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Emotion Recognition based on Multivariate Multiscale Fuzzy Entropy Analysis of EEG Recordings
Dae-Young Lee, Young-Seok Choi
http://doi.org/10.5626/JOK.2020.47.3.227
An Electroencephalogram (EEG) signal is an immediate and continuous signal that records brain activity, and it is mainly used for emotional analysis since it can directly reflect the changes of human emotional states. Among the methods of analyzing the EEG signals, entropy analysis is one of the measures for quantifying the complexity of time series. This quantitative analysis of complexity is promising for investigating non-stationary and nonlinear physiological signals. In this paper, we propose a multivariate multiscale fuzzy entropy (MMFE) analysis method that quantifies the complexity of multivariate time series over various time scales to analyze emotional states using EEG signals recorded from multiple electrodes as input. A public database, DEAP, is used as input data in this analysis, and the results show the possibility that emotional states can be distinguished through the binary classification of high/low arousal and high/low valence.
Cross-Entropy Planning with Prior Updates
HyeongJoo Hwang, Youngsoo Jang, Jaeyoung Park, Kee-Eung Kim
http://doi.org/10.5626/JOK.2020.47.1.88
This paper introduces a method of cross-entropy planning which updates prior probability for planning optimization process. Cross-entropy planning is a popular method in online planning and involves the extraction of samples from a simulation environment and selection of optimal action based on the values of the extracted samples. The performance of the cross-entropy planning is limited due to involvement of optimization processes without usage of previous planning results. We propose a method that updates prior probabilities for the optimization process based on the action sequences acquired from the cross-entropy planning. The proposed method improves the performance of cross-entropy planning with progression of planning epoch. We evaluated the proposed method based on the comparison with the cross-entropy planning in a physical-based simulation (OpenAI Gym) environment.
A Study on Two-dimensional Array-based Technology to Identify Obfuscatied Malware
Seonbin Hwang, Hogyeong Kim, Junho Hwang, Taejin Lee
http://doi.org/10.5626/JOK.2018.45.8.769
More than 1.6 milion types of malware are emerging on average per day, and most cyber attackes are generated by malware. Moreover, malware obfuscation techniques are becoming more intelligent through packing or encryption to prevent reverse engineering analysis. In the case of static analysis, there is a limit to the analysis when the analytical file becomes obfuscated, and a countermeasure is needed. In this paper, we propose an approach based on String, Symbol, and Entropy as a way to identify malware even during obfuscation. Two-dimensional arrays were applied for fixed feature-set processing as well as non-fixed feature-set processing, and 15,000 malware/benign samples were tested using the Deep Neural Network. This study is expected to operate in a complementary manner in conjunction with various malicious code detection methods in the future, and it is expected that it can be utilized in the analysis of obfuscated malware variants.
Design and Implementation of an In-Memory File System Cache with Selective Compression
http://doi.org/10.5626/JOK.2017.44.7.658
The demand for large-scale storage systems has continued to grow due to the emergence of multimedia, social-network, and big-data services. In order to improve the response time and reduce the load of such large-scale storage systems, DRAM-based in-memory cache systems are becoming popular. However, the high cost of DRAM severely restricts their capacity. While the method of compressing cache entries has been proposed to deal with the capacity limitation issue, compression and decompression, which are technically difficult to parallelize, induce significant processing overhead and in turn retard the response time. A selective compression scheme is proposed in this paper for in-memory file system caches that rapidly estimates the compression ratio of incoming cache entries with their Shannon entropies and compresses cache entries with low compression ratio. In addition, a description is provided of the design and implementation of an in-kernel in-memory file system cache with the proposed selective compression scheme. The evaluation showed that the proposed scheme reduced the execution time of benchmarks by approximately 18% in comparison to the conventional non-compressing in-memory cache scheme. It also provided a cache hit ratio similar to the all-compressing counterpart and reduced 7.5% of the execution time by reducing the compression overhead. In addition, it was shown that the selective compression scheme can reduce the CPU time used for compression by 28% compared to the case of the all-compressing scheme.
Recovery of Software Module-View using Dependency and Author Entropy of Modules
Jung-Min Kim, Chan-Gun Lee, Ki-Seong Lee
In this study, we propose a novel technique of software clustering to recover the software module-view by using the dependency and author entropy of modules. The proposed method first performs clustering of modules based on structural and logical dependencies, then it migrates selected modules from the clustered result by utilizing the author entropy of each module. In order to evaluate the proposed method, we calculated the MoJoFM values of the recovery result by applying the method to open-source projects among which ground-truth decompositions are well-known. Compared to the MoJoFM values of previously studied techniques, we demonstrated the effectiveness of the proposed method.
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