• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.139-145
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• 2023

These days, protecting location privacy has become essential and really challenging, especially protecting it from smart applications and services that rely on Location-Based Services (LBS). As the technology and the services that are based on it are developed, the capability and the experience of the attackers are increased. Therefore, the traditional protection ways cannot be enough and are unable to fully ensure and preserve privacy. Previously, a hybrid approach to privacy has been introduced. It used an obfuscation technique, called Double-Obfuscation Approach (DOA), to improve the privacy level. However, this approach has some weaknesses. The most important ones are the fog nodes that have been overloaded due to the number of communications. It is also unable to prevent the Tracking and Identification attacks in the Mix-Zone technique. For these reasons, this paper introduces a developed and enhanced approach, called Multi-Obfuscation Approach (MOA that mainly depends on the communication between neighboring fog nodes to overcome the drawbacks of the previous approach. As a result, this will increase the resistance to new kinds of attacks and enhance processing. Meanwhile, this approach will increase the level of the users' privacy and their locations protection. To do so, a big enough memory is needed on the users' sides, which already is available these days on their devices. The simulation and the comparison prove that the new approach (MOA) exceeds the DOA in many Standards for privacy protection approaches.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.19-38
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• 2017

Collaborative filtering (CF) algorithm has been popularly used for implementing recommender systems. Until now, there have been many prior studies to improve the accuracy of CF. Among them, some recent studies adopt 'hybrid recommendation approach', which enhances the performance of conventional CF by using additional information. In this research, we propose a new hybrid recommender system which fuses CF and the results from the social network analysis on trust and distrust relationship networks among users to enhance prediction accuracy. The proposed algorithm of our study is based on memory-based CF. But, when calculating the similarity between users in CF, our proposed algorithm considers not only the correlation of the users' numeric rating patterns, but also the users' in-degree centrality values derived from trust and distrust relationship networks. In specific, it is designed to amplify the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the trust relationship network. Also, it attenuates the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the distrust relationship network. Our proposed algorithm considers four (4) types of user relationships - direct trust, indirect trust, direct distrust, and indirect distrust - in total. And, it uses four adjusting coefficients, which adjusts the level of amplification / attenuation for in-degree centrality values derived from direct / indirect trust and distrust relationship networks. To determine optimal adjusting coefficients, genetic algorithms (GA) has been adopted. Under this background, we named our proposed algorithm as SNACF-GA (Social Network Analysis - based CF using GA). To validate the performance of the SNACF-GA, we used a real-world data set which is called 'Extended Epinions dataset' provided by 'trustlet.org'. It is the data set contains user responses (rating scores and reviews) after purchasing specific items (e.g. car, movie, music, book) as well as trust / distrust relationship information indicating whom to trust or distrust between users. The experimental system was basically developed using Microsoft Visual Basic for Applications (VBA), but we also used UCINET 6 for calculating the in-degree centrality of trust / distrust relationship networks. In addition, we used Palisade Software's Evolver, which is a commercial software implements genetic algorithm. To examine the effectiveness of our proposed system more precisely, we adopted two comparison models. The first comparison model is conventional CF. It only uses users' explicit numeric ratings when calculating the similarities between users. That is, it does not consider trust / distrust relationship between users at all. The second comparison model is SNACF (Social Network Analysis - based CF). SNACF differs from the proposed algorithm SNACF-GA in that it considers only direct trust / distrust relationships. It also does not use GA optimization. The performances of the proposed algorithm and comparison models were evaluated by using average MAE (mean absolute error). Experimental result showed that the optimal adjusting coefficients for direct trust, indirect trust, direct distrust, indirect distrust were 0, 1.4287, 1.5, 0.4615 each. This implies that distrust relationships between users are more important than trust ones in recommender systems. From the perspective of recommendation accuracy, SNACF-GA (Avg. MAE = 0.111943), the proposed algorithm which reflects both direct and indirect trust / distrust relationships information, was found to greatly outperform a conventional CF (Avg. MAE = 0.112638). Also, the algorithm showed better recommendation accuracy than the SNACF (Avg. MAE = 0.112209). To confirm whether these differences are statistically significant or not, we applied paired samples t-test. The results from the paired samples t-test presented that the difference between SNACF-GA and conventional CF was statistical significant at the 1% significance level, and the difference between SNACF-GA and SNACF was statistical significant at the 5%. Our study found that the trust/distrust relationship can be important information for improving performance of recommendation algorithms. Especially, distrust relationship information was found to have a greater impact on the performance improvement of CF. This implies that we need to have more attention on distrust (negative) relationships rather than trust (positive) ones when tracking and managing social relationships between users.

• Progress in Medical Physics
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• v.20 no.2
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• pp.80-87
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• 2009

Phase transformation, superelastic characteristics and variation of surface residual stress were studied for Nitinol shape memory alloy through application of UNSM technology, and life extension methods of stent were also studied by using elastic resilience and corrosion resistance. Nitinol wire of ${\phi}1.778$ mm showed similar surface roughness before and after UNSM treatment, but drawing traces and micro defects were all removed by UNSM treatment. It also changed the surface residual stress from tensile to compressive values, and XRD result showed less intensive austenite peak and clear martensite and additional R-phase peaks after UNSM treatment. Fatigue resistance could be greatly improved through removal of surface defects and rearrangement of surface residual stress from tensile to compressive state, and development of surface modification system to improve not only bio-compatability but also resistance to corrosion and wear will make it possible to develop vascular stent which can be used for circulating system diseases which run first cause of death of recent Koreans.

• The Journal of the Korea Contents Association
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• v.15 no.9
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• pp.21-28
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• 2015

In the semi-conductor process, a simulation process is performed to detect defects by analyzing the behavior of the impurity through the physical quantity calculation of the inner element. In order to perform the simulation, Finite-Difference Time-Domain(FDTD) algorithm is used. The improvement of semiconductor which is composed of nanoscale elements, the size of simulation is getting bigger. Problems that a processor such as CPU or GPU cannot perform the simulation due to the massive size of matrix or a computer consist of multiple processors cannot handle a massive FDTD may come up. For those problems, studies are performed with parallel/distributed computing. However, in the past, only single type of processor was used. In GPU's case, it performs fast, but at the same time, it has limited memory. On the other hand, in CPU, it performs slower than that of GPU. To solve the problem, we implemented a computing model that can handle any FDTD simulation regardless of size on the cluster which consist of heterogeneous processors. We tested the simulation on processors using MPI libraries which is based on 'point to point' communication and verified that it operates correctly regardless of the number of node and type. Also, we analyzed the performance by measuring the total execution time and specific time for the simulation on each test.

• Journal of The Korean Association of Information Education
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• v.25 no.5
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• pp.733-740
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• 2021

The purpose of this study is to present the direction of elementary school AI education by analyzing cases of classes related to AI education in actual school settings. For this purpose, 19 classes were collected as elementary school class cases based on AI education. According to the result of analyzing the class case, it was confirmed that the class was designed in a hybrid aspect of learning content and method using AI. As a result of analyzing the achievement standards and learning goals, action verbs related to memory, understanding, and application were found in 8 classes using AI from a tool perspective. When class was divided into introduction, development, and rearrangement stages, the AI education element appeared the most in the development stage. On the other hand, when looking at the ratio of learning content and learning method of AI education elements in the development stage, the learning time for approaching AI education as a learning method was overwhelmingly high. Based on this, the following implications were derived. First, when designing the curriculum for schools and grades, it should be designed to comprehensively deal with AI as a learning content and method. Second, to supplement the understanding of AI, in the short term, it is necessary to secure the number of hours in practical subjects or creative experience activities, and in the long term, it is necessary to secure information subjects.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.377-383
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• 2021

The purpose of this study is to present the direction of elementary school AI education by analyzing cases of classes related to AI education in actual school settings. For this purpose, 19 classes were collected as elementary school class cases based on AI education. According to the result of analyzing the class case, it was confirmed that the class was designed in a hybrid aspect of learning content and method using AI. As a result of analyzing the achievement standards and learning goals, action verbs related to memory, understanding, and application were found in 8 classes using AI from a tool perspective. When class was divided into introduction, development, and rearrangement stages, the AI education element appeared the most in the development stage. On the other hand, when looking at the ratio of learning content and learning method of AI education elements in the development stage, the learning time for approaching AI education as a learning method was overwhelmingly high. Based on this, the following implications were derived. First, when designing the curriculum for schools and grades, it should be designed to comprehensively deal with AI as a learning content and method. Second, to supplement the understanding of AI, in the short term, it is necessary to secure the number of hours in practical subjects or creative experience activities, and in the long term, it is necessary to secure information subjects.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.18 no.8
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• pp.880-886
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• 2007

We have proposed a new "hybrid" envelope elimination and restoration(EER) transmitter architecture using an efficiency optimized power amplifier(PA) and modified bias modulator. The efficiency of the PA at the average drain voltage is very important for the overall transmitter efficiency because the PA operates mostly at the average power region of the modulation signal. Accordingly, the efficiency of the PA has been optimized at the region. Besides, the bias modulator has been accompanied with the emitter follower for the minimization of memory effect. A saturation amplifier, class $F^{-1}$ is built using a 5-W PEP LDMOSFET for forward-link single-carrier wideband code-division multiple-access(WCDMA) at 1-GHz. For the interlock experiment, the bias modulator has been built with the efficiency of 64.16% and peak output voltage of 31.8 V. The transmitter with the proposed PA and bias modulator has been achieved an efficiency of 44.19%, an improvement of 8.11%. Besides, the output power is enhanced to 32.33 dBm due to the class F operation and the PAE is 38.28% with ACLRs of -35.9 dBc at 5-MHz offset. These results show that the proposed architecture is a very good candidate for the linear and efficient high power transmitter.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.113-127
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• 2016

Among the techniques for recommendation, collaborative filtering (CF) is commonly recognized to be the most effective for implementing recommender systems. Until now, CF has been popularly studied and adopted in both academic and real-world applications. The basic idea of CF is to create recommendation results by finding correlations between users of a recommendation system. CF system compares users based on how similar they are, and recommend products to users by using other like-minded people's results of evaluation for each product. Thus, it is very important to compute evaluation similarities among users in CF because the recommendation quality depends on it. Typical CF uses user's explicit numeric ratings of items (i.e. quantitative information) when computing the similarities among users in CF. In other words, user's numeric ratings have been a sole source of user preference information in traditional CF. However, user ratings are unable to fully reflect user's actual preferences from time to time. According to several studies, users may more actively accommodate recommendation of reliable others when purchasing goods. Thus, trust relationship can be regarded as the informative source for identifying user's preference with accuracy. Under this background, we propose a new hybrid recommender system that fuses CF and social network analysis (SNA). The proposed system adopts the recommendation algorithm that additionally reflect the result analyzed by SNA. In detail, our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and trust relationship information between users when calculating user similarities. For this, our system creates and uses not only user-item rating matrix, but also user-to-user trust network. As the methods for calculating user similarity between users, we proposed two alternatives - one is algorithm calculating the degree of similarity between users by utilizing in-degree and out-degree centrality, which are the indices representing the central location in the social network. We named these approaches as 'Trust CF - All' and 'Trust CF - Conditional'. The other alternative is the algorithm reflecting a neighbor's score higher when a target user trusts the neighbor directly or indirectly. The direct or indirect trust relationship can be identified by searching trust network of users. In this study, we call this approach 'Trust CF - Search'. To validate the applicability of the proposed system, we used experimental data provided by LibRec that crawled from the entire FilmTrust website. It consists of ratings of movies and trust relationship network indicating who to trust between users. The experimental system was implemented using Microsoft Visual Basic for Applications (VBA) and UCINET 6. To examine the effectiveness of the proposed system, we compared the performance of our proposed method with one of conventional CF system. The performances of recommender system were evaluated by using average MAE (mean absolute error). The analysis results confirmed that in case of applying without conditions the in-degree centrality index of trusted network of users(i.e. Trust CF - All), the accuracy (MAE = 0.565134) was lower than conventional CF (MAE = 0.564966). And, in case of applying the in-degree centrality index only to the users with the out-degree centrality above a certain threshold value(i.e. Trust CF - Conditional), the proposed system improved the accuracy a little (MAE = 0.564909) compared to traditional CF. However, the algorithm searching based on the trusted network of users (i.e. Trust CF - Search) was found to show the best performance (MAE = 0.564846). And the result from paired samples t-test presented that Trust CF - Search outperformed conventional CF with 10% statistical significance level. Our study sheds a light on the application of user's trust relationship network information for facilitating electronic commerce by recommending proper items to users.

• Korean Journal of Food Science and Technology
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• v.49 no.6
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• pp.649-658
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• 2017

The potential of the ethyl acetate fraction from Pteridium aquilinum (EFPA) to improve the cognitive function in high-fat diet (HFD)-induced diabetic mice was investigated. EFPA-treatment resulted in a significant improvement in the spatial, learning, and memory abilities compared to the HFD group in behavioral tests, including the Y-maze, passive avoidance, and Morris water maze. The diabetic symptoms of the EFPA-treated groups, such as fasting glucose and glucose tolerance, were alleviated. The administration of EFPA reduced the acetylcholinesterase (AChE) activity and malondialdehyde (MDA) content in mice brains, but increased the acetylcholine (ACh) and superoxide dismutase (SOD) levels. Finally, kaempferol-3-o-glucoside, a major physiological component of EFPA, was identified by using high-performance liquid chromatography coupled with a hybrid triple quadrupole-linear ion trap mass spectrometer (QTRAP LC-MS/MS).