AsynchronousByzantine Fault Tolerance(BFT) consensus protocols are gaining attention for their resilience against network attacks. Among them,Multi-valued Byzantine Agreement(MVBA) protocols play a critical role, which accepts input values from each replica and returns a consistent output. The state-of-the-art MVBA protocol, sMVBA, has a good-case latency of$6\delta$and an expected bad-case latency of$12\delta$, with$\delta$representing the network delay. Additionally, sMVBA exhibits a communication of$O(n^{2})$in both good and bad cases. Although it outperforms other MVBA protocols, sMVBA still lags behind partially-synchronous counterparts. For instance, PBFT achieves a good-case latency of$3\delta$, and HotStuff boasts a good-case communication of$O(n)$. This paper introduces a novel MVBA protocol, Pako, aiming for performance comparable to partially-synchronous protocols. Pako leverages an existing MVBA protocol as a black box and introduces an additional view with an optimistic path to commit values efficiently. Two Pako variants, Pako1 and Pako2, provide a trade-off between latency and communication. To be more specific, Pako1 achieves a good-case latency of$3\delta$with$O(n^{2})$communication, while Pako2 reduces the communication to$O(n)$with a slightly higher good-case latency of$5\delta$. A series of experiments demonstrate Pako's significant outperformance of counterparts.

Sandwich arbitrage is a popular strategy for blockchain-based decentralized exchange (DEX) users to facilitate decentralized token exchanges and make profits. However, conventional Sandwich arbitrage only focuses on one big-deal transaction and executes arbitrary actions within an intrablock, leading to a huge loss of profit opportunities. In this article, we present Lasagne, a novel high-profit arbitrage strategy that allows the exploration of profit opportunities from multiple small-deal transactions across interblocks. Specifically, Lasagne involves a suite of novel strategies: fishing, bundle, and grid, each of which is tailored to a liquidity pool with diverse transaction frequencies and skewness. The experimental results demonstrate the superiority of Lasagne over a state-of-the-art sandwich in terms of feasibility and effectiveness.

In wearable sensing applications, data is inevitable to be irregularly sampled or partially missing, which pose challenges for any downstream application. An unique aspect of wearable data is that it is time-series data and each channel can be correlated to another one, such as x, y, z axis of accelerometer. We argue that traditional methods have rarely made use of both times-series dynamics of the data as well as the relatedness of the features from different sensors. We propose a model, termed as DynImp, to handle different time point’s missingness with nearest neighbors along feature axis and then feeding the data into a LSTM-based denoising autoen-coder which can reconstruct missingness along the time axis. We experiment the model on the extreme missingness scenario (> 50% missing rate) which has not been widely tested in wearable data. Our experiments on activity recognition show that the method can exploit the multi-modality features from related sensors and also learn from history time-series dynamics to reconstruct the data under extreme missingness.