Analysis of the capacity of google trends to measure interest in conservation topics and the role of online news

With the continuous growth of internet usage, Google Trends has emerged as a source of information to investigate how social trends evolve over time. Knowing how the level of interest in conservation topics--approximated using Google search volume--varies over time can help support targeted conservation science communication. However, the evolution of search volume over time and the mechanisms that drive peaks in searches are poorly understood. We conducted time series analyses on Google search data from 2004 to 2013 to investigate: (i) whether interests in selected conservation topics have declined and (ii) the effect of news reporting and academic publishing on search volume. Although trends were sensitive to the term used as benchmark, we did not find that public interest towards conservation topics such as climate change, ecosystem services, deforestation, orangutan, invasive species and habitat loss was declining. We found, however, a robust downward trend for endangered species and an upward trend for ecosystem services. The quantity of news articles was related to patterns in Google search volume, whereas the number of research articles was not a good predictor but lagged behind Google search volume, indicating the role of news in the transfer of conservation science to the public

Shark detection from aerial imagery using region-based CNN, a study

© Springer Nature Switzerland AG 2018. Shark attacks have been a very sensitive issue for Australians and many other countries. Thus, providing safety and security around beaches is very fundamental in the current climate. Safety for both human beings and underwater creatures (sharks, whales, etc.) in general is essential while people continue to visit and use the beaches heavily for recreation and sports. Hence, an efficient, automated and real-time monitoring approach on beaches for detecting various objects (e.g. human activities, large fish, sharks, whales, surfers, etc.) is necessary to avoid unexpected casualties and accidents. The use of technologies such as drones and machine learning techniques are promising directions in such challenging circumstances. This paper investigates the potential of Region-based Convolutional Neural Networks (R-CNN) for detecting various marine objects, and Sharks in particular. Three network architectures namely Zeiler and Fergus (ZF), Visual Geometry Group (VGG16), and VGG_M were considered for analysis and identifying their potential. A dataset consisting of 3957 video frames were used for experiments. VGG16 architecture with faster-R-CNN performed better than others, with an average precision of 0.904 for detecting Sharks

Supporting Literacy and Digital Literacy Development in Early Childhood Education Using Storytelling Activities

Educators have increasingly adopted formalized approaches for teaching literacy skills in early childhood education. In line with an emergent critique of this approach, the present study investigated the design and effectiveness of a literacy intervention that blended Gagné’s nine events of instructional design with storytelling. Three classes in a public preschool in Indonesia participated in an experimental study involving 45 children, aged 5–6 years. Across 3 weeks, one experimental condition received storytelling activities and a second experimental condition received digital storytelling activities. The control condition received regular literacy classroom activities. Before, and after, the 3-week storytelling intervention, measures of literacy and digital literacy skills were administered to all groups. In the digital storytelling condition, children’s literacy skills increased significantly compared to children in the control condition. Other exploratory data analyses suggested that both types of storytelling activities enhanced digital literacy skills. The findings need to be replicated with an extended series of storytelling activities that involve larger groups of participants