Hwa-Young Jeong

Bio: Hwa-Young Jeong is an academic researcher from Kyung Hee University. The author has contributed to research in topics: Cloud computing & The Internet. The author has an hindex of 16, co-authored 142 publications receiving 792 citations.

A secure authentication scheme with anonymity for session initiation protocol using elliptic curve cryptography

Abstract: As a signaling protocol for controlling communication on the internet, establishing, maintaining, and terminating the sessions, the Session Initiation Protocol (SIP) is widely used in the world of multimedia communication. To ensure communication security, many authentication schemes for the SIP have been proposed. However, those schemes cannot ensure user privacy since they cannot provide user anonymity. To overcome weaknesses in those authentication schemes with anonymity for SIP, we propose an authentication scheme with anonymity using elliptic curve cryptograph. By a sophisticated analysis of the security of the proposed protocol, we show that the proposed scheme not only overcomes weaknesses in previous schemes but also is very efficient. Therefore, it is suitable for applications with higher security requirements.

Training soft skills via e‐learning: international chain hotels

Abstract: Purpose – The purpose of this study is to investigate the perceived importance of soft skills for hotel employees, their willingness to use electronic learning (e‐learning) as a training tool to improve their soft skills, and the impact of hotel employees' individual characteristics (i.e. motivation, self‐efficacy, technology anxiety) on their intentions to use e‐learning across different age groups.Design/methodology/approach – The sample was randomly selected from hotel employees working at various upscale international chain hotels in South Korea. The data were analyzed using structural equation modeling (SEM) to simultaneously measure the impact of four independent variables on the intention to use e‐learning for both younger and older learners.Findings – The analysis revealed that responsibility, self‐esteem, sociability, and working with diverse groups were rated more important by younger hotel employees. The results suggest that learners who have higher extrinsic motivations in using e‐learning wil...

Personalized Learning Course Planner with E-learning DSS using user profile

Abstract: Highlights? We design Personalized Learning Course Planner (PLCP) applied to E-learning systems. ? PLCP analyzes student's implicit requirements. ? Learning courses are organized according to characteristics and educational levels of students. ? English learning system consisting of PLCP is implemented. ? Learning effectiveness and student satisfaction is improved in experiment results. Various methods of E-learning systems, based on information and communications, and geared towards improving learning effectiveness and students' attention span, have been studied. However, most E-learning systems force students to follow the learning course or content established by a teacher. These methods are convenient, but they limit the effectiveness of E-learning.To overcome this limitation and increase effective learning, new techniques that reflect alternative learning styles, such as adaptive learning and personalized learning, have been studied. In this study, we proposed a Personalized Learning Course Planner (PLCP) that allows students to easily select the learning course they desire. User profile data was collected from the students' initial priorities about learning contents as well as the test scores after their study. E-Learning Decision Support System (EL-DSS) in PLCP suggests an appropriate learning course organization, according to calculated results based on the user profile data.To verify the effectiveness of the proposed system, we implemented an English learning system consisting of PLCP. We conducted an experiment with 30 university students and evaluated students' satisfaction by questionnaire analysis. The results indicate that the proposed system improved learning effectiveness and student satisfaction. Further investigation of the participants indicated that suggesting a learning course suitable for students' previous test scores and priorities encouraged students to concentrate on the lesson.

KDE based outlier detection on distributed data streams in multimedia network

Abstract: Multimedia networks hold the promise of facilitating large-scale, real-time data processing in complex environments. Their foreseeable applications will help protect and monitor military, environmental, safety-critical, or domestic infrastructures and resources. Cloud infrastructures promise to provide high performance and cost effective solutions to large scale data processing problems. This paper focused on the outlier detection over distributed data stream in real time, proposed kernel density estimation (KDE) based outlier detection algorithm KDEDisStrOut in Storm, firstly formalized the problem of outlier detection using the kernel density estimation technique and update the transported data incrementally between the child node and the coordinator node which reduces the communication cost. Then the paper adopted the exponential decay policy to keep pace with the transient and evolving natures of stream data and changed the weight of different data in the sliding window adaptively made the data analysis more reasonable. Theoretical analysis and experiments on Storm with synthetic and real data show that the KDEDisStrOut algorithm is efficient and effective compared with existing outlier detection algorithms, and more suitable for data streams.

Best web service selection based on the decision making between QoS criteria of service

Abstract: Recently, extensive studies have been carried out on web service standards because of the necessity of developing large-scale applications in open environments. In particular, they enable services to be dynamically bound. However, current techniques fail to address the critical problem of selecting the right service instances. Service selection should be determined based on customer preferences and service level. We propose a best web service selection method which helps to find a service provider providing the optimal quality. Web service selection process was described with multi-criteria decision making approach(e.g. PROMETHEE) on the basis of evaluated values of qualities and the defined service level. The PROMETHEE method has advantages in comparison with the others(e.g. MAUT, AHP) as follows. First, the PROMETHEE method classifies alternatives which is difficult to be compared because of a trade-off relation of evaluation standards as non-comparable alternatives. Second, the PROMETHEE method is different from the AHP method in that there's no need to perform a pair-wise comparison again when comparative alternatives are added or deleted. Therefore, this method is a suitable approach in the web service selection problem. Because the problem has a lot of quality parameters which are measured and evaluated at the same time and frequently induces a drop of another quality parameter by the improvement of one quality attribute. Consequently, our approach enables applications to be dynamically configured at runtime in a manner that continually adapts to the preferences of the customers. We verify our approach through case study.

Analyzing linguistic data: a practical introduction to statistics using R

Abstract: The complete title of this book runs ‘Analyzing Linguistic Data: A Practical Introduction to Statistics using R’ and as such it very well reflects the purpose and spirit of the book. The author guides the reader in about 350 pages from descriptive and basic statistical methods over classification and clustering to (generalised) linear and mixed models. Each of the methods is introduced in the context of concrete linguistic problems and demonstrated on exciting datasets from current research in the language sciences. In line with its practical orientation, the book focuses primarily on using the methods and interpreting the results. This implies that the mathematical treatment of the techniques is held at a minimum if not absent from the book. In return, the reader is provided with very detailed explanations on how to conduct the analyses using R [1]. The first chapter sets the tone being a 20-page introduction to R. For this and all subsequent chapters, the R code is intertwined with the chapter text and the datasets and functions used are conveniently packaged in the languageR package that is available on the Comprehensive R Archive Network (CRAN). With this approach, the author has done an excellent job in enabling researchers and students alike to reproduce the analyses and learn by doing. Another quality as a textbook is the fact that every chapter ends with Workbook sections where the user is invited to exercise his or her analysis skills on supplemental datasets. Full solutions including code, results and comments are given in Appendix A (30 pages). Instructors are therefore very well served by this text, although they might want to balance the book with some more mathematical treatment depending on the target audience. After the introductory chapter on R, the book opens on graphical data exploration. Chapter 3 treats probability distributions and common sampling distributions. Under basic statistical methods (Chapter 4), distribution tests and tests on means and variances are covered. Chapter 5 deals with clustering and classification. Strangely enough, the clustering section has material on PCA, factor analysis, correspondence analysis and includes only one subsection on clustering, devoted notably to hierarchical partitioning methods. The classification part deals with decision trees, discriminant analysis and support vector machines. The regression chapter (Chapter 6) treats linear models, generalised linear models, piecewise linear models and a substantial section on models for lexical richness. The final chapter on mixed models is particularly interesting as it is one of the few text book accounts that introduce the reader to using the (innovative) lme4 package of Douglas Bates which implements linear mixed-effects models. Moreover, the case studies included in this

The effects of integrating mobile devices with teaching and learning on students' learning performance

Abstract: Mobile devices such as laptops, personal digital assistants, and mobile phones have become a learning tool with great potential in both classrooms and outdoor learning. Although there have been qualitative analyses of the use of mobile devices in education, systematic quantitative analyses of the effects of mobile-integrated education are lacking. This study performed a meta-analysis and research synthesis of the effects of integrated mobile devices in teaching and learning, in which 110 experimental and quasiexperimental journal articles published during the period 1993-2013 were coded and analyzed. Overall, there was a moderate mean effect size of 0.523 for the application of mobile devices to education. The effect sizes of moderator variables were analyzed and the advantages and disadvantages of mobile learning in different levels of moderator variables were synthesized based on content analyses of individual studies. The results of this study and their implications for both research and practice are discussed. This is a meta-analysis and research synthesis study for mobile-integrated education.110 published journal articles that were written over a 20-year period were coded and analyzed.The application of mobile devices to education has a moderate mean effect size.The effect sizes of moderator variables were analyzed.The benefits and drawbacks of mobile learning were synthesized.