In recent years, with the rapid development of Internet technology, online shopping has become a mainstream way for users to purchase and consume. Sentiment analysis of a large number of user reviews on e-commerce platforms can effectively improve user satisfaction. This paper proposes a new sentiment analysis model-SLCABG, which is based on the sentiment lexicon and combines Convolutional Neural Network (CNN) and attention-based Bidirectional Gated Recurrent Unit (BiGRU). In terms of methods, the SLCABG model combines the advantages of sentiment lexicon and deep learning technology, and overcomes the shortcomings of existing sentiment analysis model of product reviews. The SLCABG model combines the advantages of the sentiment lexicon and deep learning techniques. First, the sentiment lexicon is used to enhance the sentiment features in the reviews. Then the CNN and the Gated Recurrent Unit (GRU) network are used to extract the main sentiment features and context features in the reviews and use the attention mechanism to weight. And finally classify the weighted sentiment features. In terms of data, this paper crawls and cleans the real book evaluation of dangdang.com, a famous Chinese e-commerce website, for training and testing, all of which are based on Chinese. The scale of the data has reached 100000 orders of magnitude, which can be widely used in the field of Chinese sentiment analysis. The experimental results show that the model can effectively improve the performance of text sentiment analysis.

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Sentiment Analysis for E-Commerce Product Reviews in Chinese Based on Sentiment Lexicon and Deep Learning

Significant progress is made in the field of object tracking recently. Especially, trackers based on deep learning and correlation filters both have achieved excellent performance. However, object tracking still faces some challenging problems such as deformation and illumination. In such kinds of situations, the accuracy and precision of tracking algorithms plunge as a result. It is imminent to find a solution to this situation. In this paper, we propose a tracking algorithm based on features extracted by residual network called Resnet features and cascaded correlation filters to improve precision and accuracy. Firstly, features extracted by a deep residual network trained on other image processing datasets, are robust enough and retain higher resolution, therefore, we exploit Resnet-101 pretrained offline to obtain features extracted by middle and high layers for target appearance model representation. Resnet-101 is deeper compared with other deep neural networks which means it contains more semantic information. Then, the method we propose to combine our correlation filters is superior. We propose cascaded correlation filters generated by handcraft, middle-level and high-level features from residual network to gain better competence. Handcraft features localize target precisely because they contain more spatial details while Resnet features are robust to the target appearance change because they retain more semantic information. Finally, we conduct extensive experiments on OTB2013 and OTB2015 benchmark. The experimental results show that our tracker achieves high performance under all kinds of challenges and performs favorably against other state-of-the-art trackers.

Visual object tracking based on residual network and cascaded correlation filters

To improve the issue of low-frequency and high-frequency components from feature maps being treated equally in existing image super-resolution reconstruction methods, the paper proposed an image super-resolution reconstruction method using attention mechanism with feature map to facilitate reconstruction from original low-resolution images to multi-scale super-resolution images. The proposed model consists of a feature extraction block, an information extraction block, and a reconstruction module. Firstly, the extraction block is used to extract useful features from low-resolution images, with multiple information extraction blocks being combined with the feature map attention mechanism and passed between feature channels. Secondly, the interdependence is used to adaptively adjust the channel characteristics to restore more details. Finally, the reconstruction module reforms different scales high-resolution images. The experimental results can demonstrate that the proposed method can effectively improve not only the visual effect of images but also the results on the Set5, Set14, Urban100, and Manga109. The results can demonstrate the proposed method has structurally similarity to the image reconstruction methods. Furthermore, the evaluating indicator of Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) has been improved to a certain degree, while the effectiveness of using feature map attention mechanism in image super-resolution reconstruction applications is useful and effective.

Image super-resolution reconstruction based on feature map attention mechanism

Currently, many efforts have been done towards secure data privacy protection and reliable information trace, however the conventional solutions are still vulnerable to information loss, privacy leakage and other attacks till the blockchain technology emerged. Blockchain can record historical data by establishing a collectively maintained and tamper-resistant public ledger to ensure the security and reliability of the data stored in a distributed network. It realizes a decentralized network architecture, which can bring new solutions to many fields such as information tracing and privacy protection. In recent years, blockchain technology has gradually attracted the close attention of all industries, and this paper summarizes the existing blockchain-based systems and applications. We mainly review the applications of blockchain traceability technology in various fields, the blockchain decentralized applications, and other blockchain applications in data security protection, respectively. This work may bring new opportunities and challenges for the development of various industries in the future.

/pdf/blockchain-based-systems-and-applications-a-survey-5evv94acv9.pdf

Blockchain-based Systems and Applications: A Survey

Existing image inpainting algorithms based on neural network models are affected by structural distortions and blurred textures on visible connectivity. As a result, overfitting and overlearning phenomena can easily emerge during the image inpainting procedure. Image inpainting refers to the repairing of missing parts of an image, given an image that is broken or incomplete. After the repairing operation is complete, there are obvious signs of repair in damaged areas, semantic discontinuities, and unclearness. This paper proposes an improved image inpainting method based on a new encoder combined with a context loss function. In order to obtain clear repaired images and ensure that the semantic features of images are fully learned, a generative network based on the fusion model of squeeze-and-excitation networks deep residual learning has been proposed to improve the application of network features in order to obtain clear images and reduce network parameters. At the same time, a discriminative network based on the squeeze-and-excitation residual Network has been proposed to strengthen the capability of the discriminative network. In order to make the generated image more realistic, so that the restored image will be more similar to the original image, a joint context-awareness loss training method (contextual perception loss network) has also been proposed to generate the similarity of the local features of the network constraint, with the result that the repaired image is closer to the original picture and more realistic. The experimental results can demonstrate that the proposed algorithm demonstrates better adaptive capability than the comparison algorithms on a number of image categories. In addition, the processing results of the image inpainting procedure were also superior to those of five state-of-the-art algorithms.

The improved image inpainting algorithm via encoder and similarity constraint

In order to enhance the robustness to complicated changes of multiple objects and complex background scene, the visual object tracking algorithm based on Adaptive Combination Kernel has been proposed in the paper. The object tracking procedure has been decomposed into two subtasks: Translation Filter and Scale Filter to estimate the object’s details. Firstly, the Translation Kernel Tracker has used the adaptive combination of Linear Kernel Filter and Gaussian Kernel Filter. The objective function has been developed to obtain the weight coefficients for Linear Kernel filter and the Gaussian Kernel filter, which incorporates not only empirical risk but also maximum value of response output for each kernel. The Adaptive Combination Kernel has the advantages of both local kernel and global kernel. Secondly, the tracking position has been calculated according to the response output of adaptive combination kernel correlation filter. Thirdly, according to the maximum response value, the scene-adaptive learning rate has been designed in the translation filter. The translation filter can be updated with the adaptive learning rate. Finally, one-dimensional scale filter has been used to estimate the object scale. The extensive experimental results have shown that the proposed algorithm is optimal on OTB-50 dataset in success rate and distance precision parameters, which is 6.8 percentage points and 4.1% points than those of KCF and is 2.0 percentage points and 3.2% points than those of BSET. The proposed algorithm has better robustness to the deformation and occlusion than others.

https://link.springer.com/content/pdf/10.1007/s12652-021-03500-6.pdf

RETRACTED ARTICLE: The visual object tracking algorithm research based on adaptive combination kernel

The context information of images had been lost due to the low resolution of features, and due to repeated combinations of max-pooling layer and down-sampling layer. When the feature extraction process had been performed using a convolutional network, the result of semantic image segmentation loses sensitivity to the location of the object. The semantic image segmentation based on a feature fusion model with context features layer-by-layer had been proposed. Firstly, the original images had been pre-processed by the Gaussian Kernel Function to generate a series of images with different resolutions to form an image pyramid. Secondly, inputting an image pyramid into the network structure in which the plurality of fully convolutional network was been combined in parallel to obtain a set of initial features with different granularities by expanding receptive fields using Atrous Convolutions, and the initialization of feature fusion with different layer-by-layer granularities in a top-down method. Finally, the score map of feature fusion model had been calculated and sent to the conditional random field, modeling the class correlations between image pixels of the original image by the fully connected conditional random field, and the spatial position information and color vector information of image pixels were jointed to optimize and obtain results. The experiments on the PASCAL VOC 2012 and PASCAL Context datasets had achieved better mean Intersection Over Union than the state-of-the-art works. The proposed method has about 6.3% improved to the conventional methods.

Runlong Xia

Papers

Image super-resolution reconstruction based on feature map attention mechanism

RETRACTED ARTICLE: The visual object tracking algorithm research based on adaptive combination kernel

The improved image inpainting algorithm via encoder and similarity constraint

Research of improving semantic image segmentation based on a feature fusion model

Improved anti-occlusion object tracking algorithm using Unscented Rauch-Tung-Striebel smoother and kernel correlation filter