flow classification
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Author(s):  
Siwadol Sateanpattanakul ◽  
Duangpen Jetpipattanapong ◽  
Seksan Mathulaprangsan

Decompilation is the main process of software development, which is very important when a program tries to retrieve lost source codes. Although decompiling Java bytecode is easier than bytecode, many Java decompilers cannot recover originally lost sources, especially the selection statement, i.e., if statement. This deficiency affects directly decompilation performance. In this paper, we propose the methodology for guiding Java decompiler to deal with the aforementioned problem. In the framework, Java bytecode is transformed into two kinds of features called frame feature and latent semantic feature. The former is extracted directly from the bytecode. The latter is achieved by two-step transforming the Java bytecode to bigram and then term frequency-inverse document frequency (TFIDF). After that, both of them are fed to the genetic algorithm to reduce their dimensions. The proposed feature is achieved by converting the selected TFIDF to a latent semantic feature and concatenating it with the selected frame feature. Finally, KNN is used to classify the proposed feature. The experimental results show that the decompilation accuracy is 93.68 percent, which is obviously better than Java Decompiler.


2021 ◽  
pp. 107483
Author(s):  
Lu He ◽  
Sreenath Chalil Madathil ◽  
Greg Servis ◽  
Mohammad T. Khasawneh

2021 ◽  
Author(s):  
Jie Zhang ◽  
Wei Wang ◽  
Yan Shen ◽  
Yajie Li ◽  
Yongli Zhao ◽  
...  

Author(s):  
Diogo Barradas ◽  
Nuno Santos ◽  
Luis Rodrigues ◽  
Salvatore Signorello ◽  
Fernando M. V. Ramos ◽  
...  

2021 ◽  
Vol 333 ◽  
pp. 02001
Author(s):  
Yasuya Nakayama ◽  
Toshihisa Kajiwara

Mathematically, the problem of flow field classification can be analyzed by the eigenanalysis of the deformation-rate tensor; however, such analysis technique have not been commonly applied in fluid processing. We derive a simplified objective flow classification scheme based on the invariants of the strain-rate tensor and the vorticity tensor. Multiaxiality of flow, which is related to the type of elongation, and converging/bifurcating flow, is characterized by the strain-rate tensor, while rotation contribution that protects fluid element from stretching is characterized by the relative intensity of an objective vorticity to the strain-rate. The spatial distributions of flow classification quantities offer an essential tool in understanding the flow pattern structure, and therefore can be useful to get insights into the connection between the geometry and the process performance.


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