• Journal of the Korea Institute of Information Security & Cryptology
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• v.15 no.6
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• pp.37-47
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• 2005

Recently, the malicious e-mail worm-viruses have been widely spreaded over the Internet. If the recipient opens the e-mail attachment or an e-mail itself that contains the worm-virus, the worm-virus can be activated and then cause a tremendous damage to the system by propagating itself to everyone on the mailing list in the user's e-mail package. In this paper, we have designed and implemented two methods blocking e-mail worm-viruses. In the fist method, each e-mail is transmitted only by sender activity such as the click of button on a mail client application. In the second one, we insert the two modules into the sender side, where the one module transforms a recipient's address depending on a predefined rule only in time of pushing button and the other converts the address reversely with the former module whenever an e-mail is sent. The lader method also supports a polymorphism model in order to cope with the new types of e-mail worm-virus attacks. The two methods are designed not to work for the e-mail viruses. There is no additional fraction on the receiver's side of the e-mail system. Experimental results show that the proposed methods can screen the e-mail worm-viruses efficiently with a low overhead.

• The Journal of the Korea Contents Association
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• v.22 no.5
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• pp.56-66
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• 2022

Due to the recent increase in information encountered on social media, algorithm-based recommendation formats selectively provide information based on user information, which often causes a filter bubble effect by an Echo Chamber. Eco-chamber refers to a phenomenon in which beliefs are amplified or strengthened by communication only in an enclosed system, and filter bubbles refer to a phenomenon in which information providers provide customized information according to users' interests, and users encounter only filtered information. The purpose of this study is to propose a method of efficiently selecting information as a way to improve the filter bubble phenomenon by such an echo chamber. The research progress method analyzed recommended algorithms used on YouTube, Facebook and Amazon. In this study, humanities solutions such as training critical thinking skills of social media users and strengthening objective ethical standards according to self-preservation laws, and technical solutions of model-based cooperative filtering or cross-recommendation methods were presented. As a result, recommended algorithms should continue to supplement technology and develop new techniques, and humanities should make efforts to overcome cognitive dissonance and prevent users from falling into confirmation bias through critical thinking training and political communication education.

• Journal of agricultural medicine and community health
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• v.9 no.1
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• pp.56-66
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• 1984

Giardia lamblia is a pathogenic flagellate causing intestinal disorders such as diarrhea, abdominal pain and malabsorption of nutrients. Giardia is mainly infected by the ingestion of contaminated foods per os. Craun (1979) has recently reported that mass infection of this flagellate through the contaminated water supply systems is one of public health hazards. Also, so-called traveller's diarrhea is sometimes caused by Giardia infection (CDC, U.S.A., 1971). However, a few epidemiological studies figuring out the mode of infection or control measures of Giardia infection has been done so far in Korea. The present study was aimed to know the prevalence of Giardia infection in several Korean populations, detectability of this flagellate in water systems and the viability of the cysts against sewages and disinfectants applying to drinking water. In the present study, 388 stool specimens from orphanage children in Chun-joo, Chung-joo, On-yang and Chun-an areas and 538 stool specimens from inhabitants in Woo-do, In-chon, and Chun-joo were examined by formalin-ether concentration technique to detect out Giardia cysts. On the other hand, water samples from 14 sites of Han River and its tributaries were collected in May through July, 1984. Fifty liter of water sample in each sampling site was then filtered through water filtering system deviced by U.S. Environmental Proutection Agency and the sediments rinsed out from the thread rolls, a part of water filtering system, were examined to detect out the Giardia cysts. In order to observe the viability of Giardia cysts in the sewage samples, the cysts were treated in it at $4^{\circ}C$ or $25^{\circ}C$ for 7 through 28 days. For this purpose, the cysts were also exposed to various concentrations of disinfectants such as chlorine, iodine and ozone gas for proper time intervals. After treatment, the viability test of the Giardia cysts were carried out by method of Rice and Schaefer (1981) with minor modification. The results obtained in this study were as follows : 1) The detection rates of G lamblia cysts in the stool specimens were 18.3% in orphans and 4.3% in general examinees. 2) The prevalences of Giardia Infection were higher in the young age groups than in-adults. The highest positive rate was 18.4% in the age group less than 10. 3) Of 14 water specimens sampled from Han River system and its tributaries around the Seoul area, the Giardia cysts were detected from 4 samples, and no cyst was found in the water supply systems. 4) The cysts treated in the sewage survived for 28 days at $4^{\circ}C$ and for 13 days at $25^{\circ}C$. 5) The cysts were completely destroyed within 60 minutes by exposure to 8 mg/l of residual chlorine at 4g and within 30 minutes by exposure to the same concentration of chlorine at $25^{\circ}C$. 6) The cysts were all dead when exposed to 1 mg/1 of iodine for 60 minutes at $4^{\circ}C$ or $25^{\circ}C$. 7) The cysts were destroyed after 10 minute exposure in 0.15 mg to 0.25mg of residual ozone gas per liter. Summarizing the above results, it is considered that Giardia infection is regarded as water-borne disease and the cysts are able to be controlled by the application with the disinfectants in the water supply systems.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.39 no.1
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• pp.33-43
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• 2003

The midwater pair trawl which is being used at present in Korea have several problems. Firstly, it is difficult to control the net height on high towing speed. Secondly, net breaking often occurs owing to floats and thirdly, the volume of net on the net drum is so large. This study is aiming for examining the possibility of application for the Korean midwater pair trawl through the model experiment of non-float midwater pair trawl. The model of non-float midwater pair trawl was manufactured as 1/100 of the full scale net which is being used in bottom pair trawl for 850ps class considering the Tauti's Similarity law. The model experiment was carried out to analyze the opening efficiency according to the variation of lower warp length and the opening efficiency was investigated between th proto type and non-float type. The results obtained can be summarized as follows ; 1. The hydrodynamic resistance of non-float type was about 10~20% smaller than that of the proto type and it increased about 1ton according to the increase of dL at the condition of the same flow speed. The resistance acting on the lower warp decreased about 5% but that of the upper warp increased according to the increase of lower warp length (dL) at the condition of the same flow speed. 2. The net height of the non-float type decreased almost linearly according as the increased of flow speed and it increased in a logarithmic functional form with the increase of the lower warp length at the condition of the same flow speed. On the decreasing rate of the net height, the non-float type was lower than the proto type and the difference of the decreasing rate was about 12% at 3.0 knot, 25% at 4.0 knot, 25% at 4.0 knot respectively when dL was 30m. 3. The net width of non-float type was not varied so much as only 2m range and was larger than that of proto type. 4. The mouth area of non-float type decreased in a exponential functional form. On the decreasing rate of the mouth area, the non-float type was lower than the proto type. The filtering volume increased in a logarithmic functional form with increasing flow speed and the filtering volume of proto type decreased steeply over 3.0knot, but that of non-float type increased until 4.0knot. 5. The optimal length of lower warp was when the value of dL was about 30m and the optimal position of front weight was at the connection point of four net pendants.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.133-148
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• 2014

Recently, with the advent of various information channels, the number of has continued to grow. The main cause of this phenomenon can be found in the significant increase of unstructured data, as the use of smart devices enables users to create data in the form of text, audio, images, and video. In various types of unstructured data, the user's opinion and a variety of information is clearly expressed in text data such as news, reports, papers, and various articles. Thus, active attempts have been made to create new value by analyzing these texts. The representative techniques used in text analysis are text mining and opinion mining. These share certain important characteristics; for example, they not only use text documents as input data, but also use many natural language processing techniques such as filtering and parsing. Therefore, opinion mining is usually recognized as a sub-concept of text mining, or, in many cases, the two terms are used interchangeably in the literature. Suppose that the purpose of a certain classification analysis is to predict a positive or negative opinion contained in some documents. If we focus on the classification process, the analysis can be regarded as a traditional text mining case. However, if we observe that the target of the analysis is a positive or negative opinion, the analysis can be regarded as a typical example of opinion mining. In other words, two methods (i.e., text mining and opinion mining) are available for opinion classification. Thus, in order to distinguish between the two, a precise definition of each method is needed. In this paper, we found that it is very difficult to distinguish between the two methods clearly with respect to the purpose of analysis and the type of results. We conclude that the most definitive criterion to distinguish text mining from opinion mining is whether an analysis utilizes any kind of sentiment lexicon. We first established two prediction models, one based on opinion mining and the other on text mining. Next, we compared the main processes used by the two prediction models. Finally, we compared their prediction accuracy. We then analyzed 2,000 movie reviews. The results revealed that the prediction model based on opinion mining showed higher average prediction accuracy compared to the text mining model. Moreover, in the lift chart generated by the opinion mining based model, the prediction accuracy for the documents with strong certainty was higher than that for the documents with weak certainty. Most of all, opinion mining has a meaningful advantage in that it can reduce learning time dramatically, because a sentiment lexicon generated once can be reused in a similar application domain. Additionally, the classification results can be clearly explained by using a sentiment lexicon. This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of movie reviews. Additionally, various parameters in the parsing and filtering steps of the text mining may have affected the accuracy of the prediction models. However, this research contributes a performance and comparison of text mining analysis and opinion mining analysis for opinion classification. In future research, a more precise evaluation of the two methods should be made through intensive experiments.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.29-56
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• 2021

Recently, various types of products have been launched with the rapid growth of the e-commerce market. As a result, many users face information overload problems, which is time-consuming in the purchasing decision-making process. Therefore, the importance of a personalized recommendation service that can provide customized products and services to users is emerging. For example, global companies such as Netflix, Amazon, and Google have introduced personalized recommendation services to support users' purchasing decisions. Accordingly, the user's information search cost can reduce which can positively affect the company's sales increase. The existing personalized recommendation service research applied Collaborative Filtering (CF) technique predicts user preference mainly use quantified information. However, the recommendation performance may have decreased if only use quantitative information. To improve the problems of such existing studies, many studies using reviews to enhance recommendation performance. However, reviews contain factors that hinder purchasing decisions, such as advertising content, false comments, meaningless or irrelevant content. When providing recommendation service uses a review that includes these factors can lead to decrease recommendation performance. Therefore, we proposed a novel recommendation methodology through CNN-based review usefulness score prediction to improve these problems. The results show that the proposed methodology has better prediction performance than the recommendation method considering all existing preference ratings. In addition, the results suggest that can enhance the performance of traditional CF when the information on review usefulness reflects in the personalized recommendation service.

• Journal of the Korea Society of Computer and Information
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• v.16 no.2
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• pp.249-256
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• 2011

Finding a new set of features representing textured images is one of the most important studies in textured image analysis. This is because it is impossible to construct a perfect set of features representing every textured image, and it is inevitable to choose some relevant features which are efficient to on-going image processing jobs. This paper intends to find relevant features which are efficient to textured image segmentation. In this regards, this paper presents a different method for the segmentation of textured images based on the Gabor filter. Gabor filter is known to be a very efficient and effective tool which represents human visual system for texture analysis. Filtering a real-valued input image by the Gabor filter results in complex-valued output data defined in the spatial frequency domain. This complex value, as usual, gives the module and the phase. This paper focused its attention on the phase information, rather than the module information. In fact, the module information is considered very useful at region analysis in texture, while the phase information was considered almost of no use. But this paper shows that the phase information can also be fully useful and effective at region analysis in texture, once a good method introduced. We now propose "phase derivated method", which is an efficient and effective way to compute the useful phase information directly from the filtered value. This new method reduces effectively computing burden and widen applicable textured images.

• Korean Journal of Weed Science
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• v.13 no.2
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• pp.104-113
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• 1993

The experiment was conducted in 1992 to find out the approach to the development of prediction model of weed population in paddy fields. The weed seeds of 88% over were separated from the soil by using $K_2CO_3$ 50% solution with specific gravity 1.34. The weed seeds which were floated on the solution due to the difference of specific gravity between soil particles and the seeds were effectively withdrawn by using a vaccum pump attached with an aspirator. The seeds withdrawn together with solution were taken by filtering with a nylon net of $0.31{\times}0.16mm$ mesh. The pressing method was more efficient and practical for the viability test of weed seeds separated from the soil compared with the germination test and the TTC test. For the prediction of weed population by the number of weed seedlings emerged at the sampled soil, the sampling method of 0-10cm deep at 5-6 sites per field was applicable. At the prediction method by the number of seedlings emerged, the smaller the seed sizes, the lower the prediction coefficients of weed species. It was considered that the prediction method by the number of seedlings emerged was more practical than the prediction method by the number of seeds separated from sampled soil, in relation to similarities to weed population, time and expenses required for examining, technical difficulties and applicability of weed species.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.4
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• pp.479-488
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• 2002

In recent years the amount of digital video used has risen dramatically to keep pace with the increasing use of the Internet and consequently an automated method is needed for indexing digital video databases. Textual information, both superimposed and embedded scene texts, appearing in a digital video can be a crucial clue for helping the video indexing. In this paper, a new method is presented to extract both superimposed and embedded scene texts in a freeze-frame of news video. The algorithm is summarized in the following three steps. For the first step, a color image is converted into a gray-level image and applies contrast stretching to enhance the contrast of the input image. Then, a modified local adaptive thresholding is applied to the contrast-stretched image. The second step is divided into three processes: eliminating text-like components by applying erosion, dilation, and (OpenClose+CloseOpen)/2 morphological operations, maintaining text components using (OpenClose+CloseOpen)/2 operation with a new Geo-correction method, and subtracting two result images for eliminating false-positive components further. In the third filtering step, the characteristics of each component such as the ratio of the number of pixels in each candidate component to the number of its boundary pixels and the ratio of the minor to the major axis of each bounding box are used. Acceptable results have been obtained using the proposed method on 300 news images with a recognition rate of 93.6%. Also, my method indicates a good performance on all the various kinds of images by adjusting the size of the structuring element.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.147-161
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• 2010

As broadcasting and communication are converged recently, communication is jointed to TV. TV viewing has brought about many changes. The IPTV (Internet Protocol Television) provides information service, movie contents, broadcast, etc. through internet with live programs + VOD (Video on demand) jointed. Using communication network, it becomes an issue of new business. In addition, new technical issues have been created by imaging technology for the service, networking technology without video cuts, security technologies to protect copyright, etc. Through this IPTV network, users can watch their desired programs when they want. However, IPTV has difficulties in search approach, menu approach, or finding programs. Menu approach spends a lot of time in approaching programs desired. Search approach can't be found when title, genre, name of actors, etc. are not known. In addition, inserting letters through remote control have problems. However, the bigger problem is that many times users are not usually ware of the services they use. Thus, to resolve difficulties when selecting VOD service in IPTV, a personalized service is recommended, which enhance users' satisfaction and use your time, efficiently. This paper provides appropriate programs which are fit to individuals not to save time in order to solve IPTV's shortcomings through filtering and recommendation-related system. The proposed recommendation system collects TV program information, the user's preferred program genres and detailed genre, channel, watching program, and information on viewing time based on individual records of watching IPTV. To look for these kinds of similarities, similarities can be compared by using ontology for TV programs. The reason to use these is because the distance of program can be measured by the similarity comparison. TV program ontology we are using is one extracted from TV-Anytime metadata which represents semantic nature. Also, ontology expresses the contents and features in figures. Through world net, vocabulary similarity is determined. All the words described on the programs are expanded into upper and lower classes for word similarity decision. The average of described key words was measured. The criterion of distance calculated ties similar programs through K-medoids dividing method. K-medoids dividing method is a dividing way to divide classified groups into ones with similar characteristics. This K-medoids method sets K-unit representative objects. Here, distance from representative object sets temporary distance and colonize it. Through algorithm, when the initial n-unit objects are tried to be divided into K-units. The optimal object must be found through repeated trials after selecting representative object temporarily. Through this course, similar programs must be colonized. Selecting programs through group analysis, weight should be given to the recommendation. The way to provide weight with recommendation is as the follows. When each group recommends programs, similar programs near representative objects will be recommended to users. The formula to calculate the distance is same as measure similar distance. It will be a basic figure which determines the rankings of recommended programs. Weight is used to calculate the number of watching lists. As the more programs are, the higher weight will be loaded. This is defined as cluster weight. Through this, sub-TV programs which are representative of the groups must be selected. The final TV programs ranks must be determined. However, the group-representative TV programs include errors. Therefore, weights must be added to TV program viewing preference. They must determine the finalranks.Based on this, our customers prefer proposed to recommend contents. So, based on the proposed method this paper suggested, experiment was carried out in controlled environment. Through experiment, the superiority of the proposed method is shown, compared to existing ways.