Distributed randomness beacons are essential for distributed systems (e.g., blockchain and MPC), providing un biased and unpredictable shared randomness. However, implementations in asynchronous networks often suffer from poor scal ability, low throughput, and high resource consumption when deployed as independent protocols. The state-of-the-art HashRand (CCS'24) achieves high throughput and low computational in tensity using only lightweight post-quantum cryptographic primitives for an independent asynchronous beacon. Building further on this, we propose Rubato, a low-overhead, high-throughput beacon protocol that leverages lightweight batched Asynchronous Complete Secret Sharing with Byzantine Atomic Broadcast-based state machine replication (via our tailored RubatoSMR). Rubato reduces communication complexity by an O(clogn) factor compared to HashRand and resolves the circular dependency between beacon and BAB-SMR in asynchronous settings. Experiments on AWSdemonstrate that Rubato achieves ideal overall performance in scalability, resource usage, and throughput; for instance, at n = 121nodes, it produces an average of 174 beacons per minute, with RubatoSMR further optimizing memory and bandwidth consumption.

A hidden Markov model (HMM) has been applied to describe the dynamic process of driver fatigue over time. However, during the HMM training, the initial value selection of the confusion matrix has a great influence on the accuracy ofthe HHM, which brings a lot of inconveniences to the practical application. Therefore, this study applied a particle swarm optimization (PSO) algorithm to simplify the training process without affecting the accuracy of the HMM. In the beginning, an improved HMM was built based on the PSO algorithm, and then was trained by the adoption of the collected data of percentage of eye closing time over a certain period (PERCLOS), which was measured from twenty participants in a driving simulator experiment. Finally, the improved model was compared with the original model, and it didn’t require the initial value selection process based on the prior condition to achieve the global optimum and improve accuracy. It indicates that the proposed method can provide an effective way for driver fatigue identification.