• STI Policy Review
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• v.5 no.2
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• pp.96-121
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• 2014

This study identifies and evaluates inter-ministerial coordination for developing digital content technology in Korea and Japan. It is conducted through a comparative analysis between Korean and Japanese governmental organizations and their decision making process. Media content had been regulated or promoted by ministries involving culture in both countries. The digitalization of traditional media, however, blurred boundaries between the cultural, technological, and industrial spheres, so ministries involving science and technology, economy and trade, or foreign affairs started to promote digital content technology in the late 1990s. This has been the cause of conflicts among ministries and sometimes led to policy duplication, which in turn weakens policy effectiveness. The competition and coordination of ministries and agencies can be seen through establishing or amending related laws, organizations, and programs. Structural holes are founded in the networks drawn among governmental agencies in charge of digital content, particularly in the field of intellectual property in Korea and online distribution technology in Japan.

• The Korean Journal of Air & Space Law and Policy
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• v.35 no.2
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• pp.281-312
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• 2020

Despite the recent economic difficulties, the on-line market is overtaking the off-line market. Since US Amazon CEO Jeff Bezos mentioned that a delivery service using drones is possible, it has been creating new perspectives and values that have never been seen before. Drones are being used in various fields. Among them, the delivery service using drones will be the future growth engine of Korea in cooperation with the 4th industrial revolution. However, as drones are put into practical use, problems such as human life and property damage and personal information protection due to public collisions or falls are expected. The practical use of future drones is inevitable, not optional. As a method for commercialization of drones, first, securing safety through drone use and securing a national certification system, which is the minimum standard system for drone safety, and second, securing various infrastructures by activating drone use, and third, aviation regulations and personal information protection, etc. It needs to be supplemented in terms of laws and regulations.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.109-125
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• 2020

The Ministry of National Defense is pushing for the Defense Acquisition Program to build strong defense capabilities, and it spends more than 10 trillion won annually on defense improvement. As the Defense Acquisition Program is directly related to the security of the nation as well as the lives and property of the people, it must be carried out very transparently and efficiently by experts. However, the excessive diversification of laws and regulations related to the Defense Acquisition Program has made it challenging for many working-level officials to carry out the Defense Acquisition Program smoothly. It is even known that many people realize that there are related regulations that they were unaware of until they push ahead with their work. In addition, the statutory statements related to the Defense Acquisition Program have the tendency to cause serious issues even if only a single expression is wrong within the sentence. Despite this, efforts to establish a sentence comparison system to correct this issue in real time have been minimal. Therefore, this paper tries to propose a "Comparison System between the Statement of Military Reports and Related Laws" implementation plan that uses the Siamese Network-based artificial neural network, a model in the field of natural language processing (NLP), to observe the similarity between sentences that are likely to appear in the Defense Acquisition Program related documents and those from related statutory provisions to determine and classify the risk of illegality and to make users aware of the consequences. Various artificial neural network models (Bi-LSTM, Self-Attention, D_Bi-LSTM) were studied using 3,442 pairs of "Original Sentence"(described in actual statutes) and "Edited Sentence"(edited sentences derived from "Original Sentence"). Among many Defense Acquisition Program related statutes, DEFENSE ACQUISITION PROGRAM ACT, ENFORCEMENT RULE OF THE DEFENSE ACQUISITION PROGRAM ACT, and ENFORCEMENT DECREE OF THE DEFENSE ACQUISITION PROGRAM ACT were selected. Furthermore, "Original Sentence" has the 83 provisions that actually appear in the Act. "Original Sentence" has the main 83 clauses most accessible to working-level officials in their work. "Edited Sentence" is comprised of 30 to 50 similar sentences that are likely to appear modified in the county report for each clause("Original Sentence"). During the creation of the edited sentences, the original sentences were modified using 12 certain rules, and these sentences were produced in proportion to the number of such rules, as it was the case for the original sentences. After conducting 1 : 1 sentence similarity performance evaluation experiments, it was possible to classify each "Edited Sentence" as legal or illegal with considerable accuracy. In addition, the "Edited Sentence" dataset used to train the neural network models contains a variety of actual statutory statements("Original Sentence"), which are characterized by the 12 rules. On the other hand, the models are not able to effectively classify other sentences, which appear in actual military reports, when only the "Original Sentence" and "Edited Sentence" dataset have been fed to them. The dataset is not ample enough for the model to recognize other incoming new sentences. Hence, the performance of the model was reassessed by writing an additional 120 new sentences that have better resemblance to those in the actual military report and still have association with the original sentences. Thereafter, we were able to check that the models' performances surpassed a certain level even when they were trained merely with "Original Sentence" and "Edited Sentence" data. If sufficient model learning is achieved through the improvement and expansion of the full set of learning data with the addition of the actual report appearance sentences, the models will be able to better classify other sentences coming from military reports as legal or illegal. Based on the experimental results, this study confirms the possibility and value of building "Real-Time Automated Comparison System Between Military Documents and Related Laws". The research conducted in this experiment can verify which specific clause, of several that appear in related law clause is most similar to the sentence that appears in the Defense Acquisition Program-related military reports. This helps determine whether the contents in the military report sentences are at the risk of illegality when they are compared with those in the law clauses.