The World Wide Web is a large, wealthy, and accessible information system whose users are increasing rapidly nowadays. To retrieve information from the web as per users’ requests, search engines are built to access web pages. As search engine systems play a significant role in cybernetics, telecommunication, and physics, many efforts were made to enhance their capacity.However, most of the data contained on the web are unmanaged, making it impossible to access the entire network at once by current search engine system mechanisms. Web Crawler, therefore, is a critical part of search engines to navigate and download full texts of the web pages. Web crawlers may also be applied to detect missing links and for community detection in complex networks and cybernetic systems. However, template-based crawling techniques could not handle the layout diversity of objects from web pages. In this paper, a web crawler module was designed and implemented, attempted to extract article-like contents from 495 websites. It uses a machine learning approach with visual cues, trivial HTML, and text-based features to filter out clutters. The outcomes are promising for extracting article-like contents from websites, contributing to the search engine systems development and future research gears towards proposing higher performance systems.

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Art in general and fine arts, in particular, play a significant role in human life, entertaining and dispelling stress and motivating their creativeness in specific ways. Many well-known artists have left a rich treasure of paintings for humanity, preserving their exquisite talent and creativity through unique artistic styles. In recent years, a technique called ’style transfer’ allows computers to apply famous artistic styles into the style of a picture or photograph while retaining the shape of the image, creating superior visual experiences. The basic model of that process, named ’Neural Style Transfer,’ has been introduced promisingly by Leon A. Gatys; however, it contains several limitations on output quality and implementation time, making it challenging to apply in practice. Based on that basic model, an image transform network was proposed in this paper to generate higher-quality artwork and higher abilities to perform on a larger image amount. The proposed model significantly shortened the execution time and can be implemented in a real-time application, providing promising results and performance. The outcomes are auspicious and can be used as a referenced model in color grading or semantic image segmentation, and future research focuses on improving its applications.

Invasive species threaten the biodiversity and the function of ecosystems. Drone image, satellite images, and image analysis software were used to create the map of invasive distribution and the potential spreading of invasive plants. 13 most invasive plants were identified with 11 species listed as invasive species in Southeast Asia and 5 of them in the 100 world’s invasive species by IUCN. Three species Merremia boisiana (Gagn.) van Ooststr., Ipomoea eberhardtii Gagn, and Mimosa pigra were identified as the species with high-ranking impacts on biodiversity and ecosystem biodiversity in Ba Na - Nui Chua Nature Reserve (BNNR). Ipomoea eberhardtii Gagn shows the highest spreading rate at 0.65 ± 0.06 ha/month, followed by Merremia boisiana (Gagn.) van Ooststr) and Mimosa pigra at 0.12 ± 0.01 ha/month and 0.01 ± 0.001 ha/month respectively. Fresh biomass of Ipomoea eberhardtii Gagn; Merremia boisiana (Gagn.); Mimosa pigra and Sphagnetola trilobata (L.) Pruski in BNNR are 15.67; 14.9; 8.1 and 6.8 ton/ha. The database of invasive plant distribution and potential spreading will be used to monitor strategies and invasive weeds management in BNNR.

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