Towards Building a Personalized Online Web Spam Detector in Intelligent Web Browsers

Author(s):  
Cailing Dong ◽  
Bin Zhou ◽  
Lina Zhou
Keyword(s):  
Web Spam ◽  
2011 ◽  
Vol 35 (3) ◽  
pp. 21
Author(s):  
Christopher Glick
Keyword(s):  

This paper explains TeachNet, a website for Rikkyo University English teachers. It covers the website’s goals and development from the start to my term of supervision. Problems and suggestions for such sites are provided along with a brief analysis of actual website usage, as measured with a commercial online web statistics firm. 本論では、立教大学の英語教員向け情報・教材データベースTeachNetについて概説する。このウェブサイトの目標や、開設以降の発展状況も示す。さらに、このようなサイトの問題点を指摘し、提案を行い、オンライン統計会社による利用者のデータ分析結果も提示する。


2019 ◽  
Vol 12 (3) ◽  
pp. 202-211
Author(s):  
Yuancheng Li ◽  
Rong Huang ◽  
Xiangqian Nie

Background: With the rapid development of the Internet, the number of web spam has increased dramatically in recent years, which has wasted search engine storage and computing power on a massive scale. To identify the web spam effectively, the content features, link features, hidden features and quality features of web page are integrated to establish the corresponding web spam identification index system. However, the index system is highly correlation dimension. Methods: An improved method of autoencoder named stacked autoencoder neural network (SAE) is used to realize the reduction of the web spam identification index system. Results: The experiment results show that our method could reduce effectively the index of web spam and significantly improves the recognition rate in the following work. Conclusion: An autoencoder based web spam indexes reduction method is proposed in this paper. The experimental results show that it greatly reduces the temporal and spatial complexity of the future web spam detection model.


Author(s):  
Chun-ying Huang ◽  
Yun-chen Cheng ◽  
Guan-zhang Huang ◽  
Ching-ling Fan ◽  
Cheng-hsin Hsu

Real-time screen-sharing provides users with ubiquitous access to remote applications, such as computer games, movie players, and desktop applications (apps), anywhere and anytime. In this article, we study the performance of different screen-sharing technologies, which can be classified into native and clientless ones. The native ones dictate that users install special-purpose software, while the clientless ones directly run in web browsers. In particular, we conduct extensive experiments in three steps. First, we identify a suite of the most representative native and clientless screen-sharing technologies. Second, we propose a systematic measurement methodology for comparing screen-sharing technologies under diverse and dynamic network conditions using different performance metrics. Last, we conduct extensive experiments and perform in-depth analysis to quantify the performance gap between clientless and native screen-sharing technologies. We found that our WebRTC-based implementation achieves the best overall performance. More precisely, it consumes a maximum of 3 Mbps bandwidth while reaching a high decoding ratio and delivering good video quality. Moreover, it leads to a steadily high decoding ratio and video quality under dynamic network conditions. By presenting the very first rigorous comparisons of the native and clientless screen-sharing technologies, this article will stimulate more exciting studies on the emerging clientless screen-sharing technologies.


2006 ◽  
Vol 40 (2) ◽  
pp. 11-24 ◽  
Author(s):  
Carlos Castillo ◽  
Debora Donato ◽  
Luca Becchetti ◽  
Paolo Boldi ◽  
Stefano Leonardi ◽  
...  

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