National Taichung University of Science and Technology

About: National Taichung University of Science and Technology is a education organization based out in Taichung, Taiwan. It is known for research contribution in the topics: Wireless sensor network & Cloud computing. The organization has 1103 authors who have published 1504 publications receiving 17481 citations. The organization is also known as: Guólì Táizhōng Kējì Dàxué & National Taichung Institute of Technology.

An Improved Reversible Data Hiding in Encrypted Images Using Side Match

Abstract: This letter proposes an improved version of Zhang's reversible data hiding method in encrypted images. The original work partitions an encrypted image into blocks, and each block carries one bit by flipping three LSBs of a set of pre-defined pixels. The data extraction and image recovery can be achieved by examining the block smoothness. Zhang's work did not fully exploit the pixels in calculating the smoothness of each block and did not consider the pixel correlations in the border of neighboring blocks. These two issues could reduce the correctness of data extraction. This letter adopts a better scheme for measuring the smoothness of blocks, and uses the side-match scheme to further decrease the error rate of extracted-bits. The experimental results reveal that the proposed method offers better performance over Zhang's work. For example, when the block size is set to 8 8, the error rate of the Lena image of the proposed method is 0. 34%, which is significantly lower than 1.21% of Zhang's work.

A Software Defined Fog Node Based Distributed Blockchain Cloud Architecture for IoT

Abstract: The recent expansion of the Internet of Things (IoT) and the consequent explosion in the volume of data produced by smart devices have led to the outsourcing of data to designated data centers However, to manage these huge data stores, centralized data centers, such as cloud storage cannot afford auspicious way There are many challenges that must be addressed in the traditional network architecture due to the rapid growth in the diversity and number of devices connected to the internet, which is not designed to provide high availability, real-time data delivery, scalability, security, resilience, and low latency To address these issues, this paper proposes a novel blockchain-based distributed cloud architecture with a software defined networking (SDN) enable controller fog nodes at the edge of the network to meet the required design principles The proposed model is a distributed cloud architecture based on blockchain technology, which provides low-cost, secure, and on-demand access to the most competitive computing infrastructures in an IoT network By creating a distributed cloud infrastructure, the proposed model enables cost-effective high-performance computing Furthermore, to bring computing resources to the edge of the IoT network and allow low latency access to large amounts of data in a secure manner, we provide a secure distributed fog node architecture that uses SDN and blockchain techniques Fog nodes are distributed fog computing entities that allow the deployment of fog services, and are formed by multiple computing resources at the edge of the IoT network We evaluated the performance of our proposed architecture and compared it with the existing models using various performance measures The results of our evaluation show that performance is improved by reducing the induced delay, reducing the response time, increasing throughput, and the ability to detect real-time attacks in the IoT network with low performance overheads

Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model.

Abstract: This study developed and empirically tested a model to predict the factors affecting students' behavioral intentions toward using mobile learning (m-learning). This study explored the behavioral intention to use m-learning from the perspective of consumers by applying the extended unified theory of acceptance and use of technology (UTAUT) model with the addition of perceived enjoyment, mobile self-efficacy, satisfaction, trust, and perceived risk moderators. A cross-sectional study was conducted by employing a research model based on multiple technology acceptance theories. Data were derived from an online survey with 1,562 respondents and analyzed using structural equation modeling. Partial least squares (PLS) regression was used for model and hypothesis testing. The results revealed that (1) behavioral intention was significantly and positively influenced by satisfaction, trust, performance expectancy, and effort expectancy; (2) perceived enjoyment, performance expectancy, and effort expectancy had positive associations with behavioral intention; (3) mobile self-efficacy had a significantly positive effect on perceived enjoyment; and (4) perceived risk had a significantly negative moderating effect on the relationship between performance expectancy and behavioral intention. Our findings correspond with the UTAUT model and provide a practical reference for educational institutions and decision-makers involved in designing m-learning for implementation in universities.