Emotion and Adaptation

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

Analyzing linguistic data: a practical introduction to statistics using R

PROMETHEE: A comprehensive literature review on methodologies and applications

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.

/pdf/the-effects-of-integrating-mobile-devices-with-teaching-and-279uya3lib.pdf

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

Handbook of the International Phonetic Association: A Guide to the Use of the International Phonetic Alphabet

https://link.springer.com/content/pdf/10.1007/s11042-022-13883-z.pdf

Retraction Note: A study on the control of smart device set-top box using image based reverse mirroring technology

This study introduced the work of analyzing the meaning by using the computer information processing method. We extracted the painting poetry of Mukjukdo (Bamboo Paintings) in the early Joseon Dynasty from the Hanguk Munjip Chonggan (Korean Literary Collections in Classical Chinese) of Hanguk Gojeon Jonghap DB (Korea Classics DB). Through the data mining method, we divided the types by extracting and cataloging the painting poetry from the Korean classical literature. Then, the scope was narrowed down to painting poetry of Mukjukdo in the early Joseon Dynasty, and the text was analyzed in units of syllable corpus. 

A Study on Data Mining for Type of Korean Painting Poetry

All of system and process in this world is for human. Thus, system designers and developers consider the human characteristics at first when they think and design their system. For this consideration, user oriented learning system was introduced in education area. Especially, AEHS aims to provide learning contents that fits the individual learning style of users. That is, AEHS analyzes users' learning patterns and their learning process and according to the analyzed learning performance, the system identifies the most appropriate process for each user. But, in AEHS, it is difficult for users to choose learning contents fitted for their preference or learning ability. Also, system development process has many changes from traditional ways to new technology, ubiquitous and cloud computing. This change is to provide more convenient and efficient system process to developers and users. In this paper, we proposed AEHS considering user's characteristics in cloud computing environment. For this environment, we use the process as a service for learning system. In order to consider user's characteristics, we use a method that enables users to choose a content delivery and a difficulty level of the learning contents according to their styles such as gender, learning experience and preferences. For this process, we added the module, learning contents preference and learning difficulty preference, to user profile of AEHS.

Service based AEHS for human centric learning environments

In this paper, we wish to propose method to provide personalization recommendation service that is optimized to user using Service Delivery Platform in IMS environment. So we studied method to create personalization recommendation service base on web service. Creation process of personalization recommendation service for user must analyze user basis information and service use pattern in IMS Environment. And we can create and offer suitable personalization recommendation service with this. I can provide integration service using SDP that is development to provide more effective personalization recommendation service in IMS environment. And we are thought to provide order style service to user according to recommendation algorithm and Guideline.

A Personalization Recommendation Service Using Service Delivery Platform in IMS Networks

https://link.springer.com/content/pdf/10.1007/s11042-022-13872-2.pdf

Hwa-Young Jeong

Papers

Retraction Note: A study on the control of smart device set-top box using image based reverse mirroring technology

A Study on Data Mining for Type of Korean Painting Poetry

Service based AEHS for human centric learning environments

A Personalization Recommendation Service Using Service Delivery Platform in IMS Networks

Retraction Note: The big data visualization technology based ecosystem cycle on high speed network