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An Analysis Model of English Text Coherence Based on RST Dependency Relationship 4

An Analysis Model of English Text Coherence Based on RST Dep...

引用

4th International Conference on Information Science, Parallel and Distributed Systems, ISPDS 2023

作者： Huang, Guimin Shuai, Wanying Guo, Qingkai Deng, Nanxiao Lin, Jianxing School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798350337181

Text coherence analysis is an important and challenging task that is essential for subtasks such as automatic summarisation, viewpoint extraction and machine translation in natural language processing (NLP). A large body of previous work has used linguistic features such as lexical, syntactic and entity features to capture relatively shallow coherence features, while ignoring deeper logically relevant features hidden in the text. We propose an end-to-end English text coherence analysis model (RGCM for short) that incorporates RST and graph convolutional neural networks (GCN). The intra- and inter-sentence logical relations of the input text are first mapped onto a discourse relationship tree, and then transformed into an RST-dependent context graph by certain pruning strategies. Subsequently, we propose a GCN-based text coherence assessment framework to capture intra- and inter-sentence interactions to assess text coherence. We conducted experiments on two different coherence assessment datasets and achieved accuracy rates of 95.5% and 97.8% respectively. © 2023 IEEE.

关键词： Convolutional neural networks

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4th International Conference on Information Science, Parallel and Distributed Systems, ISPDS 2023

作者： Zhou, Ya Zhao, Ning Huang, Guimin Deng, Nanxiao Guo, Qingkai School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798350337181

In sentence similarity research methods, sentence similarity is often calculated from semantic aspects, however, the influence of other features is ignored. For example, the influence of sentence syntactic structure and word order on sentence similarity is not considered. Therefore, This paper proposes a method to measure the degree of similarity between sentences based on a multi-feature fusion of semantics, syntactic similarity and word order similarity, respectively. For the aspect of semantic similarity, in this paper, we first preprocess the sentence and obtain the word vectors, and then obtains sentence vector features through neural networks, so as to calculate the sentence semantic similarity;Finally, for the aspect of word order similarity, word order is considered as the auxiliary feature information to calculate sentence similarity since word order has a smaller effect on sentence similarity compared to the above information. The experimental results show that the proposed model has an accuracy of 77.4 % and an F1 value of 84.3% on the MRPC dataset. © 2023 IEEE.

关键词： Semantics

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A Deep Knowledge Tracking Model Based on Forgetting Features and Self-Attention Mechanism 4

A Deep Knowledge Tracking Model Based on Forgetting Features...

引用

4th International Symposium on computer Engineering and intelligent Communications, ISCEIC 2023

作者： Huang, Guimin Wei, Xiong Guo, Qingkai Deng, Nanxiao Lin, Jianxing Guilin University of Electronic Technology School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798350306323

The integration of large education and artificial intelligence technologies is gradually deepening, and how to provide personalized user profiling services for learners is an important research problem. In response to the long-term dependency problem and lack of learning features of today's deep knowledge tracking models, a deep knowledge tracking model (FSA-MuLSTM) based on forgetting features and the self-attentiveness mechanism is proposed. Firstly, the model combines the learner's historical learning interaction tuple sequences with learning times and memory residuals to obtain the input vector based on the LSTM;secondly, the correlation weights between tuples are captured using the self-attention mechanism;finally, the deep information between tuple sequences is captured using the stacking of multilayer LSTMs. The probability of correctly answering the current question is derived from the hidden vectors and the self-attentive weights of the output of the multilayer LSTM. Two public datasets are used for the experiments, and the combined analysis outperforms existing knowledge-tracking models in terms of performance and improves the accuracy of prediction. © 2023 IEEE.

关键词： Multilayers

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Optimization algorithm for global k-means algorithm 13

Optimization algorithm for global k-means algorithm

引用

13th International Conference on intelligent Control and Information processing, ICICIP 2025

作者： Li, Sen Li, Fang Luo, Xiaonan School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798331516147

The k-means method is widely utilized for clustering. Its simplicity, efficacy, and swiftness make it a favored choice among clustering algorithms. It faces the challenge of sensitivity to the initial class center. The enhancement strategy for the k-means algorithm focuses on refining the initial selection of cluster centroids, no longer using its random selection of initial class center points, but starting from the entire dataset, choosing the most distant point to serve as the starting cluster centroid. This can also prevent the local optimization problem of the k-means algorithm. The improved algorithm proposed in this article is an effective method that can select the initial class center point from a global perspective and reduce computation time. The suggested approach has undergone rigorous testing and comparative analysis across multiple datasets, yielding commendable outcomes. © 2025 IEEE.

关键词： Optimization algorithms

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Photovoltaic Array Fault Detection Based on Manifold Learning and Neural Network 5

Photovoltaic Array Fault Detection Based on Manifold Learnin...

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5th International Conference on computer Vision, Image and Deep Learning, CVIDL 2024

作者： Wang, Kai Zhong, Yanru Luo, Xiaonan Guilin University of Electronic Science and Technology Guangxi Key Laboratory of Intelligent Processing of Computer Images and Graphic Guilin China Guilin University of Electronic Science and Technology Satellite Navigation Positioning and Location Services National and Local Joint Engineering Research Center Guilin China

ISBN: (纸本)9798350373820

This paper endeavors to devise an ingenious methodology rooted in artificial intelligence, which, in turn, can be effectively employed to identify sundry categories of faults that are closely associated with photovoltaic arrays. This novel approach amalgamates the manifold learning algorithm with a formidable multilayer perceptron neural network (MLP). Intriguingly, it substantiates that the initial reduction in data dimensionality is achieved through the remarkable Isomap algorithm. Consequently, this compressed data is seamlessly channeled into the MLP, thereby yielding remarkable outcomes concerning fault detection. To validate and scrutinize the aforementioned proposition, a quintessential circuit representing a photovoltaic array is initially simulated utilizing the venerable Matlab/Simulink tool. During this simulation, an extensive corpus of both normal and faulty data is meticulously generated. Subsequently, the manifold learning algorithm is artfully applied to drastically diminish the dimensionality of this extensive dataset The resultant compressed data is then deftly fed into the MLP, allowing it to discern the salient characteristics pertaining to the sundry fault types. Through incessant iterations of intensive training, these faults can be astutely detected and discerned with remarkable accuracy. The results conclusively demonstrate the efficacy of combining manifold-based learning and multilayer perceptron neural network in detecting faults in photovoltaic arrays. The proposed method achieves high precision and effectiveness, surpassing the traditional approach by 2.52%. © 2024 IEEE.

关键词： Fault detection

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Tomato Leaf Disease Identification and Growth Dataset Augmentation Based on UAE-GAN 13

Tomato Leaf Disease Identification and Growth Dataset Augmen...

引用

13th International Conference on intelligent Control and Information processing, ICICIP 2025

作者： Li, Xin Li, Fang Luo, Xiaonan School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798331516147

To delve into the characterization of growth disorders in different crops, it is important to support the model with a large amount of image data that includes a variety of disease types and disease levels to capture the typical and subtle differences of various diseases on plant leaves. However, the actual process of gathering data is challenging, sample coverage is challenging to accomplish, data capture is impeded, and the quality of the data is subpar. This work aims to address the issue of data shortages by employing technical methods. In particular, we creatively investigated the UAE-GAN approach, which naturally combines CycleGAN, U-Net, Variational Autoencoder VAE, and Autoencoder to increase the data. Among these, U-Net can precisely extract the small details of disease locations in crop photos and provide a strong basis for further processing thanks to its special codec architectural benefits. The Variational Autoencoder (VAE) significantly enhances the diversity of data by mapping the image to the latent space and sampling based on a certain probability distribution, so producing new image samples that are distinct from the original image yet inherently connected. Learning the coding and decoding of the original image is the foundation of autoencoders. If a mild disruption is introduced into the coding process, it can achieve data augmentation in another dimension and create a sequence of new images with just little modifications to the original image. The aforementioned models are closely linked with CycleGAN to efficiently map and convert in a variety of picture domains and to fully leverage CycleGAN's remarkable unsupervised image conversion capabilities. The perception ability, feature capture ability, and information conversion ability of the fusion model for crop image data are significantly improved, and the key elements of each link in the data enhancement process are comprehensively considered to ensure that the generated new image data can not o

关键词： Fruits

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Power equipment fault entity recognition based on BERT-CRF model 5

Power equipment fault entity recognition based on BERT-CRF m...

引用

5th International Conference on Artificial Intelligence and computer Science, AICS 2023

作者： Zhang, Yanfang Zhong, Yanru Luo, Xiaonan Guangxi Key Laboratory of Intelligent Processing of Computer Images and Graphic Guilin University of Electronic Technology Guangxi Guilin541004 China National Local Joint Engineering Research Center of Satellite Navigation and Location Service Guilin University of Electronic Technology Guangxi Guilin541004 China

ISBN: (纸本)9781510668621

With the ongoing development of the national power grid, fault data from power equipment exhibits various characteristics, including a wide range of entity types, weak interrelationships, and high complexity. Consequently, traditional methods fail to extract adequate entity information and struggle with situations involving the multiple meanings of terminology in different contexts. To tackle these challenges, this paper proposes a method for recognizing power equipment fault entities based on the BERT-CRF model. The method initially employs web crawlers to retrieve literature related to power equipment faults. It then inductively designs five distinct types of power equipment fault entities and annotates a dataset accordingly. Subsequently, the BERT model is combined with the CRF model to recognize power equipment fault entities. The experimental results are then compared with those of the BiLSTM-CRF model and other domain datasets to evaluate the impact of power equipment fault entity types and label quantities. The experimental results confirm the feasibility and effectiveness of the BERT-CRF model, enabling the extraction of a wider range of power equipment fault entities from a smaller corpus of data. © 2023 SPIE.

关键词： Web crawler

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A English Pronunciation Error Detection and Quality Evaluation Model Based on English Text 4

A English Pronunciation Error Detection and Quality Evaluati...

引用

4th International Symposium on computer Engineering and intelligent Communications, ISCEIC 2023

作者： Guo, Qingkai Huang, Guimin Lin, Jianxing Zhou, Ya Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China

ISBN: (纸本)9798350306323

In English learning, speaking practice is crucial, but traditional classroom teaching can hardly meet the needs of most learners. In this paper, we investigate and improve the two essential techniques of pronunciation error detection and pronunciation quality evaluation in the computer Assisted Language Learning (CALL) system. In terms of pronunciation error detection, We extracted phoneme pairs' discriminative acoustic and phonological features. We used Support Vector Machine (SVM) for pronunciation error detection, and the accuracy was improved to 81.26%. For pronunciation quality evaluation, we added two new dimensions of pronunciation fluency and intonation, innovatively designed the word duration ratio, and introduced the Support Vector Regression (SVR) algorithm to achieve the effective integration of different dimensional features. The experiments show that the improved pronunciation quality evaluation model has higher performance and practical value. © 2023 IEEE.

关键词： Support vector machines

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A Deep Knowledge Tracing Model Based on Multi-Head Self-Attention Mechanism

A Deep Knowledge Tracing Model Based on Multi-Head Self-Atte...

引用

2023 International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, AHPCAI 2023

作者： Zhou, Ya Wu, Fengzhen Huang, Guimin Lin, Jianxing Deng, Nanxiao Guo, Qingkai School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9781510671881

How to provide individualized instruction and personalized teaching for students and effectively track their knowledge status in a targeted manner is a key concern in the field of education. The target of knowledge tracing (KT) is to use the learner's behavior in historical learning activities to model the students' educational process and forcast the future performance of students. Recently, self-attention has been used several times in the KT field. Traditional models have the problem of losing information under long sequence data. Inspired by Transformer, In this paper, we present a deep knowledge tracing model (MA-DKT) based on the multi-head self-attention mechanism, which employs the LSTM model combined with the multi-head self-attention mechanism to implement a deep knowledge tracing model that is able to catch the data characteristics of the students at different temporal scales. Our model works well on publicly available datasets and achieves some improvement in AUC. © 2023 SPIE. All rights reserved.

关键词： Students

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Smart Power Safety Hazard Inspection System Based on YOLOv7 1st

Smart Power Safety Hazard Inspection System Based on YOLOv7

引用

1st International Conference on Applied Intelligence, ICAI 2023

作者： Liang, Yiheng Li, Xiaoming Deng, Zhenrong School of Computer and Information Security Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9789819708260

Power safety is closely related to people’s well-being. Electric power sectors regularly inspect and maintain power lines to guarantee people’s safe and stable use of electricity, and the current mainstream inspection method is to use UAVs for power inspection. Traditional inspection and detection methods have many shortcomings, such as high labor costs, slow inspection speed, low detection efficiency, and single detection targets. In this paper, a set of smart power inspection systems based on YOLOv7 was designed to check the common potential security risks of power lines such as bird nests, insulators, garbage, and hardware. After the detection, the detection report could be generated. The system adopted the YOLOv7 target detection algorithm. The accuracy rate of all detection targets was up to 93.9%, and the mAP @.5% reached 95.9%. Therefore, the algorithm could greatly improve the detection efficiency and greatly facilitate the electric power sectors to check power lines, so it has a high use value. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Inspection

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