A time-aware query-focused summarization of an evolving microblogging stream via sentence extraction

With the number of social media users ramping up, microblogs are generated and shared at record levels. The high momentum and large volumes of short texts bring redundancies and noises, in which the users and analysts often find it problematic to elicit useful information of interest. In this paper, we study a query-focused summarization as a solution to address this issue and propose a novel summarization framework to generate personalized online summaries and historical summaries of arbitrary time durations. Our framework can deal with dynamic, perpetual, and large-scale microblogging streams. Specifically, we propose an online microblogging stream clustering algorithm to cluster microblogs and maintain distilled statistics called Microblog Cluster Vectors (MCV). Then we develop a ranking method to extract the most representative sentences relative to the query from the MCVs and generate a query-focused summary of arbitrary time durations. Our experiments on large-scale real microblogs demonstrate the efficiency and effectiveness of our approach.     

基于Watir的物联网数据处理方法研究

构建基于Watir的物联网Web事件处理系统框架。首先搭建Zigbee无线传感器网络,把终端节点的属性描述到网页并进行服务器部署,其中用微数据来描述每一个传感器的静态属性,用Js文件描述动态属性,然后采用Web自动化测试框架Watir,通过Css Selector定位技术实现对动态页面数据的实时采集,将实时采集的数据通过预处理后添加事件处理,提取关键和有效的事件数据并保存。同时利用Watir对多个事件条件下不同平台的物联网网页进行测试,结果表明基于Watir的物联网Web事件处理方法能准确和高效地获取事件数据。     

The role of media in user participation: Focusing on the knowledge activity in online space

Media technology have been considered a catalyst for boosting communication between the sources of information. Contrary to traditional media, new media which use online communication shows high interactivity properties. This characteristic allows people to participate in collective activities such as discussion, evaluation, or creating documents. Consequently, the role of media in online space has focused on delivering individual knowledge to others rather than just adopting selected information. We studied the effect of media usage patterns on creating knowledge in online communities. In our study, we divided user groups into three based on the degree to which people participate in knowledge collaboration in the online space: active participant, passive participant, and bystander. The results indicate that the active participant group is most likely to use smartphone and tablet PC to create knowledge in online space which means they prefer high accessibility rather than familiar mediators.     

French Abstract

To what extent do frame‐building and frame‐setting processes manifest themselves in the interplay between online public discourse and traditional (offline) media discourse? Employing a content analysis of 206 online posts and 114 news reports regarding a sociopolitical incident in China, we test the associations and causal relationships between the salience of opinion frames and media frames. Online public opinion plays an important role in transforming the original local event into a nationally prominent issue. It also exerts a significant frame‐building impact on subsequent media reports but only in the early stage of coverage. However, the media are not passive in this two‐way process and adapt online frames as necessary. Although media coverage is the primary source of information for netizens, it does not set frames for online discourse. Noticeably, significant associations between concurrent opinion frames and media frames lend strong support to frame‐interacting effects. Discussion focuses on governmental influences in the frame‐building process and the potential of netizen autonomy to attenuate frame‐setting effects.     

Recommendation agents and information sharing through social media for coronavirus outbreak

The novel outbreak of coronavirus disease (COVID-19) was an unexpected event for tourism in the world as well as tourism in the Netherlands. In this situation, the travelers’ decision-making for tourism destinations was heavily affected by this global event. Social media usage has played an essential role in travelers’ decision-making and increased the awareness of travel-related risks from the COVID-19 outbreak. Online consumer media for the outbreak of COVID-19 has been a crucial source of information for travelers. In the current situation, tourists are using electronic word of mouth (eWOM) more and more for travel planning. Opinions provided by peer travelers for the outbreak of COVID-19 tend to reduce the possibility of poor decisions. Nevertheless, the increasing number of reviews per experience makes reading all feedback hard to make an informed decision. Accordingly, recommendation agents developed by machine learning techniques can be effective in the analysis of such social big data for the identification of useful patterns from the data, knowledge discovery, and real-time service recommendations. The current research aims to adopt a framework for the recommendation agents through topic modeling to uncover the most important dimensions of COVID-19 reviews in the Netherland forums in TripAdvisor. This study demonstrates how social networking websites and online reviews can be effective in unexpected events for travelers’ decision making. We conclude with the implications of our study for future research and practice.     

The study of youth online: a critical review and agenda

The increasing and pervasive use of online technologies, especially social media, has inspired scholars to investigate how the Internet influences communication. As young people represent the fastest growing adopters of new online technology, much of this research targets youth activity online. But how is the communication discipline taking up this issue, broadly? Typically left to new media and computer-mediated communication scholars, we argue youth online activity raises new and exciting possibilities for researchers across the communication discipline. In this paper, we present a qualitative content analysis of communication research about youth and the Internet. Our analysis of over 700 journal articles provides a clear picture of past and present trends in communication research of youth online activity. Furthermore, we discuss the top four content themes, including: uses and gratifications, engagement, identity, and the uniqueness of youth experience. Critically, we articulate how such research organizes and positions youth in meaningful ways, paving the way for issues of inclusion. We call for a shift from a “difference” framework to one that more explicitly considers “complexity” before suggesting opportunities for future research that crosses and unites subdisciplinary boundaries.     

Performance analysis and testing of personal influence algorithm in online social networks

Social influence is the key factor to drive information propagation in online social networks and can be modeled and analyzed with social networking data.As a kind of classical personal influence algorithm,two parallel implementation versions of a PageRank based method were introduced.Furthermore,extensive experiments were conducted on a large-scale real dataset to test the performance of these parallel methods in a distributed environment.The results demonstrate that the computational efficiency of the personal influence algorithm can be improved significantly in massive data sets by virtue of existing big data processing framework,and provide an empirical reference for the future research and optimization of the algorithm as well.     

Picture-perfect streaming over the Internet: is there hope?

Quality of service in streaming of continuous media over the Internet is poor, which is partly due to variations in delays, bandwidth limitations, and packet losses. Although continuous media applications can tolerate some missing data, non-recoverable information loss degrades these applications' QoS. Consequently, a number of application areas (e.g., those related to the entertainment industry) have backed away from streaming of their content over the Internet. Inability to control the resulting visual and auditory quality of the resulting streamed presentation is an important reason for such a trend. We believe that this trend can be reversed. To this end, our work focuses on providing high-quality streaming through the exploitation of multiple paths existing in the network. By high quality, we mean with significant bandwidth requirements, of relatively long duration, and without information loss or hiccups in data delivery. In this article we present evidence that multi-path streaming is a promising approach.     

Research on Chinese social media users’ communication behaviors during public emergency events

In recent years, China has been in a period of social transition. Public emergency events have occurred frequently, and social media have developed rapidly. Social media users in China not only represent traditional audiences but also play an increasingly important role in crisis communication during public emergency events by expressing their views, discussing events with others and sharing information both online and offline. According to national telephone survey data from China, nearly two-thirds of the respondents engaged in communication behaviors during public emergency events, and more than forty percent of those users communicated by social media. Hundreds of millions of Chinese social media users are becoming the driving force of the public opinion field. To better understand social media users’ online information dissemination behaviors and influencing factors, we developed the hierarchical logistic regression model and observed that demographic variables (gender and age), social media use, people’s concerns regarding public emergencies and people’s need to monitor the government’s performance during public emergencies significantly influence online information dissemination behaviors. Our study has practical significance and academic value for understanding the online public opinion field and online political participation in China.     

Soft ARQ for Layered Streaming Media

A growing and important class of traffic in the Internet is so-called “streaming media,” in which a server transmits a packetized multimedia signal to a receiver that buffers the packets for playback. This playback buffer, if adequately sized, counteracts the adverse impact of delay jitter and reordering suffered by packets as they traverse the network, and if large enough also allows lost packets to be retransmitted before their playback deadline expires. We call this framework for retransmitting lost streaming-media packets “soft ARQ” since it represents a relaxed form of Automatic Repeat reQuest (ARQ). While state-of-the-art media servers employ such strategies, no work to date has proposed an optimal strategy for delay-constrained retransmissions of streaming media—specifically, one which determines what is the optimal packet to transmit at any given point in time. In this paper, we address this issue and present a framework for streaming media retransmission based on layered media representations, in which a signal is decomposed into a discrete number of layers and each successive layer provides enhanced quality. In our approach, the source chooses between transmitting (1) newer but critical coarse information (e.g., a first approximation of the media signal) and (2) older but less important refinement information (e.g., added details) using a decision process that minimizes the expected signal distortion at the receiver. To arrive at the proper mix of these two extreme strategies, we derive an optimal strategy for transmitting layered data over a binary erasure channel with instantaneous feedback. To provide a quantitative performance comparison of different transmission policies, we conduct a Markov-chain analysis, which shows that the best transmission policy is time-invariant and thus does not change as the frames' layers approach their expiration times.     

Communication framework for MMOG based on custom protocol

This paper presents a real-time communication framework for massive multiplayer online games (MMOG), which is to ensure the efficiency of communication and software readability at the same time. Firstly, we compare the current mainstream serialization technologies and demonstrate the significance of custom protocol through analysis of those technical limitations. Then, the paper puts forward the concept of protocol-bean innovatively. By this means, we implement an object-oriented custom protocol method, which has higher performance and more readability. Finally, through the comparison of data bandwidth and real-time response time, experimental data shows this framework could ensure the lowest data bandwidth without destroying software structure. And compared with real-time server Red5, response time is improved by 40 times.     

On-line classroom visual tracking and quality evaluation by an advanced feature mining technique

With the rapid development of computer vision and multimedia technology, especially the visual tracking technology and network transmission, teacher-centered education is popular nowadays. The shortcomings of the conventional classroom teaching mode by manually student behavior analysis are gradually becoming less effective. Aiming at the main problems existing in the application of classroom teaching video resources in multimedia teaching, in this paper, we proposed an online classroom visual data tracking system, associated with an advanced tracking quality evaluation method based on data mining. Our proposed framework can offer a scientific basis for improving the quality of online education by discovering students’ learning patterns from their online learning data. The evaluation results can effectively demonstrated that the mining of various learning information of students is useful, and obtained the classification rules that affect the learning effect toward students. These clues can be adopted to uncover the learning effect of students and provide individual guidance for students’ learning behaviors. This work can reveal the pattern online classroom image teaching behavior from the perspective of behavior chain. We also noticed the online classroom visual tracking behavior can be divided into several components: selection, presentation, mapping, analysis and collection, as well as the analysis from students facial expression.     

Robust face tracking via collaboration of generic and specific models.

Significant appearance changes of objects under different orientations could cause loss of tracking, "drifting." In this paper, we present a collaborative tracking framework to robustly track faces under large pose and expression changes and to learn their appearance models online. The collaborative tracking framework probabilistically combines measurements from an offline-trained generic face model with measurements from online-learned specific face appearance models in a dynamic Bayesian network. In this framework, generic face models provide the knowledge of the whole face class, while specific face models provide information on individual faces being tracked. Their combination, therefore, provides robust measurements for multiview face tracking. We introduce a mixture of probabilistic principal component analysis (MPPCA) model to represent the appearance of a specific face under multiple views, and we also present an online EM algorithm to incrementally update the MPPCA model using tracking results. Experimental results demonstrate that the collaborative tracking and online learning methods can handle large pose changes and are robust to distractions from the background.     

智能时代有线电视转型升级的新思路

随着信息科技水平的不断进步和发展,大数据平台逐渐成为媒体发展的“主战场”,人工智能也伴随着大数据时代的到来逐渐与媒体融合。而有线电视作为传统媒体的代表,储存着海量信息,要想在新媒体时代破茧成蝶,就必须掌握正确的转型方式,提高市场竞争力。结合大数据、人工智能、5G等新兴技术的发展趋势,从智能内容审核、电视终端智能收集、市场决策方式以及5G技术方面进行分析,为有线电视提供发展的新思路与新选择。     

基于大数据的安全事件挖掘框架

安全态势感知是掌控网络空间的听觉和视觉,安全事件的获取是安全态势感知的基础。在大数据时代,安全事件的挖掘是一个典型的大数据问题。运用大数据技术进行网络安全研究,构建了一种安全事件挖掘框架,结合在线反馈和离线分析两种方式,从海量、多源、异构的原始数据中,提取有效的安全事件,发现安全风险、潜在威胁和未知攻击。挖掘结果可用于安全态势感知、攻击追踪溯源、攻击知识学习等进一步研究。     

Invisibility and application functionalities in perceptualwatermarking an overview

Digital watermarking consists of hiding subliminal information into digital media content, also called host data. It can be the basis of many applications, including security and media asset management. In this paper we focus on the imperceptibility requirement for image watermarking. We first provide a functional inventory of image watermarking applications and emphasize the dependency between the application purpose and its need for invisibility. Then, we present a global framework common to most existing watermarking systems. It illustrates the methodology followed to translate human vision research into watermarking technology. It suggests future prospects and highlights the need for dedicated inputs from the human vision community     

Neural network methods for volumetric magnetic resonance imaging ofthe human brain

Brain magnetic resonance (MR) images contain massive information requiring lengthy and complex interpretation (as in the identification of significant portions of the image), quantitative evaluation (as in the determination of the size of certain significant regions), and sophisticated interpretation (as in determining any image portions which indicate signs of lesions or of disease). In this paper we first survey the clinical and research needs for brain imaging. We present the state-of-the-art in relevant image analysis techniques. We then discuss our recent work on the use of novel artificial neural networks which have a recurrent structure to extract precise morphometric information from MRI scans of the human brain. Finally, experimental data using our novel approach is presented and suggestions are made for future research     

The Dynamics of Audience Fragmentation: Public Attention in an Age of Digital Media

Audience fragmentation is often taken as evidence of social polarization. Yet the tools we use to study fragmentation provide limited information about how people allocate their attention across digital media. We offer a theoretical framework for understanding fragmentation and advocate for more audience‐centric studies. This approach is operationalized by applying network analysis metrics to Nielsen data on television and Internet use. We find extremely high levels of audience duplication across 236 media outlets, suggesting overlapping patterns of public attention rather than isolated groups of audience loyalists.     

基于关联性的目标动向热点分析

为了从海量传感器数据中及时发现重要目标的动向,提出了一种目标动向信息表征及关联方法,即从多源异类传感器获取的信息中抽取出目标关联要素进行动向表征,利用语义决策树实现动向要素聚类,通过知识规则进行关联匹配扩展,从而发现目标动向的热点,并进一步统计分析目标活动规律与发展趋势。实验表明所提出的基于关联性的目标动向热点分析算法准确率高,具有实用价值。