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prediction study for articles containing the two most frequent verifiability flaws in Spanish Wikipedia: articles which do not cite any references or sources at all (denominated Unreferenced) and articles that need additional citations for verification (so-called Refimprove).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% .     

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。     

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。     

融合深度学习和链路预测的细粒度技术预测研究——以合成生物技术为例

[目的/意义]面向专利文本进行更细粒度的技术实体识别和技术预测,利于更详细地把握专利技术布局与趋势。[方法/过程]首先利用深度学习方法自动识别专利技术术语类实体,通过实验对比多组深度学习算法的优劣。其次,提出新的半监督标注和自定义标注方案,提高人工标注效率。最后,执行训练得到的最优模型,结合链路预测方法,对合成生物技术进行细粒度的技术预测。[结果/结论]实证结果表明RoBERTa-BiLSTM-CRF模型更适用于语义复杂的专利技术实体识别,F1值可达到86.8%,技术识别结果比传统IPC分析方法更精细。同时,细粒度的技术预测结果表明,合成生物学的合成方法在不断改进创新,合成物研究向合成燃料发展。     

单点与双点拉剪点焊试样的疲劳特性比较

对单点与两点焊拉剪点焊疲劳试样分别进行了疲劳试验,观测了疲劳裂纹的形状和萌生位置,分析了两种试样的疲劳性能。通过有限元模型分析了同等载荷作用下两种试样的变形,对比两种试样的载荷幅寿命关系,认为两点试样的承载能力约为单点试样的两倍。基于此,使用单点试样的载荷幅寿命曲线,预测了两点试样的疲劳寿命,寿命预测结果与实验寿命结果一致。     

辽河柴油十六烷指数的预测

十六烷值是衡量柴油质量的重要指标,本文给出了利用生产数据对辽河柴油进行十六烷指数计算公式.     

Talent identification and selection in elite youth football: An Australian context

We identified the perceptual–cognitive skills and player history variables that differentiate players selected or not selected into an elite youth football (i.e. soccer) programme in Australia. A sample of elite youth male football players (n?=?127) completed an adapted participation history questionnaire and video-based assessments of perceptual–cognitive skills. Following data collection, 22 of these players were offered a full-time scholarship for enrolment at an elite player residential programme. Participants selected for the scholarship programme recorded superior performance on the combined perceptual–cognitive skills tests compared to the non-selected group. There were no significant between group differences on the player history variables. Stepwise discriminant function analysis identified four predictor variables that resulted in the best categorization of selected and non-selected players (i.e. recent match-play performance, region, number of other sports participated, combined perceptual–cognitive performance). The effectiveness of the discriminant function is reflected by 93.7% of players being correctly classified, with the four variables accounting for 57.6% of the variance. Our discriminating model for selection may provide a greater understanding of the factors that influence elite youth talent selection and identification.