• Asian Pacific Journal of Cancer Prevention
• /
• v.17 no.12
• /
• pp.5237-5242
• /
• 2016

Background: Cancer screening rates are lower in Japan than those in western countries. Health professionals publish procancer screening messages on the internet to encourage audiences to undergo cancer screening. However, the information provided is often difficult to read for lay persons. Further, anti-cancer screening activists warn against cancer screening with messages on the Internet. We aimed to assess and compare the readability of pro- and anti-cancer screening online messages in Japan using a measure of readability. Methods: We conducted web searches at the beginning of September 2016 using two major Japanese search engines (Google.jp and Yahoo!.jp). The included websites were classified as "anti", "pro", or "neutral" depending on the claims, and "health professional" or "non-health professional" depending on the writers. Readability was determined using a validated measure of Japanese readability. Statistical analysis was conducted using two-way ANOVA. Results: In the total 159 websites analyzed, anti-cancer screening online messages were generally easier to read than pro-cancer screening online messages, Messages written by health professionals were more difficult to read than those written by non-health professionals. Claim ${\times}$ writer interaction was not significant. Conclusion: When health professionals prepare pro-cancer screening materials for publication online, we recommend they check for readability using readability assessment tools and improve text for easy comprehension when necessary.

• Child Health Nursing Research
• /
• v.25 no.4
• /
• pp.435-448
• /
• 2019

Purpose: The purpose of this study was to review sexual abuse prevention education program for Korean elementary school students. Methods: Whittemore and Knafl's integrative review methods were used and Gough's weight of evidence was employed as a quality appraisal tool. Articles published in Korean or English were identified through electronic search engines and scholarly web sites using three keywords: "elementary school student", "sexual abuse", and "prevention education". Peer-reviewed articles published between 2000 and 2018 were included in this review. Results: Twelve articles met the inclusion criteria and were appraised as being high-quality. Among the 12 selected studies, seven were descriptive, while five were intervention studies. Sexual abuse prevention education programs were effective in improving perceptions, knowledge, attitudes, and preventive behaviors among elementary school students. However, deficiencies were found in the variety of educational methods, utilization of experts, and standardization of the content of sexual abuse prevention education. Conclusion: We need to provide various educational methods that are appropriate for specific developmental stages, and the sexual abuse prevention content should draw upon the current sexual education program administered to this population. Furthermore, parents and trained teachers or school health teachers should be included to provide effective education programs for elementary students.

• International Journal of Computer Science & Network Security
• /
• v.22 no.2
• /
• pp.406-412
• /
• 2022

Storing large amounts of data has always been a big problem from the beginning of computing history. Big Data has made huge advancements in improving business processes by finding the customers' needs using prediction models based on web and social media search. The main purpose of big data stream processing frameworks is to allow programmers to directly query the continuous stream without dealing with the lower-level mechanisms. In other words, programmers write the code to process streams using these runtime libraries (also called Stream Processing Engines). This is achieved by taking large volumes of data and analyzing them using Big Data frameworks. Streaming platforms are an emerging technology that deals with continuous streams of data. There are several streaming platforms of Big Data freely available on the Internet. However, selecting the most appropriate one is not easy for programmers. In this paper, we present a detailed description of two of the state-of-the-art and most popular streaming frameworks: Apache Ignite and Hazelcast. In addition, the performance of these frameworks is compared using selected attributes. Different types of databases are used in common to store the data. To process the data in real-time continuously, data streaming technologies are developed. With the development of today's large-scale distributed applications handling tons of data, these databases are not viable. Consequently, Big Data is introduced to store, process, and analyze data at a fast speed and also to deal with big users and data growth day by day.

• Journal of Korean Critical Care Nursing
• /
• v.16 no.2
• /
• pp.1-14
• /
• 2023

Purpose : Sleep disturbances in patients in the intensive care unit (ICU) are related to health problems after discharge. Therefore, active prevention and management are required. Hence, identification of the factors that affect sleep in patients who are critically ill is necessary. Methods : The PubMed, Cochrane Library, CINAHL, EMBASE, and Web of Science databases were searched. Selection criteria were observational and experimental studies that assessed sleep as an outcome, included adult patients admitted to the ICU, and published between November 2015 and April 2022. Results : A total of 21,136 articles were identified through search engines and manual searches, and 42 articles were selected. From these, 22 influencing factors and 11 interventions were identified. Individual factors included disease severity, age, pain, delirium, comorbidities, alcohol consumption, sex, sleep disturbance before hospitalization, chronic obstructive pulmonary disease (COPD), cardiovascular disease, and high diastolic blood pressure (DBP), low hemoglobin (Hb), and low respiratory rate (RR). Environmental factors included light level, noise level, and temperature. Furthermore, treatment-related factors included use of sedatives, melatonin administration, sleep management guidelines, ventilator application, nursing treatment, and length of ICU stay. Regarding sleep interventions, massage, eye mask and earplugs, quiet time and multicomponent protocols, aromatherapy, acupressure, sounds of the sea, adaptive intervention, circulation lighting, and single occupation in a room were identified. Conclusion : Based on these results, we propose the development and application of various interventions to improve sleep quality in patients who are critically ill.

• Journal of Digital Convergence
• /
• v.20 no.4
• /
• pp.367-376
• /
• 2022

In traditional criminal cases, there is a limit to information collection because information on the subject of investigation is provided only with personal information held by the national organization of legal. Surface web-based OSINT(Open Source Intelligence), including SNS and portal sites that can be searched by general search engines, can be used for meaningful profiling for criminal investigations. The Korean-style OSINT workflow can effectively profile based on OSINT, but in the case of individuals, OSINT that can be collected is limited because it begins with "name", and the reliability is limited, such as collecting information of the persons with the same name. In order to overcome these limitations, this paper defines information related to individuals, i.e., equivalent information, and enables efficient and accurate information collection based on this. Therefore, we present an improved workflow that can extract information related to a specific person, ie., equivalent information, from OSINT. For this purpose, different workflows are presented according to the person's profile. Through this, effective profiling of a person (individuals) is possible, thereby increasing reliability in collecting investigation information. According to this study, in the future, by developing a system that can automate the analysis process of information collected using artificial intelligence technology, it can lay the foundation for the use of OSINT in criminal investigations and contribute to diversification of investigation methods.

• The KIPS Transactions:PartB
• /
• v.10B no.7
• /
• pp.743-750
• /
• 2003

In this paper, we propose a query expansion and weighting method for document filtering to increase precision of the result of Web search engines. Query expansion for document filtering uses ConceptNet, encyclopedia and documents of 10% high similarity. Term weighting method is used for calculation of query-documents similarity. In the first step, we expand an initial query into the first expanded query using ConceptNet and encyclopedia. And then we weight the first expanded query and calculate the first expanded query-documents similarity. Next, we create the second expanded query using documents of top 10% high similarity and calculate the second expanded query- documents similarity. We combine two similarities from the first and the second step. And then we re-rank the documents according to the combined similarities and filter off non-relevant documents with the lower similarity than the threshold. Our experiments showed that our document filtering method results in a notable improvement in the retrieval effectiveness when measured using both precision-recall and F-Measure.

• The Korean Journal of Pain
• /
• v.22 no.2
• /
• pp.141-145
• /
• 2009

Background: There is no assessment of internet sites that carry information on chronic pain disease. So we assessed the quality of information about postherpetic neuralgia available on Korean internet sites. Methods: The keywords 'postherpetic neuralgia', 'herpes zoster, neuropathic pain', 'herpes zoster, pain', 'herpes zoster' were searched in Korean on four search engines in Korea between the 1st to the 15th of May, 2009. We evaluated the outcome on two factors; the aspect of the contents which is subdivided into two categories, the content and authorship, and the technical aspect including web design, and efficiency. Results: A total of 26 internet sites were found. Among these sites, 6 (23%) informed by anesthesiologist. The average score of the 26 internet sites was only $37.4{\pm}20.1$ out of a total of 100. A mean score of the contents was $13.3{\pm}8.3$ out of 40 points, the authorship was $10.0{\pm}6.7$ out of 20 points, the design was $9.2{\pm}5.3$ out of 20 points, the efficiency was $6.8{\pm}4.3$ out of 20 points. When comparing the score between anesthesiologist and non-anesthesiologist, the contents was $18.7{\pm}7.4$ vs. $11.7{\pm}7.9$, the authorship was $13.4{\pm}4.7$ vs. $9.0{\pm}6.8$, the design was $12.5{\pm}4.2$ vs. $8.3{\pm}5.2$ and the efficiency was $6.8{\pm}4.5$ vs. $4.3{\pm}4.0$ (P < 0.05). Conclusions: There is a need for more accurate information about postherpetic neuralgia on the Korean internet by anesthesiologists.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.24 no.1
• /
• pp.7-15
• /
• 2014

Image retrieval has become a huge part of computer vision and data mining. Although commercial image retrieval systems such as Google show great performances, the improvement on the performances are constantly on demand because of the rapid growth of data on web space. To satisfy the demand, many re-ranking methods, which enhance the performances by reordering retrieved results with independent algorithms, has been proposed. Conventional re-ranking algorithms are based on the assumption that visual patterns are not used on initial image retrieval stage. However, image search engines in present have begun to use the visual and the assumption is required to be reconsidered. Also, though it is possible to suspect that integration of multiple retrieval systems can improve the overall performance, the research on the topic has not been done sufficiently. In this paper, we made the condition that other manner than cooperation cannot improve the ranking result. We evaluate the algorithm on toy model and show that propose module can improve the retrieval results.

• Journal of Distribution Science
• /
• v.8 no.3
• /
• pp.49-56
• /
• 2010

At the initial stage of Internet advertising, banner advertising came into fashion. As the Internet developed into a central part of daily lives and the competition in the on-line advertising market was getting fierce, there was not enough space for banner advertising, which rushed to portal sites only. All these factors was responsible for an upsurge in advertising prices. Consequently, the high-cost and low-efficiency problems with banner advertising were raised, which led to an emergence of keyword advertising as a new type of Internet advertising to replace its predecessor. In the beginning of 2000s, when Internet advertising came to be activated, display advertisement including banner advertising dominated the Net. However, display advertising showed signs of gradual decline, and registered minus growth in the year 2009, whereas keyword advertising showed rapid growth and started to outdo display advertising as of the year 2005. Keyword advertising refers to the advertising technique that exposes relevant advertisements on the top of research sites when one searches for a keyword. Instead of exposing advertisements to unspecified individuals like banner advertising, keyword advertising, or targeted advertising technique, shows advertisements only when customers search for a desired keyword so that only highly prospective customers are given a chance to see them. In this context, it is also referred to as search advertising. It is regarded as more aggressive advertising with a high hit rate than previous advertising in that, instead of the seller discovering customers and running an advertisement for them like TV, radios or banner advertising, it exposes advertisements to visiting customers. Keyword advertising makes it possible for a company to seek publicity on line simply by making use of a single word and to achieve a maximum of efficiency at a minimum cost. The strong point of keyword advertising is that customers are allowed to directly contact the products in question through its more efficient advertising when compared to the advertisements of mass media such as TV and radio, etc. The weak point of keyword advertising is that a company should have its advertisement registered on each and every portal site and finds it hard to exercise substantial supervision over its advertisement, there being a possibility of its advertising expenses exceeding its profits. Keyword advertising severs as the most appropriate methods of advertising for the sales and publicity of small and medium enterprises which are in need of a maximum of advertising effect at a low advertising cost. At present, keyword advertising is divided into CPC advertising and CPM advertising. The former is known as the most efficient technique, which is also referred to as advertising based on the meter rate system; A company is supposed to pay for the number of clicks on a searched keyword which users have searched. This is representatively adopted by Overture, Google's Adwords, Naver's Clickchoice, and Daum's Clicks, etc. CPM advertising is dependent upon the flat rate payment system, making a company pay for its advertisement on the basis of the number of exposure, not on the basis of the number of clicks. This method fixes a price for advertisement on the basis of 1,000-time exposure, and is mainly adopted by Naver's Timechoice, Daum's Speciallink, and Nate's Speedup, etc, At present, the CPC method is most frequently adopted. The weak point of the CPC method is that advertising cost can rise through constant clicks from the same IP. If a company makes good use of strategies for maximizing the strong points of keyword advertising and complementing its weak points, it is highly likely to turn its visitors into prospective customers. Accordingly, an advertiser should make an analysis of customers' behavior and approach them in a variety of ways, trying hard to find out what they want. With this in mind, her or she has to put multiple keywords into use when running for ads. When he or she first runs an ad, he or she should first give priority to which keyword to select. The advertiser should consider how many individuals using a search engine will click the keyword in question and how much money he or she has to pay for the advertisement. As the popular keywords that the users of search engines are frequently using are expensive in terms of a unit cost per click, the advertisers without much money for advertising at the initial phrase should pay attention to detailed keywords suitable to their budget. Detailed keywords are also referred to as peripheral keywords or extension keywords, which can be called a combination of major keywords. Most keywords are in the form of texts. The biggest strong point of text-based advertising is that it looks like search results, causing little antipathy to it. But it fails to attract much attention because of the fact that most keyword advertising is in the form of texts. Image-embedded advertising is easy to notice due to images, but it is exposed on the lower part of a web page and regarded as an advertisement, which leads to a low click through rate. However, its strong point is that its prices are lower than those of text-based advertising. If a company owns a logo or a product that is easy enough for people to recognize, the company is well advised to make good use of image-embedded advertising so as to attract Internet users' attention. Advertisers should make an analysis of their logos and examine customers' responses based on the events of sites in question and the composition of products as a vehicle for monitoring their behavior in detail. Besides, keyword advertising allows them to analyze the advertising effects of exposed keywords through the analysis of logos. The logo analysis refers to a close analysis of the current situation of a site by making an analysis of information about visitors on the basis of the analysis of the number of visitors and page view, and that of cookie values. It is in the log files generated through each Web server that a user's IP, used pages, the time when he or she uses it, and cookie values are stored. The log files contain a huge amount of data. As it is almost impossible to make a direct analysis of these log files, one is supposed to make an analysis of them by using solutions for a log analysis. The generic information that can be extracted from tools for each logo analysis includes the number of viewing the total pages, the number of average page view per day, the number of basic page view, the number of page view per visit, the total number of hits, the number of average hits per day, the number of hits per visit, the number of visits, the number of average visits per day, the net number of visitors, average visitors per day, one-time visitors, visitors who have come more than twice, and average using hours, etc. These sites are deemed to be useful for utilizing data for the analysis of the situation and current status of rival companies as well as benchmarking. As keyword advertising exposes advertisements exclusively on search-result pages, competition among advertisers attempting to preoccupy popular keywords is very fierce. Some portal sites keep on giving priority to the existing advertisers, whereas others provide chances to purchase keywords in question to all the advertisers after the advertising contract is over. If an advertiser tries to rely on keywords sensitive to seasons and timeliness in case of sites providing priority to the established advertisers, he or she may as well make a purchase of a vacant place for advertising lest he or she should miss appropriate timing for advertising. However, Naver doesn't provide priority to the existing advertisers as far as all the keyword advertisements are concerned. In this case, one can preoccupy keywords if he or she enters into a contract after confirming the contract period for advertising. This study is designed to take a look at marketing for keyword advertising and to present effective strategies for keyword advertising marketing. At present, the Korean CPC advertising market is virtually monopolized by Overture. Its strong points are that Overture is based on the CPC charging model and that advertisements are registered on the top of the most representative portal sites in Korea. These advantages serve as the most appropriate medium for small and medium enterprises to use. However, the CPC method of Overture has its weak points, too. That is, the CPC method is not the only perfect advertising model among the search advertisements in the on-line market. So it is absolutely necessary that small and medium enterprises including independent shopping malls should complement the weaknesses of the CPC method and make good use of strategies for maximizing its strengths so as to increase their sales and to create a point of contact with customers.

• Asia pacific journal of information systems
• /
• v.21 no.1
• /
• pp.103-122
• /
• 2011

Recently, numerous documents have been made available electronically. Internet search engines and digital libraries commonly return query results containing hundreds or even thousands of documents. In this situation, it is virtually impossible for users to examine complete documents to determine whether they might be useful for them. For this reason, some on-line documents are accompanied by a list of keywords specified by the authors in an effort to guide the users by facilitating the filtering process. In this way, a set of keywords is often considered a condensed version of the whole document and therefore plays an important role for document retrieval, Web page retrieval, document clustering, summarization, text mining, and so on. Since many academic journals ask the authors to provide a list of five or six keywords on the first page of an article, keywords are most familiar in the context of journal articles. However, many other types of documents could not benefit from the use of keywords, including Web pages, email messages, news reports, magazine articles, and business papers. Although the potential benefit is large, the implementation itself is the obstacle; manually assigning keywords to all documents is a daunting task, or even impractical in that it is extremely tedious and time-consuming requiring a certain level of domain knowledge. Therefore, it is highly desirable to automate the keyword generation process. There are mainly two approaches to achieving this aim: keyword assignment approach and keyword extraction approach. Both approaches use machine learning methods and require, for training purposes, a set of documents with keywords already attached. In the former approach, there is a given set of vocabulary, and the aim is to match them to the texts. In other words, the keywords assignment approach seeks to select the words from a controlled vocabulary that best describes a document. Although this approach is domain dependent and is not easy to transfer and expand, it can generate implicit keywords that do not appear in a document. On the other hand, in the latter approach, the aim is to extract keywords with respect to their relevance in the text without prior vocabulary. In this approach, automatic keyword generation is treated as a classification task, and keywords are commonly extracted based on supervised learning techniques. Thus, keyword extraction algorithms classify candidate keywords in a document into positive or negative examples. Several systems such as Extractor and Kea were developed using keyword extraction approach. Most indicative words in a document are selected as keywords for that document and as a result, keywords extraction is limited to terms that appear in the document. Therefore, keywords extraction cannot generate implicit keywords that are not included in a document. According to the experiment results of Turney, about 64% to 90% of keywords assigned by the authors can be found in the full text of an article. Inversely, it also means that 10% to 36% of the keywords assigned by the authors do not appear in the article, which cannot be generated through keyword extraction algorithms. Our preliminary experiment result also shows that 37% of keywords assigned by the authors are not included in the full text. This is the reason why we have decided to adopt the keyword assignment approach. In this paper, we propose a new approach for automatic keyword assignment namely IVSM(Inverse Vector Space Model). The model is based on a vector space model. which is a conventional information retrieval model that represents documents and queries by vectors in a multidimensional space. IVSM generates an appropriate keyword set for a specific document by measuring the distance between the document and the keyword sets. The keyword assignment process of IVSM is as follows: (1) calculating the vector length of each keyword set based on each keyword weight; (2) preprocessing and parsing a target document that does not have keywords; (3) calculating the vector length of the target document based on the term frequency; (4) measuring the cosine similarity between each keyword set and the target document; and (5) generating keywords that have high similarity scores. Two keyword generation systems were implemented applying IVSM: IVSM system for Web-based community service and stand-alone IVSM system. Firstly, the IVSM system is implemented in a community service for sharing knowledge and opinions on current trends such as fashion, movies, social problems, and health information. The stand-alone IVSM system is dedicated to generating keywords for academic papers, and, indeed, it has been tested through a number of academic papers including those published by the Korean Association of Shipping and Logistics, the Korea Research Academy of Distribution Information, the Korea Logistics Society, the Korea Logistics Research Association, and the Korea Port Economic Association. We measured the performance of IVSM by the number of matches between the IVSM-generated keywords and the author-assigned keywords. According to our experiment, the precisions of IVSM applied to Web-based community service and academic journals were 0.75 and 0.71, respectively. The performance of both systems is much better than that of baseline systems that generate keywords based on simple probability. Also, IVSM shows comparable performance to Extractor that is a representative system of keyword extraction approach developed by Turney. As electronic documents increase, we expect that IVSM proposed in this paper can be applied to many electronic documents in Web-based community and digital library.