• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권9호
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• pp.4555-4571
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• 2019

Due to increasing interest in distributed agile software development, there is a need to systematically review the literature on challenges encountered in the agile software development environment. Using the Systematic Literature Review (SLR) approach, 32 relevant publications, dated between 2013 and 2018 were selected from four electronic databases. Data from these publications were extracted to identify the key challenges across the system development life cycle (SDLC) phases, which essentially are short phases in each agile-based iteration. 5 types of key challenges were identified as impacting the SDLC phases; these challenges are Communication, Coordination, Cooperation, Collaboration and Control. In the context of the SLDC phases, the Communication challenge was discussed the most often (79 times, 33%). The least discussed challenges were Cooperation and Collaboration (26 times, 11% each). The 5 challenges occur because of distances which occur in distributed environment. This SLR identified 4 types of distances which contribute to the occurrence of these key challenges - physical, temporal, social-cultural and knowledge/experience. Of the 32 publications, only 4 included research which proposed new solutions to address challenges in agile distributed software development. The authors of this article believe that the findings in this SLR are a resource for future research work to deepen the understanding of and to develop additional solutions to address the challenges in distributed agile software development.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권12호
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• pp.4776-4794
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• 2020

We propose a rapid adaptive learning framework for streaming object detection, called EER-ASSL. The method combines the expected error reduction (EER) dependent rollback learning and the active semi-supervised learning (ASSL) for a rapid adaptive CNN detector. Most CNN object detectors are built on the assumption of static data distribution. However, images are often noisy and biased, and the data distribution is imbalanced in a real world environment. The proposed method consists of collaborative sampling and EER-ASSL. The EER-ASSL utilizes the active learning (AL) and rollback based semi-supervised learning (SSL). The AL allows us to select more informative and representative samples measuring uncertainty and diversity. The SSL divides the selected streaming image samples into the bins and each bin repeatedly transfers the discriminative knowledge of the EER and CNN models to the next bin until convergence and incorporation with the EER rollback learning algorithm is achieved. The EER models provide a rapid short-term myopic adaptation and the CNN models an incremental long-term performance improvement. EER-ASSL can overcome noisy and biased labels in varying data distribution. Extensive experiments shows that EER-ASSL obtained 70.9 mAP compared to state-of-the-art technology such as Faster RCNN, SSD300, and YOLOv2.

• Journal of Information Technology Applications and Management
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• 제28권2호
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• pp.1-16
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• 2021

In recent years, as IT technologies such as cloud computing and mobile payment have evolved and Internet users have increased, the Internet financial market has become intelligent, mobile, and platformed. This study considers the impact of the psychological characteristics of platform systems and users on the performance of fintech platforms. The results of this study are as follows. Information quality affected trust and commitment, service quality affected commitment only, and system quality affected trust and commitment. The perceived risk affected trust and commitment, and the perceived benefit only affected trust and was shown to have an insignificant relationship with immersion. Trust has been shown to have a significant relationship with commitment, and both trust and commitment affected performance. In the validation of mediation effects, trust has shown a partially mediated effect between information quality, system quality, perceived risks, and perceived benefits and performance. There was no mediation effect between service quality and performance. Immersion has been shown to have a partial mediating effect between information quality, service quality, system quality, perceived risk and performance, and there is no mediating effect between perceived benefits and performance. This study showed what are the main factors that affect the performance of the fintech platform and will be used as a useful foundation for increasing the performance of the platform in the future.

• Molecules and Cells
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• 제44권11호
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• pp.843-850
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• 2021

The rapid increase in collateral omics and phenotypic data has enabled data-driven studies for the fast discovery of cancer targets and biomarkers. Thus, it is necessary to develop convenient tools for general oncologists and cancer scientists to carry out customized data mining without computational expertise. For this purpose, we developed innovative software that enables user-driven analyses assisted by knowledge-based smart systems. Publicly available data on mutations, gene expression, patient survival, immune score, drug screening and RNAi screening were integrated from the TCGA, GDSC, CCLE, NCI, and DepMap databases. The optimal selection of samples and other filtering options were guided by the smart function of the software for data mining and visualization on Kaplan-Meier plots, box plots and scatter plots of publication quality. We implemented unique algorithms for both data mining and visualization, thus simplifying and accelerating user-driven discovery activities on large multiomics datasets. The present Q-omics software program (v0.95) is available at http://qomics.sookmyung.ac.kr.

• BMB Reports
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• 제55권9호
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• pp.429-438
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• 2022

Cervical cancer is the fourth most common malignancy in women worldwide. Although infection from human papillomavirus (HPV) has been the leading cause of cervical cancer, HPV-negative cervical cancer accounts for approximately 3-8% of all cases. Previous research studies on cervical cancer have focused on HPV-positive cervical cancer due to its prevalence, resulting in HPV-negative cervical cancer receiving considerably less attention. As a result, HPV-negative cervical cancer is poorly understood. Its etiology remains elusive mainly due to limitations in research methodology such as lack of defined markers and model systems. Moreover, false HPV negativity can arise from inaccurate diagnostic methods, which also hinders the progress of research on HPV-negative cervical cancer. Since HPV-negative cervical cancer is associated with worse clinical features, greater attention is required to understand HPV-negative carcinoma. In this review, we provide a summary of knowledge gaps and current limitations of HPV-negative cervical cancer research based on current clinical statistics. We also discuss future directions for understanding the pathogenesis of HPV-independent cervical cancer.

• Structural Engineering and Mechanics
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• 제82권2호
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• pp.233-244
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• 2022

Following a loss of control, the impact against a road barrier in a turn predominates among the most severe motorcyclist accidents. These road restraint devices can be equipped with a motorcycle screen, the function of which is to restrain the rider and minimize the consequences of the impact in terms of the severity of injuries. The performance of these screens is evaluated by the European normative procedure EN1317-8, which specifies the test conditions, based on one or two configurations. In practice, however, these impact conditions are very diverse, difficult to extrapolate from accident analysis and therefore poorly investigated. This study is interested in improving knowledge of these impact conditions in terms of impact speed, impact angle and particularly position of the rider. A finite element model has been developed to simulate the dynamic behavior of the rider from loss of control to impact on the screen. Statistical analysis of the results shows a high variability of the impact conditions, in particular with regard to the direction of turn (to the right or to the left). Some improvements are suggested in order to overcome the limitations inherent in standard procedures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권12호
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• pp.4024-4041
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• 2022

Robust reversible watermarking has attracted widespread attention in the field of information hiding in recent years. It should not only have robustness against attacks in transmission but also meet the reversibility of distortion-free transmission. According to our best knowledge, the most recent robust reversible watermarking methods adopt a single image as the carrier, which might lead to low efficiency in terms of carrier utilization. To address the issue, a novel dual-image robust reversible watermarking framework is proposed in this paper to effectively utilize the correlation between both carriers (namely dual images) and thus improve the efficiency of carrier utilization. In the dual-image robust reversible watermarking framework, a two-layer robust watermarking mechanism is designed to further improve the algorithm performances, i.e., embedding capacity and robustness. In addition, an optimization model is built to determine the parameters. Finally, the proposed framework is applied in different domains (namely domain-independent), i.e., Slantlet Transform and Singular Value Decomposition domain, and Zernike moments, respectively to demonstrate its effectiveness and generality. Experimental results demonstrate the superiority of the proposed dual-image robust reversible watermarking framework.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.175-175
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• 2023

Detection and monitoring of wildfires are essential for limiting their harmful effects on ecosystems, human lives, and property. In this research, we propose a novel method running in the Google Earth Engine platform for identifying and characterizing burnt regions using a hybrid of Sentinel-1 (C-band synthetic aperture radar) and Sentinel-2 (multispectral photography) images. The 2022 Uljin wildfire, the severest event in South Korean history, is the primary area of our investigation. Given its documented success in remote sensing and land cover categorization applications, we select the Random Forest (RF) method as our primary classifier. Next, we evaluate the performance of our model using multiple accuracy measures, including overall accuracy (OA), Kappa coefficient, and area under the curve (AUC). The proposed method shows the accuracy and resilience of wildfire identification compared to traditional methods that depend on survey data. These results have significant implications for the development of efficient and dependable wildfire monitoring systems and add to our knowledge of how machine learning and remote sensing-based approaches may be combined to improve environmental monitoring and management applications.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.945-951
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• 2022

The major objective of this research is to evaluate performance of improved intersections for response time to emergency vehicle preemption. Smart technologies have been introduced to civil infrastructure systems for resilient communities. The technologies need to evaluate their effectiveness and feasibility to confirm their introduction. This research focuses on the performance of emergency vehicle preemption, represented by response time, when smart intersections are introduced in a community. The response time is determined by not only intersections but also a number of factors such as traffic, distance, road conditions, and incident types. However, the evaluation of emergency response has often ignored factors related to emergency vehicle routes. In this respect, this research synthetically analyzes geospatial and incident data using each route of emergency vehicle and conducts before-and-after evaluations. The changes in performance are analyzed by the impact of smart intersections on response time through Bayesian regression models. The result provides measures of the project's performance. This study will contribute to the body of knowledge on modeling the impacts of technology application and integrating heterogeneous data sets. It will provide a way to confirm and prove the effectiveness of introducing smart technologies to our communities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권4호
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• pp.1100-1122
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• 2023

Scheduling user-submitted cloud tasks to the appropriate virtual machine (VM) in cloud computing is critical for cloud providers. However, as the demand for cloud resources from user tasks continues to grow, current evolutionary algorithms (EAs) cannot satisfy the optimal solution of large-scale cloud task scheduling problems. In this paper, we first construct a large- scale multi-objective cloud task problem considering the time and cost functions. Second, a multi-objective optimization algorithm based on multi-factor optimization (MFO) is proposed to solve the established problem. This algorithm solves by decomposing the large-scale optimization problem into multiple optimization subproblems. This reduces the computational burden of the algorithm. Later, the introduction of the MFO strategy provides the algorithm with a parallel evolutionary paradigm for multiple subpopulations of implicit knowledge transfer. Finally, simulation experiments and comparisons are performed on a large-scale task scheduling test set on the CloudSim platform. Experimental results show that our algorithm can obtain the best scheduling solution while maintaining good results of the objective function compared with other optimization algorithms.