Range charts and no-space graphs

No-space graphs present one solution to the familiar problem: given data on the occurrence of fossil taxa in separate, well-sampled sections, determine a range chart; that is, a reasonable working hypothesis of the total range in the area in question of each taxon studied.The solution presented here treats only the relative sequence of biostratigraphic events (first and last occurrences of taxa) and does not attempt to determine an amount of spacing between events.Relative to a hypothesized sequence, observed events in any section may be in-place or out-of-place. Out-of-place events may indicate (1) the event in question reflects a taxon that did not fill its entire range (unfilled-range event), or (2) the event in question indicates a need for the revision of the hypothesized sequence.A graph of relative position only (no-space graph) can be used to facilitate the recognition of in-place and out-of-place events by presenting a visual comparison of the observations from each section with the hypothesized sequence. The geometry of the graph as constructed here is such that in-place events will lie along a line series and out-of-place events will lie above or below it. First-occurrence events below the line series and last-occurrence events above the line series indicate unfilled ranges. First-occurrence events above the line series and last-occurrence events below the line series indicate a need for the revision of the hypothesis. Knowing this, the stratigrapher considers alternative positionings of the line series as alternative range hypotheses and seeks the line series that best fits his geologic and paleontologic judgment.No-space graphs are used to revise an initial hypothesis until a final hypothesis is reached. In this final hypothesis every event is found in-place in at least one section, and all events in all sections may be interpreted to represent in-place events or unfilled-range events. No event may indicate a need for further range revision.The application of the no-space graph method requires the assumption of lack of reworking and the assumption that taxa that are present in a single horizon indicate taxa whose ranges overlap. When applied to hypothetical and actual data, the no-space graph technique produces geologically reasonable range charts that compare favorably with results produced by other methods.     

基于对象建议算法的自然场景文本检测

对象建议算法(object proposals)是对象检测中的常用算法,用于快速定位物体区域。根据自然场景文本的特点,将对象建议算法应用到文本检测中,并与经典的最稳定极值区域算法相结合;然后,通过贝叶斯模型融合了笔画宽度特征、视觉散度特征和边缘梯度特征,并将文本和非文本区域的区分问题转换成一个二值标记问题,通过最小化能量函数寻找最佳标记;最后,通过均值漂移聚类寻找文本区域的中心生成文本行。经实验证明,本算法在常用的自然场景文本检测数据集上速度得到了提高,并且一定程度上解决了传统最稳定极值区域算法对光照敏感的问题,获得了较高的查全率。     

Text detection in chart images

Common OCR (Optical Character Recognition) systems fail to detect and recognize small text strings of few characters, in particular when a text line is not horizontal. Such text regions are typical for chart images. In this paper we present an algorithm that is able to detect small text regions regardless of string orientation and font size or style. We propose to use this algorithm as a preprocessing step for text recognition with a common OCR engine. According to our experimental results, one can get up to 20 times better text recognition rate, and 15 times higher text recognition precision when the proposed algorithm is used to detect text location, size and orientation, before using an OCR system. Experiments have been performed on a benchmark set of 1000 chart images created with the XML/SWF Chart tool, which contain about 14000 text regions in total.     

Deadlock detection without wait-for graphs

Deadlock detection is an important service that the run-time system of a parallel environment should provide. In parallel programs deadlock can occur when the different processes are waiting for various events, as opposed to concurrent systems, where deadlock occurs when processes wait for resources held by other processes. Therefore classical deadlock detection techniques such as checking for cycles in the wait-for graph are unapplicable. An alternative algorithm that checks whether all the processes are blocked is presented. This algorithm deals with situations in which the state transition from blocked to unblocked is indirect, as may happen when busy-waiting is used.     

Text line detection in handwritten documents

In this paper, we present a new text line detection method for handwritten documents. The proposed technique is based on a strategy that consists of three distinct steps. The first step includes image binarization and enhancement, connected component extraction, partitioning of the connected component domain into three spatial sub-domains and average character height estimation. In the second step, a block-based Hough transform is used for the detection of potential text lines while a third step is used to correct possible splitting, to detect text lines that the previous step did not reveal and, finally, to separate vertically connected characters and assign them to text lines. The performance evaluation of the proposed approach is based on a consistent and concrete evaluation methodology.     

面向文本情感分析的商品评价信息检测

为及时有效地获取商品评价信息,提出了基于评价对象识别的商品评价信息检测方法。首先在中文分词的基础上,依据词性标注结果抽取商品评价信息中的候选评价对象;然后基于规则过滤和共现扩展的方法得到精准评价对象;最后实现了基于评价对象识别的商品评价信息检测方法。实验结果表明,与基本模型相比,提出的商品评价信息检测方法的F-Measure提高了34.8%,证明了充分挖掘商品评价信息中的评价对象可以非常有效地改善检测方法的性能。     

Overlapping community detection in labeled graphs

We present a new approach for the problem of finding overlapping communities in graphs and social networks. Our approach consists of a novel problem definition and three accompanying algorithms. We are particularly interested in graphs that have labels on their vertices, although our methods are also applicable to graphs with no labels. Our goal is to find k communities so that the total edge density over all k communities is maximized. In the case of labeled graphs, we require that each community is succinctly described by a set of labels. This requirement provides a better understanding for the discovered communities. The proposed problem formulation leads to the discovery of vertex-overlapping and dense communities that cover as many graph edges as possible. We capture these properties with a simple objective function, which we solve by adapting efficient approximation algorithms for the generalized maximum-coverage problem and the densest-subgraph problem. Our proposed algorithm is a generic greedy scheme. We experiment with three variants of the scheme, obtained by varying the greedy step of finding a dense subgraph. We validate our algorithms by comparing with other state-of-the-art community-detection methods on a variety of performance measures. Our experiments confirm that our algorithms achieve results of high quality in terms of the reported measures, and are practical in terms of performance.     

Fault detection and diagnosis using statistical control charts and artificial neural networks

In order to operate a successful plant or process, continuous improvement must be made in the areas of safety, quality and reliability. Central to this continuous improvement is the early or proactive detection and correct diagnosis of process faults. This research examines the feasibility of using cumulative summation (CUSUM) control charts and artificial neural networks together for fault detection and diagnosis (FDD). The proposed FDD strategy was tested on a model of the heat transport system of a CANDU nuclear reactor.The results of the investigation indicate that a FDD system using CUSUM control charts and a radial basis function (RBF) neural network is not only feasible but also of promising potential. The control charts and neural network are linked by using a characteristic fault signature pattern for each fault which is to be detected and diagnosed. When tested, the system was able to eliminate all false alarms at steady state, promptly detect six fault conditions, and correctly diagnose five out of the six faults. The diagnosis for the sixth fault was inconclusive.     

Text detection and recognition in images and video frames

This paper presents a new method for detecting and recognizing text in complex images and video frames. Text detection is performed in a two-step approach that combines the speed of a text localization step, enabling text size normalization, with the strength of a machine learning text verification step applied on background independent features. Text recognition, applied on the detected text lines, is addressed by a text segmentation step followed by an traditional OCR algorithm within a multi-hypotheses framework relying on multiple segments, language modeling and OCR statistics. Experiments conducted on large databases of real broadcast documents demonstrate the validity of our approach.     

The economic design of control charts with repair cost depending on detection delay

This study extends Duncan's [1] model to two different manufacturing process models in which the processes continue and discontinue in operations during the search for the assignable cause. A more realistic assumption considered in this paper is that the cost of repair and the net hourly out-of-control income are functions of detection delay. In the continuous model, detection delay is defined as the elapsed time from the time when the shift of the process occurs until it is identified by control charts and the assignable cause is eliminated. The discontinuous model defines detection delay as the time interval from the occurrence of the process shift to the completion of testing a set of samples and interpreting the results. An efficient procedure is developed to determine the optimal designs without using any approximation approach. Thus, the proposed procedure can obtain the truly optimal designs rather than those approximate designs determined by Duncan [1] and other subsequent researchers. This paper illustrates several numerical examples and makes some relevant comparisons. The results indicate that this optimal solution procedure is more accurate than that of Panagos et al. [2]. Also, detection delay is sensitive to the economic design of control charts.     

一种基于梯度差的文档图像文本行检测算法

在分析文本行特点的基础上,提出了一种利用水平梯度差进行文档图像的文本行检测算法。该算法首先对输入的文档图像进行水平梯度差计算,然后在局部窗口中求解最大梯度差并进行文本行区域的合并,通过非文本区域过滤来消除字符阶跃的跳变,最后将文档图像以行块的形式进行显示。实验结果表明,与投影算法进行相比,该算法对于行间距较小的文档图像的检测效果较好,时间复杂度较低并且检测的正确率较高,具有一定的鲁棒性和较好的适应性。     

边缘与灰度检测相结合的场景图像文本定位

自然场景图像中包含大量的图像和文本信息,其文本字符能够提供重要的语义信息。利用计算机自动检测并识别自然场景中的文本信息,是模式识别和文字信息处理领域重要的研究内容。本文提出一种有效的从场景图像中定位文本的方法,其原理为：首先基于边缘检测进行文本区域粗定位,对定位到的区域进行灰度检测,来确定文本域中的字符位置,其后对所得到的检测区域进行筛选,去掉噪声区域,获取到目标文本域。实验结果表明,本文提出的方法对字体的大小、样式、颜色、以及排布方向具有较强的鲁棒性, 能够准确定位并提取自然场景下的文本信息。     

KTMiner: Distributed k-truss detection in big graphs

Discovering cohesive subgraphs is an important issue in the analysis of massive graphs. A new type of cohesive subgraphs, k-truss, has gained a lot of attentions in recent years. Although different methods have been proposed to extract k-truss subgraphs, they are unable to deal with big graphs, and they suffer from the lack of efficiency. To deal with the problem of finding k-truss subgraphs in big graphs, we propose a novel efficient distributed algorithm, called KTMiner, based on the MapReduce paradigm and the key–value structure. KTMiner is deployed on the Spark platform, a big data framework. Given a specific k value, KTMiner finds edges belonging to the k-truss subgraphs. It includes three consequent phases. First, a novel distributed k-core routine is employed to prune unnecessary vertices from the input graph. Then, the triangle information per edge in the form of a new data structure, called Triangle Set (TSet), is produced. Finally, using a distributed iterative procedure, the desired k-truss subgraphs are detected. KTMiner perfectly caches the reusable data in the distributed memory. In addition, it forms an efficient load balancing mechanism by designing appropriate data structures. This results in a fine-grained parallelism. The superiority of our solution over the state-of-the-art methods is shown by the experiments on real-world graphs.     

基于笔画角度变换和宽度特征的自然场景文本检测

针对光照不均和背景复杂度所导致的自然场景文本检测中文本的漏检和错检现象,提出一种基于笔画角度变换和宽度特征的自然场景文本检测方法。分析发现与非文本相比,文本具有较稳定的笔画角度变换次数和笔画宽度,针对这两个特性提出笔画外边界优劣角变换次数和增强笔画支持像素面积比两种特征。前者分段统计笔画外轮廓角度变换次数;后者计算笔画宽度稳定区域在笔画总面积的占比,用来分别反映笔画角度和宽度变化稳定特性。为降低文本漏检率,采用多通道最大稳定极值区域（maximally stable extremal regions,MSER）检测,合并所有候选区域,提取候选区域的笔画特征和纹理特征,利用支持向量机完成文本和非文本区域分类。在ICDAR2015数据库上,算法的精确率和召回率分别达到79.3%和72.8%,并在一定程度上解决了光照不均和复杂背景的问题。     

基于MCA与判别字典学习的场景图文字检测方法

传统的文字检测方法在场景图像复杂背景、噪声污染和文字的多种形态特征的干扰下,检测的准确率很低,漏检、误检非常严重.针对这些问题,提出了基于形态成分分析(MCA)与判别字典学习的场景图像文字检测的方法.通过学习过完备字典将文字检测问题转化成稀疏和鲁棒表示的问题.利用MCA与改进的Fisher判别准则学习一个过完备字典,求解待检测图像文字部分的稀疏系数,重建待检测图像中的文字图像,进行文字检测.通过在ICDAR2003/2005/2011和MSRA-TDS00数据库中的大量的实验证明了与其他文字检测方法相比,该方法能有效提高检测准确率.     

Clique detection for nondirected graphs: Two new algorithms

Making use of special tree search algorithms the present paper describes two new methods for determining all maximal complete subgraphs (cliques) of a finite nondirected graph. In both methods the blockwise generation of all cliques induces characteristic properties, which guarantee an efficient calculation of special clique subsets, especially the set of all cliques of maximal length. Moreover, by their structure both algorithms allow to calculate the complete clique set by parallel processing. The algorithms have been tested for many series of characteristic graphs and compared with the algorithm of Bron-Kerbosch (Algorithm 457 of CACM) the most efficient algorithm which is known to the authors.     

基于DCSSM的文本特征提取及文本挖掘研究

提出了用模式作为复杂类型数据的知识表示方法,结合结构化数据挖掘给出了基于复杂类型数据知识发现的结构模型——发现特征子空间模型DCSSM。在此基础上讨论了文本特征提取及文本挖掘的方法。