• Journal of Information Technology Applications and Management
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• v.25 no.2
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• pp.53-72
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• 2018

This empirical study explored characteristics that affect the usefulness of online reviews, in the China e-commerce platform, and implemented multiple regressions to find factors that significantly influence on product sales, ultimately. Till now, prior studies have continuously revealed what factor affects usefulness of online review or product sales, only in respective terms. The point of our study is that we built two-level regression models, thereby being able to comprehensively analyze these two different targets. Before plunging into running regressions, we carefully collected 192,764 online review data for 200 products extracted from the Jingdong, the second biggest e-commerce platform in China. Also, we gathered "review sentimental scores" variable from each review and used that one as a core variable in our regression model, thus we were able to implement both quantitative and qualitative research. The evidences from the two-level regression models showed that the extent to which a product is experience good positively affects both usefulness of a review and product sales, again the usefulness of a review contributes to product sales in sequence. Also, the property of experience good has interaction effect on both for two-level regression models. Our main findings highlight the importance of role of online review to business performance of e-commerce firms.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.9
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• pp.259-266
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• 2020

In this paper, we propose a novel scheme for product effect analysis, termed PEM, to find out the effectiveness of products used for improving the current condition, such as health supplements and cosmetics, by utilizing online customer reviews. The proposed technique preprocesses online customer reviews to remove advertisements automatically, constructs the word dictionary composed of symptoms, effects, increases, and decreases, and measures products' effects from online customer reviews. Using Naver Shopping Review datasets collected through crawling, we evaluated the performance of PEM compared to those of two methods using traditional sentiment dictionary and an RNN model, respectively. Our experimental results shows that the proposed technique outperforms the other two methods. In addition, by applying the proposed technique to the online customer reviews of atopic dermatitis and acne, effective treatments for them were found appeared on online social media. The proposed product effect analysis technique presented in this paper can be applied to various products and social media because it can score the effect of products from reviews of various media including blogs.

• Journal of Korean Physical Therapy Science
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• v.19 no.3
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• pp.23-30
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• 2012

The purpose of this study was to apply treadmill training through motor learning to cerebral palsy children and examine its effects on their Gross Motor Functions. The subjects of this study were 13 spastic diplegia children who had difficulty in independent gait, and GMFCS level III, IV. We performed treadmill gait training using the principle of weight bearing, based on 4times a week for 30 minutes before and after each session physical therapy we gave weight bearing treadmill training 5 to 10 minutes, during 7 weeks(April 9, 2012~May 26, 2012) fittingly for the children's gait characteristics. In order to test how the weight bearing treadmill training affects spastic diplegia children's gross motor functions, we measured body mobility with Gross Motor Function Measure (GMFM). These data were collected before and after the experiment and analyzed through comparison. Data collected from the 13 spastic diplegia children the results were as follows. For evaluating with regard to change in body mobility, significant difference was observed between before and after the experiment in measured gross motor functions, which were crawling, kneeling, standing, walking, jumping and running(p<0.05). According to the results of this study, when gait training through motor learning was applied to spastic cerebral palsy children, it made significant changes in their body mobility. Accordingly, for the effective application of gait training through motor learning to cerebral palsy children, it is considered necessary to make research from different angle on how such training affects children's mobility, activity of muscles in the lower limbs, and gait characteristics.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.550-555
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• 2018

This study aims to identify and compare contents and keywords used in articles related to blockchain applications to various industries. The text mining and Semantic Network Analysis, as methods of keyword network analysis, were used to analyze articles including terms of 'finance' 'energy' and 'logistics', which media and government frequently mentioned as areas that can apply blockchain technologies. For this study, data were collected from 43,093 articles from January, 2017 through July, 2018. Data crawling was carried out by using Python BeautifulSoup and data cleaning was performed in order to eliminate mutual redundancies of the three terms. After that, text mining and semantic network analysis were performed using Textom and UCInet for network analysis between keywords. The results showed that all the three terms were similar in terms of 'technology', but there were differences in the contents of 'government policy' or 'industry' issues. In addition, there were differences in frequencies and centralities of these terms.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.6
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• pp.1317-1330
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• 2017

To collect server information and construct network map enable us to prevent security breach, prepare for national cyber warfare and make effective policies. In this paper, we analyze well-known network scanners, Nmap and ZMap, and construct network map using banner grabbing. We use multiple threads in order to increase scanning speed and arrange IP lists by specific order to reduce the load on information gathering targets. Also, we applied performance tests to compare the real-time banner grabbing tool with the existing network scanners. As a result, we gathered server information from domestic and overseas servers and derived a risk index based on the collected database. Although there are slight differences among countries, we can identify the risky situation that many users in every country are exposed to several security breaches.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.6
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• pp.1491-1498
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• 2017

As the use of smartphones and the launch of various apps have increased rapidly, the number of malicious apps has also increased, and the damage is continuing. The Google Market where Android apps are registered is inevitably present at the same time as normal apps and malicious apps even though there are regulations for app registration. Especially, as social networks are activated, users are connected with social networks, and the ratings, downloads and awareness information are reflected in the number of downloaded apps. As a result, when users choose their apps by simply reflecting ratings, popularity, popular comments, and highly-categorized apps, malicious app downloads can sometimes cause significant harm. Therefore, this study first analyzed the tendency of malicious apps by directly crawling and analyzing long-term social information in the currently active Android market.

• KIISE Transactions on Computing Practices
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• v.21 no.10
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• pp.670-679
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• 2015

With a rapid development of internet, many users these days refer to various recommender sites when buying items, movies, music and more. However, there are malicious users (Sybil) who raise or lower item ratings intentionally in these recommender sites. And as a result, a recommender system (RS) may recommend incomplete or inaccurate results to normal users. We suggest a recommender algorithm to separate ratings generated by users into normal ratings and outlier ratings, and to minimize the effects of malicious users. Specifically, our algorithm first ensures a stable RS against three kinds of attack models (Random attack, Average attack, and Bandwagon attack) which are the main recent security issues in RS. To prove the performance of the method of suggestion, we conducted performance analysis on real world data that we crawled. The performance analysis demonstrated that the suggested method performs well regardless of Sybil size and type when compared to existing algorithms.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.10
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• pp.251-258
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• 2018

Ransomware, a malicious software that requires a ransom by encrypting a file, is becoming more threatening with its rapid propagation and intelligence. Rapid detection and risk analysis are required, but real-time analysis and reporting are lacking. In this paper, we propose a ransomware infection detection system using social big data mining technology to enable real-time analysis. The system analyzes the twitter stream in real time and crawls tweets with keywords related to ransomware. It also extracts keywords related to ransomware by crawling the news server through the news feed parser and extracts news or statistical data on the servers of the security company or search engine. The collected data is analyzed by data mining algorithms. By comparing the number of related tweets, google trends (statistical information), and articles related wannacry and locky ransomware infection spreading in 2017, we show that our system has the possibility of ransomware infection detection using tweets. Moreover, the performance of proposed system is shown through entropy and chi-square analysis.

• Journal of Ocean Engineering and Technology
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• v.27 no.6
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• pp.81-89
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• 2013

This paper considers the steering characteristics of a four-row tracked vehicle crawling on extremely cohesive soft soil, where each side is composed of two parallel tracks. The four-row tracked vehicle (FRTV) is assumed to be a rigid body with 6-DOF. A dynamic analysis program for the tracked vehicle is developed using the Newmark-${\beta}$ method based on an incremental-iterative scheme. A terra-mechanics model of an extremely cohesive soft soil is implemented in the form of the relationships of the normal pressure to the sinkage, the shear resistance to the shear displacement, and the dynamic sinkage to the shear displacement. In order to investigate the steering characteristics of the four-row tracked vehicle, a series of dynamic simulations is conducted with respect to the distance between the left and right tracks (pitch), steering ratios, driving velocity, reference track velocity, lengths of the tracks, and properties of the cohesive soft soil. Through these numerical simulations, the possibility of using a kinematic steering ratio is explored.

• Journal of Convergence for Information Technology
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• v.7 no.2
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• pp.31-36
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• 2017

In this paper, we try to search and reproduce the image part of a specific cast from a large number of images. The conventional method must manually set the offset value when searching for a scene or viewing a corner. However, in this paper, the proposed method learns the main character 's face, then finds the main character in the image recognition and moves to the scene where the main character appears to reproduce the image. Data for specific performers is extracted and collected using crawl techniques. Based on the collected data, we learn using convolutional neural network algorithm and perform performance evaluation using it. The performance evaluation measures the accuracy by extracting and judging a specific performer learned in the extracted key frame while playing the drama. The performance confirmation of how quickly and accurately the learned scene is searched has obtained about 93% accuracy. Based on the derived performance, it is applied to the image service such as viewing, searching for person and detailed information retrieval per corner