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With Gile's Efforts Model as the theoretical foundation, this study aims at analyzing the effect of pre-interpreting preparation on business interpreting between English and Chinese, particularly focusing on the following two questions: 1. Does pre-interpreting preparation affect the quality of students' business interpreting output? If so, how it affects the various factors of interpreting output quality? 2. Will the pre-interpreting effect change with the different language levels? To answer these two questions, we carried out an experimental study by testing sample students' interpretation on a selected business interview without any preparation and with adequate preparation respectively. With the data collected, we made both quantitative and qualitative analysis. Based on the analysis, the following conclusions are reached: First, pre-interpreting preparation has a positive effect on the overall quality of business interpreting output. Second, in business scenarios, pre-interpreting preparation have different degrees of impact on the factors of interpretation quality. Specifically, it has a relatively large impact on the accuracy of terminology and the logical coherence of the target-language speech, while exerting a minimal impact on the voice quality. Third, pre-interpreting preparation has a greater influence on the interpreting output of the intermediate language level group than it has on the advanced language level group. It is hoped that this study could provide some new perspectives and offer some help to the teaching of business English interpreting. [ABSTRACT FROM AUTHOR]

Cost-sensitive support vector machine is one of the most popular tools to deal with class-imbalanced problem such as fault diagnosis. However, such data appear with a huge number of examples as well as features. Aiming at class-imbalanced problem on big data, a cost-sensitive support vector machine using randomized dual coordinate descent method (CSVM-RDCD) is proposed in this paper. The solution of concerned subproblem at each iteration is derived in closed form and the computational cost is decreased through the accelerating strategy and cheap computation. The four constrained conditions of CSVM-RDCD are derived. Experimental results illustrate that the proposed method increases recognition rates of positive class and reduces average misclassification costs on real big class-imbalanced data. [ABSTRACT FROM AUTHOR]