Flexible and Robust Model Matching based on Association Graph for Form Image Understanding

A new method of image understanding for forms based on model matching is proposed in this paper as the basis of an OCR which can read a variety of forms. The outline of this method is described as follows. First, ruled lines are extracted from the input image of a form. After that, several lines are grouped as one to be recognised as data corresponding to a sub-form. These lines and sub-forms are both used for understanding the form, taking into account their feature attributes and the relationships between them. Each feature in the input image of a form is expected to correspond to a feature in one of the model forms, which are described as structured features. This correspondence is represented by a node in an association graph, where an arc represents compatible correspondences established on the basis of feature relationships. The best match is found as the largest maximal clique in the association graph. Experimental results show the method is robust and effective for document images of poor quality, and also for various styles of forms.     

基于多粒度注意力网络的知识超图链接预测

在知识图谱中进行链接预测是图谱补全的有效方法,可以有效地改善知识图谱的数据质量.然而,现实生活中的关系往往是多元的,这些包含多元关系的知识图谱可称为知识超图(knowledgehypergraph,KHG).然而,现有的知识超图链接预测模型忽略了多元关系的平等性(多元关系中实体不存在先后关系)与整体性(多元关系缺少一个实体则不成立).针对以上问题,首先提出了一种知识超图多元关系表示模型,可以直接建模知识超图中的多元关系;然后研究了一种基于多粒度神经网络的链接预测方法(hyperedgepredictionbasedonmulti-granular attentionnetwork,HPMG).该模型将关系划分为多重粒度进行学习,从不同粒度联合完成知识超图的学习和预测,充分考虑了知识超图中不同维度多元关系的整体性.接下来,针对HPMG特征融合不充分的问题,提出了基于多粒度注意力网络的知识超图链接预测方法 HPMG+,结合全局和局部注意力,实现了不同特征的有区分融合,进一步提高了模型的性能.最后,真实数据集上的大量实验结果验证了所提方法的效果显著地优于所有基线方法.     

Approximate mining of maximal frequent itemsets in data streams with different window models

A data stream is a massive, open-ended sequence of data elements continuously generated at a rapid rate. Mining data streams is more difficult than mining static databases because the huge, high-speed and continuous characteristics of streaming data. In this paper, we propose a new one-pass algorithm called DSM-MFI (stands for Data Stream Mining for Maximal Frequent Itemsets), which mines the set of all maximal frequent itemsets in landmark windows over data streams. A new summary data structure called summary frequent itemset forest (abbreviated as SFI-forest) is developed for incremental maintaining the essential information about maximal frequent itemsets embedded in the stream so far. Theoretical analysis and experimental studies show that the proposed algorithm is efficient and scalable for mining the set of all maximal frequent itemsets over the entire history of the data streams.     

Distribution and determinants of maximal physical work capacity of Korean male metal workers

The distribution of maximal physical work capacity (MPWC) can be used to establish an upper limit for energy expenditure during work (EEwork). If physically demanding work has wearing effects, there will be a negative relationship between MPWC and workload. This study was conducted to investigate the distribution of MPWC among Korean metal workers and to examine the relationship between workload and MPWC. MPWC was estimated with a bicycle ergometer using a submaximal test. Energy expenditure was estimated by measuring heart rates during work. The study subjects were 507 male employees from several metal industries in Korea. They had a lower absolute VO_2max than the Caucasian populations described in previous studies. The older workers had a lower physical capacity and a greater overload at work. A negative relationship was found between MPWC and workload across all age groups. Upper limits for EEwork for all age groups and for older age groups are recommended based on the 5th percentile value of MPWC.     

基于数据相容填补的极大相容块构造算法

极大相容块技术在不完备信息处理方面具有一定优势,但极大相容块获取本身是个耗时的过程。通过分析不完备信息系统中极大相容块的性质特点,给出极大相容块中不完备数据的相容填补方法;在不完备信息系统中使用相容填补方法,得到一种新的极大相容块构造算法;基于构造算法的特点,引入决策树存储结构对算法进行优化。使用标准的数据集验证了构造算法的有效性。实验结果表明,构造算法在较大规模的数据集上有较好的性能。     

一种基于垂直分布的非数值数据聚类算法

在本文中，我们提出了一种新的非数值数据聚类算法-VBCCD．VBCCD算法由关系表计算关系的一维分割，再由关系的分割来构造一个超图，而后通过超图分割算法，对构造出来的超图进行优化分割，得到最终的聚类结果。试验结果表明，该算法比传统的针对数值数据设计的聚类算法有更好的效果。     

一种发现函数依赖集的方法及应用

介绍了一种发现最小函数依赖集的方法.这种方法基于一致集的概念,根据一致集导出最大集及其补集,然后生成最小非平凡函数依赖集.通过使用带状划分数据库减少求一致集的运算次数,使用逐层求精的算法来计算最小非平凡函数依赖集的左部.其结果可用于数据库的重新组织和设计、属性约简、聚类、关联规则提取等知识发现工作中.     

模糊-超图聚类模型在土地评价中的应用研究

针对具体的土地评价问题，构建结构清晰、具有可靠性的土地评价的超图数据聚类模型，应用改进的聚类算法，设计聚类的有效性评价指标。并通过实例验证该方法在城镇土地定级中聚类结果的有效性和准确性。     

一般k阶余因式的k超连接表达式和分解定理

本文应用有向超图理论提出了线性系统不定参数矩阵Y的一般k阶余因式Y(ii)的两个k超连接表达式,并根据它导出了Y(ii)的一个分解定理。应用该定理容易对任意线性大系统进行多层撕裂和分析。这是一种新的多层拓扑分析方法,它可以扩大一台计算机所能拓扑分析的系统规模。