The Arabic language suffers from a great shortage of datasets suitable for training deep learning models, and the existing ones include general non-specialized classifications. In this work, we introduce a new Arab medical dataset, which includes two thousand medical documents collected from several Arabic medical websites, in addition to the Arab Medical Encyclopedia. The dataset was built for the task of classifying texts and includes 10 classes (Blood, Bone, Cardiovascular, Ear, Endocrine, Eye, Gastrointestinal, Immune, Liver and Nephrological) diseases. Experiments on the dataset were performed by fine-tuning three pre-trained models: BERT from Google, Arabert that based on BERT with large Arabic corpus, and AraBioNER that based on Arabert with Arabic medical corpus.

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Kubernetes emerged as docker containers' most popular orchestration, it is widely used for developing microservices and deploying applications. Because of advancements in containerization technology, information technology organizations use Kubernetes to manage their systems and report benefits in the deployment process. However, security concerns have been highlighted as challenges in Kubernetes deployment, The hackers can exploit the security vulnerabilities to cause damage to company assets. This work will shed the light on the Kubernetes orchestration platform and how attacks can be contacted against subevents manifest. we also demonstrate 10 security best practices in the Kubernetes cluster based on practitioners' reports, which we should follow to help protect our infrastructure.