Nikos Papatheodorou, George Hatzivasilis, Nikos Papadakis
Self-sovereign identity (SSI) is an emerging model for digital identity management that empowers individuals to control their credentials without reliance on centralized authorities. This work presents YouGovern, a blockchain-based SSI system deployed on Binance Smart Chain (BSC) and compliant with W3C Decentralized Identifier (DID) standards. The architecture includes smart contracts for access control, decentralized storage using the Inter Planetary File System (IPFS), and long-term persistence via Web3.Storage. YouGovern enables users to register, share, and revoke identities while preserving privacy and auditability. The system supports role-based permissions, verifiable claims, and cryptographic key rotation. Performance was evaluated using Ganache and Hardhat under controlled stress tests, measuring transaction latency, throughput, and gas efficiency. Results indicate an average DID registration latency of 0.94 s and a peak throughput of 12.5 transactions per second. Compared to existing SSI systems like Sovrin and uPort, YouGovern offers improved revocation handling, lower operational costs, and seamless integration with decentralized storage. The system is designed for portability and real-world deployment in academic, municipal, or governmental settings.
Roman Kashitsyn, Robin Künzler, Ognjen Marić, Lara Schmid
Reentrancy is a well-known source of smart contract bugs on Ethereum, leading e.g. to double-spending vulnerabilities in DeFi applications. But less is known about this problem in other blockchains, which can have significantly different execution models. Sharded blockchains in particular generally use an asynchronous messaging model that differs substantially from the synchronous and transactional model of Ethereum. We study the features of this model and its effect on reentrancy bugs on three examples: the Internet Computer (ICP) blockchain, NEAR Protocol, and MultiversX. We argue that this model, while useful for improving performance, also makes it easier to introduce reentrancy bugs. For example, reviews of the pre-production versions of some of the most critical ICP smart contracts found that 66% (10/15) of the reviewed contracts -- written by expert authors -- contained reentrancy bugs of medium or high severity, with potential damages in tens of millions of dollars. We evaluate existing Ethereum programming techniques (in particular the effects-checks-interactions pattern, and locking) to prevent reentrancy bugs in the context of this new messaging model and identify some issues with them. We then present novel Rust and Motoko patterns that can be leveraged on ICP to solve these issues. Finally, we demonstrate that the formal verification tool TLA+ can be used to find and eliminate such bugs in real world smart contracts on sharded blockchains.
Trace-based properties are the gold standard for program behaviour analysis. One of the domains of application of this type of analysis is cryptocurrency ledgers, both for the purpose of analyzing the behaviour of the ledger itself, and any user-defined programs called by it, known as smart contracts. The (extended) UTxO ledger model is a kind of ledger model where all smart contract code is stateless, and additional work must be done to model stateful programs. We formalize the application of trace-based analysis to UTxO ledgers and contracts, expressing it in the languages of topology, as well as graph and category theory. To describe valid traces of UTxO ledger executions, and their relation to the behaviour of stateful programs implemented on the ledger, we define a category of simple graphs, infinite paths in which form an ultra-metric space. Maps in this category are arbitrary partial sieve-define homomorphisms of simple graphs. Programs implemented on the ledger correspond to non-expanding maps out of the graph of valid UTxO execution traces. We reason about safety properties in this framework, and prove properties of valid UTxO ledger traces.
Cryptocurrency price dynamics are driven largely by microstructural supply demand imbalances in the limit order book (LOB), yet the highly noisy nature of LOB data complicates the signal extraction process. Prior research has demonstrated that deep-learning architectures can yield promising predictive performance on pre-processed equity and futures LOB data, but they often treat model complexity as an unqualified virtue. In this paper, we aim to examine whether adding extra hidden layers or parameters to "blackbox ish" neural networks genuinely enhances short term price forecasting, or if gains are primarily attributable to data preprocessing and feature engineering. We benchmark a spectrum of models from interpretable baselines, logistic regression, XGBoost to deep architectures (DeepLOB, Conv1D+LSTM) on BTC/USDT LOB snapshots sampled at 100 ms to multi second intervals using publicly available Bybit data. We introduce two data filtering pipelines (Kalman, Savitzky Golay) and evaluate both binary (up/down) and ternary (up/flat/down) labeling schemes. Our analysis compares models on out of sample accuracy, latency, and robustness to noise. Results reveal that, with data preprocessing and hyperparameter tuning, simpler models can match and even exceed the performance of more complex networks, offering faster inference and greater interpretability.
Ensuring traceability, compliance certification and cold chain integrity in frozen food supply chains remains a persistent challenge, exacerbated by fragmented monitoring systems, manual audits and vulnerability to data manipulation. This study presents a conceptual design for a blockchain-enabled compliance architecture that addresses these challenges by integrating real-time Internet of Things (IoT) data acquisition, permissioned blockchain-based data storage and smart contract-driven compliance automation. Following a Design Science Research (DSR) methodology, the research focuses on the initial phases (problem identification, objective specification and artefact conceptualization) providing a structured foundation for future demonstration and evaluation. The proposed design is structured across three interdependent layers: (1) a Data Acquisition Layer that ensures continuous and secure sensor-based monitoring; (2) a Data Storage Layer that leverages blockchain for immutable recording and transparent auditability; and (3) an Application Layer that integrates smart contracts for automated compliance enforcement and user interfaces for stakeholder interaction. By translating regulatory compliance requirements into a modular, blockchain-based design, this work contributes to the theoretical grounding of decentralized regulatory infrastructures in agri-food systems. The proposed architecture embodies design principles that may inform similar traceability systems across other regulated supply chains. Although empirical validation is forthcoming, the conceptualization serves as a scaffold for future DSR iterations and contributes to design knowledge in the domain of digital compliance architectures.
Bonnet, Severin, Di Francesco Maesa, Damiano, Loporchio, Matteo, Tietze, Frank
Currently, artificial intelligence (AI) models – particularly those of, but not limited to, Large Language Models – are trained over large amounts of data. Training often happens with little consideration, and even less remuneration, of input data that is content protected by Intellectual Property (IP) rights (e.g., copyrights). The recent rise in sophistication and popularity of generative AI models has further highlighted this issue, as traditional IP licensing models remain largely inadequate. In this paper, we present a proof of concept for an automated, fair, and trustworthy remuneration system for AI model training data contributors leveraging Distributed Ledger Technology. We propose the use of attribution methods for rewarding the most relevant sources for any given request, and smart contracts for the enforcement of the mutually beneficial revenue-sharing agreements between the model creator and training data copyright holders.
Jun 6, 2025·Innovations in Digital Finance and Intelligent Technologies: A Deep Dive into AI, Machine Learning, Cloud Computing, and Big Data in Transforming Global Payments and Financial Services
The financial sector is pervaded by high uncertainty and at a constant risk of damage to multiple stakeholders, either voluntarily or involuntarily. The highly unpredictable, multi-stakeholder, and multi-dimensional implications of machine learning assurances in finance have caused regulators around the world to impose strict regulations on their use. The heavy documentation requirements imposed on AI systems primarily aim to increase transparency through collaborative scrutiny of different stakeholders by allowing audits to be performed. This auditability requirement raises additional challenges for the implementation of distributed ledger-based systems and can discourage companies from utilizing the advantages such technologies convey. Nonetheless, operating in a system lacking collaborative transparency can pose even higher risks. Hence, the use of AI systems in finance needs to be adequately scrutinized in a manner that maintains the advantages of decentralization while ensuring the maintenance of internal and external compliance.
Stablecoins face an unresolved trilemma of balancing decentralization, stability, and regulatory compliance. We present a hybrid stabilization protocol that combines crypto-collateralized reserves, algorithmic futures contracts, and cross-chain liquidity pools to achieve robust price adherence while preserving user privacy. At its core, the protocol introduces stabilization futures contracts (SFCs), non-collateralized derivatives that programmatically incentivize third-party arbitrageurs to counteract price deviations via adaptor signature atomic swaps. Autonomous AI agents optimize delta hedging across decentralized exchanges (DEXs), while zkSNARKs prove compliance with anti-money laundering (AML) regulations without exposing identities or transaction details. Our cryptographic design reduces cross-chain liquidity concentration (Herfindahl-Hirschman Index: 2,400 vs. 4,900 in single-chain systems) and ensures atomicity under standard cryptographic assumptions. The protocol's layered architecture encompassing incentive-compatible SFCs, AI-driven market making, and zero-knowledge regulatory proofs. It provides a blueprint for next-generation decentralized financial infrastructure.
Jun 6, 2025·Innovations in Digital Finance and Intelligent Technologies: A Deep Dive into AI, Machine Learning, Cloud Computing, and Big Data in Transforming Global Payments and Financial Services
Although blockchain technology has been around since the launch of Bitcoin, it is still little understood and often dismissed as merely another digital gimmick. Considered technology’s biggest innovation in the last five years, blockchain is a decentralized, distributed ledger on which transactions are recorded in an immutable way, deployable across diverse sectors – from finance to agriculture, from healthcare to smart cities. Crypto-currencies are one of the first and best-known practical applications of blockchain, but multiple other uses are being developed and adopted by start-ups and large enterprises alike.
Decentralized Finance (DeFi) has revolutionized traditional banking paradigms, offering transparent, peer-to-peer financial services without intermediaries. This paper presents a novel DeFi banking system that leverages advanced blockchain technologies including Solana and Ethereum networks, integrated through React, Node.js, and Metamask. The system facilitates seamless ETH transactions both sending and receiving across multiple networks using Hard Hat for simulation and testing. By implementing decentralized transaction history tracking, it aims to enhance transparency and user autonomy in digital banking. Our project addresses key issues of scalability, security, and ease of access, which are fundamental in current decentralized applications. We analyze the interplay between decentralized systems and traditional banking infrastructures, shedding light on how DeFi could offer faster, cheaper, and more secure financial services. Additionally, we discuss potential challenges, such as regulatory uncertainties and smart contract vulnerabilities, which need to be addressed for DeFi systems to gain widespread adoption. Through this system, we envision a future where DeFi can complement, rather than disrupt, traditional banking by providing secure, scalable, and user-centric financial services.
Efficient and secure sharing of scientific data remains a key challenge in the Open Science framework, especially in terms of data authenticity, provenance and privacy. Traditional digital repositories improve access but often lack decentralized mechanisms that guarantee integrity and traceability. Blockchain technology provides a potential solution through tamper-proof records and distributed consensus, while Zero Knowledge Proofs (ZKP) can enhance privacy protection. This study explores how blockchain and ZKP can be integrated for decentralized scientific data management. A systematic literature review reveals limited application of these combined technologies in Open Science, highlighting a research gap and the need for solutions that support transparent, secure and privacy-preserving data sharing in accordance with FAIR principles.
The rise of Industry 5.0 focuses on merging advanced intelligence, automation, and human-centered teamwork in industrial settings. However, keeping interconnected IoT networks secure is still a challenging problem. This paper proposes a new security framework that combines Blockchain, Federated Transfer Learning, and zero trust network (ZTN) principles to improve IoT security in Industry 5.0. Blockchain is a decentralized ledger that ensures secure data sharing and protects model updates. Federated Transfer Learning allows model training across distributed IoT devices to keep data private. The ZTN approach enforces strict access rules, assuming that no entity is trusted by default. The proposed framework offers a scalable and resilient solution to protect next-generation industrial IoT networks, using Blockchain for data security, transfer learning for adaptability, and ZTN for strict access control. The ZTN architecture strengthens security by checking every access request and keeping the IoT system safe. The experimental results show good performance of the proposed method, with better accuracy, precision, recall, and F1 scores. The model achieved an accuracy of 0.85, 0.88, and 0.87 for learning rates of 0.01, 0.001, and 0.0001, respectively, at 100 epochs. The precision values reached 0.84, 0.87, and 0.86, while the recall scores were 0.82, 0.86, and 0.85, respectively. The F1-scores were recorded at 0.83, 0.86, and 0.85, which confirms the robustness of our model.
The counterfeit medication infiltration within global supply chains poses a major public health threat. To address this, a collaborative effort among governments, regulators, and pharmaceutical companies is essential to secure the global/local supply chain. This paper proposes a novel approach that leverages blockchain technology, polymorphic encryption, and cloud storage to tackle security risks and privacy concerns in medication supply chains. The framework integrates a drug supply chain decentralized application (also called SCMapp) within the Ethereum blockchain, enabling functionalities like secure supplier onboarding, encrypted data management, cloud storage integration, and efficient data retrieval. This approach aims to revolutionize drug supply chain management by enhancing security, transparency, and overall efficiency, ensuring adherence to global health regulations. A safe and effective method for managing drug supply chains is provided by the suggested Drug Supply Chain Management System. The proposed model outperformed existing solutions in terms of security, efficiency, and traceability. The combination of encryption, blockchain, and cloud storage provided a comprehensive approach to address the challenges of drug supply chain management. The comparison analysis highlighted the unique advantages of the proposed model over other methods.
Ahmed Sayed M. Metwally, Yazeed Alhumaidan, Saad Alzahrani, Mohamed H. Abdelati
Implementing artificial intelligence (AI) and blockchain technology in management systems transforms traditional libraries into advanced information centers that are data-driven and effectively managed. While these technologies enhance efficiency and operational capabilities, they also present two critical challenges: data privacy and ethical concerns. This study examines the role of AI and blockchain in library management, focusing on issues related to data privacy and ethical challenges that arise from their use. It also offers best practices to ensure safe implementation. The research adopts a comprehensive mixed-methods approach, involving qualitative interviews and quantitative surveys to identify these challenges within the system architecture, assess the effectiveness of current designs, and propose a complete framework using privacy-preserving technologies. This framework incorporates innovative cryptographic techniques, including homomorphic encryption, differential privacy, and zero-knowledge proofs, providing a novel model for the ethical use of AI in libraries. The findings indicate that robust data protection, transparency, and accountability are essential to building trust in AI-powered library services.
This study examines the temporary impact of major global news on bitcoin absolute price changes from 2018 to 2023, focusing on information related to the COVID-19 pandemic, inflation, and the Russia-Ukraine conflict. Using Bloomberg news and high-frequency data, the analysis is conducted in two stages. First, hourly price data and only highly significant news are analysed over the entire period. Second, second-by-second data from the CME Bitcoin Real Time Index (BRTI) is employed for key dates, incorporating broader news categories. The results show that bitcoin investors need approximately 45 minutes to process each news item on COVID-19 and war as information continuously flows into the market. This constant information processing enables investors to anticipate highly significant news on these topics up to two hours before its publication. Conversely, inflation-related news exhibits concentrated effects around scheduled release times. The findings highlight the necessity of selecting appropriate time frequencies for the analysis to avoid misinterpretation. Overall, the study highlights the significant impact that relevant global news has on bitcoin price volatility, suggesting that bitcoin markets are becoming increasingly integrated with traditional financial markets.
Abstract The concept of the metaverse, an integrated environment encompassing virtual reality (VR), augmented reality (AR), artificial intelligence (AI), and blockchain technology, is revolutionizing brand-consumer engagement. This academic paper integrates data from quantitative surveys, expert interviews, and real-life brand applications to investigate the transformative impact of immersive and interactive environments on marketing. It examines how these technologies foster emotional engagement, customer loyalty, and digital innovation. Furthermore, the study delves into strategic implementations, including non-fungible tokens (NFTs), virtual brand ambassadors, and AI-driven brand communications, while also addressing the ethical challenges associated with this new digital frontier. The metaverse—a convergence of virtual reality (VR), augmented reality (AR), artificial intelligence (AI), and blockchain—is redefining how brands engage with consumers. This research paper combines insights from quantitative surveys, expert interviews, and real-world brand case studies to explore how immersive, interactive environments are driving emotional resonance, loyalty, and digital innovation. Highlighting strategies involving NFTs, virtual influencers, and AI-powered brand engagement, this paper presents a thorough analysis of opportunities and challenges that define metaverse marketing today.
Open access
Digital Marketing and Social Media
Consumer Behavior in Brand Consumption and Identification
In increasingly dynamic organizational and computational environments, workflows controlled by multi-agent systems face often face context changes, blurred task borders, and unexpected behaviors. This research proposes a new model for coping with decision drift in adaptive workflows by modeling latent boundary negotiation. By using concepts from complex systems theory, cognitive modeling, and distributed artificial intelligence, we define agents as autonomous systems which are able to perceive, report, and re-negotiate internal role expectations through localized negotiation protocols. The designed model merges drift detection with multi-agent coordination techniques and correlates them to simulated workflow processes typical for high risk, time constrained settings like crisis management, distributed logistics, and agile development teams. Simulation results show that latent negotiation improves task coherence, decreases agent interference, and stabilizes the performance of workflows under ambiguous and changing boundary conditions much more than negotiation less systems. The benefits of negotiation-aware multi-agent systems in supporting sustained alignment of decisions and flexibility in the system are highlighted through comparison with static and rule-based systems. The results outline latent boundary negotiation as a key feature towards the implementation of intelligent self-organizing work systems in decentralized multi agent ecosystems.
This article presents cutting-edge developments in data-passing architectures that are revolutionizing AI-driven learning systems. By examining recent breakthroughs in streaming data architectures, data lakehouse designs, and feature stores, the article identifies how these innovations overcome traditional bottlenecks in distributed training environments. It explores critical challenges in multi-platform data passing, including data quality maintenance, security considerations, and performance optimization. The discussion extends to self-healing architectures that significantly enhance system resilience through autonomous fault detection and recovery mechanisms. Additionally, emerging trends in data-sharing protocols, from blockchain-based decentralized architectures to federated learning approaches, demonstrate how collaborative AI ecosystems can maintain privacy while maximizing data utility. Through a comprehensive analysis of these architectural innovations, the article illustrates how organizations can create more powerful, resilient, and collaborative AI-driven learning systems that operate seamlessly across previously siloed environments.
Introduction. Blockchain technology has emerged as a transformative innovation in distributed computing, providing a secure, transparent, and decentralized mechanism for data management. Initially introduced as the backbone of cryptocurrencies, blockchain has expanded into various sectors, including finance, healthcare, supply chain management, and governance. However, despite its numerous advantages, blockchain faces significant challenges, including scalability, transaction speed, and energy consumption. This article presents a comprehensive analysis of blockchain technology, focusing on its classification, consensus mechanisms, scalability solutions, and future trends. The study explores the comparative advantages and limitations of different blockchain architectures and evaluates emerging optimization techniques such as hybrid consensus algorithms and artificial intelligence-based enhancements. Purpose of the Work. The objective of this study is to conduct an in-depth analysis of blockchain technology, investigating its core principles, operational mechanisms, and performance optimization strategies. The research aims to provide a systematic comparison of consensus algorithms, including Proof of Work (PoW), Proof of Stake (PoS), Delegated Proof of Stake (DPoS), and Byzantine Fault Tolerance (BFT) variations, assessing their impact on transaction speed, energy efficiency, and security. Additionally, the study examines Layer 1 (L1) and Layer 2 (L2) scaling solutions such as sharding, rollups, and sidechains to address blockchain's scalability challenges. The research also highlights emerging trends in blockchain development, particularly hybrid models and AI-driven optimization techniques, which can enhance blockchain efficiency and security. Results. The analysis reveals that different blockchain architectures exhibit varying trade-offs between decentralization, security, and scalability. Public blockchains, such as Bitcoin and Ethereum, prioritize decentralization and security but suffer from limited scalability. Private blockchains, in contrast, offer higher transaction throughput but compromise decentralization. Hybrid blockchains aim to balance these aspects by integrating the strengths of both models. A detailed comparison of consensus mechanisms indicates that PoW, while highly secure, is energy-intensive and slow, whereas PoS and its variations provide faster and more energy-efficient alternatives. The study also finds that Byzantine Fault Tolerance-based mechanisms, such as PBFT and DBFT, offer high-speed consensus suitable for enterprise applications. Furthermore, Layer 1 improvements, including sharding, enhance on-chain transaction processing, while Layer 2 solutions, such as optimistic rollups and zero-knowledge rollups, significantly increase throughput by offloading computations to secondary layers. The research highlights recent advancements, such as AI-assisted transaction validation and adaptive consensus algorithms, as promising directions for blockchain scalability and security. Conclusions. The study underscores the importance of optimizing blockchain scalability and consensus mechanisms to enable broader adoption across industries. While Layer 1 and Layer 2 solutions provide significant improvements in throughput and efficiency, their integration remains a key challenge. The findings suggest that hybrid consensus models and AI-based optimizations could further enhance blockchain performance, reducing energy consumption while maintaining security and decentralization. Future research should focus on developing dynamic sharding techniques, parallel consensus mechanisms, and predictive analytics for transaction management to advance blockchain's applicability in large-scale real-world scenarios. The continued evolution of blockchain technology will play a critical role in shaping secure, efficient, and decentralized digital ecosystems. Keywords: blockchain, decentralization, consensus mechanisms, optimistic rollups, sharding, transaction validation.
In this work, we present a concretely efficient lattice-based zero-knowledge proof system that satisfies straight-line extractability. At the heart of our construction is a novel, non-generic application of the Katsumata transform (CRYPTO 2021) to the widely adopted zero-knowledge framework of Lyubashevsky, Nguyen and Plançon (CRYPTO 2022). Our approach significantly optimizes the proof size, achieving a 2X reduction compared to prior generic techniques. This makes our scheme particularly well-suited for anonymous credential systems, secure multi-party computation, and proving quantum security, where straight-line extractability is essential.
This study analyses the price discovery between bitcoin exchange-traded funds (ETFs) and their underlying asset (bitcoin spot) after the introduction of bitcoin ETFs on US exchanges. Using 5-min data, starting from the launch of bitcoin ETFs on 11 January 2024 and nine months later, until 11 October 2024, we calculate three price discovery measures, namely Information Share (IS), Component Share (CS) and Information Leadership Share (ILS). Our ILS results suggest that bitcoin ETFs, especially the most actively traded ETFs such as IBIT, FBTC and GBTC, dominate price discovery over bitcoin spot about 85 per cent of the time during the sample period. These findings indicate an increasing investor preference for the more accessible and liquid ETFs, supported by the US SEC approval, and underline the growing appeal of bitcoin ETFs for investors seeking efficient bitcoin exposure through brokerage accounts. The study contributes to the literature on price discovery in the cryptocurrency market and provides insights for academics, investors, regulators and policymakers.
Web3 technology echoes the paradigm of a secure, transparent, and privacy-preserving decentralized internet. Unfortunately, this digital transformation has opened up new opportunities for cybercriminals, for example online gambling bookies can utilize the Blockchain Domain Name System (BDNS) running on Web3 as a way to avoid tracking and blocking. This analysis aims to map the potential threats from misuse of BDNS technology through a two-dimensional scenario matrix approach using two main types of variables: (1) Native Web3 integration into popular browsers, and (2) Level of BDNS adoption by online gambling sites. The four scenarios produced will describe various levels of risk that require different mitigation strategies, in certain combination conditions will create the highest threat conditions where the conventional domain name blocking system is no longer effective in blocking online gambling sites. The results of this study are expected to identify weak signals from the evolution of online gambling threats, as well as become a basis for government agencies or policy makers in preparing anticipatory steps in dealing with the dynamics of digital threats in the Web3 era.
Jun 6, 2025·Artificial Intelligence and Financial Transformation: Unlocking the Power of Fintech, Predictive Analytics, and Public Governance in the Next Era of Economic Intelligence
In a bank-centered financial system, digital payments basically mean how customers access their bank deposits to settle payments. Retail payments are mostly settled through government and commercial banks, but at RTGS level, payments occur through settlement controls of the central banks. In contrast, digital payments in a decentralized currency-based financial system involve no banks or financial institutions. In brief, it is the entire banking system and not just its accounts that do not participate in digital payments. Digital payments are the electric wiring that connects everyone at the ‘exchange’ and ‘conversion’ levels of the macroeconomic circuit, including consumers, businesses, banks, and central bank. In addition to money transfers, digital payments also provide complementary services needed in consumption and Ecommerce, such as invoicing, matching buyers and sellers, clearing, and settlement. Unlike most circuit activities, digital payments do not have a multiplier effect because of digital payments’ one-for-one service charge. Digital payments also have a major role in the rapid and invisible collection of indirect taxes. Digital payment systems, whether wallet-based or interbank-based, extricate the economy from the “cash flow and multiply” mechanism that has characterized the world economy ever since barter systems were replaced by elaborate currencies. Paper currencies also have become obsolete and are headed for complete replacement by digital payments (Gai et al., 2018; Chen et al., 2019; Arner et al., 2020).
A zero-knowledge proof (ZKP) is an interactive proof between a prover and a verifier, with the prover's goal to convince the verifier that a solution of a computation problem exists without revealing any information about the solution itself. A card-based physical ZKP is an implementation of a ZKP using physical cards. Several metrics are used to measure the efficiency of card-based ZKPs, such as the numbers of required cards and shuffles. In this paper, we propose the first card-based ZKP for a puzzle Sudoku that uses only one shuffle. To achieve this, we use a technique of applying private operations interactively to prepare inputs for the protocol.