While resources for English language are fairly sufficient to understand content on social media, similar resources in Arabic are still immature. The main reason that the resources in Arabic are insufficient is that Arabic has many dialects in addition to the standard version (MSA). Arabs do not use MSA in their daily communications; rather, they use dialectal versions. Unfortunately, social users transfer this phenomenon into their use of social media platforms, which in turn has raised an urgent need for building suitable AI models for language-dependent applications. Existing machine translation (MT) systems designed for MSA fail to work well with Arabic dialects. In light of this, it is necessary to adapt to the informal nature of communication on social networks by developing MT systems that can effectively handle the various dialects of Arabic. Unlike for MSA that shows advanced progress in MT systems, little effort has been exerted to utilize Arabic dialects for MT systems. While few attempts have been made to build translation datasets for dialectal Arabic, they are domain dependent and are not OSN cultural-language friendly. In this work, we attempt to alleviate these limitations by proposing an online social network-based multidialect Arabic dataset that is crafted by contextually translating English tweets into four Arabic dialects: Gulf, Yemeni, Iraqi, and Levantine. To perform the translation, we followed our proposed guideline framework for content translation, which could be universally applicable for translation between foreign languages and local dialects. We validated the authenticity of our proposed dataset by developing neural MT models for four Arabic dialects. Our results have shown a superior performance of our NMT models trained using our dataset. We believe that our dataset can reliably serve as an Arabic multidialectal translation dataset for informal MT tasks.

Despite achieving accuracies higher than 90% on recognizing upper-limb movements through sEMG (surface Electromyography) signal with the state of art classifiers in the laboratory environment, there are still issues to be addressed for a myo-controlled prosthesis achieve similar performance in real environment conditions. Thereby, the main goal of this review is to expose the latest researches in terms of strategies in each block of the system, giving a global view of the current state of academic research. A systematic review was conducted, and the retrieved papers were organized according to the system step related to the proposed method. Then, for each stage of the upper limb motion recognition system, the works were described and compared in terms of strategy, methodology and issue addressed. An additional section was destined for the description of works related to signal contamination that is often neglected in reviews focused on sEMG based motion classifiers. Therefore, this section is the main contribution of this paper. Deep learning methods are a current trend for classification stage, providing strategies based on time-series and transfer learning to address the issues related to limb position, temporal/inter-subject variation, and electrode displacement. Despite the promising strategies presented for contaminant detection, identification, and removal, there are still some factors to be considered, such as the occurrence of simultaneous contaminants.