scholarly journals English Grammar Error Detection Using Recurrent Neural Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Zhenhui He
Keyword(s):  
Error Detection ◽  
Detection Algorithm ◽  
Sequence Information ◽  
Position Information ◽  
English Grammar ◽  
Text Information ◽  
Automatic Reading ◽  
Encoding Method ◽  
Reading System ◽  
Low Dimensional

Automatic marking of English compositions is a rapidly developing field in recent years. It has gradually replaced teachers’ manual reading and become an important tool to relieve the teaching burden. The existing literature shows that the error of verb consistency and the error of verb tense are the two types of grammatical errors with the highest error rate in English composition. Hence, the detection results of verb errors can reflect the practicability and effectiveness of an automatic reading system. This paper proposes an English verb’s grammar error detection algorithm based on the cyclic neural network. Since LSTM can effectively retain the valid information in the context during training, this paper decided to use LSTM to model the labeled training corpus. At the same time, how to convert the text information in English compositions into numerical values for subsequent calculation is also an important step in automatic reading. Most mainstream tools use the word bag model, i.e., each word is encoded according to the order of each word in the dictionary. Although this encoding method is simple and easy to use, it not only causes the vector to lose the sequence information of the text but also is prone to dimensional disaster. Therefore, word embedding model is adopted in this paper to encode the text, and the text information is sequentially mapped to a low-dimensional vector space. In this way, the position information of the text is not lost, and the dimensional disaster is avoided. The proposed work collects some corpus samples and compares the proposed algorithm with Jouku and Bingguo. The verification results show the superiority of the proposed algorithm in verb error detection.

scholarly journals Deep Learning-Based Congestion Detection at Urban Intersections

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2052
Author(s):  
Xinghai Yang ◽  
Fengjiao Wang ◽  
Zhiquan Bai ◽  
Feifei Xun ◽  
Yulin Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Optical Flow ◽  
Traffic Congestion ◽  
Detection Algorithm ◽  
Input Image ◽  
Vehicle Speed ◽  
Position Information ◽  
Traffic State ◽  
State Discrimination ◽  
Discrimination Method

In this paper, a deep learning-based traffic state discrimination method is proposed to detect traffic congestion at urban intersections. The detection algorithm includes two parts, global speed detection and a traffic state discrimination algorithm. Firstly, the region of interest (ROI) is selected as the road intersection from the input image of the You Only Look Once (YOLO) v3 object detection algorithm for vehicle target detection. The Lucas-Kanade (LK) optical flow method is employed to calculate the vehicle speed. Then, the corresponding intersection state can be obtained based on the vehicle speed and the discrimination algorithm. The detection of the vehicle takes the position information obtained by YOLOv3 as the input of the LK optical flow algorithm and forms an optical flow vector to complete the vehicle speed detection. Experimental results show that the detection algorithm can detect the vehicle speed and traffic state discrimination method can judge the traffic state accurately, which has a strong anti-interference ability and meets the practical application requirements.

scholarly journals Two SNP Markers Identified Using Genotyping-by-Sequencing Are Associated with Remontancy in a Segregating F1 Population of Syringa meyeri ‘Palibin’ × S. pubescens ‘Penda’ Bloomerang®

2020 ◽  
Vol 145 (2) ◽  
pp. 104-109
Author(s):  
Hsuan Chen ◽  
Jason D. Lattier ◽  
Kelly Vining ◽  
Ryan N. Contreras
Keyword(s):  
Ornamental Plants ◽  
Genotyping By Sequencing ◽  
Snp Markers ◽  
Sequence Information ◽  
Specific Marker ◽  
Nucleotide Polymorphism ◽  
Additive Interaction ◽  
Single Nucleotide ◽  
Position Information ◽  
Flowering Season

Lilacs (Syringa sp.) have been used as ornamental plants since the mid-16th century and remain important in modern gardens due to their attractive and fragrant flowers. However, a short flowering season is a critical drawback for their ornamental value. Breeders have identified remontancy (reblooming) in dwarf lilac (Syringa pubescens), and have tried to introgress this trait into related species by interspecific hybridization. Molecular tools for lilac breeding are limited because of the shortage of genome sequence knowledge and currently no molecular markers are available to use in breeding for remontancy. In this study, an F1 population from crossing Syringa meyeri ‘Palibin’ × S. pubescens ‘Penda’ Bloomerang® Purple was created and subjected to genotyping-by-sequencing (GBS) analysis and phenotyped for remontancy. Plants were categorized as remontant, semi-remontant, and nonremontant based on the relative quantity of inflorescences during the second flush of flowers. A total of 20,730 single-nucleotide polymorphism (SNP) markers from GBS were used in marker-trait association to find remontant-specific marker(s) without marker position information. Two SNP markers, TP70580 (A locus) and TP82604 (B locus), were correlated with remontancy. The two loci showed a partial epistasis and additive interaction effects on the level of remontancy. Accumulation of recessive alleles at the two loci was positively correlated with increased reblooming. For example, 87% of aabb plants were remontant, and only 9% were nonremontant. In contrast, 100% of AaBB plants were nonremontant. These two SNP markers associated with remontancy will be useful in developing markers for future breeding and demonstrate the feasibility of developing markers for breeding woody ornamental taxa that lack a reference genome or extensive DNA sequence information.

Community detection in complex network by network embedding and density clustering

2021 ◽  
pp. 1-12
Author(s):  
JinFang Sheng ◽  
Huaiyu Zuo ◽  
Bin Wang ◽  
Qiong Li
Keyword(s):  
Complex Network ◽  
Community Detection ◽  
Dimensional Space ◽  
Detection Algorithm ◽  
Superior Performance ◽  
Network Embedding ◽  
Detection Algorithms ◽  
Density Clustering ◽  
Community Detection Algorithm ◽  
Low Dimensional

 In a complex network system, the structure of the network is an extremely important element for the analysis of the system, and the study of community detection algorithms is key to exploring the structure of the complex network. Traditional community detection algorithms would represent the network using an adjacency matrix based on observations, which may contain redundant information or noise that interferes with the detection results. In this paper, we propose a community detection algorithm based on density clustering. In order to improve the performance of density clustering, we consider an algorithmic framework for learning the continuous representation of network nodes in a low-dimensional space. The network structure is effectively preserved through network embedding, and density clustering is applied in the embedded low-dimensional space to compute the similarity of nodes in the network, which in turn reveals the implied structure in a given network. Experiments show that the algorithm has superior performance compared to other advanced community detection algorithms for real-world networks in multiple domains as well as synthetic networks, especially when the network data chaos is high.

scholarly journals Novel Error Detection Algorithm for LZSS Compressed Data

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 8940-8947 ◽  
Author(s):  
Beom Kwon ◽  
Myongsik Gong ◽  
Sanghoon Lee
Keyword(s):  
Error Detection ◽  
Detection Algorithm ◽  
Compressed Data

Analysis of Dynamic Method of Line Scales Detection

2009 ◽  
Vol 147-149 ◽  
pp. 576-581
Author(s):  
A. Barakauskas ◽  
Albinas Kasparaitis ◽  
Saulius Kausinis ◽  
R. Lazdinas
Keyword(s):  
Error Detection ◽  
Dynamic Method ◽  
Optical Microscope ◽  
Detection Algorithm ◽  
Uncertainty In Measurement ◽  
Line Scale ◽  
Scale Calibration ◽  
Calibration Program ◽  
Long Stroke ◽  
Scale Detection

The main causes of uncertainty in measurement regarding long-stroke line scales are line detection errors and external factors, especially temperature effects. The number of calibration errors of this sort increases with the extension of calibration time. Therefore, a dynamic method of line scale detection for modern long-stroke line scale comparators is used [1, 2, 3]. The article discusses the dynamic method of line scale detection by means of an optical microscope equipped with a photosensitive cell matrix and a line scale detection algorithm. Advantages of the dynamic method of scale calibration in terms of rate, accuracy and throughput are presented. The method’s error (detection parameters) correlations with detection rate, number of nominal lines, measuring rate, exposition delay are analyzed and mathematical models are described. The optimal values of these parameters are estimated. We are particularly interested in the improvement of the dynamic calibration program algorithm and minimization of uncertainty in measurement. The method was implemented and tested on the long-stroke line scale comparator, which has been developed and realized by JSC Precizika Metrology [3, 4, 5] in cooperation with VGTU and KUT.

scholarly journals An Unsupervised Classification Technique for Detection of Flipped Orientations in Document Images

2016 ◽  
Vol 6 (5) ◽  
pp. 2140
Author(s):  
Vijayashree CS ◽  
Shobha Rani ◽  
Vasudev T
Keyword(s):  
Unsupervised Learning ◽  
Unsupervised Classification ◽  
Document Image ◽  
Document Images ◽  
Orientation Direction ◽  
Average Accuracy ◽  
Direction Detection ◽  
Automatic Reading ◽  
Reading System ◽  
Specific Orientation

<table width="593" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p>Detection of text orientation in document images is of preliminary concern prior to processing of documents by Optical Character Reader. The text direction in document images should exist generally in a specific orientation, i.e.,   text direction for any automated document reading system. The flipped text orientation leads to an unambiguous result in such fully automated systems. In this paper, we focus on development of text orientation direction detection module which can be incorporated as the perquisite process in automatic reading system. Orientation direction detection of text is performed through employing directional gradient features of document image and adapts an unsupervised learning approach for detection of flipped text orientation at which the document has been originally fed into scanning device. The unsupervised learning is built on the directional gradient features of text of document based on four possible different orientations. The algorithm is experimented on document samples of printed plain English text as well as filled in pre-printed forms of Telugu script. The outcome attained by algorithm proves to be consistent and adequate with an average accuracy around 94%.</p></td></tr></tbody></table>

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