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Atmospheric turbulence is a crucial factor that affects the quality of long-distance imaging. Though current deep learning models can effectively suppress geometric displacement and spatial blurring caused by atmospheric turbulence, such models require a large number of parameters and computational resources. To tackle this problem, this paper proposed a lightweight atmospheric turbulence degraded image restoration model based on generative inversion that entailed three core mo-dules: the DeBlur module, the remove shift module, and the turbulence regeneration module. The DeBlur module used high-dimensional feature mapping blocks, detail feature extraction blocks, and feature compensation blocks to suppress image blurring caused by turbulence. The remove shift module compensated for pixel displacement caused by turbulence using two convolutional layers. The turbulence regeneration module regenerated turbulence degraded images through convolutional operations. In the DeBlur module, it designed an attention-based feature compensation module that integrated the channel attention mechanism and the spatial mixed attention mechanism to focus on essential detail information in the image during training. The proposed model achieved peak signal-to-noise ratios of 19.94 dB and 23.51 dB, and structural similarity values of 0.688 2 and 0.752 1 on publicly available dataset Heat Chamber and self-built dataset Helen, respectively. Furthermore, it reduced the number of parameters and computational resources, compared to the current state-of-the-art(SOTA) method. The experimental results demonstrate the effectiveness of this method in restoring atmospheric turbulence degraded images. [ABSTRACT FROM AUTHOR]

COVID-19 is spreading rapidly around the world. In order to diagnose it quickly and accurately and thus block the chain of epidemic transmission, the study proposed a deep learning-based classification network DLDA-A-DenseNet. Firstly, DenseNet-201 combined deep layer dense aggregation to aggregate feature information at different stages to enhance its ability to identify and localize lesions. Secondly, this paper proposed efficient multi-scale long-range attention to refine the aggregated features. Moreover, this paper used a balanced sampling training strategy to eliminate the bias for the class imbalance problem of CT image dataset. Testing on the China consortium of chest CT image investigation dataset, the method improved 2.24%,3.09%,2.09%,2.60% and 3.48% in accuracy, recall, precision, F1 score and Kappa coefficient compared with DenseNet-201,and achieved an optimal accuracy of 99.50% on COVID-CISet image dataset. The results show that the proposed COVID-19 CT image classification method can fully extract the lesion features of CT slices compared with other methods, and has higher classification accuracy and good generalizability. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence, digital images have been widely used in various fields. However, due to the appearance of image editing software, a large number of images have been tampered with maliciously, which seriously affects the authenticity of image content. Different from the general object detection, the study of image tampering detection needs to pay more attention to the tamper information of the image itself, which is often manifested in a weak form. Therefore, the detection model needs to focus on learning more abundant tamper features. This paper proposes a dual-stream Faster R-CNN model that combines gradient edge information and attention mechanism, and the model can realize detection and location of regions with different tampering types. One of the two streams is the color stream, which uses the attention mechanism to extract the surface features of the image, such as brightness contrast, visual difference of tampering with the boundary, etc. The second of the two streams is a gradient stream. A Gradient high-pass filter is used to enhance the anomaly edge features between the real area and the tampered area, making it easier for the model to find faint tampered traces in the tampered image. Finally, the features of color stream and gradient stream are fused by means of compact bilinear pooling. Due to the relatively small size of publicly available image tampering data sets, the Pascal VOC 2012 is used to create an image tampering detection data set which containing 10 010 images for model pre-training. The experimental results on COVER, Columbia, and CASIA data sets show that the model proposed in this paper improves the detection accuracy by 7.1% to 9.6% compared to the latest models, and exhibits higher robustness under JPEG compression and image blur attacks. [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]

Shield load is the main performance indicator of the shield, accurate load prediction is significant to ensure the safety and efficiency of the shield and the stability of the surrounding environment. Recognizing the limitations of the traditional prediction methods, this paper proposes a hybrid model (CBM), combining convolutional neural network (CNN), bi-directional long short-term memory (BiLSTM) and attention mechanism, to predict the shield load accurately based on the high-dimensional feature and time series characteristic of the data. The proposed model not only can extract the high-dimensional features and time series characteristics of the data, but also can highlight the importance of high-dimensional features and important time node information. The experiment results show that compared with the existing models, the proposed model achieves a higher prediction performance, the prediction accuracy of the thrust and torque is 94.2% and 96.2%. [ABSTRACT FROM AUTHOR]

For the beamforming design of intelligent reflector assisted large-scale MISO systems, most of the current researches are basing on the fully known channel state information, and do not consider the coupling relationship between the active beamforming matrix at the base station and the reflection coefficient matrix of intelligent reflector. The intelligent reflector assisted channel is a cascade channel, which is difficult to estimate and has high pilot frequency overhead. In view of this, this paper proposed a hybrid intelligent reflective surface architecture containing active and passive components. Firstly, based on the sparse characteristics of millimeter-wave channels, it used the compressed sensing algorithm to estimate the channels, and designed a two-stage convolutional network framework integrating attention mechanism to jointly optimize the reflection coefficient matrix and beamforming matrix with the aim of maximizing and rate. The experimental results show that compared to existing methods, the proposed method can achieve better sum rate performance with fewer pilots, effectively reducing pilot loss and reducing computational time complexity. And when the communication environment changes, the network also has good robustness. [ABSTRACT FROM AUTHOR]

To address the problem of poor recommendation accuracy in the session recommendation model, this paper proposed a capsule-based graph convolutional disentanglement for session-aware recommendation (CGCD) method.Specifically, it used disentangled learning technique to transition item embedding into factor embedding based on multiple sub-channels. Then, it used a capsule dynamic fusion strategy to aggregate different factors to obtain a new item embedding.In addition, it used a multi-head attention mechanism to assign weights to each item in the session. Finally, it aggregated the item embedding with other items in the current session according to the assigned weights, and then generated an accurate session representation to achieve item recommendation. Experiments on two publicly available real datasets show that the proposed model can improve the Pre@10, Pre@20, MRR@10 and MRR@20 by 5.17%, 2.99%, 6.56% and 2.94% on average of the recommended performance, which verifies the effectiveness and efficiency of the method in this paper. [ABSTRACT FROM AUTHOR]

Existing lip-reading algorithms for Chinese sentences are inadequate at feature extraction and visual ambiguity resolution, which leads to low accuracy. Aiming at this problem, this paper proposed a lip-reading algorithm for Chinese sentences based on temporal convolutional network and 3D convolutional neural network（3DT-CHLipNet）.Firstly, it used a Temporal Convolutional Network to extract long-term features from lip dynamics sequences, which has a much larger receptive field than the Long Short Term Memory Network. Secondly, in order to minimize the visual ambiguity in Chinese lipreading, it adopted a Transformer model with the self-attention mechanism to capture the context information and improve the accuracy of sentence prediction. Finally, it introduced a temporal masking data augmentation strategy in the data preprocessing to further reduce the error rate of the algorithm. Comparison experiments on CMLR, the largest open-source Sequence-to-Sequence Chinese Mandarin Lip Reading dataset, show that the improvement in accuracy over representative lip reading algorithms for Chinese sentences ranges from 2.17%to 23.99%. [ABSTRACT FROM AUTHOR]

Air pollution seriously affects human health and social sustainable development. However, the multi-variable air quality data obtained by sensors often have missing values, which brings difficulties to data analysis and processing. Currently, many analysis methods for changes in a certain air component only rely on time data and spatial data of this attribute, ignoring the influence of other air components on the trend of this attribute in the same time interval. In addition, it is difficult to achieve ideal results in filling discrete missing data. This paper proposes a Time Attention Model (TAM) based on deep learning, which uses attention mechanism to focus on the correlation between different timestamps and the correlation between different feature time series, and combines short-term historical data to fill missing values in multi-variable air quality data. The proposed model is evaluated using air quality data from Beijing, and the experimental results show that TAM has advantages over ten other baseline models. [ABSTRACT FROM AUTHOR]

Miao nationality is one of the oldest nationalities in the history of China. Miao costumes are a distinctive and colorful ethnic culture formed in the long historical process. The colorful Miao costume patterns highly summarize the pursuit of the national spirit, and the styles, colors and textures are the unique style characteristics of the costume patterns. Therefore, it is of great academic significance and application value for the digital protection and inheritance of Miao costume culture to efficiently and accurately segment minority costume patterns. At present, some scholars have carried out researches on minority costume images, but most of them are based on fuzzy C-means clustering algorithm and active contour model, and few of them are based on the deep learning algorithm for minority costume image segmentation. The traditional image segmentation algorithm used to segment the Miao costume patterns leads to the loss of some characteristics of spatial information and low efficiency and accuracy of image segmentation. In order to promote the digital protection of minority costume images and the inheritance of ethnic culture, aiming at the problems of large color differences, diversified styles and textures of Miao costume patterns, this paper proposes a Miao costume pattern segmentation model based on the attention mechanism. FCN is used as the main structure, and the feature weight of the input image is adjusted by the attention mechanism to improve the segmentation performance, so that the model can better associate the features of interest from the local level to the global level. Firstly, data enhancement is used to preprocess the image data to improve the generalization ability and robustness of the model and avoid over-fitting. Then, the feature extraction of Miao costume patterns is carried out by using the full convolutional network model fused with attention module (CBAM) to reduce the loss of spatial information, so as to effectively improve the segmentation accuracy of the model and reduce the loss rate. The model has a total of nine network layers and uses convolution, Batch Normalization (BN), CBAM, add, pooling, and concat operations. The convolution operation is used to extract image features and double the number of channels. BN operation is mainly used to normalize the training image to prevent the model from over-fitting. CBAM layers make the model pay more attention to foreground pixels while learning network weights. The add layer increases the amount of information under image features. The pooling operation performs down-sampling operation on the image to reduce the image size by two times, retain the main features while reducing the number of parameters, and improve the model generalization ability. Concat operation is used for skip connection between corresponding feature layers, so that the model can extract more rich feature information. Except Sigmoid activation function for the last convolutional layer, ReLU function is used for other layers. Analyzing the visual characteristics of ethnic costume from the perspective of deep learning and computer vision is not only convenient for researchers to store and retrieve ethnic costume images, but also conducive to the digital protection of ethnic costume images and the inheritance of ethnic culture. Finally, the experimental results on the Miao costume pattern data set show that compared with the traditional U-Net and FCN models, the IoU increases by 14.79% and 18.21%, respectively, and the Dice coefficient increases by 11.03% and 13.95%, respectively using less than half of the training parameters. Therefore, this paper provides an effective and feasible method for the research of Miao costume pattern segmentation algorithm. In the future, in-depth research regarding the mapping relationship between costume style feature points and image feature points after image segmentation will be conducted. On a certain basis, the research results play a role in the development and protection of minority costume culture, and also provide certain reference for minority costume image segmentation algorithm research. [ABSTRACT FROM AUTHOR]

In dynamic gesture recognition methods based on deep learning, aiming at the problems of large network scale, simple dimension of extracted spatiotemporal features, and insufficient extraction ability of effective features, this paper proposed a deep network framework. Firstly, this paper applied a novel spatiotemporal convolution module based on multiscale information fusion in the time domain to improve the 3D residual network structure, greatly reduced the size of the network and obtained rich spatiotemporal receptive field characteristics. Then it introduced a spatiotemporal feature channel attention mechanism with global information synchronization, and used a few parameters to construct the global dependency between feature maps, and the module could obtain the key features of dynamic gestures more efficiently. The experimental results on the self-built gesture dataset SHC and the public gesture dataset SKIG show that the proposed gesture recognition method has fewer parameters and more powerful multiscale spatiotemporal feature extraction ability, and gains a higher recognition rate than the current mainstream algorithms. [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]

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]

[Objectives]The posture of meat goose is an important indicator for early warning of the meat goose’s abnormal behavior and evaluation of the meat goose’s health status. Since manual observation of the meat goose’s posture on traditional farms was time-consuming, laborious and highly subjective, a new method based on deep learning was proposed in this paper. The detection algorithm recognized the posture of meat goose automatically. [Methods]The YOLO v5 model was used to identify the four postures of Yangzhou goose (standing, resting, drinking and feather preening). Three attention modules including SENet, CBAM and ECA were added to the YOLO v5 model to improve the network structure and the recognition ability of the model. Light and dark experiments and dense scene experiments were designed to further verify the robustness of the model in complex scenes. [Results]The average detection accuracy (mAP) of the YOLO v5+ECA model was 88.93%,which was 2.27% higher than that of YOLO v5. In terms of recognition accuracy (AP), the standing posture was 91.85%,the resting posture was 93.42%,the drinking posture was 90.02%,and the feather preening posture was 80.42%. In the light and dark experiments and dense scene experiments, the performance of the YOLO v5+ECA model was stable, and the missing detection and false detection were relatively rare. [Conclusions]The posture of the meat goose can be detected rapidly and accurately in the farm complex scene with this model. The data can support the following research on the behavior monitoring and epidemic prevention of the meat goose. [ABSTRACT FROM AUTHOR]

Aiming at the problems of similar appearance and occlusion of people in the video person re-identification, a video-based person re-identification model based on pyramid segmentation and attention mechanism was studied and designed. First, in order to enhance the recognition ability of the graph model for the local features of pedestrians, a multi-scale horizontal pyramid segmentation method was proposed. In addition, given that the simple spatiotemporal attention module was prone to damage person features due to occlusion, the spatiotemporal attention module was improved using the spatiotemporal correlation attention method, which gradually learns and aggregates spatially local information while interacting in time sequence to suppress person interference features and enhance discriminative features. This paper evaluates the model on Mars and DukeMTMC-VideoReID datasets, and the experimental results confirm the effectiveness of the proposed method. [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]

Aiming at the problem of fine-grained visual categorization, this paper proposed an effective algorithm to achieve end-to-end fine-grained visual categorization. The ECA module in ECA-Net was a channel attention mechanism with significant performance advantages. It model-fused with the classic network ResNet-50 to form the ResEca. Then, it used the object-level image positioning module and the part-level image generation module to generate object-level and part-level images. Those images combined with original images could be as the input of the new constructed network Tb-ResEca-Net. This paper trained the 95. 1% and 95. 3% respectively on the test set of the corresponding dataset. The experimental results show that this method has higher classification accuracy and stronger robustness compares with other traditional fine-grained classification methods, which is an effective fine-grained image classification method. [ABSTRACT FROM AUTHOR]

Single object tracking( SOT) is a technique to analyze the motion status and provide localization of the single target by using target appearance and context information in video. It has promising applications in intelligent surveillance, intelligent interaction, navigation and guidance, etc. However, problems such as occlusion, background interference and appearance variation led to slow progress in practical applications. With the rapid development of deep learning in recent years, the study of using deep learning techniques to optimize SOT algorithm has become one of the hot spots in computer vision. Around the SOT algorithm based on deep learning, this paper respectively provided outline and analysis of each of the six aspects of correlation filter, Siamese networks, meta-learning, attention, recurrent neural networks and generative adversarial networks according to the core algorithms, after analyzing the basic principles of SOT. In addition, this paper summarized the current state of research and proposed the development trend and optimization ideas of the algorithms. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low accuracy and poor generalization of industrial aluminum surface defect detection, this paper proposes a multi task feature fusion detection method based on improved segmentation decision network. Firstly, aiming at the common pit defects in industry, it constructs the defect detection data set of this paper; secondly, on the basis of the original segmentation detection model,it also adds the aluminum area segmentation network to reduce the demand for samples in the network structure; in addition, the Squeeze-and-Excitation module is introduced to make the model focus more on the dimensional features related to aluminum defects and improve the detection accuracy of the network. Finally, a comparative experiment is carried out on the data set of dirty spots on the aluminum surface. The structure shows that the segmentation accuracy of the proposed method is improved by 1.6% and the classification accuracy is improved by 4.01% compared with the original model; compared with FCN and u-net network, the detection accuracy of image segmentation is 0. 76% higher than that of FCN and U-Net network, and 1. 12% higher than that of VGG and Resnet50 network. The experimental results have proved that this method has sufficient detection accuracy in the detection of dirty spots on the aluminum surface, and is stable and efficient. [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]

With the popularity and development of deep learning, the existence of adversarial samples is a serious threat to the security of the deep learning model. For the attack problem of adversarial samples in the physical world, this paper proposed a physical adversarial sample detection method based on attention mechanism. The method combined attention mechanism and feature compression to detect pertinently of local visual adversarial samples, excluded effects of non-major regions, and reduced calculation effort. It effectively combined multiple feature compression methods to deal with the main areas of the sample, which destroyed the structure of the antagonistic block and made them unaggressive. Perform defense tests on different adversarial attacked on the MNIST and CIFAR-10 data sets and compared with other countermeasures. The experimental results show that the defense accuracy of the method can reach more than 95%. This method is higher versatility and stronger stability than other local adversarial sample defense methods, and can effectively defense the attack of local visual adversarial samples. [ABSTRACT FROM AUTHOR]

Aiming at the problems of complex background, variety of change types, missing detection and rough boundary recognition in high-resolution remote sensing image of the same region, this paper proposed a high-resolution remote sensing image building change detection algorithm based on Siam-UNet++network. The algorithm used UNet++as the backbone extraction network. In the encoder phase, it applied the Siam-diff structure to extract the change features of the two sequential images, and employed the attention mechanism after the up sampling and lateral jump path connection in the decoding stage to highlight the building change features and inhibit the network learning from other types of features. Meanwhile, it used the MSOF strategy to weight and fuse feature information of different semantic levels, which improved the accuracy of building change detection. Finally, it adopted a sliding window method to predict large-scale remote sensing images, reducing the hole pattern generated by the change result map during the splicing process. The experimental results demonstrate that proposed algorithm shows better performance than other models. [ABSTRACT FROM AUTHOR]

At present, the application of convolutional neural network in remote sensing image classification requires a lot of computation and occupies a lot of memory. This paper proposed a knowledge distillation method based on pruning network to classify remote sensing images. Based on the model pruning theory, it introduced attention mechanism into the network structure to strengthen the extraction of important features, and carried out model pruning on the network. Then it introduced knowledge distillation technology to carry out transfer learning to compensate the loss of classification accuracy after pruning. In order to prove the advance and reliability of the method, it carried out a comparison experiment with similar algorithms on NWPU-RESISC45 remote sensing satellite data set. The experimental results show that the proposed method not only has better performance in classification accuracy, but also has more advantages in model size. [ABSTRACT FROM AUTHOR]

Due to the various species of fish, and the influence of different light and background environments, the recognition accuracy of some traditional fish recognition algorithms bases on color texture or feature point extrac tion is reduced, and good classification results cannot be achieved. To solve the problem, based on the existing Alex Net convolutional neural network, this paper proposes one method to speed up model training by reducing redundant convolutional layers. An item-based soft attention algorithm is applied to the improved AlexNet convolutional neural network model, which consists of four convolutional layers, one item-based soft attention layer, and two fully-con nected layers. Meanwhile, a fish recognition model is established using transfer learning methods. The test results show that the average recognition accuracy of the proposed algorithm achieves 97. 43%, which is 4.08% higher than the original AlexNet model, and the average recognition rate of some fishes achieves 99.31% and time consumption of fish recognition is reduced by 35%. In conclusion, compared with state-of-the-art fish recognition algorithms. the fish recognition algorithm proposed in this study achieve higher accuracy, lower model complexity, and stronger robustness. [ABSTRACT FROM AUTHOR]

With the development of deep learning, more and more deep learning models have been applied to the task of relation extraction, but traditional deep learning models can't solve long distance dependence problems. At the same time, distant supervision will inevitably generate wrong labels. For these two problems, this paper proposed a distant supervision relationship extraction method based on GRU (gated recurrent unit) and the attention mechanism. First, it adopted the GRU neural network to extract text features and solve long-distance dependence problems. Second, it constructed a sentence-level attention mechanism on entity pairs to reduce the weight of noise sentences. Finally, based on the real data set, by calculating the accuracy rate and recall rate, and drawing the PR curve to prove the proposed method has achieved significant progress compared with some existing methods. [ABSTRACT FROM AUTHOR]

In the mass music, how to analyze the user’s needs according to the user’s history' listening record to implement song recommendation is one of the challenging topics in the music recommendation field. The existing music recommendation method simply uses all the music the user has heard as the context of the music recommendation, which results in the same weight distribution of contexts learned by different types of music,which seriously affects the accuracy of the music recommendation. In response to this problem,this paper proposed a music recommendation method based on attention mechanism, which dynamically allocated different attentions to different users’ historical listening music, that was learns different contex-tual weights so as to make the recommendation result more in line with the user’s actual preference. And through the test on public music dataset named million song dataset,the recommended accuracy of the method proposed has greatly improved. [ABSTRACT FROM AUTHOR]