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Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Synthetic minority oversampling technique (SMOTE) has become one of the mainstream methods for dealing with unbalanced data due to its ability to effectively deal with minority samples, and many SMOTE improvement algorithms have been proposed, but very little research existing considers popular algorithmic-level improvement methods. Therefore a more comprehensive analysis of existing SMOTE class algorithms is provided. Firstly, the basic principles of the SMOTE method are elaborated in detail, and then the SMOTE class algorithms are systematically analyzed mainly from the two levels of data level and algorithmic level, and the new ideas of the hybrid improvement of data level and algorithmic level are introduced. Data-level improvement is to balance the data distribution by deleting or adding data through different operations during preprocessing; algorithmic-level improvement will not change the data distribution, and mainly strengthens the focus on minority samples by modifying or creating algorithms. Comparison between these two kinds of methods shows that, data- level methods are less restricted in their application, and algorithmic-level improvements generally have higher algorithmic robustness. In order to provide more comprehensive basic research material on SMOTE class algorithms, this paper finally lists the commonly used datasets, evaluation metrics, and gives ideas of research in the future to better cope with unbalanced data problem. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Recommendation system can help users quickly find useful information and improve the retrieval efficiency of users effectively. However, the recommendation system has problems such as data sparsity and cold start, most of the existing recommendation algorithms that integrate social relations ignore the sparsity of social relations data, and there are few recommendation algorithms that integrate social relations and item attribute data at the same time. This paper proposes a recommendation model that is multi-task feature learning approach for social relationship and knowledge graph enhanced recommendation (MSAKR) in response to solve the above problems. Firstly, the algorithm extracts the user's social relations through the graph convolutional neural network to get the user's feature vector, then selects the neighbor by the graph centrality, and generates the virtual neighbor by the word2vec model, so as to alleviate the sparsity of the social data. This paper uses the attention mechanism to gather the neighbors. Secondly, multi-task learning and semantic-based matching model are used to extract the information of attribute knowledge graph to obtain the feature vector of the item. Finally, comprehensive recommendation is made to the user based on the obtained user and item feature vectors. In order to assess the performance of the recommendation algorithm, experiments are carried out on real datasets Douban and Yelp. Click-through rate prediction and Top-K recommendation are used to evaluate the performance of the model respectively. Experimental results show that the proposed model is superior to other benchmark models. [ABSTRACT FROM AUTHOR]

Aiming at the problem of incomplete location and fuzzy edge of salient region, this paper proposes a method of RGB-D salient target detection. Firstly, a cross-modal feature fusion module is designed to integrate RGB and depth information layer by layer. Six modal fusion feature outputs are obtained. This module reduces the discrepancy between RGB and depth information, providing more common and complementary deep features for the subsequent advanced semantic repair. Multiple levels of information is obtained based on the above modules. This paper uses the last three layer features to jointly extract richer high-level semantic information, and the initial salient map is obtained. After that, the network structure of U-Net is used to fuse from top to the bottom of the network. After upsampling, each layer is fused with the next layer in channel dimension. The first three layers of bottom features are guided by advanced semantic features before and after fusion, to complete the repair of the lowlevel features. Finally, the final salient map is obtained. The proposed cross-modal feature fusion module can adaptively fuse multi-modal features, highlight the commonness and complementarity of fusion features, and reduce the ambiguity of fusion. The proposed advanced semantic repair strategy is helpful to accurately detect the salient region and improve the edge clarity. Experimental results show that the proposed algorithm outperforms most excellent methods on five datasets, including NJU2K, NLPR, STERE, DES and SIP, which achieves relatively advanced performance. [ABSTRACT FROM AUTHOR]

TVM (tensor virtual machine) is a deep learning compiler, that translates the deep learning operators described by tensor expression to TVM IR (TVM intermediate representation) programs. After a series of operatorlevel optimizations on TVM IR, TVM generates the target code across diverse hardware back-ends. Tensor expression, a domain- specific language for tensor computation, performs loop transformation to operators. The result of loop transformation is a number of complicated expressions emerging in nested loop statements, which contain loop invariant code. However, in the context of deep learning applications, the traditional loop invariant code motion algorithm has severe limitations. Firstly, it's difficult to determine the extra-benefit of moving certain invariant code out of loops. Secondly, it's difficult to detect loop invariant code which has different orders of operands. Thirdly, it cannot process nested condition expressions well. Furthermore, there are conflicts with target hardware compiler optimizations. The application of loop invariant code motion technique is constrained by the aforementioned problems. In this paper, a new loop invariant code motion algorithm is proposed, which takes deep learning application characteristics into consideration in a heuristics way. The algorithm normalizes the program by manipulating the expression operands and simplifying the nested condition expression. This paper introduces a new cost model, which evaluates the cost of moving certain loop invariant code while the characteristics of TVM IR and target hardware back-ends are fully considered. The algorithm is implemented as a registered TVM pass on opensource compiler TVM version 0.7. To testify the effectiveness and correctness of this algorithm, experiments are conducted on TVM TOPI benchmark with 27 operators and 511 test cases under different input. Experimental results show that this algorithm improves 47.6% of operators'performance, and achieves speedups up to 40.0%. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The continuous development of deep learning technology has provided new ideas for image repair research over the years, and the image repair methods can understand the semantic information of image through the study of massive image data. Although the existing image repair methods have been able to generate desirable repair results, it is insufficient to deal with the details of missing part from the image when facing the image with more complex missing part, thus the restoration results are excessively smooth or blurry, and the complex structural information that misses from the image cannot be repaired well. In order to solve the issues above, an edge-guided image repair method based on generative adversarial networks technology and the corresponding algorithm are proposed in this paper, and the repair process is divided into two stages. First, the edge repair model is trained to generate more realistic edge information of the missing area. Then, the content generation model is trained to fill in the missing content information based on the edge information that has been repaired. Lastly, the experimental verification is conducted on the CelebA dataset and ParisStreet-View dataset to compare with the Shift-Net model, deep image prior (DIP) model and field factorization machine (FFM) model, and the visual qualitative analysis and quantitative index analysis are carried out on the experimental repair results. The experimental results prove that the repair method proposed in this paper for the missing complex structure information in the image is superior to the existing methods, and also reflect the edge information plays a crucial role in image repair. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

SM9 identity-based encryption algorithm is a commercial identity-based encryption algorithm independently designed by our country, which has become the standard of the domestic identity-based encryption algorithm industry, and is widely used in e-mail, electronic voting and online negotiation, etc. However, SM9 identity-based encryption algorithm can't effectively protect the identity privacy of the sender. Based on SM9 identity-based encryption algorithm, combined with the deniable authentication protocol, this paper proposes SM9 identity-based encryption algorithm with deniable authentication. This algorithm allows the sender to deny its participation after the protocol runs, and only the intended receiver can identify the true source of the given message. At the same time, the receiver can't convince any other third party that the message is sent by a specific sender. Under the assumption of DBDH's difficult problem, the formal definition and security model of SM9 identity-based encryption algorithm with deniable authentication are given, and the security analysis of the algorithm is given under the random oracle model, which proves that the algorithm can satisfy denial, confidentiality and deniable authentication at the same time. Theoretical analysis and simulation experiments show that the proposed algorithm not only maintains the efficiency advantage of SM9 identity-based encryption algorithm, but also has a lower computational overhead than other identity-based encryption algorithms with deniable authentication. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Insufficient feature extraction of the backbone network and lack of semantic information in the shallow convolution layer often lead to poor detection results on small objects. In order to improve the accuracy and robustness of small object detection, this paper proposes a deep small object detection algorithm that integrates attention mechanism. Firstly, to address the problem of insufficient feature extraction capability of the backbone network, Darknet-53 is selected as the network of feature extraction, and a new grouped residual connection is proposed to replace the residual connection structure in the original Darknet-53. This forms a new enhanced backbone network named I-Darknet53. This grouped residual structure can effectively increase the size of the receptive field by interweaving the feature information of different channels. Secondly, in the multi-scale detection phase, a shallow feature enhancement network is proposed to obtain shallow enhanced features by fusing the shallow layer and deep layer. The network including feature enhancement module and an efficient feature fusion strategy guided by channel attention mechanism is used to improve the lack of semantic information of shallow features. Experimental results show that the proposed algorithm has better performance than the SSD algorithm on PASCAL VOC dataset. When the input image size is 300 x 300, the average accuracy of the proposed model is 80.2%; when the input image size is 500 x 500, the average accuracy of the proposed model is 82.3%. In addition, it can effectively improve the detection accuracy of small objects under the premise of ensuring the detection speed. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)