A Novel Explainable Artificial Intelligence Model in Image Classification problem

In recent years, artificial intelligence is increasingly being applied widely in many different fields and has a profound and direct impact on human life. Following this is the need to understand the principles of the model making predictions. Since most of the current high-precision models are black boxes, neither the AI scientist nor the end-user deeply understands what's going on inside these models. Therefore, many algorithms are studied for the purpose of explaining AI models, especially those in the problem of image classification in the field of computer vision such as LIME, CAM, GradCAM. However, these algorithms still have limitations such as LIME's long execution time and CAM's confusing interpretation of concreteness and clarity. Therefore, in this paper, we propose a new method called Segmentation - Class Activation Mapping (SeCAM) that combines the advantages of these algorithms above, while at the same time overcoming their disadvantages. We tested this algorithm with various models, including ResNet50, Inception-v3, VGG16 from ImageNet Large Scale Visual Recognition Challenge (ILSVRC) data set. Outstanding results when the algorithm has met all the requirements for a specific explanation in a remarkably concise time.Comment: Published in the Proceedings of FAIC 202

BUILDING THE TEACHING STAFF OF DEFENSE EDUCATION AND SECURITY IN UNIVERSITIES TO MEET CURRENT DEVELOPMENT REQUIREMENTS IN VIETNAM

In Vietnam today, strengthening defense and security education for all classes of people, especially students at universities, is an essential job. In particular, one of the critical factors contributing to improving the quality of defense and security education for university students is the teaching staff of defense and security education. However, the teaching staff of defense and security education in Vietnamese universities needs more quantity and has certain limitations in quality. The article outlines several requirements posed to the teaching staff of national defense and security education and, on that basis, proposes some solutions to build the contingent of defense and security education lecturers in Vietnamese universities to meet the current development requirements of the country.  Article visualizations

Evaluating model teacher education and training at Vietnam's universities of technology and education

The traditional educational paradigm has become outdated as a result of changes in both the cultural and socioeconomic setting. A more sustainable and acceptable teacher is needed in education. A quantitative study was conducted. 95 administrators and teachers at the University of Technology and Education participated in this study with the aim of analyzing the current status of the teacher education model at the institution. According to the findings of the study, the model for educator preparation has been put into practice primarily through the processes of planning, organizing and directing activities related to educator preparation as well as inspecting and assessing the quality of education. The outcomes of the study indicate that it is essential to design educational programs that are appropriate for the present context. In particular, the study suggests that one of the most important steps towards achieving success is to incorporate technology into teaching methods. The process of educating teachers with the right degree of expertise and skills should be emphasized by educators and policymakers by developing relationships with other educational institutions and allowing teachers to participate in internships

Incorporating Stratified Negation into Query-Subquery Nets for Evaluating Queries to Stratified Deductive Databases

Most of the previously known evaluation methods for deductive databases are either breadth-first or depth-first (and recursive). There are cases when these strategies are not the best ones. It is desirable to have an evaluation framework for stratified DatalogN that is goal-driven, set-at-a-time (as opposed to tuple-at-a-time) and adjustable w.r.t. flow-of-control strategies. These properties are important for efficient query evaluation on large and complex deductive databases. In this paper, by incorporating stratified negation into so-called query-subquery nets, we develop an evaluation framework, called QSQNSTR, with such properties for evaluating queries to stratified DatalogN databases. A variety of flow-of-control strategies can be used for QSQNSTR. The generic evaluation method QSQNSTR for stratified DatalogN is sound, complete and has a PTIME data complexity

Challenges in Speaking English in ASEAN

Studies reveal that learning English by non-native individuals within any country across the globe increases the nature of problem-solving criteria adopted worldwide. People tend to adopt and embrace a new trajectory that can enhance efficiency in solving the problems that the designated group might face. Immigrants tend to meet a lot of issues within the nation. The leading cause of these problems is usually a result of the language barrier, which can be easily breached by introducing the English language within the region of ASEAN (Yi & Jang 2020). Proficiency in the language creates an atmosphere that is conducive for the aspect of solving the existing problems within the environment. The nature of problem-solving is sophisticated and crucial for the operations exhibited within ASEAN. This paper outlines the challenges faced by ASEANic people when speaking English. &nbsp

Factors Affecting Credit Access of Individual Business Households in ho chi Minh City, Vietnam

Purpose: The study studies the factors affecting access to credit of individual business households in Ho Chi Minh City (HCMC), Vietnam. The authors proposed policy implications based on research results that improve access to credit for individual business households.   Theoretical framework: Access to formal credit can be understood in many ways. This article considers that access to proper credit is the ability of a customer to use the capital of a traditional credit institution based on meeting financial obligations - emphasizing the ability to repay both principal and interest.   Design/methodology/approach: The authors chose a mixed method to conduct the study. This is a suitable research method to answer the research questions raised. Mixed methods include both qualitative and quantitative methods. However, this combination is the implementation of interlacing, replacing the two approaches to solve each problem, the research design's specific goal. The authors analyzed Data from May 2022 to December 2022 in HCMC based on the EFA and CFA, using the structural equation model analysis (SEM) method with SPSS 20.0 software and Amos.   Findings: The article's findings showed five factors affecting access to credit of individual business households in HCMC, Vietnam. In particular, the loan procedure factor substantially impacts the five factors affecting credit access.   Research, Practical & Social implications: The study has provided empirical evidence on the factors affecting formal credit access by a linear structural model and provides evidence that factors such as the loan procedure affect traditional credit affecting access to credit.   Originality/value: The authors give some policy implications for state management agencies, credit institutions, customers, and mass organizations, such as increasing the role of local Government and increasing the ability to use banks, E-commerce, design new loan products to help individual business households access more formal capital, and at the same time, reduce black credit

G-CAME: Gaussian-Class Activation Mapping Explainer for Object Detectors

Nowadays, deep neural networks for object detection in images are very prevalent. However, due to the complexity of these networks, users find it hard to understand why these objects are detected by models. We proposed Gaussian Class Activation Mapping Explainer (G-CAME), which generates a saliency map as the explanation for object detection models. G-CAME can be considered a CAM-based method that uses the activation maps of selected layers combined with the Gaussian kernel to highlight the important regions in the image for the predicted box. Compared with other Region-based methods, G-CAME can transcend time constraints as it takes a very short time to explain an object. We also evaluated our method qualitatively and quantitatively with YOLOX on the MS-COCO 2017 dataset and guided to apply G-CAME into the two-stage Faster-RCNN model.Comment: 10 figure

The roles of, activities of, and competencies for, community nursing services in rural Vietnam: Implications for policy decisions

Community health workforce plays a vital role in providing primary health care services as per the needs of residents; however, few studies have examined how nurses work within commune health centers (CHCs). Using qualitative methods including interviews and focus group discussions with key stakeholders, this study explores the roles, activities, and competencies required of community nursing services in rural districts within Vietnam. Two primary roles were identified: CHC nursing and family nursing. For the latter, in addition to providing people with general health care and health communication, they were expected to also deliver psychological care. CHC nursing fulfilled more roles and required four specific competencies: clinical care, communication, management, and planning/coordination activities. Despite these various roles serving people within a community, few ongoing efforts at either the local or national level are aimed at supporting these nurses. The study highlights the need for policy decisions via either developing a new job position policy or adapting the existing policy by integrating new roles into the existing positions of CHC nurses in Vietnam. © 2018 John Wiley & Sons, Ltd. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Nguyen Huy" is provided in this record*