There are many physical biometrics such as iris patterns and fingerprints. There are also interactive gestures like how a person types on a keyboard, moves a mouse, holds a phone, or even taps a touch screen. Keystroke dynamics or typing dynamics is an automatic method that confirms the identity of an individual based on the manner and the way of the user typing on a keyboard. There are two types of keystroke systems, Fixed-text system, and free-text system and each of them has it is own importance. In this research paper, we are investigating the possibility of classifying individuals using features extracted from their keystroke dynamics with two different datasets: (1) fixed-text dataset with different difficulty levels and (2) free-text dataset with no restrictions what a user types on the keyboard. Investigation was done using several classification techniques: RandomForest (RF), Support Vector Machines (SVM), BayesNet (BN), and K-Nearest Neighbors (KNN). The highest accuracy achieved with the fixed-text dataset was 98.8% using RF for classification while the highest achieved accuracy with the freetext dataset was 87.58 % using RF classifier.

Visual Question Answering (VQA) is the problem of automatically answering a natural language question about a given image or video. Standard Arabic is the sixth most spoken language around the world. However, to the best of our knowledge, there are neither research attempts nor datasets for VQA in Arabic. In this paper, we generate the first Visual Arabic Question Answering (VAQA) dataset, which is fully automatically generated. The dataset consists of almost 138k Image-Question-Answer (IQA) triplets and is specialized in yes/no questions about real-world images. A novel database schema and an IQA ground-truth generation algorithm are specially designed to facilitate automatic VAQA dataset creation. We propose the first Arabic-VQA system, where the VQA task is formulated as a binary classification problem. The proposed system consists of five modules, namely visual features extraction, question pre-processing, textual features extraction, feature fusion, and answer prediction. Since it is the first research for VQA in Arabic, we investigate several approaches in the question channel, to identify the most effective approaches for Arabic question pre-processing and representation. For this purpose, 24 Arabic-VQA models are developed, where two question-tokenization approaches, three word-embedding algorithms, and four LSTM networks with different architectures are investigated. A comprehensive performance comparison is conducted between all these Arabic-VQA models on the VAQA dataset. Experiments indicate that the performance of all Arabic-VQA models ranges from 80.8 to 84.9%, while utilizing Arabic-specified question pre-processing approaches of considering the special case of separating the question tool "Image missing" and embedding the question words using fine-tuned Word2Vec models from AraVec2.0 have significantly improved the performance. The best-performing model is which treats the question tool "Image missing" as a separate token, embeds the question words using AraVec2.0 Skip-Gram model, and extracts the textual feature using one-layer unidirectional LSTM. Further, our best Arabic-VQA model is compared with related VQA models developed on other popular VQA datasets in a different natural language, considering their performance only on yes/no questions according to the scope of this paper, showing a very comparable performance.

Undoubtedly, Information and Communication Technology (ICT) is the most popular and widespread trend of many fields around the globe, and a smart tool for the emergence of several technological services such as web service and virtual technology. Nowadays, development and growth in computers and communications field are dynamically changing. ICT plays a key role in the digital transformation which led to the appearance of a new age called the digital age. Hence, many organizations and countries have supported modern technological trends such as the Virtual University (VU), Virtual Reality (VR) and Virtual Learning Environments (VLE), as a virtual technology. ICT can be used in positive and negative aspects, so it must be observed and considered. As a virtual technology, VU can offer extraordinary opportunities to avoid obstacles caused by critical circumstances.

In recent times, Information and Communication Technology (ICT) has been developed and widely spread around world. ICT is used in various sectors and considered a basis in the emergence of some important technologies such as virtual reality technology. Virtual Reality (VR) is a special technology as an advanced technology connected to several fields, e.g. training, learning, science, engineering, medicine, military, etc. VR has great potentials which enabled to perform several phenomena and experiments. Hence, several scenarios have become available. The purpose of this study is to shed light on virtual reality technology and list a glimpse of common publications and studies involved. I.

In this paper, different machine learning algorithms, ensemble algorithms, and deep learning algorithms are applied to Arabic tweets to detect whether it human-generated or not. The tweets are used twice as preprocessed and nonpreprocessed to measure the effectiveness of Arabic preprocessing in the classification process. The data is also tokenized with various methods like unigram, trigram, and Term Frequency-Inverse Document Frequency. The experiments show that the support vector machine with the non-preprocessed tweets and unigram tokenization has the best performance of 83.11% and a precision of 0.9516 while it predicts the spam or not in a relatively small time.