• Korean journal of applied entomology
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• v.14 no.3 s.24
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• pp.97-109
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• 1975

In an attempt to develop an effective integrated system of controlling blast disease of rice caused by Pyricularia oryzae Cav., the possibility of minimizing the disease incidence by proper application of fertilizers has been investigated. Thus the effect of silicate, nitrogen, phosphorus and potassium fertilizers on the development of blast disease as well as the correlation between the rice varieties an4 strains of P. oryzae were studied. The experiments were made in 1971 and 1973 by artificial inoculation and under natural development of the blast disease on rice plants. The results obtained are summarized as follows. 1. Application of silicate fertilizer resulted in the increase of silicate as well as total sugar and potassium content but decrease of total nitrogen and phosphorus in tile leaf blades of rice plants. 2. The ratios of total C/total N. $ SiO_2/total$ N, and $K_2O/total$ N in leaf blades of rice plants increased by the application of silicate fertilizers. There was high level of negative correlation between the ratios mentioned above and the incidence of rice blast disease. 3. Application of silicate fertilizer reduced the incidence of rice blast disease. 4. The over dressing of nitrogen fertilizer resulted in the increase of total nitrogen and decrease of silicate and total sugar content in leaf blades, thus disposing the rice plants more susceptible to blast disease. 5. Over dressing of phosphorus fertilizer resulted in the increase of both total nitrogen and Phosphorus, and decrease of silicate content in the leaf blades inducing the rice plants to become more susceptible to blast disease. 6. Increased dressing of potash resulted in the increase of silicate content and $K_2O/total$ N ratio but decrease of total nitrogen content in leaf blades. When potassium content is low in the leaf blades of rice plants, the additional dressing of potash to rice plant contributed to the increase of resistance to blast disease. However, there was no significant correlation between additional potassium application and the resistance to blast disease when the potassium content is already high in the leaf blades. 7. When four rice varieties were artificially inoculated with three strains of P. oryzae, the incidence of blast disease was most severe on Pungok, least severe on Jinheung and moderate on Pungkwang and Paltal varieties. 8. Disease incidence was most severe on the second leaf from top and less sever on top and there leaf regardless of the fertilizer application when 5-6 leaf stage rice seedlings of four rice varieties were artificially inoculated with three strains of P. oryzae. 9. The pathogenicity of three strains of P. oryzae was in the order of $P_1,\;P_2,\;and\;P_3$ in their virulence when inoculated to Jinheung, Paltal, Pungkwang varieties but not with Pungok. The interaction between strains of P. oryzae and rice varieties was significant.

• Tuberculosis and Respiratory Diseases
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• v.55 no.3
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• pp.250-256
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• 2003

Background : Anthracofibrosis, a descriptive term for multiple black pigmentation with fibrosis on bronchoscopic examination, has a close relationship with active tuberculosis (TB). However, TB activity is determined in the later stage by the TB culture results in some cases of anthracofibrosis. Therefore, it is necessary to identify early markers of TB activity in anthracofibrosis. There have been several reports investigating the serum levels of IL-2 $sR{\alpha}$, IFN-${\gamma}$ and TBGL antibody for the evaluation of TB activity. In the present study, we tried to measure the above mentioned serologic markers for the evaluation of TB activity in patients with anthracofibrosis. Methods : Anthracofibrosis was defined when there was deep pigmentation (in more than two lobar bronchi) and fibrotic stenosis of the bronchi on bronchoscopic examination. The serum of patients with anthracofibrosis was collected and stored under refrigeration before the start of anti-TB medication. The serum of healthy volunteers (N=16), patients with active TB prior to (N=22), and after (N=13), 6 month-medication was also collected and stored. Serum IL-2 $sR{\alpha}$, IFN-${\gamma}$ were measured with ELISA kit (R&D system, USA) and serum TBGL antibody was measured with TBGL EIA kit (Kyowa Inc, Japan). Results : Serum levels of IL-2 $sR{\alpha}$ in healthy volunteers, active TB patients before and after medication, and patients with anthracofibrosis were $640{\pm}174$, $1,611{\pm}2,423$, $953{\pm}562$, and $863{\pm}401$ pg/ml, respectively. The Serum IFN-${\gamma}$ levels were 0, $8.16{\pm}17.34$, $0.70{\pm}2.53$, and $2.33{\pm}6.67$ pg/ml, and TBGL antibody levels were $0.83{\pm}0.80$, $5.91{\pm}6.71$, $6.86{\pm}6.85$, and $3.22{\pm}2.59$ U/ml, respectively. The serum level of TBGL antibody was lower than of other groups (p<0.05). There was no significant difference of serum IL-2 $sR{\alpha}$ and IFN-${\gamma}$ levels among the four groups. Conclusion : The serum levels of IL-2 $sR{\alpha}$, IFN-${\gamma}$ and TBGL antibody were not useful in the evaluation of TB activity in patients with anthracofibrosis. More useful ways need to be developed for the differentiation of active TB in patients with anthracofibrosis.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.29-44
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• 2017

In Internet commerce, consumers are heavily influenced by product reviews written by other users who have already purchased the product. However, as the product reviews accumulate, it takes a lot of time and effort for consumers to individually check the massive number of product reviews. Moreover, product reviews that are written carelessly actually inconvenience consumers. Thus many online vendors provide mechanisms to identify reviews that customers perceive as most helpful (Cao et al. 2011; Mudambi and Schuff 2010). For example, some online retailers, such as Amazon.com and TripAdvisor, allow users to rate the helpfulness of each review, and use this feedback information to rank and re-order them. However, many reviews have only a few feedbacks or no feedback at all, thus making it hard to identify their helpfulness. Also, it takes time to accumulate feedbacks, thus the newly authored reviews do not have enough ones. For example, only 20% of the reviews in Amazon Review Dataset (Mcauley and Leskovec, 2013) have more than 5 reviews (Yan et al, 2014). The purpose of this study is to analyze the factors affecting the usefulness of online product reviews and to derive a forecasting model that selectively provides product reviews that can be helpful to consumers. In order to do this, we extracted the various linguistic, psychological, and perceptual elements included in product reviews by using text-mining techniques and identifying the determinants among these elements that affect the usability of product reviews. In particular, considering that the characteristics of the product reviews and determinants of usability for apparel products (which are experiential products) and electronic products (which are search goods) can differ, the characteristics of the product reviews were compared within each product group and the determinants were established for each. This study used 7,498 apparel product reviews and 106,962 electronic product reviews from Amazon.com. In order to understand a review text, we first extract linguistic and psychological characteristics from review texts such as a word count, the level of emotional tone and analytical thinking embedded in review text using widely adopted text analysis software LIWC (Linguistic Inquiry and Word Count). After then, we explore the descriptive statistics of review text for each category and statistically compare their differences using t-test. Lastly, we regression analysis using the data mining software RapidMiner to find out determinant factors. As a result of comparing and analyzing product review characteristics of electronic products and apparel products, it was found that reviewers used more words as well as longer sentences when writing product reviews for electronic products. As for the content characteristics of the product reviews, it was found that these reviews included many analytic words, carried more clout, and related to the cognitive processes (CogProc) more so than the apparel product reviews, in addition to including many words expressing negative emotions (NegEmo). On the other hand, the apparel product reviews included more personal, authentic, positive emotions (PosEmo) and perceptual processes (Percept) compared to the electronic product reviews. Next, we analyzed the determinants toward the usefulness of the product reviews between the two product groups. As a result, it was found that product reviews with high product ratings from reviewers in both product groups that were perceived as being useful contained a larger number of total words, many expressions involving perceptual processes, and fewer negative emotions. In addition, apparel product reviews with a large number of comparative expressions, a low expertise index, and concise content with fewer words in each sentence were perceived to be useful. In the case of electronic product reviews, those that were analytical with a high expertise index, along with containing many authentic expressions, cognitive processes, and positive emotions (PosEmo) were perceived to be useful. These findings are expected to help consumers effectively identify useful product reviews in the future.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.51-69
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• 2020

Now, it is a stylized fact that a small number of technology firms such as Apple, Alphabet, Microsoft, Amazon, Facebook and a few others have become larger and dominant players in an industry. Coupled with the rise of these leading firms, we have also observed that a large number of young firms have become an acquisition target in their early IPO stages. This indeed results in a sharp decline in the number of new entries in public exchanges although a series of policy reforms have been promulgated to foster competition through an increase in new entries. Given the observed industry trend in recent decades, a number of studies have reported increased concentration in most developed countries. However, it is less understood as to what caused an increase in industry concentration. In this paper, we uncover the mechanisms by which industries have become concentrated over the last decades by tracing the changes in industry concentration associated with a firm's status change in its early IPO stages. To this end, we put emphasis on the case in which firms are acquired shortly after they went public. Especially, with the transition to digital-based economies, it is imperative for incumbent firms to adapt and keep pace with new ICT and related intelligent systems. For instance, after the acquisition of a young firm equipped with AI-based solutions, an incumbent firm may better respond to a change in customer taste and preference by integrating acquired AI solutions and analytics skills into multiple business processes. Accordingly, it is not unusual for young ICT firms become an attractive acquisition target. To examine the role of M&As involved with young firms in reshaping the level of industry concentration, we identify a firm's status in early post-IPO stages over the sample periods spanning from 1990 to 2016 as follows: i) being delisted, ii) being standalone firms and iii) being acquired. According to our analysis, firms that have conducted IPO since 2000s have been acquired by incumbent firms at a relatively quicker time than those that did IPO in previous generations. We also show a greater acquisition rate for IPO firms in the ICT sector compared with their counterparts in other sectors. Our results based on multinomial logit models suggest that a large number of IPO firms have been acquired in their early post-IPO lives despite their financial soundness. Specifically, we show that IPO firms are likely to be acquired rather than be delisted due to financial distress in early IPO stages when they are more profitable, more mature or less leveraged. For those IPO firms with venture capital backup have also become an acquisition target more frequently. As a larger number of firms are acquired shortly after their IPO, our results show increased concentration. While providing limited evidence on the impact of large incumbent firms in explaining the change in industry concentration, our results show that the large firms' effect on industry concentration are pronounced in the ICT sector. This result possibly captures the current trend that a few tech giants such as Alphabet, Apple and Facebook continue to increase their market share. In addition, compared with the acquisitions of non-ICT firms, the concentration impact of IPO firms in early stages becomes larger when ICT firms are acquired as a target. Our study makes new contributions. To our best knowledge, this is one of a few studies that link a firm's post-IPO status to associated changes in industry concentration. Although some studies have addressed concentration issues, their primary focus was on market power or proprietary software. Contrast to earlier studies, we are able to uncover the mechanism by which industries have become concentrated by placing emphasis on M&As involving young IPO firms. Interestingly, the concentration impact of IPO firm acquisitions are magnified when a large incumbent firms are involved as an acquirer. This leads us to infer the underlying reasons as to why industries have become more concentrated with a favor of large firms in recent decades. Overall, our study sheds new light on the literature by providing a plausible explanation as to why industries have become concentrated.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.163-176
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• 2014

Social media is becoming the platform for users to communicate their activities, status, emotions, and experiences to other people. In recent years, microblogs, such as Twitter, have gained in popularity because of its ease of use, speed, and reach. Compared to a conventional web blog, a microblog lowers users' efforts and investment for content generation by recommending shorter posts. There has been a lot research into capturing the social phenomena and analyzing the chatter of microblogs. However, measuring television ratings has been given little attention so far. Currently, the most common method to measure TV ratings uses an electronic metering device installed in a small number of sampled households. Microblogs allow users to post short messages, share daily updates, and conveniently keep in touch. In a similar way, microblog users are interacting with each other while watching television or movies, or visiting a new place. In order to measure TV ratings, some features are significant during certain hours of the day, or days of the week, whereas these same features are meaningless during other time periods. Thus, the importance of features can change during the day, and a model capturing the time sensitive relevance is required to estimate TV ratings. Therefore, modeling time-related characteristics of features should be a key when measuring the TV ratings through microblogs. We show that capturing time-dependency of features in measuring TV ratings is vitally necessary for improving their accuracy. To explore the relationship between the content of microblogs and TV ratings, we collected Twitter data using the Get Search component of the Twitter REST API from January 2013 to October 2013. There are about 300 thousand posts in our data set for the experiment. After excluding data such as adverting or promoted tweets, we selected 149 thousand tweets for analysis. The number of tweets reaches its maximum level on the broadcasting day and increases rapidly around the broadcasting time. This result is stems from the characteristics of the public channel, which broadcasts the program at the predetermined time. From our analysis, we find that count-based features such as the number of tweets or retweets have a low correlation with TV ratings. This result implies that a simple tweet rate does not reflect the satisfaction or response to the TV programs. Content-based features extracted from the content of tweets have a relatively high correlation with TV ratings. Further, some emoticons or newly coined words that are not tagged in the morpheme extraction process have a strong relationship with TV ratings. We find that there is a time-dependency in the correlation of features between the before and after broadcasting time. Since the TV program is broadcast at the predetermined time regularly, users post tweets expressing their expectation for the program or disappointment over not being able to watch the program. The highly correlated features before the broadcast are different from the features after broadcasting. This result explains that the relevance of words with TV programs can change according to the time of the tweets. Among the 336 words that fulfill the minimum requirements for candidate features, 145 words have the highest correlation before the broadcasting time, whereas 68 words reach the highest correlation after broadcasting. Interestingly, some words that express the impossibility of watching the program show a high relevance, despite containing a negative meaning. Understanding the time-dependency of features can be helpful in improving the accuracy of TV ratings measurement. This research contributes a basis to estimate the response to or satisfaction with the broadcasted programs using the time dependency of words in Twitter chatter. More research is needed to refine the methodology for predicting or measuring TV ratings.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

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.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.79-104
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• 2020

Recently, as deep learning has attracted attention, the use of deep learning is being considered as a method for solving problems in various fields. In particular, deep learning is known to have excellent performance when applied to applying unstructured data such as text, sound and images, and many studies have proven its effectiveness. Owing to the remarkable development of text and image deep learning technology, interests in image captioning technology and its application is rapidly increasing. Image captioning is a technique that automatically generates relevant captions for a given image by handling both image comprehension and text generation simultaneously. In spite of the high entry barrier of image captioning that analysts should be able to process both image and text data, image captioning has established itself as one of the key fields in the A.I. research owing to its various applicability. In addition, many researches have been conducted to improve the performance of image captioning in various aspects. Recent researches attempt to create advanced captions that can not only describe an image accurately, but also convey the information contained in the image more sophisticatedly. Despite many recent efforts to improve the performance of image captioning, it is difficult to find any researches to interpret images from the perspective of domain experts in each field not from the perspective of the general public. Even for the same image, the part of interests may differ according to the professional field of the person who has encountered the image. Moreover, the way of interpreting and expressing the image also differs according to the level of expertise. The public tends to recognize the image from a holistic and general perspective, that is, from the perspective of identifying the image's constituent objects and their relationships. On the contrary, the domain experts tend to recognize the image by focusing on some specific elements necessary to interpret the given image based on their expertise. It implies that meaningful parts of an image are mutually different depending on viewers' perspective even for the same image. So, image captioning needs to implement this phenomenon. Therefore, in this study, we propose a method to generate captions specialized in each domain for the image by utilizing the expertise of experts in the corresponding domain. Specifically, after performing pre-training on a large amount of general data, the expertise in the field is transplanted through transfer-learning with a small amount of expertise data. However, simple adaption of transfer learning using expertise data may invoke another type of problems. Simultaneous learning with captions of various characteristics may invoke so-called 'inter-observation interference' problem, which make it difficult to perform pure learning of each characteristic point of view. For learning with vast amount of data, most of this interference is self-purified and has little impact on learning results. On the contrary, in the case of fine-tuning where learning is performed on a small amount of data, the impact of such interference on learning can be relatively large. To solve this problem, therefore, we propose a novel 'Character-Independent Transfer-learning' that performs transfer learning independently for each character. In order to confirm the feasibility of the proposed methodology, we performed experiments utilizing the results of pre-training on MSCOCO dataset which is comprised of 120,000 images and about 600,000 general captions. Additionally, according to the advice of an art therapist, about 300 pairs of 'image / expertise captions' were created, and the data was used for the experiments of expertise transplantation. As a result of the experiment, it was confirmed that the caption generated according to the proposed methodology generates captions from the perspective of implanted expertise whereas the caption generated through learning on general data contains a number of contents irrelevant to expertise interpretation. In this paper, we propose a novel approach of specialized image interpretation. To achieve this goal, we present a method to use transfer learning and generate captions specialized in the specific domain. In the future, by applying the proposed methodology to expertise transplant in various fields, we expected that many researches will be actively conducted to solve the problem of lack of expertise data and to improve performance of image captioning.

• The Korean Journal of Archival Studies
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• no.26
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• pp.103-149
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• 2010

Documentation strategy has been experimented in various subject areas and local regions since late 1980's when it was proposed as archival appraisal and selection methods by archival communities in the United States. Though it was criticized to be too ideal, it needs to shed new light on the potentialities of the strategy for documenting local regions in digital environment. The purpose of this study is to analyse the implementation issues of documentation strategy and to suggest the directions for documenting local regions of Korea through the application of the strategy. The documentation strategy which was developed more than twenty years ago in mostly western countries gives us some implications for documenting local regions even in current digital environments. They are as follows; Firstly, documentation strategy can enhance the value of archivists as well as archives in local regions because archivist should be active shaper of history rather than passive receiver of archives according to the strategy. It can also be a solution for overcoming poor conditions of local archives management in Korea. Secondly, the strategy can encourage cooperation between collecting institutions including museums, libraries, archives, cultural centers, history institutions, etc. in each local region. In the networked environment the cooperation can be achieved more effectively than in traditional environment where the heavy workload of cooperative institutions is needed. Thirdly, the strategy can facilitate solidarity of various groups in local region. According to the analysis of the strategy projects, it is essential to collect their knowledge, passion, and enthusiasm of related groups to effectively implement the strategy. It can also provide a methodology for minor groups of society to document their memories. This study suggests the directions of documenting local regions in consideration of current archival infrastructure of Korean as follows; Firstly, very selective and intensive documentation should be pursued rather than comprehensive one for documenting local regions. Though it is a very political problem to decide what subject has priority for documentation, interests of local community members as well as professional groups should be considered in the decision-making process seriously. Secondly, it is effective to plan integrated representation of local history in the distributed custody of local archives. It would be desirable to implement archival gateway for integrated search and representation of local archives regardless of the location of archives. Thirdly, it is necessary to try digital documentation using Web 2.0 technologies. Documentation strategy as the methodology of selecting and acquiring archives can not avoid subjectivity and prejudices of appraiser completely. To mitigate the problems, open documentation system should be prepared for reflecting different interests of different groups. Fourth, it is desirable to apply a conspectus model used in cooperative collection management of libraries to document local regions digitally. Conspectus can show existing documentation strength and future documentation intensity for each participating institution. Using this, documentation level of each subject area can be set up cooperatively and effectively in the local regions.

• The Korean Journal of Archival Studies
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• no.22
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• pp.157-199
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• 2009

The organization for managing records and archives did not emerge together with the founding of the Chinese Communist Party. Such management became active with the establishment of the Department of Documents (文書科) and its affiliated offices overseeing reading and safekeeping of official papers, after the formation of the Central Secretariat(中央秘書處) in 1926. Improving the work of the Secretariat's organization became the focus of critical discussions in the early 1930s. The main criticism was that the Secretariat had failed to be cognizant of its political role and degenerated into a mere "functional organization." The solution to this was the "politicization of the Secretariat's work." Moreover, influenced by the "Rectification Movement" in the 1940s, the party emphasized the responsibility of the Resources Department (材料科) that extended beyond managing documents to collecting, organizing and providing various kinds of important information data. In the mean time, maintaining security with regard to composing documents continued to be emphasized through such methods as using different names for figures and organizations or employing special inks for document production. In addition, communications between the central political organs and regional offices were emphasized through regular reports on work activities and situations of the local areas. The General Secretary not only composed the drafts of the major official documents but also handled the reading and examination of all documents, and thus played a central role in record processing. The records, called archives after undergoing document processing, were placed in safekeeping. This function was handled by the "Document Safekeeping Office(文件保管處)" of the Central Secretariat's Department of Documents. Although the Document Safekeeping Office, also called the "Central Repository(中央文庫)", could no longer accept, beginning in the early 1930s, additional archive transfers, the Resources Department continued to strengthen throughout the 1940s its role of safekeeping and providing documents and publication materials. In particular, collections of materials for research and study were carried out, and with the recovery of regions which had been under the Japanese rule, massive amounts of archive and document materials were collected. After being stipulated by rules in 1931, the archive classification and cataloguing methods became actively systematized, especially in the 1940s. Basically, "subject" classification methods and fundamental cataloguing techniques were adopted. The principle of assuming "importance" and "confidentiality" as the criteria of management emerged from a relatively early period, but the concept or process of evaluation that differentiated preservation and discarding of documents was not clear. While implementing a system of secure management and restricted access for confidential information, the critical view on providing use of archive materials was very strong, as can be seen in the slogan, "the unification of preservation and use." Even during the revolutionary movement and wars, the Chinese Communist Party continued their efforts to strengthen management and preservation of records & archives. The results were not always desirable nor were there any reasons for such experiences to lead to stable development. The historical conditions in which the Chinese Communist Party found itself probably made it inevitable. The most pronounced characteristics of this process can be found in the fact that they not only pursued efficiency of records & archives management at the functional level but, while strengthening their self-awareness of the political significance impacting the Chinese Communist Party's revolution movement, they also paid attention to the value possessed by archive materials as actual evidence for revolutionary policy research and as historical evidence of the Chinese Communist Party.

• Korean Journal of Heritage: History & Science
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• v.52 no.2
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• pp.196-219
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• 2019

According to the Samguk Yusa, the nine-story wooden pagoda at Hwangnyongsa Temple was built by a Baekje artisan named Abiji in 645. Until the temple was burnt down completely during the Mongol invasion of Korea in 1238, it was the greatest symbol of the spiritual culture of the Korean people at that time and played an important role in the development of Buddhist thought in the country for about 700 years. At present, the only remaining features of Hwangnyongsa Temple, which is now in ruins, are the pagoda's stylobate and several foundation stones. In the past, many researchers made diverse inferences concerning the restoration of the original structure and the overall architecture of the wooden pagoda at Hwangnyongsa Temple, based on written records and excavation data. However, this information, together with the remaining external structure of the pagoda site and the assumption that it was a simple wooden structure, actually suggest that it was a rectangular-shaped nine-story pagoda. It is assumed that such ideas were suggested at a time when there was a lack of relevant data and limited knowledge on the subject, as well as insufficient information about the technical lineage of the wooden pagoda at Hwangnyongsa Temple; therefore, these ideas should be revised in respect of the discovery of new data and an improved level of awareness about the structural features of large ancient Buddhist pagodas. This study focused on the necessity of raising awareness of the lineage and structure of the wooden pagoda at Hwangnyongsa Temple and gaining a broader understanding of the structural system of ancient Buddhist pagodas in East Asia. The study is based on a reanalysis of data about the site of the wooden pagoda obtained through research on the restoration of Hwangnyongsa Temple, which has been ongoing since 2005. It is estimated that the wooden pagoda underwent at least two large-scale repairs between the Unified Silla and Goryeo periods, during which the size of the stylobate and the floor plan were changed and, accordingly, the upper structure was modified to a significant degree. Judging by the features discovered during excavation and investigation, traces relating to the nine-story wooden pagoda built during the Three Kingdoms Period include the earth on which the stylobate was built and the central pillar's supporting stone, which had been reinstalled using the rammed earth technique, as well as other foundation stones and stylobate stone materials that most probably date back to the ninth century or earlier. It seems that the foundation stones and stylobate stone materials were new when the reliquaries were enshrined again in the pagoda after the Unified Silla period, so the first story and upper structure would have been of a markedly different size to those of the original wooden pagoda. In addition, during the Goryeo period, these foundation stones were rearranged, and the cover stone was newly installed; therefore, the pagoda would seem to have undergone significant changes in size and structure compared to previous periods. Consequently, the actual structure of the original wooden pagoda at Hwangnyongsa Temple should be understood in terms of the changes in large Buddhist pagodas built in East Asia at that time, and the technical lineage should start with the large Buddhist pagodas of the Baekje dynasty, which were influenced by the Northern dynasty of China. Furthermore, based on the archeological data obtained from the analysis of the images of the nine-story rock-carved pagoda depicted on the Rock-carved Buddhas in Tapgok Valley at Namsan Mountain in Gyeongju, and the gilt-bronze rail fragments excavated from the lecture hall at the site of Hwangnyongsa Temple, the wooden pagoda would appear to have originally been an octagonal nine-story pagoda with a dual structure, rather than a simple rectangular wooden structure.