作者机构:
[Yu, Xin Guo; He, Bin; Zhuang, Jiao Jiao; Sun, Jia Yu; Dai, Zi Chun] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
通讯机构:
[Yu, Xin Guo] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
会议名称:
3rd International Conference on Mechatronics and Intelligent Robotics (ICMIR)
会议时间:
MAY 25-26, 2019
会议地点:
Kunming, PEOPLES R CHINA
会议主办单位:
[Yu, Xin Guo;Sun, Jia Yu;He, Bin;Zhuang, Jiao Jiao;Dai, Zi Chun] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Peoples R China.
摘要:
This paper designs and implements the automatic invigilation functions using the embedded technology. It proposes a framework for automatic invigilation, which conducts the invigilation functions of the entire examination process. In the examination preparation stage, the framework collects the registration details of examinees and verifies the details with the database through the remote server. During the examination ongoing stage, it keeps checking the consistence between each examinee and his examination materials by photographing the examinee and scanning the QR codes on examination papers, sketch papers, and answer sheets. In the examination ending stage, it checks the consistence of an examination bag and the materials being put into it by scanning and verifying the QR codes on them. The framework reduces the human workload by using automatic functions to replace the human work. The tests demonstrates that the framework can perform the designed functions. (C) 2020 The Authors. Published by Elsevier B.V.
摘要:
This article presents an algorithm for reading both single and multiple digital video clocks by using a context-aware pixel periodicity method and a deep learning technique. Reading digital video clocks in real time is a very challenging problem. The first challenge is the clock digit localization. The existing pixel periodicity is not applicable to localizing multiple second-digit places. This article proposes a context-aware pixel periodicity method to identify the second-pixels of each clock. The second challenge is clock-digit recognition. For this task, the algorithms based a domain knowledge and deep learning technique is proposed to recognize clock digits. The proposed algorithm is better than the existing best one in two aspects. The first one is that it can read not only single digit video clock but also multiple digit video clocks. The other is that it requires a short length of a video clip. The experimental results show that the proposed algorithm can achieve 100% of accuracy in both localization and recognition for both single and multiple clocks.
期刊:
Lecture Notes in Computer Science,2020年 11994: 194-205 ISSN:0302-9743
作者机构:
National Engineering Research Center for E-LearningCentral China Normal UniversityWuhanChina
摘要:
This paper presents an algorithm for reading digital video clocks by using two phases of connected deep networks to avoid the demerits of existing heuristic algorithms. The problem of reading digital video clocks can divided into two phases: locating the clock area and reading the clock digits. First, a phase of connected deep networks is a chain of neural networks to localize the clock area. Each of these neural networks takes use the properties of the working digital video clocks to work on one task. Its key step is to localize the place of second place by using the constancy and the periodicity of the pixels belong to second place. Second, the other phase of deep networks is a batch of custom digit recognizers that are designed based on deep networks and the properties of the working digital video clocks. The proposed method gets rid of the tedious heuristic procedure to find the accurate locations of all digits. Thus this paper forms the first algorithm that key tasks are taken by different neural networks. The experimental results show that the proposed algorithm can achieve a high accuracy in localizing and reading all the digits of clocks.
期刊:
Lecture Notes in Computer Science,2020年11994:172-182 ISSN:0302-9743
作者机构:
National Engineering Research Center for E-LearningCentral China Normal UniversityWuhanChina
摘要:
This paper designs a structure of 3D convolutional neural network to detect the global exam events in invigilation videos. Exam events in invigilation videos are defined according to the human activity performed at a certain phase in the entire exam process. Unlike general event detection which involves different scenes, global event detection focuses on differentiating different collective activities in the exam room ambiance. The challenges lie in the great intra-class variations within the same type of events due to various camera angles and different exam room ambiances, as well as inter-class similarities which are challengeable. This paper adopts the 3D convolutional neural network based on its ability in extracting spatio-temporal features and its effectiveness in detecting video events. Experiment results show the designed 3D convolutional neural network achieves an accuracy of its capability of 93.94% in detecting the global exam events, which demonstrates the effectiveness of our model.
作者机构:
National Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China
关键词:
Intelligent Classroom;Traditional Classroom;Teaching Design;Advantage Perspective;Cooperative Development
摘要:
The rise of the future classroom is accompanied by many obstacles, including limited funds, technical and theoretical immaturity. In order to better carry out the construction of educational informatization, this paper uses big data analysis methods to rank the importance of each link and equipment in teaching activities, namely the perspective advantages. Then we propose a new model, which focuses on the advantage perspective input and construction, trying to construct the cooperative development of future classrooms and traditional classrooms on the basis of existing classrooms and maximizes the role of the perspective of superiority, so as to improve the quality of teaching, and discusses this new model in this paper. The feasibility and necessity of the idea.
摘要:
Automatically understanding natural language problems is a long-standing challenging research problem in automatic solving. This paper models the understanding of geometry problems as a problem of relation extraction, instead of as the problem of semantic understanding of natural language. Then it further proposes a supervised machine learning method to extract geometric relations, targeting to produce a group of relations to represent the given geometry problem. This method identifies the actual geometric relations from the relation candidates using a classifier trained from the labelled examples. The formalized geometric relations can then be transformed into the target system-native representations for manipulation in various tasks. Experiments conducted on the test problem dataset show that the proposed method can extract geometric relations at high F-1 scores. The comparisons also demonstrate that the proposed method can achieve good performance against the baseline methods. Integrating the automatic understanding method with different geometry systems will greatly enhance the efficiency and intelligence in geometry tutoring.
作者机构:
[Zheng, Lina] Cent China Normal Univ, Cent China Normal Univ Wollongong Joint Inst, Wuhan, Hubei, Peoples R China.;[Yu, Xinguo; Zhang, Ting] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.
会议名称:
15th IAPR International Conference on Document Analysis and Recognition (ICDAR) / 2nd Workshop of Machine Learning (WML)
会议时间:
SEP 21-22, 2019
会议地点:
Sydney, AUSTRALIA
会议主办单位:
[Zheng, Lina] Cent China Normal Univ, Cent China Normal Univ Wollongong Joint Inst, Wuhan, Hubei, Peoples R China.^[Zhang, Ting;Yu, Xinguo] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.
会议论文集名称:
Proceedings of the International Conference on Document Analysis and Recognition
关键词:
Handwritten symbol recognition;Chemical organic ring structure symbols;convolutional neural networks
摘要:
Many types of data exhibit characteristic of rotational symmetry. Chemical Organic Ring Structure(ORS) Symbol is such a case. In this paper, we focus on offline handwritten chemical ORS Symbols recognition using convolutional neural networks(CNNs), from application point of view, in order to relax the inconvenience and ineffectiveness of the traditional click-and-drag style of interaction when input chemical notations into electronic devices; from scientific point of view, to explore the capacity of rotation invariance of CNNs using data augmentation. We propose a VGGNet-based classifier for offline handwritten chemical ORS Symbols. To evaluate it, a new dataset of 3600 samples are collected of which 90% is for training while 10% is for test. The recognition accuracy is 84.3% with VGGNet-16 and 92.4% with VGGNet-19.
期刊:
Lecture Notes in Computer Science,2019年11854:116-127 ISSN:0302-9743
通讯作者:
Song, Wu
作者机构:
[Yu, Xinguo; Song, Wu] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
通讯机构:
[Song, Wu] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
会议名称:
9th Pacific-Rim Symposium on Image and Video Technology (PSIVT)
会议时间:
NOV 18-22, 2019
会议地点:
Sydney, AUSTRALIA
会议主办单位:
[Song, Wu;Yu, Xinguo] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Peoples R China.
会议论文集名称:
Lecture Notes in Computer Science
关键词:
Exam invigilation video;Video scene classification;Convolutional neural network
摘要:
This paper presents a double channel 3D convolution neural network to classify the exam scenes of invigilation videos. The first channel is based on the C3D convolution neural network, which is the status-of-arts method of the video scene classification. The structure of this channel is redesigned for classifying the exam-room scenes of invigilation videos. Another channel is based on the two-stream convolution neural network using the optical flow graph sequence as its input. This channel uses the data from the optical flow of video to improve the performance of the video scene classification. The formed double channel 3D convolution neural network has appropriate size of convolution kernel and pooling kernel design. Experiments show that the proposed neural network can classify the exam-room scenes of invigilation videos faster and more accurately than the existing methods.
作者:
Yu, Xinguo(余新国);Wang, Mingshu;Gan, Wenbin;He, Bin*;Ye, Nan
期刊:
International Journal of Pattern Recognition and Artificial Intelligence,2019年33(7):1940005:1-1940005:21 ISSN:0218-0014
通讯作者:
He, Bin
作者机构:
[Yu, Xinguo; Gan, Wenbin; He, Bin; Wang, Mingshu] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.;[Ye, Nan] Univ Queensland, Brisbane, Qld 4072, Australia.
通讯机构:
[He, Bin] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.
关键词:
Automatic solver;relation extraction;syntax-semantic model;arithmetic word problems;plane geometry theorems
摘要:
This paper presents a framework for solving math problems stated in a natural language (NL) and applies the framework to develop algorithms for solving explicit arithmetic word problems and proving plane geometry theorems. We focus on problem understanding, that is, the transformation of a NL description of a math problem to a formal representation. We view this as a relation extraction problem, and adopt a greedy algorithm to extract the mathematical relations using a syntax-semantics model, which is a set of patterns describing how a syntactic pattern is mapped to its formal semantics. Our method yields a human readable solution that shows how the mathematical relations are extracted one at a time. We apply our framework to solve arithmetic word problems and prove plane geometry theorems. For arithmetic word problems, the extracted relations are transformed into a system of equations, and the equations are then solved to produce the solution. For plane geometry theorems, these extracted relations are input to an inference system to generate the proof. We evaluate our approach on a set of arithmetic word problems stated in Chinese, and two sets of plane geometry theorems stated in Chinese and English. Our algorithms achieve high accuracies on these datasets and they also show some desirable properties such as brevity of algorithm description and legibility of algorithm actions.
期刊:
Lecture Notes in Computer Science,2018年10749:314-325 ISSN:0302-9743
通讯作者:
He, Bin
作者机构:
[Yu, Xinguo; He, Bin; Jian, Pengpeng; Xia, Meng] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.;[Zhao, Gang] Cent China Normal Univ, Sch Educ Informat Technol, Wuhan, Hubei, Peoples R China.
通讯机构:
[He, Bin] C;Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.
会议名称:
8th Pacific-Rim Symposium on Image and Video Technology (PSIVT)
会议时间:
NOV 20-24, 2017
会议地点:
Wuhan, PEOPLES R CHINA
会议主办单位:
[Yu, Xinguo;Jian, Pengpeng;He, Bin;Xia, Meng] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan, Hubei, Peoples R China.^[Zhao, Gang] Cent China Normal Univ, Sch Educ Informat Technol, Wuhan, Hubei, Peoples R China.
摘要:
This paper presents an algorithm for understanding problems from circuit schematics in exercise problems in physics at secondary school. This paper models the problem understanding as a problem of extracting a set of relations that can be used to solve problems with enough information. The challenges lie in not only analyzing the circuit schematics but also extracting the proper relations for a given exercise problem. To face these challenges a novel approach is proposed to detect circuit nodes with their current flows to extract the current equations for nodes. And the other novel approach is proposed to extract voltage equations of independent loops. The proposed approach was tested with a dataset collected from the text books and the exam papers for the students at secondary schools. Experimental results show that the effect of recognition and analysis we designed delivers promising result, and our approach can be adapted to more complex electrical circuit analysis.
摘要:
Extracting algebraic relations from a given circuit image is still a challenge task due to the complex topology of considered circuit. This paper presents an approach for extracting algebraic relations from circuit images through producing a set of atomic topologies from the complex topology of a given circuit. In which, algebraic relations, in form of a set of equations involving voltage, current and resistance relations from atomic topologies that is obtained by an iteratively operation of transforming a complex series/parallel connection into a series of atomic connection topology breaking down and shrinking. The extracted algebraic relations can be used to solve the exercise problem described by the circuit. Experimental results on 20 exercise problems show that the proposed algorithm can obtain a complete set of algebraic relations that can be used to solve the given problem. Further experiments conducted on a dataset of 200 scanned circuit images from the text books and exam papers demonstrate the proposed algorithm is the robustness and effectiveness.