Mobile computing is a new technology emerging with the development of mobile communication, Internet, database, distributed computing, and other technologies. Mobile computing technology will enable computers or other information intelligent terminal devices to realize data transmission and resource sharing in the wireless environment. Its role is to bring useful, accurate, and timely information to any customer at anytime, anywhere, and to change the way people live and work. In mobile computing environment, a lot of Internet rumors hidden among the huge amounts of information communication network can cause harm to society and people’s life; this paper proposes a model of social network rumor detection based on convolution networks, the use of adjacency matrix between the nodes represent user and the relationship between the constructions of social network topology. We use a high-order graph neural network (K-GNN) to extract the rumor posting features. At the same time, the graph attention network (GAT) is used to extract the association features of other nodes of the network topology. The experimental results show that the method of the detection model in this paper improves the accuracy of prediction classification compared with deep learning methods such as RNN, GRU, and attention mechanism. The innovation of the paper proposes a rumor detection model based on the graph convolutional network, which lies in considering the propagation structure among users. It has a strong practical value.
Currently, universities have rising demands to apply the incredible recent developments in computer technology that support students to achieve skills necessary for developing applied critical thinking in the contexts of online society. Medical and engineering subjects’ practical learning and education scenarios are crucial to attain a set of competencies and applied skills. These recent developments allow sharing and resource allocation, which brings savings and maximize use, and therefore offer centralized management, increased security, and scalability. This paper describes the implantation of Virtual Desktop Infrastructure (VDI) to access the virtual laboratories that bring efficient use of resources as one of Al Balqa Applied University’s (BAU) Private Cloud services. The concept of desktop virtualization implements the sharing of capabilities utilizing legacy machines, which reduces the cost of infrastructure and introduces increased security, mobility, scalability, agility, and high availability. Al Balqa Applied University uses the service extensively to facilitate in/off-campus learning, teaching, and administrative activities and continue performing their work and education functions remotely to cope with the COVID-19 pandemic.
The rise in mobile Internet usage and increased reliance on cloud computing have led to increased fear of cloud database security. Mobile cloud computing has emerged as the only promising way of providing solutions for the mobile computing environment, including computation offloading and data binding. This paper discusses the overview of mobile cloud computing features and its prone computing security issues and how to walk over them with the most promising solutions. More specifically, it explores in detail a wide range of threats that may attack the mobile cloud-computing platform and the various devices and applications that work extremely well in supporting and mitigating the wide range of problems related to security issues in mobile applications. Moreover, this paper studies some of the ways to make mobile cloud computing more secure and productive no matter the intensity of the required computation. This study takes into consideration, the most common threats that affect the security issues of the mobile cloud database and its solutions. It is deemed necessary to note that, the duty of various cloud service providers is to keep all mobile cloud data safe. Consequently, they must come up with solutions to the problems affecting the day-to-day mobile cloud database security.
With the development and popularization of e-commerce and Internet, more and more attention has been paid to personalized recommendation for users. The traditional user interest model only considers the user’s behavior on the project, ignoring the user’s context at that time. Pointing to the shortage that context-related factors are not considered in previous works, combining the characteristics of a mobile computing environment, this paper studies the algorithm and model of mobile service recommendation. A recommendation algorithm based on specified context filtering in mobile computing environment is proposed. The context of the classification is aggregated, by grouping the scenarios of the same category together. Through experiments, we found that the improved personalized recommendation algorithms are superior to the common collaborative filtering algorithm.
The present value of interest in the amortization schedule or the sinking fund schedule is derived in the theorem. Even though that people prefer the level payments with smaller total amount of interest, the sum of the present value of interest and the present value of principal remains unchanged in all payment methods. The sum is just the loan amount. Rather than the traditional spreadsheet, the free MIT App Inventor is applied to create the amortization schedule and the sinking fund schedule in the mobile computing environment.