (1) embedding-based matching, computing cosine similarity between class embeddings from DOLCE and ITSMO; and (2) LLM-based matching, prompting a language model (GPT-4o) to evaluate class compatibility on a numeric scale. We also experiment with GraphSAGE GNN to enrich embeddings with ontology structure. Z-score clustering is applied to similarity scores to select top candidate mappings while filtering out outliers from the top cluster. The methodology operates with no annotated data and was validated using three-steps approach: GPT-4o as a surrogate expert for baseline class matching evaluation, expert spot-check, and OWL reasoner (Pellet and HermiT) to prove logical consistency (Glimm et al., 2014; Sirin et al., 2007). Results: The automated method successfully mapped ITSMO classes under DOLCE, yielding an integrated ontology (80 classes) that extends DOLCE into the ITIL domain with minimal expert intervention (expert consolidated suggestions into a result ontology). The LLM-based approach (GPT-4o) achieved the best performance with 73.5% accuracy for top-1 mappings and 82.4% for top-3 (cluster) inclusion. Transformer-based embeddings (e.g., DeBERTa) also performed well (up to 39.3% top-1, outperform random matching with 27.6% accuracy), but classical graph embeddings (RDF2Vec/Node2Vec) failed due to the small ontology size. Incorporating a GNN provided smoother embedding distributions and increased correct mappings within top-3 clusters, but it slightly reduced top-1 precision in this small-graph setting. These findings underscore the effectiveness of LLMs in zero-shot ontology alignment and the limitations of purely structural methods on limited data. Conclusions: This work demonstrates, as a proof-of-concept, that an upper-level ontology can be extended to a domain ontology automatically, with no or minimal expert involvement, by leveraging AI-based mapping techniques. The resulting new ontology integrates ITSMO into DOLCE, providing a consistent semantic foundation for IT domain knowledge graphs. The approach is immediately applicable to ITSM and suggests a generalizable framework for ontology expansion in other domains. Future work will focus on scaling the method to larger ontologies, automatically discovering new classes/relations from text, and evaluating the approach’s practical impact on IT service management processes.
Abstract Decentralized autonomous organizations (DAOs), like blockchains, were expected to revolutionize the economy by solving the principal–agent problem in the corporate form. Yet, the very first DAO (“The DAO”) suffered a catastrophic hack that led to the Ethereum blockchain’s forking. Despite this failure, and the 2022 crypto markets collapse, enthusiasm for DAOs as corporate alternatives persists in crypto circles. Nine years on, however, DAOs remain a niche structure with little traction outside crypto projects. This chapter posits that this is due to fundamental flaws in the case for DAOs as corporate replacements. Crypto proponents often dismiss centralization and opacity as wholly undesirable, overlooking their functional value in business organizations. They also fail to recognize that blockchain’s security, transparency, and predictability are narrowly bound and come at a cost, rendering DAOs poorly equipped to handle the risks organizations face in the real world. These trade-offs explain why DAOs are less a revolution, more naïve wishful thinking.
Ajibola Oluwafemi Oyeleye, Onyeka Franca Asuzu, Adaobi Vivian Ibeh
This paper presents a conceptual model for raising Accounts Payable (AP) accuracy in research institutions by embedding process intelligence across the procure-to-pay lifecycle. The model integrates process mining, rule-based controls, and machine-learning anomaly detection with grant compliance logic to reduce mismatches, duplicate payments, and breaches. It addresses the context of universities and research hospitals, where varied funding sources, sponsor terms, and decentralized purchasing create transaction patterns and compliance risk. The model positions AP as a data-driven assurance hub connecting principal investigators, central finance, and suppliers. The architecture has four layers: first, data acquisition that unifies ERP, e-procurement, and grant management logs via standardized event schemas; second, conformance engines encoding sponsor allowability, period of performance, three-way match, and delegation rules; third, analytics and prediction that combine process discovery, first-pass-yield forecasting, vendor normalization, and exception clustering; and fourth, workflow orchestration that returns prescriptive alerts to case managers and routes exceptions to approvers for timely resolution. Methodologically, the model adopts a design-science and DMAIC hybrid. Teams baseline cycle time, touchpoints, and first-pass accuracy; mine event logs to map as-is variants; prioritize failure modes through FMEA; implement targeted controls; and measure effects with interrupted time series and segmented regression. Data quality is elevated through master-data maintenance, vendor deduplication, and invoice OCR confidence thresholds with human-in-the-loop review. Expected outcomes include higher first-pass yield, fewer late-payment penalties, improved sponsor billing, and cleaner audit trails. Leading indicators exception rate, conformance score, and rework loops feed a control chart to sustain gains, while lagging indicators write-offs, questioned costs, and audit findings confirm risk reduction. The model also incorporates equity and accessibility by simplifying small-supplier onboarding and enabling transparent status notifications to reduce inquiry volume and payment anxiety. A change-management plan aligns incentives across finance, research administration, and procurement, with skills uplift delivered through training and playbooks. This conceptualization offers a scalable blueprint aligning AP accuracy with research integrity, stewardship of public funds, and overall operational resilience, enabling institutions to realize predictable, compliant payables operations and stronger supplier relationships.
R. Priscilla, J Gold Beulah Patturose, Jerrish Bright J M, M Sanjay · 6 authors
This project focuses on the development of a decentralized voting platform using blockchain technology, integrated with ERC-4337 standards and Non-Fungible Tokens (NFTs) to improve election integrity, transparency, and security. Traditional voting systems face vulnerabilities such as fraud, manipulation, and data breaches, which undermine public trust. By utilizing blockchain’s immutable ledger, this platform ensures that votes are securely recorded and verifiable, preventing any tampering after submission. ERC-4337 introduces account abstraction, which simplifies user interaction by offering flexible account management. This enhances the voting experience, making it more accessible for non-technical users, thereby increasing voter participation. NFTs are leveraged to represent candidates uniquely, providing a secure, transparent method to verify their identities and ensure authenticity throughout the election process. The combination of blockchain's decentralized structure, ERC-4337's user-friendly account management, and NFTs for candidate representation addresses major shortcomings of existing voting systems. This decentralized approach eliminates the need for intermediaries, reduces the risk of fraud, and enhances transparency with real-time verifiable results. The proposed platform can be adapted for various types of elections, ranging from local to national, offering a scalable, secure, and efficient alternative to conventional voting methods, ultimately modernizing and safeguarding the democratic process.
Block reorganization (reorg) may occur when a blockchain fork is deliberately instigated and remains poorly solved on Ethereum. We utilize an agent-based modeling approach to simulate the block generation and decision-making processes of reorg attackers and ordinary validators. We analyze the impact of six validator network structures (i.e., random, regular, small-world, scale-free, hierarchical, and community networks) and four fork selection rules (i.e., LMD GHOST, HLMD GHOST, Goldfish, and RLMD GHOST) on the success rate of reorg attacks. The results show that the community network is more vulnerable to reorg attacks, while the hierarchical network is more resilient to such attacks. In addition, the Goldfish fork-choice rule can significantly reduce the success rate of reorg attacks.
The UpOn platform leverages cutting-edge modular smart contract architecture to redefine Real World Asset (RWA) management by creating a legally compliant, decentralized ecosystem for virtual office investment, launching, and rental. Built with a focus on transparency, security, and compliance, UpOn allows users to seamlessly participate in real estate investment through Non-Fungible Tokens (NFTs) that represent virtual office spaces. By integrating law firms for legal oversight, UpOn ensures that all transactions align with global legal standards, providing users and investors with trust and security. The platform comprises four main components-Invest, Launch, Rent and Legal-each of which serves a unique function within the ecosystem. The Invest section enables investors to fund new office projects through NFT minting, while Launch allows owners of established offices to tokenize their assets. In the Rent section, users can securely lease office NFTs with contract-backed agreements tailored to local regulations, and the dashboard provides realtime data retrieval from smart contracts, ensuring a transparent user experience. Finally, the Legal component, backed by law firms, safeguards user rights and enforces compliance in all jurisdictions. With its modular design and elimination of centralized databases, UpOn showcases the full potential of Web3 by simplifying the traditional complexities of RWA projects, lowering costs, and offering unprecedented accessibility to global markets.
The rapid evolution of Ethereum’s infrastructure calls for innovative mechanisms to enhance scalability, security, and performance. This paper introduces BeamSNARKS, a cutting-edge framework designed to address critical challenges in zero-knowledge proof systems. BeamSNARKS encompasses two groundbreaking innovations: the Dynamic zkSNARKS Generation Optimization Mechanism and the Dynamic SNARKification Technology. The former revolutionizes computational efficiency by dynamically retrieving state data relevant to proof generation, minimizing bandwidth and storage requirements while maintaining validation accuracy. The latter introduces adaptive circuit design and hierarchical proof aggregation to optimize transaction throughput and reduce the computational and financial overhead of Layer 1 submissions. Together, these innovations establish BeamSNARKS as a pivotal advancement in scalable, efficient, and resource-optimized zero-knowledge proof systems. Through comprehensive analysis and targeted experiments, this paper evaluates the performance of BeamSNARKS’s innovations, demonstrating their potential to transform Ethereum’s decentralized ecosystem and lay the groundwork for future high-throughput applications.
With the G-20 prioritising the development of a solution that will address the challenges of cross-border payment, a lot of focus and research has been exploring ways of using a decentralised ledger technology (DLT) solution to address this challenge. Some of the issues that have been identified with the DLT option are interoperability, scalability, security, and privacy. Central Bank Digital Currencies (CBDCs) implementing DLT have also been acknowledged by experts as having the potential to address some of these challenges. Given the focus on CBDCs as an alternative technology that could address the shortcomings of traditional cross-border payment systems, some of the design challenges that are impacting their widespread adoption have come to the fore. Considering that early CBDC projects were in reaction to emerging digital currencies and therefore were meant to address the domestic needs of each implementing jurisdiction, the lack of a common development framework for developing the technology contributed to the challenge of the interoperability of the various CBDCs across jurisdictions and has impacted it’s fullscale adoption as an alternative to existing cross-border payment systems. With specific focus on regulatory frameworks used by various jurisdictions and also an identified challenge of CBDCs, this paper explores Customer Due Diligence (CDD) regulation as a mechanism for combating anti-money laundering (AML) and counter-terrorist financing (CFT). As part of the efforts to adapt cross-border payment system into a world of distributed ledgers relying on smart contracts, it reviews the impact of divergent regulatory frameworks that limit the interoperability of CBDCs. It discusses some of the existing techniques in place for conducting CDD, such as Know Your Customer (KYC) and how they fit with the new technology. It concludes with a proposal on how the incongruence of legal frameworks and AML/CFT regulations, can be addressed and standardised using new technologies such as Large Language Models (LLMs).
The group signature with designated traceability (GSdT) is a kind of group signatures (GS) which aim to restrict the opening authority of the group manager; by setting an access structure over openers’ attributes at the signing, a signer is able to control openers who can open the signature. A generic construction of GSdT was given when the notion was introduced, then a pairing-based construction and a symmetric-key-based one were presented. Nonetheless, it remains open whether or not a post-quantum GSdT that has full anonymity can be really constructed.In this paper, we give a lattice-based GSdT scheme that has full anonymity for the first time. In our construction, the lattice-based ciphertext-policy attribute-based encryption (CP-ABE) by Tsabary and the lattice-based group signatures (GS) by Libert et al. are employed. The CP-ABE is based on the Regev public-key encryption, while the GS uses a non-interactive zero-knowledge proof to prove the correctness of the encryption in the signing process. Based on the compatibility, we combine and modify them to build up a GSdT scheme.
Ken Huang, Youwei Yang, Fan Zhang, Xi Chen · 5 authors
Chapter 9 offers a comprehensive exploration of decentralized autonomous organizations (DAOs) and their role in the formation of Web3 organizations. It begins by defining DAOs, tracing their history, and outlining the benefits they bring to the digital world. The chapter then delves into the various types of DAOs, including protocol DAOs such as MakerDAO and Lido, charity DAOs such as GitcoinDAO and Ukraine DAO, and investment DAOs, represented by The LAO and MetaCartel Ventures. The chapter also examines Art Collection, Social, Fans, Sports, Media, Tool, and multipurpose DAOs, with examples such as Aragon DAO and Coordinape DAO. However, it is not without noting the disadvantages of DAOs, such as slow decision-making, low active participation rates, legal and tax issues, centralization risk, and vulnerability to flash loan attacks. The future of DAOs is also a topic of discussion, with speculations on how they could be combined with artificial intelligence (AI), attract large institutional investments, or be endorsed by big brands. Furthermore, the chapter considers the possibility of DAOs functioning as virtual countries and their potential role in inclusive finance. The chapter provides a summary, followed by a series of thought-provoking questions for further reflection.
Akshatha Kamatha, A. S. Mamatha, B S Sushma, Valmeeki Thakshith · 6 authors
Traditional file-sharing systems relying on centralized servers pose significant security risks, leaving users vulnerable to data leaks and cyberattacks. As a result, individuals and organizations exercise caution when sharing sensitive information through such platforms. Proposed system introduces a cutting-edge decentralized file-sharing system, achieved by seamlessly integrating the InterPlanetary File System (IPFS) with blockchain technology. This innovative approach revolutionizes data exchange, ensuring secure and efficient file sharing and storage without the need for a centralized middleman. This offers an in-depth exposition of the system's architecture and design, highlighting the integration of smart contracts for safe transactions and IPFS for distributed file storage. Notably, the proposed system covers the unique features brought forth by smart contracts and IPFS, such as trustless data exchange, distributed file access control, and dynamic file sharing permissions. The research contributes significantly to the growing body of knowledge on blockchain-based file sharing platforms, demonstrating the transformative potential of this secure and decentralized data exchange paradigm. As blockchain technology continues to evolve, this work opens up new possibilities compared to traditional file-sharing paradigms and ushering in a more equitable and resilient data-sharing ecosystem.