Sketch retrieval based on qualitative shape similarity matching: Towards a tool for teaching geometry to children

The knowledge on geometry are of great importance for the understanding of reality. Spatial notion and geometrical concepts, graphical skills and habits are an important part of the study of geometrical knowledge in elementary school as propedeutics of the system course on geometry in the next school levels. In the recent years, education in Bulgaria follows the trends imposed by the European Union related to the acquiring of some basic key competencies. They promote to the improvement of knowledge, skills, abilities and attitudes of students and their more successful social development. From the school year 2016/2017, the education in the Bulgarian schools is in accordance with the new Law on pre-school and school education. Under this law, students are teached under new curriculum and teaching kits for the corresponding class. According to the new curriculum, the general education of the students of I-IV grade, covers basic groups of key competencies. Here, much more attention is paid also to the results of international researches on the students’ performance in mathematics. Primary school students participate in international competitions and Olympiads, which lead to the need of working on more mathematical problems with geometric content of the relevant specific types. This allows to study and use author’s various mathematical problems for teaching geometry. Their purpose is to contribute to the expansion of space notions of the students, to develop their thinking and imagination. This article is dedicated to the application of author’s various mathematical problems and exercises for teaching students from the third grade through which the geometrical knowledge and skills of the students develop and build. The solving of the mathematical problems is realized on a rich visual-practical basis, providing conditions for inclusion of the students in various activities. The proposed various mathematical problems are developed by themes including fully geometric problems and exercises for teaching mathematics to third grade students. Teaching by using the various mathematical problems was held with 149 students from third grade, from five schools - three in Sofia and two in smaller towns, in the school year 2016/2017.

Download Full-text

Mixing realities for sketch retrieval in Virtual Reality

The 17th International Conference on Virtual-Reality Continuum and its Applications in Industry ◽

10.1145/3359997.3365751 ◽

2019 ◽

Cited By ~ 1

Author(s):

Daniele Giunchi ◽

Stuart James ◽

Donald Degraen ◽

Anthony Steed

Keyword(s):

Virtual Reality ◽

Sketch Retrieval

Download Full-text

A Densely Connected GRU Neural Network Based on Coattention Mechanism for Chinese Rice-Related Question Similarity Matching

Agronomy ◽

10.3390/agronomy11071307 ◽

2021 ◽

Vol 11 (7) ◽

pp. 1307

Author(s):

Haoriqin Wang ◽

Huaji Zhu ◽

Huarui Wu ◽

Xiaomin Wang ◽

Xiao Han ◽

...

Keyword(s):

Related Question ◽

Similarity Matching ◽

Frequency Method ◽

Text Data ◽

Information Platform ◽

Document Frequency ◽

Proposed Model ◽

Related Text ◽

Text Features ◽

Matching Models

In the question-and-answer (Q&A) communities of the “China Agricultural Technology Extension Information Platform”, thousands of rice-related Chinese questions are newly added every day. The rapid detection of the same semantic question is the key to the success of a rice-related intelligent Q&A system. To allow the fast and automatic detection of the same semantic rice-related questions, we propose a new method based on the Coattention-DenseGRU (Gated Recurrent Unit). According to the rice-related question characteristics, we applied word2vec with the TF-IDF (Term Frequency–Inverse Document Frequency) method to process and analyze the text data and compare it with the Word2vec, GloVe, and TF-IDF methods. Combined with the agricultural word segmentation dictionary, we applied Word2vec with the TF-IDF method, effectively solving the problem of high dimension and sparse data in the rice-related text. Each network layer employed the connection information of features and all previous recursive layers’ hidden features. To alleviate the problem of feature vector size increasing due to dense splicing, an autoencoder was used after dense concatenation. The experimental results show that rice-related question similarity matching based on Coattention-DenseGRU can improve the utilization of text features, reduce the loss of features, and achieve fast and accurate similarity matching of the rice-related question dataset. The precision and F1 values of the proposed model were 96.3% and 96.9%, respectively. Compared with seven other kinds of question similarity matching models, we present a new state-of-the-art method with our rice-related question dataset.

Download Full-text