Deep learning and remote sensing techniques have significantly advanced water surface monitoring; however, the need for annotated data remains a challenge. This is particularly problematic in wetland detection, where water extent varies over time and space, demanding multiple annotations for the same area. In this paper, we present DEEPAQUA, a deep learning model inspired by knowledge distillation (a.k.a. teacher–student model) to generate labeled data automatically and eliminate the need for manual annotations during the training phase. We utilize the Normalized Difference Water Index (NDWI) as a teacher model to train a Convolutional Neural Network (CNN) for segmenting water from Synthetic Aperture Radar (SAR) images. To train the student model, we exploit cases where optical- and radar-based water masks coincide, enabling the detection of both open and vegetated water surfaces. DEEPAQUA represents a significant advancement in computer vision techniques for water detection by effectively training semantic segmentation models without any manually annotated data. Experimental results show that DEEPAQUA outperforms other unsupervised methods by improving accuracy by 3%, Intersection Over Union by 11%, and F1-score by 6%. This approach offers a practical solution for monitoring wetland water extent changes without the need of ground truth data, making it highly adaptable and scalable for wetland monitoring.

Code Search is a practical tool that helps developers navigate growing source code repositories by connecting natural language queries with code snippets. Platforms such as StackOverflow resolve coding questions and answers; however, they cannot perform a semantic search through the code. Moreover, poorly documented code adds more complexity to search for code snippets in repositories. To tackle this challenge, this paper presents SIAMBERT, a BERT-based model that gets the question in natural language and returns relevant code snippets. The SIAMBERT architecture consists of two stages, where the first stage, inspired by Siamese Neural Network, returns the top K relevant code snippets to the input questions, and the second stage ranks the given snippets by the first stage. The experiments show that SIAMBERT outperforms non-BERT-based models having improvements that range from 12% to 39% on the Recall@1 metric and improves the inference time performance, making it 15x faster than standard BERT models.

Nowadays, we can find machine learning (ML) applications in nearly every aspect of modern life, and we see that more developers are engaged in the field than ever. In order to facilitate the development of new ML applications, it would be beneficial to provide services that enable developers to share, access, and search for source code easily. A step towards making such a service is to cluster source code by functionality. In this work, we present COCLUBERT, a BERT-based model for source code embedding based on their functionality and clustering them accordingly. We build COCLUBERT using CuBERT, a variant of BERT pre-trained on source code, and present three ways to fine-tune it for the clustering task. In the experiments, we compare COCLUBERT with a baseline model, where we cluster source code using CuBERT embedding without fine-tuning. We show that COCLUBERT significantly outperforms the baseline model by increasing the Dunn Index metric by a factor of 141, the Silhouette Score metric by a factor of two, and the Adjusted Rand Index metric by a factor of 11.