• Journal of Korean Ophthalmic Optics Society
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• v.11 no.4
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• pp.329-335
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• 2006

The aim of this study was to evaluate the relation between Asthenopia of near lateral phoria and fusional reserve and also to provide fundamental clinical data. A total of 97 subjects, aged between 17 and 35 years old, who had no strabismus, an eye trouble or whole body disease, were examined nacked visual acuity, corrected visual acuity, corrected diopter, phoria, fusional reserve tests from October of 2005 to July of 2006. We excluded 8 subjects for the following reasons: if they had an amblyopia affecting binocular vision or inaccurate data. After these exclusions, 87 subjects remained. The results were as follow. According to interview results was that in near works, exophoria and esophoria with asthenopia was 59.6%, 64.7%, and 52.6% respectively. The subjects who have exophoria of $0-6{\Delta}$ in the range of normal state was 19.1%. The subjects who have exophoria of $7{\Delta}$ over in the range of abnormal state was 80.9%. The fusional reserve was in inverse proportion to phoria. The fusional reserve was twice over of phoria were 30.3%, and twice under were 69.7%. The asthenopia complain persons were 33.9% with the twice over fusional reserve of phoria. The asthenopia no complain persons were 66.1% with the twice under fusional reserve of phoria. In conclusion, our research has shown conclusively that there is a link between asthenopia of lateral phoria and fusional reserve and we also find that fusional reserve must be examined when we prescribe for a patient who has phoria.

• Communications of Mathematical Education
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• v.31 no.1
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• pp.17-48
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• 2017

This case study examined mathematics anxiety of a public high school sophomore who was unable to perform well in mathematics but later overcame his fear of mathematics. In this study, he showed high levels of mathematics anxiety in the assessment tools that evaluate mathematical anxiety factors. Cognitive and behavior treatments were carried out to alleviate his anxiety. First, cognitive treatments that were implemented include: understanding his own problems, writing down his thoughts on a record sheet, and changing intermediate and core beliefs. This paper explored cognitive and affective changes and reactions during the treatment process. Second, behavioral treatments that were conducted include: the divided-page method and peer tutoring. The divided-page technique involves the test subject to write down and solve his problems on a note to see what kind of cognitive and affective changes occur during the process. This paper also explored how Su-chul, an overly competitive student, changed and reacted cognitively and affectively through peer tutoring. The results revealed that Su-chul's exam anxiety, as well as other factors, has decreased. Moreover, he regained his self-confidence by solving math problems that he had felt difficult. His competitive attitude also has turned into a cooperative and thoughtful one.

• Journal of Convergence for Information Technology
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• v.11 no.12
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• pp.1-13
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• 2021

This study tried to suggest ways to improve the indicator system to strengthen the personal information protection. For this purpose, the components of indicator system are derived through domestic and foreign literature, and it was selected as main the diagnostic indicators through FGI/Delphi analysis for personal information protection experts and a survey for personal information protection officers of public institutions. As like this, this study was intended to derive an inspection standard that can be reflected as a separate index system for personal information protection, by classifying the specific IT technologies of the 4th industrial revolution, such as big data, cloud, Internet of Things, and artificial intelligence. As a result, from the planning and design stage of specific technologies, the check items for applying the PbD principle, pseudonymous information processing and de-identification measures were selected as 2 common indicators. And the checklists were consisted 2 items related Big data, 5 items related Cloud service, 5 items related IoT, and 4 items related AI. Accordingly, this study expects to be an institutional device to respond to new technological changes for the continuous development of the personal information management level diagnosis system in the future.

• The Journal of the Convergence on Culture Technology
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• v.8 no.2
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• pp.83-89
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• 2022

Recentely, the military needs to apply various technologies for the improvement of teamwork. The government should take the non-face-to-face system due to the social interest of young military members. In this study we investigated collective cohesion by helping soldiers who have difficulty expressing their feelings and delivering messages while living in groups, or who are unable to adapt to group life due to psychological disorders such as relationship anxiety. We proposed the Multi-perspective Relationship Experience program as a new VR application. We showed feeling a sense of reality equivalent to the actual situation, interpersonal tension and social distance were significantly reduced, and communication, which was difficult to actually do, was naturally achieved. In addition, positive effects were confirmed on the sense of belonging and leadership among all participants. We will be effectively used in manpower management policies that improve the collective cohesion of soldiers and support the adaptability of the military environment in line with the rapidly changing social interaction method.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.159-185
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• 2016

Planning a multi-day trip is a complex, yet time-consuming task. It usually starts with selecting a list of points of interest (POIs) worth visiting and then arranging them into an itinerary, taking into consideration various constraints and preferences. When choosing POIs to visit, one might ask friends to suggest them, search for information on the Web, or seek advice from travel agents; however, those options have their limitations. First, the knowledge of friends is limited to the places they have visited. Second, the tourism information on the internet may be vast, but at the same time, might cause one to invest a lot of time reading and filtering the information. Lastly, travel agents might be biased towards providers of certain travel products when suggesting itineraries. In recent years, many researchers have tried to deal with the huge amount of tourism information available on the internet. They explored the wisdom of the crowd through overwhelming images shared by people on social media sites. Furthermore, trip planning problems are usually formulated as 'Tourist Trip Design Problems', and are solved using various search algorithms with heuristics. Various recommendation systems with various techniques have been set up to cope with the overwhelming tourism information available on the internet. Prediction models of recommendation systems are typically built using a large dataset. However, sometimes such a dataset is not always available. For other models, especially those that require input from people, human computation has emerged as a powerful and inexpensive approach. This study proposes CYTRIP (Crowdsource Your TRIP), a multi-day trip itinerary planning system that draws on the collective intelligence of contributors in recommending POIs. In order to enable the crowd to collaboratively recommend POIs to users, CYTRIP provides a shared workspace. In the shared workspace, the crowd can recommend as many POIs to as many requesters as they can, and they can also vote on the POIs recommended by other people when they find them interesting. In CYTRIP, anyone can make a contribution by recommending POIs to requesters based on requesters' specified preferences. CYTRIP takes input on the recommended POIs to build a multi-day trip itinerary taking into account the user's preferences, the various time constraints, and the locations. The input then becomes a multi-day trip planning problem that is formulated in Planning Domain Definition Language 3 (PDDL3). A sequence of actions formulated in a domain file is used to achieve the goals in the planning problem, which are the recommended POIs to be visited. The multi-day trip planning problem is a highly constrained problem. Sometimes, it is not feasible to visit all the recommended POIs with the limited resources available, such as the time the user can spend. In order to cope with an unachievable goal that can result in no solution for the other goals, CYTRIP selects a set of feasible POIs prior to the planning process. The planning problem is created for the selected POIs and fed into the planner. The solution returned by the planner is then parsed into a multi-day trip itinerary and displayed to the user on a map. The proposed system is implemented as a web-based application built using PHP on a CodeIgniter Web Framework. In order to evaluate the proposed system, an online experiment was conducted. From the online experiment, results show that with the help of the contributors, CYTRIP can plan and generate a multi-day trip itinerary that is tailored to the users' preferences and bound by their constraints, such as location or time constraints. The contributors also find that CYTRIP is a useful tool for collecting POIs from the crowd and planning a multi-day trip.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.83-98
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• 2015

Many TV viewers use mainly portal sites in order to retrieve information related to broadcast while watching TV. However retrieving information that people wanted needs a lot of time to retrieve the information because current internet presents too much information which is not required. Consequentially, this process can't satisfy users who want to consume information immediately. Interactive video is being actively investigated to solve this problem. An interactive video provides clickable objects, areas or hotspots to interact with users. When users click object on the interactive video, they can see additional information, related to video, instantly. The following shows the three basic procedures to make an interactive video using interactive video authoring tool: (1) Create an augmented object; (2) Set an object's area and time to be displayed on the video; (3) Set an interactive action which is related to pages or hyperlink; However users who use existing authoring tools such as Popcorn Maker and Zentrick spend a lot of time in step (2). If users use wireWAX then they can save sufficient time to set object's location and time to be displayed because wireWAX uses vision based annotation method. But they need to wait for time to detect and track object. Therefore, it is required to reduce the process time in step (2) using benefits of manual annotation method and vision-based annotation method effectively. This paper proposes a novel annotation method allows annotator to easily annotate based on face area. For proposing new annotation method, this paper presents two steps: pre-processing step and annotation step. The pre-processing is necessary because system detects shots for users who want to find contents of video easily. Pre-processing step is as follow: 1) Extract shots using color histogram based shot boundary detection method from frames of video; 2) Make shot clusters using similarities of shots and aligns as shot sequences; and 3) Detect and track faces from all shots of shot sequence metadata and save into the shot sequence metadata with each shot. After pre-processing, user can annotates object as follow: 1) Annotator selects a shot sequence, and then selects keyframe of shot in the shot sequence; 2) Annotator annotates objects on the relative position of the actor's face on the selected keyframe. Then same objects will be annotated automatically until the end of shot sequence which has detected face area; and 3) User assigns additional information to the annotated object. In addition, this paper designs the feedback model in order to compensate the defects which are wrong aligned shots, wrong detected faces problem and inaccurate location problem might occur after object annotation. Furthermore, users can use interpolation method to interpolate position of objects which is deleted by feedback. After feedback user can save annotated object data to the interactive object metadata. Finally, this paper shows interactive video authoring system implemented for verifying performance of proposed annotation method which uses presented models. In the experiment presents analysis of object annotation time, and user evaluation. First, result of object annotation average time shows our proposed tool is 2 times faster than existing authoring tools for object annotation. Sometimes, annotation time of proposed tool took longer than existing authoring tools, because wrong shots are detected in the pre-processing. The usefulness and convenience of the system were measured through the user evaluation which was aimed at users who have experienced in interactive video authoring system. Recruited 19 experts evaluates of 11 questions which is out of CSUQ(Computer System Usability Questionnaire). CSUQ is designed by IBM for evaluating system. Through the user evaluation, showed that proposed tool is useful for authoring interactive video than about 10% of the other interactive video authoring systems.

• Journal of Korean Philosophical Society
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• v.123
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• pp.347-377
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• 2012

We have numerous terms representing mind. We can understand them largely as the relationship of 'Discernible Mind' and 'Indiscernible Mind.' Because, our understanding mind is formed by linguistic discernment. When any discernment arise from our mind, we recognize the mind shown by discernment[Discernible Mind]. At the same time, we can think orignal mind[Indiscernible Mind] outside that discernment. Buddhism, generally, has understood mind in the relation with everything. That is to say, they have understood it from the perspective of dependent co-arising. In the early Buddhism and the abhidharma Buddhism, approaches to mind were mainly made by the discerning method. They explained arising and vanishing of 'Discernible Mind' by the law of dependent arising. Co-arisen 'Discernible Mind' is impermanent and temporary. But they never be denied on 'Discernible Mind' as an vainness. In $Mah{\bar{a}}y{\bar{a}}na$ Buddhism, $N{\bar{a}}garjuna$ understood the essence of dependent arising as the ${\acute{s}}{\bar{u}}nyata$ (emptiness) and the law of dependent arising as simultaneous dependence, not gradual dependences. $N{\bar{a}}garjuna$ criticized on vainness of Discernible Mind through ${\acute{s}}{\bar{u}}nyata$, and made possible to directly perceive Indiscernible Mind, before Discernment. Undiscriminating Mind can not be explained for being stayed beyond the state linguistic discernment(false discrimination), however, had been approached from various other names to potential consciousness or original nature. While ${\acute{s}}{\bar{u}}nata$ thought focused on criticizing vainness of discernment, Hwaeum thought suggested aspect of Indiscernible mind from the aspect of $ekay{\bar{a}}na$ dependant co-arising that everything has been co-arisen, the truth of discrimination. Furthermore, it opened the path to affirm the both indiscernible mind and discernible mind by illuminating that everything is manifestation of original nature itself, i.e. nature-arising. Hwaeum thought focused on perfect understanding by explicating the relation both indiscernible mind and discernible mind from the view point of non-abiding, rather than clarifying 'Discernible Mind' and 'Indiscernible Mind', itself. That is to say, from the aspect of dependant co-arising, Hwaeum thought plays a role that enters the indiscernible world from discernible world, and also, another role, from the aspect of nature-arising that is manifesting discernible world from indiscernible world. These aspects are important for righteous understanding on mind, and also simultaneously, very effective for healing disease of obsession, a kind of metal disease.

• The Korean Society of Law and Medicine
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• v.24 no.4
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• pp.67-101
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• 2023

The utilization of generative AI in the medical field is also being rapidly researched. Access to vast data sets reduces the time and energy spent in selecting information. However, as the effort put into content creation decreases, there is a greater likelihood of associated issues arising. For example, with generative AI, users must discern the accuracy of results themselves, as these AIs learn from data within a set period and generate outcomes. While the answers may appear plausible, their sources are often unclear, making it challenging to determine their veracity. Additionally, the possibility of presenting results from a biased or distorted perspective cannot be discounted at present on ethical grounds. Despite these concerns, the field of generative AI is continually advancing, with an increasing number of users leveraging it in various sectors, including biomedical and life sciences. This raises important legal considerations regarding who bears responsibility and to what extent for any damages caused by these high-performance AI algorithms. A general overview of issues with generative AI includes those discussed above, but another perspective arises from its fundamental nature as a large-scale language model ('LLM') AI. There is a civil law concern regarding "the memorization of training data within artificial neural networks and its subsequent reproduction". Medical data, by nature, often reflects personal characteristics of patients, potentially leading to issues such as the regeneration of personal information. The extensive application of generative AI in scenarios beyond traditional AI brings forth the possibility of legal challenges that cannot be ignored. Upon examining the technical characteristics of generative AI and focusing on legal issues, especially concerning the protection of personal information, it's evident that current laws regarding personal information protection, particularly in the context of health and medical data utilization, are inadequate. These laws provide processes for anonymizing and de-identification, specific personal information but fall short when generative AI is applied as software in medical devices. To address the functionalities of generative AI in clinical software, a reevaluation and adjustment of existing laws for the protection of personal information are imperative.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.39-53
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• 2013

The emergence of the internet technology and SNS has increased the information flow and has changed the way people to communicate from one-way to two-way communication. Users not only consume and share the information, they also can create and share it among their friends across the social network service. It also changes the Social Media behavior to become one of the most important communication tools which also includes Social TV. Social TV is a form which people can watch a TV program and at the same share any information or its content with friends through Social media. Social News is getting popular and also known as a Participatory Social Media. It creates influences on user interest through Internet to represent society issues and creates news credibility based on user's reputation. However, the conventional platforms in news services only focus on the news recommendation domain. Recent development in SNS has changed this landscape to allow user to share and disseminate the news. Conventional platform does not provide any special way for news to be share. Currently, Social News Service only allows user to access the entire news. Nonetheless, they cannot access partial of the contents which related to users interest. For example user only have interested to a partial of the news and share the content, it is still hard for them to do so. In worst cases users might understand the news in different context. To solve this, Social News Service must provide a method to provide additional information. For example, Yovisto known as an academic video searching service provided time dependent metadata from the video. User can search and watch partial of video content according to time dependent metadata. They also can share content with a friend in social media. Yovisto applies a method to divide or synchronize a video based whenever the slides presentation is changed to another page. However, we are not able to employs this method on news video since the news video is not incorporating with any power point slides presentation. Segmentation method is required to separate the news video and to creating time dependent metadata. In this work, In this paper, a time dependent metadata-based framework is proposed to segment news contents and to provide time dependent metadata so that user can use context information to communicate with their friends. The transcript of the news is divided by using the proposed story segmentation method. We provide a tag to represent the entire content of the news. And provide the sub tag to indicate the segmented news which includes the starting time of the news. The time dependent metadata helps user to track the news information. It also allows them to leave a comment on each segment of the news. User also may share the news based on time metadata as segmented news or as a whole. Therefore, it helps the user to understand the shared news. To demonstrate the performance, we evaluate the story segmentation accuracy and also the tag generation. For this purpose, we measured accuracy of the story segmentation through semantic similarity and compared to the benchmark algorithm. Experimental results show that the proposed method outperforms benchmark algorithms in terms of the accuracy of story segmentation. It is important to note that sub tag accuracy is the most important as a part of the proposed framework to share the specific news context with others. To extract a more accurate sub tags, we have created stop word list that is not related to the content of the news such as name of the anchor or reporter. And we applied to framework. We have analyzed the accuracy of tags and sub tags which represent the context of news. From the analysis, it seems that proposed framework is helpful to users for sharing their opinions with context information in Social media and Social news.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.57-71
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• 2013

Over a billion people in the world generate new news minute by minute. People forecasts some news but most news are from unexpected events such as natural disasters, accidents, crimes. People spend much time to watch a huge amount of news delivered from many media because they want to understand what is happening now, to predict what might happen in the near future, and to share and discuss on the news. People make better daily decisions through watching and obtaining useful information from news they saw. However, it is difficult that people choose news suitable to them and obtain useful information from the news because there are so many news media such as portal sites, broadcasters, and most news articles consist of gossipy news and breaking news. User interest changes over time and many people have no interest in outdated news. From this fact, applying users' recent interest to personalized news service is also required in news service. It means that personalized news service should dynamically manage user profiles. In this paper, a content-based news recommendation system is proposed to provide the personalized news service. For a personalized service, user's personal information is requisitely required. Social network service is used to extract user information for personalization service. The proposed system constructs dynamic user profile based on recent user information of Facebook, which is one of social network services. User information contains personal information, recent articles, and Facebook Page information. Facebook Pages are used for businesses, organizations and brands to share their contents and connect with people. Facebook users can add Facebook Page to specify their interest in the Page. The proposed system uses this Page information to create user profile, and to match user preferences to news topics. However, some Pages are not directly matched to news topic because Page deals with individual objects and do not provide topic information suitable to news. Freebase, which is a large collaborative database of well-known people, places, things, is used to match Page to news topic by using hierarchy information of its objects. By using recent Page information and articles of Facebook users, the proposed systems can own dynamic user profile. The generated user profile is used to measure user preferences on news. To generate news profile, news category predefined by news media is used and keywords of news articles are extracted after analysis of news contents including title, category, and scripts. TF-IDF technique, which reflects how important a word is to a document in a corpus, is used to identify keywords of each news article. For user profile and news profile, same format is used to efficiently measure similarity between user preferences and news. The proposed system calculates all similarity values between user profiles and news profiles. Existing methods of similarity calculation in vector space model do not cover synonym, hypernym and hyponym because they only handle given words in vector space model. The proposed system applies WordNet to similarity calculation to overcome the limitation. Top-N news articles, which have high similarity value for a target user, are recommended to the user. To evaluate the proposed news recommendation system, user profiles are generated using Facebook account with participants consent, and we implement a Web crawler to extract news information from PBS, which is non-profit public broadcasting television network in the United States, and construct news profiles. We compare the performance of the proposed method with that of benchmark algorithms. One is a traditional method based on TF-IDF. Another is 6Sub-Vectors method that divides the points to get keywords into six parts. Experimental results demonstrate that the proposed system provide useful news to users by applying user's social network information and WordNet functions, in terms of prediction error of recommended news.