Learning models are highly dependent on data to work effectively, and they give a better performance upon training on big datasets. Massive research exists in the literature to address the dataset adequacy issue. One promising approach for solving dataset adequacy issues is the data augmentation (DA) approach. In DA, the amount of training data instances is increased by making different transformations on the available data instances to generate new correct and representative data instances. DA increases the dataset size and its variability, which enhances the model performance and its prediction accuracy. DA also solves the class imbalance problem in the classification learning techniques. Few studies have recently considered DA in the Arabic language. These studies rely on traditional augmentation approaches, such as paraphrasing by using rules or noising-based techniques. In this paper, we propose a new Arabic DA method that employs the recent powerful modeling technique, namely the AraGPT-2, for the augmentation process. The generated sentences are evaluated in terms of context, semantics, diversity, and novelty using the Euclidean, cosine, Jaccard, and BLEU distances. Finally, the AraBERT transformer is used on sentiment classification tasks to evaluate the classification performance of the augmented Arabic dataset. The experiments were conducted on four sentiment Arabic datasets, namely AraSarcasm, ASTD, ATT, and MOVIE. The selected datasets vary in size, label number, and unbalanced classes. The results show that the proposed methodology enhanced the Arabic sentiment text classification on all datasets with an increase in F1 score by 4% in AraSarcasm, 6% in ASTD, 9% in ATT, and 13% in MOVIE.

Kratom（KT）通常发挥抗抑郁药（AD）效应。但是，评估哪种形式的KT提取物具有类似于标准AD氟西汀（FLU）的AD特性仍然具有挑战性。在这里，我们采用了称为ANET的基于自动编码器（AE）的异常检测器，以衡量响应KT休假提取物和AD流感的小鼠局部场电位（LFP）特征的相似性。响应KT糖浆的功能与响应AD流感的人的相似性最高，为85.62 $ \ pm $ 0.29％。这一发现表明，将KT糖浆用作抑郁剂治疗的替代物质的可行性比KT生物碱和KT水（这是本研究中的其他候选者）。除了相似性测量外，我们还将ANET用作多任务AE，并评估了与不同KT提取物和AD流感效果相对应的多级LFP响应的性能。此外，我们分别以定性和定量为T-SNE投影和最大平均差异距离，可视化LFP响应之间的潜在特征。分类结果报告的准确性和F1得分为79.78 $ \ pm $ 0.39％和79.53 $ \ pm $ 0.00％。总而言之，这项研究的结果可能有助于治疗设计设备进行替代物质概况评估，例如在现实世界应用中基于KRATOM的形式。