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study for articles containing the two most frequent verifiability flaws in Spanish Wikipedia: articles which do not cite any references or sources at all (denominated Unreferenced) and articles that need additional citations for verification (so-called Refimprove).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

台阶试验质疑

台阶试验是一项由BROHA建立的用以评价机体对强体力负荷的适应能力和身体机能的恢复能力的运动负荷试验,研究表明:台阶试验指数与运动耐力和最大耗氧量之间无高度相关,因此,不能对心血管系统功能作出准确评价;台阶试验指数只能够对受试者的状况作出好与不好的评价,而不能作出精确评价;台阶试验指数不能有效反映以最大耗氧量、最大心输出量为代表的心脏功能增龄性变化规律及其性别差异。鉴于以上原因,台阶试验作为心血管系统功能评价的运动负荷试验尚待进一步研究。     

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% .     

放松训练研究的回顾与展望

概括了放松训练的国内外研究现状，探讨了放松训练的一些主要技术(如渐进性放松、自生训练、表象放松、生物反馈放松以及其它的一些放松训练技术)和效果，并对放松训练研究的未来提出了展望。     

我国国内旅游发展前景及对策

本文从人们收入水平的提高、闲暇时间的增多、消费观念的改变、旅游意识的增强等方面分析了我国国内旅游的发展前景.并提出了促进国内旅游进一步发展的措施政府主导,携手培育大旅游;加强旅游法制建设;以市场为导向,开发特色旅游产品和优势旅游产品;重视旅游消费引导;加强旅游人才培训,提高服务质量.     

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。