THE METHOD OF THEATRICALIZATION IN TEACHING YOUNGER STUDENTS TO SOLVE COMPOSITE TASKS

The method of theatricalization is described, which is used in solving textual mathematical problems in mathematics lessons in elementary grades at all stages of working with a composite problem. This improves students’ understanding of the structure of the task, its semantic parts, increases the likelihood that the task will be solved correctly, and there will be a versatile development of students.

Comparing part-based representations of deep convolutional neural networks with those of human vision through representational similarity analysis

Multiple theories of human object recognition argue for the importance of semantic parts in the formation of intermediate representations. However, the role of semantic parts in Deep Convolutional Neural Networks (DCNN), which encapsulate the most recent and successful computer vision models, is poorly examined. We extract representations of DCNNs corresponding to differential performance with stimuli in which different parts of the same exemplar are deleted, and then compare these representations with those of human observers obtained in a behavioral experiment, using representational similarity analysis (RSA). We find that DCNN representations correlate strongly with those of observers, while acknowledging that these DCNN representations may not be part-based given an equally high correlation between DCNN output and part size. Additionally, the exemplars incorrectly identified by DCNNs tend to have less “human-like” representations, which demonstrates RSA as a potential novel method for interpreting error in intermediate processes of recognition of DCNNs.

The role of semantics in the perceptual organization of shape

Establishing correspondence between objects is fundamental for object constancy, similarity perception and identifying transformations. Previous studies measured point-to-point correspondence between objects before and after rigid and non-rigid shape transformations. However, we can also identify ‘similar parts’ on extremely different objects, such as butterflies and owls or lizards and whales. We measured point-to-point correspondence between such object pairs. In each trial, a dot was placed on the contour of one object, and participants had to place a dot on ‘the corresponding location’ of the other object. Responses show correspondence is established based on similarities between semantic parts (such as head, wings, or legs). We then measured correspondence between ambiguous objects with different labels (e.g., between ‘duck’ and ‘rabbit’ interpretations of the classic ambiguous figure). Despite identical geometries, correspondences were different across the interpretations, based on semantics (e.g., matching ‘Head’ to ‘Head’, ‘Tail’ to ‘Tail’). We present a zero-parameter model based on labeled semantic part data (obtained from a different group of participants) that well explains our data and outperforms an alternative model based on contour curvature. This demonstrates how we establish correspondence between very different objects by evaluating similarity between semantic parts, combining perceptual organization and cognitive processes.

Cultural code on the example of servants in the Russian Empire in the post-reform period

The article is devoted to national, religious, mental and cultural particularities in the field of labor recruitment of servants. Conditionally, the article can be divided into two semantic parts: 1) manifestation of national features in the servant’s work; 2) the impact of reforms and laws on the relationship between the employer and employee. As the examples the authors chose Russians, Estonians and Finns (Chukhonts), Latvians, Tatars, Jews, Poles and the peoples of the Caucasus. The task was to show the characteristics and colorful features of the servants of these representatives. After all, the specificity of the work of some national groups was unstated, and its originality had only traditional character. At the same time, Jews had their own rules of interaction in the field of servants’ hiring. The examples given in this article clearly illustrate the importance of taking into account the national, religious and cultural practices of peoples for the implementation of mutually beneficial cooperation between employers and workers.

Automated Essay Grading System using NLP Techniques

The purpose of the study is to develop an automated essay grading system (AES) which can grade students essays based on various factors. Our proposed system performs grading of essays based on two features. Simple features consist of finding syntactic errors such as spelling mistakes, grammatical errors, punctuations and sentence proportions. Complex features consist of finding semantic errors through discourse analysis, thematic analysis and detection of undesirable style of writing. Many existing AES systems fail to consider the semantic parts of the essay which is addressed in this study. Calculation of score would be done based on what is specified in rubrics. The proposed system is evaluated using datasets from kaggle. The accuracy of model and obtained results show an agreement with teachers’ grading. This gives us an indication that the model can be deployed for assessment of students’ essay, thereby leading to reduction in time, efforts and cost for evaluating an essay

(DE)CONSTRUCTING LETTERS TO LEADERS: ANALYSIS OF STUDENTS’ APPEALS TO THE AUTHORITIES IN THE 1920s

The article examines students’ appeals to the authorities as a specific type of epistolary narrative discourse. The author focuses on the creation of an epistolary text as a part of socio-discursive communication practice. The aim of the research is to identify the narrative structure of student letters and the ways of their argumentation. The study is (Де)конструируя письма … 173 complicated by the fact that it presents a complex of small disparate narratives that are not related to each other. A structural approach is used for the narrative analysis of student letter texts. The letters of students to the authorities are compositionally divided into two groups – simple, consisting of one or two semantic parts, and complex, consisting of three, four or more semantic parts. The studied letters help identify both general patterns of the narrative structure of appeals to the authorities and particular cases of plotting. The compositional structure of the students’ narrative was determined by the problem of treatment, the genre of writing, social origin, gender, and level of "language personality" of the authors. The texts of selected letters are analyzed separately according to the structural parts of the composition: the initial part, the main part, and the final part. The applicants’ arguments were both rational and emotional, including threats to commit suicide in case of default request. The narratives are presented as “small” stories of “small” people who constructed their stories in accordance with the social norms of the period. The story of each author of the letter was individual, but the experiences of difficulties were collective. Students could not share their stories with each other, which, along with generational habitus, probably determined semantic similarities in the epistolary narrative.

Parts4Feature: Learning 3D Global Features from Generally Semantic Parts in Multiple Views

Deep learning has achieved remarkable results in 3D shape analysis by learning global shape features from the pixel-level over multiple views. Previous methods, however, compute low-level features for entire views without considering part-level information. In contrast, we propose a deep neural network, called Parts4Feature, to learn 3D global features from part-level information in multiple views. We introduce a novel definition of generally semantic parts, which Parts4Feature learns to detect in multiple views from different 3D shape segmentation benchmarks. A key idea of our architecture is that it transfers the ability to detect semantically meaningful parts in multiple views to learn 3D global features. Parts4Feature achieves this by combining a local part detection branch and a global feature learning branch with a shared region proposal module. The global feature learning branch aggregates the detected parts in terms of learned part patterns with a novel multi-attention mechanism, while the region proposal module enables locally and globally discriminative information to be promoted by each other. We demonstrate that Parts4Feature outperforms the state-of-the-art under three large-scale 3D shape benchmarks.