Quality flaw prediction in Spanish Wikipedia: A case of study with verifiability flaws

In this work, we present the first quality flaw prediction study for articles containing the two most frequent verifiability flaws in Spanish Wikipedia: articles which do not cite any references or sources at all (denominated Unreferenced) and articles that need additional citations for verification (so-called Refimprove). Based on the underlying characteristics of each flaw, different state-of-the-art approaches were evaluated. For articles not citing any references, a well-established rule-based approach was evaluated and interesting findings show that some of them suffer from Refimprove flaw instead. Likewise, for articles that need additional citations for verification, the well-known PU learning and one-class classification approaches were evaluated. Besides, new methods were compared and a new feature was also proposed to model this latter flaw. The results showed that new methods such as under-bagged decision trees with sum or majority voting rules, biased-SVM, and centroid-based balanced SVM, perform best in comparison with the ones previously published.     

Cross-company customer churn prediction in telecommunication: A comparison of data transformation methods

Cross-Company Churn Prediction (CCCP) is a domain of research where one company (target) is lacking enough data and can use data from another company (source) to predict customer churn successfully. To support CCCP, the cross-company data is usually transformed to a set of similar normal distribution of target company data prior to building a CCCP model. However, it is still unclear which data transformation method is most effective in CCCP. Also, the impact of data transformation methods on CCCP model performance using different classifiers have not been comprehensively explored in the telecommunication sector. In this study, we devised a model for CCCP using data transformation methods (i.e., log, z-score, rank and box-cox) and presented not only an extensive comparison to validate the impact of these transformation methods in CCCP, but also evaluated the performance of underlying baseline classifiers (i.e., Naive Bayes (NB), K-Nearest Neighbour (KNN), Gradient Boosted Tree (GBT), Single Rule Induction (SRI) and Deep learner Neural net (DP)) for customer churn prediction in telecommunication sector using the above mentioned data transformation methods. We performed experiments on publicly available datasets related to the telecommunication sector. The results demonstrated that most of the data transformation methods (e.g., log, rank, and box-cox) improve the performance of CCCP significantly. However, the Z-Score data transformation method could not achieve better results as compared to the rest of the data transformation methods in this study. Moreover, it is also investigated that the CCCP model based on NB outperform on transformed data and DP, KNN and GBT performed on the average, while SRI classifier did not show significant results in term of the commonly used evaluation measures (i.e., probability of detection, probability of false alarm, area under the curve and g-mean).     

人体上下肢长预测方法的研究与应用

人体生长发育过程中，具有非等比性和向心律的特点，在生长发育结束以前测评出的形态比例关系，不能直接说明成熟后的情况。通过对7～18岁儿童少年发育状况的调研，提出了人体上下肢长与身高比例关系的预测方法。     

关于影响蔡民十项全能主要因素的分析及成绩的预测研究

应用灰色系统对十项全能的影响因素进行了分析,找出了影响其成绩变化的优势因素.并运用灰色动态模型进行了近期和短期的预测,结果证明这一方法对十项成绩的预测准确性较好,残差一般为±0.25% .     

三维影相的新算法——修正的直接线性变换

通过引入摄影机镜头的内、外方位元素的约束条件,导出了一种修正的DLT(MDLT)方法。该方法用于影片和录相的实测,得到一些有意义的结果。当测量的标定控制点点数较少(6～8个),且这些点的位置在空间和成像面分布不均匀时,用MDLT计算的结果比DLT的结果要好,误差可降低80％以上。     

骨骼肌基因转移系统的建立

以EGFP为标记基因，利用重新构建的pAV-EGFP质粒，采用直接注射方法，进行了离体和在体基因转移研究。实验结果显示，经脂质体包裹后，质粒可以有效地进行细胞转移；DNA表达载体肌肉直接注射后，利用共聚焦显微镜可以在注射点周围的肌细胞观察到绿色荧光蛋白；而在对侧肢PCR和共聚焦显微镜的结果均为阴性。实验结果表明，肌肉直接注射DNA可以在肌肉得到表达，将会为治疗肌肉损伤带来新的方法。     

求解法在数字电路课程教学中的应用

触发器是时序逻辑电路的基本单元,在数字电路系统中具有十分重要的地位。文章提出一种“问题求解教学法”———即先引出问题,寻求问题的解决方法,最终圆满解决问题。教学实践表明,该教学法对激发学生的学习兴趣,引导学生独立思考,提高其创新思维能力,加深对触发器理解和记忆,均有很好的积极意义。     

商务英语专业学生英语阅读理解常见错误的实证研究

文章通过对高职商务英语专业学生阅读中难句翻译理解错误的分析及其与阅读理解得分之间的相关分析，发现翻译错误与阅读理解高度正相关；母语参与阅读理解思维活动有利于学习者对原文的理解；在影响学习者阅读理解的各因素中，词汇影响最大；中国学生在阅读理解中，对某些结构和词汇的省略并不会对阅读理解造成重大影响。     

知行统一观在篮球比赛中的运用研究

以全国航空管理系统篮球赛14个队的知行能力获胜率为研究对象，运用问卷调查等方法，对知行统一观在篮球比赛中的运用情况进行研究。结果显示：比赛是知行能力的竞争，知行统一是通变的基础；比赛中知的核心是知已知彼的认识过程，行的原则是避实击虚的实践过程。     

销售量预测模型及其应用

运用回归分析方法建立的某类销售量预测模型,对季节变化关联较强的商品的销售量有较高的预测精确度。