Problem statement: Digital multimedia watermarking technology was suggested in the last decade to embed copyright information in digital objects such images, audio and video. However, the increasing use of relational database systems in many real-life applications created an ever increasing need for watermarking database systems. As a result, watermarking relational database systems is now merging as a research area that deals with the legal issue of copyright protection of database systems. Approach: In this study, we proposed an efficient database watermarking algorithm based on inserting binary image watermarks in non-numeric mutli-word attributes of selected database tuples. Results: The algorithm is robust as it resists attempts to remove or degrade the embedded watermark and it is blind as it does not require the original database in order to extract the embedded watermark. Conclusion: Experimental results demonstrated blindness and the robustness of the algorithm against common database attacks.

Arabic language is distinguished by its morphological richness, which forces the workers in the field of Arabic language Processing (i.e., information retrieval, document's classification, text summarizing) to deal with many words that seem to be different but in reality they came from an identical root word. One of the methods to overcome this problem is to return the words to their roots. This research aims to provide a new algorithm, that returns roots of Arabic words using n-gram technique without using morphological rules in order to avoid the complexity arising from the morphological richness of the language in one hand and the multiplicity of morphological rules in other hand. The proposed algorithm uses a list that contains over 4,500 identical roots words.

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Text categorization is the process of grouping documents into categories based on their contents. This process is important to make information retrieval easier, and it became more important due to the huge textual information available online. The main problem in text categorization is how to improve the classification accuracy. Although Arabic text categorization is a new promising field, there are a few researches in this field. This paper proposes a new method for Arabic text categorization using vector evaluation. The proposed method uses a categorized Arabic documents corpus, and then the weights of the tested document's words are calculated to determine the document keywords which will be compared with the keywords of the corpus categorizes to determine the tested document's best category.

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