ID 原文 译文
1163 但这些方案并未给出外包解密的并行化方法，存在解密效率低的问题。 However, these schemes do not give the specific parallelization method of outsourcing decryption in cloud server, and there are problems of low efficiency of cloud decryption.
1164 本文提出一种支持解密外包的 KP-ABE 方案。 To solve these prob-lems, this paper presents a KP-ABE scheme for decryption outsourcing.
1165 在该方案中，把大部分解密计算外包给Spark 平台; In this scheme, most of decryption computation isoutsourced to Spark platform;
1166 并根据 KP-ABE 的解密特点设计并行化解密算法，完成对叶子节点和根节点的并行化解密。 and according to the decryption characteristics of KP-ABE, a decryption parallelization algo-rithm is designed to complete the parallel decryption of leaf nodes and root nodes.
1167 性能分析表明，用户端仅需进行一次指数运算即可解密出共享数据，同时并行化设计能有效提高云端解密速率。 The performance analysis shows that mostof decryption computing is done by cloud servers and the client can decrypt the shared data by shared access tree with only once exponential operation, and the parallel design can effectively improve the cloud decryption rate.
1168 有限域上线性互补对偶( LCD) 码具有良好的结构和性质，并在双用户加法器信道中得到了广泛的应用。 Video object segmentation (VOS)is a research hotspot in the field of computer vision.
1169 自正交码是编码理论中一类重要的线性码，常被用于构造量子纠错码。本文根据有限域上线性码是厄米特 LCD 码或厄米特自正交码的判定条件，通过选取合适的定义集，构造出了四类四元厄米特 LCD 码和厄米特自正交码。同时，本文还研究了这四类线性码的厄米特对偶码，并得到了一些四元最优线性码。 Traditional VOS based on deep learning fine-tunes the deep network online, which leads to long time-consuming segmentation and is difficult to meet real-time requirements.
1170 针对目前视频压缩感知重构算法对不同特征的视频序列重构质量参差不齐的问题，结合双稀疏对轮廓、细节的高清晰重构以及多假设算法对高频噪声有效抑制的优点， The existing approaches to reconstruct compressed video sensing achieve heavy quality fluctuation when re-constructing videos with different motion feature. To solve this problem, combing the merits of two CS(Compressed Sens-ing)methods:the clearly edges and fine details reconstruction of the dual sparsity representation and the effectively high fre-quency noise suppression of multi-hypothesis prediction,
1171 本文提出一种基于视频运动特征的多假设-双稀疏重构算法(VF-MH-DSR)。 this paper proposes a video motion features based multi-hypothesis-dual-sparsity reconstruction algorithm(VF-MH-DSR)for compressed video sensing(CVS).
1172 基本思路是基于每个视频组(GOP)的运动特征，采取相应的多假设-双稀疏重构策略。 The basic thinking of VF-MH-DSR is that adopting a corresponding MH-DSR method to each video group(GOP)based on their motion features.