火狐3 老扩展 新青春

火狐2实用插件Greasemonkey:让用户实现改变网页的源码,修改网页的内容。ColorfulTabs:不同浏览标签页面以不同的颜色区分。Download Statusbar:可以管理你的Internet下载,且不会影响当前的网页浏览。     

空间信息管理可视化在Access数据库中的应用

空间信息处理,就是对客观实体的空间属性（位置和状态）信息的处理， 在Access中采用映射的方法,实现空间信息可视化处理的，其基本方法是：把客观实体R（x,y）映射到窗体上的一个标签矩阵B（x,y），而客观实体中每个元素的状态用标签矩阵的相应标签的不同颜色来映射，这样客观实体的状态,就可以通过标签的颜色直观的显示出来。     

支持多维标签云的移动餐厅推荐系统仿真研究

实现了一个可提供基于位置的移动餐厅推荐及导航服务的系统。结合最新的Web2.0应用数据,实现了多模式的餐厅信息推送服务,包括基于模糊条件约束的查询机制,基于位置的地图浏览模式和基于三维标签云的协同信息共享机制,为用户提供实时的基于位置的餐厅个性推荐及导航。同时,针对移动手机屏幕较小,显示内容受限的问题,研究并提出了一个“面向移动终端的多维标签云可视化方法”,并实现了一种标签云关联分析和聚类算法,实现了基于用户偏好的快速导航。     

基于序列模式的用户浏览行为提取与分析

当今互联网所提供的功能和服务越来越多,Web内容也越来越丰富,移动应用越来越流行。然而,复杂的Web服务应用对用户提出了更高的要求,给用户浏览带来了很多问题,很多时候用户会感到无所适从。文中提出基于用户浏览序列模式的用户行为提取与分析方法。该方法可以分为浏览模式分析和用户聚类两部分。在浏览模式分析时,首先根据用户行为数据得到浏览序列,然后运用序列模式挖掘PrefixSpan算法获取用户习惯的浏览模式,最后把分析获取的用户浏览模式应用到Web浏览中,为不同的用户需求提供个性化的服务。在用户聚类时,运用层次聚类方法按照浏览模式的相似性对用户进行聚类,以分析用户的不同属性（如年龄、职业、学历等）对用户浏览模式的影响。实验结果表明,文中采用的PrefixSpan算法和层次聚类方法在用户浏览模式分析和研究方面具有很好的可行性和有效性。     

MathWorks推出MATLAB重要新版本

正MathWorks推出MATLAB的重要新版本。作为2014b版本的一部分,重大的更新功能包括了全新的图形系统、大数据功能以及经过改进用于打包和分享代码及源控制集成的协作功能。工程师和科学家利用这些新增功能,能够在所有主要工业领域更加轻松地分析数据并实现数据可视化。MATLAB全新图形系统中的默认颜色、字体和样式经过更新,可以更加轻松地解释数据,使用户能够进行深入的了解。使用更改图形对象属性的新语法,可以更加方便地自定义可视化。新增功能包括可旋转的轴勾选标签、多语言文本和符号支持,以及日期和时间勾选标签的自动更新。     

一种通用的数据可视化模型设计与实现

随着互联网、物联网、云计算等信息技术的迅猛发展,大量非结构化信息不断涌现,对数据可视化与可视化分析的需求不断增加,同时可视化开发成本不断提高。数据可视化模型的研究对于可视化开发必不可少。基于此背景,根据数据信息的分类研究提出一种通用、模块化的数据可视化模型,将可视化流程分为可视化结构映射、可视化空间布局、可视化视图映射三个主要模块,并基于数据可视化模型利用开源的Echarts图表工具包和D3函数库形成可视化工具DVL.js(Data Visualization Layout)。该工具可应用于Web平台,包含丰富的图形展示方式如柱状图、折线图、时序图、饼图、雷达图、层次树、力导向图、标签云等,这些展示方式都提供了完备的数据与交互接口,能应对不同场合的可视化需求。     

一种多维数据的聚类算法及其可视化研究

提出了一种基于主次属性划分的聚类方法和一种新的数据可视化方法.首先,利用数据的主属性和次属性的特征值对数据集进行聚类;然后,采用彩色刺激光谱投影到RGB颜色空间的原理,通过色度学中麦克斯韦的三角平面坐标色度图对各聚类结果进行可视化显示.实验证明了文中方法算法简单、容易实现,可视化结果有利于用户全面地理解数据,为数据的预测、决策起到重要作用.     

网站导航设计分析

网站导航的基本作用是为了让用户在浏览网站过程中不至迷失,并且可以方便地回到网站首页以及其他相关内容的页面。网站导航设计的好坏与否影响着用户对网站的感受,也是网站信息是否可以有效地传递给用户的重要影响因素之一。因此,网站导航系统也成为评价网站是否专业、是否具有网络营销导向的基本指标之一。     

基于颜色属性的车辆阴影去除方法

颜色属性,即语言颜色标签,可以表示世界中的所有颜色。视频帧图像中的阴影的颜色属性为黑色,因此提出一种基于颜色属性的车辆阴影去除方法。利用概率潜在语义模型学习颜色属性,建立颜色名概率字典,并实现对视频帧图像的颜色名映射,将表征阴影的黑色区域二值化为背景,而非阴影区域二值化为前景。同时,将二值化图与背景差分图进行"与"操作,去除阴影,再去除阴影外边缘的噪声。最后,对其进行先膨胀再连通域填充处理,以得到去除阴影后的车辆目标。实验证明,该方法在一定场景下可以很好地去除阴影,获得相对完整的运动车辆目标。     

基于WebGIS的海水淡化工程空间信息可视化应用研究

针对海水淡化工程空间信息共享与可视化服务需求,本文借鉴WebGIS在海洋信息可视化领域的先进理念和成功经验,研究建立海水淡化工程空间数据库,设计开发海水淡化工程空间信息可视化系统。系统实现了地图浏览、专题查询及统计分析等功能,能够为用户及时、准确掌握海水淡化工程相关属性信息与空间信息提供一定支撑。     

一种基于子空间学习的图像标签推荐方法

Represented by Flickr and Picasa, online photo albums allow users to tag images, hoping to make it more convenient as well as efficient to organize and retrieve image resources. Recently, automatic tag recommendation system has become a hot research field considering the increasing request that high-quality tags be provided. In this thesis, a new method for tag recommendation system is proposed. Unlike the traditional one which only depends on frequency information or visual feature similarity while neglecting the relation between visual content and the semantic meaning contained in tags thus leading to unsatisfactory recommendations, the new method can find out a latent subspace shared by visual features and tag contents using matrix factorization. As for an untagged image, recommendations can be made when its visual features are projected into the latent subspace and the relevance level it has with others tags is figured out. This new method has been proved efficient after being tested on NUS-WIDE data set with more satisfactory results.     

Detection of cloaked web spam by using tag-based methods

Web spam attempts to influence search engine ranking algorithm in order to boost the rankings of specific web pages in search engine results. Cloaking is a widely adopted technique of concealing web spam by replying different content to search engines’ crawlers from that displayed in a web browser. Previous work on cloaking detection is mainly based on the differences in terms and/or links between multiple copies of a URL retrieved from web browser and search engine crawler perspectives. This work presents three methods of using difference in tags to determine whether a URL is cloaked. Since the tags of a web page generally do not change as frequently and significantly as the terms and links of the web page, tag-based cloaking detection methods can work more effectively than the term- or link-based methods. The proposed methods are tested with a dataset of URLs covering short-, medium- and long-term users’ interest. Experimental results indicate that the tag-based methods outperform term- or link-based methods in both precision and recall. Moreover, a Weka J4.8 classifier using a combination of term and tag features yields an accuracy rate of 90.48%.     

Multi-modal tag localization for mobile video search

Given the tremendous growth of mobile videos, video tag localization, which localizes the relevant video clips for an associated semantic tag, is becoming increasingly important to influence users browsing and searching experience. However, most existing approaches adopt and depend to large degree on carefully selected visual features, which are manually designed by experts and do not take multi-modality into consideration. Aiming to take into account complementarity of different modalities, in this paper, we propose a multi-modal tag localization framework by exploiting deep learning to learn visual, auditory, and semantic features of videos for tag localization. Furthermore, we showcase that the framework can be applied to two novel mobile video search applications: (1) automatic time-code-level tags generation and (2) query-dependent video thumbnail selection. Extensive experiments on the public dataset show that the proposed approach achieves promising results, which obtains \(7.6~\%\) improvement beyond the state-of-the-arts. Finally, the subjective evaluation of usability demonstrates the proposed applications can significantly improve the user’s mobile video search experience.     

The influence of visual complexity on initial user impressions: testing the persuasive model of web design

ABSTRACT

Shortly after fixating on webpages, users form initial impressions. These initial impressions influence how much users will use and return to websites. Researchers have understudied how objective design features (e.g. visual complexity) influence subjective perceptions of website content and the favorability of initial user impressions. In the present study, the influence of two dimensions of visual complexity – feature complexity and design complexity – were tested within the boundaries of the persuasive model of web design. More specifically, the study examined how visual complexity influences perceptions of visual informativeness, cues for engagement, favourable initial impressions, and behavioural intentions in a sample of young adults (N?=?277). Results suggest relationships for both dimensions of visual complexity on all outcome variables using ANOVA and OLS regression procedures and that perceptions of visual informativeness and cues for engagement mediate the relationship between visual complexity and favourable initial impressions and behavioural intentions. The study offers support for the utility of the persuasive model of web design for linking objective design features with subjective design perceptions to better understand favourable initial user impressions.     

A theoretical model for the automatic generation of tag clouds

This paper presents a new approach to information retrieval from non-structured attributes in databases, which involves the processing of text attributes. To make retrieval more effective, frequent text sequences are extracted and mathematically represented as intermediate forms which permit a clearer and more precise definition of operations on texts. These intermediate forms appear to users in the form of tag clouds to facilitate content identification, exploration, and querying. In this sense, tag cloud visualization is a simple, user-friendly visual interface to data. This paper proposes a theoretical model for the representation of frequent text sequences and their operations as well as a general procedure for generating tag clouds from text attributes in databases. The tag clouds thus obtained were compared with conventional tag clouds composed of single terms. Our study showed that automatically generated multi-term tag clouds provide better results than mono-term tag clouds.     

The Engagement of Website Initial Aesthetic Impressions: An Experimental Investigation

ABSTRACT

This study investigates how website design features, web page order and visual complexity, influence users’ initial website aesthetic impressions and how such impressions subsequently enhance engagement and intention to use the website. A laboratory experiment was conducted to test the hypotheses using different levels of web page order (high vs. low), visual complexity (high vs. low), and exposure time (one-second vs. no-time-constraint). Overall, the results from structural equation modeling (SEM) analysis suggest that web page order significantly influences visual appeal, engagement, and intention. In addition, the results of multigroup SEM analysis reveal that users evaluate website design very quickly (within 1 s), and that these evaluations remain remarkably consistent over time.     

SparkClouds: visualizing trends in tag clouds

Tag clouds have proliferated over the web over the last decade. They provide a visual summary of a collection of texts by visually depicting the tag frequency by font size. In use, tag clouds can evolve as the associated data source changes over time. Interesting discussions around tag clouds often include a series of tag clouds and consider how they evolve over time. However, since tag clouds do not explicitly represent trends or support comparisons, the cognitive demands placed on the person for perceiving trends in multiple tag clouds are high. In this paper, we introduce SparkClouds, which integrate sparklines into a tag cloud to convey trends between multiple tag clouds. We present results from a controlled study that compares SparkClouds with two traditional trend visualizations—multiple line graphs and stacked bar charts—as well as Parallel Tag Clouds. Results show that SparkClouds ability to show trends compares favourably to the alternative visualizations.     

Interactive resource recommendation algorithm based on tag information

With the popularization of social media and the exponential growth of information generated by online users, the recommender system has been popular in helping users to find the desired resources from vast amounts of data. However, the cold-start problem is one of the major challenges for personalized recommendation. In this work, we utilized the tag information associated with different resources, and proposed a tag-based interactive framework to make the resource recommendation for different users. During the interaction, the most effective tag information will be selected for users to choose, and the approach considers the users’ feedback to dynamically adjusts the recommended candidates during the recommendation process. Furthermore, to effectively explore the user preference and resource characteristics, we analyzed the tag information of different resources to represent the user and resource features, considering the users’ personal operations and time factor, based on which we can identify the similar users and resource items. Probabilistic matrix factorization is employed in our work to overcome the rating sparsity, which is enhanced by embedding the similar user and resource information. The experiments on real-world datasets demonstrate that the proposed algorithm can get more accurate predictions and higher recommendation efficiency.     

Personalized Tag Recommendation Using Social Influence

Tag recommendation encourages users to add more tags in bridging the semantic gap between human concept and the features of media object,which provides a feasible solution for content-based multimedia information retrieval.In this paper,we study personalized tag recommendation in a popular online photo sharing site - Flickr.Social relationship information of users is collected to generate an online social network.From the perspective of network topology,we propose node topological potential to characterize user’s social influence.With this metric,we distinguish different social relations between users and find out those who really have influence on the target users.Tag recommendations are based on tagging history and the latent personalized preference learned from those who have most influence in user’s social network.We evaluate our method on large scale real-world data.The experimental results demonstrate that our method can outperform the non-personalized global co-occurrence method and other two state-of-the-art personalized approaches using social networks.We also analyze the further usage of our approach for the cold-start problem of tag recommendation.     

User continuance intention to use cloud storage service

Cloud computing is a huge and important change in the field of network application in recent years to provide users with a completely different IT service and delivery mode. Among various cloud services, cloud storage is a service most closely related to web users’ need because it involves the storage of users’ all important data and backup files. In this study, a sample survey was conducted in Taiwan, and key factors influencing individual users’ adoption of the cloud storage service were analyzed and discussed based on Task-Technology Fit theory. The research results indicate that “cloud storage service”, “unstructured task”, “cloud storage self-efficacy” and “opinion of reference groups” all have significant positive influences on the “perceived usefulness”, which further has influence on users’ continuance intention to use the cloud storage service. The findings also support that the privacy protection risk and the lack of privacy-policy risk in the cloud storage service produce negative moderating effects on the perceived usefulness and the continuance intention.