• BMB Reports
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• v.52 no.6
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• pp.397-402
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• 2019

Middle East respiratory syndrome coronavirus (MERS-CoV) uses the spike (S) glycoprotein to recognize and enter target cells. In this study, we selected two epitope peptide sequences within the receptor binding domain (RBD) of the MERS-CoV S protein. We used a complex consisting of the epitope peptide of the MERS-CoV S protein and CpG-DNA encapsulated in liposome complex to immunize mice, and produced the monoclonal antibodies 506-2G10G5 and 492-1G10E4E2. The western blotting data showed that both monoclonal antibodies detected the S protein and immunoprecipitated the native form of the S protein. Indirect immunofluorescence and confocal analysis suggested strong reactivity of the antibodies towards the S protein of MERS-CoV virus infected Vero cells. Furthermore, the 506-2G10G5 monoclonal antibody significantly reduced plaque formation in MERS-CoV infected Vero cells compared to normal mouse IgG and 492-1G10E4E2. Thus, we successfully produced a monoclonal antibody directed against the RBD domain of the S protein which could be used in the development of diagnostics and therapeutic applications in the future.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.5
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• pp.25-34
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• 2009

Many conference systems over the Internet require authenticated group key agreement (AGKA) for secure and reliable communication. After Shamir [1] proposed the ID-based cryptosystem in 1984, ID-based AGKA protocols have been actively studied because of the simple public key management. In 2006, Zhou et al. [12] proposed two-round ID-based AGKA protocol which is very efficient in communication and computation complexity. However, their protocol does not provide user identification and suffers from the impersonation attack by malicious participants. In this paper, we propose improved ID-based AGKA protocol to prevent impersonation attack from Zhou et al.'s protocol. In our protocol, the malicious insider cannot impersonate another participants even if he knows the ephemeral group secret value. Moreover, our protocol reduces the computation cost from Zhou et al.'s protocol.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.742-756
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• 2022

A flood of information has occurred with the rise of the internet and digital devices in the fourth industrial revolution era. Every millisecond, massive amounts of structured and unstructured data are generated; smartphones, wearable devices, sensors, and self-driving cars are just a few examples of devices that currently generate massive amounts of data in our daily. Machine learning has been considered an approach to support and recognize patterns in data in many areas to provide a convenient way to other sectors, including the healthcare sector, government sector, banks, military sector, and more. However, the conventional machine learning model requires the data owner to upload their information to train the model in one central location to perform the model training. This classical model has caused data owners to worry about the risks of transferring private information because traditional machine learning is required to push their data to the cloud to process the model training. Furthermore, the training of machine learning and deep learning models requires massive computing resources. Thus, many researchers have jumped to a new model known as "Federated Learning". Federated learning is emerging to train Artificial Intelligence models over distributed clients, and it provides secure privacy information to the data owner. Hence, this paper implements Federated Averaging with a Deep Neural Network to classify the handwriting image and protect the sensitive data. Moreover, we compare the centralized machine learning model with federated averaging. The result shows the centralized machine learning model outperforms federated learning in terms of accuracy, but this classical model produces another risk, like privacy concern, due to the data being stored in the data center. The MNIST dataset was used in this experiment.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.11
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• pp.473-478
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• 2022

Evaluation learning based on code testing is becoming a popular solution in programming education via Online judge(OJ). In the recent past, many papers have been published on how to detect plagiarism through source code similarity analysis to support OJ. However, deep learning-based research to support automated tutoring is insufficient. In this paper, we propose Input & Output side FiLM models to predict whether the input code will pass or fail. By applying Feature-wise Linear Modulation(FiLM) technique to GRU, our model can learn combined information of Java byte codes and problem information that it tries to solve. On experimental design, a balanced sampling technique was applied to evenly distribute the data due to the occurrence of asymmetry in data collected by OJ. Among the proposed models, the Input Side FiLM model showed the highest performance of 73.63%. Based on result, it has been shown that students can check whether their codes will pass or fail before receiving the OJ evaluation which could provide basic feedback for improvements.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.549-561
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• 2022

In the Khmer writing system, the Khmer script is the official letter of Cambodia, written from left to right without a space separator; it is complicated and requires more analysis studies. Without clear standard guidelines, a space separator in the Khmer language is used inconsistently and informally to separate words in sentences. Therefore, a segmented method should be discussed with the combination of the future Khmer natural language processing (NLP) to define the appropriate rule for Khmer sentences. The critical process in NLP with the capability of extensive data language analysis necessitates applying in this scenario. One of the essential components in Khmer language processing is how to split the word into a series of sentences and count the words used in the sentences. Currently, Microsoft Word cannot count Khmer words correctly. So, this study presents a systematic library to segment Khmer phrases using the bi-directional maximal matching (BiMM) method to address these problematic constraints. In the BiMM algorithm, the paper focuses on the Bidirectional implementation of forward maximal matching (FMM) and backward maximal matching (BMM) to improve word segmentation accuracy. A digital or prefix tree of data structure algorithm, also known as a trie, enhances the segmentation accuracy procedure by finding the children of each word parent node. The accuracy of BiMM is higher than using FMM or BMM independently; moreover, the proposed approach improves dictionary structures and reduces the number of errors. The result of this study can reduce the error by 8.57% compared to FMM and BFF algorithms with 94,807 Khmer words.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.377-379
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• 2022

POI refers to the point of Interest in Location-Based Social Networks (LBSNs). With the rapid development of mobile devices, GPS, and the Web (web2.0 and 3.0), LBSNs have attracted many users to share their information, physical location (real-time location), and interesting places. The tremendous demand of the user in LBSNs leads the recommendation systems (RSs) to become more widespread attention. Recommendation systems assist users in discovering interesting local attractions or facilities and help social network service (SNS) providers based on user locations. Therefore, it plays a vital role in LBSNs, namely POI recommendation system. In the machine learning model, most of the training data are stored in the centralized data storage, so information that belongs to the user will store in the centralized storage, and users may face privacy issues. Moreover, sharing the information may have safety concerns because of uploading or sharing their real-time location with others through social network media. According to the privacy concern issue, the paper proposes a recommendation model to prevent user privacy and eliminate traditional RS problems such as cold-start and data sparsity.

• 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 Intelligence and Information Systems
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• v.17 no.4
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• pp.255-277
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• 2011

Product Service System(PSS), which is an integrated combination of product and service, provides new value to customer and makes companies sustainable as well. The objective of this paper draws Critical Successful Factors(CSF) of PSS through multiple case study. First, we review various concepts and types in PSS and Platform business literature currently available on this topic. Second, after investigating various cases with the characteristics of PSS and platform business, we select four cases of 'iPod of Apple', 'Kindle of Amazon', 'Zune of Microsoft', and 'e-book reader of Sony'. Then, the four cases are categorized as successful and failed cases according to criteria of case selection and PSS classification. We consider two methodologies for the case selection, i.e., 'Strategies for the Selection of Samples and Cases' proposed by Bent(2006) and the seven case selection procedures proposed by Jason and John(2008). For case selection, 'Stratified sample and Paradigmatic cases' is adopted as one of several options for sampling. Then, we use the seven case selection procedures such as 'typical', 'diverse', 'extreme', 'deviant', 'influential', 'most-similar', and 'mostdifferent' and among them only three procedures of 'diverse', 'most?similar', and 'most-different' are applied for the case selection. For PSS classification, the eight PSS types, suggested by Tukker(2004), of 'product related', 'advice and consulancy', 'product lease', 'product renting/sharing', 'product pooling', 'activity management', 'pay per service unit', 'functional result' are utilized. We categorize the four selected cases as a product oriented group because the cases not only sell a product, but also offer service needed during the use phase of the product. Then, we analyze the four cases by using cross-case pattern that Eisenhardt(1991) suggested. Eisenhardt(1991) argued that three processes are required for avoiding reaching premature or even false conclusion. The fist step includes selecting categories of dimensions and finding within-group similarities coupled with intergroup difference. In the second process, pairs of cases are selected and listed. The second step forces researchers to find the subtle similarities and differences between cases. The third process is to divide the data by data source. The result of cross-case pattern indicates that the similarities of iPod and Kindle as successful cases are convenient user interface, successful plarform strategy, and rich contents. The differences between the successful cases are that, wheares iPod has been recognized as the culture code, Kindle has implemented a low price as its main strategy. Meanwhile, the similarities of Zune and PRS series as failed cases are lack of sufficient applications and contents. The differences between the failed cases are that, wheares Zune adopted an undifferentiated strategy, PRS series conducted high-price strategy. From the analysis of the cases, we generate three hypotheses. The first hypothesis assumes that a successful PSS system requires convenient user interface. The second hypothesis assumes that a successful PSS system requires a reciprocal(win/win) business model. The third hypothesis assumes that a successful PSS system requires sufficient quantities of applications and contents. To verify the hypotheses, we uses the cross-matching (or pattern matching) methodology. The methodology matches three key words (user interface, reciprocal business model, contents) of the hypotheses to the previous papers related to PSS, digital contents, and Information System (IS). Finally, this paper suggests the three implications from analyzed results. A successful PSS system needs to provide differentiated value for customers such as convenient user interface, e.g., the simple design of iTunes (iPod) and the provision of connection to Kindle Store without any charge. A successful PSS system also requires a mutually benefitable business model as Apple and Amazon implement a policy that provides a reasonable proft sharing for third party. A successful PSS system requires sufficient quantities of applications and contents.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.10
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• pp.920-929
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• 2014

Recently, embedded system which demand is explosively increasing in the fields of communication, traffic, medical and industry facilities, expands to cyber physical system (CPS) which monitors and controls the networked embedded systems. In addition, internet of things(IoT) technology using wearable devices such as Google Glass, Samsung Galaxy Gear and Sony Smart Watch are gaining attention. In this situation, Samsung Smart Home and LG Home Chat are released one after another. However, since these services can be available only between smart phones and home appliances, there is a disadvantage that information cannot be passed to other terminals without commercial global messaging server. In this paper, to solve above issues, we propose the structure of an indoor location network based on unit space, which prevents the information of the devices or each individual person from leaking to outside and can selectively communicate to all existent terminals in the network using IoT chatting. Also, it is possible to control general devices and prevent external leakage of private information.

• Cartoon and Animation Studies
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• s.37
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• pp.491-508
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• 2014

As number of making 3D feature animation films is increasing, 3D production pipeline become more complicated and more artists are needed than before. Major studios in foreign countries, in burden of producing high quality films with limited amount of budget and time, have been handling such difficulties by hiring technical directors in each department such as animation, rigging, cloth hair, and effect. Technical director is new occupation which appears after trend of producing animation is changed from 2D to 3D. Importance of technical director is increasing in respect to studios' needs which are related to complication in production time, manpower, budget, and production pipeline. This research is based on the researcher's work experience as an animation TD at Walt Diseny Animation Studio and Sony Pictures Imageworks, interview with working professionals, and related books and thesis. It focuses on the role of animation technical director in Disney's 3D feature animation film from two perspectives, 'Designing Production Pipeline' and 'Analyzing Problem of Shot'. Animation technical directors design and test production pipeline so that they can detect and solve problems that may arise in production process as early as possible. They not only analyze numerous problems of characters or shots limited to animation department but also in other departments such as modeling, mapping, character rigging, cloth, hair, lighting, rendering, software development in order to support artists to complete their shots according to the production schedule. In accordance with recent trend of increasing number of 3D feature animation film production in South Korea and collaboration with foreign studios outside of South Korea, it is vital to train animation technical directors who can develop production pipeline, analyze various problems of shots and characters to escalate efficiency in production.