In this paper, we present a novel blockchain-enabled approach to opportunistic federated learning (OppCL) for intelligent transportation systems (ITS). Our approach integrates blockchain with OppCL to streamline the learning of autonomous vehicle models while addressing data privacy and trust challenges. We deploy resilient countermeasures, incentivized mechanisms, and a secure gradient distribution to combat single-point failure verification attacks. Additionally, we integrate the Byzantine fault-tolerant algorithm (BFT) into the node verification component of the delegated proof of stake (DPoS) to minimize verification delays. We validate our approach through experiments on the MNIST, SVHN, and CIFAR-10 datasets, showing convergence rates and prediction accuracy comparable to traditional OppCL approaches.

A blockchain-oriented continuous micropayment system forms an integral element of the digital economy, enabling seamless transactions and encouraging minor purchases. However, due to the inherent throughput constraints of blockchain, payment channels (PCs) are customarily deployed for managing high-volume transactions in an off-chain mode. Despite this, the integration of a PC necessitates a trusted intermediary to safeguard transactional security, thereby imposing an extra security assumption as only the initial and concluding transactions are visible to other system participants. To circumvent this limitation, we introduce an innovative micropayment structure utilizing lockable signatures. Each transaction within the PC coalesces into a continuous microtransaction hash-chain (CMHC), precluding unscrupulous users from accessing transactional data during the process. Additionally, all locks within the CMHC remain concealed throughout the transaction, with all information relayed in a tamper-proof manner. We provide a comprehensive security analysis and perform a gamut of evaluations. Empirical evidence indicates that our proposed system outperforms existing state-of-the-art solutions in transaction time and verification expenses. This lockable signature-dependent micropayment system not only ensures robust security but also enhances transactional speed and efficiency, thereby catalyzing the growth of the digital economy.

At present, more and more consumers and manufacturers are aware of the importance of product traceability, so it is very important to strengthen the traceability in the supply chain. The existing centralized database system has isolated data storage, and many parties involved in data query lack mutual trust. Based on the distributed ledger of the blockchain, this paper attempts to solve this problem by transforming the traceability information of physical goods into the digital of the blockchain, so as to realize the collaborative traceability of entities in the supply chain. At present, the relationship between components and products in manufacturing process cannot be captured, which limits the ability to track the source of products. This paper proposes a supply chain traceability model based on blockchain, which uses smart contracts to complete multi-party agreements on the chain. In the contract, the products produced by the manufacturer are transferred in the specified alliance chain entity, and in each transfer process, the products will be marked with new traceability information in the form of digital authentication. This mechanism retains the possibility of products being traced. The model is tested under the framework of hyperledger fabric. By building a blockchain network, the entity objects participating in the supply chain can be authenticated to ensure the closed-loop circulation of goods. It also indirectly proves the feasibility of commodity traceability application based on blockchain technology.

In this paper, the research and experimental analysis of cross-project application software defect prediction is carried out, and the TCA model is used to improve the application function of its prediction. The models pointed out in this paper usually include: normalization processing model and mathematical linear kernel mathematical statistics The difference between the functional SVM classifier and the extended migration component analysis TCA+ model is that the model pointed out in this paper not only satisfies the prediction of software defects within the project suitable for TCA, but also meets the prediction of software defects in the cross-project of TCA+, so the most appropriate normalization can be selected. Optimized processing options to improve cross-project software defect prediction capabilities.

A blockchain-based continuous micropayment is a crucial component of the digital economy as it facilitates transactions and promotes small purchases. However, due to the throughput limitations of blockchain, payment channels (PC) are often used to process a large volume of transactions through an off-chain mode. Nevertheless, the introduction of PC requires a trusted third party to ensure transaction security, which creates an additional security assumption since only the first and last transactions can be witnessed by other system users. To address this issue, we propose a novel micropayment scheme based on lockable signatures. All transactions in the PC form a continuous microtransaction hash-chain (CMHC) to prevent malicious users from obtaining transaction information during the process. Furthermore, all locks in the CMHC are invisible during the entire transaction process, and all information is transferred in a tamper-resistant manner. We provide corresponding security analysis and conduct a series of evaluations. The results show that the proposed scheme performs better than the state-of-the-art solutions in terms of transaction time and verification costs. This lockable signature-based micropayment scheme not only guarantees security but also improves transaction speed and efficiency, thereby promoting the development of the digital economy.