Eliminating Noisy Information in Web Pages based on Source Code Shrinking

2012 ◽  
Vol 4 (18) ◽  
pp. 390-397
Author(s):  
Hu Fei ◽  
Li Ming ◽  
Ma Yan
Keyword(s):  
Source Code ◽  
Web Pages ◽  
Noisy Information

Removal of Noisy Information in Web Pages

2014 ◽  
Author(s):  
Hiteshwar Kumar Azad ◽  
Rahul Raj ◽  
Rahul Kumar ◽  
Harshit Ranjan ◽  
Kumar Abhishek ◽  
...  
Keyword(s):  
Web Pages ◽  
Noisy Information

scholarly journals WAVES: a Web Application for Versatile Enhanced bioinformatic Services

2018 ◽  
Author(s):  
Marc Chakiachvili ◽  
Sylvain Milanesi ◽  
Anne-Muriel Arigon Chifolleau ◽  
Vincent Lefort
Keyword(s):  
Web Services ◽  
General Public ◽  
Web Application ◽  
Source Code ◽  
Web Pages ◽  
Tool Integration ◽  
Web Framework ◽  
Bioinformatic Tool ◽  
Restful Web Services ◽  
General Public License

AbstractSummary: WAVES is a web application dedicated to bioinformatic tool integration. It provides an efficient way to implement a service for any bioinformatic software. Such services are automatically made available in three ways: web pages, web forms to include in remote websites, and a RESTful web services API to access remotely from applications. In order to fulfill the service’s computational needs, WAVES can perform computation on various resources and environments, such as Galaxy instances.Availability and implementation: WAVES was developed with Django, a Python-based web framework. It was designed as a reusable web application. It is fully portable, as only a Python installation is required to run Django. It is licensed under GNU General Public License. Source code, documentation with examples and demo are available from http://www.atgc-montpellier.fr/waves/.Contact:[email protected]

Deep Web Data Extraction Based on Regular Expression

2013 ◽  
Vol 718-720 ◽  
pp. 2242-2247 ◽  
Author(s):  
Tao Lin ◽  
Bao Hua Qiang ◽  
Shi Long ◽  
He Qian
Keyword(s):  
Regular Expression ◽  
Data Extraction ◽  
Source Code ◽  
Deep Web ◽  
Web Pages ◽  
Web Data ◽  
Common Path ◽  
Web Data Integration ◽  
The Common ◽  
Target Data

Data extraction is an important issue in Deep web data integration. In order to extract the query results of the Deep Web, it is firstly required to locate the target data block correctly. Due to the html source code of web pages can be parsed as well structured DOM, we proposed an effective algorithm for discerning the common path based on hierarchical DOM. Based on the common path and our predefined regular expression, the target data of the Deep Web can be extracted effectively. The experimental results on real websites show that our proposed algorithm is highly effective.

Appropriate Targets for Search Advertising as Part of Online Gatekeeping for Suicide Prevention

Crisis ◽  
2018 ◽  
Vol 39 (3) ◽  
pp. 197-204 ◽  
Author(s):  
Hajime Sueki ◽  
Jiro Ito
Keyword(s):  
Suicide Prevention ◽  
Daily Life ◽  
Web Pages ◽  
Service Users ◽  
Online Questionnaire ◽  
Consultation Service ◽  
Self Disclosure ◽  
Search Advertising ◽  
Consultation Services ◽  
Age Range

Abstract. Background: Gatekeeper training is an effective suicide prevention strategy. However, the appropriate targets of online gatekeeping have not yet been clarified. Aim: We examined the association between the outcomes of online gatekeeping using the Internet and the characteristics of consultation service users. Method: An advertisement to encourage the use of e-mail-based psychological consultation services among viewers was placed on web pages that showed the results of searches using suicide-related keywords. All e-mails received between October 2014 and December 2015 were replied to as part of gatekeeping, and the obtained data (responses to an online questionnaire and the content of the received e-mails) were analyzed. Results: A total of 154 consultation service users were analyzed, 35.7% of whom were male. The median age range was 20–29 years. Online gatekeeping was significantly more likely to be successful when such users faced financial/daily life or workplace problems, or revealed their names (including online names). By contrast, the activity was more likely to be unsuccessful when it was impossible to assess the problems faced by consultation service users. Conclusion: It may be possible to increase the success rate of online gatekeeping by targeting individuals facing financial/daily life or workplace problems with marked tendencies for self-disclosure.

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