• Title/Summary/Keyword: network-selection

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Quality Evaluation Model on Information Protection Product (정보보호제품 품질 평가 모델에 관한 연구)

  • Yoon, Hyung-Deuk;Lee, kyung-ho
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.27 no.6
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    • pp.1535-1547
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    • 2017
  • The purchase of information protection products accounts for the largest portion of corporate information protection activity budgets. However, there are no evaluation factors and evaluation models that should be applied to objectively compare information protection products, and therefore, product selection is difficult. Therefore, in this study, we study the inherent quality characteristics of information security products and select evaluation factors accordingly. The selected evaluation factors are analyzed and a quality determination model is given by weighting according to importance. The target is limited to the network information protection product and can be extended to all information protection products.

Design and Implementation of the SMIL Authoring Tool (SMIL(Synchronized Multimedia Integration Language) 전용 저작도구의 설계 및 구현)

  • Shin, Dong-Kyoo;Shin, Dong-Il;Kim, Se-Young
    • The KIPS Transactions:PartB
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    • v.10B no.1
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    • pp.103-110
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    • 2003
  • SMIL is a declarative markup language based on XML, which enables users to make effective multimedia presentations on the web by synchronizing media objects. In this paper, we design and implement a SMIL authoring tool which support Intuitive creation of web multimedia documents and diverse practical use of created information. Besides, creation, management and authoring module of SMIL document, it offers template interface depending on network bandwidth selection for general users to create multimedia presentation a convenient way. In addition, we implement SMIL document management facility for a large number of users, by connecting SMIL contents database sever.

Default Prediction for Real Estate Companies with Imbalanced Dataset

  • Dong, Yuan-Xiang;Xiao, Zhi;Xiao, Xue
    • Journal of Information Processing Systems
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    • v.10 no.2
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    • pp.314-333
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    • 2014
  • When analyzing default predictions in real estate companies, the number of non-defaulted cases always greatly exceeds the defaulted ones, which creates the two-class imbalance problem. This lowers the ability of prediction models to distinguish the default sample. In order to avoid this sample selection bias and to improve the prediction model, this paper applies a minority sample generation approach to create new minority samples. The logistic regression, support vector machine (SVM) classification, and neural network (NN) classification use an imbalanced dataset. They were used as benchmarks with a single prediction model that used a balanced dataset corrected by the minority samples generation approach. Instead of using prediction-oriented tests and the overall accuracy, the true positive rate (TPR), the true negative rate (TNR), G-mean, and F-score are used to measure the performance of default prediction models for imbalanced dataset. In this paper, we describe an empirical experiment that used a sampling of 14 default and 315 non-default listed real estate companies in China and report that most results using single prediction models with a balanced dataset generated better results than an imbalanced dataset.

Current Status of Liver Transplantation (간이식의 현재)

  • Kim, Hong-Jin
    • Journal of Yeungnam Medical Science
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    • v.18 no.1
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    • pp.1-12
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    • 2001
  • Liver transplantation is widely accepted as an effective therapeutic modality for a variety of irreversible acute and chronic liver diseases for which no satisfactory therapy is available. Following the first unsuccessful efforts at human liver transplantation in 1963, development of the procedure evolved at first slowly and steadily for 20 years and then rapidly over the past two decades. The growth of liver transplantation was facilitated by the conclusion of the national institutes of health consensus development conference in 1983 that liver transplantation is not an experimental procedure but an effective therapy that deserves broader application. The number of liver transplantations increased 2.4-fold(from 1.713 to 4.058) from 1988 to 1996. but the number of patients on the UNOS(united network of organ sharing) liver list increased 12.1-fold(from 616 to 7,467); as would be expected, the number of deaths of listed patients increased 4.9-fold(from 195 to 954), The current supply of donor livers is insufficient to meet this need, and organ donation has been stagnant or increased by only a few percent in recent years. These facts underscore the importance of the appropriate selection of candidates for liver transplantation and the development of operative procedures, such as living donor liver transplant, split liver transplant and auxiliary partial liver transplant.

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A study on Stage-Based Flow Graph Model for Expressing Cyber Attack Train Scenarios (사이버 공격 훈련 시나리오 표현을 위한 Stage 기반 플로우 그래프 모델 연구)

  • Kim, Moon-Sun;Lee, Man-Hee
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.31 no.5
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    • pp.1021-1030
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    • 2021
  • This paper proposes S-CAFG(Stage-based Cyber Attack Flow Graph), a model for effectively describing training scenarios that simulate modern complex cyber attacks. On top of existing graph and tree models, we add a stage node to model more complex scenarios. In order to evaluate the proposed model, we create a complicated scenario and compare how the previous models and S-CAFG express the scenario. As a result, we confirm that S-CAFG can effectively describe various attack scenarios such as simultaneous attacks, additional attacks, and bypass path selection.

A history-based peer selection algorithm for WebRTC-based P2P-assisted DASH systems (WebRTC 기반 P2P 통신 병용 DASH 시스템을 위한 이력 기반 피어 선택 알고리듬)

  • Choi, Seong Hyun;Seo, Ju Ho;Kim, Sang Jin;Jeon, Jae Young;Kim, Yong Han
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • 2018.11a
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    • pp.110-113
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    • 2018
  • 최근 인터넷 미디어 스트리밍 수요의 증가로 인해 CDN(Content Delivery Network) 서버 비용이 크게 증가하였으며 이를 절감하기 위한 방안의 필요성이 날로 증가하고 있다. 이러한 상황에 맞춰 최근 CDN 서버 비용을 절감할 수 있는 WebRTC(Web Real-Time Communication) 표준 기반의 P2P(Peer-to-Peer) 통신을 병용하는 DASH(Dynamic Adaptive Streaming over HTTP) 기술이 등장하였다. 본 논문에서는 이 기술의 CDN 서버 부하 절감 효과를 크게 개선할 수 있는 알고리듬을 제안한다. 또한 실제 모바일 네트워크 환경과 유사하게 실험 조건을 설정한 후, 이 알고리듬을 구현하여 그 성능을 측정한 결과, 기존과 비교하여 더 높은 절감 효과를 달성할 수 있음을 실험실 내 실험을 통해 보인다.

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A Novel Framework for Resource Orchestration in OpenStack Cloud Platform

  • Muhammad, Afaq;Song, Wang-Cheol
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.12 no.11
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    • pp.5404-5424
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    • 2018
  • This work is mainly focused on two major topics in cloud platforms by using OpenStack as a case study: management and provisioning of resources to meet the requirements of a service demanded by remote end-user and relocation of virtual machines (VMs) requests to offload the encumbered compute nodes. The general framework architecture contains two subsystems: 1) An orchestrator that allows to systematize provisioning and resource management in OpenStack, and 2) A resource utilization based subsystem for vibrant VM relocation in OpenStack. The suggested orchestrator provisions and manages resources by: 1) manipulating application program interfaces (APIs) delivered by the cloud supplier in order to allocate/control/manage storage and compute resources; 2) interrelating with software-defined networking (SDN) controller to acquire the details of the accessible resources, and training the variations/rules to manage the network based on the requirements of cloud service. For resource provisioning, an algorithm is suggested, which provisions resources on the basis of unused resources in a pool of VMs. A sub-system is suggested for VM relocation in a cloud computing platform. The framework decides the proposed overload recognition, VM allocation algorithms for VM relocation in clouds and VM selection.

Analysis of the supportive care needs of the parents of preterm children in South Korea using big data text-mining: Topic modeling

  • Park, Ji Hyeon;Lee, Hanna;Cho, Haeryun
    • Child Health Nursing Research
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    • v.27 no.1
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    • pp.34-42
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    • 2021
  • Purpose: The purpose of this study was to identify the supportive care needs of parents of preterm children in South Korea using text data from a portal site. Methods: In total, 628 online newspaper articles and 1,966 social network service posts published between January 1 and December 31, 2019 were analyzed. The procedures in this study were conducted in the following order: keyword selection, data collection, morpheme analysis, keyword analysis, and topic modeling. Results: The term "yirundung-yi", which is a native Korean word referring to premature infants, was confirmed to be a useful term for parents. The following four topics were identified as the supportive care needs of parents of preterm children: 1) a vague fear of caring for a baby upon imminent neonatal intensive care unit discharge, 2) real-world difficulties encountered while caring for preterm children, 3) concerns about growth and development problems, and 4) anxiety about possible complications. Conclusion: Supportive care interventions for parents of preterm children should include general parenting methods for babies. A team composed of multidisciplinary experts must support the individual growth and development of preterm children and manage the complications of prematurity using highly accessible media.

An Anti-Virus Vaccine Selection Model Based on Stackelberg Game (슈타켈버그 게임 기반 Anti-virus 백신 선택 모형)

  • Sung, Si-Il;Choi, In-Chan
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.19 no.1
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    • pp.135-144
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    • 2009
  • This paper deals with an information security problem that involves the strategies of both an attacker and an administrator of a web-based system. A game-theoretic model for the problem, based on an Stackelberg game environment, is presented. In the model, the administrator selects a set of anti-virus vaccines to cope with potential system attackers and the intruder chooses attacking modes that are most effective against the administrator's chosen set of vaccines. Moreover, the model considers a number of practical constraints, such as a budget limit on the vaccine purchase and a limit on the system performance. In addition, two different scenario analyses are provided, based on the results of the proposed model applied to a simulated pseudo-real-world data.

Socially Aware Device-to-multi-device User Grouping for Popular Content Distribution

  • Liu, Jianlong;Zhou, Wen'an;Lin, Lixia
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.11
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    • pp.4372-4394
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    • 2020
  • The distribution of popular videos incurs a large amount of traffic at the base stations (BS) of networks. Device-to-multi-device (D2MD) communication has emerged an efficient radio access technology for offloading BS traffic in recent years. However, traditional studies have focused on synchronous user requests whereas asynchronous user requests are more common. Hence, offloading BS traffic in case of asynchronous user requests while considering their time-varying characteristics and the quality of experience (QoE) of video request users (VRUs) is a pressing problem. This paper uses social stability (SS) and video loading duration (VLD)-tolerant property to group VRUs and seed users (SUs) to offload BS traffic. We define the average amount of data transmission (AADT) to measure the network's capacity for offloading BS traffic. Based on this, we formulate a time-varying bipartite graph matching optimization problem. We decouple the problem into two subproblems which can be solved separately in terms of time and space. Then, we propose the socially aware D2MD user selection (SA-D2MD-S) algorithm based on finite horizon optimal stopping theory, and propose the SA-D2MD user matching (SA-D2MD-M) algorithm to solve the two subproblems. The results of simulations show that our algorithms outperform prevalent algorithms.