图像质量的HVS评价方法

在充分分析人类视觉系统（HVS）的亮度、纹理、边缘等特性,并给出相应的视觉特性计算模型后,提出一种结合HVS特性的加权峰值信噪比计算方法。实验结果表明该模型计算出的HVS值能充分反映人类视觉系统的屏蔽特性;该图像质量评价方法的评价结果与主观评价结果相一致,效果优于传统图像质量评价方法。     

Can spiral of silence and civility predict click speech on Facebook?

Opinion expressions on Facebook are characterized by “click speech” in which people express their opinions and support (or disagreement) of posts through the “like,” “comment,” and “share” buttons. This study uses a 2 (low vs. high opinion congruency) x 2 (message civility vs. incivility) between-subject factorial experiment to examine the spiral of silence on participants' likelihood to interact with social media. We randomly assigned 502 participants to one of four experimental conditions. Results indicate that the fear of isolation increased the likelihood of commenting on Facebook posts and a civil climate increased the likelihood of liking comments on Facebook posts. Findings suggest that “click-speech” could be considered a form of opinion expression.     

Pessimists and optimists: Improving collaborative filtering through sentiment analysis

This work presents a novel application of Sentiment Analysis in Recommender Systems by categorizing users according to the average polarity of their comments. These categories are used as attributes in Collaborative Filtering algorithms. To test this solution a new corpus of opinions on movies obtained from the Internet Movie Database (IMDb) has been generated, so both ratings and comments are available. The experiments stress the informative value of comments. By applying Sentiment Analysis approaches some Collaborative Filtering algorithms can be improved in rating prediction tasks. The results indicate that we obtain a more reliable prediction considering only the opinion text (RMSE of 1.868), than when apply similarities over the entire user community (RMSE of 2.134) and sentiment analysis can be advantageous to recommender systems.     

The effects of a lack of social recognition on online communication behavior

Online services and applications extend the influencing opportunities of traditional word-of mouth (WOM). Unlike traditional word-of-mouth, the online environment allows for special features such as anonymity in user-generated content. Furthermore, the personality of online users affects their motivation when creating this content. The aim of this paper is to link specific online activities, i.e., the posting of product ratings and participation in discussions in online forums, with certain personality traits within an empirical study. The findings, based on an online survey with more than 16,900 completed questionnaires, indicate that opinion leaders in the online environment cannot be compared with traditional opinion leaders in terms of their articulation and personality structure. In regard to online activities with a high influencing potential, the results of moderated regression analyses show that persons with an introverted personality are more active as online opinion leaders due to the lack of social recognition they experience. The results have implications for how marketers should present incentive structures to address and integrate potential online opinion leaders, and how scholars should understand the role of opinion leaders in the online environment.     

Understanding social viewing through discussion network and emotion: A focus on South Korean presidential debates

With the development of social media, television viewing is perceived no longer as an isolated activity. This study explores the underlying mechanism of the effects of social viewing discussion networks (i.e., bridging and bonding social viewing networks) on emotions (i.e., anger, fear, and enthusiasm), and moreover on opinion consolidation as well as opinion weakening in the context of the 2017 South Korean presidential debates. Overall, the main results are: a) Social viewing discussion networks influenced emotional states of social viewers, b) Emotions influenced attitude formation during social viewing, and c) Enthusiasm served as a catalyst that links bonding and bridging social viewing, and opinion consolidation. Implications and theoretical contributions are discussed.     

Region based foreground segmentation combining color and depth sensors via logarithmic opinion pool decision

In this paper we present a novel foreground segmentation system that combines color and depth sensors information to perform a more complete Bayesian segmentation between foreground and background classes. The system shows a combination of spatial-color and spatial-depth region-based models for the foreground as well as color and depth pixel-wise models for the background in a Logarithmic Opinion Pool decision framework used to correctly combine the likelihoods of each model. A posterior enhancement step based on a trimap analysis is also proposed in order to correct the precision errors that the depth sensor introduces. The results presented in this paper show that our system is robust in front of color and depth camouflage problems between the foreground object and the background, and also improves the segmentation in the area of the objects’ contours by reducing the false positive detections that appear due to the lack of precision of the depth sensors.     

数字图书馆容灾系统建设研究

本文在阐述数据容灾概念与技术的基础上,简单分析了当前几种关键技术,并针对目前我国数字图书馆的可用性与安全性要求高的特点,提出了应用SAN技术实现数字图书馆容灾系统的设计方案,然后对此方案进行了分析,为其他容灾系统的建设起参考与借鉴作用。     

基于二次线性模型的网络舆情演化研究与实现

针对目前网络舆情形成迅速,对社会影响巨大,主要讨论在加强互联网信息监管的同时．研究网络舆情的演化趋势并研究相关算法以对其发展做出科学的预测．目的是对网络舆情的特性有一定的了解,掌握其基本的变化规律,以便提早采取应对措施,对于及时应对网络突发的公共事件和全面掌握社会民情民意有重大意义。     

Studying users’ perception of IoT mobile companion apps

Internet of Things (IoT) products provide over-the-net capabilities such as remote activation, monitoring, and notifications. An associated mobile app is often provided for more convenient usage of these capabilities. The perceived quality of these companion apps can impact the success of the IoT product. We investigate the perceived quality and prominent issues of smart-home IoT mobile companion apps with the aim of deriving insights to: (i) provide guidance to end users interested in adopting IoT products; (ii) inform companion app developers and IoT producers about characteristics frequently criticized by users; (iii) highlight open research directions. We employ a mixed-methods approach, analyzing both quantitative and qualitative data. We assess the perceived quality of companion apps by quantitatively analyzing the star rating and the sentiment of 1,347,799 Android and 48,498 iOS user reviews. We identify the prominent issues that afflict companion apps by performing a qualitative manual analysis of 1,000 sampled reviews. Our analysis shows that users’ judgment has not improved over the years. A variety of functional and non-functional issues persist, such as difficulties in pairing with the device, software flakiness, poor user interfaces, and presence of issues of a socio-technical impact. Our study highlights several aspects of companion apps that require improvement in order to meet user expectations and identifies future directions.