Recently, In-Network Cache (INC) systems have been proposed to promote the performance of remote storage systems. INC offloads cache onto programmable switches between the clients and the servers, responding to clients’ data queries within a sub-RTT time. However, most existing INC solutions do not take applications’ linearizability requirement into consideration, which could lead to query errors and storage state errors in the runtime. We propose a new INC system — NetKV-L, which preserves the high-performance I/O without compromising the linearizability. NetKV-L devises a sequentiality enforcement mechanism, a PSN correction mechanism, and a response memorization mechanism to guarantee linearizability under possible unreliable network conditions. Our prototype and experiments show that NetKV-L could achieve almost the same performance as the state-of-the-art systems while additionally guaranteeing linearizability.

Ancient books that record the history of different periods are precious for human civilization. But the protection of them is facing serious problems such as aging. It is significant to repair the damaged characters in ancient books and restore their original textures. The requirement of the restoration of the damaged character is keeping the stroke shape correct and the font style consistent. In order to solve these problems, this paper proposes a new restoration method based on generative adversarial networks. We use the shape restoration network to complete the stroke shape recovery and the font style recovery. The texture repair network is responsible for reconstructing texture details. In order to improve the accuracy of the generator in the shape restoration network, we use the adversarial feature loss (AFL), which can update the generator and discriminator synchronously to replace the traditional perceptual loss. Meanwhile, the font style loss is proposed to maintain the stylistic consistency for the whole character. Our model is evaluated on the datasets Yi and Qing, and shows that it outperforms current state-of-the-art techniques quantitatively and qualitatively. In particular, the Structural Similarity has increased by 8.0% and 6.7% respectively on the two datasets.

The rejoining of oracle bone rubbings is a fundamental topic for oracle research. However, it is a tough task to reassemble severely broken oracle bone rubbings because of detail loss in manual labeling, the great time consumption of rejoining, and the low accuracy of results. To overcome the challenges, we introduce a novel CFDA&CAP algorithm that consists of the Curve Fitting Degree Analysis (CFDA) algorithm and the Correlation Analysis of Pearson (CAP) algorithm. First, the orthogonalization system is constructed to extract local features based on the curve features analysis. Second, the global feature descriptor is depicted by using coordinate points sequences. Third, we screen candidate curves based on the features as well as the CFDA algorithm, so the search range of the candidates is narrowed down. Finally, image recommendation libraries for target curves are generated by adopting the CAP algorithm, and the rank for each target matching curve generates simultaneously for result evaluation. With experiments, the proposed method shows a good effect in rejoining oracle bone rubbings automatically: (1) it improves the average accuracy rate of curve matching up to 84%, and (2) for a low-resource task, the accuracy of our method has 25% higher accuracy than that of other methods.