네이버, 다음, 구글 등의 웹 상에서 제공하는 지도 서비스와 KML, GML, GeoRSS와 같은 기술들을 이용하여 하나의 위치 정보로 통합한 지리정보를 사용자에게 제공해 줄 수 있는 연구들은 현재까지 활발히 진행되어 왔다. 그러나 이러한 연구들은 위치 정보만 통하여 줄 뿐 의미 정보까지 통합하여 사 용자들에게 다양한 정보들을 제공해주지 못한다. 이 논문에서는 KML, GML, GeoRSS 등으로 표현된 풍부한 지리정보들을 통합하여 웹 기반 지도 서비스에 제공해주는 시스템을 제안한다. 또한 지리정보 들의 스키마 통합을 위해 어댑터 기반 의미 처리 방법과 정적/동적 의미 관리 기반 접근 방법을 혼합 한 하이브리드 스키마 매칭(Hybrid Schema Matching, HSM) 방법을 제안하고, 제안 시스템의 평가를 위해 스키마 매칭을 위한 4가지 접근 방법과 비교 평가를 수행한다. 평가의 결과로 제안 시스템은 의 미 해석에 대한 신뢰성이 보장되고 시스템 구축 비용과 데이터 통합 비용이 상대적으로 낮다는 특징 을 지닌다.

Hemophilia A patients with inhibitors receiving emicizumab prophylaxis (EP) have the advantage of reducing annual bleeding rate (ABR) compared with on-demand therapy using bypassing agents (OD-BPA) (87.0% reduction, 95% CI: 72.3%-94.3%). Change in the route of administration from intravenous injection of OD-BPA to subcutaneous injection of emicizumab is deemed beneficial as well. The aim of this study is to examine the perceived improvement of quality of life (QOL) of EP compared with OD-BPA in Korean hemophilia patients. A self-administered survey involving 70 hemophilia patients (Either type A or B) at least 20 years of age was conducted at Korea Hemophilia Association Camp on August 10th, 2018. In the questionnaire, two hypothetical treatment scenarios (i.e., EP vs OD-BPA) describing overall symptoms and discomfort of hemophilia A with inhibitors, treatment effectiveness, and route of administration were provided. Then, hemophilia subjects completed their perceived QOL for each scenario using the EuroQol 5-dimensional 5-level instrument (EQ-5D-5L; EQ-5D™ is a trade mark of the EuroQol Research Foundation. (https://euroqol.org/eq-5d-instruments/eq-5d-5l-about/faqs/)). EQ-5D-5L responses were converted to utility weights ranging from 0 to 1 using the Korean tariff. The mean age of the respondents was 40 years old (SD: 15.92). Of the respondents, 55 (79%) were patients with type A hemophilia and 11 (16%) had inhibitors. The average utility for EP (0.77±0.16) was significantly higher than that of OD-BPA (0.63±0.20, p<0.05). The improvement of utility for EP compared to OD-BPA was more apparent in patients with inhibitors (0.77 vs. 0.61) than those without inhibitors (0.76 vs. 0.64). The significant improvement of perceived QOL for EP compared to OD-BPA suggests that EP would be a preferred intervention option for hemophilia A patients with inhibitors.

In this paper, we propose a method for identifying the adaptation period when a problem occurs in a system in order to reduce the unnecessary adaptation of self-adaptive software. Consequently, the dangerous situation information is defined, the behavior information at the time of problem occurrence is learned, and the adaptive performance is determined by comparing it with the existing similar situations by using the k-nearest neighbors algorithm. By the use of the proposed method, a situation where an unnecessary adaptation process is performed while running the self-adaptive system could be avoided, system load may be reduced, and service quality may be enhanced. Keywords—Self-adaptive software; Machine learning; Problem recognition

This letter proposes a comprehensive assessment of the mission-level damage caused by cyberattacks on an entire defense mission system. We experimentally prove that our method produces swift and accurate assessment results and that it can be applied to actual defense applications. This study contributes to the enhancement of cyber damage assessment with a faster and more accurate method.

Motor-related symptoms of patients with Parkinson's disease (PD) are caused by the decrease of dopamine (DA) in the midbrain. Decrease in DA induces imbalance between DA and acetylcholine (ACh), leading to an increase in the relative Ach level, which in turn contributes to motor symptoms again. Therefore, administration of acetylcholinesterase inhibitor (ACHEI), which inhibits the degradation of ACh, to PD patients should be avoided as it may exacerbate the imbalance between DA and Ach. However, a lot of PD patients are using ACHEI for the treatment of cognitive symptoms. Thus, we aimed to analyze the actual use of ACHEI in elderly patients with PD in Korea. PD patients were defined as those having at least one claim record with a diagnosis of PD (ICD-10 codes: G20-21) from 2016 Health Insurance Review and Assessment Service (HIRA) - Adult Patients Sample (APS) data, composed of 20% random sample of patient aged 65 and over in Korea. ACHEI users were defined as those with at least one prescription record of donepezil, rivastigmine or galantamine. Demographic characteristics were compared between ACHEI users and non-users. Of 24,524 PD patients identified from the 2016 HIRA-APS data, 8,928 patients (36%) were ACHEI users. ACHEI use increased with age (25% in 65-74, 42% in 75-84, and 49% in 85 and over, p <0.0001). The use of ACHEI was more common in women (38%) than men (34%, p <0.0001). Medical Aid beneficiaries (40%) tended to use ACHEI more than National Health Insurance beneficiaries (36%, p <0.0001). The use of ACHEI among PD patients in Korea is high. In-depth analysis should be conducted to identify factors affecting ACHEI prescribing in PD patients and to establish effective strategy to avoid potentially inappropriate use of ACHEI at clinical and the national level.

To evaluate the cost-effectiveness of emicizumab prophylaxis compared to bypassing agent (BPA) on-demand treatments (aPCC or rFVII) for hemophilia A patients with inhibitors under the Korean healthcare system. A lifetime Markov model was developed with two health states: 'Alive' and 'Death.' The Alive state included two nested states: bleeding events represented by annualized bleeding rates (ABR) and arthroplasty. Based on the comparative effectiveness measured as the reduction rate of ABR between the two interventions from HAVEN 1 trial (87.0%, 95% CI: 72.3%-94.3%), and average ABR observed among Korean patients receiving BPA on-demand treatment (46.6 ABR), we calculated the expected number of treated bleeding among those with emicizumab prophylaxis (6.1 ABR). Costs were estimated using the Korea National Health Insurance claims data and the consultations with expert panels. From the restricted societal perspective, the base-case analysis result showed that emicizumab prophylaxis was a dominant case, indicating better effectiveness and cost-saving compared to BPA on-demand treatment. When a patient starts receiving emicizumab prophylaxis from the age of 2, it is expected to avert about 800 treated bleeds and save 1.7 million US dollars over a lifetime as opposed to BPA on-demand treatments. Although the sensitivity analysis showed that the cost-effectiveness results were sensitive to the effectiveness of emicizumab and the number of ABR among patients treated with BPA on-demand therapy, the robustness of the base-case results was confirmed because the dominant status of emicizumab prophylaxis remained in most of the cases. The incremental cost had minus value, and the incremental effectiveness was positive for nearly all scenarios considered, indicating that emicizumab prophylaxis is a robust cost-saving option.

ContextIn recent years, software environments such as the cloud and Internet of Things (IoT) have become increasingly sophisticated, and as a result, development of adaptable software has become very important. Self-adaptive software is appropriate for today's needs because it changes its behavior or structure in response to a changing environment at runtime. To adapt to changing environments, runtime verification is an important requirement, and research that integrates traditional verification with self-adaptive software is in high demand.ObjectiveModel checking is an effective static verification method for software, but existing problems at runtime remain unresolved. In this paper, we propose a self-adaptive software framework that applies model checking to software to enable verification at runtime.MethodThe proposed framework consists of two parts: the design of self-adaptive software using a finite state machine and the adaptation of the software during runtime. For the first part, we propose two finite state machines for self-adaptive software called the self-adaptive finite state machine (SA-FSM) and abstracted finite state machine (A-FSM). For the runtime verification part, a self-adaptation process based on a MAPE (monitoring, analyzing, planning, and executing) loop is implemented.ResultsWe performed an empirical evaluation with several model-checking tools (i.e., NuSMV and CadenceSMV), and the results show that the proposed method is more efficient at runtime. We also investigated a simple example application in six scenarios related to the IoT environment. We implemented Android and Arduino applications, and the results show the practical usability of the proposed self-adaptive framework at runtime.ConclusionsWe proposed a framework for integrating model checking with a self-adaptive software lifecycle. The results of our experiments showed that the proposed framework can achieve verify self-adaptation software at runtime.

The Internet of Things (IoT) connects several objects within environments that dynamically change, and so requirements may be added and changed at runtime. Therefore, requirements may be satisfied at dynamic change. Self-adaptive software can alter their behavior to satisfy requirements in dynamic environments. In this perspective, the concept of self-adaptive software is suitable for IoT environments. In this study, a self-adaptive framework is proposed for decision making in IoT environments at runtime. The framework includes finite-state machine model designs and game theoretic decision-making methods to extract efficient strategies. The framework is implemented as a prototype, and experiments are performed to evaluate runtime performance. The results demonstrate that the proposed framework can be applied to IoT environments at runtime.

Over the past five years, research on big data analysis has been actively conducted, and many services have been developed to find valuable data. However, low quality of raw data and data loss problem during data analysis make it difficult to perform accurate data analysis. With the enormous generation of both unstructured and structured data, refinement of data is becoming increasingly difficult. As a result, data refinement plays an important role in data analysis. In addition, as part of efforts to ensure research reproducibility, the importance of reuse of researcher data and research methods is increasing; however, the research on systems supporting such roles has not been conducted sufficiently. Therefore, in this paper, we propose a big data analysis system named the unified data analytics suite (UDAS) that focuses on data refinement. UDAS performs data refinement based on the big data platform and ensures the reusability and reproducibility of refinement and analysis through the visual programming language interface. It also recommends open source and visualization libraries to users for statistical analysis. The qualitative evaluation of UDAS using the functional evaluation factor of the big data analysis platform demonstrated that the average satisfaction of the users is significantly high.

A smart greenhouse monitors the internal and external context information of the greenhouse in real time and keeps the internal environment in an optimal condition for the growth of crops. In this paper, we propose a smart greenhouse system based on MAPE-K that can automatically control the greenhouse. All information of the proposed system is stored and managed through an ontology knowledge repository based on ISO-IEC 11179 (metadata registry, MDR). Furthermore, we design a low cost smart greenhouse by interoperating the smart devices.

In order to reduce the effect on the eyes location caused by the variation of illumination and expression, this paper proposes a human eye location algorithm based on the multi-scale self-quotient image and morphological filtering. Firstly, the multi-scale self-quotient image is used to offset the lighting effects on the face, then the morphological open-close operation will be taken to enhance the local features around the eyes and relevant coefficient is used to roughly position the eyes. At last, the variance projection method will be used to analyze the roughly-positioned areas and binarize them to position accurately the central point of the eye. The experiments on the images from JAFFE Database, Yale B Database and AR database have shown that the proposed algorithm can well position the center of the eye, and it is robust to deal with the changes of illumination and expressions.

Cloud-based electronic health record (EHR) systems enable medical documents to be exchanged between medical institutions; this is expected to contribute to improvements in various medical services in the future. However, as the system architecture becomes more complicated, cloud-based EHR systems may introduce additional security threats when compared with the existing singular systems. Thus, patients may experience exposure of private data that they do not wish to disclose. In order to protect the privacy of patients, many approaches have been proposed to provide access control to patient documents when providing health services. However, most current systems do not support fine-grained access control or take into account additional security factors such as encryption and digital signatures. In this paper, we propose a cloud-based EHR model that performs attribute-based access control using extensible access control markup language. Our EHR model, focused on security, performs partial encryption and uses electronic signatures when a patient's document is sent to a document requester. We use XML encryption and XML digital signature technology. Our proposed model works efficiently by sending only the necessary information to the requesters who are authorized to treat the patient in question.

Twitter is a microblogging website, which has different characteristics from any other social networking service (SNS) in that it has one-directional relationships between users with short posts of less than 140 characters. These characteristics make Twitter not only a social network but also a news media. In addition, Twitter posts have been used and analyzed in various fields such as marketing, prediction of presidential elections, and requirement analysis. With an increase in Twitter usage, we need a more effective method to analyze Twitter content. In this paper, we propose a method for content analysis based on the influence of Twitter content. For measuring Twitter influence, we use the number of followers of the content author, retweet count, and currency of time. We perform experiments to compare the proposed method, frequency, numerical statistics, user influence, and sentiment score. The results show that the proposed method is slightly better than the other methods. In addition, we discuss Twitter characteristics and a method for an effective analysis of Twitter content.

In this research, we propose a lighting control system for environments with multiple light sources, including a natural light source and an artificial light source, based on a self–adaptive software control system. We also propose an algorithm for optimization between control devices in a multi-lighting environment, and evaluation methods for self-adaptive software in an Internet of Things environment. Based on these proposals, a simulation is carried out.

The goal of a group recommendation involves providing appropriate information for all members in a group. Most extant studies use aggregation methods to determine group preferences. An aggregation method is an approach that aggregates individual preferences of group members to recommend items to a group. Previous studies on aggregation methods only consider high averages, counts, and rankings to provide recommendations. However, the most important component of a group recommendation involves ensuring that majority of the members in a group are satisfied with the recommended results. Therefore, it is necessary to consider the deviation as an important element in aggregation methods. The present study involves proposing an upward leveling (UL) aggregation method that considers deviations for group recommendations. The UL recommends items with low deviations and high averages in conjunction with frequency of positive rating counts for group members. Furthermore, the effectiveness of the UL is validated to perform a comparative evaluation with existing aggregation methods by using the normalized discounted cumulative gain (NDCG) and diversity. The results indicate that the UL outperforms all the baselines and that the deviation plays an important role in the aggregation method. (C) 2017 Elsevier Ltd. All rights reserved.

Global urbanization has increased the importance of military operations in urban terrain (MOUT) in recent wars. However, the defense modeling and simulation community in Korea has faced challenges in applying the characteristics of urban terrain and MOUT in the analysis of contemporary wars. To overcome these issues, we enhanced the semi-automated behaviors in the One Semi-Automated Forces (OneSAF) simulation system for the building clearing operation, which is one of the most important operations in MOUT. Existing OneSAF behaviors have limitations when applying the actual actions of each combatant in the simulation. However, the enhanced behaviors can make the simulation more realistic. Through simulation experiments, we proved that these behaviors yield reliable simulation results and contribute to reasonable and effective MOUT simulations of the Korean battlefield environment.

The 5G network evolution opens up a new era wherein useful information can be easily acquired from dispersed Big Data because powerful infrastructures for data integration are provided to users. However, to fully utilize the advantages of the 5G networks during data integration, some issues must be resolved. This paper discusses some issues and the big picture of efficient data management under the 5G network evolution.

Self-adaptive software can change its own behavior in order to achieve an intended objective in a changing environment. Consequently, self-adaptive software requires practical runtime verification and validation. We propose an approach for runtime verification of self-adaptive software by using a designed transition system model. The proposed approach consists of two phases: pre-computing phase and runtime phase. In the precomputing phase, we assume that the self-adaptive software is designed as a transition system. In this phase, the proposed approach translates the designed transition system into equations for runtime verification. For translation, we suggest an algorithm based on state elimination and reachability. After the pre-computing phase, the results of the translated equations are verified in the runtime phase. In order to demonstrate the suitability of our proposed approach, we performed experiments to evaluate the performance of the pre-processing phase and the runtime phase. In comparison with other model-checking tools, our approach achieved excellent results.

Interoperability and functional redundancy between devices are important issues on IoT device group management because an IoT device group consists of diverse devices on heterogeneous platform. However, previous approaches for IoT device management are conducted from the perspective of the manufacture such as usage-based design and pre-sales analytics. In addition, product-based device management has a limitation in dealing with multifunctional smart devices and feature-reconfigurable devices on heterogeneous IoT environment. In this paper, we propose a user-centric approach for proactive product recommendation on heterogeneous IoT device platform. For the user-centric device management on heterogeneous platform, user and user group device profiles are integrated with feature-level device profiles which are owned by user and user group, respectively. The user's application usage of feature level is visualized to the comparison through a circular-coordinate diagram. For the recommendation of new supplement and substitute devices, the device scores are calculated with the interoperability score, redundancy score, and superior score.