Current Issue : October-December Volume : 2022 Issue Number : 4 Articles : 5 Articles
To find out whether there is any vulnerability in software programs where conditional judgment is ignored, this article proposes a software vulnerability detection method based on complex network community. First, the method abstracts the software system into a directed weighted graph by using the software algebraic component model and then preprocesses the directed weighted graph to get a complex network graph. Then, by using the partition algorithm, the complex network graph is divided into the communities, and the key nodes in communities are found by nRank algorithm. Finally, the graph of the key nodes with high influence is matched with the complex network graph that has been preprocessed. In order to evaluate the effectiveness of the community partition algorithm and the nRank algorithm, comparative experiments are carried out on two datasets. The experimental results show that the community partition algorithm is better than the comparison algorithm in precision, recall, and comprehensive evaluation index, and the nRank algorithm is closer to the result of degree centrality measurement index than the PageRank algorithm and the LeaderRank algorithm. The spring-shiro-training project is used to verify the vulnerability detection method based on complex network community, and the results show that the method is effective....
Oracle bone inscription is the ancestor of modern Chinese characters. Character recognition is an essential part of the research of oracle bone inscription. In this paper, we propose an improved neural network model based on Inception-v3 for oracle bone inscription character recognition. We replace the original convolution block and add the Contextual Transformer block and the Convolutional Block Attention Module. We conduct character recognition experiments with the improved model on two oracle bone inscription character image datasets, HWOBC and OBC306, and the results indicate that the improved model can still achieve excellent results in the cases of blurred, occluded, and mutilated characters. We also select AlexNet, VGG-19, and Inception-v3 neural network models for the same experiments, and the comparison result shows that the proposed model outperforms other models in three evaluation indicators, namely, Top-1 Accuracy, Top-3 Accuracy, and Top-5 Accuracy, which indicate the correctness and excellence of our proposed model....
Background: Circular RNAs (circRNAs) are a class of non-coding RNAs formed by premRNA back-splicing, which are widely expressed in animal/plant cells and often play an important role in regulating microRNA (miRNA) activities. While numerous databases have collected a large amount of predicted circRNA candidates and provided the corresponding circRNA-regulated interactions, a stand-alone package for constructing circRNA-miRNA-mRNA interactions based on user-identified circRNAs across species is lacking. Results: We present CircMiMi (circRNA-miRNA-mRNA interactions), a modular, Python-based software to identify circRNA-miRNA-mRNA interactions across 18 species (including 16 animals and 2 plants) with the given coordinates of circRNA junctions. The CircMiMi-constructed circRNA-miRNA-mRNA interactions are derived from circRNA-miRNA and miRNA-mRNA axes with the support of computational predictions and/or experimental data. CircMiMi also allows users to examine alignment ambiguity of back-splice junctions for checking circRNA reliability and examine reverse complementary sequences residing in the sequences flanking the circularized exons for investigating circRNA formation. We further employ CircMiMi to identify circRNAmiRNA- mRNA interactions based on the circRNAs collected in NeuroCirc, a large-scale database of circRNAs in the human brain. We construct circRNA-miRNA-mRNA interactions comprising differentially expressed circRNAs, and miRNAs in autism spectrum disorder (ASD) and cross-species analyze the relevance of the targets to ASD. We thus provide a rich set of ASD-associated circRNA-miRNA-mRNA axes and a useful starting point for investigation of regulatory mechanisms in ASD pathophysiology. Conclusions: CircMiMi allows users to identify circRNA-mediated interactions in multiple species, shedding light on regulatory roles of circRNAs. The software package and web interface are freely available at https:// github. com/ Trees Lab/ CircM iMi and http:// circm imi. genom ics. sinica. edu. tw/, respectively....
This study combines particle swarm optimization programming and computer-aided translation to model English-Chinese translation and designs a computer-aided English-Chinese translation system with a particle swarm optimization algorithm as a collaborative evolutionary strategy. The system is refined into a user environment model, userspace adjustment, network optimization judgment scheme, multi-intelligent body position adjustment scheme, and particle swarm optimization algorithm; the fitness function, particle structure, population structure, and particle update strategy particle swarm algorithm are designed. In this study, we propose to improve the system of the Seq2seq model and offer the conversion layer. In the existing Seq2seq model, the source language sequence generates a representation vector through the encoder. Then the representation vector is directly used as the initial state of the decoder to generate the target language sequence. The application of computer-aided translation software to study abroad text translation is a brand-new attempt worth exploring. To improve translation speed and efficiency of study abroad texts, this study explores the practical process of using CAT software-assisted text translation, summarizes the translation practice, and puts forward relevant suggestions by analyzing the operation steps and problems that arise in the three stages of retranslation, translation, and posttranslation. It is found that computer-aided software is applicable to improve the translation efficiency of texts to a certain extent....
With the continuous progress of society, computer technology and information technology are also experiencing rapid development. Especially in recent years, the application of computer technology has rapidly entered into people’s daily life. As people’s lives become richer, these applications have become particularly complex. For some large software, tens of thousands of function points or millions of lines of source code may be triggered to support it when performing related tasks. As a result, the security of such a complicated and excellent software becomes quite essential. The most effective way to ensure software security is to test the security of software products during the development process. A precise and effective security testing process is the basis for ensuring that software is tested for security. Without a detailed scientific software security testing model to guide software development for security testing, software security testing will become very difficult. This not only wastes more time and money but also does not guarantee the security of the software. A great security testing methodology should be able to find security problems that may be hidden deep within the software. In addition, a scientific process management can greatly facilitate the implementation of software security testing. As a result, it is relatively meaningful to establish a complete software security testing process model, generate excellent security test cases, and develop security process management tools for software security testing. At the same time, in recent years, deep learning has gradually entered more and more people’s lives. However, the widespread application of deep learning systems can bring convenience to human life but also bring some hidden dangers. Hence, deep neural networks must be adequately tested to eliminate as many security risks as possible in some safety-critical software that involves personal and property safety. As the foundation of deep learning systems, deep neural networks should be adequately tested for security. However, deep learning systems are fundamentally different from traditional software testing, so traditional software testing techniques cannot be directly applied to deep neural network testing. In recent years, many scholars in related fields have proposed coverage guidelines based on deep learning testing, but the usefulness of these guidelines is still debatable. Based on the complexity of the large software development process and the fact that the interrelationship between nodes often constitutes a complex network of collaborative relationships, this study applies coverage-based testing in deep neural networks to test the security of software. To be specific, this research applies metrics such as peak coverage, speed to peak, and computational speed to evaluate coverage criteria and to investigate the feasibility of using coverage to guide test case selection to select solutions for security testing....
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