LinkingPark: An automatic semantic table interpretation system

In this paper, we present LinkingPark, an automatic semantic annotation system for tabular data to knowledge graph matching. LinkingPark is designed as a modular framework which can handle Cell-Entity Annotation (CEA), Column-Type Annotation (CTA), and Columns-Property Annotation (CPA) altogether. It is built upon our previous SemTab 2020 system, which won the 2nd prize among 28 different teams after four rounds of evaluations. Moreover, the system is unsupervised, stand-alone, and flexible for multilingual support. Its backend offers an efficient RESTful API for programmatic access, as well as an Excel Add-in for ease of use. Users can interact with LinkingPark in near real-time, further demonstrating its efficiency.     

Seismic behavior of geosynthetic-reinforced retaining walls backfilled with cohesive soil

This study investigates the seismic performance of geosynthetic-reinforced modular block retaining walls backfilled with cohesive, fine grained clay-sand soil mixture. Shaking table tests were performed for three ½ scaled (wall height 190 cm) and ¼ scaled model walls to investigate the effects of backfill type, the influence of reinforcement length and reinforcement stiffness effects. The El Centro and Kobe earthquake records of varying amplitudes were used as base acceleration. Displacement of the front wall, accelerations at different locations, strains on the reinforcements, and the visual observations of the facing and the backfill surface were used to evaluate the seismic performance of model walls. The model walls were subjected to rigorous shaking and the walls did not exhibit any stability problems or signs of impending failure. The maximum deformations observed on the models with cohesive backfill was less than half of the deformation of the sand model. The load transfers between the geogrid and cohesive soil was comparable to that of sand and hence the needed reinforcement length was similar as well. As a result; the model walls with cohesive backfills performed within acceptable limits under seismic loading conditions when compared with granular backfilled counterparts.     

The development of a single-item Food Choice Questionnaire

Based on the multi-item Food Choice Questionnaire (FCQ) originally developed by Steptoe and colleagues (1995), the current study developed a single-item FCQ that provides an acceptable balance between practical needs and psychometric concerns. Studies 1 (N = 1851) and 2 (2a (N = 3290), 2b (N = 4723), 2c (N = 270)) showed that the single-item FCQ scale has good convergent and discriminant validity. Generally, the results showed the highest correlations with the related multi-item dimensions (>0.40). Study 2 refined the scale. Only the items for convenience (Study 2a), sensory appeal (Study 2b) and mood (Study 2c) needed to be revised (as Study 1 showed a correlation between the multi-item and the single-item below the threshold of 0.60). The results also showed comparable predictive validity. Both methods revealed similar association patterns between food motives and consumption behaviours (Fisher’s z tests revealed agreements of 86.2% for Study 1, 92.9% for Study 2a and 100% for Studies 2b and 2c). Study 3 (N = 6062) showed an example of the added value of a context-specific application for the single-item FCQ. Different motives were shown to be relevant across contexts, and the context-specific motives had additional explained variance beyond the general multi-item FCQ. Studies 2b and 3 also showed the performance of the single-item FCQ in an international context. In sum, the results indicate that the single-item FCQ can be used as a flexible and short substitute for the multi-item FCQ. The study also discusses the conditions that should be considered when using the single-item scale.     

一种基于Spark的不确定数据集频繁模式挖掘算法

如何在海量不确定数据集中提高频繁模式挖掘性能是目前研究的热点.传统算法大多是以期望、概率或者权重等单一指标为数据项集支持度，在大数据背景下，同时考虑概率和权重支持度的算法难以兼顾其执行效率.为此，本文提出一种基于Spark的不确定数据集频繁模式挖掘算法（UWEFP），首先，为了同时兼顾数据项的概率和权重，计算一项集的最大概率权重值并进行剪枝；然后，为了减少对数据集的多次扫描，结合Spark框架的优点，设计了一种具有FP-tree特征的新颖的UWEFP-tree结构进行模式树的构建及挖掘；最后在Spark环境下，以UCI数据集进行实验验证.实验结果表明本文的方法在保证挖掘结果的同时，提高了效率.     

基于粗糙集信息观的决策表属性约简方法

粗糙集理论是近年来发展起来的一种有效的处理不精确、不确定、含糊信息的数学理论方法，它被广泛应用于相容和不相容决策表的属性约简和核属性计算。利用反例指出目前基于粗糙集信息观[2、6]的决策表属性约简和核属性计算方法的局限性。对决策表的性质作了深入的研究，研究发现文献[2、6]方法的不足原因是：它们没有考虑U／ind（C）中等价类的相客性。给出了基于U／ind（C）中等价类相客性的属性约简定义和核属性定义，并给出了一种新的基于粗糙集信息观的决策表属性约简和核属性计算方法。讨论了该方法同文献[2、6]方法的区别。最后用相同实例验证了该方法的有效性。     

基于属性相关表的关系模式全部候选关键字求法

本文提出了属性相关表这样一种新的概念，定义了相应的运算并研究了它的有关性质及定理，在此基础上提出了用属性相关表求关系模式全部主属性的等级，并给出了用主属性来组合关系模式全部候选关键字的算法，上述算法的时间复杂度均为Ｏ（ｎ＾２）。     

三维施工中SN388炮点表的设置

本文提供了SN388在三维施工中炮点表的几种设置方法，并进行了相应的对比说明，最后给出了改进炮点表设置的有关设想。     

Examining a dimensional representation of depression and anxiety disorders' comorbidity in psychiatric outpatients with item response modeling.

The current study replicated, in a sample of 2,300 outpatients seeking psychiatric treatment, a previous study (R. F. Krueger & M. S. Finger, 2001) that implemented an item response theory approach for modeling the comorbidity of common mood and anxiety disorders as indicators along the continuum of a shared latent factor (internalizing). The 5 disorders examined were major depressive disorder, social phobia, panic disorder/agoraphobia, specific phobia, and generalized anxiety disorder. The findings were consistent with the prior research. First, a confirmatory factor analysis yielded sufficient evidence for a nonspecific factor underlying the 5 diagnostic indicators. Second, a 2-parameter logistic item response model showed that the diagnoses were represented in the upper half of the internalizing continuum, and each was a strongly discriminating indicator of the factor. Third, the internalizing factor was significantly associated with 3 indexes of social burden: poorer social functioning, time missed from work, and lifetime hospitalizations. Rather than the categorical system of presumably discrete disorders presented in DSM-IV, these 5 mood and anxiety disorders may be alternatively viewed as higher end indicators of a common factor associated with social cost. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

ERCM与数据库设计

本文介绍了一种实用的数据库设计方法，它以实体分析法为理论基础，通过对我们日常使用的表进行聚集和归类，完成数据库的逻辑设计，所设计的数据库满足ＢＮＯＦ范式。同时该方法可操作性较强，本文还介绍了自行设计的ＥＲＣＭ数据库辅助设计工具。     

提高定制产品物料需求预测的准确性

提高定制产品物料需求预测准确性是提高企业竞争能力的必由之路。通过某公司提高定制产品物料需求预测准确性的具体项目实践，介绍了关于提高物料需求预测准确性的具体措施。通过建立一种能为各部门所共同接受的产品语言来加强各部门的信息交流，并减少从客户的实际需求到物料预测之间的中间环节来提高整个物料预测的准确性，并以实例验证其有效性。