Taras Maksymyuk, Francesco Meloni, Matias Torres Diaz, Domenico Romano · 6 authors
This paper presents a blockchain-centered system architecture for cultural heritage provenance that replaces fragmented, paper-based tracking with a tamper-evident, auditable digital workflow. We assume that each object can be reliably bound to a stable physical fingerprint through an established scan-based pipeline, and we focus on how that fingerprint is represented, stored, and verified within a practical distributed ledger design. The proposed framework separates high-assurance settlement events, such as registration and ownership transfer, from high-volume operational records, such as condition updates and monitoring logs, by routing data across multiple layers and committing verifiable summaries of frequent activity to a high-security anchor chain. We also describe a deployable decentralized application stack that integrates standard token interfaces for asset representation, event-driven synchronization for user-facing services, and scalable node access to reduce read latency without requiring institutions to maintain their own node infrastructure. The result is a concrete system model that clarifies how the end-to-end provenance trail remains verifiable under realistic performance constraints.
This technical report presents the reference implementation of Ternary Moral Logic (TML) within the Ethereum Virtual Machine (EVM) ecosystem. It addresses the limitations of traditional "Code is Law" architectures by introducing a finite state machine that enforces a mandatory third state—the "Sacred Zero" or Epistemic Hold—allowing smart contracts to pause execution when pre-defined ethical conditions are unmet. The report moves beyond theoretical ethics to specify the Solidity design patterns, storage layouts, and cryptographic verification methods required to make TML enforcement non-bypassable and auditable. Key Technical Contributions: Finite State Machine (FSM): Implements a mandatory "Sacred Zero" state (State 0) that acts as an "Epistemic Hold," distinguishing between valid (1), invalid (-1), and uncertain (0) transaction states. Dual-Lane Latency Architecture: Defines a "Fast Lane" for synchronous, clear-cut transactions and a "Slow Lane" for ambiguous cases requiring governance or oracle resolution, preventing head-of-line blocking. Cryptographic Provenance: Utilizes EIP-712 typed data signing to bind off-chain AI/Oracle verdicts to on-chain execution, preventing replay attacks and ensuring distinct domain separation. Privacy Preservation: Integrates Zero-Knowledge Proofs (ZK-SNARKS) to verify the execution of moral logic models without revealing sensitive input data or proprietary model weights ("Glass Box" architecture). Immutable Core Pattern: Rejects standard upgradeable proxy patterns in favor of an "Immutable Core" architecture to eliminate administrative "God Mode" and ensure constitutional constraints cannot be bypassed by key holders. Formal Verification: Demonstrates safety and liveness properties (e.g., "No Silent Pause," "Eventual Resolution") using TLA+ (Temporal Logic of Actions) to mathematically prove the system's robustness.
Non-fungible tokens (NFTs) on Ethereum currently follow a binary mobility paradigm: ERC-721 enables unrestricted transfers, whereas SBTs (ERC-5192) prohibit transfers entirely. We identify a design gap in which no standard mechanism supports bounded transferability, where ownership mobility is allowed but limited to a finite number of programmable transfers. We study counted NFT transfers and introduce ERC-7634 as a minimal realization compatible with ERC-721. The design augments each token with a transfer counter and configurable cap L, allowing ownership to evolve under a finite transfer budget. ERC-7634 defines a minimal extension interface with three lightweight functions (transferCountOf, setTransferLimit, and transferLimitOf), two events, and native-transfer hooks, requiring fewer than 60 additional lines of Solidity while preserving full backward compatibility with existing NFT infrastructure. We analyze behavioral and economic consequences of counted transfers. Our results reveal (i) a mobility premium induced by remaining transfer capacity, (ii) a protocol-level costing signal that can deter wash trading in cap-aware markets through irreversible budget consumption, (iii) bounded recursive collateralization enabled by limited ownership turnover, and (iv) associated security and gas-cost implications, including wrapper-bypass trade-offs. Evaluation on calibrated simulations shows that moderate limits (e.g., L = 10) affect fewer than 15% of tokens under representative transfer distributions, while repeated manipulation becomes unprofitable after a few cycles in a cap-aware pricing model; the additional gas overhead remains below 11% per transfer. We further position ERC-7634 within the NFT mobility design space, derive practical cap-selection guidelines, and discuss post-cap ownership outcomes including soulbound conversion, auto-burn, and provenance freeze.
Investigations of cybercrime today require forensic architectures that natively traverse multiple blockchains with ease while protecting and scaling evidence processing. Although blockchains support tamper- evident logs, their original single-chain architecture limits cross-platform interoperability and forensic scaling. Recent developments overcome these limitations such as zero-knowledge proofs supporting private but verifiable evidence verification, sharding architectures splitting state without compromising latency, and AI-based anomaly detectors identifying subtle tampering. But challenges remains like zero- knowledge proofs are computationally expensive, sharding poses intricate state-consistency problems and AI models need to be retrained constantly, incurring operational burden. Future research needs to make these pieces work for real- time, large-scale forensic applications by designing light-weight zero-knowledge constructs, self-tuning shard governance systems and compact AI with incremental-update threads. Integrating such abilities into single frameworks will offer privacy, scalability and security, supporting forensic processes for which courts will give credit in various, changing block-chain environments.
Efficiently matching patients to clinical trials is essential for advancing medical research and ensuring reliable outcomes. However, current matching methods face several challenges. These include data integrity issues from tampered records, privacy risks caused by weak anonymization, and manual processes that delay recruitment. In addition, centralized systems lack transparency, expose sensitive patient data to security vulnerabilities, and suffer from single points of failure that reduce resilience and trust. In this paper, we propose a blockchain and Large Language Models (LLMs)-driven solution for secure, trustworthy, traceable, decentralized, and transparent patient–clinical trial matching. Blockchain ensures data integrity, security, and transparency by eliminating single points of failure and enabling tamper-proof records. LLMs enhance patient–trial matching by automating the interpretation of complex eligibility criteria, improving accuracy, and significantly reducing the time required for manual review. Our approach uses Ethereum-based smart contracts to automate workflows such as trial registration, eligibility assessment, and consent tracking. We fine-tune GPT-4, T5, and Gemini on synthetic data derived from real clinical trial records and employ majority voting to ensure consistent and unbiased eligibility decisions. A prototype Gradio interface was developed as a minimum viable product (MVP) to demonstrate seamless interaction between LLMs and smart contracts. Performance evaluation based on accuracy (0.800), precision (0.733), recall (1.000), and F1-score (0.846) demonstrates reliable eligibility prediction. Cost analysis confirms affordability, and security evaluation verifies resilience against known threats. Comparison with existing solutions highlights the framework’s advantages in transparency, trust, and automation. The smart contract code is publicly available on GitHub.
Current commercial Large Language Model (LLM) architectures enforce a "server-side memory" paradigm, where user cognitive state is stored, managed, and monetized by the provider. This centralization creates two critical vulnerabilities: the economic inefficiency of "token inflation" (re-processing redundant context) and the epistemological risk of "rented cognition" (lack of user sovereignty over identity). This paper proposes a disruptive architectural shift: Remember Me AI, formally defined as the Client-Side Narrative Protocol (CSNP). By integrating Cross-Session Narrative Memory (CSNM) with a novel Semantic Compression Layer and Distributed Local Storage, we demonstrate a mechanism to reduce context token costs by approximately 40x while maintaining longitudinal coherence. We argue that this architecture commoditizes the inference layer, forcing a market transition from "Memory-as-a-Service" to "Compute-as-a-Commodity." This restores epistemological sovereignty to the user and neutralizes the lock-in mechanisms of hyperscale providers. The protocol includes Merkle-CRDT synchronization for multi-device consistency, Zero-Knowledge Safety Proofs for regulatory compliance, and a Polyglot Transpiler to ensure interoperability across proprietary model endpoints.
AI development requires reliable datasets, yet today’s data supply chains face challenges in traceability, authenticity and ethical compliance. This study introduces a blockchain-assisted data integrity framework that ensures transparent and verifiable provenance for AI model training. The proposed system uses smart contracts to record data lineage, ownership, preprocessing transformations and annotation events. IPFS-based off-chain storage reduces blockchain load while ensuring immutability. A verification engine allows auditors to evaluate dataset compliance with ethical and regulatory standards, including bias mitigation and consent validation. Experiments utilized three real AI workflows: medical imaging, sentiment analysis and environmental sensor classification. Findings show a 92 percent reduction in provenance disputes and an improvement in audit efficiency by 41 percent. The system also provides tamper-resistant documentation supporting responsible AI governance. Latency tests show minimal performance impact due to parallelized validation nodes. This research demonstrates that blockchain can provide a robust backbone for ethical AI ecosystems, where transparency and trust are critical. Future work will explore confidentiality enhancements using zero-knowledge proofs.
Modern digital ecosystems rely heavily on Open Source Software (OSS), but maintaining license compliance is still a major and unsolved problem. Current approaches rely on either manual audits, which are expensive, sluggish, and prone to error, or automatic scanners, which frequently fail with dual or bespoke licenses. Businesses, entrepreneurs, and academic institutions are exposed to serious legal, financial, and reputational concerns as a result of this divide. This project suggests a multi-layered OSS License Verification Framework that incorporates human-in-the-loop learning, logical reasoning, evidence-based validation, provenance tracking, and cryptographic assurance in order to overcome these constraints. To establish technical ground truth, the system starts with SBOM and SPDX provenance data, builds an attestation graph, and uses binary inference and differential tracing. License requirements are represented as vectors of obligations, assessed using a constraint solver and validated using zero-knowledge proofs (zk-proofs) to give auditors reliable proof of compliance. A human oracle ensures adaptation to changing license ecosystems by resolving ambiguities and continuously enhancing the knowledge base. The suggested framework seeks to provide an end-to-end, intelligent, and auditable solution for OSS licensing compliance by fusing automation with verifiability and adaptability. The results will help a variety of stakeholders, such as businesses looking to reduce risk, startups seeking quicker innovation, and academic institutions using OSS responsibly, all of which will contribute to a more secure and reliable opensource ecosystem.
Abstract - The paper referenced proposes a decentralized marketplace model for trading, verifying, and managing the ownership of AI models by means of blockchain and NFTs. Given the need for trusted exchange and provenance in the management of AI assets, the authors propose a system wherein AI models and datasets are represented as NFTs on a public blockchain, providing transparent, traceable, and secure transactions. Smart contracts automate auctions, royalty distributions, and ownership transfers. Further security and privacy are provided by TEEs, proxy re-encryption, and decentralized storage (IPFS). Collaboration is enabled through the architecture, which allows contributors to improve and resell models, while royalty schemes guarantee fair compensation for creators. Details of the implementation include smart contracts in Solidity and cost analyses for transaction efficiency. Evaluation in terms of security is resilient against Sybil and Eclipse threats. It is also set up as broadly adaptable to both public and private AI assets and easily generalizable to other situations of digital assets to ensure robust provenance, fair remuneration, and trustless exchange. Key Words: Blockchain, Non-Fungible Tokens(NFTs), Decentralized AI marketplace, Smart Contracts, Digital Ownership
Keybyte Systems, Intentix Lab , Melbourne, Australiapronab@keybytesystems.com.au Supported by AusIndustry Grant IR2405165 __________________________________AbstractModern cloud-native applications distribute business logic across multiple layers: application code, orchestration frameworks, service meshes, and infrastructure configurations. This distribution creates ”hidden logic”—execution rules embedded in infrastructure that are invisible during design and difficult to trace at runtime. We present Intention Space , a computing model built on the CPUX (Common Path of Understanding and Execution) paradigm that consolidates all business logic into explicit, design-time declarations using plain-language state pulses. In our model, Design Nodes (DNs) contain computation while Gatekeepers declare execution conditions as named pulses (e.g., ”payment validated”: Y). The infrastructure provides only mechanical enforcement through an Intention Loop that matches runtime state to Gatekeepers without adding decision logic. We demonstrate that complex workflows—traditionally requiring nested if-then branching and explicit loops—can be expressed as linear CPUX sequences where execution paths emerge from data state rather than code branching. Our Golang implementation shows complete elimination of orchestration code while maintaining full cognitive traceability. Beyond technical innovation, CPUX addresses a critical social computing crisis: the lack of accountability in distributed social platforms. By creating unique, device-level CPUX footprints for every interaction, our model enables verifiable traceability from device identity through user intention to executed action—restoring accountability to social computing while preserving privacy. We argue this separation of intent (CPUX) from enforcement (infrastructure) is essential for building LLM-integrated, auditable, and socially responsible distributed systems.Keywords: CPUX, Intention Space, Design Nodes, Cognitive Computing, Data-Driven Execution, Microservices Architecture, Cloud Computing, LLM Integration, Social Computing Accountability__________________________________1. Introduction1.1 The Hidden Logic ProblemConsider a typical e-commerce order processing system deployed on Kubernetes with Istio service mesh:// order-service/main.go (Business Logic Layer) func ProcessOrder(order Order) error { if order.Amount > 1000 { if err := premiumValidator.Validate(order); err != nil { return retry(premiumValidator.Validate, 3, order) } } else { standardValidator.Validate(order) } // … more branching logic }# k8s/hpa.yaml (Infrastructure Layer) spec: metrics: - type: Resource resource: name: cpu target: type: Utilization averageUtilization: 80 # Hidden rule: Scale when CPU > 80%# istio/retry-policy.yaml (Service Mesh Layer) spec: http: - retries: attempts: 3 perTryTimeout: 2s # Hidden rule: Retry 3 times on failureQuestion: What is the complete execution flow for a $1500 order that fails validation on first attempt?Answer: One must read and correlate:Application code (branching logic)Kubernetes manifests (scaling rules)Istio configurations (retry policies)Service mesh observability logs (runtime behavior)This hidden logic distribution creates fundamental problems:Traceability : No single artifact shows complete flowTestability : Must test infrastructure + code interactionsAuditability : Business stakeholders cannot validate logicMaintainability : Changes require coordinating multiple layersLLM Integration : No structured representation for AI reasoningSocial Accountability : Cannot trace interactions to source devices/users1.2 The Core InsightWe observe that traditional computing conflates two distinct concerns:What should happen(business intent)How to make it happen(mechanical execution)Current architectures intertwine these concerns across code, configuration, and infrastructure, making systems cognitively opaque.Our Contribution: We introduce CPUX (Common Path of Understanding and Execution) , a paradigm that separates business intent from infrastructure enforcement:CPUX Structure : Declares all possible execution paths as sequences of Design Nodes (DNs) with plain-language Gatekeeper conditionsInfrastructure : Provides mechanical execution (Intention Loop) that enforces CPUX declarations without adding decision logicDevice-Level Identity : Each CPUX execution tied to unique device fingerprint + user intention, enabling social computing accountabilityResult : Complete business logic is visible in CPUX; infrastructure remains purely mechanical; every social interaction is traceable1.3 Key ContributionsFormal Model : CPUX as cognitive execution contract with Design Nodes, Intentions, Objects, and Pulses as primitive componentsElimination of Hidden Logic : All business decisions visible in design-time CPUX declarations; infrastructure adds zero decision logicPlain-Language State Declarations : Execution conditions expressed as named pulses (e.g., ”inventory confirmed”: Y) enabling business stakeholder review and LLM integrationData-Driven Execution : Runtime branching eliminated from code; execution paths emerge from pulse state matching via SyncTestSocial Computing Accountability : Device-level CPUX fingerprints create unique, traceable identity for every social interaction, addressing the accountability crisis in platforms like Facebook, Twitter, TikTokImplementation & Evaluation : Golang framework code sample with concrete use case demonstrating zero orchestration code while maintaining full traceability1.4 Paper OrganizationSection 2 examines related work. Section 3 presents the PnR computing model and CPUX formalism. Section 4 details the architecture and implementation. Section 5 evaluates our approach through metrics and case studies. Section 6 discusses LLM integration. Section 7 introduces CPUX for social computing accountability—the urgent global need. Section 8 concludes with future directions.__________________________________2. Related Work2.1 Workflow Orchestration SystemsAWS Step Functions [1] and Azure Logic Apps [2] provide visual workflow definition with explicit state machines. However, they:Use proprietary JSON/XML DSLs (not plain language)Embed conditional logic in workflow definitions (still branching)Remain platform-specific (vendor lock-in)Require reading workflow definitions to understand flowCannot trace to device/user identityApache Airflow [3] and Temporal [4] define workflows as code with DAG structures. They improve on step functions ,recoverability but:Business logic still in code (if-then branches)Workflow orchestration separate from executionNo plain-language condition declarationsNo device-level traceabilityCPUX Advantage : All logic in plain-language pulses, platform-agnostic, no explicit branching in declarations, device-level identity for every execution, recoverability built into platform. 2.2 Service Mesh & OrchestrationIstio [5] and Linkerd [6] provide traffic management, retries, circuit breaking. Kubernetes Operators [7] encode reconciliation logic. These systems:Hide business rules in YAML configurationsDistribute logic across mesh config + operator codeFocus on infrastructure concerns (not business flow)Lack unified view of complete execution pathNo user/device attributionCPUX Advantage : Consolidates all execution logic in CPUX; infrastructure config aligned with business intent; device identity integral.2.3 Event-Driven ArchitecturesApache Kafka [8], AWS EventBridge [9] enable event-driven systems with loose coupling. Reactive systems [10] promote message-passing. However:Event flows implicit (must trace message paths)Conditional logic in event handlers (code-level branching)No design-time declaration of all possible flowsNo provenance tracking to source deviceCPUX Advantage : Explicit declaration of all event-driven paths as DN sequences with visible Gatekeepers; device identity in event provenance.2.4 Intent-Based SystemsIntent-Based Networking [11] translates high-level intents to network configurations. Policy-based management [12] separates policy from mechanism. Closest to our work, but:Focus on infrastructure (not application logic)Policies often domain-specific (not general computing)Limited plain-language expressivenessNo user accountabilityCPUX Advantage : General-purpose computing model with full plain-language pulse declarations applicable to any domain; device-level user accountability.2.5 Formal Methods & Model CheckingTLA+ [13], Alloy [14], and Petri Nets [15] enable formal specification and verification. These are powerful but:Require specialized formal notation (high learning curve)Specification separate from implementation (sync problems)Not designed for runtime executionNo social computing traceabilityCPUX Advantage : Declarations are executable; CPUX structure IS the implementation contract; device identity embedded.2.6 Social Computing & AccountabilityBlockchain-based identity [16] and zero-knowledge proofs [17] address digital identity but:Focus on cryptographic primitives (not execution tracing)Don’t integrate with application logicNo cognitive representation of intentFederated social networks [18] (Mastodon, ActivityPub) improve decentralization but:Still lack device-level traceabilityNo structured intent representationCannot prove user intended specific actionCPUX Advantage : First system to integrate device identity, user intention, and execution trace in single cognitive framework.2.7 PositioningCPUX is the first system to combine:Plain-language execution conditions (like Intent-Based Networking)Executable specifications (unlike formal methods)Complete flow visibility (unlike distributed orchestration)Zero hidden infrastructure logic (unique contribution)Device-level social accountability (unique contribution)__________________________________3. The PnR Computing Model3.1 Core Abstractions3.1.1 Pulse: Atomic State UnitA Pulse is the fundamental data unit representing a named state with optional response and trivalence:Pulse = (Name: String, Response: Value, Trivalence: {Y, N, U})Name : Plain-language identifier (e.g., ”payment validated”)Response : Optional value (e.g., transaction ID)Trivalence : Y (yes/true), N (no/false), U (undecided)
AI systems rely heavily on high-quality training data, yet provenance tracking remains fragmented and vulnerable to manipulation. This study presents a blockchain-enabled data provenance framework designed to bring transparency and verifiability to AI training pipelines. The architecture records dataset lineage, preprocessing steps, annotation events and model updates using immutable smart contract transactions. A lightweight off-chain storage mechanism reduces blockchain overhead while maintaining audit guarantees. The system was tested with three machine learning pipelines involving image classification, text processing and sensor analytics. Results show a 95 percent reduction in provenance disputes and full traceability across all data contributors. Smart contracts automate compliance checks and access permissions, ensuring that only validated datasets feed into the training process. The framework improves accountability for AI ethics, model bias evaluation and regulatory reporting. Experiments confirm that blockchain latency does not significantly affect pipeline throughput due to parallelized validation nodes. The work demonstrates how decentralized technologies can support trustworthy AI development. Future research will explore integration with zero-knowledge proofs to further enhance confidentiality.
ExecMesh introduces cryptographically verifiable computation as a foundational primitive for regulatory compliance and audit trail requirements in AI/ML systems [1–3]. By combining commitmentbased verification with secure multi-party oracles and a two-tier regulatory architecture, ExecMesh enables enterprises to meet FDA, SEC, and EU AI Act requirements while maintaining the benefits of decentralized infrastructure. Immediate Value Proposition: ExecMesh provides immediate value as an audit trail and provenance layer for regulated AI systems, independent of advances in zero-knowledge proof technology. Even without full verification of large neural networks, the system delivers cryptographic guarantees for data integrity, execution timestamps, and pipeline reproducibility—meeting core regulatory requirements today.
Kavian Amirmozafarisabet, Meisam Namazi, Mohammad Naserameri
Rapid deployment of AI, particularly in sectors such as healthcare, finance and smart infrastructure, necessitates reliable capabilities (1) to assure data integrity (2) model update verifiability and (3) privacy preservation along the AI lifecycles. In this paper, we propose a blockchain based framework for trustful AI and traceable end-to-end training and inference. The framework leverages a distributed ledger to immutably bind the hash-locked commitment of data and model parameters through deployed smart contracts defining automated verification protocols. Secure multiparty computation [PADDP13] and zero-knowledge proofs are some of the privacy mechanisms used during collaborative training in federated learning environments to keep sensitive data safe. In expanding on our work in blockchain-based AI integrity frameworks, the new architecture shown integrates anomaly detection and provenance tracking to prevent malicious contributions. Extensive experimental evaluations over standard datasets confirm the ability of our solution to efficiently provide reliable verification without additional computational background for domain independence. This work has the potential to form the bedrock of future AI deployments that are transparent, safe, and ethically responsible.
Academic credential fraud and falsification of research outputs remain persistent challenges in higher education and research communities. Traditional centralized credential verification systems are vulnerable to tampering, slow verification processes, and lack of transparency. This research introduces a robust blockchain-based consortium framework for transparent and tamper-proof verification of academic credentials and research outputs. Unlike prior works that primarily address identity or degree validation, our system integrates universities, accreditation authorities, and publishers into a multi-layered consortium blockchain, ensuring trust among multiple stakeholders. To preserve privacy, zero-knowledge proofs (ZKPs) are applied, enabling credential verification without disclosing sensitive personal data. The framework also introduces a dynamic revocation mechanism to handle fraudulent, plagiarized, or revoked certificates and publications. A prototype implementation on Hyperledger Fabric demonstrates feasibility, achieving high throughput (182 TPS), low latency (1.2 s average block confirmation), and efficient scalability with multiple nodes. Our results highlight the potential of blockchain in building a global, tamper-proof, privacy-preserving academic verification ecosystem, addressing credential fraud and ensuring research integrity.
K.Kalaiselvi, Mohammad Musa Al-Momani, E.Sivajothi, T. Vijetha · 6 authors
Embedded systems are at the heart of critical infrastructure facilities in areas such as energy, transportation, healthcare, defense; where integrity, traceability, and auditability of data generated by the system are of great importance. However, existing data provenance security solutions for embedded settings are highly unsatisfactory because centralized design is vulnerable in embedded environment, they do not scale well and have poor privacy mechanisms. This work presents a novel method of combining immutable data provenance and anonymous blockchain solutions to solve these issues and promote the trustworthiness of embedded systems. The framework takes advantage of privacy-preserving cryptographic methods such as ring signatures, stealth addresses, and zero-knowledge proofs to support tamper-evident decentralized storage of data events without losing source privacy. It's designed to run efficiently under the resource constraints of the embedded platforms, to be low point compatible with low-power devices, without sacrificing the responsiveness of the system or the authenticity of the data. The architecture is designed for real time monitoring and auditability on distributed embedded devices that are installed in critical infrastructure networks. A lightweight consensus algorithm designed for embedded environments allows secure synchronization and validation of data without the need for the heavy computation of a public blockchain. The framework was experimentally validated through prototype implementation and simulation in multiple use-case scenarios, showing its effectiveness against data forgery, unauthorized access and provenance tampering. Performance evaluation demonstrates that the model is scalable, low latency and high throughput under restrained resource environments. This work demonstrated that, by building immutable and anonymous data provenance into embedded systems, in addition to increasing transparency, trustworthiness, and robustness of operation, it is also possible to lay the foundation for a novel class of secure, decentralized infrastructure monitoring tools suitable for adversarial deployments. Results demonstrate a robustness for deployment into actual applications with high-assured data traceability supported with privacy protection.
This project applies the latest cutting-edge Web3 technologies to design a Blockchain-Based Proof of Contribution (PoC) Framework aimed at improving efficiency, security, and transparency in managing research outputs and safeguarding intellectual property in collaborative settings. This framework uses the features of smart contracts on Ethereum and blockchain technology to guarantee transparency and accountability by automating the process involved in awarding the funds raised, verification of donations, and resolving disputes. After hashing a contribution cryptographically, every one of them stays on the blockchain, generating an irreversible record of its veracity. The application of IPFS in this makes decentralized and safe offchain storage possible, scalable, and intact. It has an automated dispute resolution process, which guarantees that the outcomes will be fair and not sent out for arbitration from outside. The framework deals with issues of trust, data security, and intellectual property in order to provide a safe and effective setting in which researchers can focus on creativity, knowing their work is safely documented, appropriately acknowledged, and protected from misuse.
Rene Casanova, Fernán A Villa-Garzón, John W. Branch
Background: Health information systems (HIS) are critical for digital health transformation, yet fragmentation and poor interoperability adoption remains a major challenge. Objectives: This study systematically reviews architectural patterns used in HIS and evaluates their alignment with ecosystem-level requirements. Methods: Following PRISMA 2020 guidelines, a systematic literature review was conducted across Scopus, IEEE Xplore, PubMed, and Web of Science (2020-2025). Eligible studies described, evaluated, or proposed HIS solutions. Results: From an initial set of 304 records, 89 met the inclusion criteria. Service-based and decentralized/distributed ledger architectures were predominant, with emerging models integrating edge computing and modular design. FHIR-based contracts are found as stabilizers of interfaces, enabling validation and reducing integration costs. However, gaps persist in cross-border care, sustainability, and artificial intelligence integration. Conclusion: While microservices dominate current HIS architectures, achieving resilient, interoperable ecosystems requires greater architectural diversity and intersectoral collaboration.
Recent advances in large language models (LLMs) have enabled the emergence of intelligent agents capable of performing complex multi-step tasks across various domains. In parallel, the growth of Web3 has introduced a decentralized web infrastructure, yet remains largely inaccessible to non-technical users due to operational complexity, fragmented information, and security risks. In this article, we present Web3Agent , an AI agent system that integrates LLM-based interaction with blockchain environments to enable language-driven on-chain operations. Web3Agent automatically decomposes user instructions into structured workflows, dynamically queries blockchain data and APIs, and performs multi-step operations such as asset transfers, token swaps, and smart contract execution. Web3Agent incorporates real-time inspection, error handling, and interaction transparency across its operation log, and flow visualization components. We evaluate the system and perform ablation study with customized dataset in a simulated environment, demonstrating its feasibility in orchestrating complex Web3 tasks and highlighting implications for agent-based abstraction in decentralized systems.
J. Wenzel, Alam, Syeda Umaima, Andreas Schmidt, Hanwei Zhang · 5 authors
An ever increasing number of high-stake decisions are made or assisted by automated systems employing brittle artificial intelligence technology. There is a substantial risk that some of these decision induce harm to people, by infringing their well-being or their fundamental human rights. The state-of-the-art in AI systems makes little effort with respect to appropriate documentation of the decision process. This obstructs the ability to trace what went into a decision, which in turn is a prerequisite to any attempt of reconstructing a responsibility chain. Specifically, such traceability is linked to a documentation that will stand up in court when determining the cause of some AI-based decision that inadvertently or intentionally violates the law. This paper takes a radical, yet practical, approach to this problem, by enforcing the documentation of each and every component that goes into the training or inference of an automated decision. As such, it presents the first running workflow supporting the generation of tamper-proof, verifiable and exhaustive traces of AI decisions. In doing so, we expand the DBOM concept into an effective running workflow leveraging confidential computing technology. We demonstrate the inner workings of the workflow in the development of an app to tell poisonous and edible mushrooms apart, meant as a playful example of high-stake decision support.
This student propose a new methodology for academic credential verification. The system focuses on tamper resistance, transparency, and scalability-key factor for building trust among institution, employers and student Developed on block chain technology, the system creates an immutable record that can be used in order to store academic credentials that are safe to prevent any third-party modifications to ensure that their alteration is prevented to a significant extent and the fear of fraud is reduced in the system. It integrates the Interplanetary File System (IPFS) that can be used to store data in a decentralized to guarantee fast access to documents and safe guard against tampering. Biometric hashing offers individual-specific users, which increases security and prevents Fraud, and zero-knowledge proofs (zkSNARKs) prove the credentials without expressing crucial confidential data, hence strikes the equilibrium between privacy and verification. Future AI algorithms could also optimize the detection of Fraud by detecting patterns and anomalies that further enhance security and user trust. The system is set in such a way that it is flexible to integrate with the existing block chain networks with significant contributions being seen in increased accuracy in verifications, security integrity, identity confirmation and reduced fraudulent activities. Such a system is essential ultimately to establish trust and credibility in academic credentials across borders thus strengthening the credibility of educational degree.
The ethical tension surrounding AI-generated art often arises from misconceptions that anthropomorphize the algorithmic process. The accusation that “AI steals human creativity” overlooks the mediating role of human design and data literacy. This paper reframes the debate as a problem of informational asymmetry rather than morality. It proposes that Non-Fungible Tokens (NFTs) and Digital Object Identifiers (DOIs) can visualize and authenticate the flow of creative tension within a transparent ecosystem. NFTs serve as formal anchors—recording authorship, signature, and temporal origin—while DOIs preserve the conceptual framework and creative process. When linked, these two systems transform authorship into a traceable circulation of knowledge, allowing the boundary between plagiarism, homage, and originality to be objectively determined. This dual-layer provenance model presents an ethical infrastructure for creation in the age of generative AI.
Open access
2 source records
Scientific Computing and Data Management
Ethics and Social Impacts of AI
Artificial Intelligence in Healthcare and Education