Decrease in Free Computer Science Papers Found through Google Scholar

Purpose - Google Scholar was used to locate free full-text versions of computer science research papers to determine what proportion could be freely accessed.Design/methodology/approach - A sample of 1967 conference papers and periodical articles from 2003-2010, indexed in the ACM Guide to Computing Literature, was searched for manually in Google Scholar, using the paper or article title and the first author’s surname and supplementary searches as needed. Findings - Free full-text versions were found for 52% of the conference papers and 55% of the periodical articles. Documents with older publication dates were more likely to be freely accessible than newer documents, with free full-text versions found for 71% of items published in 2003 and 43% of items published 2010. Many documents did not indicate what version of the document was presented. Research limitations/implications - Results were limited to the retrieval of known computer science publications via Google Scholar. The results may be different for other computer science publications, subject areas, types of searches, or search engines. Practical implications - Users of Google Scholar for finding free full-text computer science research papers may be hindered by the lower access to recent publications. Because many papers are freely available, libraries and scholarly publishers may be better served by promoting services they provide beyond simple access to papers. Originality/value – Previous research showed lower levels of free access than we found for computer science, but the decline found in this study runs contrary to increases found in previous research

Focused image search in the social Web.

Recently, social multimedia-sharing websites, which allow users to upload, annotate, and share online photo or video collections, have become increasingly popular. The user tags or annotations constitute the new multimedia meta-data . We present an image search system that exploits both image textual and visual information. First, we use focused crawling and DOM Tree based web data extraction methods to extract image textual features from social networking image collections. Second, we propose the concept of visual words to handle the image\u27s visual content for fast indexing and searching. We also develop several user friendly search options to allow users to query the index using words and image feature descriptions (visual words). The developed image search system tries to bridge the gap between the scalable industrial image search engines, which are based on keyword search, and the slower content based image retrieval systems developed mostly in the academic field and designed to search based on image content only. We have implemented a working prototype by crawling and indexing over 16,056 images from flickr.com, one of the most popular image sharing websites. Our experimental results on a working prototype confirm the efficiency and effectiveness of the methods, that we proposed