ID 原文 译文
45576 通过真实数据和合成数据将 LR-Tree 与 3DR-Tree、SETI 及 TB-Tree 进行对比， Using both real and synthetic datasets, the LR-Tree was extensively evaluated in comparison with 3DR-Tree, SETI and TB-Tree.
45577 实验表明 LR-Tree 具有更好的剪枝能力，从而验证了所提算法及索引的有效性。 The experimental results demonstrate that LR-Tree showing better pruning ability, and verify the effectiveness of proposed algorithm and index.
45578 为了保证无线体域网（WBAN, wireless body area network）中病人生理数据的安全和隐私，通信双方必须进行相互认证。 To ensure the security and privacy of patients’ health data in wireless body area network (WBAN), communication parties must be mutual authenticated.
45579 现有的一些方案使用双线性对导致用户计算代价较大，其采用树形结构进行撤销会导致用户的存储代价较大。 Now some bilinear pairings led to a larger computation cost for users and tree structure revocation would lead to larger user storage cost.
45580 为了实现撤销同时降低用户端的代价，构造了基于椭圆曲线的可撤销无证书远程匿名认证协议，基于即时更新时间密钥技术进行撤销。 In order to achieve revocation and reduce the cost of the user side, a novel revocable certificate less remote anonymous authentication protocol for WBAN was proposed by using elliptic curve cryptography and revoke algorithm that could revoke users by updating their time-private-keys.
45581 协议满足匿名性，相互认证和会话密钥建立等安全需求。 Security requirements including anonymity, mutual authentication and session key establishment were satisfied in proposed scheme.
45582 与现有方案相比，实验分析表明认证协议用户端的计算代价和存储代价大幅降低，更适用于资源受限的无线体域网。 Compared with the existing schemes, the experimental analysis shows that the computation cost and storage cost of the authentication protocol are greatly reduced, which is more suitable for resource-constrained WBAN.
45583 安全性分析证实了协议在随机预言模型下是安全的。 Security analysis also shows that the protocol is secure in the random oracle model.
45584 针对传统主动学习单一策略算法在挑选最有价值未标记样本时出现的抖动和不稳定的现象， In order to improve the phenomena of jitter and instability of the traditional active learning single strategy algorithm in selecting the most valuable unlabeled samples.
45585 引入集成学习（ensemble learning）分类器的加权组合思想，提出一种基于组合策略的联合挑选（ESAL）方法，将模型的组合衍生至策略的组合，从而实现单一模型多策略的融合，获得更高的稳定性。 The idea of weighted combination of ensemble learning classifier and proposes a joint selection based on the combination strategy method (ESAL, ensemble strategy active learning)was introduced, the combination of the model was extended to the combination of the strategy so as to achieve the fusion of multiple strategies in a single model and achieve higher stability.