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huge irrelevant search hits. In this paper, we propose an improved method for ranking of search results to reduce human efforts on locating interesting hits. The search results are re-ranked using adaptive user interest hierarchies (AUIH), which considers both investigator-defined keywords and user interest learnt from
the device to strengthen the defense. To enhance the security of the back-end application servers, we use keyword filtering and re-treatment to rule out the blacklist, and to adjust the system settings so that it can effectively block the assaults or reduce the possibility of successful attacks. In addition, we also
expansion of the keyword lexicon used to discover IED related web pages, which identified new relevant terms for inclusion. Additionally, we present an improved web page feature representation designed to better capture the structural and stylistic cues revealing of genres of communication, and a series of experiments
Abnormal remarks on World Wide Web, such as violence, threat, superstition, etc. may disturb the social order and public morality. Most traditional methods filter a page as long as it contains a keyword in a predefined blacklist. Such methods cannot provide a quantitative measure of how sensitive the content is. In
keyword queries through a standard search engine and receive up-to-date database information. The system was then tested to determine if it could return results that were similar to those submitted using SQL. We also looked at whether a standard search engine such as Google could actually index the database content
applications in cloud deployment. First, the method obtains the SQL keywords through the analysis of lexical regulation for SQL statement. Then, it analyses the syntax regulation of SQL statement to create the rule tree. Finally, it traverses ternary tree on the basis of the model which established by SQL syntax regulation to
gambling is under strict regulations. However, there are so many websites that it is rather difficult to regulate Internet gambling and rather challenging to identify them. It may introduce many false positives or false negatives, if we simply grep contents of websites with keywords. In this paper, we find that the behavior
5th GrC model is the formal model specified into the category of sets. It is a theory of ordered granules, namely, granules are ordered ldquosubsetsrdquo of the universe, We extract a 5th GrC model from a set of Web pages. A granule is a high frequent sequence of keywords, It is a tuple in a relation and naturally
the tokens or between the checksums indicates a same origin violation. To reduce the scheme's performance ovrhead, this matching is performed only when a request originated from a page with no submission form has suspicious keywords. We analyze the protection potential, security, and performance overhead of our scheme.
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