User Satisfaction with Tablet PC Features

Previous studies of tablet personal computers have concentrated on their use in education and healthcare. The current study focused instead on personal usage, investigating how satisfied users are with their own tablets after having used them in their daily lives. The objective was to identify the major features for tablets and to investigate how form factors affect the preference of functions by performing a comparison of iPad1 and Galaxy Tab. Also, gender and ethnicity were analyzed to determine whether they influence satisfaction with the devices. For e‐mail and web browsing functions, users’ ratings showed more satisfaction with the iPad1 since it has a larger display; for the e‐book reader function, users indicated higher satisfaction with the Galaxy Tab. Male users evaluated their devices by the function itself, whereas female users were mainly concerned with aesthetic aspects. Koreans indicated that they were less satisfied with their tablets than were other ethnic groups.     

随机加密图像中高效特定图像定位方法仿真

在随机加密图像中高效特定图像定位过程中,由于图像在加密后,源图像的像素变换会较为置乱,使得图像定位特征衰退明显,无法形成可以查询的规则。当前的图像定位算法,是通过提取图像共有特征的方法进行特定图像定位,加密后的图像不能精确剖析特定的细致特征,特征无法形成关联。为此,提出改进关联挖掘算法的随机加密图像中高效图像快速定位方法。对随机加密图像中所含有多余及重复的特定图像特征信息进行二次校验过滤,进而缩小定位特定图像错误范围,将在随机加密图像中定位特定图像的问题转换为关联规则挖掘中寻找频繁项集的问题,通过在随机加密图像中查找与特定图像的特征相似的频繁项集,完成对特定图像精准定位。仿真结果证明,提出的改进关联挖掘算法的高效图像快速定位方法精准度高,耗时短,为高效图像快速定位提供了科学手段。     

Effective composite image detection method based on feature inconsistency of image components

This paper proposes an effective composite image detection method that uses the feature inconsistency of image components of the composite image to detect tampered regions. The composite image is first divided into image components. Next, the variance of the noise remaining after de-noising in each image component is calculated and used as a feature. Finally, tampered regions are detected using this feature based on a tampering detection rule. Experimental results show that the proposed method has good composite image detection performance.     

Toward an efficient and scalable feature selection approach for internet traffic classification

There is significant interest in the network management and industrial security community about the need to identify the “best” and most relevant features for network traffic in order to properly characterize user behaviour and predict future traffic. The ability to eliminate redundant features is an important Machine Learning (ML) task because it helps to identify the best features in order to improve the classification accuracy as well as to reduce the computational complexity related to the construction of the classifier. In practice, feature selection (FS) techniques can be used as a preprocessing step to eliminate irrelevant features and as a knowledge discovery tool to reveal the “best” features in many soft computing applications. In this paper, we investigate the advantages and disadvantages of such FS techniques with new proposed metrics (namely goodness, stability and similarity). We continue our efforts toward developing an integrated FS technique that is built on the key strengths of existing FS techniques. A novel way is proposed to identify efficiently and accurately the “best” features by first combining the results of some well-known FS techniques to find consistent features, and then use the proposed concept of support to select a smallest set of features and cover data optimality. The empirical study over ten high-dimensional network traffic data sets demonstrates significant gain in accuracy and improved run-time performance of a classifier compared to individual results produced by some well-known FS techniques.     

宜春某建筑场地岩溶发育特征

江西省宜春市宜阳新区位于可溶岩区,岩溶作用形成的溶洞、土洞对地基的稳定性存在重要影响.根据对宜春某建筑场地桩基础超前钻获得溶洞、溶蚀带和断裂构造的位置,分析其空间分布规律,对桩基础结构设计及施工具有关键指导作用.     

一种基于横切特征分析的软件体系结构自动重构方法

软件体系结构中的横切关注点增加了软件体系结构的复杂性,从而加剧了体系结构演化与维护的困难.这种设计问题可以通过体系结构层面的重构来进行改善.在已有的横切特征分析方法基础上,提出了一种面向横切特征分析的体系结构自动重构方法.该方法首先基于特征与构件之间的追踪关系分析横切特征,然后将与横切特征有直接追踪关系的构件从初始体系结构中提取出来,实现方面构件,完成体系结构重构.在面向方面体系结构描述语言AO-ADL基础上开发了相应的体系结构重构工具,并针对一个业务系统进行了体系结构重构实验.实验结果表明,该方法能有效地实现体系结构横切特征的自动化重构.     

基于最小平方中值定理的立体视觉里程计

提出了一种基于最小平方中值定理（LMedS）的立体视觉里程计方法。利用图像中尺度不变的SIFT特征点作为路标,基于KD树的最邻近点搜索算法来实现左右图像对特征点的匹配和前后帧间特征点跟踪。通过特征点的三维重建,基于最小平方中值定理估计出机器人的运动距离和方向信息。实验表明该方法在不同图像间匹配、三维路标跟踪和机器人运动估计中具有很强的鲁棒性。     

基于动态RBF神经网络的遥感图像中烟辨识

针对森林火灾的突发性和分散性特点,根据森林火灾中的烟影像的特征,结合RBF神经网络分类器通用性的特点,采用PCA方法降低图像的维数和Fisher线性判别方法提取遥感图像中烟的影像特征,用特征值数代替训练样本数,并作为RBF网络输入数,在对样本训练的过程中提出一种改进型的混合算法。仿真实验表明,该算法对烟图像识别的准确率较高。