FUNCTIONS OF FORECASTING AND PROJECTING IN THE SYSTEM OF FOREIGN STUDENTS’ SOCIAL AND PEDAGOGICAL SUPPORT

The article presents the theoretical characteristics of the strategic functions of the system of foreign student’s social and pedagogical support, namely, the prognostic and the project functions. The features of forecasting during the adaptation process are considered, which are in the consideration of the entire spectrum of probable actions and potential problem situations. This will allow to find a solution during the preparatory stage, to form the appropriate skills, to overcome them and carry out the practical activity more effectively in future. Thus, the prognostic function plays an important role in the system of foreign students’ socio-pedagogical support, allowing them to make prognostic activities both the potential events that will occur when performing an action and the consequences of choosing a particular approach or cultural pattern when communicating with a representative of another country and culture. Adaptive activity is not only about the formation of the necessary skills to be able to interact with representatives of other culture and to conduct an effective academic process. But this kind of activity can be carried out only as a result of achieving the goals set by the projective function. The features of which are: to formulate the purpose of the whole adaptation process, that will move to the next stages of socio-pedagogical support projecting; to have a goal that allows you to find and approve the tools that must be used in the adaptation process; to build a sequence of actions that will help to achieve the goal, that provides the selection of stages; to take into account the importance of evaluating the results that will be formed during the adaptation activity to determine the level of development of knowledge and skills of a foreign student, to determine the need for additional training or retraining. All these components allow us to talk about the necessity to build a model by the foreign student and consultant, which will demonstrate the sequence of actions aimed at projecting adaptation activities. This kind of model will allow to formulate the stages of the preparatory process, including the development of an adaptation program.

Zinc ore supplier evaluation and recommendation method based on nonlinear adaptive online transfer learning

<p style='text-indent:20px;'>Purchasing decisions determine the purchasing cost, which is the largest section of the production cost of zinc smelting enterprise(ZSE). An excellent supplier recommendation is significant for ZSE to reduce the cost. However, during the supplier recommendation process, the nonlinear demand feature of purchasing department varies with the production environment, and there are wrong samples that can affect the supplier recommendation effect. To handle these problems, the recommendation strategy based on a multiple-layer perceptron adaptive online transfer learning algorithm(AOTLMLP) are proposed. In this method, the original prediction function is modified based on MLP nonlinear projective function and adaptive loss function, which enables the AOTLMLP algorithm to tackle the nonlinear classification problems and efficiently follow the demand change of purchasing department, thereby improving the result of the recommendation. The performance of the AOTLMO algorithm is evaluated through a common dataset and a purchasing dataset from a zinc smelter that generated by a supplier evaluation model. It can be assumed that AOTLMLP can ignore the influence of wrong samples and provide an effective recommendation confronting the characteristic of zinc ore purchasing.</p>

Extract the Relational Information of Static Features and Motion Features for Human Activities Recognition in Videos

Both static features and motion features have shown promising performance in human activities recognition task. However, the information included in these features is insufficient for complex human activities. In this paper, we propose extracting relational information of static features and motion features for human activities recognition. The videos are represented by a classical Bag-of-Word (BoW) model which is useful in many works. To get a compact and discriminative codebook with small dimension, we employ the divisive algorithm based on KL-divergence to reconstruct the codebook. After that, to further capture strong relational information, we construct a bipartite graph to model the relationship between words of different feature set. Then we use ak-way partition to create a new codebook in which similar words are getting together. With this new codebook, videos can be represented by a new BoW vector with strong relational information. Moreover, we propose a method to compute new clusters from the divisive algorithm’s projective function. We test our work on the several datasets and obtain very promising results.