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In this work, we propose a new approach for face recognition using low-resolution images. By cleverly combining conventional interpolation methods with the state-of-the-art classification approach, i.e. convolutional neural network, we introduce a new approach to efficiently leverage low-resolution images in classification task, especially in face recognition. Besides, we also do experiments on some recent popular methods, our approach outperforms some of them. Additionally, we propose a specific transfer learning strategy based on the preexisting well-known concept dedicated to low-resolution transfer learning. It boosts performance and reduces training time significantly. We also investigate on scalability by applying Bayesian optimization for hyper-parameter search. Therefore, our approach is able to be widely applied in many kinds of datasets and low-resolution classification tasks due to automatically seeking optimal hyper-parameters, which makes our method competitive to others. [ABSTRACT FROM AUTHOR]

A social network is one of the efficient tools for information propagation. The content is the bridge between the product and its customers. Evaluating the user's content creation is a valuable feature to improve information spreading on the social network. This paper proposes a method for extracting brand value with influencers by combining the user's amplification and content creation in influencer marketing. The amplification factors are studied based on the propagation of the posts on the social network in a duration time. Those factors are more valuable than before when using influencer marketing at a determined time. Moreover, the content creation score is also studied to measure content creation based on the passion point with a brand and its quality. The amplification factors and content creation score are combined to analyze posts' interest in detecting the emerging influent users for a product in the influencer marketing campaign. Using the amplification factors, the passion points, and the content creation score, a system to manage the influencer marketing on Facebook has been constructed and tested in the real-world campaign. The experimental results show that the proposed method's influencers bring the conversion rate's efficiency and revenue in the influencer marketing campaign. [ABSTRACT FROM AUTHOR]

The intelligent chatbot has many applications in the real world, especially for supporting e-learning. An educational chatbot requires a complete knowledge base to help students to search knowledge content of courses. This paper proposes a model for integrating multiple knowledge domains, called the Integ-Rela model. Each knowledge domain is organized by an ontology Real-model, which is a knowledge model of relations. The Integ-Rela model manages the knowledge base of an intelligent chatbot that can intellectually retrieve information on multiple domains. Moreover, searching problems in a field have been studied and solved, and the issues for retrieval on numerous knowledge domains. The proposed method is applied to construct an intelligent chatbot for searching programming content in a university's Introduction to Programming and Object-oriented Programming courses. It can act as a tutor by providing the required knowledge of both approaches to students. The experimental results show that it is the potential to apply in practice. [ABSTRACT FROM AUTHOR]

Featured Application: This paper presents a method to design a system to manage influencer marketing campaigns. The designed system can determine emerging influencers for a brand by using these measures about amplification factors for evaluating information propagation, the passion point to measure the favorite of a user of a brand, and the content creation score for determining the ability of post-content creation. In a marketing campaign, the designing system manages the parameters of emerging influencers, such as the information diffusion of each influencer, and determines statistically their effectiveness by presenting the conversion rate of clicks and orders, the number of clicks on interactions, and their revenue. Moreover, this system also supports the customer's decision at that moment based on producing the data visualization of the campaign's results. Therefore, the designed management system is helpful for application in an influencer marketing campaign in practice. In the fourth technology revolution, influencer marketing is an essential kind of digital marketing. This marketing uses identified influencers to viral the information of products to target customers. It is useful to support brands exposed to more valuable online consumers. The influencer marketing campaign needs a management system to manage on a social network. This system helps to increase the efficiency of a campaign. This paper proposes a management system for the influencer marketing campaign, called the ADVO system. This system provides a tool for collecting data on a social network and detecting potential brand influencers for the marketing campaign. The meaningful measures for users include amplification factors for evaluating the information propagation, the passion point to measure a user's favorite when it comes to a brand, and the content creation score for determining the ability of post-content creating. The ADVO system helps the brand to make the decision through real-time visual reports of the campaign. It is a foundation to create commercial activities and construct an advocate community of the related brand. [ABSTRACT FROM AUTHOR]

Nowadays, building intelligent systems for science, technology, engineering, and math (STEM) education is necessary to support the studying of learners. Intelligent problem solver (IPS) is a system that can be able to solve or tutor how to solve the problems automatically. Learners only declare hypothesis and goal of problems based on a sufficient specification language. They can request the program to solve it automatically or to give instructions that help them to solve it themselves. Knowledge representation plays a vital role in these kinds of intelligent systems. There are various methods for knowledge representation; however, they do not meet the requirements of an IPS in STEM education. In this paper, we propose the criteria of a knowledge model for an IPS in education. These criteria orient to develop a method for knowledge representation to meet actual requirements in practice, especially pedagogical requirements. For proving the effectiveness of these criteria, a knowledge model is also constructed. This model can satisfy these criteria and be applied to build IPS for courses, such as mathematics and physics. [ABSTRACT FROM AUTHOR]