由于对不同部门的电子芯片的需求不断增长，因此，半导体公司被授权离岸其制造流程。这一不必要的事情使他们对筹码的筹码有关，并引起了硬件攻击的创造。在这种情况下，半导体供应链中的不同实体可以恶意行事，并对从设备到系统的设计计算层进行攻击。我们的攻击是一个硬件特洛伊木马，在不受信任的铸造厂中插入了在面具的生成/制造过程中。特洛伊木马在制造，通过添加，删除或设计单元的变化中留下了脚印。为了解决这个问题，我们在这项工作中提出了可解释的视觉系统，用于硬件测试和保证（EVHA），可以检测以低成本，准确和快速的方式对设计的最小变化。该系统的输入是从正在检查的集成电路（IC）中获取的扫描电子显微镜（SEM）图像。系统输出是通过添加，删除或在单元格级的设计单元格中使用任何缺陷和/或硬件木马来确定IC状态。本文概述了我们的防御系统的设计，开发，实施和分析。

The machine translation mechanism translates texts automatically between different natural languages, and Neural Machine Translation (NMT) has gained attention for its rational context analysis and fluent translation accuracy. However, processing low-resource languages that lack relevant training attributes like supervised data is a current challenge for Natural Language Processing (NLP). We incorporated a technique known Active Learning with the NMT toolkit Joey NMT to reach sufficient accuracy and robust predictions of low-resource language translation. With active learning, a semi-supervised machine learning strategy, the training algorithm determines which unlabeled data would be the most beneficial for obtaining labels using selected query techniques. We implemented two model-driven acquisition functions for selecting the samples to be validated. This work uses transformer-based NMT systems; baseline model (BM), fully trained model (FTM) , active learning least confidence based model (ALLCM), and active learning margin sampling based model (ALMSM) when translating English to Hindi. The Bilingual Evaluation Understudy (BLEU) metric has been used to evaluate system results. The BLEU scores of BM, FTM, ALLCM and ALMSM systems are 16.26, 22.56 , 24.54, and 24.20, respectively. The findings in this paper demonstrate that active learning techniques helps the model to converge early and improve the overall quality of the translation system.