The development of a dynamic game model for planning a double and duplication of supervision based on the indeterminate appointment probability and order of supervisors on the grounds of the current causes of planning and supervision collusion in real estate land is presented in this paper and the question how to separate the planning from supervision availably is analyzed. The dynamic game is ...

This document outlines guidelines for supervision of students in health service psychology education and training programs. The goal was to capture optimal performance expectations for psychologists who supervise. It is based on the premises that supervisors (a) strive to achieve competence in the provision of supervision and (b) employ a competency-based, meta-theoretical approach to the super...

There must be few categories of childhood injury for which parental supervision, or the lack of it, has not been invoked as a contributory factor. Since 1990 alone, lack ofparental supervision has been proposed as a cause of road traffic injury,' electrical burn injury,2 and infant walker related injury.3 In most cases the identification of the aetiological role of parental supervision is intui...

AbstractThe success of deep learning is usually accompanied by the growth in neural network depth. However, traditional training method only supervises at its last layer and propagates supervision layer-by-layer, which leads to hardship optimizing intermediate layers. Recently, has been proposed add auxiliary classifiers layers networks. By these with supervised task loss, can be applied shallo...

Educational supervision is regular supervision taking place in the context of a recognized training programme in order to determine learning needs and review progress. Patel’s article [1] effectively evaluates postgraduate educational supervision in one UK trust. This was a mixed methods study in which educational supervisors and trainees working within a large UK trust were surveyed online abo...

In this paper, we propose a method for training neural networks when we have a large set of data with weak labels and a small amount of data with true labels. In our proposed model, we train two neural networks: a target network, the learner and a confidence network, the meta-learner. The target network is optimized to perform a given task and is trained using a large set of unlabeled data that...