A Convolutional Neural Network (CNN) Based Approach for the Recognition and Evaluation of Classroom Teaching Behavior

To improve classroom teaching behavior recognition and evaluation accuracy, this paper proposes a new model based on deep learning. First, we obtain the classroom teaching behavior characteristic data through the SVM’s linear separable initial and determine the relationship of the characteristic sample data in the hyperplane. Then, we obtain the heterogeneous support vector of the online learning behavior characteristic sample data in the SVM’s hyperplane and complete the extraction of data with the help of convolutional neural networks. We then use a decision matrix to analyze the hierarchical process, determine the weight of classroom teaching behavior indicators, verify their consistency, and complete the evaluation by calculating the membership of evaluation factors. The experimental results show that the identification and evaluation method of classroom teaching behavior in this paper can effectively improve the identification accuracy of the classroom teaching behavior.

Polynomial Identification of $$\omega $$-Automata

We study identification in the limit using polynomial time and data for models of $$\omega $$-automata. On the negative side we show that non-deterministic $$\omega $$-automata (of types Büchi, coBüchi, Parity or Muller) can not be polynomially learned in the limit. On the positive side we show that the $$\omega $$-language classes $$\mathbb {IB}$$, $$\mathbb {IC}$$, $$\mathbb {IP}$$, and $$\mathbb {IM}$$ that are defined by deterministic Büchi, coBüchi, parity, and Muller acceptors that are isomorphic to their right-congruence automata (that is, the right congruences of languages in these classes are fully informative) are identifiable in the limit using polynomial time and data. We further show that for these classes a characteristic sample can be constructed in polynomial time.

EXACT GRAYSCALE IMAGE RECONSTRUCTION FROM PROJECTIONS

This paper proposes a new method for the exact reconstruction of gray-scale images from projections. The image projections construct an accumulator array, which is used afterwards to reconstruct the original grayscale image by applying the proposed decomposition algorithm. The proposed method determines the number of projections and the number of rays in each projection that are required in order to achieve the reconstruction. These two parameters also define the dimensions of the accumulator array. Using an accumulator array with proper dimensions ensures that there is always a unique characteristic sample for each pixel, which is used during the reconstruction process to extract the pixel's grayscale value. During the reconstruction phase, the sinusoidal contribution of each pixel is removed from the accumulator array. At the end of the decomposition process the accumulator array becomes empty and the original image is exactly reconstructed. The experimental results confirm the robustness and efficiency of the proposed method.

The Self-Referent Attitudes of Neurotic and Inadequate Personalities

Recent improvements in the treatment of psychotic patients (Ministry of Health, 1960; Registrar General 1957 and 1958) have, by contrast, drawn attention to the non-psychotic population of mental hospitals. This paper deals with this latter group. Their self-referent attitudes have been chosen for study as there is a persistent tradition in psychology which asserts their importance. This can be traced through McDougall (1923), the writings of Jung on Individuation (1939), Hilgard (1954), Sherif and Cantril (1947), Symonds (1951) and Carl Rogers (1951). Associated with this tradition is the concept that an important change in the attitude of a person to himself may lead to important changes in behaviour. This, if true, holds out hopes of therapeutic advance. This paper therefore studies the self-referent attitudes found in a characteristic sample population of non-psychotic patients in mental hospitals.