ID 原文 译文
25355 在无线可充电传感器网络中，针对提高充电能量效率问题，提出了一种按需多节点顺带充电调度方案。 To improve the charging energy efficiency in wireless rechargeable sensor networks (WＲSNs), this paperproposes an on-demand multi-node passerby charging (OMPC) scheduling scheme．
25356 考虑移动充电器在为请求节点充电时，允许其为覆盖范围内的非请求节点同时充电。 Mobile chargers are considered to be allowed to simultaneously charge unrequested nodes within the range of coverage while charging the request nodes．
25357 为了进一步减少分组个数，通过对已有的算法改进提出了一种更有效的启发式算法，并证明了停止点选择的正确性。 To further reduce the number of groups, a more effective heuristic algorithm is proposed compared with the existing algorithms, and the correctness of the selection of the stopping points is proved．
25358 分析了低效率分组对充电性能的影响，并提出了拆分低效率分组的有效策略，以局部优化行驶路径。 In this paper, the effect of low-efficiency grouping on charging performance is analyzed, and an effective strategy for splitting low-efficiency grouping is proposed to optimize the travel path locally.
25359 仿真结果表明，在离线和按需场景中，所提算法和方案可以减少能耗和提高充电能效。 Simulation results show that in offline and on-demand scenarios, the proposed algorithm and scheme can reduce energy consumption and improve charging efficiency．
25360 近年来，自动化沙箱被广泛部署并应用于恶意代码分析与检测，然而随着恶意代码数量的激增和抗分析能力的增强，如何有效应对海量恶意代码分析任务，提高沙箱系统分析效率，是增强网络安全防御能力的一个重要研究方向。 In recent years, automated sandboxes have been widely deployed for malware analysis and detection. However, with the rapid increase column of malware and the enhancement of anti-analysis capabilities, how to effectively handle these massive malware analysis tasks and improve the efficiency of sandbox system is an important research topic．
25361 本文利用不同学习方式以及恶意代码动、静态特征的特点，提出了一种基于混合学习模型的恶意代码检测方法，分别提取恶意代码的静态模糊哈希特征和动态行为特征，然后将无监督聚类学习与有监督的分类学习相结合用于恶意代码检测。 Based on the characteristics of different learning methods and malware dynamic and static features, this paper proposes a malware detection method based on a hybrid learning model. We extract static fuzzy-hash features and dynamic behavior features ofmalware, then unsupervised clustering learning is combined with supervised classification learning.
25362 实验表明，在不影响检测精度的情况下，只利用了原有系统 0. 02% 分析时间提高了整个系统 25.6%的检测速度。 Experiments show that using only 0. 02% of the analysis time improves the detection speed of the entire system by 25. 6% without affecting the detection accuracy．
25363 为了更好地学习节点依赖并利用结构信息，本文提出一种以完全依赖树作为直接输入的新方法，利用图卷积网络并结合两个并行的注意力模块，自主学习如何有选择地关注对关系抽取任务有用的信息。 To better learn node dependence and make use of structural information, this paper proposes a new method that takes the tree of complete dependence as the direct input． The method uses the graph convolutional network and combines two parallel attention modules to learn how to select the useful information．
25364 该方法将样本表示成图上的各节点，一个模块用于计算节点特征位置之间的影响，使特征向量可以包含更广范围的语义信息，另一个用于计算节点依赖的关系特征，以增强节点间的全局依赖。 The method represents the samples as nodes on the graph. One module is used to compute the influence between positions of node features, which allows the feature vector to contain a wider range of semantic information. The other one is used to compute the relational features of node dependence, so as to enhance the global dependence between nodes．