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As a sub-direction of image retrieval, person re-identification (Re-ID) is usually used to solve the security problem of cross camera tracking and monitoring. A growing number of shopping centers have recently attempted to apply Re-ID technology. One of the development trends of related algorithms is using an attention mechanism to capture global and local features. We notice that these algorithms have apparent limitations. They only focus on the most salient features without considering certain detailed features. People’s clothes, bags and even shoes are of great help to distinguish pedestrians. We notice that global features usually cover these important local features. Therefore, we propose a dual branch network based on a multi-scale attention mechanism. This network can capture apparent global features and inconspicuous local features of pedestrian images. Specifically, we design a dual branch attention network (DBA-Net) for better performance. These two branches can optimize the extracted features of different depths at the same time. We also design an effective block (called channel, position and spatial-wise attention (CPSA)), which can capture key fine-grained information, such as bags and shoes. Furthermore, based on ID loss, we use complementary triplet loss and adaptive weighted rank list loss (WRLL) on each branch during the training process. DBA-Net can not only learn semantic context information of the channel, position, and spatial dimensions but can integrate detailed semantic information by learning the dependency relationships between features. Extensive experiments on three widely used open-source datasets proved that DBA-Net clearly yielded overall state-of-the-art performance. Particularly on the CUHK03 dataset, the mean average precision (mAP) of DBA-Net achieved 83.2%.
Denghua Fan; Liejun Wang; Shuli Cheng; Yongming Li. Dual Branch Attention Network for Person Re-Identification. Sensors 2021, 21, 5839 .
AMA StyleDenghua Fan, Liejun Wang, Shuli Cheng, Yongming Li. Dual Branch Attention Network for Person Re-Identification. Sensors. 2021; 21 (17):5839.
Chicago/Turabian StyleDenghua Fan; Liejun Wang; Shuli Cheng; Yongming Li. 2021. "Dual Branch Attention Network for Person Re-Identification." Sensors 21, no. 17: 5839.
Caching and transcoding at multi-access edge computing (MEC) server and wireless resource allocation in eNodeB interact with each other and together determine the quality of experience (QoE) of dynamic adaptive streaming over HTTP (DASH) clients. However, the relationship among the three factors has not been explored, which has led to limited improvement in clients' QoE. Therefore, we propose a joint optimization framework of video segment caching and transcoding in MEC servers and resource allocation to improve the QoE of DASH clients. Based on the established framework, we develop an MEC caching management mechanism that consists of the MEC caching partition, video segment deletion, and MEC caching space transfer. Then, a joint optimization algorithm that combines the video segment caching and transcoding in the MEC server and resource allocation is proposed. In the algorithm, the clients' channel state and the playback status and cooperation among MEC servers are employed to estimate the client's priority, video segment representation switch and continuous playback time. Considering the above four factors, we develop a utility function model of clients' QoE. Then, we formulate a mixed-integer nonlinear programming mathematical model to maximize the total utility of DASH clients, where the video segment caching and transcoding strategy and resource allocation strategy are jointly optimized. To solve this problem, we propose a low-complexity heuristic algorithm that decomposes the original problem into multiple subproblems. The simulation results show that our proposed algorithms efficiently improve client's throughput, received video quality and hit ratio of video segments while decreasing the playback rebuffering time, video segment representation switch and system backhaul traffic.
Xinyu Huang; Lijun He; Liejun Wang; Fan Li. Towards 5G: Joint Optimization of Video Segment Caching, Transcoding and Resource Allocation for Adaptive Video Streaming in a Multi-access Edge Computing Network. IEEE Transactions on Vehicular Technology 2021, PP, 1 -1.
AMA StyleXinyu Huang, Lijun He, Liejun Wang, Fan Li. Towards 5G: Joint Optimization of Video Segment Caching, Transcoding and Resource Allocation for Adaptive Video Streaming in a Multi-access Edge Computing Network. IEEE Transactions on Vehicular Technology. 2021; PP (99):1-1.
Chicago/Turabian StyleXinyu Huang; Lijun He; Liejun Wang; Fan Li. 2021. "Towards 5G: Joint Optimization of Video Segment Caching, Transcoding and Resource Allocation for Adaptive Video Streaming in a Multi-access Edge Computing Network." IEEE Transactions on Vehicular Technology PP, no. 99: 1-1.
Considerable research and surveys indicate that skin lesions are an early symptom of skin cancer. Segmentation of skin lesions is still a hot research topic. Dermatological datasets in skin lesion segmentation tasks generated a large number of parameters when data augmented, limiting the application of smart assisted medicine in real life. Hence, this paper proposes an effective feedback attention network (FAC-Net). The network is equipped with the feedback fusion block (FFB) and the attention mechanism block (AMB), through the combination of these two modules, we can obtain richer and more specific feature mapping without data enhancement. Numerous experimental tests were given by us on public datasets (ISIC2018, ISBI2017, ISBI2016), and a good deal of metrics like the Jaccard index (JA) and Dice coefficient (DC) were used to evaluate the results of segmentation. On the ISIC2018 dataset, we obtained results for DC equal to 91.19% and JA equal to 83.99%, compared with the based network. The results of these two main metrics were improved by more than 1%. In addition, the metrics were also improved in the other two datasets. It can be demonstrated through experiments that without any enhancements of the datasets, our lightweight model can achieve better segmentation performance than most deep learning architectures.
Yuying Dong; Liejun Wang; Shuli Cheng; Yongming Li. FAC-Net: Feedback Attention Network Based on Context Encoder Network for Skin Lesion Segmentation. Sensors 2021, 21, 5172 .
AMA StyleYuying Dong, Liejun Wang, Shuli Cheng, Yongming Li. FAC-Net: Feedback Attention Network Based on Context Encoder Network for Skin Lesion Segmentation. Sensors. 2021; 21 (15):5172.
Chicago/Turabian StyleYuying Dong; Liejun Wang; Shuli Cheng; Yongming Li. 2021. "FAC-Net: Feedback Attention Network Based on Context Encoder Network for Skin Lesion Segmentation." Sensors 21, no. 15: 5172.
Recently, deep learning to hash has extensively been applied to image retrieval, due to its low storage cost and fast query speed. However, there is a defect of insufficiency and imbalance when existing hashing methods utilize the convolutional neural network (CNN) to extract image semantic features and the extracted features do not include contextual information and lack relevance among features. Furthermore, the process of the relaxation hash code can lead to an inevitable quantization error. In order to solve these problems, this paper proposes deep hash with improved dual attention for image retrieval (DHIDA), which chiefly has the following contents: (1) this paper introduces the improved dual attention mechanism (IDA) based on the ResNet18 pre-trained module to extract the feature information of the image, which consists of the position attention module and the channel attention module; (2) when calculating the spatial attention matrix and channel attention matrix, the average value and maximum value of the column of the feature map matrix are integrated in order to promote the feature representation ability and fully leverage the features of each position; and (3) to reduce quantization error, this study designs a new piecewise function to directly guide the discrete binary code. Experiments on CIFAR-10, NUS-WIDE and ImageNet-100 show that the DHIDA algorithm achieves better performance.
Wenjing Yang; Liejun Wang; Shuli Cheng; Yongming Li; Anyu Du. Deep Hash with Improved Dual Attention for Image Retrieval. Information 2021, 12, 285 .
AMA StyleWenjing Yang, Liejun Wang, Shuli Cheng, Yongming Li, Anyu Du. Deep Hash with Improved Dual Attention for Image Retrieval. Information. 2021; 12 (7):285.
Chicago/Turabian StyleWenjing Yang; Liejun Wang; Shuli Cheng; Yongming Li; Anyu Du. 2021. "Deep Hash with Improved Dual Attention for Image Retrieval." Information 12, no. 7: 285.
When taking pictures of electronic screens or objects with high-frequency textures, people often run across colorful rainbow patterns that are known as “moire”, seriously affecting the image quality and subsequent processing. Current methods for removing moire patterns mostly extract multiscale information by downsampling pooling layers, which may inevitably cause information loss. To address this issue, this paper proposes a demoireing method in the wavelet domain. By employing both discrete wavelet transform (DWT) and inverse discrete wavelet transform (IDWT) instead of traditional downsampling and upsampling, this method can effectively increase the network receptive field without information loss. In addition, to further reconstruct more details of moire patterns, this paper proposes an efficient attention fusion module (EAFM). With a combination of efficient channel attention, spatial attention and local residual learning, this module can self-adaptively learn various weights of feature information at different levels and inspire the network to focus more on effective information such as moire details to improve learning and demoireing performance. Extensive experiments based on public datasets have shown that this suggested method can efficiently remove moire patterns and has a good quantitative and qualitative performance.
Chunyun Sun; Huicheng Lai; Liejun Wang; Zhenghong Jia. Efficient Attention Fusion Network in Wavelet Domain for Demoireing. IEEE Access 2021, 9, 53392 -53400.
AMA StyleChunyun Sun, Huicheng Lai, Liejun Wang, Zhenghong Jia. Efficient Attention Fusion Network in Wavelet Domain for Demoireing. IEEE Access. 2021; 9 (99):53392-53400.
Chicago/Turabian StyleChunyun Sun; Huicheng Lai; Liejun Wang; Zhenghong Jia. 2021. "Efficient Attention Fusion Network in Wavelet Domain for Demoireing." IEEE Access 9, no. 99: 53392-53400.
Learning-based single image super-resolution (SISR) aims to learn a versatile mapping from low resolution (LR) image to its high resolution (HR) version. The critical challenge is to bias the network training towards continuous and sharp edges. For the first time in this work, we propose an implicit boundary prior learnt from multi-view observations to significantly mitigate the challenge in SISR we outline. Specifically, the multi-image prior that encodes both disparity information and boundary structure of the scene supervise a SISR network for edge-preserving. For simplicity, in the training procedure of our framework, light field (LF) serves as an effective multi-image prior, and a hybrid loss function jointly considers the content, structure, variance as well as disparity information from 4D LF data. Consequently, for inference, such a general training scheme boosts the performance of various SISR networks, especially for the regions along edges. Extensive experiments on representative backbone SISR architectures constantly show the effectiveness of the proposed method, leading to around 0.6 dB gain without modifying the network architecture.
Dingjian Jin; Mengqi Ji; Lan Xu; Gaochang Wu; Liejun Wang; Lu Fang. Boosting Single Image Super-Resolution Learnt From Implicit Multi-Image Prior. IEEE Transactions on Image Processing 2021, 30, 3240 -3251.
AMA StyleDingjian Jin, Mengqi Ji, Lan Xu, Gaochang Wu, Liejun Wang, Lu Fang. Boosting Single Image Super-Resolution Learnt From Implicit Multi-Image Prior. IEEE Transactions on Image Processing. 2021; 30 ():3240-3251.
Chicago/Turabian StyleDingjian Jin; Mengqi Ji; Lan Xu; Gaochang Wu; Liejun Wang; Lu Fang. 2021. "Boosting Single Image Super-Resolution Learnt From Implicit Multi-Image Prior." IEEE Transactions on Image Processing 30, no. : 3240-3251.
Image-text matching aims to find the relationship between image and text data and to establish a connection between them. The main challenge of image-text matching is the fact that images and texts have different data distributions and feature representations. Current methods for image-text matching fall into two basic types: methods that map image and text data into a common space and then use distance measurements and methods that treat image-text matching as a classification problem. In both cases, the two data modes used are image and text data. In our method, we create a fusion layer to extract intermediate modes, thus improving the image-text processing results. We also propose a concise way to update the loss function that makes it easier for neural networks to handle difficult problems. The proposed method was verified on the Flickr30K and MS-COCO datasets and achieved superior matching results compared to existing methods.
Depeng Wang; Liejun Wang; Shiji Song; Gao Huang; Yuchen Guo; Shuli Cheng; Naixiang Ao; Anyu Du. Fusion layer attention for image-text matching. Neurocomputing 2021, 442, 249 -259.
AMA StyleDepeng Wang, Liejun Wang, Shiji Song, Gao Huang, Yuchen Guo, Shuli Cheng, Naixiang Ao, Anyu Du. Fusion layer attention for image-text matching. Neurocomputing. 2021; 442 ():249-259.
Chicago/Turabian StyleDepeng Wang; Liejun Wang; Shiji Song; Gao Huang; Yuchen Guo; Shuli Cheng; Naixiang Ao; Anyu Du. 2021. "Fusion layer attention for image-text matching." Neurocomputing 442, no. : 249-259.
Target tracking has been a research hotspot in computer vision, and the correlation filtered target tracking algorithm has the benefits of low computational complexity and fast speed. Still, the tracking effect is not good when dealing with complicated circumstances. This paper proposes a multi-feature fusion target repositioning tracking algorithm for the target tracking problem in complex environments. First, a multi-feature weighted fusion algorithm is presented. Since each feature has different advantages in different environments, we combine HOG, CN, ULBP, and image edge features and use the weighted coefficient method to adaptively fuse each feature component. Second, to address the target occlusion problem, an occlusion judgment mechanism is introduced, and the target is re-located by fusion weighted filtering. Third, the scale pool is established, and the scale filter is trained by the classification search method. Finally, an adaptive model update strategy is proposed. We conduct comparison experiments with current mainstream algorithms on the publicly available datasets OTB-2015, VOT2018, UAV123, and TColor-128, respectively, and the experimental results show that our proposed algorithm is more robust in complex scenarios.
Qingzhong Shu; Huicheng Lai; Liejun Wang; Zhenhong Jia. Multi-Feature Fusion Target Re-Location Tracking Based on Correlation Filters. IEEE Access 2021, 9, 28954 -28964.
AMA StyleQingzhong Shu, Huicheng Lai, Liejun Wang, Zhenhong Jia. Multi-Feature Fusion Target Re-Location Tracking Based on Correlation Filters. IEEE Access. 2021; 9 ():28954-28964.
Chicago/Turabian StyleQingzhong Shu; Huicheng Lai; Liejun Wang; Zhenhong Jia. 2021. "Multi-Feature Fusion Target Re-Location Tracking Based on Correlation Filters." IEEE Access 9, no. : 28954-28964.
In the SAR change detection algorithm based on self-supervised learning, speckle noise reduces the difference image (DI) quality. Therefore, the contrast of the DI is low, and its change area is not significant. Moreover, the preclassification algorithm with the poor robustness makes the classification results of the low-quality DI inaccurate. When the wrong labels are sent into the classification network, the accuracy of the final detection results is reduced. First, to improve the quality of the initial DI, we design an adaptive gamma correction algorithm that adjusts the contrast according to the mean value of the initial DI and the variation coefficient β. The contrast of the new DI generated by this algorithm is higher. Furthermore, to suppress the noise, we adopt a new algorithm based on popular ranking to obtain the saliency map of the new DI. Combining the initial DI with this saliency map, a high-quality DI with a low noise level is obtained. After that, we introduce the structure tensor into the fuzzy local information c-means clustering algorithm (FLICM) to classify the DI more accurately. The new clustering algorithm improves the accuracy of preclassification, especially its hierarchical version. Besides, we use the structure tensor to generate the structure maps of the original images. Finally, according to the prior information obtained from the preclassification, we use a convolution wavelet neural network (CWNN) to supervise and train the structure maps of the original images. Experimental results show that the DI generated by us is closer to the ground-truth than other methods. Our preclassification algorithm performs better. Our algorithm shows higher detection accuracy for SAR images with strong noise than some advanced change detection algorithms.
Wenhui Meng; Liejun Wang; Anyu Du; Yongming Li. SAR Image Change Detection Based on Data Optimization and Self-Supervised Learning. IEEE Access 2020, 8, 217290 -217305.
AMA StyleWenhui Meng, Liejun Wang, Anyu Du, Yongming Li. SAR Image Change Detection Based on Data Optimization and Self-Supervised Learning. IEEE Access. 2020; 8 (99):217290-217305.
Chicago/Turabian StyleWenhui Meng; Liejun Wang; Anyu Du; Yongming Li. 2020. "SAR Image Change Detection Based on Data Optimization and Self-Supervised Learning." IEEE Access 8, no. 99: 217290-217305.
At present, occlusion and appearance similarity pose severe challenges to person re-identification tasks. Although many robust deep convolutional neural networks alleviate these problems, convolutional layers with limited receptive fields cannot model global semantic information well. In addition, in the person re-identification model, many metric losses ignore or destroy the intra-class structure of the sample, which makes the model difficult to be optimized. Therefore, we design a discriminative Re-identification model with global-local attention and adaptive weighted rank list loss (GLWR). Specifically, our global-local attention (GL-Attention) learns the semantic context in the channel and spatial dimensions. By learning the dependencies between features, GL-Attention integrates global semantic information into local features to extract discriminative features. Unlike rank list loss, our adaptive weighted rank list loss (WRLL) adaptively assigns weights according to the metric distance between the negative sample and the input image, which further improves the performance of the model. Experimental studies on three public datasets (Market-1501, DukeMTMC-ReID and CUHK03) indicate that the performance of our GLWR is significantly superior to many of the latest algorithms.
Yongchang Gong; Liejun Wang; Yongming Li; Anyu Du. A Discriminative Person Re-Identification Model With Global-Local Attention and Adaptive Weighted Rank List Loss. IEEE Access 2020, 8, 203700 -203711.
AMA StyleYongchang Gong, Liejun Wang, Yongming Li, Anyu Du. A Discriminative Person Re-Identification Model With Global-Local Attention and Adaptive Weighted Rank List Loss. IEEE Access. 2020; 8 ():203700-203711.
Chicago/Turabian StyleYongchang Gong; Liejun Wang; Yongming Li; Anyu Du. 2020. "A Discriminative Person Re-Identification Model With Global-Local Attention and Adaptive Weighted Rank List Loss." IEEE Access 8, no. : 203700-203711.
Deep hashing is the mainstream algorithm for large-scale cross-modal retrieval due to its high retrieval speed and low storage capacity, but the problem of reconstruction of modal semantic information is still very challenging. In order to further solve the problem of unsupervised cross-modal retrieval semantic reconstruction, we propose a novel deep semantic-preserving reconstruction hashing (DSPRH). The algorithm combines spatial and channel semantic information, and mines modal semantic information based on adaptive self-encoding and joint semantic reconstruction loss. The main contributions are as follows: (1) We introduce a new spatial pooling network module based on tensor regular-polymorphic decomposition theory to generate rank-1 tensor to capture high-order context semantics, which can assist the backbone network to capture important contextual modal semantic information. (2) Based on optimization perspective, we use global covariance pooling to capture channel semantic information and accelerate network convergence. In feature reconstruction layer, we use two bottlenecks auto-encoding to achieve visual-text modal interaction. (3) In metric learning, we design a new loss function to optimize model parameters, which can preserve the correlation between image modalities and text modalities. The DSPRH algorithm is tested on MIRFlickr-25K and NUS-WIDE. The experimental results show that DSPRH has achieved better performance on retrieval tasks.
Shuli Cheng; Liejun Wang; Anyu Du. Deep Semantic-Preserving Reconstruction Hashing for Unsupervised Cross-Modal Retrieval. Entropy 2020, 22, 1266 .
AMA StyleShuli Cheng, Liejun Wang, Anyu Du. Deep Semantic-Preserving Reconstruction Hashing for Unsupervised Cross-Modal Retrieval. Entropy. 2020; 22 (11):1266.
Chicago/Turabian StyleShuli Cheng; Liejun Wang; Anyu Du. 2020. "Deep Semantic-Preserving Reconstruction Hashing for Unsupervised Cross-Modal Retrieval." Entropy 22, no. 11: 1266.
The diversity of network attacks poses severe challenges to intrusion detection systems (IDSs). Traditional attack recognition methods usually adopt mining data associations to identify anomalies, which has the disadvantages of a high false alarm rate (FAR), low recognition accuracy (ACC) and poor generalization ability. To ameliorate the comprehensive capabilities of IDS and strengthen network security, we propose a novel intrusion detection method based on the adaptive synthetic sampling (ADASYN) algorithm and an improved convolutional neural network (CNN). First, we use the ADASYN method to balance the sample distribution, which can effectively prevent the model from being sensitive to large samples and ignore small samples. Second, the improved CNN is based on the split convolution module (SPC-CNN), which can increase the diversity of features and eliminate the impact of interchannel information redundancy on model training. Then, an AS-CNN model mixed with ADASYN and SPC-CNN is used for intrusion detection tasks. Finally, the standard NSL-KDD dataset is selected to test AS-CNN. The simulation illustrates that the accuracy is 4.60% and 2.79% higher than that of the traditional CNN and RNN models, and the detection rate (DR) increased by 11.34% and 10.27%, respectively. Additionally, the FAR decreased by 15.58% and 14.57%, respectively, compared with the two models.
Zhiquan Hu; Liejun Wang; Lei Qi; Yongming Li; Wenzhoong Yang. A Novel Wireless Network Intrusion Detection Method Based on Adaptive Synthetic Sampling and an Improved Convolutional Neural Network. IEEE Access 2020, 8, 1 -1.
AMA StyleZhiquan Hu, Liejun Wang, Lei Qi, Yongming Li, Wenzhoong Yang. A Novel Wireless Network Intrusion Detection Method Based on Adaptive Synthetic Sampling and an Improved Convolutional Neural Network. IEEE Access. 2020; 8 ():1-1.
Chicago/Turabian StyleZhiquan Hu; Liejun Wang; Lei Qi; Yongming Li; Wenzhoong Yang. 2020. "A Novel Wireless Network Intrusion Detection Method Based on Adaptive Synthetic Sampling and an Improved Convolutional Neural Network." IEEE Access 8, no. : 1-1.
The Kullback-Leibler divergence (KLD), which is widely used to measure the similarity between two distributions, plays an important role in many applications. In this article, we address the KLD metric-learning task, which aims at learning the best KLD-type metric from the distributions of datasets. Concretely, first, we extend the conventional KLD by introducing a linear mapping and obtain the best KLD to well express the similarity of data distributions by optimizing such a linear mapping. It improves the expressivity of data distribution, which means it makes the distributions in the same class close and those in different classes far away. Then, the KLD metric learning is modeled by a minimization problem on the manifold of all positive-definite matrices. To deal with this optimization task, we develop an intrinsic steepest descent method, which preserves the manifold structure of the metric in the iteration. Finally, we apply the proposed method along with ten popular metric-learning approaches on the tasks of 3-D object classification and document classification. The experimental results illustrate that our proposed method outperforms all other methods.
Shuyi Ji; Zizhao Zhang; Shihui Ying; Liejun Wang; Xibin Zhao; Yue Gao. Kullback-Leibler Divergence Metric Learning. IEEE Transactions on Cybernetics 2020, 1 -12.
AMA StyleShuyi Ji, Zizhao Zhang, Shihui Ying, Liejun Wang, Xibin Zhao, Yue Gao. Kullback-Leibler Divergence Metric Learning. IEEE Transactions on Cybernetics. 2020; (99):1-12.
Chicago/Turabian StyleShuyi Ji; Zizhao Zhang; Shihui Ying; Liejun Wang; Xibin Zhao; Yue Gao. 2020. "Kullback-Leibler Divergence Metric Learning." IEEE Transactions on Cybernetics , no. 99: 1-12.
The existing learning-based unsupervised hashing method usually uses a pre-trained network to extract features, and then uses the extracted feature vectors to construct a similarity matrix which guides the generation of hash codes through gradient descent. Existing research shows that the algorithm based on gradient descent will cause the hash codes of the paired images to be updated toward each other's position during the training process. For unsupervised training, this situation will cause large fluctuations in the hash code during training and limit the learning efficiency of the hash code. In this paper, we propose a method named Deep Unsupervised Hashing with Gradient Attention (UHGA) to solve this problem. UHGA mainly includes the following contents: (1) use pre-trained network models to extract image features; (2) calculate the cosine distance of the corresponding features of the pair of images, and construct a similarity matrix through the cosine distance to guide the generation of hash codes; (3) a gradient attention mechanism is added during the training of the hash code to pay attention to the gradient. Experiments on two existing public datasets show that our proposed method can obtain more discriminating hash codes.
Shaochen Jiang; Liejun Wang; Shuli Cheng; Anyu Du; Yongming Li. Unsupervised Hashing with Gradient Attention. Symmetry 2020, 12, 1193 .
AMA StyleShaochen Jiang, Liejun Wang, Shuli Cheng, Anyu Du, Yongming Li. Unsupervised Hashing with Gradient Attention. Symmetry. 2020; 12 (7):1193.
Chicago/Turabian StyleShaochen Jiang; Liejun Wang; Shuli Cheng; Anyu Du; Yongming Li. 2020. "Unsupervised Hashing with Gradient Attention." Symmetry 12, no. 7: 1193.
Integrated learning can be used to combine weak classifiers in order to improve the effect of emotional classification. Existing methods of emotional classification on micro-blogs seldom consider utilizing integrated learning. Personality can significantly influence user expressions but is seldom accounted for in emotional classification. In this study, a micro-blog emotion classification method is proposed based on a personality and bagging algorithm (PBAL). Introduce text personality analysis and use rule-based personality classification methods to divide five personality types. The micro-blog text is first classified using five personality basic emotion classifiers and a general emotion classifier. A long short-term memory language model is then used to train an emotion classifier for each set, which are then integrated together. Experimental results show that compared with traditional sentiment classifiers, PBAL has higher accuracy and recall. The F value has increased by 9%.
Wenzhong Yang; Tingting Yuan; Liejun Wang. Micro-Blog Sentiment Classification Method Based on the Personality and Bagging Algorithm. Future Internet 2020, 12, 75 .
AMA StyleWenzhong Yang, Tingting Yuan, Liejun Wang. Micro-Blog Sentiment Classification Method Based on the Personality and Bagging Algorithm. Future Internet. 2020; 12 (4):75.
Chicago/Turabian StyleWenzhong Yang; Tingting Yuan; Liejun Wang. 2020. "Micro-Blog Sentiment Classification Method Based on the Personality and Bagging Algorithm." Future Internet 12, no. 4: 75.
In modern recommender systems, matrix factorization has been widely used to decompose the user–item matrix into user and item latent factors. However, the inner product in matrix factorization does not satisfy the triangle inequality, and the problem of sparse data is also encountered. In this paper, we propose a novel recommendation model, namely, metric factorization with item cooccurrence for recommendation (MFIC), which uses the Euclidean distance to jointly decompose the user–item interaction matrix and the item–item cooccurrence with shared latent factors. The item cooccurrence matrix is obtained from the colike matrix through the calculation of pointwise mutual information. The main contributions of this paper are as follows: (1) The MFIC model is not only suitable for rating prediction and item ranking, but can also well overcome the problem of sparse data. (2) This model incorporates the item cooccurrence matrix into metric learning so it can better learn the spatial positions of users and items. (3) Extensive experiments on a number of real-world datasets show that the proposed method substantially outperforms the compared algorithm in both rating prediction and item ranking.
Honglin Dai; Liejun Wang; Jiwei Qin. Metric Factorization with Item Cooccurrence for Recommendation. Symmetry 2020, 12, 512 .
AMA StyleHonglin Dai, Liejun Wang, Jiwei Qin. Metric Factorization with Item Cooccurrence for Recommendation. Symmetry. 2020; 12 (4):512.
Chicago/Turabian StyleHonglin Dai; Liejun Wang; Jiwei Qin. 2020. "Metric Factorization with Item Cooccurrence for Recommendation." Symmetry 12, no. 4: 512.
In recent years, the common algorithms for image super-resolution based on deep learning have been increasingly successful, but there is still a large gap between the results generated by each algorithm and the ground-truth. Even some algorithms that are dedicated to image perception produce more textures that do not exist in the original image, and these artefacts also affect the visual perceptual quality of the image. We believe that in the existing perceptual-based image super-resolution algorithm, it is necessary to consider Super-Resolution (SR) image quality, which can restore the important structural parts of the original picture. This paper mainly improves the Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) algorithm in the following aspects: adding a shallow network structure, adding the dual attention mechanism in the generator and the discriminator, including the second-order channel mechanism and spatial attention mechanism and optimizing perceptual loss by adding second-order covariance normalization at the end of feature extractor. The results of this paper ensure image perceptual quality while reducing image distortion and artefacts, improving the perceived similarity of images and making the images more in line with human visual perception.
Can Li; Liejun Wang; Shuli Cheng; Naixiang Ao. Generative Adversarial Network-Based Super-Resolution Considering Quantitative and Perceptual Quality. Symmetry 2020, 12, 449 .
AMA StyleCan Li, Liejun Wang, Shuli Cheng, Naixiang Ao. Generative Adversarial Network-Based Super-Resolution Considering Quantitative and Perceptual Quality. Symmetry. 2020; 12 (3):449.
Chicago/Turabian StyleCan Li; Liejun Wang; Shuli Cheng; Naixiang Ao. 2020. "Generative Adversarial Network-Based Super-Resolution Considering Quantitative and Perceptual Quality." Symmetry 12, no. 3: 449.
The secure transmission of data within a network has received great attention. As the core of the security management mechanism, the key management scheme design needs further research. In view of the safety and energy consumption problems in recent papers, we propose a key management scheme based on the pairing-free identity based digital signature (PF-IBS) algorithm for heterogeneous wireless sensor networks (HWSNs). Our scheme uses the PF-IBS algorithm to complete message authentication, which is safer and more energy efficient than some recent schemes. Moreover, we use the base station (BS) as the processing center for the huge data in the network, thereby saving network energy consumption and improving the network life cycle. Finally, we indirectly prevent the attacker from capturing relay nodes that upload data between clusters in the network (some cluster head nodes cannot communicate directly). Through performance evaluation, the scheme we proposed reasonably sacrifices part of the storage space in exchange for entire network security while saving energy consumption.
Erdong Yuan; Liejun Wang; Shuli Cheng; Naixiang Ao; Qingrui Guo. A Key Management Scheme Based on Pairing-Free Identity Based Digital Signature Algorithm for Heterogeneous Wireless Sensor Networks. Sensors 2020, 20, 1543 .
AMA StyleErdong Yuan, Liejun Wang, Shuli Cheng, Naixiang Ao, Qingrui Guo. A Key Management Scheme Based on Pairing-Free Identity Based Digital Signature Algorithm for Heterogeneous Wireless Sensor Networks. Sensors. 2020; 20 (6):1543.
Chicago/Turabian StyleErdong Yuan; Liejun Wang; Shuli Cheng; Naixiang Ao; Qingrui Guo. 2020. "A Key Management Scheme Based on Pairing-Free Identity Based Digital Signature Algorithm for Heterogeneous Wireless Sensor Networks." Sensors 20, no. 6: 1543.
Low-rank tensor factorization can not only mine the implicit relationships between data but also fill in the missing data when working with complex data. Compared with the traditional collaborative filtering (CF) algorithm, the changes are essentially proposed, from traditional matrix analysis to three-dimensional spatial analysis. Based on low-rank tensor factorization, this paper proposes a recommendation model that comprehensively considers local information and global information, in other words, combining the similarity between trust users and low-rank tensor factorization. First, the similarity between trusted users is measured to capture local information between users by trusting similar preferences of users when selecting items. Then, the users’ similarity is integrated into the tensor, and the low-rank tensor factorization is used to better maintain and describe the internal structure of the data to obtain global information. Furthermore, based on the idea of the alternating least squares method, the conjugate gradient (CG) optimization algorithm for the model of this paper is designed. The local and global information is used to generate the optimal expected result in an iterative process. Finally, we conducted a large number of comparative experiments on the Ciao dataset and the FilmTrust dataset. Experimental results show that the algorithm has less precision loss under the data set with lower density. Thus, not only can a perfect compromise between accuracy and coverage be achieved, but also the computational complexity can be reduced to meet the need for real-time results.
Pei Ma; Liejun Wang; Jiwei Qin. A Low-Rank Tensor Factorization Using Implicit Similarity in Trust Relationships. Symmetry 2020, 12, 439 .
AMA StylePei Ma, Liejun Wang, Jiwei Qin. A Low-Rank Tensor Factorization Using Implicit Similarity in Trust Relationships. Symmetry. 2020; 12 (3):439.
Chicago/Turabian StylePei Ma; Liejun Wang; Jiwei Qin. 2020. "A Low-Rank Tensor Factorization Using Implicit Similarity in Trust Relationships." Symmetry 12, no. 3: 439.
The protection of video data has become a hot topic of research. Researchers have proposed a series of coding algorithms to ensure the safe and efficient transmission of video information. We propose an encryption scheme that can protect video information with higher security by combining the video coding algorithm with encryption algorithm. The H.264/AVC encoding algorithm encodes the video into multiple slices, and the slices are independent of each other. With this feature, we encrypt each slice while using the cipher feedback (CFB) mode of the advanced encryption standard (AES) with the dynamic key. The key is generated by the pseudo-random number generator (PRNG) and updated in real time. The encryption scheme goes through three phases: constructing plaintext, encrypting plaintext, and replacing the original bitstream. In our scheme, we encrypt the code stream after encoding, so it does not affect the coding efficiency. The purpose of the CFB mode while using the AES encryption algorithm is to maintain the exact same bit rate and produce a format compatible bitstream. This paper proposes a new four-dimensional (4-D) hyperchaotic algorithm to protect data privacy in order to further improve the security of video encryption. Symmetric encryption requires that the same key is used for encryption and decoding. In this paper, the symmetry method is used to protect the privacy of video data due to the large amount of video encrypted data. In the experiment, we evaluated the proposed algorithm while using different reference video sequences containing motion, texture, and objects.
Shuli Cheng; Liejun Wang; Naixiang Ao; Qingqing Han. A Selective Video Encryption Scheme Based on Coding Characteristics. Symmetry 2020, 12, 332 .
AMA StyleShuli Cheng, Liejun Wang, Naixiang Ao, Qingqing Han. A Selective Video Encryption Scheme Based on Coding Characteristics. Symmetry. 2020; 12 (3):332.
Chicago/Turabian StyleShuli Cheng; Liejun Wang; Naixiang Ao; Qingqing Han. 2020. "A Selective Video Encryption Scheme Based on Coding Characteristics." Symmetry 12, no. 3: 332.