• Journal of KIISE:Computing Practices and Letters
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• v.10 no.3
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• pp.221-230
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• 2004

In general, remote monitoring systems monitor the status of distributed hosts and/or applications in real-time for diverse managerial purposes. However, most of the extant systems have a few undesirable problems. First of all, they are platform-dependent and are not resilient to network and/or host failures. Moreover, they normally focus on the resource usage trends in monitored hosts, rather than on the status change of the applications running on them. We strongly believe that the latter has more direct and profound effect on the resource usage patterns on each host. In this paper, we present the design and implementation of the Remote Applications Monitoring System (RAMS) that enables us to effectively manage distributed applications through a real-time monitoring of their respective resource usages. The RAMS is a centralized system that consists of many distributed agents and a single centralized manager. An agent on each host is in charge of collecting and reporting the status of local applications. The manager handles agent registration and provides a central access point to the selection and monitoring of distributed applications. The salient features of the system include robustness and portability The adoption of JINI greatly facilitates an automatic recovery from partial network failure and host failure.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.5
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• pp.345-349
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• 2009

Social bookmarking systems are a typical web 2.0 service based on folksonomy, providing the platform for storing and sharing bookmarking information. Spammers in social bookmarking systems denote the users who abuse the system for their own interests in an improper way. They can make the entire resources in social bookmarking systems useless by posting lots of wrong information. Hence, it is important to detect spammers as early as possible and protect social bookmarking systems from their attack. In this paper, we applied a diverse set of machine learning approaches, i.e., decision tables, decision trees (ID3), $na{\ddot{i}}ve$ Bayes classifiers, TAN (tree-augment $na{\ddot{i}}ve$ Bayes) classifiers, and artificial neural networks to this task. In our experiments, $na{\ddot{i}}ve$ Bayes classifiers performed significantly better than other methods with respect to the AUC (area under the ROC curve) score as veil as the model building time. Plausible explanations for this result are as follows. First, $na{\ddot{i}}ve$> Bayes classifiers art known to usually perform better than decision trees in terms of the AUC score. Second, the spammer detection problem in our experiments is likely to be linearly separable.

• The Journal of Korean Association of Computer Education
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• v.23 no.4
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• pp.1-11
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• 2020

The purpose of this study is to propose the direction of AI education in K-12 education through investigating and analyzing aspects of the purpose, content, and methods of AI education as the curriculum and teacher training factors. We collected and analyzed 9 papers as the primary literature and 11 domestic and foreign policy reports as the secondary literature. The collected literatures were analyzed by applying a descriptive reviews, and the implications were derived by analyzing the curriculum components and TPACK elements for multi-dimensional analysis. As a result of this study, AI education targets were divided into three steps: AI users, utilizer, and developers. In K-12 education, the user and utilizer stages are appropriate, and artificial intelligence literacy must be included for user education. Based on the current computing thinking ability and coding ability for utilizer education, the implication was derived that it is necessary to target the ability to create creative output by applying the functions of artificial intelligence. In addition to the pedagogical knowledge and the ability to use the platform, The teacher training is necessary because teachers need content knowledge such as problem-solving, reasoning, learning, perception, and some applied mathematics, cognitive / psychological / ethical of AI.

• Journal of Internet Computing and Services
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• v.18 no.4
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• pp.109-119
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• 2017

This paper is to analyse empirically using the data collected from Korea portal Naver's ad management system and show online ad platform may not be two-sided market. It is aim of this study to propose the appropriate approach to define the market, based on the empirical result. Here are two research questions to be reviewed. First, is there any consistency between business model of search advertising and definition of two-sided market which Rochet-Tirole proposed in 2006? Second, do indirect network externalities exist significantly in search advertising market? if so, this study is going to estimate the level of it through empirical measurement. Based on Luchetta's paper which suggested that google may be one-sided market, it performed the correlation & regression analysis to prove his suggestion. The result is that online search advertising costs increased by more than 50 won when advertisers increased by one unit. However, there was no significant correlation and regression between the search frequency and online search advertising cost. It means that there is little possibility to identify two-sidedness in online search advertising service(market) because of no(or little) indirect network externalities which are a necessary condition for two-sided market. This result has three implications, such as the availability to adapt traditional market definition tools to online search advertising market, the possibility enhancement to find the fundamental competition elements in defined market and promotion of the powers of persuasion in competitive market reality. It is significant that the gap between legal scholars including regulatory practitioners and economists can be overcome to some extent. who have shown the different perspective on the two-sided market.

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.15-26
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• 2014

This study focus on a economic value of the Big Data technologies by real options model using big data technology company's stock price to determine the price of the economic value of incremental assessed value. For estimating stochastic process of company's stock price by big data technology to extract the incremental shares, Generalized Moments Method (GMM) are used. Option value for Black-Scholes partial differential equation was derived, in which finite difference numerical methods to obtain the Big Data technology was introduced to estimate the economic value. As a result, a option value of big data technology investment is 38.5 billion under assumption which investment cost is 50 million won and time value is a about 1 million, respectively. Thus, introduction of big data technology to create a substantial effect on corporate profits, is valuable and there are an effects on the additional time value. Sensitivity analysis of lower underlying asset value appear decreased options value and the lower investment cost showed increased options value. A volatility are not sensitive on the option value due to the big data technological characteristics which are low stock volatility and introduction periods.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.125-139
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• 2013

Distribution of malicious applications developed by attackers is increasing along with general normal applications due to the openness of the Android-based open market. Mechanism that allows more accurate ways to distinguish normal apps and malicious apps for common mobile devices should be developed in order to reduce the damage caused by the rampant malicious applications. This paper analysed the normal event pattern from the most highly used game apps in the Android open market to analyse the event pattern from normal apps and malicious apps of mobile devices that are based on the Android platform, and analysed the malicious event pattern from the malicious apps and the disguising malicious apps in the form of a game app among 1260 malware samples distributed by Android MalGenome Project. As described, experiment that extracts normal app and malicious app events was performed using Strace, the Linux-based system call extraction tool, targeting normal apps and malicious apps on Android-based mobile devices. Relevance analysis for each event set was performed on collected events that occurred when normal apps and malicious apps were running. This paper successfully extracted event similarity through this process of analyzing the event occurrence characteristics, pattern and distribution on each set of normal apps and malicious apps, and lastly suggested a mechanism that determines whether any given app is malicious.

• Journal of Internet Computing and Services
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• v.14 no.4
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• pp.25-33
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• 2013

The procedure of the arbitration which is the tag collision is essential because the multiple tags response simultaneously in the same frequency to the request of the Reader. This procedure is known as Anti-collision and it is a key technology in the RFID system. In this paper, we propose the 4-Bit Pattern Slot Allocation(4-BPSA) algorithm for the high-speed identification of the multiple tags. The proposed algorithm is based on the tree algorithm using the time slot and identify the tag quickly and efficiently through accurate prediction using the a slot as a 4-bit pattern according to the slot allocation scheme. Through mathematical performance analysis, We proved that the 4-BPSA is an O(n) algorithm by analyzing the worst-case time complexity and the performance of the 4-BPSA is improved compared to existing algorithms. In addition, we verified that the 4-BPSA is performed the average 0.7 times the query per the Tag through MATLAB simulation experiments with performance evaluation of the algorithm and the 4-BPSA ensure stable performance regardless of the number of the tags.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.591-596
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• 2019

In this paper, we implemented a deep learning operation structure with less influence of local PC performance. In general, the deep learning model has a large amount of computation and is heavily influenced by the performance of the processing PC. In this paper, we implemented deep learning operation using AWS and streaming server to reduce this limitation. First, deep learning operations were performed on AWS so that deep learning operation would work even if the performance of the local PC decreased. However, with AWS, the output is less real-time relative to the input when computed. Second, we use streaming server to increase the real-time of deep learning model. If the streaming server is not used, the real-time performance is poor because the images must be processed one by one or by stacking the images. We used the YOLO v3 model as a deep learning model for performance comparison experiments, and compared the performance of local PCs with instances of AWS and GTX1080, a high-performance GPU. The simulation results show that the test time per image is 0.023444 seconds when using the p3 instance of AWS, which is similar to the test time per image of 0.027099 seconds on a local PC with the high-performance GPU GTX1080.

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.1
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• pp.63-76
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• 2001

As reusability becomes recognized more importantly, with the introduction of ObjectOriented Programming Languages, developers not only want to reduce development duration, but also to develop a proper system robustly and safely by renovating the Hot Spot in order to reuse the existing framework. When we perform these works, we need the development environment which is the Rapid Application Development tool, and the RAD tools provide us with the convenient development environment. The need of RAD tools is recognized by every Object-Oriented programmer, and many business enterprises are developing them. In this paper, we will present a design and implementation of module-based modeler as a method for developing technique to constmct user-driven Intranet environment for the generation of the program based on the framework. The framework modeler used Java language that is independent on platform, and applied the technique of OMT editor that provides the UML notation partially. Additionally, The modeler also includes the notations that are not supported in OMT editor. In addition to this characteristic, it is structured to develop system consistently with applying the Agent pattern, which is a design pattern suggested by ourselves, to send messages occurred between various Views. The existing MVC(Model-View-Controller) architecture does not have this function. Thus, this tool has a flexibility when user's requirements are changed, or functions are extended.

• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.27-35
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• 2018

This paper is a study to classify malicious applications in Android environment. And studying the threat and behavioral analysis of malicious Android applications. In addition, malicious apps classified by machine learning were performed as experiments. Android behavior analysis can use dynamic analysis tools. Through this tool, API Calls, Runtime Log, System Resource, and Network information for the application can be extracted. We redefined the properties extracted for machine learning and evaluated the results of machine learning classification by verifying between the overall features and the main features. The results show that key features have been improved by 1~4% over the full feature set. Especially, SVM classifier improved by 10%. From these results, we found that the application of the key features as a key feature was more effective in the performance of the classification algorithm than in the use of the overall features. It was also identified as important to select meaningful features from the data sets.