Ripple acts as a real-time settlement and payment system to connect banks and payment providers. As the consensus support of the Ripple network to ensure network consistency, Ripple consensus protocol has been widely concerned in recent years. Compared with those Byzantine fault tolerant protocols, Ripple has a significant difference that the system can reach an agreement under decentralized trust model. However, Ripple has many problems both in theory and practice, which are mentioned in the previous researches. This paper presents Ripple+, an improved scheme of Ripple consensus protocol, which improves Ripple from three aspects: (1) Ripple+ employs a specific trust model and a corresponding guideline for Unique Node List selection, which makes it easy to deploy in practice to meet the safety and liveness condition; (2) the primary and view change mechanism are joined to solve the problem discussed by the previous research that Ripple may lose liveness in some extreme scenarios; (3) we remove the strong synchrony clock and timeout during consensus periods to make it suitable for weak synchrony assumption. We implemented a prototype of Ripple+ and conducted experiments to show that Ripple+ can achieve the throughput of tens of thousands of transactions per second with no more than half a minute latency, and the view change mechanism hardly incurs additional cost.

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Abstract-In this paper, a universal method is proposed for intelligent judgement, which relies on feature vectors representing each case to enable intelligent judgement via machine learning algorithms. The process to extract feature vectors consists of three main steps: modeling the case, building feature words lists, and extracting the vectors. After feature vectors are built, kNN and SVM algorithms are used to train the classification model, and the performance is evaluated through the experiments.