• Journal of the Korean Society for Library and Information Science
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• v.55 no.4
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• pp.5-22
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• 2021

This study aimed to understand how people perceive the transition from offline to online classes at universities during the COVID-19 pandemic. To achieve the goal, we collected tweets related to online classes on Twitter and performed sentiment and time series topic analysis. We have the following findings. First, through the sentiment analysis, we found that there were more negative than positive opinions overall, but negative opinions had gradually decreased over time. Through exploring the monthly distribution of sentiment scores of tweets, we found that sentiment scores during the semesters were more widespread than the ones during the vacations. Therefore, more diverse emotions and opinions were showed during the semesters. Second, through time series topic analysis, we identified five main topics of positive tweets that include class environment and equipment, positive emotions, places of taking online classes, language class, and tests and assignments. The four main topics of negative tweets include time (class & break time), tests and assignments, negative emotions, and class environment and equipment. In addition, we examined the trends of public opinions on online classes by investigating the changes in topic composition over time through checking the proportions of representative keywords in each topic. Different from the existing studies of understanding public opinions on online classes, this study attempted to understand the overall opinions from tweet data using sentiment and time series topic analysis. The results of the study can be used to improve the quality of online classes in universities and help universities and instructors to design and offer better online classes.

• Journal of the Korean Society for information Management
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• v.39 no.4
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• pp.191-213
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• 2022

With a view to providing a high-quality policy information service beyond the existing national task service of the national policy information portal (POINT) of the National Library of Korea Sejong, it would be necessary to effectively provide the policy data needed for the implementation of the new national tasks. Accordingly, in this study, an attempt has been made to find a way to connect and develop the BRM-based national tasks and the policy information contents. Towards this end, first, the types of national tasks and the contents of each field and area of the government function's classification system were analyzed, with a focus placed on the 120 national tasks of the new administration. Furthermore, by comparing and analyzing the national tasks of the previous administration and the current information, the contents ought to be reflected for the development of contents related to the national tasks identified. Second, the method for linking and collecting the policy information was sought based on the analysis of the current status of policy information and the national information portal. As a result of the study, first, examining the 1st stage BRM of the national tasks, it turned out that there were 21 tasks for social welfare, 14 for unification and diplomacy, 17 for small and medium-sized businesses in industry and trade, 12 for general public administration, 8 for the economy, taxation and finance, 6 for culture, sports and tourism, science and technology, and education each, 5 for communication, public order and safety each, 4 for health, transportation and logistics, and environment each, 3 for agriculture and forestry, 2 for national defense and regional development each, and 1 for maritime and fisheries each, among others. As for the new administration, it is apparent that science technology and IT are important, and hence, it is necessary to consider such when developing the information services for the core national tasks. Second, to link the database with external organizations, it would be necessary to form a linked operation council, link and collect the information on the national tasks, and link and provide the national task-related information for the POINTs.

• Journal of KIISE:Information Networking
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• v.32 no.3
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• pp.279-290
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• 2005

Recently, as the serious damage caused by DDoS attacks increases, the rapid detection and the proper response mechanisms are urgent. However, existing security mechanisms do not effectively defend against these attacks, or the defense capability of some mechanisms is only limited to specific DDoS attacks. In this paper, we propose a detection architecture against DDoS attack using data mining technology that can classify the latest types of DDoS attack, and can detect the modification of existing attacks as well as the novel attacks. This architecture consists of a Misuse Detection Module modeling to classify the existing attacks, and an Anomaly Detection Module modeling to detect the novel attacks. And it utilizes the off-line generated models in order to detect the DDoS attack using the real-time traffic. We gathered the NetFlow data generated at an access router of our network in order to model the real network traffic and test it. The NetFlow provides the useful flow-based statistical information without tremendous preprocessing. Also, we mounted the well-known DDoS attack tools to gather the attack traffic. And then, our experimental results show that our approach can provide the outstanding performance against existing attacks, and provide the possibility of detection against the novel attack.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.227-252
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• 2018

The evolution of instant communication has mirrored the development of the Internet and messenger applications are among the most representative manifestations of instant communication technologies. In messenger applications, senders use emoticons to supplement the emotions conveyed in the text of their messages. The fact that communication via messenger applications is not face-to-face makes it difficult for senders to communicate their emotions to message recipients. Emoticons have long been used as symbols that indicate the moods of speakers. However, at present, emoticon-use is evolving into a means of conveying the psychological states of consumers who want to express individual characteristics and personality quirks while communicating their emotions to others. The fact that companies like KakaoTalk, Line, Apple, etc. have begun conducting emoticon business and sales of related content are expected to gradually increase testifies to the significance of this phenomenon. Nevertheless, despite the development of emoticons themselves and the growth of the emoticon market, no suitable emoticon recommendation system has yet been developed. Even KakaoTalk, a messenger application that commands more than 90% of domestic market share in South Korea, just grouped in to popularity, most recent, or brief category. This means consumers face the inconvenience of constantly scrolling around to locate the emoticons they want. The creation of an emoticon recommendation system would improve consumer convenience and satisfaction and increase the sales revenue of companies the sell emoticons. To recommend appropriate emoticons, it is necessary to quantify the emotions that the consumer sees and emotions. Such quantification will enable us to analyze the characteristics and emotions felt by consumers who used similar emoticons, which, in turn, will facilitate our emoticon recommendations for consumers. One way to quantify emoticons use is metadata-ization. Metadata-ization is a means of structuring or organizing unstructured and semi-structured data to extract meaning. By structuring unstructured emoticon data through metadata-ization, we can easily classify emoticons based on the emotions consumers want to express. To determine emoticons' precise emotions, we had to consider sub-detail expressions-not only the seven common emotional adjectives but also the metaphorical expressions that appear only in South Korean proved by previous studies related to emotion focusing on the emoticon's characteristics. We therefore collected the sub-detail expressions of emotion based on the "Shape", "Color" and "Adumbration". Moreover, to design a highly accurate recommendation system, we considered both emotion-technical indexes and emoticon-emotional indexes. We then identified 14 features of emoticon-technical indexes and selected 36 emotional adjectives. The 36 emotional adjectives consisted of contrasting adjectives, which we reduced to 18, and we measured the 18 emotional adjectives using 40 emoticon sets randomly selected from the top-ranked emoticons in the KakaoTalk shop. We surveyed 277 consumers in their mid-twenties who had experience purchasing emoticons; we recruited them online and asked them to evaluate five different emoticon sets. After data acquisition, we conducted a factor analysis of emoticon-emotional factors. We extracted four factors that we named "Comic", Softness", "Modernity" and "Transparency". We analyzed both the relationship between indexes and consumer attitude and the relationship between emoticon-technical indexes and emoticon-emotional factors. Through this process, we confirmed that the emoticon-technical indexes did not directly affect consumer attitudes but had a mediating effect on consumer attitudes through emoticon-emotional factors. The results of the analysis revealed the mechanism consumers use to evaluate emoticons; the results also showed that consumers' emoticon-technical indexes affected emoticon-emotional factors and that the emoticon-emotional factors affected consumer satisfaction. We therefore designed the emoticon recommendation system using only four emoticon-emotional factors; we created a recommendation method to calculate the Euclidean distance from each factors' emotion. In an attempt to increase the accuracy of the emoticon recommendation system, we compared the emotional patterns of selected emoticons with the recommended emoticons. The emotional patterns corresponded in principle. We verified the emoticon recommendation system by testing prediction accuracy; the predictions were 81.02% accurate in the first result, 76.64% accurate in the second, and 81.63% accurate in the third. This study developed a methodology that can be used in various fields academically and practically. We expect that the novel emoticon recommendation system we designed will increase emoticon sales for companies who conduct business in this domain and make consumer experiences more convenient. In addition, this study served as an important first step in the development of an intelligent emoticon recommendation system. The emotional factors proposed in this study could be collected in an emotional library that could serve as an emotion index for evaluation when new emoticons are released. Moreover, by combining the accumulated emotional library with company sales data, sales information, and consumer data, companies could develop hybrid recommendation systems that would bolster convenience for consumers and serve as intellectual assets that companies could strategically deploy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.116-126
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• 2020

This study analyzed the effects of auricular acupressure on improving sleep disorders of the elderly. The data was collected from domestic and foreign databases. The keywords were 'auricular,' 'sleep,' 'insomnia, and so on. RoB and RoBANS were used to evaluate the risk of bias in the selected literature. Meta-analysis was performed using RevMan 5.3. Seven of 386 studies were selected. Regarding the sleep score, those studies with an intervention period of less than four weeks showed WMD of 25.66 (95% CI; 20.18 to 31.14), which was significantly higher than that of the control group. Similarly, the studies with an intervention period longer than four weeks had significantly higher WMD than that of the control group, and the former exhibited WMD of 8.59 (95% CI: 6.26 to 10.92). In contrast, sleep satisfaction was significantly higher in the studies with a maintenance period of three days or less, which resulted in SMD of 13.37 (95% CI: 5.29 to 21.45). The score in the studies with a maintenance period longer than four days was significantly higher than that of the control group with SMD of 2.27 (95% CI: 1.82 to 2.72). Auricular acupressure was effective in alleviating sleep disorders among the elderly in terms of both sleep quality and sleep satisfaction.

• Journal of Acupuncture Research
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• v.21 no.3
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• pp.265-282
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• 2004

Objective : The aim of this study was to review systemically clinical trials on the effectiveness and safety of herbal medicines in the treatment of osteoarthritis. Methods : Computerized literature searches were carried out on seven electronic databases, and hand-searching on some chinese medical journals in library of Kyung Hee Medical Center. Trial data were extracted in a standardized, predefined manner and assessed independently. Results : 1. Thirty reports of clinical trials and two reports of meta-analyses concerning herbal medicine were collected and reviewed. Among these reports three medical herbs were applied as topical medicine and others as internal medicine. 2. The western studies established NSAIDs or placebo as their control group. Five chinese reports established formulated herb pill(Ruanshnagshenjin pill) as their control group and Six did not establish a control group at all. 3. ACR was the most highly used diagnostic criteria in the western studies while the Chinese used their official criteria established by their government or the criteria of their text books. 4. 20 reports chose the Lequesne functional index, SHAQ, WOMAC OA index, AIMS, and their own unique scoring system as the criteria of analysing the effect. Others chose clinical symptoms, articular functions, and lab finding as their criteria. 5. 7 single herbs and 19 formulated herbs were studied. Among the formulated herbs, Achyranthes japonica was studied in 10 of the studies and Angelica gigantis Radix in 8, making them the most often studied herbs among the studies.

• Journal of Digital Convergence
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• v.16 no.9
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• pp.11-20
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• 2018

The Q&A board is widely used as a means of communicating service enquiries, and the need for efficient management of the enquiry system has risen because certain questions are being repeatedly and frequently registered. This study aims to construct a student-centered FAQ, centered on the unstructured data posted on the university homepage's Q&A board. We extracted major keywords from 690 postings registered in the recent 3 years, and conducted the semantic network analysis to find the relationship between the keywords and the centrality analysis in order to carry out network visualization. The most central keywords found through the analysis, in order of centrality, were application, curriculum, credit point, completion, graduation, approval, period, major, portal, department. Also, the major keywords were classified into 8 groups of course, register, student life, scholarship, library, dormitory, IT and commute. If the most frequent questions are organized into these areas to form the FAQ, based on the results above, it is expected to contribute to user convenience and the efficiency of administration by simplifying the service enquiry process for repeated questions, as well as enabling smooth two-way communication among the members of the university.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.42 no.5 s.335
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• pp.55-62
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• 2005

In this paper, we propose an efficient symbol detection algorithm for space-frequency OFDM (SF-OFDM) transmit diversity scheme and present the implementation results of the SF-OFDM WLAN baseband processor with the proposed algorithm. When the number of sub-carriers in SF-OFDM scheme is small, the interference between adjacent sub-carriers may be generated. The proposed algorithm eliminates this interference in a parallel manner and obtains a considerable performance improvement over the conventional detection algorithm. The bit error rate (BER) performance of the proposed detection algorithm is evaluated by the simulation. In the case of 2 transmit and 2 receive antennas, at $BER=10^{-4}$ the proposed algorithm obtains about 3 dB gain over the conventional detection algorithm. The packet error rate (PER), link throughput, and coverage performance of the SF-OFDM WLAN with the proposed detection algorithm are also estimated. For the target throughput at $80\%$ of the peak data rate, the SF-OFDM WLAN achieves the average SNR gain of about 5.95 dB and the average coverage gain of 3.98 meter. The SF-OFDM WLAN baseband processor with the proposed algorithm was designed in a hardware description language and synthesized to gate-level circuits using 0.18um 1.8V CMOS standard cell library. With the division-free architecture, the total logic gate count for the processor is 945K. The real-time operation is verified and evaluated using a FPGA test system.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.42 no.12
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• pp.45-52
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• 2005

A systolic array formed by interconnecting a set of identical data-processing cells in a uniform manner is a combination of an algorithm and a circuit that implements it, and is closely related conceptually to arithmetic pipeline. High-performance computation on a large array of cells has been an important feature of systolic array. To achieve even higher degree of concurrency, it is desirable to make cells of systolic array themselves systolic array as well. The structure of systolic array with its cells consisting of another systolic array is to be called super-systolic array. This paper proposes a scalable bit-level super-systolic amy which can be adopted in the VLSI design including regular interconnection and functional primitives that are typical for a systolic architecture. This architecture is focused on highly regular computational structures that avoids the need for a large number of global interconnection required in general VLSI implementation. A bit-level super-systolic FIR filter is selected as an example of bit-level super-systolic array. The derived bit-level super-systolic FIR filter has been modeled and simulated in RT level using VHDL, then synthesized using Synopsys Design Compiler based on Hynix $0.35{\mu}m$ cell library. Compared conventional word-level systolic array, the newly proposed bit-level super-systolic arrays are efficient when it comes to area and throughput.