Sequence-to-sequence learning-based conversion of pseudo-code to source code using neural translation approach

Pseudo-code refers to an informal means of representing algorithms that do not require the exact syntax of a computer programming language. Pseudo-code helps developers and researchers represent their algorithms using human-readable language. Generally, researchers can convert the pseudo-code into computer source code using different conversion techniques. The efficiency of such conversion methods is measured based on the converted algorithm's correctness. Researchers have already explored diverse technologies to devise conversion methods with higher accuracy. This paper proposes a novel pseudo-code conversion learning method that includes natural language processing-based text preprocessing and a sequence-to-sequence deep learning-based model trained with the SPoC dataset. We conducted an extensive experiment on our designed algorithm using descriptive bilingual understudy scoring and compared our results with state-of-the-art techniques. Result analysis shows that our approach is more accurate and efficient than other existing conversion methods in terms of several performances metrics. Furthermore, the proposed method outperforms the existing approaches because our method utilizes two Long-Short-Term-Memory networks that might increase the accuracy. © 2013 IEEE

CONTRIBUTION À L’ÉVALUATION ET À LA CARTOGRAPHIE DE LA SENSIBILITÉ À L’ÉROSION HYDRIQUE DES SOLS DU SOUS BASSIN VERSANT DE L’OUED DE SAIDA (OUEST DE L’ALGÉRIE)

The sub-watershed of WadiSaidawich is a part of Macta watershed is characterized by a semiarid climate. Erratic rains, usually in stormy character, combined with anthropozoogenic pressure (deforestation, urbanization, overgrazing) cause a severe erosion. According to the National Agency of water resources, sediment yield (sediment from erosion) is estimated at 29667 t / year, which contribute to the siltation of the dam of Ouizert. This study was conducted using a Geographic Information System (GIS), allowed to characterize different areas of the sub-watershed, producing a synthetic map of the distribution of degrees of susceptibility to erosion. Indeed, Three classes of multifactorial vulnerability to water erosion were distinguished, areas with low vulnerability 40.18%; areas with medium vulnerability 24.93% and 34.88% highly vulnerable areas. Thus, classes with medium and high multifactorialvulnerability represent 60% of the area. This first mapping study is a tool to help decision makers to better manage water resources and soil and taking into account the expectations and needs of the rural populatio