ID 原文 译文
24625 根据参与者的历史行为对其进行分析，初步过滤掉一些劣质感知用户，同时利用参与者间的相似性构建混合用户模型。 We could analyze the participants according to their historical behavior, and filter out low-quality sensing users. Meanwhile, the similarity among the participants was used to build a user-hybrid model.
24626 利用概率矩阵分解对参与者的意愿值进行预测，并根据排序学习得到一个排序模型。 Then, the participants' willingness would be predicted by the probabilistic matrix factorization, and a ranking model was obtained.
24627 根据排序模型生成任务推荐列表，作为目标参与者的优选任务列表。 Finally, a task recommendation list was generated on the basis of ranking model as the preferred task list for the target participants.
24628 基于真实数据集的仿真实验结果表明，本文提出的方法有效地提高了任务分配的准确率，与此同时减少了感知用户的移动距离。 The simulation experiments based on the real dataset show that the proposed method in this paper can improve the accuracy of task assignment effectively and reduce the moving distance of sensing users simultaneously.
24629 现有关键词抽取算法缺乏对短语的有效表示，为抽取出更能反映文本主题的关键短语，本文提出一种基于短语向量的关键词抽取方法 PhraseVecRank。 Keyword extraction is a key basic problem in the field of natural language processing. The keyphrase extraction algorithms (PhraseVecRank) is proposed based on phrase embedding.
24630 首先设计基于 LSTM（Long Short-Term Memory）和 CNN（ConvolutionalNeural Network）自编码器的短语向量构建模型，解决复杂短语的语义表示问题。 Firstly, a phrase vector construction model based on LSTM（Long Short-Term Memory）and CNN（Convolutional Neural Network）is designed to solve the semantic representation of complex phrases.
24631 然后，利用短语向量对每个候选短语计算主题权重，通过主题加权排序提高关键词抽取的效果。 Then, PhraseVecRank uses phrase embedding to calculate theme weight for each candidate phrase, and uses semantic similarity between candidate phrase embedding and co-occurrence information to calculate edge weight together, which can improve the extraction effect of keyphrases through topic weighted ranking.
24632 在公共数据集和学术论文数据上的实验表明，本文提出的方法能够有效提取与文本主题信息相关的关键短语，同时利用自编码器构造的短语向量可以更好地表示短语的语义信息。 The experimental results verify that PhraseVecRank can effectively extract keyphrases covering the topic information of text, and the phrase embedding models we proposed can better represent the semantic information of phrases.
24633 合成孔径雷达（Synthetic Aperture Radar，SAR）影像中的斑点现象是地物后向散射信号相互干扰产生的，其虽然类似噪声却涉及地物目标的散射特征。 The speckle phenomenon in synthetic aperture radar (SAR) images is caused by the mutual interference of backscattering signals of ground objects. Although it is similar to noise, it involves the scattering characteristics of ground objects.
24634 同一地物目标的散射特征可通过斑点统计分布模型来刻画，因此降噪过程中可通过恢复影像斑点的统计分布特性来保留斑点包含的地物散射信息。 The scattering characteristics of the same ground object can be described by the speckle statistical distribution model. Therefore, in the process of noise reduction, the scattering information of the ground object contained in the speckle can be preserved by restoring the statistical distribution characteristics of speckle.