• Journal of Information Technology Applications and Management
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• v.21 no.3
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• pp.119-133
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• 2014

Prior to the 2000s, internal control had not been among the high priority issues in the management's agenda. Since then, however, it has become one of the hottest issues, and has received a significant attention as the means of improving the transparency, sustainability, and competitiveness of a company. The objectives of this paper are to examine if there has been any noticeable changes in the level of internal controls of Korean companies before and after the 2010, and to analyze the underlying drivers and issues thereto. Accounting manipulation and moral hazard were among the factors to cause the Korean financial crisis in 1997 and 2008. Since then, the capital market has had a strong pressure on Korean companies to enhance the transparency of management and accounting while the government has made the laws, requirements, and recommendations to alleviate the moral hazard problems of management and enhance the accounting transparency. Both market and government have driven companies to put more priority on the reliability of financial reporting and the compliance of applicable laws and regulations. Thereby, the market and governmental forces has led companies to enhance the level of internal controls which contribute to the reliability of financial reporting and the compliance The pressure on companies to enhance the level of internal controls may be different across industries. The capital market and government experiencing the severe financial crisis in 1997 and 2008 put even more pressure on financial companies such as banks to upgrade the reliability of financial reporting and the compliance of regulations to the global level than on non-financial companies. A survey is performed on the changes in the level of internal controls of 54 major companies consisting of 10 financial and 44 non-financial companies in Korea. The survey results show that the average level of internal controls of Korean companies has noticeably improved and that the change in the level of control environment factor is higher than that of IT control factor. The analysis on the industry differences shows that financial companies increased the level of control environment factor more than non-financial companies did while non-financial companies upgraded the level of IT control factor more than financial companies did relatively. Among internal control categories, the most improved area since the economic crisis is "Risk Assessment." The global best practices for risk management have been developed primarily in the financial industry and then spread to other industries. The general level of control practices of Korean companies has been improving significantly, but still appears below the global advanced practices.

• Journal of KIISE:Software and Applications
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• v.29 no.5
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• pp.326-335
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• 2002

Recently there have been a number of researches to provide code mobility to lazy functional language (LFL) programs by translating LFL programs to Java programs. These approaches are basically baled on architectural similarities between abstract machines of LFLs and Java. The abstract machines of LFLs and Java programming language, Spineless Tagless G-Machine(STGM) and Java Virtual Machine(JVM) respectively, share important common features such as built- in garbage collector and stack machine architecture. Thus, we can provide code mobility to LFLs by translating LFLs to Java utilizing these common features. In this paper, we propose a new translation scheme which fully utilizes architectural common features between STGM and JVM. By redefining STGM as an eval-apply evaluation model, we have defined a new translation scheme which utilizes Java Virtual Machine Stack for function evaluation and totally eliminates stack simulation which causes array manipulation overhead in Java. Benchmark program translated to Java programs by our translation scheme run faster on JDK 1.3 than those translated by the previous schemes.

• FLOWER RESEARCH JOURNAL
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• v.18 no.3
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• pp.157-164
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• 2010

In Chrysanthemum ${\times}$ morifolium, 30 days of continuous short days (SD) are required for flowering. The effects of alternating short day (SD) - long day (LD) - SD treatments was tested in 5 daisy-like single cultivars, 'Limelight', 'Sunlight', 'Candle Light', 'Firebrand', and 'Twilight', Thee other decorative type cultivars, 'Spirit', 'SunburstSpriit', 'Mandalay', and 'Illini Harvest' was also included. Short day treatment was given for 30 days (control: 30SD) and 5 or 10 LD were interposed following 5 or 10 SD. Different responses were noticed when data from all cultivars were combined, showing that flowering was delayed and the number of ray florets were increased by 5 SD-10 LD-25 SD. The number of ray florets, 40 florets in 'Firebrand' and 60.8 florets in 'Candlelight' was increased significantly by 5 SD-10 LD-25 SD as compared to the control. There were no adverse effects by SD-LD-SD treatment, except for the delay in flowering time from 6 to 7 days. The number of ray florets in 'Illini Harvest' and 'Limelight' were, however, not increased by any SD-LD-SD treatments. Cultivar dependent responses should further be investigated in other newly available cultivars, and this information could be used to breed new cultivars.

• The Journal of the Korea Contents Association
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• v.19 no.4
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• pp.556-563
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• 2019

This study was conducted to improve the use experience of elderly people in fast food kiosks which are actively utilizing 'un-tact' technology. First, the Kiosk design analysis criteria were discussed through literature research. Then, the UI screens of McDonald's, Lotteria and Burger King, which are currently serving kiosks in Korea, were divided into colors, layouts and buttons. Next, the usability evaluation and survey of the elderly revealed problems with the interface design of the current kiosk. As a result, older people do not recognize buttons without an outline as buttons, and errors occur when layout changes or when viewing is complicated. In addition, horizontal navigation and vertical layout menus were error-free, and the design of buttons was error-free with the same color for the same function. Research shows that the Kiosk UI design is preferred by older people. First, buttons in the form of outline or solid are recommended. Second, vertical binding of buttons combined with images and text is recommended. Third, the screen layout should be consistent with the direction of view flow and manipulation, and should avoid increasing the number of menus and information. Fourth, page navigation is recommended in horizontal form and menu arrangement is vertical form. Finally, buttons that perform the same function are efficient for buttons to use the same color, and for different functions, applying a complementary colors can reduce confusion.

• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.369-376
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• 2021

3D architectural visualization animation has become more important in architectural presentations due to the rapid development of digital technology. Unlike games and movies, architectural visualization animation most focuses on delivering visual information, and aims to express the sense of space that viewers feel in an architectural space, rather than simply providing an image of viewing buildings. The sense of space is affected not only by physical elements of architecture, but also by immaterial elements such as light, time, and human actions, and it is more advantageous to express it in animations that can contain temporality compared to a fixed image. Therefore, the purpose of this study is to search for elements to effectively convey a sense of space in architectural visualization animation. To this end, the works of renowned architectural visualization artists that are open to the public were selected and observed to search for elements to effectively convey a sense of space to viewers. The elements that convey the sense of space that are common to the investigated architectural animations can be classified into the movement and manipulation of the camera, the movement of surrounding objects, the change of the light environment, the change of the weather, the control of time, and the insertion of a surreal scene. It will be followed by a discussion on the immersion of architectural contents.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.3
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• pp.141-148
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• 2022

Recently, reinforcement learning combined with deep neural network technology has achieved remarkable success in various fields such as board games such as Go and chess, computer games such as Atari and StartCraft, and robot object manipulation tasks. However, such deep reinforcement learning describes states, actions, and policies in vector representation. Therefore, the existing deep reinforcement learning has some limitations in generality and interpretability of the learned policy, and it is difficult to effectively incorporate domain knowledge into policy learning. On the other hand, dNL-RRL, a new relational reinforcement learning framework proposed to solve these problems, uses a kind of vector representation for sensor input data and lower-level motion control as in the existing deep reinforcement learning. However, for states, actions, and learned policies, It uses a relational representation with logic predicates and rules. In this paper, we present dNL-RRL-based policy learning for transportation mobile robots in a manufacturing environment. In particular, this study proposes a effective method to utilize the prior domain knowledge of human experts to improve the efficiency of relational reinforcement learning. Through various experiments, we demonstrate the performance improvement of the relational reinforcement learning by using domain knowledge as proposed in this paper.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.57-66
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• 2022

The Road Management Administration is cracking down on overloaded vehicles by installing low-speed or high-speed WIMs at toll gates and main lines on expressways. However, in recent years, the act of intelligently evading the overloaded-vehicle control system of the Road Management Administration by illegally manipulating the variable axle of an overloaded truck is increasing. In this manipulation, when entering the overloaded-vehicle checkpoint, all axles of the vehicle are lowered to pass normally, and when driving on the main road, the variable axle of the vehicle is illegally lifted with the axle load exceeding 10 tons alarmingly. Therefore, this study developed a technology to detect the state of the variable axle of a truck driving on the road using roadside camera images. In particular, this technology formed the basis for cracking down on overloaded vehicles by lifting the variable axle after entering the checkpoint and linking the vehicle with the account information of the checkpoint. Fundamentally, in this study, the tires of the vehicle were recognized using the Mask RCNN algorithm, the recognized tires were virtually arranged before and after the checkpoint, and the height difference of the vehicle was measured from the arrangement to determine whether the variable axle was lifted after the vehicle left the checkpoint.

• Asia pacific journal of information systems
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• v.17 no.4
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• pp.31-59
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• 2007

We frequently use search engines to find relevant information in the Web but still end up with too much information. In order to solve this problem of information overload, ranking algorithms have been applied to various domains. As more information will be available in the future, effectively and efficiently ranking search results will become more critical. In this paper, we propose a ranking algorithm for the Semantic Web resources, specifically RDF resources. Traditionally, the importance of a particular Web page is estimated based on the number of key words found in the page, which is subject to manipulation. In contrast, link analysis methods such as Google's PageRank capitalize on the information which is inherent in the link structure of the Web graph. PageRank considers a certain page highly important if it is referred to by many other pages. The degree of the importance also increases if the importance of the referring pages is high. Kleinberg's algorithm is another link-structure based ranking algorithm for Web pages. Unlike PageRank, Kleinberg's algorithm utilizes two kinds of scores: the authority score and the hub score. If a page has a high authority score, it is an authority on a given topic and many pages refer to it. A page with a high hub score links to many authoritative pages. As mentioned above, the link-structure based ranking method has been playing an essential role in World Wide Web(WWW), and nowadays, many people recognize the effectiveness and efficiency of it. On the other hand, as Resource Description Framework(RDF) data model forms the foundation of the Semantic Web, any information in the Semantic Web can be expressed with RDF graph, making the ranking algorithm for RDF knowledge bases greatly important. The RDF graph consists of nodes and directional links similar to the Web graph. As a result, the link-structure based ranking method seems to be highly applicable to ranking the Semantic Web resources. However, the information space of the Semantic Web is more complex than that of WWW. For instance, WWW can be considered as one huge class, i.e., a collection of Web pages, which has only a recursive property, i.e., a 'refers to' property corresponding to the hyperlinks. However, the Semantic Web encompasses various kinds of classes and properties, and consequently, ranking methods used in WWW should be modified to reflect the complexity of the information space in the Semantic Web. Previous research addressed the ranking problem of query results retrieved from RDF knowledge bases. Mukherjea and Bamba modified Kleinberg's algorithm in order to apply their algorithm to rank the Semantic Web resources. They defined the objectivity score and the subjectivity score of a resource, which correspond to the authority score and the hub score of Kleinberg's, respectively. They concentrated on the diversity of properties and introduced property weights to control the influence of a resource on another resource depending on the characteristic of the property linking the two resources. A node with a high objectivity score becomes the object of many RDF triples, and a node with a high subjectivity score becomes the subject of many RDF triples. They developed several kinds of Semantic Web systems in order to validate their technique and showed some experimental results verifying the applicability of their method to the Semantic Web. Despite their efforts, however, there remained some limitations which they reported in their paper. First, their algorithm is useful only when a Semantic Web system represents most of the knowledge pertaining to a certain domain. In other words, the ratio of links to nodes should be high, or overall resources should be described in detail, to a certain degree for their algorithm to properly work. Second, a Tightly-Knit Community(TKC) effect, the phenomenon that pages which are less important but yet densely connected have higher scores than the ones that are more important but sparsely connected, remains as problematic. Third, a resource may have a high score, not because it is actually important, but simply because it is very common and as a consequence it has many links pointing to it. In this paper, we examine such ranking problems from a novel perspective and propose a new algorithm which can solve the problems under the previous studies. Our proposed method is based on a class-oriented approach. In contrast to the predicate-oriented approach entertained by the previous research, a user, under our approach, determines the weights of a property by comparing its relative significance to the other properties when evaluating the importance of resources in a specific class. This approach stems from the idea that most queries are supposed to find resources belonging to the same class in the Semantic Web, which consists of many heterogeneous classes in RDF Schema. This approach closely reflects the way that people, in the real world, evaluate something, and will turn out to be superior to the predicate-oriented approach for the Semantic Web. Our proposed algorithm can resolve the TKC(Tightly Knit Community) effect, and further can shed lights on other limitations posed by the previous research. In addition, we propose two ways to incorporate data-type properties which have not been employed even in the case when they have some significance on the resource importance. We designed an experiment to show the effectiveness of our proposed algorithm and the validity of ranking results, which was not tried ever in previous research. We also conducted a comprehensive mathematical analysis, which was overlooked in previous research. The mathematical analysis enabled us to simplify the calculation procedure. Finally, we summarize our experimental results and discuss further research issues.

• Journal of Korea Entertainment Industry Association
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• v.15 no.8
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• pp.145-159
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• 2021

is a contemporary experimental film that forms ambiguity in the narrative and the psychological motivation of the characters, destroys linear temporality, and reminds of manipulation possibilities in digital images through varied techniques, and it carries implication by the fact that the transformation process of human subjects and self-awareness are connected to social trauma and makes way to infer by comparing it to the historical contexts of other nations or societies. centers on the space outside the screen, absent space, and the intrinsic meaning within the space and the frame and shares the information in the visible space and the space outside the screen and arouses an active perceptual process so that the audience can deduce the information that is not presented. The film visualized the historical meaning without describing the background of the times in detail and aimed to express the conflicts and worries between the god, a transcendental existence, with humans, which are marginal beings, within the conflicting structure among humans. Moreover, attempted to resolve the sadness of loss and absence through the spatial aesthetics and the film presented the progression of the situation through the contrast of the characters and also the comparison between light and darkness. This study intends to make an attempt of interpreting the realm involving personal (characters) stories and the social and historical backgrounds together with the religious sphere and discuss the visualization of the semantic context. In addition, this study analyzed the sequence of the scenes in , which reconstructs identity and historical cases and religious values to observe the meaning and characteristics and closely analyze the general meaning pursued by the film. discussed the issues of trauma that individuals, regions, and nations confront as a representation and interpretation of the trauma connoted in the film, and consideration can be provided about the implication concerning the situation and context in South Korea. Furthermore, the film placidly discusses the growth and agony in humans and the society without expressing it excessively, so it will be a valuable research result to inspire the trend of creating films that incorporate new imaging technology and original visualization techniques.

• 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.