• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.357-359
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• 2023

This paper proposes a hybrid recommendation system (RS) model that overcomes the limitations of traditional approaches such as data sparsity, cold start, and scalability by combining collaborative filtering and context-aware techniques. The objective of this model is to enhance the accuracy of recommendations and provide personalized suggestions by leveraging the strengths of collaborative filtering and incorporating user context features to capture their preferences and behavior more effectively. The approach utilizes a novel method that combines contextual attributes with the original user-item rating matrix of CF-based algorithms. Furthermore, we integrate k-mean++ clustering to group users with similar preferences and finally recommend items that have highly rated by other users in the same cluster. The process of partitioning is the use of the rating matrix into clusters based on contextual information offers several advantages. First, it bypasses of the computations over the entire data, reducing runtime and improving scalability. Second, the partitioned clusters hold similar ratings, which can produce greater impacts on each other, leading to more accurate recommendations and providing flexibility in the clustering process. keywords: Context-aware Recommendation, Collaborative Filtering, Kmean++ Clustering.

• Journal of the Korea Convergence Society
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• v.11 no.11
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• pp.25-31
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• 2020

This study is to study a method of applying weights for quality characteristics in software test evaluation. The weight application method analyzes the text of the test report and uses the ratio according to the frequency of the text as a weight for the quality characteristics of the software test score. The feasibility review of the results of this study was conducted by comparing the results of the questionnaire survey, which made the developers and users to evaluate the importance of software, and the results of the frequency analysis of text analysis. When measuring quality based on the eight quality characteristics presented in ISO/IEC 25023, the result of this study is the software quality measurement result considering software characteristics, whereas the result of this study is the software quality measurement result by applying the same weight when measuring quality.

• Korean Journal of Computational Design and Engineering
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• v.19 no.4
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• pp.434-442
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• 2014

Most manufacturing enterprises create CAD data as a result of the product/part design process; however, the CAD data is being utilized only for production activities. Besides the processes directly related to manufacturing such as design and production, the CAD data is an important resource that can be used in variety of services (e.g., catalog production and production manuals) for manufacturing enterprises. This study proposes a software platform that can support a wide range of services for manufacturing companies in an efficient and productive way. The software platform was designed based on the functions identified by requirement analysis. The platform consists of four layers: data model layer to manage relevant data; library layer and common function layer to configure services; and application layer to install and run the software. Finally, this study evaluates the validity of the proposed platform architecture by applying it to the digital catalog system.

• Journal of the Korea Society of Computer and Information
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• v.22 no.1
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• pp.143-151
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• 2017

In this paper, we suggest a method utilizing OSHW(Open Source HardWare) in order to raise up students who are competent in IT convergence and integration as a basic research to improve the university software education. Software education cannot be too much emphasized in the age of big change of Fourth Industrial Revolution. It hardly seems to have changes in the software education area of university where has to train competent technicians to be deployed into the industrial field, although software education is planned even in elementary, middle, and high school. In this situation, we expect that utilizing OSHW in software education can result in gaining a meaningful effect. However, we don't have various and systemic approach which use it as a education system component, unlike the response and necessity OSHW market. Therefore in this paper we suggest models which constitute software education environment based on OSHW and exemplify how to use it in each model. In addition, we compare and analyze each model in order to give a criteria to choice one of them according to the condition.

• Journal of Information Technology Services
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• v.11 no.4
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• pp.295-308
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• 2012

Software and Systems Product Line(SSPL) is a paradigm that has been developed and applied by European Union(EU) to achieve the productivity and competitiveness of EU industries on the world market. It is not just a simple system or software development methodology, but a sophisticated technology requiring capabilities for a high level of mass customization, platforms, processes and convergence of software and systems. EU has applied SSPL for the five selected industrial sectors including aerospace, automobile, medical equipment, consumer electronics and telecommunication equipment since 1990s and led the way to other industry sectors to stimulate the application of SSPL from 2006. In order for Korea to secure competitiveness in the manufacturing and service industries in the competitive borderless market, it is essential to gain the high level of capabilities for software development and convergence of software and systems. SSPL can be a powerful means to achieve this end. This paper discusses the paradigmatic concept of SSPL, how EU's major industries and companies have secured competitiveness through SSPL, key capabilities that are necessary for successful institutionalization of SSPL in Korea, and finally suggestions on core strategies to materialize the benefits of SSPL for Korea.

• Journal of Internet Computing and Services
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• v.22 no.5
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• pp.27-33
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• 2021

With the broad adoption of the Internet of Things (IoT) in a variety of scenarios and application services, management and orchestration entities require upgrading the traditional architecture and develop intelligent models with ultra-reliable methods. In a heterogeneous network environment, mission-critical IoT applications are significant to consider. With erroneous priorities and high failure rates, catastrophic losses in terms of human lives, great business assets, and privacy leakage will occur in emergent scenarios. In this paper, an efficient resource slicing scheme for optimizing federated learning in software-defined IoT (SDIoT) is proposed. The decentralized support vector regression (SVR) based controllers predict the IoT slices via packet inspection data during peak hour central congestion to achieve a time-sensitive condition. In off-peak hour intervals, a centralized deep neural networks (DNN) model is used within computation-intensive aspects on fine-grained slicing and remodified decentralized controller outputs. With known slice and prioritization, federated learning communications iteratively process through the adjusted resources by virtual network functions forwarding graph (VNFFG) descriptor set up in software-defined networking (SDN) and network functions virtualization (NFV) enabled architecture. To demonstrate the theoretical approach, Mininet emulator was conducted to evaluate between reference and proposed schemes by capturing the key Quality of Service (QoS) performance metrics.

• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.1-8
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• 2021

With the rapid growth of intelligent devices and communication technologies, 5G network environment has become more heterogeneous and complex in terms of service management and orchestration. 5G architecture requires supportive technologies to handle the existing challenges for improving the Quality of Service (QoS) and the Quality of Experience (QoE) performances. Among many challenges, traffic steering is one of the key elements which requires critically developing an optimal solution for smart guidance, control, and reliable system. Mobile edge computing (MEC), software-defined networking (SDN), network functions virtualization (NFV), and deep learning (DL) play essential roles to complementary develop a flexible computation and extensible flow rules management in this potential aspect. In this proposed system, an accurate flow recommendation, a centralized control, and a reliable distributed connectivity based on the inspection of packet condition are provided. With the system deployment, the packet is classified separately and recommended to request from the optimal destination with matched preferences and conditions. To evaluate the proposed scheme outperformance, a network simulator software was used to conduct and capture the end-to-end QoS performance metrics. SDN flow rules installation was experimented to illustrate the post control function corresponding to DL-based output. The intelligent steering for network communication traffic is cooperatively configured in SDN controller and NFV-orchestrator to lead a variety of beneficial factors for improving massive real-time Internet of Things (IoT) performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.3875-3891
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• 2021

In this study, a Velostat pressure sensor was manufactured to develop a plantar pressure measurement system and a C#-based application was developed to monitor and collect plantar pressure data in real time. In order to evaluate the characteristics of the proposed plantar pressure measurement system, the accuracy of plantar pressure index and personal classification was verified by comparing with MatScan, a commercial plantar pressure measurement system. As a result, the output characteristics according to the weight of the Velostat pressure sensor were evaluated and a trend line with the reliability of r² = 0.98 was detected. The Root Mean Square Error(RMSE) of the weighted area was 11.315 cm², the RMSE of the x coordinate of Center of Pressure(CoP_x) was 1.036 cm and the RMSE of the y coordinate of Center of Pressure(CoP_y) was 0.936 cm. Finally, inaccuracy of personal classification, the proposed system was 99.47% and MatScan was 96.86%. Based on the advantage of being simple to implement and capable of manufacturing at low cost, it is considered that it can be applied to various fields of measuring vital signs such as sitting posture and breathing in addition to the plantar pressure measurement system.

• International Journal of Contents
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• v.13 no.1
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• pp.38-44
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• 2017

Increased use of software and complexity of software functions, as well as shortened software quality evaluation periods, have increased the importance and necessity for automation of software testing. Automating software testing by using machine learning not only minimizes errors in manual testing, but also allows a speedier evaluation. Research on machine learning in automated software testing has so far focused on solving special problems with algorithms, leading to difficulties for the software developers and testers, in applying machine learning to software testing automation. This paper, proposes a new machine learning framework for software testing automation through related studies. To maximize the performance of software testing, we analyzed and categorized the machine learning algorithms applicable to each software test phase, including the diverse data that can be used in the algorithms. We believe that our framework allows software developers or testers to choose a machine learning algorithm suitable for their purpose.

• Journal of Convergence for Information Technology
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• v.7 no.2
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• pp.67-76
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• 2017

Along with shortening the life cycle of software utilization and supporting various types of user functions, the importance of software architecture development has been emphasized recently. If a software architecture is developed flexibly and reliably for expansion to support new functionality, it can quickly cope with new market demands. This paper proposes an architecture design method based on design recovery of open source software to reuse the software in the development of sustainable software system. When using open source software to develop a software system based on software architecture, we can develop a software system rapidly and also can improve the reliability of the system because we use the already proven open source software in the development.