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Fiber communication receiver models based on the multi-head attention mechanism

Chinese Optics Letters 2023年第3期21卷 29-34页

作者：臧裕斌于振明徐坤陈明华杨四刚陈宏伟 and Department of Electronic Engineering Tsinghua UniversityBeijing 100084China State Key Laboratory of Information Phonetics and Optical Communications Beijing University of Post and TelecommunicationsBejing 100876 China

In this paper,an artificial-intelligence-based fiber communication receiver model is put *** the multi-head attention mechanism it contains,this model can extract crucial patterns and map the transmitted signals into the bit *** appropriately trained,it can obtain the ability to restore the information from the signals whose transmission distances range from 0 to 100 km,signal-to-noise ratios range from 0 to 20 dB,modulation formats range from OOK to PAM4,and symbol rates range from 10 to 40 *** validity of the model is numerically demonstrated via MATLAB and Pytorch scenarios and compared with traditional communication receivers.

关键词： fiber receiver model neural networks multi-head attention mechanism

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multi-scale persistent spatiotemporal transformer for long-term urban traffic flow prediction

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Journal of Electronic Science and Technology 2024年第1期22卷 53-69页

作者： Jia-Jun Zhong Yong Ma Xin-Zheng Niu Philippe Fournier-Viger Bing Wang Zu-kuan Wei School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China College of Computer Science&Software Engineering Shenzhen UniversityShenzhen518060China School of Computer Science Southwest Petroleum UniversityChengdu610500China

Long-term urban traffic flow prediction is an important task in the field of intelligent transportation,as it can help optimize traffic management and improve travel *** improve prediction accuracy,a crucial issue is how to model spatiotemporal dependency in urban traffic *** recent years,many studies have adopted spatiotemporal neural networks to extract key information from traffic ***,most models ignore the semantic spatial similarity between long-distance areas when mining spatial *** also ignore the impact of predicted time steps on the next unpredicted time step for making long-term ***,these models lack a comprehensive data embedding process to represent complex spatiotemporal *** paper proposes a multi-scale persistent spatiotemporal transformer(MSPSTT)model to perform accurate long-term traffic flow prediction in *** adopts an encoder-decoder structure and incorporates temporal,periodic,and spatial features to fully embed urban traffic data to address these *** model consists of a spatiotemporal encoder and a spatiotemporal decoder,which rely on temporal,geospatial,and semantic space multi-head attention modules to dynamically extract temporal,geospatial,and semantic *** spatiotemporal decoder combines the context information provided by the encoder,integrates the predicted time step information,and is iteratively updated to learn the correlation between different time steps in the broader time range to improve the model’s accuracy for long-term *** on four public transportation datasets demonstrate that MSPSTT outperforms the existing models by up to 9.5%on three common metrics.

关键词： Graph neural network multi-head attention mechanism Spatio-temporal dependency Traffic flow prediction

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Using Recurrent Neural Network Structure and multi-head attention with Convolution for Fraudulent Phone Text Recognition

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Computer Systems Science & Engineering 2023年第8期46卷 2277-2297页

作者： Junjie Zhou Hongkui Xu Zifeng Zhang Jiangkun Lu Wentao Guo Zhenye Li School of Information and Electrical Engineering Shandong Jianzhu UniversityJinan250000China

Fraud cases have been a risk in society and people’s property security has been greatly *** recent studies,many promising algorithms have been developed for social media offensive text recognition as well as sentiment *** algorithms are also suitable for fraudulent phone text *** to these tasks,the semantics of fraudulent words are more complex and more difficult to *** Neural Networks(RNN),the variants ofRNN,ConvolutionalNeuralNetworks(CNN),and hybrid neural networks to extract text features are used by most text classification ***,a single network or a simple network combination cannot obtain rich characteristic knowledge of fraudulent phone texts ***,a new model is proposed in this *** the fraudulent phone text,the knowledge that can be learned by the model includes the sequence structure of sentences,the correlation between words,the correlation of contextual semantics,the feature of keywords in sentences,*** new model combines a bidirectional Long-Short Term Memory Neural Network(BiLSTM)or a bidirectional Gate Recurrent United(BiGRU)and a multi-head attention mechanism module with convolution.A normalization layer is added after the output of the final hidden *** or BiGRU is used to build the encoding and decoding ***-head attention mechanism module with convolution(MHAC)enhances the ability of the model to learn global interaction information and multi-granularity local interaction information in fraudulent sentences.A fraudulent phone text dataset is produced by us in this *** THUCNews data sets and fraudulent phone text data sets are used in *** results show that compared with the baseline model,the proposed model(LMHACL)has the best experiment results in terms of Accuracy,Precision,Recall,and F1 score on the two data *** the performance indexes on fraudulent phone text data sets are all above 0.94.

关键词： BiLSTM BiGRU multi-head attention mechanism CNN

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multi-head attention-based long short-term memory model for speech emotion recognition

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Journal of Southeast University(English Edition) 2022年第2期38卷 103-109页

作者： Zhao Yan Zhao Li Lu Cheng Li Sunan Tang Chuangao Lian Hailun School of Information Science and Engineering Southeast University Nanjing 210096 China School of Biological Science and Medical Engineering Southeast University Nanjing 210096 China

To fully make use of information from different representation subspaces,a multi-head attention-based long short-term memory(LSTM)model is proposed in this study for speech emotion recognition(SER).The proposed model uses frame-level features and takes the temporal information of emotion speech as the input of the LSTM ***,a multi-head time-dimension attention(MHTA)layer was employed to linearly project the output of the LSTM layer into different subspaces for the reduced-dimension context *** provide relative vital information from other dimensions,the output of MHTA,the output of feature-dimension attention,and the last time-step output of LSTM were utilized to form multiple context vectors as the input of the fully connected *** improve the performance of multiple vectors,feature-dimension attention was employed for the all-time output of the first LSTM *** proposed model was evaluated on the eNTERFACE and GEMEP corpora,*** results indicate that the proposed model outperforms LSTM by 14.6%and 10.5%for eNTERFACE and GEMEP,respectively,proving the effectiveness of the proposed model in SER tasks.

关键词： speech emotion recognition long short-term memory(LSTM) multi-head attention mechanism frame-level features self-attention

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Discharge Summaries Based Sentiment Detection Using multi-head attention and CNN-BiGRU

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Computer Systems Science & Engineering 2023年第7期46卷 981-998页

作者： Samer Abdulateef Waheeb School of Computer Science and Engineering Northwestern Polytechnical UniversityXi’an710072China

Automatic extraction of the patient’s health information from the unstructured data concerning the discharge summary remains *** summary related documents contain various aspects of the patient health condition to examine the quality of treatment and thereby help improve decision-making in the medical *** a sentiment dictionary and feature engineering,the researchers primarily mine semantic text ***,choosing and designing features requires a lot of *** proposed approach is an unsupervised deep learning model that learns a set of clusters embedded in the latent space.A composite model including Active Learning(AL),Convolutional Neural Network(CNN),BiGRU,and multi-attention,called ACBMA in this research,is designed to measure the quality of treatment based on discharge summaries text sentiment *** is utilized for extracting the set of local features of text *** BiGRU network was utilized to extract the text’s global features to solve the issues that a single CNN cannot obtain global semantic information and the traditional Recurrent Neural Network(RNN)gradient *** prove that the ACBMA method can demonstrate the effectiveness of the suggested method,achieve comparable results to state-of-arts methods in sentiment detection,and outperform them with accurate ***,several algorithm studies ultimately determined that the ACBMA method is more precise for discharge summaries sentiment analysis.

关键词： Sentiment analysis lexicon discharge summaries active learning multi-head attention mechanism

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Predictive Model of Live Shopping Interest Degree Based on Eye Movement Characteristics and Deep Factorization Machine

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Journal of Donghua University(English Edition) 2022年第4期39卷 353-360页

作者： SHI Xiujin LI Hao SHI Hang WANG Shaoyu SUN Guohao School of Computer Science and Technology Donghua UniversityShanghai 201620China

In the live broadcast process,eye movement characteristics can reflect people’s attention to the ***,the existing interest degree predictive model research does not consider the eye movement *** order to obtain the users’interest in the product more effectively,we will consider the key eye movement *** first collect eye movement characteristics based on the self-developed data processing algorithm fast discriminative model prediction for tracking(FDIMP),and then we add data dimensions to the original data set through information *** addition,we apply the deep factorization machine(DeepFM)architecture to simultaneously learn the combination of low-level and high-level *** order to effectively learn important features and emphasize relatively important features,the multi-head attention mechanism is applied in the interest *** experimental results on the public data set Criteo show that,compared with the original DeepFM algorithm,the area under curve(AUC)value was improved by up to 9.32%.

关键词： eye movement interest degree predictive deep factorization machine(DeepFM) multi-head attention mechanism

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