ID 原文 译文
39956 为了解决文本分类任务中未标注数据无法即时标注及成本过高的问题，提出一种面向文本分类的不确定性主动学习方法。 To solve the problem that the unlabeled data in the text classification task cannot be immediately marked and the cost is too high, this paper proposes an active learning method for uncertainty based on text classification.
39957 提出MSDL(Measure sample density by LDA)算法对未标注样本密集度进行计算，引入新的度量样本聚集情况的密集度计算方式， The MSDL(Measure sample density by LDA) algorithm is proposed to calculate the unlabeled sample density, and the new metric sample aggregation situation is introduced.
39958 在密集度高的样本区域选取初始训练集样本，从而使初始训练集更具代表性； The initial training set sample is selected in the densely sampled region, thus making the initial The training set is more representative.
39959 从未标注样本中选取更具不确定性的样本加入到训练集中，并基于信息熵对样本进行加权训练，迭代更新分类器模型，直至达到预期终止条件。 The more uncertain samples from the unlabeled samples are added to the training set, the samples are weighted based on the information entropy, and the classifier model is iteratively updated until the expected termination condition is reached.
39960 实验结果表明，在文本分类任务中，该方法相较于其他传统主动学习算法性能更优。 Experimental results show that this method is better than other traditional active learning algorithms in text classification tasks.
39961 在对抗攻击研究领域，黑盒攻击相比白盒攻击更具挑战性和现实意义。 For adversarial attacks, black-box attacks are more challenging and applicable than white-box attacks.
39962 目前实现黑盒攻击的主流方法是利用对抗样本的迁移性， Recently, black-box attacks based on the transferability of adversarial examples have become mainstream methods.
39963 然而现有大多数方法所得的对抗样本在黑盒攻击时效果不佳。 However, the adversarial examples generated by most existing methods exhibit low efficiency in black-box attacks.
39964 本文提出了一种基于高斯噪声和翻转组合策略方法来增强对抗样本的迁移性，进而提升其黑盒攻击性能。 In this paper, a combination strategy based on Gaussian noise and flipping is proposed to enhance the transferability of adversarial examples, thus achieving higher black-box attack success rates.
39965 同时，该方法可与现有基于梯度的攻击方法相结合形成更强的对抗攻击。 Moreover, this strategy can be integrated into any gradient-based method to obtain stronger attacks.