Tables are widely used in documents because of their compact and structured representation information. In particular, scientific papers, tables can sum up novel discoveries summarize experimental results, making the research comparable easily understandable by scholars. Since layout is highly variable, it would be useful to interpret content classify them into categories. This could helpful di...

Developing a high-performance text classification model in low-resource language is challenging due to the lack of labeled data. Meanwhile, collecting large amounts data cost-inefficient. One approach increase amount create synthetic using augmentation techniques. However, most available techniques work on English and are highly language-dependent as they perform at word sentence level, such re...

Data augmentation is an effective method for the performance enhancement of neural machine translation (NMT) by generating additional bilingual data. In this paper, we propose a novel data strategy translation. Unlike existing methods that simply modify words with same probability across different sentences, introduce sentence-specific approach word selection based on syntactic roles in sentenc...

Abstract Artificial neural networks are currently applied in a wide variety of fields, and they near to achieving performance similar humans many tasks. Nevertheless, vulnerable adversarial attacks the form small intentionally designed perturbation, which could lead misclassifications, making these models unusable, especially applications where security is critical. The best defense against att...

Convolutional Neural Networks (CNNs) are used in many domains but the requirement of large datasets for robust training sessions and no overfitting makes them hard to apply medical fields similar fields. However, when quantities samples cannot be easily collected, various methods can still applied stem problem depending on sample type. Data augmentation, rather than other methods, has recently ...