With the popularization of the Internet and the rapid development of computer network technology, human beings have entered a brand new era - the information age. This kind of network technology beyond space not only brings well-being to people, but also subtly affects the ideas and behaviors of teenagers. It not only changes their lifestyle and values, but also quietly makes them mentally ill, resulting in an endless series of problems of juvenile cybercrimes. For the purpose of promoting the governance of Internet crimes among young people effectively and avoiding crimes among special groups of young people, this paper plans to base on the concept of Internet crimes of teenagers, summarize the characteristics of youth crimes in our country, analyze its influence factors and propose the measures to deal with it.

This paper provides a comprehensive review of SURF (speeded up robust features) feature descriptor, commonly used technique for image feature extraction. The SURF algorithm has obtained significant popularity because to its robustness, efficiency, and invariance to various image transformations. In this paper, an in-depth analysis of the underlying principles of SURF, its key components, and its use in computer vision tasks such as object recognition, image matching, and 3D reconstruction are proposed. Furthermore, we discuss recent advancements and variations of the SURF algorithm and compare it with other popular feature descriptors. Through this review, the aim is to provide a clear understanding of the SURF feature descriptor and its significance in the area of computer vision.

Idiomatic phrase are one of the lexical units.Many second-language learners showing great enthusiasm for using idiomatic expressions because of the rich cultural factors inherent in them and the vibrant,hilarious language that is close to life-like.However,  the idiomatic terms are so complicated that they frequently cause foreign learners to struggle with learning and comprehending Chinese.With its own advantages, the idea of lexical chunks has the potential to be a game changer in the teaching of idiomatic.

The effects of climate change are already being felt, including the failure to harvest several agricultural products. On the other hand, peatland requires good management because it is a high carbon store and is vulnerable as a contributor to high emissions if it catches fire. This study aims to determine the potential for livelihood options through land management with an agroforestry pattern in peatlands. The methods used are field observation and in-depth interviews. The research location is in Kuburaya Regency, West Kalimantan, Indonesia. Several land use scenarios are presented using additional secondary data. The results show that agroforestry provides more livelihood options than monoculture farming or wood. The economic contribution is very important so that people reduce slash-and-burn activities that can increase carbon emissions and threaten the sustainability of peatland.

Monitoring marine biodiversity is a challenge in some vulnerable and difficult-to-access habitats, such as underwater caves. Underwater caves are a great focus of biodiversity, concentrating a large number of species in their environment. However, most of the sessile species that live on the rocky walls are very vulnerable, and they are often threatened by different pressures. The use of these spaces as a destination for recreational divers can cause different impacts on the benthic habitat. In this work, we propose a methodology based on video recordings of cave walls and image analysis with deep learning algorithms to estimate the spatial density of structuring species in a study area. We propose a combination of automatic frame overlap detection, estimation of the actual extent of surface cover, and semantic segmentation of the main 10 species of corals and sponges to obtain species density maps. These maps can be the data source for monitoring biodiversity over time. In this paper, we analyzed the performance of three different semantic segmentation algorithms and backbones for this task and found that the Mask R-CNN model with the Xception101 backbone achieves the best accuracy, with an average segmentation accuracy of 82%.