<p class="0abstract"> </p><p class="0abstract">This paper researches the evolution process of what is called two-factor authentication technique and its adaptation related to the educational system through the Internet. This technique is a measure of security employed, particularly in scopes which have valuable information like bank services. It witnesses developments so far as today, in parallel with the developments occurring in technology. Since this technique consists of two phases, the security is going to be developed. Today, bank services, devices using the Internet of things, tickets of public transportation and lots of other scopes are utilized. In the information field, the researchers and scientists always update the techniques of two-factor authentication to resist the attacks related to security. Last years, the researchers studied novel technologies like behavioral biometric or biometrics. The training through the Internet may become much more useful than going to someplace to study a specific course. Mostly, the participants in the trainings through the Internet get many certificates for success, participation, etc. The principal problem is how to certify the truthiness of the participant who desires to get the certification. In this paper, and by researching the techniques of two-factor authentication, the Mimic Control Method with Sound Intensity (MCMSI) is proposed to be used for the training through the Internet.</p>

The promising services offered by cloud computing environments have led to huge amount of data that need to be processed and stored. Wireless cloud networks rely on Transmission Control Protocol/Internet Protocol (TCP/IP) for reliable transfer of data traffic between the cloud end-users and servers and vise-versa. Even though TCP has been successful for several applications, it, however, does not perform well in wireless cloud environments. The many-to-one communication pattern used in such environments with such huge amount of data resulted in TCP incast problem. Transmission Control Protocol incast problem happens in cluster based storage workloads where a lot of end-users communicate simultaneously to a server in the cloud through a bottleneck router, creating buffers overflows which lead to high packet loss. This paper presents an empirical study on TCP incast in current wireless cloud networks and how it is caused. It evaluates TCP-Vegas and TCP-Sack to examine their behaviors and suitability for short-lived connections in terms of queue occupancy level, packet drops, throughput, link utilization and bandwidth unfairness between the TCP connections. It was found that both protocols suffer from high packet loss and link underutilization with comparable throughput.

Named-Entity-Recognition (NER) is one of the most important Information-Extraction (IE) use cases, which is used to improve the performance of Natural Languages Processing (NLP) tasks, such as Relation-Extraction (RE), Question-Answering (QA). Recently, Arabic NER is tackled in different ways by researchers. In this study, we assess the performance of two widely used models, namely, LSTM and Bi-LSTM on the NER task in the Arabic language and perform a comparative study between these models. In contrast to the traditional data partition technique widely used during the training, we employ the technique of k-fold cross-validation to improve the performance of each model. The experimental results reveal that the performance of all models is improved when k-fold cross-validation is applied. Additionally, according to our experiment results, the Bi-LSTM model outperforms the LSTM model in terms of our evaluation metric. We achieve the best F1 score of 94.17% with CNN-Bi-LSTM-CRF. An ablation study on k-fold cross-validation demonstrates that the F1 score increased from 87.28 to 94.17%.

This article investigates the use of a transformer encoder for Arabic Named Entity Recognition (NER). The classic transformer that was originally proposed for machine translation adopts the absolute sinusoidal position embedding which is aware of distance but unfortunately is not aware of the directionality. However, in the NER task, both distance and orientation are crucial. Therefore, in this study, instead of using absolute sinusoidal position encoding, we employ relative positional encoding and incorporate the directionality information in our NER model. More specifically, our proposed model uses Bidirectional Long Short-Term Memory (BiLSTM) for encoding every input token. Then, the output of the encoder is fed to the multi-head attention where both the distance and directionality information are incorporated. The decoder layer with a simple fully connected layer takes as input, the result of the attention layer, and the prediction layer with Conditional Random Fields (CRF) predicts the tag of each token. We validate our proposed approach on two merged public datasets, namely, ANER corp and AQMAR. Our experiment results demonstrate significant improvements when compare to the vanilla Transformer with absolute sinusoidal position encoding while achieving a state-of-the-art result on a merged two Arabic public datasets.