Efficient parallel automata construction for hybrid resource-impelled data-matching

We are presenting an innovative, massively-parallel heterogeneous architecture for the very fast construction and implementation of very large Aho–Corasick and Commentz-Walter pattern-matching automata, commonly used in data-matching applications, and validate its use with large sets of data actively used in intrusion detection systems. Our approach represents the first known hybrid-parallel model for the construction of such automata and the first to allow self-adjusting pattern-matching automata in real-time by allowing full-duplex transfers at maximum throughput between the host (CPU) and the device (GPU). The architecture we propose is easily scalable to multi-GPU and multi-CPU systems and benefits greatly from GPU acceleration, also relying on a highly-efficient storage model for the automata and includes on-demand support for regular-expression matching, as well as support for custom heuristics to be built on top of the architecture, at different processing stages.     

Investigation of a measurement scheme based on IGES

The integration research of measuring machine and CAD information, just CMM/CAD integration technology, is far behind the information integration of mechanical processing and CAD. With the rapid development of information integration industry and in the manufacturing process of the parts with complex curve surface, it is crucial to integrate the measurement and machining procedures based on IGES as that IGES is a general ANSI standard of information exchange for CAD/CAM among different computer systems. The measuring path planning determines the realization of measuring each sampling point and the completion of all sampling points safely and effectively by using measuring equipment. Based on IGES in this paper, the calculation method of normal direction at the location of sample point is studied for different surfaces, so as to improve the measuring precision. Then the calculation strategy for the positioning points and the measuring range is established, and the identification method for the topological relation between surfaces is also researched for improving the measuring efficiency and security. At last, the measuring sequence of the sampling points to be planned reasonably based on TSP is given. It is believed that the research results can provide some useful contributions towards the development of automated inspection task planning and systems, especially for coordinate measuring machines.     

基于云计算的分布式通信电源控制系统研究

通信基站中的电源系统大多以一种分布式的状态存在,其运行数据繁多而复杂,因此应用云计算平台来进行电源系统的数据管理是一种良好的策略。在设计通信电源云计算平台的基础上,采取数据分类匹配的方法对数据进行预处理,以减少云平台的数据冗余,从而达到提高电源系统数据管理效率的目的。     

Innovation Subsidies: Does the Funding Source Matter for Innovation Intensity and Performance? Empirical Evidence from Germany

In this paper we analyze the effect of European and national funding on innovation input and output at the firm level. In terms of innovation input, we do not find evidence that one policy crowds out the effect of the other. Instead, the policies are complements. In terms of output, we find that subsidy recipients are more active with respect to patenting. A citation analysis of patents reveals that the subsidy recipients file patents that are more valuable (in terms of forward citations) than those filed in the counterfactual situation of receiving no public support. These results suggest that public funding triggers socially beneficial research projects and that the co-existence of national and European policies does not lead to crowding-out effects when compared to a hypothetical world of a closed economy with no supplemental European policies.     

大数据相似性连接查询技术研究进展

为了深入理解和全面把握大数据相似性连接查询技术的研究进展，更好地促进其在图片聚类、实体解析、相似文档检测、相似轨迹检索等领域的广泛应用，对大数据相似性连接查询技术相关研究工作进行了深入调研和分析。首先对相似性连接查询的基本概念进行了介绍，然后分别对集合、向量、空间数据、概率数据、字符串等不同类型大数据的相似性连接查询相关研究工作进行了深入研究，对其优缺点进行了分析和总结。最后，指出了大数据相似性连接查询面临的若干挑战性问题及未来的研究重点。     

Convolutional neural network based deep conditional random fields for stereo matching

Stereo matching has been studied for many years and is still a challenge problem. The Markov Random Fields (MRF) model and the Conditional Random Fields (CRF) model based methods have achieved good performance recently. Based on these pioneer works, a deep conditional random fields based stereo matching algorithm is proposed in this paper, which draws a connection between the Convolutional Neural Network (CNN) and CRF. The object knowledge is used as a soft constraint, which can effectively improve the depth estimation accuracy. Moreover, we proposed a CNN potential function that learns the potentials of CRF in a CNN framework. The inference of the CRF model is formulated as a Recurrent Neural Network (RNN). A variety of experiments have been conducted on KITTI and Middlebury benchmark. The results show that the proposed algorithm can produce state-of-the-art results and outperform other MRF-based or CRF-based methods.     

Efficient image matching using weighted voting

Spectral decomposition subject to pairwise geometric constraints is one of the most successful image matching (correspondence establishment) methods which is widely used in image retrieval, recognition, registration, and stitching. When the number of candidate correspondences is large, the eigen-decomposition of the affinity matrix is time consuming and therefore is not suitable for real-time computer vision. To overcome the drawback, in this letter we propose to treat each candidate correspondence not only as a candidate but also as a voter. As a voter, it gives voting scores to other candidate correspondences. Based on the voting scores, the optimal correspondences are computed by simple addition and ranking operations. Experimental results on real-data demonstrate that the proposed method is more than one hundred times faster than the classical spectral method while does not decrease the matching accuracy.     

基于语义相似度的Web信息可信分析

互联网上的虚假事实陈述严重影响人们有效地获取信息,如何判定事实陈述是否可信成为一个亟待解决的问题.文中提出了一种针对事实陈述的可信判定模型MFSV.该模型针对事实陈述的特点,从互联网上搜集与待判定事实陈述相关的文本信息,度量其与对应事实陈述的语义相似度;同时,该模型考虑了相关文本信息在可信度上的差别,从受欢迎程度和重要程度两个方面度量了相关文本信息来源的可信度,并获取了相关文本信息的可信度排序;根据语义相似度以及可信度排序,衡量了相关文本信息对对应事实陈述可信判定所做出的贡献,并以此为基础实现了待判定事实陈述的可信判定.一系列的实验验证了该模型的合理性及可信判定的准确性.     

Robust object tracking based on adaptive templates matching via the fusion of multiple features

Moving object tracking under complex scenes remains to be a challenging problem because the appearance of a target object can be drastically changed due to several factors, such as occlusions, illumination, pose, scale change and deformation. This study proposes an adaptive multi–feature fusion strategy, in which the target appearance is modeled based on timed motion history image with HSV color histogram features and edge orientation histogram features. The variances based on the similarities between the candidate patches and the target templates are used for adaptively adjusting the weight of each feature. Double templates matching, including online and offline template matching, is adopted to locate the target object in the next frame. Experimental evaluations on challenging sequences demonstrate the accuracy and robustness of the proposed algorithm in comparison with several state-of-the-art algorithms.     

Comparative analysis of discretization methods in Bayesian networks

A key step in implementing Bayesian networks (BNs) is the discretization of continuous variables. There are several mathematical methods for constructing discrete distributions, the implications of which on the resulting model has not been discussed in literature. Discretization invariably results in loss of information, and both the discretization method and the number of intervals determines the level of such loss. We designed an experiment to evaluate the impact of commonly used discretization methods and number of intervals on the developed BNs. The conditional probability tables, model predictions, and management recommendations were compared and shown to be different among models. However, none of the models did uniformly well in all comparison criteria. As we cannot justify using one discretization method against others, we recommend caution when discretization is used, and a verification process that includes evaluating alternative methods to ensure that the conclusions are not an artifact of the discretization approach.