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Copyright of Journal of Computer Engineering & Applications 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 Computer Engineering & Applications 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 Computer Engineering & Applications 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 Computer Engineering & Applications 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 Computer Engineering & Applications 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 Computer Engineering & Applications 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.)

As deep neural networks are widely used in various fields, it is particularly important to test and evaluate them and ensure their safety. When the test dataset is large and the labelled cost is expensive, the test input selection method can select and sort the test samples to improve the test efficiency and test coverage. In order to further understand the research progress in the field of test input selection for deep neural networks, 91 academic papers in related fields over the past five years are systematically sorted out. Firstly, the basic concepts and processes of deep neural networks testing are introduced, including the construction of deep learning systems, test input selecting and the test metrics. Secondly, the paper outlines and analyzes the applicable scenarios and shortcomings of various metrics and test input selection methods, as well as the interconnections among them. Finally, current challenges and opportunities for deep neural networks test input selection and metrics are pointed out. [ABSTRACT FROM AUTHOR]

The representative researches on data augmentation are mainly carried out on common classification benchmark datasets such as ImageNet. Considering intra- class and inter- class relation in fine- grained visual classification (FGVC) datasets is so different from ordinary classification datasets, data augmentation methods for FGVC need to be further studied. Therefore, this paper proposes a fine- grained semantic image patch mixing method by commonsense (ComSipmix), starting from the fine-grained recognition task and the special properties of the dataset. The proposed method exploits common sense knowledge to mine potential associations between sample labels, and designs a multi-branch convolutional neural network structure for structured image mixing strategy based on this, so that the image mixing process pays more attention to the subtle differences of targets. Through extensive performance tests, it can be verified that the performance of the proposed method is significantly better than the mainstream image mixing-based data augmentation methods. At the same time, through experimental verification, the common sense knowledge proposed in this paper helps to improve the performance of various data augmentation models based on mixed image classes. [ABSTRACT FROM AUTHOR]

Network failures cause a large number of packet losses and seriously affect network performance. How to deal with network failures efficiently and quickly is the basic requirement and main task of designing routing protocols. Currently, the most effective methods for Internet deployment are open shortest path first (OSPF) and intermediate systemto- intermediate system (IS-IS). Network faults are resolved by dynamic routing protocols, but a large number of packets are still discarded during the dynamic convergence of protocols. Therefore, router manufacturers widely adopt the route protection method with better performance to overcome network faults. However, the existing route protection schemes generally have high implementation complexity or low fault protection rate. Aiming at the above problems, this paper realizes the network programmability based on SRv6. In this paper, a research on intra-domain routing protection scheme based on SRv6 (RPSRv6) is proposed. Firstly, the calculation rules of backup paths are proposed. According to the backup path calculation rule, the backup path is calculated for all pairs of source and destination nodes affected by link failure in the incremental shortest path tree generated. On this basis, the calculation rule of Segment List is proposed, that is, the value and number of SIDs in the Segment List are calculated. In the process of calculating the backup path, each node affected by the fault and other nodes can be accessed at most once, so the time complexity of RPSRv6 has good performance. The experimental results show that compared with existing DC (downstream criterion) rules and U-Turn algorithms, RPSRv6 algorithm has better experimental results in fault protection rate and path stretch, reaching 100% fault protection rate and the optimal path in path stretch. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications 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.)