Examining Correlates of Attitudes toward Gay Men Among Vietnamese College Students

The current study investigated correlates of attitudes toward gay men among 455 Vietnamese students (219 females). The Vietnamese Version of Attitudes toward Gay Men scale (VATG) was used as the dependent variable in four hierarchical multiple regression analyses, with gender, traditional male role attitudes, knowledge about gay issues, and number of gay friends as the independent variables. The VATG formed four factors: Social Distance, Positive Attitudes, Moral Condemnation, and Effeminacy. The results indicated that knowledge about gay issues predicted four subscales of the VATG. These relationships were moderated by gender. Effects of traditional male role attitudes on four subscales of the VATG were significant for both genders. Social Distance and Moral Condemnation were predicted by the number of gay friends. This study suggested a potential role of knowledge about gay issues in improving Vietnamese attitudes toward gay men

ベトナムにおける男性同性愛者に対する態度とそれに関連する要因および介入効果

内容の要約広島大学(Hiroshima University)博士(心理学)Doctor of Philosophy in Psychologydoctora

A BRANCH AND BOUND ALGORITHM FOR WORKFLOW SCHEDULING

Nowadays, people are connected to the Internet and use different Cloud solutions to store, process and deliver data. The Cloud consists of a collection of virtual servers that promise to provision on-demand computational and storage resources when needed. Workflow data is becoming an ubiquitous term in both science and technology and there is a strong need for new  tools and techniques to process and analyze large-scale complex datasets that are growing exponentially. scientific workflow is a sequence of connected tasks with large data transfer from parent task to children tasks. Workflow scheduling is the activity of assigning tasks to execution on servers and satisfying resource constraints and this is an NP-hard problem. In this paper, we propose a scheduling algorithm for workflow data that is derived from the Branch and Bound Algorithm

Real-Time 6DOF Pose Relocalization for Event Cameras with Stacked Spatial LSTM Networks

We present a new method to relocalize the 6DOF pose of an event camera solely based on the event stream. Our method first creates the event image from a list of events that occurs in a very short time interval, then a Stacked Spatial LSTM Network (SP-LSTM) is used to learn the camera pose. Our SP-LSTM is composed of a CNN to learn deep features from the event images and a stack of LSTM to learn spatial dependencies in the image feature space. We show that the spatial dependency plays an important role in the relocalization task and the SP-LSTM can effectively learn this information. The experimental results on a publicly available dataset show that our approach generalizes well and outperforms recent methods by a substantial margin. Overall, our proposed method reduces by approx. 6 times the position error and 3 times the orientation error compared to the current state of the art. The source code and trained models will be released.Comment: 7 pages, 5 figure

LAPFormer: A Light and Accurate Polyp Segmentation Transformer

Polyp segmentation is still known as a difficult problem due to the large variety of polyp shapes, scanning and labeling modalities. This prevents deep learning model to generalize well on unseen data. However, Transformer-based approach recently has achieved some remarkable results on performance with the ability of extracting global context better than CNN-based architecture and yet lead to better generalization. To leverage this strength of Transformer, we propose a new model with encoder-decoder architecture named LAPFormer, which uses a hierarchical Transformer encoder to better extract global feature and combine with our novel CNN (Convolutional Neural Network) decoder for capturing local appearance of the polyps. Our proposed decoder contains a progressive feature fusion module designed for fusing feature from upper scales and lower scales and enable multi-scale features to be more correlative. Besides, we also use feature refinement module and feature selection module for processing feature. We test our model on five popular benchmark datasets for polyp segmentation, including Kvasir, CVC-Clinic DB, CVC-ColonDB, CVC-T, and ETIS-LaribComment: 7 pages, 7 figures, ACL 2023 underrevie

Online Shopping in an Emerging Market

The emerging market is currently witnessing the active development of online shopping. This study was conducted to analyze the key factors affecting the intention of customers to purchase in Vietnam. This study is conducted by two methods, including "secondary research method" and "quantitative method" for research. Firstly, the author uses the "secondary research method" to refer to previous academic sources for this research. Secondly, the author collected a sample of 349 volunteers in Vietnam who have an understanding and interest in e-commerce. Perceived Usefulness, Perceived Ease of Use, Perceived Transaction Security are reported to have a positive relationship with online purchase intention. They are significant factors affecting the purchase intention of Vietnamese online customers. Other demographic factors can affect online shopping intention, such as age and income, so research cannot represent all customers. Businesses can use the findings to further understand about emerging markets. This trend is the basis for business managers to develop customer attraction strategies and improve the quality of online shopping services. The potential for online shopping is becoming more attractive, especially in emerging markets. This study is in line with the trend of the online shopping industry. The results of this study can help marketers understand more about customers' intentions, thereby building long-term relationships between businesses and customers

FACTORS AFFECTING THE ACADEMIC RESULTS OF MASTER STUDENTS IN MATHEMATICS EDUCATION AT CAN THO UNIVERSITY, VIETNAM: A SURVEY STUDY

The study results were based on the survey data of 24 students  studying the master program in math education at Can Tho University, Vietnam. We used the questionnaire to find out the factors affecting students' learning outcomes: Learning time, learning conditions, learning environment, personal level, learning methods, collaborative learning, learning attitudes. The results show factors such as learning conditions, learning environment, time for leaning, qualifications, teaching methods, learning methods, cooperation in learning, attitude in learning are factors that significantly affect the learning of master students in Mathematics education. Therefore, universities with high-level training programs should have adequate facilities for students' learning; lecturers know how to use teaching methods to promote self-study and self-study for students, improve their ability to work independently, the ability to cooperate in the learning and research process of students. In other words, universities must uphold  their responsibilities when implementing intensive training programs, helping learners with necessary competencies as expected of the community and society.  Article visualizations

Probabilistic task modelling for meta-learning

We propose probabilistic task modelling -- a generative probabilistic model for collections of tasks used in meta-learning. The proposed model combines variational auto-encoding and latent Dirichlet allocation to model each task as a mixture of Gaussian distribution in an embedding space. Such modelling provides an explicit representation of a task through its task-theme mixture. We present an efficient approximation inference technique based on variational inference method for empirical Bayes parameter estimation. We perform empirical evaluations to validate the task uncertainty and task distance produced by the proposed method through correlation diagrams of the prediction accuracy on testing tasks. We also carry out experiments of task selection in meta-learning to demonstrate how the task relatedness inferred from the proposed model help to facilitate meta-learning algorithms.Comment: Accepted at UAI 202