Content-based Recommendation Based on Social Network for Personalized News Services (개인화된 뉴스 서비스를 위한 소셜 네트워크 기반의 콘텐츠 추천기법)
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- 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 User interest changes over time and many people have no interest in outdated news.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.
Recently, increase of demand for big data analysis has been driving the vigorous development of related technologies and tools. In addition, development of IT and increased penetration rate of smart devices are producing a large amount of data. According to this phenomenon, data analysis technology is rapidly becoming popular. Also, attempts to acquire insights through data analysis have been continuously increasing. It means that the big data analysis will be more important in various industries for the foreseeable future. Big data analysis is generally performed by a small number of experts and delivered to each demander of analysis. However, increase of interest about big data analysis arouses activation of computer programming education and development of many programs for data analysis. Accordingly, the entry barriers of big data analysis are gradually lowering and data analysis technology being spread out. As the result, big data analysis is expected to be performed by demanders of analysis themselves. Along with this, interest about various unstructured data is continually increasing. Especially, a lot of attention is focused on using text data. Emergence of new platforms and techniques using the web bring about mass production of text data and active attempt to analyze text data. Furthermore, result of text analysis has been utilized in various fields. Text mining is a concept that embraces various theories and techniques for text analysis. Many text mining techniques are utilized in this field for various research purposes, topic modeling is one of the most widely used and studied. Topic modeling is a technique that extracts the major issues from a lot of documents, identifies the documents that correspond to each issue and provides identified documents as a cluster. It is evaluated as a very useful technique in that reflect the semantic elements of the document. Traditional topic modeling is based on the distribution of key terms across the entire document. Thus, it is essential to analyze the entire document at once to identify topic of each document. This condition causes a long time in analysis process when topic modeling is applied to a lot of documents. In addition, it has a scalability problem that is an exponential increase in the processing time with the increase of analysis objects. This problem is particularly noticeable when the documents are distributed across multiple systems or regions. To overcome these problems, divide and conquer approach can be applied to topic modeling. It means dividing a large number of documents into sub-units and deriving topics through repetition of topic modeling to each unit. This method can be used for topic modeling on a large number of documents with limited system resources, and can improve processing speed of topic modeling. It also can significantly reduce analysis time and cost through ability to analyze documents in each location or place without combining analysis object documents. However, despite many advantages, this method has two major problems. First, the relationship between local topics derived from each unit and global topics derived from entire document is unclear. It means that in each document, local topics can be identified, but global topics cannot be identified. Second, a method for measuring the accuracy of the proposed methodology should be established. That is to say, assuming that global topic is ideal answer, the difference in a local topic on a global topic needs to be measured. By those difficulties, the study in this method is not performed sufficiently, compare with other studies dealing with topic modeling. In this paper, we propose a topic modeling approach to solve the above two problems. First of all, we divide the entire document cluster(Global set) into sub-clusters(Local set), and generate the reduced entire document cluster(RGS, Reduced global set) that consist of delegated documents extracted from each local set. We try to solve the first problem by mapping RGS topics and local topics. Along with this, we verify the accuracy of the proposed methodology by detecting documents, whether to be discerned as the same topic at result of global and local set. Using 24,000 news articles, we conduct experiments to evaluate practical applicability of the proposed methodology. In addition, through additional experiment, we confirmed that the proposed methodology can provide similar results to the entire topic modeling. We also proposed a reasonable method for comparing the result of both methods.
Word of Mouth (WOM) is a behavior used by consumers to transfer or communicate their product or service experience to other consumers. Due to the popularity of social media such as Facebook, Twitter, blogs, and online communities, electronic WOM (e-WOM) has become important to the success of products or services. As a result, most enterprises pay close attention to e-WOM for their products or services. This is especially important for movies, as these are experiential products. This paper aims to identify the network factors of an online movie community that impact box office revenue using social network analysis. In addition to traditional WOM factors (volume and valence of WOM), network centrality measures of the online community are included as influential factors in box office revenue. Based on previous research results, we develop five hypotheses on the relationships between potential influential factors (WOM volume, WOM valence, degree centrality, betweenness centrality, closeness centrality) and box office revenue. The first hypothesis is that the accumulated volume of WOM in online product communities is positively related to the total revenue of movies. The second hypothesis is that the accumulated valence of WOM in online product communities is positively related to the total revenue of movies. The third hypothesis is that the average of degree centralities of reviewers in online product communities is positively related to the total revenue of movies. The fourth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. The fifth hypothesis is that the average of betweenness centralities of reviewers in online product communities is positively related to the total revenue of movies. To verify our research model, we collect movie review data from the Internet Movie Database (IMDb), which is a representative online movie community, and movie revenue data from the Box-Office-Mojo website. The movies in this analysis include weekly top-10 movies from September 1, 2012, to September 1, 2013, with in total. We collect movie metadata such as screening periods and user ratings; and community data in IMDb including reviewer identification, review content, review times, responder identification, reply content, reply times, and reply relationships. For the same period, the revenue data from Box-Office-Mojo is collected on a weekly basis. Movie community networks are constructed based on reply relationships between reviewers. Using a social network analysis tool, NodeXL, we calculate the averages of three centralities including degree, betweenness, and closeness centrality for each movie. Correlation analysis of focal variables and the dependent variable (final revenue) shows that three centrality measures are highly correlated, prompting us to perform multiple regressions separately with each centrality measure. Consistent with previous research results, our regression analysis results show that the volume and valence of WOM are positively related to the final box office revenue of movies. Moreover, the averages of betweenness centralities from initial community networks impact the final movie revenues. However, both of the averages of degree centralities and closeness centralities do not influence final movie performance. Based on the regression results, three hypotheses, 1, 2, and 4, are accepted, and two hypotheses, 3 and 5, are rejected. This study tries to link the network structure of e-WOM on online product communities with the product's performance. Based on the analysis of a real online movie community, the results show that online community network structures can work as a predictor of movie performance. The results show that the betweenness centralities of the reviewer community are critical for the prediction of movie performance. However, degree centralities and closeness centralities do not influence movie performance. As future research topics, similar analyses are required for other product categories such as electronic goods and online content to generalize the study results.
According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.
Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.
In the pandemic of infectious disease, restrictions of individual liberty have been justified in the name of public health and public interest. In March 2020, the National Assembly of the Republic of Korea passed the revised bill of the 「Infectious Disease Control and Prevention Act.」 The revised bill newly established the legal basis for forced testing and disclosure of the information of confirmed cases, and also raised the penalties for violation of self-isolation and treatment refusal. This paper examines whether and how these individual liberty limiting clauses be justified, and if so on what ethical and philosophical grounds. The authors propose the theories of the philosophy of law related to the justifiability of liberty-limiting measures by the state and conceptualized the dual-aspect of applying the liberty-limiting principle to the infected patient. In COVID-19 pandemic crisis, the infected person became the 'Patient as Victim and Vector (PVV)' that posits itself on the overlapping area of 'harm to self' and 'harm to others.' In order to apply the liberty-limiting principle proposed by Joel Feinberg to a pandemic with uncertainties, it is necessary to extend the harm principle from 'harm' to 'risk'. Under the crisis with many uncertainties like COVID-19 pandemic, this shift from 'harm' to 'risk' justifies the state's preemptive limitation on individual liberty based on the precautionary principle. This, at the same time, raises concerns of overcriminalization, i.e., too much limitation of individual liberty without sufficient grounds. In this article, we aim to propose principles regarding how to balance between the precautionary principle for preemptive restrictions of liberty and the concerns of overcriminalization. Public health crisis such as the COVID-19 pandemic requires a population approach where the 'population' rather than an 'individual' works as a unit of analysis. We propose the second expansion of the harm principle to be applied to 'population' in order to deal with the public interest and public health. The new concept 'risk to population,' derived from the two arguments stated above, should be introduced to explain the public health crisis like COVID-19 pandemic. We theorize 'the extended harm principle' to include the 'risk to population' as a third liberty-limiting principle following 'harm to others' and 'harm to self.' Lastly, we examine whether the restriction of liberty of the revised 「Infectious Disease Control and Prevention Act」 can be justified under the extended harm principle. First, we conclude that forced isolation of the infected patient could be justified in a pandemic situation by satisfying the 'risk to the population.' Secondly, the forced examination of COVID-19 does not violate the extended harm principle either, based on the high infectivity of asymptomatic infected people to others. Thirdly, however, the provision of forced treatment can not be justified, not only under the traditional harm principle but also under the extended harm principle. Therefore it is necessary to include additional clauses in the provision in order to justify the punishment of treatment refusal even in a pandemic.
This paper examined the poem world of Simjae(深齋) Cho Geung-seop(曺兢燮: 1873-1933) in the turning point of the modern era, focused on his Hwaunsi (和韻詩: Poems written by using the rhymes of other poets' poems). In his poems, there are lots of Hwaunsi on the poems of Tu Fu(杜甫) and Su Shi(蘇軾), especially. This makes him regarded as a medieval poet, engaged in Chinese poem creation in the most traditional method in the turbulent period. Looking at the Hawunsi(和韻詩) alone, Simjae's creative life became the starting point of turnaround at around 40 years old. Before the age of 40, the poets in the Tang Dynasty and Song Dynasty and Ming Dynasty and Qing Dynasty and Korean figures like Lee Hwang(李滉), as well as Tu Fu and Su Shi were the subjects of his Hwanunsi. After the age of 40, some examples of writing poems using the rhymes of other poets' poems, especially Korean figures related to regions, are often found, reducing Hwaunsi on Tu Fu and Su Shi. Simjae called Tu Fu the integration of poets, talking about the integrity of poetic talent and his being highly proficient in mood and view. As reflecting such an awareness, the themes and moods and views are demonstrated diversely in Simjae's Hwaunsi. Although, he did not reveal his thinking about the poems of Su Shi, he seemed to love Su Shi's poems to some degree. The closeness to the original poems, the poems of Tu Fu are relatively higher than those of Su Shi. Roughly speaking, Simjae tried to find his own individuality, intending to follow Tu Fu, but, he seemed to attempt to reveal his intention using Su Shi's poems, rather than trying to imitate. To carefully examine, Simjae wrote Hwaunsi, but he did not just imitate, but revealed the aesthetics of comparison and difference. In many cases, he made new meanings by implanting his intentions in the poems, while sharing the opportunity of creation, rather than bringing the theme and mood and view as they are. The Hwaunsi on Su Shi's poems reveal the closeness to the original poems relatively less. This can be the trace of an effort to make his own theme and individuality, not being dominated by the Hwaun(和韻: using the rhymes of other poets' poems) entirely, as he used the creative method having many restrictions. However, it is noted that the Hwaunsi on Tu Fu's poems was not written much, after the age of 40. Is this the reason why he realized literary reality that he could not cope with anymore with only his effort within the Hwaunsi? For example, he wrote four poems by borrowing Su Shi's Okjungsi(獄中詩: poem written in jail) rhymes and also wrote Gujung Japje(拘中雜題), in 1919, while he was detained. In these poems, his complex contemplation and emotion, not restricted by any poet's rhymes, are revealed diversely. Simjae's Hwaunsi testifies the reality, in which Chinese poetry's habitus existed and the impressive existence mode at the turning point of the modern era. Although, the creation of Hwaunsi reflects his disposition of liking the old things, it is judged that his psychology, resisting modern characters' change, affected to some degree in the hidden side. In this regard, Simaje's Hwaunsi encounters limitation on its own, however, it has significance in that some hidden facts were revealed in the modern Chinese poetry history, which was captured with attention under the name of novelty, eccentricity and modernity.
The paintings Gyeongpodae Pavilion and Chongseokjeong Pavilion were recently donated to the National Museum of Korea and unveiled to the public for the first time at the 2019 special exhibition "Through the Eyes of Joseon Painters: Real Scenery Landscapes of Korea." These two paintings carry significant implications for understanding Joseon art history. Because the fact that they were components of a folding screen produced after a sightseeing tour of the Gwandong regions in 1557 has led to a broadening of our understanding of sixteenth-century landscape painting. This paper explores the art historical meanings of Gyeongpodae Pavilion and Chongseokjeong Pavilion by examining the contents in the two paintings, dating them, analyzing their stylistic characteristics, and comparing them with other works. The production background of Gyeongpodae Pavilion and Chongseokjeong Pavilion can be found in the colophon of Chongseokjeong Pavilion. According to this writing, Sangsanilro, who is presumed to be Park Chung-gan (?-1601) in this paper, and Hong Yeon(?~?) went sightseeing around Geumgangsan Mountain (or Pungaksan Mountain) and the Gwandong region in the spring of 1557, wrote a travelogue, and after some time produced a folding screen depicting several famous scenic spots that they visited. Hong Yeon, whose courtesy name was Deokwon, passed the special civil examination in 1551 and has a record of being active until 1584. Park Chung-gan, whose pen name was Namae, reported the treason of Jeong Yeo-rip in 1589. In recognition of this meritorious deed, he was promoted to the position of Deputy Minister of the Ministry of Punishments, rewarded with the title of first-grade pyeongnan gongsin(meritorious subject who resolved difficulties), and raised to Lord of Sangsan. Based on the colophon to Chongseokjeong Pavilion, I suggest that the two paintings Gyeongpodae Pavilion and Chongseokjeong Pavilion were painted in the late sixteenth century, more specifically after 1557 when Park Chung-gan and Hong Yeon went on their sightseeing trip and after 1571 when Park, who wrote the colophon, was in his 50s or over. The painting style used in depicting the landscapes corresponds to that of the late sixteenth century. The colophon further states that Gyeongpodae Pavilion and Chongseokjeong Pavilion were two paintings of a folding screen. Chongseokjeong Pavilion with its colophon is thought to have been the final panel of this screen. The composition of Gyeongpodae Pavilion recalls the onesided three-layered composition often used in early Joseon landscape paintings in the style of An Gyeon. However, unlike such landscape paintings in the An Gyeon style, Gyeongpodae Pavilion positions and depicts the scenery in a realistic manner. Moreover, diverse perspectives, including a diagonal bird's-eye perspective and frontal perspective, are employed in Gyeongpodae Pavilion to effectively depict the relations among several natural features and the characteristics of the real scenery around Gyeongpodae Pavilion. The shapes of the mountains and the use of moss dots can be also found in Welcoming an Imperial Edict from China and Chinese Envoys at Uisungwan Lodge painted in 1557 and currently housed in the Kyujanggak Institute for Korean Studies at Seoul National University. Furthermore, the application of "cloud-head" texture strokes as well as the texture strokes with short lines and dots used in paintings in the An Gyeon style are transformed into a sense of realism. Compared to the composition of Gyeongpodae Pavilion, which recalls that of traditional Joseon early landscape painting, the composition of Chongseokjeong Pavilion is remarkably unconventional. Stone pillars lined up in layers with the tallest in the center form a triangle. A sense of space is created by dividing the painting into three planes(foreground, middle-ground, and background) and placing the stone pillars in the foreground, Saseonbong Peaks in the middle-ground, and Saseonjeong Pavilion on the cliff in the background. The Saseonbong Peaks in the center occupy an overwhelming proportion of the picture plane. However, the vertical stone pillars fail to form an organic relation and are segmented and flat. The painter of Chongseokjeong Pavilion had not yet developed a three-dimensional or natural spatial perception. The white lower and dark upper portions of the stone pillars emphasize their loftiness. The textures and cracks of the dense stone pillars were rendered by first applying light ink to the surfaces and then adding fine lines in dark ink. Here, the tip of the brush is pressed at an oblique angle and pulled down vertically, which shows an early stage of the development of axe-cut texture strokes. The contrast of black and white and use of vertical texture strokes signal the forthcoming trend toward the Zhe School painting style. Each and every contour and crack on the stone pillars is unique, which indicates an effort to accentuate their actual characteristics. The birds sitting above the stone pillars, waves, and the foam of breaking waves are all vividly described, not simply in repeated brushstrokes. The configuration of natural features shown in the above-mentioned Gyeongpodae Pavilion and Chongseokjeong Pavilion changes in other later paintings of the two scenic spots. In the Gyeongpodae Pavilion, Jukdo Island is depicted in the foreground, Gyeongpoho Lake in the middle-ground, and Gyeongpodae Pavilion and Odaesan Mountain in the background. This composition differs from the typical configuration of other Gyeongpodae Pavilion paintings from the eighteenth century that place Gyeongpodae Pavilion in the foreground and the sea in the upper section. In Chongseokjeong Pavilion, stone pillars are illustrated using a perspective viewing them from the sea, while other paintings depict them while facing upward toward the sea. These changes resulted from the established patterns of compositions used in Jeong Seon(1676~1759) and Kim Hong-do(1745~ after 1806)'s paintings of Gwandong regions. However, the configuration of the sixteenth-century Gyeongpodae Pavilion, which seemed to have no longer been used, was employed again in late Joseon folk paintings such as Gyeongpodae Pavilion in Gangneung. Famous scenic spots in the Gwandong region were painted from early on. According to historical records, they were created by several painters, including Kim Saeng(711~?) from the Goryeo Dynasty and An Gyeon(act. 15th C.) from the early Joseon period, either on a single scroll or over several panels of a folding screen or several leaves of an album. Although many records mention the production of paintings depicting sites around the Gwandong region, there are no other extant examples from this era beyond the paintings of Gyeongpodae Pavilion and Chongseokjeong Pavilion discussed in this paper. These two paintings are thought to be the earliest works depicting the Gwandong regions thus far. Moreover, they hold art historical significance in that they present information on the tradition of producing folding screens on the Gwandong region. In particular, based on the contents of the colophon written for Chongseokjeong Pavilion, the original folding screen is presumed to have consisted of eight panels. This proves that the convention of painting eight views of Gwangdong had been established by the late sixteenth century. All of the existing works mentioned as examples of sixteenth-century real scenery landscape painting show only partial elements of real scenery landscape painting since they were created as depictions of notable social gatherings or as a documentary painting for practical and/or official purposes. However, a primary objective of the paintings of Gyeongpodae Pavilion and Chongseokjeong Pavilion was to portray the ever-changing and striking nature of this real scenery. Moreover, Park Chung-gan wrote a colophon and added a poem on his admiration of the scenery he witnessed during his trip and ruminated over the true character of nature. Thus, unlike other previously known real-scenery landscape paintings, these two are of great significance as examples of real-scenery landscape paintings produced for the simple appreciation of nature. Gyeongpodae Pavilion and Chongseokjeong Pavilion are noteworthy in that they are the earliest remaining examples of the historical tradition of reflecting a sightseeing trip in painting accompanied by poetry. Furthermore, and most importantly, they broaden the understanding of Korean real-scenery landscape painting by presenting varied forms, compositions, and perspectives from sixteenth-century real-scenery landscape paintings that had formerly been unfound.
Personalized services directly and indirectly acquire personal data, in part, to provide customers with higher-value services that are specifically context-relevant (such as place and time). Information technologies continue to mature and develop, providing greatly improved performance. Sensory networks and intelligent software can now obtain context data, and that is the cornerstone for providing personalized, context-specific services. Yet, the danger of overflowing personal information is increasing because the data retrieved by the sensors usually contains privacy information. Various technical characteristics of context-aware applications have more troubling implications for information privacy. In parallel with increasing use of context for service personalization, information privacy concerns have also increased such as an unrestricted availability of context information. Those privacy concerns are consistently regarded as a critical issue facing context-aware personalized service success. The entire field of information privacy is growing as an important area of research, with many new definitions and terminologies, because of a need for a better understanding of information privacy concepts. Especially, it requires that the factors of information privacy should be revised according to the characteristics of new technologies. However, previous information privacy factors of context-aware applications have at least two shortcomings. First, there has been little overview of the technology characteristics of context-aware computing. Existing studies have only focused on a small subset of the technical characteristics of context-aware computing. Therefore, there has not been a mutually exclusive set of factors that uniquely and completely describe information privacy on context-aware applications. Second, user survey has been widely used to identify factors of information privacy in most studies despite the limitation of users' knowledge and experiences about context-aware computing technology. To date, since context-aware services have not been widely deployed on a commercial scale yet, only very few people have prior experiences with context-aware personalized services. It is difficult to build users' knowledge about context-aware technology even by increasing their understanding in various ways: scenarios, pictures, flash animation, etc. Nevertheless, conducting a survey, assuming that the participants have sufficient experience or understanding about the technologies shown in the survey, may not be absolutely valid. Moreover, some surveys are based solely on simplifying and hence unrealistic assumptions (e.g., they only consider location information as a context data). A better understanding of information privacy concern in context-aware personalized services is highly needed. Hence, the purpose of this paper is to identify a generic set of factors for elemental information privacy concern in context-aware personalized services and to develop a rank-order list of information privacy concern factors. We consider overall technology characteristics to establish a mutually exclusive set of factors. A Delphi survey, a rigorous data collection method, was deployed to obtain a reliable opinion from the experts and to produce a rank-order list. It, therefore, lends itself well to obtaining a set of universal factors of information privacy concern and its priority. An international panel of researchers and practitioners who have the expertise in privacy and context-aware system fields were involved in our research. Delphi rounds formatting will faithfully follow the procedure for the Delphi study proposed by Okoli and Pawlowski. This will involve three general rounds: (1) brainstorming for important factors; (2) narrowing down the original list to the most important ones; and (3) ranking the list of important factors. For this round only, experts were treated as individuals, not panels. Adapted from Okoli and Pawlowski, we outlined the process of administrating the study. We performed three rounds. In the first and second rounds of the Delphi questionnaire, we gathered a set of exclusive factors for information privacy concern in context-aware personalized services. The respondents were asked to provide at least five main factors for the most appropriate understanding of the information privacy concern in the first round. To do so, some of the main factors found in the literature were presented to the participants. The second round of the questionnaire discussed the main factor provided in the first round, fleshed out with relevant sub-factors. Respondents were then requested to evaluate each sub factor's suitability against the corresponding main factors to determine the final sub-factors from the candidate factors. The sub-factors were found from the literature survey. Final factors selected by over 50% of experts. In the third round, a list of factors with corresponding questions was provided, and the respondents were requested to assess the importance of each main factor and its corresponding sub factors. Finally, we calculated the mean rank of each item to make a final result. While analyzing the data, we focused on group consensus rather than individual insistence. To do so, a concordance analysis, which measures the consistency of the experts' responses over successive rounds of the Delphi, was adopted during the survey process. As a result, experts reported that context data collection and high identifiable level of identical data are the most important factor in the main factors and sub factors, respectively. Additional important sub-factors included diverse types of context data collected, tracking and recording functionalities, and embedded and disappeared sensor devices. The average score of each factor is very useful for future context-aware personalized service development in the view of the information privacy. The final factors have the following differences comparing to those proposed in other studies. First, the concern factors differ from existing studies, which are based on privacy issues that may occur during the lifecycle of acquired user information. However, our study helped to clarify these sometimes vague issues by determining which privacy concern issues are viable based on specific technical characteristics in context-aware personalized services. Since a context-aware service differs in its technical characteristics compared to other services, we selected specific characteristics that had a higher potential to increase user's privacy concerns. Secondly, this study considered privacy issues in terms of service delivery and display that were almost overlooked in existing studies by introducing IPOS as the factor division. Lastly, in each factor, it correlated the level of importance with professionals' opinions as to what extent users have privacy concerns. The reason that it did not select the traditional method questionnaire at that time is that context-aware personalized service considered the absolute lack in understanding and experience of users with new technology. For understanding users' privacy concerns, professionals in the Delphi questionnaire process selected context data collection, tracking and recording, and sensory network as the most important factors among technological characteristics of context-aware personalized services. In the creation of a context-aware personalized services, this study demonstrates the importance and relevance of determining an optimal methodology, and which technologies and in what sequence are needed, to acquire what types of users' context information. Most studies focus on which services and systems should be provided and developed by utilizing context information on the supposition, along with the development of context-aware technology. However, the results in this study show that, in terms of users' privacy, it is necessary to pay greater attention to the activities that acquire context information. To inspect the results in the evaluation of sub factor, additional studies would be necessary for approaches on reducing users' privacy concerns toward technological characteristics such as highly identifiable level of identical data, diverse types of context data collected, tracking and recording functionality, embedded and disappearing sensor devices. The factor ranked the next highest level of importance after input is a context-aware service delivery that is related to output. The results show that delivery and display showing services to users in a context-aware personalized services toward the anywhere-anytime-any device concept have been regarded as even more important than in previous computing environment. Considering the concern factors to develop context aware personalized services will help to increase service success rate and hopefully user acceptance for those services. Our future work will be to adopt these factors for qualifying context aware service development projects such as u-city development projects in terms of service quality and hence user acceptance.
Medical services are a fundamental and essential service in all urban areas. The location and accessibility of medical service facilities and institutions are critical to the diagnosis, control and prevention of illness and disease. The purpose of this paper is to present the results of a study on the location of medical facilities in Kwangju and the utilization of these facilities by the inhabitants. The following information is a summary of the findings: (1) Korea, like many countries, is now witnessing an increase in the age of its population as a result of higher living standards and better medical services. Korea is also experiencing a rapid increase in health care costs. To ensure easy access to medical consultation, diagnosis and treatment by individuals, the hierarchical efficient location of medical facilities, low medical costs, equalized medical services, preventive medical care is important. (2) In Korea, the quality of medical services has improved significantly as evident by the increased number of medical facilities and medical personnel. However, there is still a need for not only quantitative improvements but also for a more equitable distribution of and location of medical services. (3) There are 503 medical facilities in Kwangju each with a need to service 2,556 people. This is below the national average of 1,498 inhabitants per facility. The higher locational quotient and satisfactory population per medical facility showed at the civic center. On the other hand, problem regions such as the traditional residential area in Buk-Gu, Moo-deung mountain area and the outer areas of west Kwangju still maintain rural characteristics. (4) In the study area there are 86 general medicine clinics which provide basic medical services. i. e. one clinic per every 14,949 residents. As a basic service, its higher locational quotient showed in the residential area. The lower population concentration per clinic was found in the civic center and in the former town center, Songjeong-dong. In recently build residential areas and in the civic center, the lack of general medicine clinics is not a serious medical services issue because of the surplus of medical specialists in Korea. People are inclined to seek a consultation with a specialist in specific fields rather than consult a general practitioner. As a result of this phenomenon, there are 81 internal medicine facilities. Of these, 32.1% provide services to people who are not referred by a primary care physician but who self-diagnose then choose a medical facility specializing in what they believe to be their health problem. Areas in the city, called dongs, without any internal facilities make up 50% of the total 101 dongs. (5) There are 78 surgical facilities within the area, and there is little difference at the locational appearance from internal medicine facilities. There are also 71 pediatric health clinics for people under 15 years of age in this area, represents one clinic per 5,063 people. On the quantitative aspect, this is a positive situation. Accessibility is the most important facility choice factor, so it should be evenly located in proportion to demander distribution. However, 61% of 102 dongs have no pediatric clinics because of the uneven location. (6) There are 43 obstetrical and gynecological clinics in Kwangju, and the number of residents being served per clinic is 15,063. These services need to be given regularly so it should increase the numbers. There are 37 ENT clinics in the study area with the lower concentration in Dong-gu (32.4%) making no locational differences by dong. There are 23 dermatology clinics with the largest concentration in Dong-Gu. There are 17 ophthalmic clinics concentrated in the residential area because of the primary function of this type of specialization. (7) The use of general medicine clinics, internal medicine clinics, pediatric clinics, ENT clinics by the inhabitants indicate a trend toward primary or routine medical services. Obstetrics and gynecology clinics are used on a regular basis. In choosing a general medicine clinic, internal medicine clinic, pediatric clinic, and a ENT clinic, accessibility is the key factor while choice of a general hospital, surgery clinic, or an obstetrics and gynecology clinic, thes faith and trust in the medical practitioner is the priority consideration. (8) I considered the efficient use of medical facilities in the aspect of locational and management and suggest the following: First, primary care facilities should be evenly distributed in every area. In Kwangju, the number of medical facilities is the lowest among the six largest cities in Korea. Moreover, they are concentrated in Dong-gu and in newly developed areas. The desired number of medical facilities should be within 30 minutes of each person's home. For regional development there is a need to develop a plan to balance, for example, taxes and funds supporting personnel, equipment and facilities. Secondly, medical services should be co-ordinated to ensure consistent, appropriate, quality services. Primary medical facilities should take charge of out-patient activities, and every effort should be made to standardize and equalize equipment and facility resources and to ensure ongoing development and training in the primary services field. A few specialty medical facilities and general hospitals should establish a priority service for incurable and terminally ill patients. (9) The management scheme for the inhabitants' efficient use of medical service is as follows: The first task is to efficiently manage medical facilities and related services. Higher quality of medical services can be accomplished within the rapidly changing medical environment. A network of social, administrative and medical organizations within an area should be established to promote information gathering and sharing strategies to better assist the community. Statistics and trends on the rate or occurrence of diseases, births, deaths, medical and environment conditions of the poor or estranged people should be maintained and monitored. The second task is to increase resources in the area of disease prevention and health promotion. Currently the focus is on the treatment and care of individuals with illness or disease. A strong emphasis should also be placed on promoting prevention of illness and injury within the community through not only public health offices but also via medical service facilities. Home medical care should be established and medical testing centers should be located as an ordinary service level. Also, reduced medical costs for the physically handicapped, cardiac patients, and mentally ill or handicapped patients should be considered.