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In order to solve the problems of poor anti-interference and invasion of user privacy in traditional methods of using cameras for human activity recognition, this paper proposed a human activity recognition network based on spatiotemporal attention of millimeter wave radar 3D point cloud data to achieve accurate perception of intelligent application context. The network firstly used a secondary sliding time window to accumulate and separate the point cloud data generated by human activities as the input of the classifier, then used the PointLSTM unit to aggregate point features and states according to the point cloud coordinate relationship to extract the time sequence features of human activities, and then spliced temporal-spatial features, reduced the overall network computation and enhanced the network’s aggregation ability for local featured through sampling grouping modules, and finally used a stacked attention module to deeply fuse global and local features in temporal-spatial point cloud data to complete the accurate classification of human activities. This paper used millimeter wave radar to collect point cloud datasets of various human activities, the experimental results show that the average accuracy of the proposed spatiotemporal attention network can reach 98.64%,which can effectively identify complex and small-difference human activities, and meet the requirements of the human activity recognition system. [ABSTRACT FROM AUTHOR]

Local feature information plays an important role in image segmentation, however, the referring image segmentation task is dependent on the text expression, so it is impossible to extract local feature information directly from the original reference image. In order to solve this problem, this paper proposes a specific noun-guided local feature extraction deep neural network model（NgLFNet). The NgLFNet model can automatically mine the local feature information of the object to be segmented according to the key nouns in the input text expression. Specifically, the model first obtains key nouns in text through sentence analysis; secondly, extracting corresponding features through text and image encoders, and using the key nouns to obtain local features of high-interest regions through the multihead attention mechanism; then the multi-modal features are gradually fused to learn; Finally, the decoding and correction module uses the obtained local features to perform more detailed corrections on the prediction mask to obtain the final result. The method in this paper is compared with a variety of mainstream referring segmentation methods and the experimental results show that the proposed method improves the accuracy of text-based instance segmentation task. [ABSTRACT FROM AUTHOR]

In recent years, the deep learning network model based on UNet and GAN (generative adversarial network) has shown unique advantages in the field of image restoration. But, there are still artifacts, blurring, degradation of texture details, difficult to repair large-area damage, and the repaired holes are incompatible with the background image. In order to solve the problems that the current existing models are not friendly to the repair of large-area damaged images and the degraded images after repair, this paper improved the Shift-UNet model by studying the existing methods. On the basis of U-Net and GAN, this paper added an improved attention mechanism Attention-UNet between each layer of encoder and decoder and integrated it into the Shift-UNet to form Attention-Shift-UNet. And through research, it changed the activation function of the original down-sampling part from Leaky＿ ReLU to SiLU. The improved model not only achieved good results on the 64×64 central cover, but also realized the random cover, and the cover area increased from 20 % to 80 %. The experimental results show that the repair effect of the proposed model is better, especially for the repair of large-area damaged images. After testing on CelebA, Paris Architecture and Paris Stree View datasets, the evaluation indexes have been significantly improved, and SSIM has increased from 0.944 5 to 0.947 1. PSNR is increased from 27.992 7 to 28.553 6. L2 loss is decreased from 0.001 7 to 0.001 5. [ABSTRACT FROM AUTHOR]

In order to further mine the changelog information, this paper proposed a siamese Bi-lSTM network model based on the attention mechanism to classify the changelog to realize topic annotation and assist in locating the location of code defects. The model proposed a word segmentation tool with security features to realize changelog preprocessing, used Bi-lSTM network to learn changelog contextual semantic information, and solved the problem of overfilling mode existing in changelog itself through siamese neural network and expanded the data set with high quality to improve generalization ability. It carried out serialization training for the changelog composed of multiple sentences, and the influence of sentences was distinguished through the attention mechanism. For some changelogs of the defect repair class, this paper improved the LLVM tool, generated a mapping table to search the log content, and located the location of the defect module in the source code. A large number of experimental results show that the classification effect of the model in this paper has strong generalization ability, and is nearly 10% higher than the general methods in text classification methods in terms of accuracy, F, value and other indicators, has ideal log classification effect and topic learning effect. [ABSTRACT FROM AUTHOR]

Objectives: Sea-land segmentation is of great significance for tasks such as ocean target detection and coastline extraction in synthetic aperture radar (SAR) image. To solve the problem of sea-land segmentation of multi-resolution SAR image in practical applications, this paper presents a sea-land segmentation method based on context and edge attention. Methods: The proposed method uses the channel attention mechanism to fuse context features of different scales and levels, and designs an edge extraction branch to provide edge information for further improving the segmentation result of the boundary area. In addition, a sea-land segmentation dataset of multi-resolution SAR image based on the Gaofen-3 satellite data is provided. The dataset covers multiple resolution images, including various sea-land boundary types such as ports, islands. Using this multi-resolution SAR image coastline segmentation dataset, we perform experimental analyses on the effectiveness of the proposed network and the contributions of each module. Results and Conclusions: Experimental results show that the proposed method can work well for the task of sealand segmentation, the overall classification accuracy and mean intersection over union achieve 98.21% and 96.47%, respectively. [ABSTRACT FROM AUTHOR]

In order to solve the problems of large number of parameters and high GPU resource cost in binocular vision stereo matching method based on neural network in robot and unmanned driving fields, this paper proposed a multi-scale aggregation stereo matching method. Firstly, this paper proposed a multi-scale feature extraction network to obtain richer features without changing the resolution by using dilated convolution, and introduced the attention mechanism was. Then, features at different resolutions were cross-fused to improve the feature information. Secondly, the acquisition method of cost volume was changed, the cost volume was obtained by aggregation at low scale, and continuously combined the high-scale similar information to update iteratively, and cross fused multiple cost volumes to obtain the final cost volume. Finally, combined with the refinement module of attention mechanism, the outliers and discontinuous regions in the initial disparity map are corrected to obtain the final disparity map. Experimental results show that the algorithm can run under low parameter number and low cost GPU resources, and obtain good matching accuracy. [ABSTRACT FROM AUTHOR]

Most existing text classification algorithms based on graph neural network ignore the problem of over-smoothing, and ignore the problem of information loss due to graph topology, resulting in poor classification performance. To solve this problem, this paper proposed a method to measure the smoothness of multiple text graph representations WACD and a regularization term RWACD to suppress over-smoothing. Subsequently, this paper proposed an attention and residual-based network structure ARS to compensate for the loss of textual information due to graph topology differences. Finally, this paper proposed a graph convolutional neural network text classification algorithm RA-GCN. RA-GCN used ARS to fuse text representations in the graph representation learning layer, and used RWACD in the readout layer to suppress over-smoothing. This paper conducted experiments on 6 Chinese and English datasets. The experimental results demonstrate the classification performance of RA-GCN, and the effects of RWACD and ARS are verified through multiple comparative experiments. [ABSTRACT FROM AUTHOR]

In recent years, single device sound scene classification has achieved good results, however, progress in multi-device sound scene classification has been slow. To solve the problem of large differences in the number of multi-device samples, this paper proposed a pairwise feature fusion algorithm. By calculating the differences in the spectrogram for each pair of paired samples and averaging these differences after accumulation, obtaining the average spectral characteristics of each device for the conversion of device samples. The algorithm effectively improved the generalization ability of the model while increasing the number of device samples. Meanwhile, in order to obtain global information, it proposed a lightweight attention module, which could make the model focus on the training of the whole sound sequence information on the basis of reduced computation by performing self-attention operations on the input features after compression in the frequency domain. The experimental results show that the proposed algorithm has better advantages in terms of model size and classification accuracy compared with other methods. [ABSTRACT FROM AUTHOR]

There is a huge demand for weather cloud images in the observatory' s weather monitoring system. In order to solve the problems of model instability and loss of image features when the conventional generative adversarial network expands the dataset of the weather cloud images, this paper proposed a double-layer embedded adversarial image generation method based on SAU-Net DCGAN. This method consisted of two layers of networks which were nested within each other. Firstly,by the first layer of embedded network, it added the U-shaped network to the generator of the generative adversarial network. This network acted as the basic architecture and enhanced the feature recovery capability of the image by using the jump connection between the encoder and the decoder. Secondly,by the second layer of embedded network, it added SA to the U-shaped network. This attention mechanism reduced the complexity of the model by simplifying the parameters, improved effectively the feature loss in the dark part of the image. Finally, it developed a new weight calculation method to strengthen the connection between each features and improved the extraction of detail texture features from the images. The experimental results show that the quality of the images generated by this method is better than that of the conventional generative adversarial network in terms of sharpness and saturation. The evaluation indicators PSNR and SSIM have increased by 27. 06 dB and 0. 606 5 respectively. [ABSTRACT FROM AUTHOR]

In order to solve the problem of reducing the accuracy of part segmentation due to the loss of secondary features by PointNet max pooling, this paper proposed attention weighted feature aggregation Point Net network for part segmentation, and it could make full use of different features of point cloud for part segmentation. Firstly, it used the multiple layer perceptions to extract the spatial geometric features of the point cloud, and then multiplied the features and the shared weight matrix to obtain the attention scores of each feature of each point. Then it used the normalized scores as the weight to multiply the corresponding global feature and summed to obtain the aggregated global feature. Finally, it used the multiple layer perceptions to map the aggregated feature to the part segmentation results. Experimental results show that the proposed method improves the overall average mean intersection over union of part segmentation than traditional PointNet, has significant advantages in network robustness and computational complexity, and effectively optimizes PointNet. [ABSTRACT FROM AUTHOR]

For image denoising, this paper proposed CEANet which had a dual-channel dilated convolution with attention mechanism to solve the problem of information loss caused by deep neural network. Reserving block merged output feature maps of each layer to make up the loss of detail information during convolution. Dilated convolution achieved better balance between denoising performance and efficiency,extracting more features with less parameters and enhancing the representation capability of the model for noisy images. The sparse module of dilated convolution expanded the receptive field to extract significant structural information and edge features and recover details of complicated noisy images. The feature enhancement module based on attention mechanism further guided network for image denoising by fusing global features with local features. The experimental results show that CEANet achieves high peak signal-to-noise ratio and structure similarity mean value at Gaussian white noise level of 25 and 50, which can capture image detail information more efficiently and has better performance in edge retention and noise suppression. Through the above comparative experiments proved the effectiveness of the algorithm framework. [ABSTRACT FROM AUTHOR]

Due to the lack of sufficient co-occurrence information in short text, weak connections between words, and it is difficult to obtain subject words, which leads to the need to manually label a large number of training samples for short text classification, and the problems of sparse features and dimension explosion. This paper proposed a word symbiotic short text classification model based on attention mechanism and label graph（WGA-BERT）.Firstly, this paper used the pretrained BERT model to calculate the context aware text representation, and used WNTM to model the potential word group distribution of each word to obtain the topic expansion feature vector. Secondly, this paper used a tag graph construction method to capture the structure and relevance of subject words. Finally, this paper used an attention mechanism to establish the relationship between subject words and between subject words and text, which solved the problems of data sparsity and subject text heterogeneity. The experimental results show that the WGA-BERT model improves the classification accuracy by an average of 3% compared with the traditional machine learning model. [ABSTRACT FROM AUTHOR]

Relation detection for knowledge base question answering (KBQA) aims to select the best relation path that matches the question expressed by natural language from the candidate relations in the knowledge base and retrieve the answer to the question. To solve the problem of semantic information loss and inadequate attention interaction in existing relation detection methods, this paper proposed a multi-granularity relation detection model incorporating global-local features. The model firstly used bi-directional long short-term memory (Bi-LSTM) networks to encode the question and relation and modeled relations from multiple granularity, such as word-level and relation-level representation. Then, the model introduced a bi-directional attention mechanism to implement attentive interaction of the question and relation. Finally, it extracted global and local features by the aggregation operation and word-level interaction, respectively, to calculate the semantic similarity of the question and candidate relation. Experiments show that the proposed model achieves 93.5 % and 84.13% accuracy on SimpleQuestions and WebQuestionsSP datasets, respectively, improving the effect of relation detection. [ABSTRACT FROM AUTHOR]

The recommendation algorithm based on graph neural network can extract the association relationship between users and goods. Traditional methods can’t extract this relationship. At present, most of these algorithms ignore the general prefe-rences in the review data of users and products. In order to solve this problem, this paper proposed a new method. This me-thod used the graph neural network to extract association relations, and the advantage of deep learning to extract the general preferences, and carried out feature fusion to improve the recommendation effect. This paper conducted comparative experiments and ablation experiments on four sets of public data sets to verify the effectiveness of the proposed method. The evaluation indexes include the recall rate and normalized discounted cumulative gain. Experiments show that this method is more effective than the existing algorithms. The feature fusion of the two networks can improve the recommendation effect. [ABSTRACT FROM AUTHOR]

In the field of CXR image classification, current studies mostly use convolutional neural network and transfer learning technology. But in the process of rapidly constructing a network without considering the specificity of the CXR image. To solve the above problem, this paper proposed a novel XDense-RC-net. Based on the DenseNet model, this method improved the capability of feature extraction and feature fusion by introducing a new spatial attention mechanism in the densely connected layer, Optimized Transition module of DenseNet by using two different pooling strategies to enhance the noise immunity of the model. XDense-RC-net used the chest X-ray14 (multilabel and 14-class) and COVIDx (single label and 3-class) datasets for validating. In the multi-label classification experiments, the average AUC score reached 0.854, which is 0.109 higher than the benchmark method. In the single-label classification experiments, the average accuracy reached 96.75%, which is 7.75% higher than the baseline model. The results show that XDense-RC-net improves the accuracy of CXR image classification and can generalize to multilabel and single-label classification tasks. [ABSTRACT FROM AUTHOR]

Common models of text classification are mostly constructed with recurrent neural network and convolutio-nal neural network in a stacked way. Although this stacked structure can extract more high-dimensional and deeper semantic information, a part of the effective feature information is also dropped when different structures are connected . In order to solve the above problem, this paper proposed a classification model based on dual-channel word vectors, and the model used a shallower structure with attention-mechanism-based Bi-LS TM and CNN to extract features of text representation effectively. In addition, this paper presented a new method to characterize text into two forms, forward and backward, and used CNN to extract feature information of the text . By conducting classification experiments on two different five -classification datasets and comparing with a variety of benchmark models, it verifies that the model is effective and the results show that this model is superior to the other models with stacked structure. [ABSTRACT FROM AUTHOR]

In order to solve the problem of the existing research work focuses on the compatibility of paired items, this paper proposed an outfit compatibility prediction model based on hypergraph representation. The model constructs a fashion hypergraph based on the category information of fashion items and the collocation relationship between different fashion items in the existing dataset, where each hypernode represents an item, and each hyperedge represents an outfit made up of multiple items. To better infer outfit compatibility from the hypergraph, the model converts hypergraphs into traditional graphs, and the graph neural network is used to simulate the complex interaction between nodes. Finally, the attention mechanism is introduced to calculate the outfit compatibility score to strengthen the predictive ability of the model. Experimental results show that in the outfit fill-in-the-blank task and the outfit compatibility prediction task, the model achieved an accuracy rate of 77.29% and 96.23% respectively, which was significantly improved compared with other baseline models. [ABSTRACT FROM AUTHOR]

The recommender system uses knowledge graph can solve the problems of data sparsity and cold start, but it often ignores the importance of high-order collaborative information and different collaborative information to explore users' potential preferences. Therefore, this paper proposed a recommendation model combining enhanced collaborative information and knowledge graph information ( CIKG ) . This model first used the historical interactive data of users and items to obtain first-order collaboration information and high-order collaboration information. At the same time, it used the attention mechanism to capture important information and obtain enhanced collaboration information to supplement the feature representation of users and items. Secondly, this model matched interacted items with the entities in the knowledge graph and performed the propagation in the knowledge graph. It could obtain knowledge graph information and used the information to obtain the user' s preferences and enhance the interpretability of the model. Finally, the aggregator combined the enhanced collaboration information and knowledge graph information to obtain the final representation of users and items, so as to make predictions. The experimental results on Last-fm and Book-crossing datasets show that CIKG has a great improvement over other comparative models. [ABSTRACT FROM AUTHOR]

In recent years, the research of aspect level sentiment analysis attempts to model the dependency between context words and aspects by using attention mechanism and graph convolution model based on dependency tree. However, the accuracy of these methods is limited by noise information and lack of differentiated message passing mechanism. In order to solve the above problems, this paper explored a new method combining attention mechanism with syntactic knowledge, used dependency tree and location information to supervise the attention mechanism respectively, and designed and proposed a dependency tree enhanced attention model for aspect level sentiment analysis. The model could make more rational use of semantic and syntactic information and reduce the dependence on dependency tree. Experiments on three benchmark datasets verify the effectiveness and interpretability of the proposed method . [ABSTRACT FROM AUTHOR]

Aiming at the problem of missing detection and false detection caused by inaccurate motion feature extraction of existing video key frame extraction algorithms, this paper proposed a reinforcement learning key frame extraction algorithm combining multi-channel feature and attention mechanism. The algorithm extracted the human skeleton joint points from the video sequence through the human posture recognition algorithm firstly. Then it used the S-GCN and ResNet50 network to extract the motion features and static features in the video sequence respectively, and performed a weighted fusion of the two. Finally, it applied the attention mechanism to calculate the importance of the video frame of the feature sequence, and used reinforcement learning to extract and optimize key frames. The experimental results show that the algorithm can solve the problem of missing and false detection in the key frame extraction of motion video. It performs well in the detection of video frames containing key actions, with high accuracy and strong stability. [ABSTRACT FROM AUTHOR]

Nowadays intelligent power production uses electric power images widely. However, due to the development of image editing software, some power images are maliciously tampered, which seriously affects the power production process. In order to solve this problem, this paper proposed a dual-channel CenterNet model for power image splicing forgery detection. The color channel extracted the obvious visual features of the image. The stage-analysis channel explored the noise feature. At the same time, the algorithm used a feature fusion module based on attention mechanism, it integrated the dual-channel features to enhanced the feature recognition ability. Experiments show that the model can get better detection performance and has practical significance in the forgery detection application of power images. [ABSTRACT FROM AUTHOR]

At present, aspect-based sentiment analysis based on graph convolution neural network (GCN) integrates the grammatical structure of sentences. It can effectively solve the problem of inaccurate long-range lexical dependence. However, GCN has unnecessary complexity and redundant computing. In addition, it ignores the relative position relationship between attributes and context. This paper proposed a new model to solve these problems. Firstly, the model established a bidirectional GRU layer. Secondly, it used position-aware transformation to increase the importance of context words close to aspect words, and then reduced the complexity by removing nonlinearity and folding the weight matrix between successive layers. Thirdly, it fused the specific mask layer to realize the single-layer MASGC (masked simple graph convolutional networks) structure, and then generated a new attention mechanism based on retrieval context. Finally, it gave the classification results through the full connection layer. The model carries out a large number of experiments on five data sets. The experimental results show that it has higher accuracy and less training time. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the existing super-resolution algorithm based on deep learning didn't make full use of the feature information of each level, resulting in low reconstruction accuracy and large parameter quantity, this paper proposed a double dense connection structure named densely channel attention skip connection network. In the inner structure of the network, it improved the original dense cascade block to generate a channel separable dense cascade block. The outer structure adopted a densely residual connection and attention mechanism to fuse the features extracted by the dense block to achieve the goal that less convolution layer and higher precision effect. This paper tested the network models on several benchmark datasets . The results show the proposed model has higher accuracy and fewer parameters than the other models. [ABSTRACT FROM AUTHOR]

Existing deep learning based single image super-resolution (SISR) models usually improve the fitting ability of the model by increasing the number of network layers, but fail to fully extract and reuse features, leading low quality of reconstructed images. To solve this problem, this paper proposed an image super-resolution model based on feature fusion and attention mechanism. This model used residual in residual (RIR) structure in feature extraction module. The feature extraction module of the network consisted of several residual groups. Each residual group consisted of several residual blocks. This module implemented local feature fusion in each residual group and global feature fusion between each group. In addition, this model introduced coordinate attention module into each residual block and spatial attention module into each residual group. It verifies that the model is able to fully extract features and reuse features. The final experimental results show that the model is superior to the existing models in objective evaluation indexes and subjective visual effect. [ABSTRACT FROM AUTHOR]

Aiming at the problem of low person re-identification accuracy caused by similar appearance and posture of person in surveillance videos, this paper proposed a video-based person re-identification method based on a graph model, which effectively utilized the time sequence information in the video to realize the information interaction across frames and intra-frame regions. Specifically, it used the correlation information between the cross-frame block regions to establish the inherent relationship between the regional nodes, and iteratively updated the regional information through feature propagation. On the other hand, in the metric learning process, it proposed a weighted loss function strategy, which optimized the binary allocation method (that is, discarding or retaining the sample) in the previous mining strategy into a continuous score allocation method, which solved the problem that the available samples were not used efficiently. Finally, it evaluated the model on MARS and DukeMTMC-VideoReID datasets, and the experimental results confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposed a object detection method based on a fused attention mechanism with an improved single shot multibox detector( SSD) algorithm for the detection of synthetic aperture radar (SAR) image ship targets in complex backgrounds by SSD original algorithm that was prone to false detection or missed detection. Firstly, this method introduced the ResNet and improved SSD algorithm to provide rich semantic and detailed information to improve the robustness. Secondly, the fusion of channel and spatial attention enhanced the recognition ability of ship targets and suppressed the interference information such as sea clutter. Meanwhile, it improved the loss function to solve the problem of missed detection when the ships were densely distributed and improve the network training effect. Experiments on the dataset show that the mean average precision ( mAP) of the method is 87. 6%, which is 4. 2 percentage points higher than that of the SSD algorithm, and the missed and false detections of targets are significantly reduced. Compared with the SSD algorithm, this method has better discrimination ability and robustness for ship targets in complex backgrounds, and improves the anti-interference ability. [ABSTRACT FROM AUTHOR]

In speech recognition, the convolution location-based hybrid attention mechanism can not extract location information that can be valid over long term. This paper proposed a new hybrid attention mechanism to solve this problem. Firstly it convolved with the attention score generated for the current time to extract multi-channel features, followed by obtaining the feature vectors of constant dimensions via global average pooling. Then it introduced a LSTM unit as the external memory module and used the generated feature vectors as input to generate the location vectors for the next time point. Finally this paper used the classic LAS model to verify the effect of the new hybrid attention mechanism. Experiment results show that the new hybrid attention mechanism can take full consideration of the attention scores at many past time points. Compared to the convolution location-based LAS model, the label error rate of the proposed scheme on pure and noisy speech datasets is reduced by 1.8% and 2.21%, respectively. [ABSTRACT FROM AUTHOR]

Computer poetry generation is the first step towards computer writing. At present, there are many problems in computer poetry writing, such as unclear theme, the content of poetry is inconsistent with the writing intention. For solving these problems, this paper followed the process of writing poem by the ancient Chinese poet and proposed a method for generating Chinese poem with two stages. The first stage extracted the outline. During this process, it used TextRank algorithm to extract keywords from user input text, and proposed an attention-based sequence to sequence neural network model for expanding keyword. The second stage generated each line of poem based on the outline of poem. During this process, it proposed a sequence to sequence neural network model with dual-encoding and attention mechanism for generating Chinese poem. At the end, this paper verified the effectiveness of this approach by evaluation. Compared with baseline approach, the theme of the Chinese poem generated by this approach is more explicit, and the contents expressed by the poem are more consistent with the writing intention. [ABSTRACT FROM AUTHOR]

Aerial image target detection has problems such as low accuracy of multi-scale target detection, slow detection speed, serious missed detection and serious misdetection. To solve this problems, this paper proposed a target detection algorithm（proYOLOv4） that combined convolutional attention mechanism and lightweight network, and applied it to multi-scale aerial image target detection. First, the algorithm used the K-means clustering algorithm to cluster the aerial data set and optimize the anchor frame parameters, and improved the effectiveness of target detection. Secondly, the algorithm used a lightweight network structure to simplify network complexity and improve detection speed. Finally, the algorithm introduced a convolutional attention module to solve the interference of complex scenes on aerial target detection, thereby effectively reducing the false detection rate and missed detection rate. This algorithm was compared with the aerial photography dataset RSOD and NWPU VHR-10.The experimental results show that the detection effect of pro-YOLOv4 is significantly improved compared with YOLOv4,and the average detection accuracy is increased by 3.42% and 3.98%,respectively.This algorithm not only shows good detection performance for multiscale targets, but also reduces the missed detection rate of targets, and has good robustness and generalization ability. [ABSTRACT FROM AUTHOR]

In order to solve the problems of insufficient feature extraction of sEMG end-to-end gesture recognition and low accuracy of multi-gesture recognition, this paper proposed a multi-stream convolutional EMG gesture recognition network model fusing attention mechanism. The proposed model generated sub-myoelectrograms from multi-channel time-domain sEMG through sliding windows and used a multi-stream convolutional neural network to fully extract the semantic features of each sampled channel sEMG, and then aggregated them to obtain rich multi-channel gesture semantic features. At the same time, the proposed model calculated the attention distribution map of semantic features from temporal and feature channel dimensions to strengthen the useful features and weaken the useless features, further improved the accuracy of multi-gesture recognition. The experiment used the Ninapro dataset for training and testing and compared with the mainstream electromyogram recognition model. The experimental results show that the proposed model has better performance in recognition accuracy, which proves the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Due to the influence of culture and society on the expression of human emotion, the features of speech emotion in different languages vary greatly, which leads to the insufficient generalization ability of speech emotion recognition model in a single language . To solve this problem, this paper proposed a multi-lingual speech emotion recognition method based on multitask attention. By introducing the auxiliary task of language identification, the model could not only learn the emotional features shared by different languages, but also learn the unique emotional characteristics of each language, so as to improve the generalization ability of the multi-language emotion recognition model. Experiments on the dimensional affective corpora of two languages show that the proposed method improves the mean values of relative UAR of the Valence and Arousal tasks by 3 . 66% ~ 5. 58% and 1. 27% ~ 6. 51 %, respectively, compared with the benchmark methods . Experiments on discrete affective corpora of four languages show that the mean values of absolute UAR improves by 13. 82% ~ 15. 75% compared with the benchmark methods. Therefore, the proposed method can effectively extract the language-related emotion feat ures and improves the performance of multi-lingual emotion recognition. [ABSTRACT FROM AUTHOR]

Most existing recommendation algorithms using knowledge graph could not solve the problem of irrelevant entities in the knowledge graph when detecting the potential preferences of user, resulting in low accuracy of recommendations. To address this problem, this paper proposed a recommendation algorithm KG-GAT based on the knowledge graph and graph attention network. This algorithm used the knowledge graph as auxiliary information, and the graph attention network redefined the embedding of the entity by incorporating the information about the neighboring entities related to the entity in a layered attention mechanism. It obtained a more effective potential representation of users and items, generated a more accurate top-N recommendation list, and provided interpretability as well. Compared with other algorithms, the experimental results on two open datasets show that KG-GAT can effectively solve the problem of unrelated entities when detecting the potential preferences of user along with the relationships in the knowledge graph. [ABSTRACT FROM AUTHOR]

Compared with the single language short-text sentiment classification, the code-switching short-text sentiment classification has more challenges to face up with because the word that expresses emotion is not unique and the sentence has complex grammatical structure, using traditional word embedding alone cannot make the classifier learn enough useful features, resulting in poor classification. This paper proposed a dual-channel deep learning model which integrated char and word features. Firstly, in order to solve the problem of imbalanced data set, it proposed a data undersampling algorithm based on Bert semantic similarity. Secondly, it constructed dual-channel deep learning network, the original data embedded in chars and words were sent to two different module composed of CNN and LSTM with attention mechanism through two channels for extracting multi-level features, and finally features from the two channels were fused for classification. The experimental results show that the overall performance of the proposed model is further improved than the current representative deep learning models on the code-switching five-category dataset published in NLPCC2018&task 1. [ABSTRACT FROM AUTHOR]

Although great progress has been made in open domain dialogue systems in recent years, automatic evaluation methods based on these systems are still a problem to be solved. In order to solve the problem that various evaluation methods need a lot of tagged data and low accuracy, this paper proposed a model for judging whether the response pair was a real dialogue by using the long-term and short-term memory network and attention mechanism. The model was based on continuous dialogue corpus, which solved the shortcomings of the current evaluation methods based on the reference response. On the Cornell and Reddit data sets, the accuracy of the model is 57. 2% and 71. 8% respectively, which is obviously improved compared with the existing evaluation models. [ABSTRACT FROM AUTHOR]

Most of the relation extraction approaches could not learn the long distance dependence information from the long sentences with entity co-occurrence. This paper proposed a new relation extraction model to solve this problem. It was based on the recurrent convolutional neural network and the sentence-level attention mechanism. It used the Bi-GRU neural network to learn context vectors for words. And it adopted the piecewise maximum pooling method, which could obtain fine grained features. Experimental results on the NYT dataset demonstrate that this proposed method outperforms the baseline systems. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the use of softmax as a long-short-term memory network classifier leads to the lack of generalization ability of the entity relationship recognition model, it is not suitable for the extraction of TCM entity relationships. This paper proposed a bidirectional long short-term memory (BILSTM) relational identification algorithm (BILSTM-GBDT) that incorporates a gradient boosting decision tree (GBDT) Firstly, it trained the Chinese medicine text vector by word2vec, then extracted the high-order features by the bidirectional long short-term memory network based on the attention mechanism. Finally, it used the integrated classification model gradient lifting tree as the feature classifier to improve the relationship recognition effect. Experimental results on multiple relational corpora such as Chinese medicine show that the model has higher accuracy, recall and F value than traditional SVM method, GBDT method and deep learning method. [ABSTRACT FROM AUTHOR]

The BiLSTM neural network model has long training time and cannot fully learn text sentiment information. In order to solve the problems, this paper proposed a text sentiment classification model based on BiGRU-attention neural network. Firstly, it used the bidirectional gated recurrent unity (BiGRU) neural network layer to extract the features of the deep text information. Secondly, it used the attention mechanism (attention) layer to allocate the corresponding weight of the extracted text deep information. Finally, it put the text feature messages of different weights into the softmax function layer to carry out the text sentiment classifications. The experimental results on the IMDB data set show that the accuracy of the proposed neural network model is 90.54%, the loss rate is 0. 2430 and the time cost is 1 100 s and the validity of the BiGRU-attention model is verified. [ABSTRACT FROM AUTHOR]