Human activities in the sea, such as intensive fishing and exploitation of offshore wind farms, may impact negatively on the marine mega fauna. As an attempt to control such impacts, surveying, and tracking of marine animals are often performed on the sites where those activities take place. Nowadays, thank to high resolution cameras and to the development of machine learning techniques, tracking of wild animals can be performed remotely and the analysis of the acquired images can be automatized using state-of-the-art object detection models. However, most state-of-the-art detection methods require lots of annotated data to provide satisfactory results. Since analyzing thousands of images acquired during a flight survey can be a cumbersome and time consuming task, we focus in this article on the weakly supervised detection of marine animals. We propose a modification of the patch distribution modeling method (PaDiM), which is currently one of the state-of-the-art approaches for anomaly detection and localization for visual industrial inspection. In order to show its effectiveness and suitability for marine animal detection, we conduct a comparative evaluation of the proposed method against the original version, as well as other state-of-the-art approaches on two high-resolution marine animal image datasets. On both tested datasets, the proposed method yielded better F1 and recall scores (75% recall/41% precision, and 57% recall/60% precision, respectively) when trained on images known to contain no object of interest. This shows a great potential of the proposed approach to speed up the marine animal discovery in new flight surveys. Additionally, such a method could be adopted for bounding box proposals to perform faster and cheaper annotation within a fully-supervised detection framework.

O home office foi regulamentado com a reforma trabalhista no Brasil no ano de 2017. Esta pesquisa objetiva analisar os benefícios e os desafios da adoção do home office sob a perspectiva de profissionais do departamento de recursos humanos de empresas de tecnologia da informação. Trata-se de um estudo multicasos, de natureza descritiva e qualitativa. Os dados foram coletados por meio de entrevistas semiestruturadas em empresas do setor de tecnologia da informação. Os resultados principais apontam que as empresas investigadas aderiram a esse formato de trabalho gradativamente. Os principais benefícios da adoção do home officeidentificados foram: flexibilidade, produtividade, possibilidade de contratar profissionais sem restrições geográficas, redução de despesas de estrutura e de deslocamento e melhoria da qualidade de vida. Os desafios identificados incluem cultura organizacional, modelo contratual, indisciplina, falta de comprometimento, dificuldades da ausência de contato presencial com a equipe e tecnologia insuficiente.

In this article, we develop a novel feature extraction method that combines two well-established mathematical morphology concepts: watersheds and morphological attribute profiles (APs). In order to extract spatial-spectral features from remote sensing data, APs were originally defined as sequences of filtering operators on inclusion trees, i.e., the max- and min-trees, computed from the input image. In this study, we extend the AP paradigm to the more general framework of hierarchical watersheds. Moreover, we explore the semantic knowledge provided by labeled training pixels during different phases of the watershed-AP construction, namely within the construction of hierarchical watersheds from the raw image and later within the filtering of the resulting hierarchy. We illustrate the relevance of the proposed method with two applications including land cover classification and building extraction using optical remote sensing images. Experimental results show that the new profiles outperform various existing features using two public datasets (Zurich and Vaihingen), thus providing another high potential feature extraction method within the AP family.