Reviewerly/exHarmony huggingface dataset:
A Binary Matrix-Based Data Representation for Data Compression in Blockchain

Blockchain relies on storing and verifying a large volume of data across multiple nodes, making efficient data compression techniques crucial. By reducing the size of data, compression techniques enable more data to be stored within the limited space constraints of the blockchain networks. Furthermore, compressed data consumes less bandwidth for transmission and enhances the overall performance of blockchain networks by reducing the time and resources needed for data storage and retrieval. To overcome this issue, this paper presents a new data representation approach to enable efficient storage and management of diverse data types on the blockchain, ensuring scalability, cost-effectiveness, and improved network efficiency. A binary matrix M of size m x n bits can be converted to two vectors H and V of sizes m’ and n’, respectively. The compression rate expressed by (m‘ + n’ + │ Hash(M) │) x 100/(m × n) increases exponentially, i.e., 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">λ</sup> with <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">λ</inf> depends on m and n); this makes the proposed technique is very effective in data size reduction. With a matrix, for example, M = 512 x 512 bits, we achieve a rate of reduction equal to 96.42%. The original data can be recovered using H, V, and Hash(M). The conversion from M to (H, V) is simple, which optimizes energy consumption for low-power devices. Meanwhile, the challenge of recovering the original data could be exploited in a blockchain process, where the mining consensus could be identified based on the node that recovered a predefined set of vectors. Furthermore, this technique ensures that data integrity checking is available only at the nodes with a massive computation capacity.
Implementing and Evaluating a Blockchain-Based Dematerialized Public Procurement System with HyperLedger Fabric and OCDS

In Senegal, public procurement procedures are not fully dematerialized. Then it is observed that paper is used massively throughout the process, with risks of delay, corruption and lack of real-time procurement data available for citizens. Then, Blockchain technology is one of the candidate solutions to dematerialize the procurement process as it guarantees better transparency, efficiency and traceability. This paper proposes an implementation of a prototype by doing an extension of the Hyperledger Fabric Blockchain network. The chaincodes implemented in Go lang use the Open Contracting Data Standard data model and a proposed extension format for control activities. The documents are stored in Internet Planetary Files System, a decentralized file system storage. A REST API web application is developed to prove that our solution can securely dematerialize the public procurement process while protecting sensitive data. Regarding performance, the Hyperledger Caliper tool runs performance tests and analyzes metrics.
Opening Pandora's Box: An Analysis of the Usage of the Data Field in Blockchains

Since the proposal of Bitcoin in 2009 and with the inclusion of the first transaction in its genesis block, Blockchains (BC) have been used to store arbitrary data, including texts, images, and documents. However, such data is often not easily discoverable in BCs and is embedded within their binary data structures. Thus, this paper presents the design and implementation of a solution to analyze BC transactions searching for “media” content. This solution, called blockchain-parser, is capable of detecting ASCII strings and files (e.g., PDF, GIF, and SVG) embedded in BC's transactions. To evaluate such a solution, Bitcoin, Monero, and Ethereum cryptocurrencies were examined to find commonalities and differences between different BCs regarding their arbitrary data storage usage. Conclusions from such an evaluation indicate that Ethereum has been the most used BC for media data storage compared to Bitcoin and Monero.
Communicate Vehicle Accident Data to Blockchain for Secure and Reliable Record-Keeping Using Android Automotive Application

The study presented in this paper demonstrates the potential of blockchain technology to address some of the challenges present in the automotive domain, particularly in terms of providing secure and efficient data handling for real-time vehicle accident data storage. We propose an implementation on Android Automotive OS using a Substrate-based private blockchain and IPFS (InterPlanetary File System). The evaluation of the android application shows successful data communication with IPFS and the blockchain. The paper also exposes the application latency and blockchain metrics analysis.
A Framework for Asynchronous Cross-Blockchain Smart Contract Calls

Missing interoperability is a prevalent issue in today's blockchain landscape. Due to this lack of interoperability, only smart contracts deployed on the same blockchain can call each other. Smart contract interactions across the boundaries of blockchains would, however, provide an opportunity to create cross-blockchain applications. Hence, an additional mechanism for the execution of cross-blockchain smart contract calls is desirable. Unfortunately, already proposed solutions usually require one or more trusted intermediaries, which eventually leads to a lower degree of decentralization. Therefore, we propose a novel framework to enable smart contract interoperability across different blockchains. The framework enables the execution of asynchronous cross-blockchain smart contract calls. It uses blockchain relays to maintain a high degree of decentralization while placing only minor trust in intermediaries. We provide a prototypical implementation for Ethereum-based blockchains and evaluate it regarding costs and execution time.
Blockchain-Based Heuristic Study to Secure UAVs From GPS Spoofing Signals and External Attacks

This paper presents a heuristic study to introduce the use of blockchain technology for securing Unmanned Aerial Vehicle Systems (UAVs) against security threats, for example Global Positioning System (GPS) spoofing and improving their efficiency. We leverage a unique combination of “Write Once, Read Many (WORM),” and “Multi-Factor Authentication (MFA)” to create a fortified security infrastructure. Our code results validate the robustness of this combined approach, demonstrating its superiority in providing enhanced protection against data tampering and unauthorised access. The study concludes by contrasting the stand-alone WORM technique with the synergistic WORM+MFA method, evaluating them on key metrics such as level of security, cost of implementation, and user convenience to secure UAVs from GPS spoofing signals and external attacks.
Role of Blockchain in Spectrum Sharing in B5G Networks Service

Beyond 5G (B5G) Networks are envisioned to support a wide range of services with varying data profiles and traffic requirements. Unlike previous generations of wireless communication where improving BW and latency was the main theme, the primary objective of B5G is to provide the flexibility needed to support diverse network services required by different sectors of the industry (also referred to as verticals). One of the key enablers to support the heterogeneous network envisioned as part of the Industry 4.0 revolution is the introduction of unlicensed spectrum bands (mmWave and Terahertz). Optimal utilization of spectrum is crucial for the seamless working of different services in the B5G network. One of the proposals to provide on-demand spectrum usage is to use Blockchain for managing the spectrum. In this paper, we focus on a Blockchain-enabled B5G network for spectrum management. First, we provide a brief introduction to different services envisioned to be part of the B5G network based on the 3GPP vision. Second, we discuss recent work on Blockchain-enabled B5G networks and highlight the pros and cons of the proposals. The objective of this paper is to position Blockchain in the B5G framework and to demystify the pros and cons of using Blockchain focusing on spectrum management. Finally, we perform a simulation to study the effects of spectrum allocation strategies on a Blockchain network size. The results suggest that the strategy of how transactions are allocated in a Blockchain network can have either a positive or negative impact on the overall QoS of a service.
Poster Paper: Towards Blockchain-Based AI Alignment Using a Proof of Personhood Consensus Mechanism

Recent developments in the field of AI and the public availability of remarkably powerful AI tools, such as the chatbot ChatGPT, have sparked mass interest in AI, raised the possibility of timely emergence of an artificial general intelligence, and thus increased the urgency for AI safety. As AI alignment lags behind other developments in AI, this work explores how blockchain technology can support AI safety. Other works propose the use of distributed ledger technology for this purpose, but mostly without referring to novel consensus mechanisms. This paper suggests to write AI alignment rules in a blockchain that can only be updated by humans using a Proof of Personhood consensus mechanism in combination with, for instance, identity mechanisms and biometric features. Thus, relevant technologies are identified and combined to propose a system that is protected from AI interference and suitable to govern its behavior. Designing such a system is of great importance at a time when an artificial general intelligence could emerge that might one day reject human ethics, goals, and principles.
Refining Blockchain-Powered Industrial Internet-of-Things Architecture for Improved Performance

This paper introduces a high-performance blockchain solution to enhance Industrial Internet of Things (IIoT) operations, effectively addressing the challenge of reconciling blockchain's computational demands with the limited capabilities of IIoT devices. Through quantitative research, we present an IIoT blockchain-powered deep reinforcement learning performance optimization protocol. Our evaluation covers key aspects such as latency, scalability, security, and decentralization, demonstrating enhanced scalability without compromising security or latency. We establish a scalable and robust blockchain architecture tailored for high-processing IIoT systems, fine-tuning its components using deep reinforcement learning. Our simulations illustrate the framework's capacity to significantly boost the performance of blockchain-powered IIoT systems while adapting to their diverse operational dynamics.
Risk-Free Interest Rates in Decentralized Finance

Decentralized Finance (DeFi) aims to use advancements in both computation and cryptography to tackle economic problems. Therefore, it must operate within the intersection of constraints from both the computer science and economic domains. We explore a foundational question at the junction of those fields: Can we synthesize variable market-clearing risk-free yield for native tokens via smart contracts? We use a stylized model representing a large class of decentralized consensus algorithms to show this is impossible. This undecidability result places bounds on what decentralized financial products can be built and constrains the shape of future developments in DeFi. Among other limitations, our results reveal that markets in DeFi are incomplete.
Blockchain Technology in IoT and IIoT Environments

Computing has become available everywhere due to the huge spread of the Internet of Things (IoT) technologies across multiple and diverse areas. IoT expands Internet connectivity and connects several devices to the Internet, in addition to collecting and processing data for decision-making. IoT technology has grown significantly across various industrial sectors with several and varied applications. On the other hand, the Industrial Internet of Things (IIoT) technology is based on a centralized architecture, which has the disadvantage of being likely to be exposed to an attack affecting the availability or leading to decision-making errors. Therefore, Blockchain technology is an appropriate solution combined with IIoT technologies. It shows a marked potential to raise the level of security within the IIoT, when the IIoT is in the form of a distributed ledger relying on decentralization. Decentralization limits or mitigates tampering, as it maintains data records across varied and multiple locations. This distinguishing feature of Blockchain technology also resolves problems, such as heterogeneity in traditional IoT networks. However, integrating Blockchain technology with IIoT systems leads to several challenges, such as environmental operability, scalability, and standardization. Therefore, this research study provides a comprehensive overview of IIoT security and investigates how IIoT security and privacy can be enhanced by using Blockchain technology. It also discusses recent research trends in the area and the use of Blockchain technology for risk management in IIoT systems.
Routing Fee Adjustment Strategies in Payment Channels

Offloading payments from the blockchain to increase transaction throughput (tps) is a common technique that has been applied to several mainstream cryptocurrencies. In layer 2 solutions, this approach necessitates payment forwarding nodes owing to the fact that the resulting payment channel graph is not always complete, i.e., some participants might not have direct payment channels between each other. This leads to a routing problem and the solution to it is not straightforward. In this work, the efficacy of the source routing scheme of the Lightning Network is investigated. The authors show that the total routing revenue increases when the participants follow a common strategy. Several strategies are proposed and their performance is evaluated using a simulator on a filtered snapshot of the Lightning Network.
Model-Driven Approach for Generating Smart Contracts for Access Control

Access controls are principles and policies that are deployed on a system to ensure privileged access to system resources. Role-based access controls are a type of access controls which ensure access to resources based on users' roles. There has been a recent interest in generating smart contracts for the design of access controls. Smart contracts are computerized applications or protocols which execute automatically between contributory entities without an intermediary interference. The smart contracts, however, have a limitation of being dependent on the blockchain platform for which they are designed. This limitation has been resolved by iContractML framework, which offers a provision of designing and implementing smart contracts for multiple platforms. In this study, we have used a model-driven engineering (MDE) based approach to exploit iContractML for generating smart contract for role-based access controls. We have extended the meta-model of iContractML with new notations and have used the revised meta-model to generate smart contracts for role-based access controls. The generated contracts have been thoroughly tested and evaluated for correctness.
Measuring Stakeholder Agreement and Stability in a Decentralised Organisation

A decentralised organisation (DO) is a multi-stakeholder institution where decision making is assigned to various levels of the organisation. Decentralised stakeholders play an important role in the governance of a decentralised organisation. The ability to measure DO stability will help monitor the health of the organisation and acts as an early warning system for disagreement and group exit, leading to its destabilisation/collapse. For example, blockchain hard forks. We propose the organisational tension quadrilateral to study agreement between stakeholders and build a tool based on voting data (information as vote choices) to measure its stability. The stakeholders are permitted to vote their choice into an electronic ballot box. Here, each vote choice represents a measure of agreement. When voting ends, this information is aggregated and used to build a metric for DO stability. To the best of our knowledge, there are no similar tools available to measure DO stability.
A Comprehensive Distributed Framework for Cross-silo Federated Learning using Blockchain

The interest in cross-silo federated learning (FL), which involves a small number of client organizations that collaboratively train a model, has considerably increased in recent times. Although there exists a multitude of works on cross-silo FL, most of them focus on a small subset of the desired features, namely, security, privacy, performance, and scalability. In this work, we propose a comprehensive distributed framework for deploying cross-silo FL by cohesively integrating various technologies and techniques to realize these features. The proposed approach aims to overcome the challenges associated with the centralized model of federated learning by leveraging the distributed nature of blockchain networks to enable secure and efficient data sharing among multiple silos. The use of Kubernetes enables efficient management of resources required for training machine learning models. We also propose two simple, intuitive, and novel reputation-based algorithms - RepSecAgg and RepFedAvg for improved security and performance of the models, respectively. The proposed approach has the potential to significantly improve the efficiency and security of cross-silo FL, which can be applied in various fields, including healthcare, IoT, and finance. The feasibility of the proposed approach is evaluated through simulations and experiments, demonstrating its effectiveness in improving the performance and scalability of cross-silo FL.
IoT Data Sharing Scheme Based on Blockchain and Homomorphic Encryption

Through data sharing, individuals and organisations can obtain the information and value contained in the Internet of Things (IoT) data to facilitate the development of society. However, current IoT data sharing is facing data privacy and security problems. In this paper, an IoT data sharing scheme based on blockchain and homomorphic encryption is proposed. First of all, this paper proposes a fully homomorphic computing protocol based on reverse polish notation (RPN) and improved ElGamal algorithm to ensure privacy during data sharing. Subsequently, since blockchain can improve the security of data, the above fully homomorphic computing protocol is combined with blockchain to design the IoT data sharing scheme in this paper. Finally, the scheme is instantiated and its security is analysed. Through experimental tests, the performance of the scheme is evaluated. The experimental results indicate that the scheme proposed in this paper has excellent practicality, privacy and security.
Optimizing Read Performance in Lightweight Blockchain with Cooperative Storage Scheme

As a popular and innovative technology in fields including digital currencies and the Internet of Things, blockchain provides decentralized and immutable trust to multiple parties through an append-only chained structure and redundant replicas. However, the increasing length of the blockchain leads to extreme storage costs. While some lightweight storage schemes compress block data on-chain, most of them suffer from poor generality, limited compression capabilities, and imperfect read performance. To alleviate these issues, we propose a cooperative storage scheme as a lightweight solution for resource-limited blockchains. It incorporates a history-based node assignment mechanism and a timeline-based adaptive read mechanism to reduce the decoding probability when accessing transactions. The former maps chunks of erasure codes to nodes based on their history scores, while the latter redundantly caches blocks at certain nodes for easy access. Experiments demonstrate that the proposed scheme actively mitigates the influence of anomalous nodes on read requests. In contrast to typical schemes, it achieves extremely low read time with less storage cost, which is especially valuable in the long term.
Prediction of Blockchain Transaction Fraud Using a Lightweight Generative Adversarial Network

Classic blockchain protocol design is centered around a computationally-intense cryptographic scheme, such as Bitcoin's Proof-of-Work (PoW). Network scalability and efficiency are stifled with the computational resources necessary to approve new transactions, thus rendering PoW unsuitable with limited devices like Internet of Things (IoT). As a first step towards alleviating this, a novel lightweight Generative Adversarial Network (GAN) called Vector GAN (VecGAN) is introduced wherein its weights are tuned through a direct error-driven learning approach with a Bayesian estimator for the selection of random noise matrices, called Bayesian Feedback Alignment (BFA), to augment data for improved fraud prediction. The augmented data is subsequently processed by a classifier for prediction. This combination of VecGAN with a classifier is treated as a novel method of blockchain ledger decision-making to approve ground-truth data. By using the two-step process, prediction accuracy using a classifier was improved up to 8% for real-world datasets. Resource consumption comparison to existing IoT blockchain protocols in a realistic simulation environment is also provided, where lightweight approaches for VecGAN in training and noise modeling reduce computation compared to other techniques.
Modelling Blockchain-Based Systems Using Model-Driven Engineering

As the use of blockchain systems continues to grow, the need for templates and standards to analyze and design these systems becomes essential. Unlike other systems, blockchain-based systems have unique properties that require defining network participants, assets, tokens, smart contracts, and their functions. Developing Decentralized Applications (DApps) enables the creation of blockchain-based applications and interfaces to interact with these systems. Many researchers have used model-driven engineering methodologies to design blockchain-based systems and DApps, but most focus on converting Platform-Independent Models (PIMs) into Platform-Specific Models (PSMs) to generate system code. In our research, we concentrate on creating a Computation-Independent Model (CIM) and translating it into a PIM. We utilize graph grammar to generate an approach that formally verifies system requirements using graph grammar rules, allowing for the generation of blockchain-based systems.
Annotate French Clinical Data Using Large Language Model Predictions

In clinical and other specialized domains, data are scarce due to their confidential nature. This lack of data is a major problem when fine-tuning language models. Nevertheless, very large language models (LLMs) are promising for the medical domain but cannot be used directly in healthcare facilities due to data confidentiality issues. We explore an approach of annotating training data with LLMs to train smaller models more adapted to our problem. We show that this method yields promising results for information extraction tasks.