This paper presents the development of Augmented Reality (AR) for smart campus urbanization using library as the environment for the demonstration of the AR prototype. The main goal of the AR development is to help users to get information and direction easily using AR based mobile application when walking inside the library. In normal circumstances, users typically walk around and explore the library area before reaching their targeted destinations. Depending on the library size and number of reading corners, exploration and walking in the library can be time consuming. Therefore, an AR technology is introduced in this paper to improve the user experience inside the library in the right direction and information instantly. This application is developed using Vuforia software to set the image marker-based and process the output into Unity3D software, Android Studio for the Main Menu interface and IBM Watson for voice recognition. The final form of the application is successfully generated from the development of Augmented Reality (AR) application for the smart campus by using a library for the demonstration of the AR prototype. A series of application tests are conducted in each corner of the library to evaluate the effectiveness of developed AR.

Non-vocalized Arabic words are ambiguous words, because non-vocalized words may have different meanings. Therefore, these words may have more than one root. Many Arabic root extraction algorithms have been conducted to extract the roots of non-vocalized Arabic words. However, most of them return only one root and produce lower accuracy than reported when they are tested on different datasets. Arabic root extraction algorithm is an urgent need for applications like information retrieval systems, indexing, text mining, text classification, data compression, spell checking, text summarization, question answering systems and machine translation. In this work, a new rule-based Arabic root extraction algorithm is developed and focuses to overcome the limitation of previous works. The proposed algorithm is compared to the algorithm of Khoja, which is a well-known Arabic root extraction algorithm that produces high accuracy. The testing process was conducted on the corpus of Thalji, which is mainly built to test and compare Arabic roots extraction algorithms. It contains 720,000 word-root pairs from 12000 roots, 430 prefixes, 320 suffixes, and 4320 patterns. The experimental result shows that the algorithm of Khoja achieved 63%, meanwhile the proposed algorithm achieved 94% of accuracy.

Abstract-Many studies have focused recently on building, evaluating and comparing Arabic root extracting algorithm. The main challenges facing root extraction algorithms are the absence of standard data set for testing, comparing and enhancing different Arabic root extraction algorithms. In addition, the absence of complete lists of roots prefixes suffixes and patterns. In this paper, we describe the development of a new corpus driven from traditional Arabic dictionaries "mu'jams". The goal is to use the corpus, as a new gold standard data set for testing, comparing and enhancing different Arabic root extraction algorithms. This data set covers all types of words and all roots. It contains each word and its root as a pair to avoid the consultation of a human expert needed to verify the correct roots of words used in the testing or comparing process. We describe the individual phases of the corpus construction, i.e. normalisation, reading derivation words and roots as a pair, and reading each root and its definition part. We have automatically extracted (12000) roots, (430) prefixes, (320) suffixes, (4320) patterns, and (720,000) word-root pair. Konja's and Garside Arabic root extraction algorithm was tested on this corpus; the accuracy was (63%), then we test it after supplying it with our lists of roots prefixes suffixes and patterns, the accuracy of it became 84%.

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