Abstract— In this paper, the XMLSchema-driven mapping of architecture components for generating new data formats will be introduced and an investigation of how the XMLSchema can be stored in different ways will be carried out. In general, any application that has the capability to work with XML documents will need to display the structure of its related data in a different format specified for a particular occasion, due to its nature in working in heterogeneous environment. Accordingly, mapping document from one data structure to another is needed. Such a mapping process is essential, especially when dealing with XMLSchema. Actually, when the data are to be translated between XML and database there should be some means of mapping formulated for the data before they can be transferred either to the database or in the document. Most of the techniques use object relational mapping for transforming data between XML and the database. In this paper, we will present different types of mapping of XMLSchema such as tree-to-tree which means XMLSchema to another XMLSchema and XMLSchema to XHTML. Other mappings are XMLSchema to relation, XMLSchema to object relational, and XMLSchema to relational algebra. We also introduce general algorithms for many of the mapping types. The algorithms and the techniques show how XMLSchema drives the mapping of architecture components to generate a new data structure.

Abstract— Query expansion is the process of supplementing additional terms to the original query to improve the information retrieval (IR) performance. For heavily inflectional languages such as Arabic, query expansion is considered a difficult task. In this paper, the well-known approach: "The similarity thesaurus" is adopted to be applied on Arabic. Prior to applying this approach, first; datasets (three collections of Arabic documents) are pre-processed to create documents inverted index vocabularies, then, the normal indexing process is carried out. The thesaurus method is applied to create a modified (expanded) query of the original one and the target collection is indexed once more. To gauge the enhancement of retrieval process, the results of normal indexing and those of applying thesaurus approach are evaluated against each other using precision and recall measures. The results have shown that the thesaurus method has considerably enhanced the performance of the Arabic Information Retrieval (AIR) System. As the number of expansion terms increases up to a certain extent (35 terms), the performance has been improved. On the other hand, the performance will not be affected, or grow insignificantly as the number of expansion terms exceeds this limit.

Abstract With the explosive growth of the World Wide Web, Information Retrieval Systems (IRS) have recently become a focus of research. Query expansion is defined as the process of supplementing additional terms or phrases to the original query to improve the information retrieval performance. Arabic is highly inflectional and derivational language which makes the query expansion process a hard task. In this paper, the well known approach, Pseudo Relevance Feedback (PRF) is adopted to be applied on Arabic. Prior to applying PRF, first; datasets (three collections of Arabic documents) are pre-processed to create documents inverted index vocabularies, then, the normal indexing process is carried out. The PRF is applied to create a modified (expanded) query of the original one and the target collection is indexed once more. To judge the enhancement of retrieval process, the results of normal indexing and those of applying PRF are evaluated against each other using precision and recall measures. The results have shown that the PRF method has significantly enhanced the performance of the Arabic Information Retrieval (AIR) System. As the number of expansion terms increases up to a certain extent (35 terms), the performance has been improved. On the other hand, the performance will not be affected, or grow insignificantly as the number of expansion terms exceeds this limit. 

Abstract: Nowadays, enormous amounts of accessible textual information available on the Internet are phenomenal. Automatic text classification is considered an important application in natural language processing. It is the process of assigning a document to predefined categories based on its content. In this paper, the well-known Centroid-based technique developed for text classification is considered to be applied on Arabic text. Arabic language is highly inflectional and derivational which makes text processing a complex and challenging task. In the proposed work, the Centroid-based Algorithm is adopted and adapted to be applied to classify Arabic documents. The implemented algorithm is evaluated using a corpus containing a set of Arabic documents. The experimental results against a dataset of 1400 Arabic text documents covering seven distinct categories reveal that the adapted Centroid-based algorithm is applicable to classify Arabic documents. The performance criteria of the implemented Arabic classifier achieved roughly figures of 90.7%, 87.1%, 88.9%, 94.8%, and 5.2% of Micro-averaging recall, precision, F measure, accuracy, and error rates respectively.

Abstract— Automatic text categorization is considered an important application in natural language processing. It is the process of assigning a document to predefined categories based on its content. In this research, some well-known techniques developed for classifying English text are considered to be applied on Arabic. This work focuses on applying the well-known Rocchio (Centroid-based) technique on Arabic documents. This technique uses centroids to define good class boundaries. The centroid of a class c is computed as center of mass of its members. Arabic language is highly inflectional and derivational which makes text processing a complex task. In the proposed work, first Arabic text is preprocessed using tokenization and stemming techniques. Then, the Rocchio Algorithm is adopted and adapted to be applied to classify Arabic documents. The implemented algorithm is evaluated using a corpus containing a set of actual documents. The results show that the adapted Rocchio algorithm is applicable to categorize Arabic text. Ratios of 92.2%, 92.7%, and 92.1% of Micro-averaging recall, precision, and F-measure respectively are achieved, against a data set of 500 Arabic text documents covering five distinct categories.

Network layer indications are not readily available upon a link change; therefore, general dependes on the network layer may introduce unnecessary delays due to network layer signaling for a simple link layer handover. If information could be gathered at link layer to determine the need for network layer signaling, then both the delay and signaling load could be really improved over the current standards of Mobile IP.

This paper presents a Cross-layer decision on two layer network and link layers to improve the performance of Enhanced Mobile IP (E-Mobile IP) handover in which reducing packet loss and latency during handover process.

 In this paper, English language will be used as an instance of natural languages as we will be concerned with the set of all natural language texts. this research tries to employ a set of all synonyms as a way to hide secret message inside a natural language text.