• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.6
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• pp.1499-1506
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• 2017

The use of smartphones and the launch of various apps have increased exponentially, and malicious apps have also increased. Existing app recommendation systems have been limited to operate based on static information analysis such as ratings, comments, and popularity categories of other users who are online. In this paper, we first propose a robust app recommendation system that realistically uses dynamic information of apps actually used in smartphone and considers static information and dynamic information at the same time. In other words, this paper proposes a robust Android app recommendation system by partially reflecting the time of the app, the frequency of use of the app, the interaction between the app and the app, and the number of contact with the Android kernel. As a result of the performance evaluation, the proposed method proved to be a robust and efficient app recommendation system.

• Journal of Information Technology Services
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• v.11 no.4
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• pp.233-250
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• 2012

In this paper, credit card delinquency means the possibility of occurring bad debt within the certain near future from the normal accounts that have no debt and the problem is to predict, on the monthly basis, the occurrence of delinquency 3 months in advance. This prediction is typical binary classification problem but suffers from the issue of data imbalance that means the instances of target class is very few. For the effective prediction of bad debt occurrence, Support Vector Machine (SVM) with kernel trick is adopted using credit card usage and payment patterns as its inputs. SVM is widely accepted in the data mining society because of its prediction accuracy and no fear of overfitting. However, it is known that SVM has the limitation in its ability to processing the large-scale data. To resolve the difficulties in applying SVM to bad debt occurrence prediction, two stage clustering is suggested as an effective data reduction method and ensembles of SVM models are also adopted to mitigate the difficulty due to data imbalance intrinsic to the target problem of this paper. In the experiments with the real world data from one of the major domestic credit card companies, the suggested approach reveals the superior prediction accuracy to the traditional data mining approaches that use neural networks, decision trees or logistics regressions. SVM ensemble model learned from T2 training set shows the best prediction results among the alternatives considered and it is noteworthy that the performance of neural networks with T2 is better than that of SVM with T1. These results prove that the suggested approach is very effective for both SVM training and the classification problem of data imbalance.

• Convergence Security Journal
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• v.18 no.5_1
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• pp.67-73
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• 2018

From a software engineering point of view, tracing is a special form of logging that records program execution information. Tracers using dedicated hardware are often used because of the characteristics of tracers that need to generate and decode huge amounts of data in real time. Intel(R) PT uses proprietary hardware to record all information about software execution on each hardware thread. When the software execution is completed, the PT can process the trace data of the software and reconstruct the correct program flow. The hardware trace program can be integrated into the operating system, but in the case of the window system, the integration is not tight due to problems such as the kernel opening. Also, it is possible to trace only a single process and not provide a way to trace multiple process streams. In this paper, we propose a method to extend existing PT trace program to support multi - process stream traceability in Windows environment in order to overcome these disadvantages.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.589-592
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• 2016

The period of the previous multi-core could be helped to improve program performance, based on the development of the hardware. However, one of the core performance enhancements for this encounter limitations and become the common way of multi-core with multiple cores. Modern programming concurrency that improves the conventional method for using threads of the kernel level in order to use the multi-core come to the fore. Using modern lightweight thread concurrency programming is to optimize the benefits of multi-core. Also sharing the absence of available data that can change is a major consideration when writing concurrent code. This paper describes the key considerations when creating a discussion concurrent code, and these issues are being supported in any way in the language of one 'go' of technologies that support the modern concurrency, and even how to write better code concurrency.

• Convergence Security Journal
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• v.17 no.5
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• pp.153-161
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• 2017

The gain a decision by a prediction supposition future combat. Take a future combat by the method fighting of U.S. Army in the Irak war. A make combat progress is from real time information to precision bombing for a guided weapon by GPS, a intelligence satellite, a pilotless scout plane, real time simultaneous and unification combat power are the kernel element of gain a decision fighting power by network in the ground, sky, marine, universe, cyberspace. The NCW is in a sense network center war organic be connected by networking a factor of operation. Any where networking information collection, command and decision, blow system. The Study on the improvement plan for Military combat power by base of NCW abainst the future War. Construct an integrate intelligence network apply to future combat.

• Convergence Security Journal
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• v.19 no.1
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• pp.97-101
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• 2019

If the operating system's core file contains an Intel PT, the debugger can not only check the program state at the time of the crash, but can also reconfigure the control flow that caused the crash. We can also extend the execution trace scope to the entire system to debug kernel panics and other system hangs. The second-generation PT, the WinIPT library, includes an Intel PT driver with additional code to run process and core-specific traces through the IOCTL and registry mechanisms provided by Windows 10 (RS5). In other words, the PT trace information, which was limited access only by the first generation PT, can be executed by process and core by the IOCTL and registry mechanism provided by the operating system in the second generation PT. In this paper, we compare and describe methods for collecting, storing, decoding and detecting malicious codes of data packets in a window environment using 1/2 generation PT.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.55-62
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• 2021

In this paper, we propose a human-content interface that allows users to quickly and efficiently search data through friction-based scrolling with ROI(Regions of interests). Our approach, conceived from the behavior of finding information or content of interest to users, efficiently calculates ROI for a given content. Based on the kernel developed by conceiving from GMM(Gaussian mixture model), information is searched by moving the screen smoothly and quickly to the location of the information of interest to the user. In this paper, linear interpolation is applied to make one softer inertia, and this is applied to scrolls. As a result, unlike the existing approach in which information is searched according to the user's input, our method can more easily and intuitively find information or content that the user is interested in through friction-based scrolling. For this reason, the user can save search time.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.1-8
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• 2022

As an efficient file system for SSD(Solid State Drive), F2FS is employed in the kernel of Linux operating system. F2FS applies various methods to improve performance by reflecting the characteristics of flash memory. One of them is improvement of the index structure that contains addresses of data blocks for each file. This paper presents a method for further improving performance by modifying the index structure of F2FS. F2FS manages all index blocks as logical numbers, and an address mapping table is used to find the physical block addresses of index blocks on flash memory. This paper shows performance improvement by applying logical numbers to the last level index blocks only. The count of mapping table search for a data block access is reduced to 1~2 from 1~4.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.174-174
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• 2023

The impact of global warming on the south Asian summer monsoon is of critical importance for the large population of this region. This study aims to investigate the future changes of the precipitation extremes during pre-monsoon and monsoon, across this region in a more organized regional structure. The study area is divided into six major divisions based on the Köppen-Geiger's climate structure and 10 sub-divisions considering the geographical locations. The future changes of extreme precipitation indices are analyzed for each zone separately using five indices from ETCCDI (Expert Team on Climate Change Detection and Indices); R10mm, Rx1day, Rx5day, R95pTOT and PRCPTOT. 10 global climate model (GCM) outputs from the latest CMIP6 under four combinations of SSP-RCP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) are used. The GCMs are bias corrected using nonparametric quantile transformation based on the smoothing spline method. The future period is divided into near future (2031-2065) and far future (2066-2100) and then the changes are compared based on the historical period (1980-2014). The analysis is carried out separately for pre-monsoon (March, April, May) and monsoon (June, July, August, September). The methodology used to compare the changes is probability distribution functions (PDF). Kernel density estimation is used to plot the PDFs. For this study we did not use a multi-model ensemble output and the changes in each extreme precipitation index are analyzed GCM wise. From the results it can be observed that the performance of the GCMs vary depending on the sub-zone as well as on the precipitation index. Final conclusions are made by removing the poor performing GCMs and by analyzing the overall changes in the PDFs of the remaining GCMs.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.4
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• pp.234-240
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• 2022

This paper proposes a customized AI exercise recommendation service for balancing the relative amount of exercise according to the working environment by each occupation. WISDM database is collected by using acceleration and gyro sensors, and is a dataset that classifies physical activities into 18 categories. Our system recommends a adaptive exercise using the analyzed activity type after classifying 18 physical activities into 3 physical activities types such as whole body, upper body and lower body. 1 Dimensional convolutional neural network is used for classifying a physical activity in this paper. Proposed model is composed of a convolution blocks in which 1D convolution layers with a various sized kernel are connected in parallel. Convolution blocks can extract a detailed local features of input pattern effectively that can be extracted from deep neural network models, as applying multi 1D convolution layers to input pattern. To evaluate performance of the proposed neural network model, as a result of comparing the previous recurrent neural network, our method showed a remarkable 98.4% accuracy.