Constructing practice on evolutionary knowledge in ECEC

peer reviewedThis article provides a discussion on the importance of knowledge in early childhood education and also the role of knowledge dissemination process in the field. From the question of giving more freedom of movement, knowledge about this topic used in a Belgian curriculum, the article illustrates the importance of considering practice not as an application of theory, but as a co-construct and complex process that combines it with other resources in context. It illustrates the importance of going further than the production of knowledge to create appropriate practice in service

Spatial Audio and Individualized HRTFs using a Convolutional Neural Network (CNN)

Spatial audio and 3-Dimensional sound rendering techniques play a pivotal and essential role in immersive audio experiences. Head-Related Transfer Functions (HRTFs) are acoustic filters which represent how sound interacts with an individual's unique head and ears anatomy. The use of HRTFs compliant to the subjects anatomical traits is crucial to ensure a personalized and unique spatial experience. This work proposes the implementation of an HRTF individualization method based on anthropometric features automatically extracted from ear images using a Convolutional Neural Network (CNN). Firstly, a CNN is implemented and tested to assess the performance of machine learning on positioning landmarks on ear images. The I-BUG dataset, containing ear images with corresponding 55 landmarks, was used to train and test the neural network. Subsequently, 12 relevant landmarks were selected to correspond to 7 specific anthropometric measurements established by the HUTUBS database. These landmarks serve as a reference for distance computation in pixels in order to retrieve the anthropometric measurements from the ear images. Once the 7 distances in pixels are extracted from the ear image, they are converted in centimetres using conversion factors, a best match method vector is implemented computing the Euclidean distance for each set in a database of 116 ears with their corresponding 7 anthropometric measurements provided by the HUTUBS database. The closest match of anthropometry can be identified and the corresponding set of HRTFs can be obtained for personnalized use. The method is evaluated in its validity instead of the accuracy of the results. The conceptual scope of each stage has been verified and substantiated to function correctly. The various steps and the available elements in the process are reviewed and challenged to define a greater algorithm entity designed for the desired task

A methodology to estimate impacts of domestic policies on deforestation: Compensated Successful Efforts for “avoided deforestation” (REDD)

Climate change mitigation would benefit from Reduced Emissions from Deforestation and Degradation (REDD) in developing countries. The REDD mechanism is in charge of distilling the right incentives for fostering forest conservation with appropriate compensation of foregone revenues, which in turn is related to avoided deforestation (how many hectares of forests are saved). Although any prediction of deforestation rates (i.e. business-as-usual scenarios) is challenging, and any negotiated target is subject to political influence, these two ways have been prioritirized so far. In other words, proposals have focused on a baseline (or cap)-and-trade approach, which relevance is questionable because resulting financial compensations are subject to unfairness if estimations of avoided deforestation are not reliable. Rather than considering overall deforestation (predicted and observed), we argue that a REDD mechanism would gain from linking compensations to real efforts that developing countries implement for slowing deforestation rates. This would provide more efficient incentives to design and enforce suitable policies and measures. The methodology we present to measure these efforts (labeled Compensated Successful Efforts) is based on the rationale that overall deforestation is due partly to structural factors, and partly to domestic policies and measures. This typology differs from others presented in the literature such as proximate / underlying causes, or economic / institutional factors. Using an econometric model, our approach estimates efforts that are (i) independent of structural factors (economic development, population, initial forest area, agricultural export prices), (ii) estimated ex post at the end of the crediting period, and (iii) relative to other countries. In order to illustrate the methodology we apply the model to a panel of 48 countries (Asia, Latin America, Africa) and four periods between 1970 and 2005. We conclude on the feasibility to estimate avoided deforestation using the Compensated Successful Efforts approach. In addition to being conservative from an environmental perspective, this approach guarantees fairness by accounting for dramatic changes during the commitment period.avoided deforestation, REDD, climate change, baseline scenario, Forest

A methodology to estimate impacts of domestic policies on deforestation: Compensated Successful Efforts for “avoided deforestation” (REDD)

Climate change mitigation would benefit from Reduced Emissions from Deforestation and Degradation (REDD) in developing countries. The REDD mechanism is in charge of distilling the right incentives for fostering forest conservation with appropriate compensation of foregone revenues, which in turn is related to avoided deforestation (how many hectares of forests are saved). Although any prediction of deforestation rates (i.e. business-as-usual scenarios) is challenging, and any negotiated target is subject to political influence, these two ways have been prioritirized so far. In other words, proposals have focused on a baseline (or cap)-and-trade approach, which relevance is questionable because resulting financial compensations are subject to unfairness if estimations of avoided deforestation are not reliable. Rather than considering overall deforestation (predicted and observed), we argue that a REDD mechanism would gain from linking compensations to real efforts that developing countries implement for slowing deforestation rates. This would provide more efficient incentives to design and enforce suitable policies and measures. The methodology we present to measure these efforts (labeled Compensated Successful Efforts) is based on the rationale that overall deforestation is due partly to structural factors, and partly to domestic policies and measures. This typology differs from others presented in the literature such as proximate / underlying causes, or economic / institutional factors. Using an econometric model, our approach estimates efforts that are (i) independent of structural factors (economic development, population, initial forest area, agricultural export prices), (ii) estimated ex post at the end of the crediting period, and (iii) relative to other countries. In order to illustrate the methodology we apply the model to a panel of 48 countries (Asia, Latin America, Africa) and four periods between 1970 and 2005. We conclude on the feasibility to estimate avoided deforestation using the Compensated Successful Efforts approach. In addition to being conservative from an environmental perspective, this approach guarantees fairness by accounting for dramatic changes during the commitment period.avoided deforestation;REDD;climate change;baseline scenario;Forest

A response to the commentary on “Compensated Successful Efforts”

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