Context-aware technologies can make e-learning services smarter and more efficient since context-aware services are based on the user’s behavior. To add those technologies into existing e-learning services, a service architecture model is needed to transform the existing e-learning environment, which is situation-aware, into the environment that understands context as well. The context-awareness in e-learning may include the awareness of user profile and terminal context. In this paper, we propose a new notion of service that provides context-awareness to smart learning content in a cloud computing environment. We suggest the elastic four smarts (E4S)—smart pull, smart prospect, smart content, and smart push—concept to the cloud services so smart learning services are possible. The E4S focuses on meeting the users’ needs by collecting and analyzing users’ behavior, prospecting future services, building corresponding contents, and delivering the contents through cloud computing environment. Users’ behavior can be collected through mobile devices such as smart phones that have built-in sensors. As results, the proposed smart e-learning model in cloud computing environment provides personalized and customized learning services to its users.

In this paper, we measure human physiological changes from different body parts to quantify human mental stress level by using multimodal bio-sensors. By integrating these physiological responses, we generate bio-index and rule for the prediction of mental status, such as tension, normal, and relax. We also develop an inspection service middleware for analyzing health parameters such as electroencephalography (EEG), electrocardiography (ECG), oxygen saturation (SpO2), blood pressure (BP), and respiration rate (RR). In this service middleware, we use the multi-level assessment model for mental stress level that consists of three steps as follows; classification, reasoning, and decision making. The classification of datasets from bio-sensors is enabled by fuzzy logic and SVM algorithm. The reasoning uses the decision-tree model and random forest algorithm to classify the mental stress level from the health parameters. Finally, we propose a prediction model to make a decision for the wellness contents by using Expectation Maximization (EM).

Smart home and IoT-related technologies are developing rapidly, and various smart devices are being developed to help users enjoy a more comfortable lifestyle. However, the existing smart homes are limited by a scarcity of operating systems to integrate the devices that constitute the smart home environment. This is because these devices use independent IoT platforms developed by the brand or company that developed the device, and they produce these devices based on self-service modules. A smart home that lacks an integrated operating system becomes an organizational hassle because the user must then manage each device individually. Furthermore, this leads to problems such as excessive traffic on the smart home network and energy wastage. To overcome these problems, it is necessary to build an integrated management system that connects IoT devices to each other. To efficiently manage IoT, we propose three intelligent models as IoT platform application services for a smart home. The three models are intelligence awareness target as a service (IAT), intelligence energy efficiency as a service (IE²S), and intelligence service TAS (IST). IAT manages the “things” stage. IAT uses intelligent learning to acquire a situational awareness of the data values generated by things (sensors) to collect data according to the environment. IE²S performs the role of a server (IoT platform) and processes the data collected by IAT. The server uses Mobius, which is an open-source platform that follows international standards, and an artificial TensorFlow engine is used for data learning. IE²S analyzes and learns the users’ usage patterns to help provide service automatically. IST helps to provide, control, and manage the service stage. These three intelligent models allow the IoT devices in a smart home to mutually cooperate with each other. In addition, these intelligent models can resolve the problems of network congestion and energy wastage by reducing unnecessary network tasks to systematically use energy according to the IoT usage patterns in the smart home.

The recommendation system are widely adopted in today’s mainstream online sharing services, providing useful prediction of user’s rating or user’s preferences of sharing items (such as products, movies, books, and news articles). A key challenge of recommendation systems in sharing economy is to employ prediction algorithms to estimate the matching items with considering their interests and needs. The environment-context has been recognized as an important factor to consider in personalized recommender systems. Since dynamic information in environment-context describes the situation of items and users, the information affects the user’s decision process essentially to apply in recommender systems. However, most model-based collaborative filtering approaches such as Matrix Factorization do not provide an easy way of integrating context information into the model. In this paper, we introduce a Multidimensional Trust model based on Tensor Factorization. The generalization of Matrix Factorization allows for a flexible and generic integration of contextual information. According to the different types of context, the Multidimensional Trust model considers the additional dimensions for the representation of the data as a tensor. This is achieved by going through the collecting user’s behavior based on rating analysis and identification of users’ historical activity and viewing patterns. The benefits behavior solutions, which use the handle intelligently to meet the users’ needs, are the focus of this paper.

The challenge that journalism is facing these days in the Internet mobile environment is greater than ever before. Journalism is losing its revenue structure to platform operators favoring a certain markets, and also the trust of its readers in light of fake news and infected news. To alleviate this situation, we propose a blockchain technology that is applicable to journalism in order to achieve decentralization as a reasonable alternative. The journalism model based on hybrid blockchain aims to achieve the following: the delivery of articles with sharing value, what we call proof of sharing; the distribution of roles of personalized agenda settings; and finally, the use of agora to collect public opinions. With all these, we attempt to resolve the issues with current journalism with our proposed model based on blockchain.

South Korea invests a budget of trillions in national R&D projects every year, and has achieved excellent performance doing so each year. However, since the projects are planned, evaluated, and managed by different departments and institutions, duplicate planning and submission leads to insufficient sharing of research results. Currently, the National Technology Information Service (NTIS) inspects project duplication based on keywords, which leads to duplicate planning among departments and closed management of research results. Since the NTIS builds in centralized systems, the inspection systems supports one-way management for duplication checking and information sharing. Therefore, we propose a new platform, called the Trusted Information Project Platform (TIP-Platform), for easily checking for project duplication, sharing research results, and updating research results. TIP-Platform adopts a new concept for user authority setting, the distributed ledger structure, transaction structure, and service. For the adaption, the TIP-Platform uses blockchain technology that performs recording and management via blocks by distributing the right to record and managing transactions. This platform makes it easy for anyone to view and use project-related information such as research results and duplication review. In this paper, we describe how the TIP-Platform can achieve excellent research results through information sharing of a project. This platform needs to be based on trust, because it shares information and continually updates information.