基于路径的频繁子图挖掘算法研究

图挖掘是数据挖掘的一个重要研究方向,而图挖掘主要集中在图数据集内频繁子图的挖掘。频繁子图挖掘技术的关键是建立有效机制减少冗余候选子图,以便高效计算和处理所需的频繁子图。提出了一种基于路径的频繁子图挖掘算法,该算法首先找出所有频繁边从而挖掘出频繁单路径,然后通过组合、双射和操作扩展出较多的频繁路径,再通过连接操作产生所有频繁子图候选集。通过定理证明了该算法的正确性和完整性,从理论上分析了该算法时间复杂度低于现有的算法,最后进行了2个图数据集实验,在候选集产生的数量和时间性能2方面验证了算法的优越性。     

A path-based approach for web page retrieval

Use of links to enhance page ranking has been widely studied. The underlying assumption is that links convey recommendations. Although this technique has been used successfully in global web search, it produces poor results for website search, because the majority of the links in a website are used to organize information and convey no recommendations. By distinguishing these two kinds of links, respectively for recommendation and information organization, this paper describes a path-based method for web page ranking. We define the Hierarchical Navigation Path (HNP) as a new resource for improving web search. HNP is composed of multi-step navigation information in visitors’ website browsing. It provides indications of the content of the destination page. We first classify the links inside a website. Then, the links for web page organization are exploited to construct the HNPs for each page. Finally, the PathRank algorithm is described for web page retrieval. The experiments show that our approach results in significant improvements over existing solutions.     

On-line sensitivity analysis of Markov chains

Discrete-event systems modeled as continuous-time Markov processes and characterized by some integer-valued parameter are considered. The problem addressed is that of estimating performance sensitivities with respect to this parameter by directly observing a single sample path of the system. The approach is based on transforming the nominal Markov chain into a reduced augmented chain, the stationary-state probabilities which can be easily combined to obtain stationary-state probability sensitivities with respect to the given parameter. Under certain conditions, the reduced augmented chain state transitions are observable with respect to the state transitions of the system itself, and no knowledge of the nominal Markov-chain state of the transition rates is required. Applications for some queueing systems are included. The approach incorporates estimation of unknown transition rates when needed and is extended to real-valued parameters     

Kriging-based convex subspace single linkage method with path-based clustering technique for approximation-based global optimization

This paper proposes an improved approach of the Kriging-based Convex Subspace Single Linkage Method (KCSSL method), which was reported as one of approximation-based global optimization methods. The KCSSL method consists of a convex subspace clustering procedure and a local optimization procedure. For the clustering procedure, previously, the cell-based clustering technique was employed. However, this approach will involve a huge number of convexity estimations in case of a higher dimensional problem. This will cause a very high computational cost, therefore, a path-based clustering procedure is newly developed. At first, a procedure for the convexity estimation with the Kriging method is introduced. Next, outline and detailed procedure of the proposed path-based clustering technique are explained. Also, the proposed method is applied to solving some approximate optimization problems. From the numerical results, validity and effectiveness of the proposed method are discussed.     

A simple formula for the calculation of of the wiener index of hexagonal chains

The Wiener index (W) of hexagonal chains (i.e. the molecular graphs of unbranched catacondensed benzenoid hydrocarbons) is examined. The index W is a topological index defined as the sum of distances between all pairs of vertices in a molecular graph. A simple calculation formula for W is put forward. As a corollary, necessary and sufficient conditions for the coincidence of W-values of certain hexagonal chains are established.     

Hand-based single sample biometrics recognition

Currently, single sample biometrics recognition (SSBR) has emerged as one of the major research contents. It may lead to bad recognition result. To solve this problem, we present a novel approach by fusing two kinds of hand-based biometrics, i.e., palmprint and middle finger. We obtain their discriminant features by combining statistical information and structural information of each modal which are extracted using locality preserving projection (LPP) based on wavelet transform (WT). In order to reduce the influence of affine transform, we utilize mean filtering to enhance the robustness of structural information to improve the discriminant ability of palmprint high-frequency sub-bands. The two types of features are then fused at score level for the final hand-based SSBR. The experiments on the hand image database that contains 1,000 samples from 100 individuals show that the proposed feature extraction and fusion methods lead to promising performance.     

A syntactic path-based hybrid neural network for negation scope detection

The automatic detection of negation is a crucial task in a wide-range of natural language processing(NLP)applications,including medical data mining,relation extraction,question answering,and sentiment analysis.In this paper,we present a syntactic path-based hybrid neural network architecture,a novel approach to identify the scope of negation in a sentence.Our hybrid architecture has the particularity to capture salient information to determine whether a token is in the scope or not,without relying on any human intervention.This approach combines a bidirectional long shortterm memory(Bi-LSTM)network and a convolutional neural network(CNN).The CNN model captures relevant syntactic features between the token and the cue within the shortest syntactic path in both constituency and dependency parse trees.The Bi-LSTM learns the context representation along the sentence in both forward and backward directions.We evaluate our model on the Bioscope corpus,and get 90.82%F-score(78.31%PCS)on the abstract sub-corpus,outperforming features-dependent approaches.     

A sensitivity formula for risk-sensitive cost and the actor–critic algorithm

We propose for risk sensitive control of finite Markov chains a counterpart of the popular ‘actor–critic’ algorithm for classical Markov decision processes. The algorithm is based on a ‘sensitivity formula’ for the risk sensitive cost and is shown to converge with probability one to the desired solution. The proof technique is an adaptation of the ordinary differential equations approach for the analysis of two time-scale stochastic approximation algorithms.     

尺度不变单样本人脸识别方法

人脸识别是近年来图像处理、模式识别等领域的研究热点,但其面临的小样本空间、图像旋转、尺度变化等难题一直制约着人脸识别技术的发展与应用。为此,提出一种针对单样本人脸识别,提取尺度不变特征和样本脸进行匹配的识别方法:通过提取并匹配人脸图像的尺度不变特征得到特征点匹配关系,用以估计和校正图像间的旋转、尺度变化,并得到相对输入人脸图像尺度、旋转不变的样本脸,再使用子区域归一化互相关算法快速计算样本脸与输入图像之间的匹配程度,最后基于联合判决器综合特征点、样本脸两级匹配置信度综合判定识别结果。该方法有效结合了SIFT尺度不变特征对于旋转、尺度和光照变化稳定不变的优势,对于弱纹理图像也能够有效识别,具有较强的鲁棒性和适应性。     

基于压缩感知的单样本人脸识别

提出一种基于压缩感知的单样本人脸识别方法,通过局部邻域嵌入非线性降维和稀疏系数的方法产生冗余样本,则新样本包含了多种姿态和多种表情。将所有的新样本作为训练样本,运用改进后的稀疏表征分类算法进行人脸图像的识别。在单样本情况下,基于ORL人脸库和FERET人脸库的实验证明,该方法比原稀疏表征方法在识别率上分别提高了15.53%和7.67%。与RSRC、SSRC、DMMA、I-DMMA等方法相比,该方法同样具有良好的识别性能。     

Contract analysis: A performance measures and profit evaluation within two-echelon supply chains

Coordination is regarded as key in managing dependencies between distinctive members of a supply chain through the benefits of coordination mechanisms. Such coordination mechanisms are contracts, implemented to increase total supply chain profit, reduce costs and share risk among supply chain members. However, by contract implementation the retailer is constrained in his purchase by bearing the entire risk of holding the inventory (wholesale price contract) or by limited risk allocated to the supplier (buyback, revenue sharing and quantity flexibility contracts). By implementing an advanced purchase system the risk of inventory is fairly divided between the supplier and the retailer. In order to observe inventory implications on the supply chain bottom line, this article is directed towards the evaluation of performance measures and supply chain profit behavior under buyback, revenue sharing, quantity flexibility and advanced purchase discount contracts versus no coordination and wholesale price systems.     

基于单样本的手掌静脉识别方法

针对单样本手掌静脉识别率较低的问题,研究了一种结合手掌静脉2DPCA特征和分区LBP特征的识别方法。利用图像重采样和奇异值扰动方法生成虚拟样本,利用2DPCA从生成的虚拟样本图像上提取静脉特征进行识别;利用LBP从原单样本手掌静脉提取分区特征进行识别;利用决策层融合方法将以上两种方法进行融合。在Poly U手掌静脉库上的实验表明,该方法能有效地解决手掌静脉的单样本识别问题。     

Boosting quantum annealer performance via sample persistence

We propose a novel method for reducing the number of variables in quadratic unconstrained binary optimization problems, using a quantum annealer (or any sampler) to fix the value of a large portion of the variables to values that have a high probability of being optimal. The resulting problems are usually much easier for the quantum annealer to solve, due to their being smaller and consisting of disconnected components. This approach significantly increases the success rate and number of observations of the best known energy value in samples obtained from the quantum annealer, when compared with calling the quantum annealer without using it, even when using fewer annealing cycles. Use of the method results in a considerable improvement in success metrics even for problems with high-precision couplers and biases, which are more challenging for the quantum annealer to solve. The results are further enhanced by applying the method iteratively and combining it with classical pre-processing. We present results for both Chimera graph-structured problems and embedded problems from a real-world application.     

Mathematical formula identification and performance evaluation in PDF documents

An important initial step of mathematical formula recognition is to correctly identify the location of formulae within documents. Previous work in this area has traditionally focused on image-based documents; however, given the prevalence and popularity of the PDF format for dissemination, alternatives to image-based approaches are increasingly being explored. In this paper, we investigate the use of both machine learning techniques and heuristic rules to locate the boundaries of both isolated and embedded formulae within documents, based upon data extracted directly from PDF files. We propose four new features along with preprocessing and post-processing techniques for isolated formula identification. Furthermore, we compare, analyse and extensively tune nine state-of-the-art learning algorithms for a comprehensive evaluation of our proposed methods. The evaluation is carried out over a ground-truth dataset, which we have made publicly available, together with an application adaptable fine-grained evaluation metric. Our experimental results demonstrate that the overall accuracies of isolated and embedded formula identification are increased by 11.52 and 10.65 %, compared with our previously proposed formula identification approach.     

Illumination robust single sample face recognition based on ESRC

Multimedia Tools and Applications - The extended sparse representation classifier (ESRC) is one of the state-of-the-art solutions for single sample face recognition, but it performs...     

求解单一重现期暴雨强度公式的Lingo-BFGS算法

为了克服一般的非线性优化算法对初始值的依赖,针对某一地区单一重现期暴雨强度公式参数计算问题,首先将Lingo软件编程求出的结果作为初始值,然后利用拟牛顿算法对其参数进行优化。实例计算结果表明：采用Lingo软件和拟牛顿法相结合的方法求解单一重现期暴雨强度公式比传统的方法精度更高,能有效解决实际优化设计问题。     

一种渐进式的抽样策略及其性能分析

介绍了一种新的特征匹配算法-渐进式抽样一致算法,该算法首先按匹配度大小将初步匹配集合中的匹配对按匹配度作降序排列,然后根据规定的增长函数从前至后作抽样。详细介绍了算法的设计思想、增长函数的设计策略、抽样中指标准。将该算法成功的应用到图像配准问题中,实验结果表明该算法较传统算法在抽样终止标准相同条件下时间复杂度有较大改善。