درخت حوزه‌های تخصصی

The aim of this research was to investigate the effectiveness of mental imagery on the English language comprehension of sixth grade students. The method is semi-experimental and its plan involves pretest, post-test, and follow-up. Using a multi-stage random sampling method, 32 students were selected from schools in the district (2) and assigned to either the experimental or evidence group - each group consisted of 16 students. All the students within both groups were given a pretest which consisted of 20 multiple choice questions (with 4 possible answers each). There was no significant difference between the mean scores gained by the students of both groups on the pretest. The experimental group received the mental imagery teaching program and how to form pictures in the mind-e.g, how to visualize-before reading, while reading, and after reading a short story, for example, by being requested to draw pictures of the characters, scenes, or setting in the story as they perceived them. The evidence group received no instruction. To measure the effectiveness of the treatment program for each experimental condition, a research-made reading comprehension test was applied. There were  significant differences between the experimental and evidence groups, when data analysis was performed by ANOVA, using repeated measures. The findings showed that the mental imagery teaching program can improve the English language comprehension and this study suggests that the English teacher may improve her teaching strategy by optimizing the use of a variation technique in school. The teacher should be creative while delivering materials in the class.

Detecting occluded faces is a non-trivial problem for face detection in computer vision. This challenge becomes more difficult when the occlusion covers majority of the face. Despite the high performance of current state-of-the-art face detection algorithms, the detection of occluded and covered faces is an unsolved problem and is still worthy of study. In this paper, a deep-learning-face-detection model Niqab-Face-Detector is proposed along with context-based labeling technique for detecting unconstrained veiled faces such as faces covered with niqab. An experimental test was conducted to evaluate the performances of the proposed model using the Niqab-Face dataset. The experiment showed encouraging results and improved accuracy compared with state-of-the-art face detection algorithms