基于粗集的支持向量机文本分类方法研究

本文提出了一种基于粗糙集的支持向量机文本分类方法。该方法利用粗糙集的约简理论降低了支持向量的维数,同时保证了分类性能。实验表明该方法能获得较好的分类效果。     

一种基于特征编码的Word文档数字水印算法

本文基于文本数字水印的特征编码思想,通过设置Word文档中字符的下划线格式以嵌入和检测水印。实验表明,该算法有较强的不可见性,能较好的嵌入和检测水印,从而达到数字水印的一般要求。     

一种改进的基于信噪比的文本分类方法

改进了传统的信噪比文本分类方法。提出了一种改进的基于信噪比的文本分类方法。它考虑了禁用词的排除、近义词和同义词的合并以及特征项在文本中不同位置对文章分类的贡献不同。     

贝叶斯网在.NET超文本分类检索中的应用

文本分类是指在给定的分类体系下，根据文本的内容自动判别文本类别的过程，本文对、NET文本分类检索中所涉及的关键技术贝叶斯网分类方法，进行了研究和探讨，并且提出了基于向量空间的．NET文本分类检索的结构，并给出了会估计方法和实验结果。     

Understanding Greek Primary School Children's Comprehension of Sun Exposure

The purpose of this study was to assess Greek primary (1st to 6th grade) school children's understanding of sun exposure during summer vacation. Our results show that children know the damaging effects of long time exposure and the precautions that should be taken during sun bathing (sun glasses, hat, umbrella, sunscreen, etc). Nevertheless, they do not seem to avoid being exposed between 12.00 A.M. and 15.00 P.M., which is the most dangerous time. The socioeconomic status of father has no effect on the attitude of children towards taking protection measures when exposed to the sun, however, the educational status of the mother appears to influence children's reported knowledge and behaviour. Place of residence (urban or rural areas) and gender does not influence their knowledge on the sun's damaging effects and the precautions they should take. Finally, as children grow older they seem to gradually reduce the protective measures they take.     

论戴望舒诗歌创作中的悲剧意识

戴望舒作为20世纪30年代现代派诗坛的首领,他强调新诗“应该有新的情绪“.也就是在这种“内在情绪“的牵引下,戴望舒的九十余首诗篇中流露出浓重的悲剧意识.本文拟从戴望舒悲剧意识的缘起、文本呈现及其得失等三个方面作一粗浅的分析.     

The semantic mapping of words and co-words in contexts

Meaning can be generated when information is related at a systemic level. Such a system can be an observer, but also a discourse, for example, operationalized as a set of documents. The measurement of semantics as similarity in patterns (correlations) and latent variables (factor analysis) has been enhanced by computer techniques and the use of statistics; for example, in “latent semantic analysis”. This communication provides an introduction, an example, pointers to relevant software, and summarizes the choices that can be made by the analyst. Visualization (“semantic mapping”) is thus made more accessible.     

Information recall of younger and older skilled athletes: The role of display complexity,attentional resources and visual exposure duration

This study was concerned with the interactive role of experience of athletic skill with other relevant environmental variables (display complexity, attentional resources, exposure time of stimuli) on perception (recall) of structured handball game situations. Nineteen experienced (older) and 19 less experienced (younger) handball players were assigned randomly either to ball‐bouncing (diverted attention) or to calm seated (focused attention) conditions. In each of the two conditions, the subjects were exposed to a total of 36 slides containing 2–3, 5–6 and 8–10 players. Eighteen slides were exposed for 0.5 s and 18 for 1.0 s. All the slides contained structured defence and offence situations. Recall scores were subjected to repeated‐measures ANOVA using attention condition and age as between‐subject factors and task complexity and exposure duration as within‐subject factors. The results showed that the recall of less experienced players deteriorated more than the recall of more experienced players following exposure to complex displays (≥ 8 players) while engaging in a secondary task (bouncing). The findings suggest that research paradigms should be applied in the field of sport which share both ecological validity and a potential to discover the cognitive substrates underlying experience and age in skilled motor performance.     

Key phrase aware transformer for abstractive summarization

Abstractive summarization aims to generate a concise summary covering salient content from single or multiple text documents. Many recent abstractive summarization methods are built on the transformer model to capture long-range dependencies in the input text and achieve parallelization. In the transformer encoder, calculating attention weights is a crucial step for encoding input documents. Input documents usually contain some key phrases conveying salient information, and it is important to encode these phrases completely. However, existing transformer-based summarization works did not consider key phrases in input when determining attention weights. Consequently, some of the tokens within key phrases only receive small attention weights, which is not conducive to encoding the semantic information of input documents. In this paper, we introduce some prior knowledge of key phrases into the transformer-based summarization model and guide the model to encode key phrases. For the contextual representation of each token in the key phrase, we assume the tokens within the same key phrase make larger contributions compared with other tokens in the input sequence. Based on this assumption, we propose the Key Phrase Aware Transformer (KPAT), a model with the highlighting mechanism in the encoder to assign greater attention weights for tokens within key phrases. Specifically, we first extract key phrases from the input document and score the phrases’ importance. Then we build the block diagonal highlighting matrix to indicate these phrases’ importance scores and positions. To combine self-attention weights with key phrases’ importance scores, we design two structures of highlighting attention for each head and the multi-head highlighting attention. Experimental results on two datasets (Multi-News and PubMed) from different summarization tasks and domains show that our KPAT model significantly outperforms advanced summarization baselines. We conduct more experiments to analyze the impact of each part of our model on the summarization performance and verify the effectiveness of our proposed highlighting mechanism.     

The dynamic effect of interactivity on customer engagement behavior through tie strength: Evidence from live streaming commerce platforms

Interactivity, which is a key characteristic of the live streaming commerce environment, fosters users’ active attitudes and behaviors in communications and transactions. However, the literature on live streaming commerce, is scarce, and few studies examine how interactivity influences customers’ non-transactional behaviors from a dynamic perspective. In this setting, based on the stimulus-organism-response (S-O-R) framework, we developed a research model using real-time data to investigate the dynamic effect of interactivity on customer engagement behavior through tie strength in live streaming commerce, which is a relatively new derivative of social commerce. This study developed a text mining method to quantify constructs using a large-scale sample of 3,500,445 online review texts. Our empirical study found that interactivity has a curvilinear relationship with customer engagement behavior. Besides, tie strength plays an intermediary role between interactivity and customer engagement behavior. It was further observed that both tenure of membership and popularity have an important moderating relationship between interactivity and tie strength. The study enriches the relationship marketing theory and live streaming commerce literature. Moreover, this study is one of the first studies to use real-time online data for live streaming commerce research.