Low-effort place recognition with WiFi fingerprints using deep learning

Using WiFi signals for indoor localization is the main localization modality of the existing personal indoor localization systems operating on mobile devices. WiFi fingerprinting is also used for mobile robots, as WiFi signals are usually available indoors and can provide rough initial position estimate or can be used together with other positioning systems. Currently, the best solutions rely on filtering, manual data analysis, and time-consuming parameter tuning to achieve reliable and accurate localization. In this work, we propose to use deep neural networks to significantly lower the work-force burden of the localization system design, while still achieving satisfactory results. Assuming the state-of-the-art hierarchical approach, we employ the DNN system for building/floor classification. We show that stacked autoencoders allow to efficiently reduce the feature space in order to achieve robust and precise classification. The proposed architecture is verified on the publicly available UJIIndoorLoc dataset and the results are compared with other solutions

Pre-service Teacher Beliefs on the Antecedents to Bullying: A Concept Mapping Study

In this study, researchers gathered Canadian pre-service teachers’ beliefs on the antecedents to bullying. Concept mapping (Kane & Trochim, 2007) was used to analyze the data. This study’s findings identified pre-service teachers to have accurate beliefs, inaccurate beliefs, and a lack of knowledge about the antecedents to bullying. Concept maps and accompanying factor-rating tables indicate that participants believe antecedents to bullying include family factors, abuse, instability and socio-economic factors, school and academic factors, interpersonal factors, and personal factors. Results may inform pre-service teachers’ knowledge, and indicate what information pre-service teachers need to be taught

Are estuaries traps for anthropogenic nutrients? Evidence from estuarine mesocosms

A series of estuarine mesocosms is described, where nutrient budgets were used to determine rates of nitrogen and phosphorus trapping and export as a function of nutrient input level, season, and presence or absence of sediments. Regardless of treatment or season these experimental systems exported most of the N and P that they received. Control systems with sediments retained none of the inflowing N and P during summer, and 5 % of N and 25 % of P inputs during winter. Eutrophied systems with sediments initially retained 30 % of added N and P due to increases in water column and sediment nutrient standing stocks in response to daily inorganic nutrient additions; however, after 6 mo of daily nutrient loading, these treatments retained only 5 to 15 % of nutrients added. Results of this study suggest that well-mixed estuarine systems may export to offshore waters most of the nitrogen and phosphorus that they receive. For the small percentage of nutrients that were retained, there was more storage during winter than summer, more storage in treatments without sediments, and more retention of P than N. Nitrogen losses through sediment denitrification accounted for 10 to 20 % of the N input to controls, and less than 10% of the N input to eutrophied treatments. The addition of nutrients to the eutrophied treatments resulted in increases in the N and P content of surface sediments, and the rapid deposition of an N and P-rich detrital layer on the bottom of the treatments without sediments