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

• Journal of the Korean Society of Radiology
• /
• v.15 no.5
• /
• pp.675-681
• /
• 2021

Recently, the Ministry of Education stipulates in the distance class operation regulations that student lecture evaluations for distance learning subjects should be conducted at least twice per semester and the results should be disclosed to students. Therefore, the lecture evaluation of D college was compared with the first semester of 2020 and the first semester of 2021. As for the multiple-choice evaluation result of the distance learning mid-course evaluation, the overall average of the mid-course evaluation of the distance class in the first semester of 2020 increased from 4.1819 to 4.4000 in the mid-course evaluation in the first semester of 2021.In the case of the first semester of 2020, due to Corona 19, all non-face-to-face classes were held, but in the first semester of 2021, face-to-face classes increased. The overall satisfaction level rose from 4.18 points in the first semester of 2020 to 4.39 points in the first semester of 2021. The screen composition, sound and picture quality, playback time, face appearance, lecture material provision, and frequency of use of the top 3% and bottom 3% also increased. Despite the changes caused by the LMS replacement, which was a concern, student attendance, assignments, and test submission rates also increased compared to the previous year. The null hypothesis that 'the difference between the two scores is the same' is the null hypothesis because the probability of significance is 0.000 and less than 0.05 in the case of the best 3% of the test result of the test result of the mid-course evaluation of distance classes in the first semester of 2020 and the evaluation of the intermediate lectures in the first semester of 2021. As this was rejected, it can be seen that the best score for the 2021 school year has significantly increased compared to the first semester of 2020. Also, in the case of Worst 3% or less, the significance probability is 0.000, which is less than 0.05, so the null hypothesis that 'the difference between the two scores is the same' was rejected, indicating that the Worst score for the 2021 school year was significantly higher than that for the first semester of 2020.