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PAL-BERT:An Improved Question Answering Model

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 2729-2745页

作者： Wenfeng Zheng Siyu Lu Zhuohang Cai Ruiyang Wang Lei Wang lirong yin School of Automation University of Electronic Science and Technology of ChinaChengdu610054China Department of Geography and Anthropology Louisiana State UniversityBaton RougeLA70803USA

In the field of natural language processing(NLP),there have been various pre-training language models in recent years,with question answering systems gaining significant ***,as algorithms,data,and computing power advance,the issue of increasingly larger models and a growing number of parameters has ***,model training has become more costly and less *** enhance the efficiency and accuracy of the training process while reducing themodel volume,this paper proposes a first-order pruningmodel PAL-BERT based on the ALBERT model according to the characteristics of question-answering(QA)system and language ***,a first-order network pruning method based on the ALBERT model is designed,and the PAL-BERT model is ***,the parameter optimization strategy of the PAL-BERT model is formulated,and the Mish function was used as an activation function instead of ReLU to improve the ***,after comparison experiments with traditional deep learning models TextCNN and BiLSTM,it is confirmed that PALBERT is a pruning model compression method that can significantly reduce training time and optimize training *** with traditional models,PAL-BERT significantly improves the NLP task’s performance.

关键词： PAL-BERT question answering model pretraining language models ALBERT pruning model network pruning TextCNN BiLSTM

Soft Tissue Feature Tracking Based on Deep Matching Network

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Computer Modeling in Engineering & Sciences 2023年第7期136卷 363-379页

作者： Siyu Lu Shan Liu Pengfei Hou Bo Yang Mingzhe Liu lirong yin Wenfeng Zheng School of Automation University of Electronic Science and Technology of ChinaChengdu610054China College of Computer Science and Cyber Security Chengdu University of TechnologyChengdu610059China Department of Geography and Anthropology Louisiana State UniversityBaton RougeLA70803USA

Research in the field ofmedical image is an important part of themedical robot to operate human *** robot is the intersection ofmulti-disciplinary research fields,in whichmedical image is an important direction and has achieved fruitful *** this paper,amethodof soft tissue surface feature tracking basedonadepthmatching network is *** method is described based on the triangular matching ***,we construct a self-made sample set for training the depth matching network from the first N frames of speckle matching data obtained by the triangle matching *** depth matching network is pre-trained on the ORL face data set and then trained on the self-made training *** the training,the speckle matching is carried out in the subsequent frames to obtain the speckle matching matrix between the subsequent frames and the first *** this matrix,the inter-frame feature matching results can be *** this way,the inter-frame speckle tracking is *** this basis,the results of this method are compared with the matching results based on the convolutional neural *** experimental results show that the proposed method has higher matching *** particular,the accuracy of the MNIST handwritten data set has reached more than 90%.

关键词： Soft tissue feature tracking deep matching network

Measuring County Resilience After the 2008 Wenchuan Earthquake

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International Journal of Disaster Risk Science 2016年第4期7卷 393-412页

作者： Xiaolu Li Nina Lam Yi Qiang Kenan Li lirong yin Shan Liu Wenfeng Zheng School of Automation University of Electronic Science and Technology of China Department of Environmental Sciences Louisiana State University The Earth Lab University of Colorado Boulder Geographical & Sustainability Sciences Department University of Iowa

The catastrophic earthquake that struck Sichuan Province,China,in 2008 caused serious damage to Wenchuan County and surrounding areas in southwestern *** recent years,great attention has been paid to the resilience of the affected *** study applied the resilience inference measurement(RIM) model to quantify and validate the community resilience of 105 counties in the impacted *** RIM model uses cluster analysis to classify counties into four resilience levels according to the exposure,damage,and recovery *** model then applies discriminant analysis to quantify the influence of socioeconomic characteristics on the county's *** results show that counties located at the epicenter had the lowest resilience,but counties immediately adjacent to the epicenter had the highest resilience *** that were farther away from the epicenter returned to normal resiliency *** variables—including sex ratio,per capita GDP,percent of ethnic minority,and medical facilities—were identified as the most influential characteristics influencing *** study provides useful information to improve county resilience to earthquakes and support decision making for sustainable development.

关键词： China Resilience Resilience inference measurement(RIM) model Vulnerability Wenchuan earthquake

AFBNet: A Lightweight Adaptive Feature Fusion Module for Super-Resolution Algorithms

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Computer Modeling in Engineering & Sciences 2024年第9期140卷 2315-2347页

作者： lirong yin Lei Wang Siyu Lu Ruiyang Wang Haitao Ren Ahmed AlSanad Salman A.AlQahtani Zhengtong yin Xiaolu Li Wenfeng Zheng Department of Geography and Anthropology Louisiana State UniversityBaton RougeLA70803USA School of Automation University of Electronic Science and Technology of ChinaChengdu610054China College of Computer and Information Sciences King Saud UniversityRiyadh11574Saudi Arabia College of Resource and Environment Engineering Guizhou UniversityGuiyang550025China School of Geographical Sciences Southwest UniversityChongqing400715China

At present,super-resolution algorithms are employed to tackle the challenge of low image resolution,but it is difficult to extract differentiated feature details based on various inputs,resulting in poor generalization *** this situation,this study first analyzes the features of some feature extraction modules of the current super-resolution algorithm and then proposes an adaptive feature fusion block(AFB)for feature *** module mainly comprises dynamic convolution,attention mechanism,and pixel-based gating *** with dynamic convolution with scale information,the network can extract more differentiated feature *** introduction of a channel spatial attention mechanism combined with multi-feature fusion further enables the network to retain more important feature *** convolution and pixel-based gating mechanisms enhance the module’s ***,a comparative experiment of a super-resolution algorithm based on the AFB module is designed to substantiate the efficiency of the AFB *** results revealed that the network combined with the AFB module has stronger generalization ability and expression ability.

关键词： Super-resolution feature extraction dynamic convolution attention mechanism gate control

DPAL-BERT:A Faster and Lighter Question Answering Model

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Computer Modeling in Engineering & Sciences 2024年第10期141卷 771-786页

作者： lirong yin Lei Wang Zhuohang Cai Siyu Lu Ruiyang Wang Ahmed AlSanad Salman A.AlQahtani Xiaobing Chen Zhengtong yin Xiaolu Li Wenfeng Zheng Department of Geography and Anthropology Louisiana State UniversityBaton RougeLA 70803USA School of Automation University of Electronic Science and Technology of ChinaChengdu610054China College of Computer and Information Sciences King Saud UniversityRiyadh11574Saudi Arabia School of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLA 70803USA College of Resources and Environmental Engineering Guizhou UniversityGuiyang550025China School of Geographical Sciences Southwest UniversityChongqing400715China

Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering ***,with the constant evolution of algorithms,data,and computing power,the increasing size and complexity of these models have led to increased training costs and reduced *** study aims to minimize the inference time of such models while maintaining computational *** also proposes a novel Distillation model for PAL-BERT(DPAL-BERT),specifically,employs knowledge distillation,using the PAL-BERT model as the teacher model to train two student models:DPAL-BERT-Bi and *** research enhances the dataset through techniques such as masking,replacement,and n-gram sampling to optimize knowledge *** experimental results showed that the distilled models greatly outperform models trained from *** addition,although the distilled models exhibit a slight decrease in performance compared to PAL-BERT,they significantly reduce inference time to just 0.25%of the *** demonstrates the effectiveness of the proposed approach in balancing model performance and efficiency.

关键词： DPAL-BERT question answering systems knowledge distillation model compression BERT Bi-directional long short-term memory(BiLSTM) knowledge information transfer PAL-BERT training efficiency natural language processing

Improved Blending Attention Mechanism in Visual Question Answering

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Computer Systems Science & Engineering 2023年第10期47卷 1149-1161页

作者： Siyu Lu Yueming Ding Zhengtong yin Mingzhe Liu Xuan Liu Wenfeng Zheng lirong yin School of Automation University of Electronic Science and Technology of ChinaChengdu610054China College of Resource and Environment Engineering Guizhou UniversityGuiyang550025China School of Data Science and Artificial Intelligence Wenzhou University of TechnologyWenzhou325000China School of Public Affairs and Administration University of Electronic Science and Technology of ChinaChengdu611731China Department of Geography and Anthropology Louisiana State UniversityBaton Rouge70803LAUSA

Visual question answering(VQA)has attracted more and more attention in computer vision and natural language *** are committed to studying how to better integrate image features and text features to achieve better results in VQA *** of all features may cause information redundancy and heavy computational *** mechanism is a wise way to solve this ***,using single attention mechanism may cause incomplete concern of *** paper improves the attention mechanism method and proposes a hybrid attention mechanism that combines the spatial attention mechanism method and the channel attention mechanism *** the case that the attention mechanism will cause the loss of the original features,a small portion of image features were added as *** the attention mechanism of text features,a selfattention mechanism was introduced,and the internal structural features of sentences were strengthened to improve the overall *** results show that attention mechanism and feature compensation add 6.1%accuracy to multimodal low-rank bilinear pooling network.

关键词： Visual question answering spatial attention mechanism channel attention mechanism image feature processing text feature extraction

Convolution-Transformer for Image Feature Extraction

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Computer Modeling in Engineering & Sciences 2024年第10期141卷 87-106页

作者： lirong yin Lei Wang Siyu Lu Ruiyang Wang Youshuai Yang Bo Yang Shan Liu Ahmed AlSanad Salman A.AlQahtani Zhengtong yin Xiaolu Li Xiaobing Chen Wenfeng Zheng Department of Geography and Anthropology Louisiana State UniversityBaton RougeLA70803USA School of Automation University of Electronic Science and Technology of ChinaChengdu610054China College of Computer and Information Sciences King Saud UniversityRiyadh11574Saudi Arabia College of Resources and Environmental Engineering Key Laboratory of Karst Georesources and Environment(Guizhou University)Ministry of EducationGuiyang550025China School of Geographical Sciences Southwest UniversityChongqing400715China School of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLA70803USA

This study addresses the limitations of Transformer models in image feature extraction,particularly their lack of inductive bias for visual *** to Convolutional Neural Networks(CNNs),the Transformers are more sensitive to different hyperparameters of optimizers,which leads to a lack of stability and slow *** tackle these challenges,we propose the Convolution-based Efficient Transformer Image Feature Extraction Network(CEFormer)as an enhancement of the Transformer *** model incorporates E-Attention,depthwise separable convolution,and dilated convolution to introduce crucial inductive biases,such as translation invariance,locality,and scale invariance,into the Transformer ***,we implement a lightweight convolution module to process the input images,resulting in faster convergence and improved *** results in an efficient convolution combined Transformer image feature extraction *** results on the ImageNet1k Top-1 dataset demonstrate that the proposed network achieves better accuracy while maintaining high computational *** achieves up to 85.0%accuracy across various model sizes on image classification,outperforming various baseline *** integrated into the Mask Region-ConvolutionalNeuralNetwork(R-CNN)framework as a backbone network,CEFormer outperforms other models and achieves the highest mean Average Precision(mAP)*** research presents a significant advancement in Transformer-based image feature extraction,balancing performance and computational efficiency.

关键词： Transformer E-Attention depth convolution dilated convolution CEFormer

An Optimized Chinese Filtering Model Using Value Scale Extended Text Vector

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Computer Systems Science & Engineering 2023年第11期47卷 1881-1899页

作者： Siyu Lu Ligao Cai Zhixin Liu Shan Liu Bo Yang lirong yin Mingzhe Liu Wenfeng Zheng School of Automation University of Electronic Science and Technology of ChinaChengdu610054China School of Life Science Shaoxing UniversityShaoxing312000China Department of Geography and Anthropology Louisiana State UniversityBaton RougeLA70803USA College of Computer Science and Cyber Security Chengdu University of TechnologyChengdu610059China

With the development of Internet technology,the explosive growth of Internet information presentation has led to difficulty in filtering effective *** a model with high accuracy for text classification has become a critical problem to be solved by text filtering,especially for Chinese *** paper selected the manually calibrated Douban movie website comment data for ***,a text filtering model based on the BP neural network has been built;Second,based on the Term Frequency-Inverse Document Frequency(TF-IDF)vector space model and the doc2vec method,the text word frequency vector and the text semantic vector were obtained respectively,and the text word frequency vector was linearly reduced by the Principal Component Analysis(PCA)***,the text word frequency vector after dimensionality reduction and the text semantic vector were combined,add the text value degree,and the text synthesis vector was *** show that the model combined with text word frequency vector degree after dimensionality reduction,text semantic vector,and text value has reached the highest accuracy of 84.67%.

关键词： Chinese text filtering text vector word frequency vectors text semantic vectors value degree BP neural network TF-IDF doc2vec PCA

关于完善兵团第八师多元化纠纷解决机制的调查与思考

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兵团党校学报 2020年第3期 27-32页

作者：尹丽蓉兵团第八师石河子市党委党校综合教研室新疆石河子832000

多元化纠纷解决机制既是社会治理体系的重要组成部分,也是正确处理人民内部矛盾的重要机制。本文立足兵团第八师石河子市多元化纠纷解决机制建设的现状,分析八师多元化纠纷解决机制建设面临的司法资源及功能有限难以解决日益增多的矛盾... 详细信息

多元化纠纷解决机制既是社会治理体系的重要组成部分,也是正确处理人民内部矛盾的重要机制。本文立足兵团第八师石河子市多元化纠纷解决机制建设的现状,分析八师多元化纠纷解决机制建设面临的司法资源及功能有限难以解决日益增多的矛盾纠纷、部门联动作用发挥不足难以化解复杂化的矛盾纠纷等问题,提出深化法院多元化纠纷解决机制改革、建立纠纷化解工作部门联席会议机制等建议。

关键词：多元化纠纷诉讼非诉讼方式人民调解行政调解