The exponential growth of the internet and a multi-fold increase in social media users in the last decade have resulted in a massive growth of unstructured data. Aspect-Based Sentiment Analysis (ABSA) is challenging because it performs a fine-grain analysis; it is a text analysis technique where the opinions group is based on the aspect. The Aspect Extraction (AE) task is one of the core subtasks of ABSA; it helps to identify aspect terms in the text, comments, or reviews. The challenge of the Arabic AE task increases due to the complexity of the Arabic language. This work aims to develop the Arabic AE task by proposing transfer learning using State-of-art pre-trained contextual language models. We concatenate the Bidirectional Encoder Representation from Transformers (BERT) language model and contextualize string embedding (Flair embedding) as a stacked embeddings layer for better word representation for Arabic language. Then, we extend it with different deep learning network architectures. For Arabic AE, the model is developed by concatenating the Arabic contextual language model, AraBERT, and Flair embedding as a contextual stacked embeddings layer with an extended layer, BiLSTM-CRF or BiGRU-CRF, for sequence labeling. Our proposed models are called BF-BiLSTM-CRF and BF-BiGRU-CRF. The proposed model is evaluated using the Arabic Hotel's reviews dataset. For performance evaluation, we used the F1 score. The experimental results show that the proposed BF-BiLSTM-CRF configuration outperformed the baseline and other models by achieving an F1score of 79.7%.

Cloud computing recently emerged as a newparadigm that aims to deploy services via Internet. Under hybrid cloud environment, cloud bursting is a technique that combines local (organizations or in-house) resources with public cloud resources, these resources are leased based on a pay-per-use basis. It used to process the overload work within local resource or to accelerate the execution time of distributed applications with respect of the required level of QoS, also to achieve the efficient use of private resources. When Cloud bursting is applied, the important issues are determining how many and which type of resources will be provisioned. Also, the important issues that should be considered before cloud bursting decision are which workload will be burst to public cloud and when these resources will be released. These issues attract the attention of researchers to tackle them. In this paper we intend to review the recent researches that concern on cloud bursting and resource provisioning. We will explore how each study address the problem, what are the proposed solutions and what are the differences between them, the main researches are compared in terms of several criteria such as type of application, which was targeted by the research, environment that used to implement the experiment, results, and limitations.