Prior language experience and language anxiety as predictors for non-native language commercial website use intention

Based on the research model, language anxiety, prior non-native language experience, Internet self-efficacy and language self-efficacy are analyzed for the intention to use non-native language commercial web sites, respectively. Prior non-native language experience has affected language anxiety, language self-efficacy and intention to use non-native language commercial web sites, respectively. By the same token, whether or not Internet self-efficacy and language self-efficacy affected by language anxiety is also examined. A valid sample of 418 undergraduates was tested in this study. Regression analysis results fully supported the model tested. These results suggest that language anxiety, prior non-native language experience, language self-efficacy and Internet self-efficacy have an effect on the intention to use non-native language commercial web sites. Prior non-native language experience has significantly affected language anxiety, language self-efficacy and the intention to use the non-native language commercial web sites, respectively. Furthermore, language anxiety has significantly affected language self-efficacy and Internet self-efficacy, respectively. Educational research and practitioner implications are provided at the end of the paper.     

结合背景光融合及水下暗通道先验和色彩平衡的#br# 水下图像增强

水下环境、光线衰减和拍摄方式造成水下图像具有不同色调、对比度和模糊度.基于图像成像模型的水下图像复原方法通常基于暗通道先验或最大像素先验,容易受到水下复杂环境的干扰而输出低质量的复原图像,因此文中提出基于背景光融合及水下暗通道先验和色彩平衡的水下图像增强方法.首先,提出多候选背景光融合方法,估计正确的背景光.然后,基于高质量水下图像统计得出水下暗通道先验,计算更准确的RGB分量传输地图.将复原图像从RGB颜色模型转换到CIE-Lab颜色模型,对L亮度分量和a、b色彩分量分别进行归一化拉伸和优化调整,进一步提高复原后水下图像的亮度和对比度.多种定性和定量分析说明文中方法增强的图像在对比度、亮度和颜色上的显示效果优于大部分现有的水下图像增强方法复原的图像.     

基于高阶逻辑的复杂结构数据半监督聚类

半监督聚类近年来成为了机器学习和数据挖掘领域的研究热点.目前存在的半监督聚类方法都采用属性-值的知识表示方式.但属性-值语言在表示复杂结构数据时存在很多弊端,而基于高阶逻辑的知识表示语言Escher能较好地表示复杂结构数据.在Fscher的知识表示方式下,首先当先验知识是实例之间的约束信息时,提出了搜索K-Means算法的K个初始质心的方法;其次,时先验知识不完全、能够发现的初始质心的个数,r小于K的情况,提出了搜索其余的K-r个初始质心的算法MSS-KMeans和SMSS-KMeans;最后在复杂结构数据集上,验证了所提算法的可行性.最终的实验结果表明,基于高阶逻辑知识表示方式的丰监督聚类方法要优于基于属性-值语言的半监督聚类方法.     

融合先验知识的自适应行人跟踪算法

在实际监控场合中,行人的运动有着诸多不确定性,这些会对现有的跟踪算法产生干扰,从而造成跟踪丢失.基于此,文中提出一种将行人检测的先验知识融入到跟踪模型自学习过程的行人跟踪算法.首先通过离线训练,得到具有较强区分能力的子分类器集,这些子分类器蕴含了对于行人的先验知识.在跟踪过程中,使用online boosting算法从离线训练的子分类器集中学习并更新强分类器,对被跟踪行人进行动态建模.实验结果表明,该算法有效缓解算法自适应性与"漂移"之间的矛盾,能够在真实监控场合下跟踪具有复杂运动的行人.     

Students’ navigation patterns in the interaction with a mechanics hypermedia program

This study investigates the interaction of a group of freshmen enrolled in a Pre Service Physics Teacher Training Course with a mechanics hypermedia program. Data were obtained to discuss hypertextual navigation guided by the following questions: (i) How can the students’ navigation in this hypermedia program be characterized? (ii) How does this relate to their prior knowledge in mechanics? The sequence analysis of the events collected from the log files was used to characterize students’ navigation and a mechanics test assessed students’ prior knowledge. The inspection of students’ navigation graphs made it possible to associate the structure of navigation to prior knowledge in mechanics. Three patterns of navigation are proposed associated to different levels of students’ prior knowledge and to different roles performed by the program. In the organized navigation, the student who best performed in the pre test seemed to be reviewing content he already knew, using the system as a database. In the conceptual navigation the students who presented difficulties in the pre test spent different times in the pages as they were addressing conceptual difficulties, using the system as a support for learning. The students who scored the lowest in the test performed a disoriented navigation, spending much less than the adequate time to interact meaningfully with the content. The role that previous knowledge in mechanics plays in these patterns of navigation was related to the function that Ausubel’s subsumers perform in learning. The results indicate that hypertextual navigation can provide information about students’ conditions to engage in meaningful learning, which could be used to help the teacher personalize instruction.     

Higher-Order Active Contour Energies for Gap Closure

One of the main difficulties in extracting line networks from images, and in particular road networks from remote sensing images, is the existence of interruptions in the data caused, for example, by occlusions. These can lead to gaps in the extracted network that do not correspond to gaps in the real network. In this paper, we describe a higher-order active contour energy that in addition to favouring network-like regions, includes a prior term penalizing networks containing ‘nearby opposing extremities’, thereby making gaps in the extracted network less likely. The new energy term causes such extremities to attract one another during gradient descent. They thus move towards one another and join, closing the gap. To minimize the energy, we develop specific techniques to handle the high-order derivatives that appear in the gradient descent equation. We present the results of automatic extraction of networks from real remote-sensing images, showing the ability of the model to overcome interruptions.

改进暗通道先验的航空图像去雾

目前较为流行的去雾算法都存在着过度增强以及增强不足,容易造成光晕效应以及色彩严重失真。提出一种基于四叉树细分的改进大气光估计方法以及一种改进的引导滤波用来解决这些问题。首先,对非重叠暗通道使用四叉树细分方法估计更加可靠的大气光值。然后,分析引导滤波在边缘区域的光晕效应产生的原因,对其加入自适应权重因子,用改进后的引导滤波对初始传输图进行优化。最后,用估计的大气光值和优化后的传输图根据大气散射模型得到去雾图像。实验结果表明:去雾后的图像颜色较为可靠,边缘区域光晕效应减弱。从颜色可靠性和细节增强度来说,提出的算法比现阶段的去雾算法有较为出众的表现。     

TOA source localization and DOA estimation algorithms using prior distribution for calibrated source

This paper presents an a priori probability density function (pdf)-based time-of-arrival (TOA) source localization algorithms. Range measurements are used to estimate the location parameter for TOA source localization. Previous information on the position of the calibrated source is employed to improve the existing likelihood-based localization method. The cost function where the prior distribution was combined with the likelihood function is minimized by the adaptive expectation maximization (EM) and space-alternating generalized expectation–maximization (SAGE) algorithms. The variance of the prior distribution does not need to be known a priori because it can be estimated using Bayes inference in the proposed adaptive EM algorithm. Note that the variance of the prior distribution should be known in the existing three-step WLS method [1]. The resulting positioning accuracy of the proposed methods was much better than the existing algorithms in regimes of large noise variances. Furthermore, the proposed algorithms can also effectively perform the localization in line-of-sight (LOS)/non-line-of-sight (NLOS) mixture situations.     

Multiple Empirical Kernel Learning with dynamic pairwise constraints

Unlike the traditional Multiple Kernel Learning (MKL) with the implicit kernels, Multiple Empirical Kernel Learning (MEKL) explicitly maps the original data space into multiple feature spaces via different empirical kernels. MEKL has been demonstrated to bring good classification performance and to be much easier in processing and analyzing the adaptability of kernels for the input space. In this paper, we incorporate the dynamic pairwise constraints into MEKL to propose a novel Multiple Empirical Kernel Learning with dynamic Pairwise Constraints method (MEKLPC). It is known that the pairwise constraint provides the relationship between two samples, which tells whether these samples belong to the same class or not. In the present work, we boost the original pairwise constraints and design the dynamic pairwise constraints which can pay more attention onto the boundary samples and thus to make the decision hyperplane more reasonable and accurate. Thus, the proposed MEKLPC not only inherits the advantages of the MEKL, but also owns multiple folds of prior information. Firstly, MEKLPC gets the side-information and boosts the classification performance significantly in each feature space. Here, the side-information is the dynamic pairwise constraints which are constructed by the samples near the decision boundary, i.e. the boundary samples. Secondly, in each mapped feature space, MEKLPC still measures the empirical risk and generalization risk. Lastly, different feature spaces mapped by multiple empirical kernels can agree to their outputs for the same input sample as much as possible. To the best of our knowledge, it is the first time to introduce the dynamic pairwise constraints into the MEKL framework in the present work. The experiments on a number of real-world data sets demonstrate the feasibility and effectiveness of MEKLPC.     

A generalized-constraint neural network model: Associating partially known relationships for nonlinear regressions

In an attempt to enhance the neural network technique so that it can evolve from a “black box” tool into a semi-analytical one, we propose a novel modeling approach of imposing “generalized constraints” on a standard neural network. We redefine approximation problems by use of a new formalization with the aim of embedding prior knowledge explicitly into the model to the maximum extent. A generalized-constraint neural network (GCNN) model has therefore been developed, which basically consists of two submodels. One is constructed by the standard neural network technique to approximate the unknown part of the target function. The other is formed from partially known relationships to impose generalized constraints on the whole model. Three issues arising after combination of the two submodels are discussed: (a) the better approximation provided by the GCNN model compared with a standard neural network, (b) the identifiability of parameters in the partially known relationships, and (c) the discrepancy in the approximation due to removable singularities in the target function. Numerical studies of three benchmark problems show important findings that have not previously been reported in the literature. Significant benefits were observed from using the GCNN model in comparison with a standard neural network.