ID	原文	译文
1973	分析结果表明，该方案无法实现强不可伪造性，并在“malicious-but-passive”的密钥生成中心攻击下也是不安全的。	The analysis results show that this scheme can-not achieve strong unforgeability and is insecure under the " malicious-but-passive" key generation center attack.
1974	为了提升该方案的安全性，设计了一个改进的无证书签名方案。	To enhancethe security of this scheme, an improved certificateless signature scheme is presented.
1975	在标准模型中证明了改进的方案对于适应性选择消息攻击是强不可伪造的，还能抵抗恶意的密钥生成中心攻击。	The improved scheme is proved to bestrongly unforgeable against adaptive chosen-message attacks and can also resist malicious key generation center attacks.
1976	此外，改进的方案具有较低的计算开销和较短的私钥长度，可应用于区块链、车联网、无线体域网等领域。	In addition, the improved scheme has lower computational overhead and shorter private key length, and can be applied to block-chain, Internet of vehicles, wireless body area network and other fields.
1977	视频监控、备份、归档等应用产生海量存储数据，导致存储能耗急剧增加。	The applications, such as video surveillance, backup, and archiving, generate massive storage data, which make the energy consumption of storage devices increase rapidly.
1978	S-RAID 采用局部并行数据布局，可显著降低该类应用的存储能耗。为使更多磁盘待机节能，S-RAID 通常执行“小写”操作，写操作时会额外引入等量的读操作，会显著降低性能。	S-RAID can reduce the storage energy consumption of a-bove applications significantly. In order to make more disks standby for energy saving, S-RAID prefers to small write. How-ever, performing small write requires additional and equal amount of read operations for generating parity data, and the write performance of S-RAID is decreased.
1979	现有预读机制主要发生在文件级，无法感知 RAID 级小写引发的读旧数据、旧校验数据等读操作，因此不会也无法预读该类数据。	The existing prefetching mechanism mainly implements at the file system level, and cannot sense the read operations generated by small write at RAID level, such as reading old data and reading old parity.
1980	为此，提出一种面向 S-RAID 的 RAID 级小写预读算法，由小写操作触发并在 RAID 级执行预读，根据 S-RAID 的数据布局方式，大粒度异步预读小写需要的旧数据、旧校验数据，有效减少 I/O数和寻道数，提高磁盘的利用率。	Soit can not prefetch these old data and old parity data. Therefore, a RAID-level prefetching algorithm for small write of S-RAID is proposed, which is triggered by small write operations and performs at RAID level. It asynchronously prefetches theold data and old parity data required by small writes in large granularity according to the data layout of S-RAID.
1981	该方法可显著提高 S-RAID 的写性能，并且不依赖于任何额外硬件，具有更高的可用性。	The write performance of S-RAID can be improved by about 40% without any additional hardware cost.
1982	受限玻尔兹曼机(Restricted Boltzmann Machine，RBM)是一种随机网络、概率图模型，它是一种比较有效的的无监督学习模型。	Restricted Boltzmann machine (RBM )is a stochastic neural network and probabilistic graphical model, which is one of the most effective models without supervision in deep learning.