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Locomotion methods are user interaction approaches that assist users in navigating the virtual environment when using virtual reality display device of HMDs (head-mounted displays). Most previous studies have been focused on the locomotion techniques with joystick and teleport in general and gaming context. However, there are lack study of locomotion techniques in virtual museum for context of culture heritage. In this study, this paper presents a comparative evaluation with sixty participants of the three locomotions: Joystick Controller, Point & Teleport and Arm Swinging to measure sickness, presence and flow state. In order to evaluate, this paper developed the virtual museum of Himmapan animals (https://angkaew.com/Hinmapan_vr/) which consisted of digital paint art, digitization objects, animation and digitization books. The results showed that Point & teleport less sickness of nausea than other techniques and Point & teleport also highest in perceived enjoyment than other techniques. However, we found no significant difference in presence. Finally, this paper suggested implementing Point & Teleport locomotion techniques in the application of virtual museums

In this study the effects of intrinsic motivation and learning competency of virtual realitybased learning is compared to traditional text-based learning. In the study 28 students were involved from the second and third years of the library and information science faculty. The application of the fully immersive virtual 3D environment of Wieng Yong House was developed to support the virtual realitybased learning in the context of museums for culture heritage. To verify the hypothesis, the control group (traditional text-based learning), consisted of 14 students, used the course module of metadata for culture heritage in the museum whereas the experimental group (control group), consisted of 14 students, was provided virtual reality-based learning in the virtual museum. The tools of measurement were the pre-test/post-test quiz for learning competency and Intrinsic Motivation Inventory (IMI) with interview. The statistical model of paired sampled t-test assumptions was chosen for the statistical investigation for the hypothesis. The results suggest that constructing a virtual reality for learning in the context of a museum for cultural heritage might be a good way to enhance intrinsic motivation but cannot sufficiently and significantly improve the student's learning competency.

Cooking is a popular activity. Most people recently use recipes on the Website, which show the ingredients that are needed for a dish and the method of cooking. However, some of the listed ingredients sometimes are not able to prepare or use for cooking for some reasons: allergies, personal preference, or some ingredients are not available in the kitchen, etc. Therefore, it should recommend the alternative-ingredients that are similar to an exchange-ingredient and suitable with remained ingredients in the recipe. This paper proposes a recommendation system of alternative ingredients for Thai recipes. The recommended method is based on the combination of the smoothed correlation weight function and graph-based approach on the Thai recipe database. Our research contributes to enhancing the cooking beginners to cook Thai food and prepare ingredients conveniently by using the alternative-ingredients.

Online social networks like Twitter have emerged as the most popular way to discuss what is happening about real-world events. The topics related to the event may change over time due to several factors. People’s interests in the event are changed over a period of time. This paper we proposed an approach for sub-events tracking in micro-blog service (i.e., Twitter). Our algorithm can identify sub-events and track a sequence of events in chronological order. We utilizes an approach of Latent Dirichlet Allocation (LDA) for sub-event detection. Sub-event tracking task is proposed based on the relationships between sub-events by adopting community evolution discovery techniques. In our experiments, we demonstrate the applicability of our method by using Twitter messages related to the 2018 Protests in Paris.

Textual document set has become an important and rapidly growing information source in the web. Text classification is one of the crucial technologies for information organisation and management. Text classification has become more and more important and attracted wide attention of researchers from different research fields. In this paper, many feature selection methods, the implement algorithms and applications of text classification are introduced firstly. However, because there are much noise in the knowledge extracted by current data-mining techniques for text classification, it leads to much uncertainty in the process of text classification which is produced from both the knowledge extraction and knowledge usage, therefore, more innovative techniques and methods are needed to improve the performance of text classification. It has been a critical step with great challenge to further improve the process of knowledge extraction and effectively utilization of the extracted knowledge. Rough Set decision making approach is proposed to use Rough Set decision techniques to more precisely classify the textual documents which are difficult to separate by the classic text classification methods. The purpose of this paper is to give an overview of existing text classification technologies, to demonstrate the Rough Set concepts and the decision making approach based on Rough Set theory for building more reliable and effective text classification framework with higher precision, to set up an innovative evaluation metric named CEI which is very effective for the performance assessment of the similar research, and to propose a promising research direction for addressing the challenging problems in text classification, text mining and other relative fields.

Most recommender systems attempt to use collaborative filtering, content-based filtering or hybrid approach to recommend items to new users. Collaborative filtering recommends items to new users based on their similar neighbours, and content-based filtering approach tries to recommend items that are similar to new users' profiles. The fundamental issues include how to profile new users, and how to deal with the over-specialization in content-based recommender systems. Indeed, the terms used to describe items can be formed as a concept hierarchy. Therefore, we aim to describe user profiles or information needs by using concepts vectors. This paper presents a new method to acquire user information needs, which allows new users to describe their preferences on a concept hierarchy rather than rating items. It also develops a new ranking function to recommend items to new users based on their information needs. The proposed approach is evaluated on Amazon book datasets. The experimental results demonstrate that the proposed approach can largely improve the effectiveness of recommender systems.