中国学生英语书面语N元序列特征分析

语言表达是否流畅、地道在很大程度上取决于是否能够恰当使用多词序列。然而,中国学生学习英语时习惯于机械地、孤立地背诵单个词汇的意义,结果必然导致词汇的误用、滥用或少用等现象。本文以本族语语料库为参照,通过分析中国学生英语书面语的N元序列,揭示其在书面语语体运用、实意词块表达和特定语法结构选择等方面存在典型特征。同时,从语言、社会、文化和认知等方面阐释了其存在的原因。

Product image sentence annotation based on kernel descriptors and tag-rank

Dealing with issues such as too simple image features and word noise inference in product image sentence anmotation, a product image sentence annotation model focusing on image feature learning and key words summarization is described. Three kernel descriptors such as gradient, shape, and color are extracted, respectively. Feature late-fusion is executed in turn by the multiple kernel learning model to obtain more discriminant image features. Absolute rank and relative rank of the tag-rank model are used to boost the key words＇ weights. A new word integration algorithm named word sequence blocks building （WSBB） is designed to create N-gram word sequences. Sentences are generated according to the N-gram word sequences and predefined templates. Experimental results show that both the BLEU-1 scores and BLEU-2 scores of the sentences are superior to those of the state-of-art baselines.

恶意软件检测中的特征选择问题

恶意软件检测问题是一个十分重要的问题,而特征选择的好坏对于恶意软件检测具有决定性的影响。该文提出用有效性、自动性及时空效率作为恶意软件检测中选择的特征好坏评价的基本指标,并讨论了几种恶意软件检测特征选择的分类方法。对目前常见的基于n元序列、基于操作码、基于基本块和基于行为的特征选择方法进行了较为系统的回顾,分析了各种特征选择方法的基本原理,总结梳理了每种特征选择方法的优点和缺点,并对特征选择的效果进行了定性的评估,得出的结论对于选择合适的特征用于恶意软件检测具有积极的参考意义。