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Deterministic Parallelism for Symbolic Execution Programs based on a Name-Freshness Monad Library 基于名称更新Monad库的符号执行程序的确定性并行性
Pub Date : 2021-02-01 DOI: 10.9708/JKSCI.2021.26.02.001
Ki Yung Ahn
[Abstract] In this paper
【摘要】本文
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引用次数: 0
Improvement of Processing Speed for UAV Attitude Information Estimation Using ROI and Parallel Processing 利用ROI和并行处理提高无人机姿态信息估计处理速度
Pub Date : 2021-01-01 DOI: 10.9708/JKSCI.2021.26.01.155
Ha Seok Wun, Park Myeong Chul
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引用次数: 0
A Research on Paramedic Student Type of Perception for 119 Rescue Workers 急救学生对119救援人员感知类型的研究
Pub Date : 2021-01-01 DOI: 10.9708/JKSCI.2021.26.08.127
Jae-min Lee
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引用次数: 0
Development and application of Scenario-based Admission Management VR contents for nursing students 面向护生的场景化招生管理VR内容开发与应用
Pub Date : 2021-01-01 DOI: 10.9708/JKSCI.2021.26.01.209
Kim Yu Jeong
[Abstract] In this paper
【摘要】本文
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引用次数: 8
A Study on GAN Algorithm for Restoration of Cultural Property (pagoda) 文物(宝塔)修复的GAN算法研究
Pub Date : 2021-01-01 DOI: 10.9708/JKSCI.2021.26.01.077
Jin-Hyun Yoon, Byong-Kwon Lee, Byung-Wan Kim
Today, the restoration of cultural properties is done by applying the latest IT technology from relying on existing data and experts. However, there are cases where new data are released and the original restoration is incorrect. Also, sometimes it takes too long to restore. And there is a possibility that the results will be different than expected. Therefore, we aim to quickly restore cultural properties using DeepLearning. Recently, so the algorithm DcGAN made in GANs algorithm, and image creation, restoring sectors are constantly evolving. We try to find the optimal GAN algorithm for the restoration of cultural properties among various GAN algorithms. Because the GAN algorithm is used in various fields. In the field of restoring cultural properties, it will show that it can be applied in practice by obtaining meaningful results. As a result of experimenting with the DCGAN and Style GAN algorithms among the GAN algorithms, it was confirmed that the DCGAN algorithm generates a top image with a low resolution.
如今,文化遗产的修复依靠现有的资料和专家,运用最新的信息技术(IT)进行。但是,在某些情况下,发布了新数据,而原始恢复不正确。而且,有时需要很长时间才能恢复。结果也有可能与预期不同。因此,我们的目标是使用deeplelearning快速恢复文化财产。近年来,所以在gan算法中提出的DcGAN算法,以及图像的创建、扇区的恢复等都在不断发展。我们试图在各种GAN算法中找到最适合文物修复的GAN算法。因为GAN算法在各个领域都有应用。在文物修复领域,通过取得有意义的成果,表明该方法可以应用于实践。通过对GAN算法中的DCGAN算法和Style GAN算法的实验,验证了DCGAN算法生成的顶图像分辨率较低。
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引用次数: 1
A research on the possibility of restoring cultural assets of artificial intelligence through the application of artificial neural networks to roof tile(Wadang) 人工神经网络应用于瓦片修复人工智能文化资产的可能性研究(瓦当)
Pub Date : 2021-01-01 DOI: 10.9708/JKSCI.2021.26.01.019
JunOh Kim, Lee Byong Kwon
[Abstract] Cultural assets excavated in historical areas have their own characteristics based on the background of the times, and it can be seen that their patterns and characteristics change little by little according to the history and the flow of the spreading area. Cultural properties excavated in some areas represent the culture of the time and some maintain their intact appearance
[摘要]历史区域出土的文化资产,基于时代背景,具有自身的特点,可以看出,其形态和特征会随着历史和传播区域的流动而逐渐发生变化。在一些地区出土的文物代表了当时的文化,有些则保持了完整的外观
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引用次数: 1
The Effects of Digital Consumption Trust and Corporate Trust on IT Device and Service Satisfaction 数字消费信任和企业信任对IT设备和服务满意度的影响
Pub Date : 2021-01-01 DOI: 10.9708/JKSCI.2021.26.01.217
Park Seung Bae, Hong Jaewon
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引用次数: 0
Improvement of a Product Recommendation Model using Customers' Search Patterns and Product Details 基于顾客搜索模式和产品详细信息的产品推荐模型改进
Pub Date : 2021-01-01 DOI: 10.9708/JKSCI.2021.26.01.265
Lee Yunju, Lee, Jaejun, Ahn, Hyunchul
[Abstract] In this paper, we propose a novel recommendation model based on Doc2vec using search keywords and product details. Until now, a lot of prior studies on recommender systems have proposed collaborative filtering (CF) as the main algorithm for recommendation, which uses only structured input data such as customers’ purchase history or ratings. However, the use of unstructured data like online customer review in CF may lead to better recommendation. Under this background, we propose to use search keyword data and product detail information, which are seldom used in previous studies, for product recommendation. The proposed model makes recommendation by using CF which simultaneously considers ratings, search keywords and detailed information of the products purchased by customers. To extract quantitative patterns from these unstructured data, Doc2vec is applied. As a result of the experiment, the proposed model was found to outperform the conventional recommendation model. In addition, it was confirmed that search keywords and product details had a significant effect on recommendation. This study has academic significance in that it tries to apply the customers' online behavior information to the recommendation system and that it mitigates the cold start problem, which is one of the critical limitations of CF.
[摘要]本文提出了一种基于Doc2vec的基于搜索关键词和产品细节的推荐模型。到目前为止,许多关于推荐系统的研究都提出了协同过滤(CF)作为推荐的主要算法,该算法只使用结构化的输入数据,如顾客的购买历史或评分。然而,在CF中使用非结构化数据,如在线客户评论,可能会导致更好的推荐。在此背景下,我们提出使用以往研究中很少使用的搜索关键词数据和产品详细信息进行产品推荐。该模型使用CF进行推荐,同时考虑了顾客购买产品的评分、搜索关键词和详细信息。为了从这些非结构化数据中提取定量模式,应用了Doc2vec。实验结果表明,该模型优于传统推荐模型。此外,我们还证实了搜索关键词和产品细节对推荐有显著的影响。本研究具有重要的学术意义,因为它尝试将顾客在线行为信息应用到推荐系统中,并且缓解了冷启动问题,而冷启动问题是CF的关键局限性之一。
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引用次数: 0
Secure Training Support Vector Machine with Partial Sensitive Part 部分敏感部件的安全训练支持向量机
Pub Date : 2021-01-01 DOI: 10.9708/JKSCI.2021.26.04.001
Saerom Park
In this paper, we propose a training algorithm of support vector machine (SVM) with a sensitive variable. Although machine learning models enable automatic decision making in the real world applications, regulations prohibit sensitive information from being used to protect privacy. In particular, the privacy protection of the legally protected attributes such as race, gender, and disability is compulsory. We present an efficient least square SVM (LSSVM) training algorithm using a fully homomorphic encryption (FHE) to protect a partial sensitive attribute. Our framework posits that data owner has both non-sensitive attributes and a sensitive attribute while machine learning service provider (MLSP) can get non-sensitive attributes and an encrypted sensitive attribute. As a result, data owner can obtain the encrypted model parameters without exposing their sensitive information to MLSP. In the inference phase, both non-sensitive attributes and a sensitive attribute are encrypted, and all computations should be conducted on encrypted domain. Through the experiments on real data, we identify that our proposed method enables to implement privacy-preserving sensitive LSSVM with FHE that has comparable performance with the original LSSVM algorithm. In addition, we demonstrate that the efficient sensitive LSSVM with FHE significantly improves the computational cost with a small degradation of performance.
本文提出了一种带有敏感变量的支持向量机训练算法。虽然机器学习模型可以在现实世界的应用程序中实现自动决策,但法规禁止使用敏感信息来保护隐私。特别是对种族、性别、残疾等受法律保护的属性的隐私保护是强制性的。提出了一种利用全同态加密(FHE)保护部分敏感属性的高效最小二乘支持向量机(LSSVM)训练算法。我们的框架假设数据所有者同时具有非敏感属性和敏感属性,而机器学习服务提供商(MLSP)可以获得非敏感属性和加密的敏感属性。因此,数据所有者可以在不将其敏感信息暴露给MLSP的情况下获得加密的模型参数。在推理阶段,对非敏感属性和敏感属性进行加密,所有计算都在加密域上进行。通过对真实数据的实验,我们发现我们的方法能够实现具有FHE的隐私保护敏感LSSVM,并且具有与原始LSSVM算法相当的性能。此外,我们还证明了具有FHE的高效敏感LSSVM在性能下降很小的情况下显着提高了计算成本。
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引用次数: 0
The Possibility of Neural Network Approach to Solve Singular Perturbed Problems 神经网络方法求解奇异摄动问题的可能性
Pub Date : 2021-01-01 DOI: 10.9708/JKSCI.2021.26.01.069
Jee-Hyun Kim, Young-Im Cho
Recentlly neural network approach for solving a singular perturbed integro-differential boundary value problem have been researched. Especially the model of the feed-forward neural network to be trained by the back propagation algorithm with various learning algorithms were theoretically substantiated, and neural network models such as deep learning, transfer learning, federated learning are very rapidly evolving. The purpose of this paper is to study the approaching method for developing a neural network model with high accuracy and speed for solving singular perturbed problem along with asymptotic methods. In this paper, we propose a method that the simulation for the difference between result value of singular perturbed problem and unperturbed problem by using neural network approach equation. Also, we showed the efficiency of the neural network approach. As a result, the contribution of this paper is to show the possibility of simple neural network approach for singular perturbed problem solution efficiently.
近年来研究了求解奇异摄动积分微分边值问题的神经网络方法。特别是用反向传播算法训练的前馈神经网络模型与各种学习算法在理论上得到了证实,深度学习、迁移学习、联邦学习等神经网络模型的发展非常迅速。本文的目的是研究用渐近方法建立求解奇异摄动问题的高精度、快速的神经网络模型的逼近方法。本文提出了一种用神经网络逼近方程模拟奇异摄动问题与非摄动问题结果值之差的方法。此外,我们还展示了神经网络方法的有效性。因此,本文的贡献在于展示了简单神经网络方法有效求解奇异摄动问题的可能性。
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引用次数: 0
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Journal of the Korea Society of Computer and Information
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