In recent years, the use of social networks has been growing at an unprecedented speed. Changing the way people communicate, interact and learn. Instead of passively consuming information, users are actively participating in the creation of knowledge, as a part of the social connection and interaction Social Networking Sites (SNSs) are built upon. And since learning, at abstract, is a social activity, educators around the world have been embracing the notion of incorporating social networks technology into the educational environment to support and enhance the teaching-learning experience. This work investigates the usage of social networks in higher education to support collaborative learning, improve the learning process and enhance students’ engagement and academic achievements. A comprehensive review of the current literature is conducted to thoroughly analyze the use and impact of social networks on higher education students. In order to propose a framework for educational social networks that will support collaborative learning and enhance learning outcomes for higher education students.

Today, an organization’s biggest asset is its data. Knowledge of relational databases and SQL (Structured Query Language) is one of the most valued technical skills and in high demand in most organizations. Students of any major can benefit in their careers from learning these fundamental database concepts. At our school, database classes for both CS (Computer Science) and non-CS students are offered by our computer science faculty members. Teaching database concepts can be challenging especially to non-CS students. In this paper, we discuss the technical difficulties our non-CS students experience in their database class and an approach to solving these problems.

Cloud Computing has managed to attract the entire buzz in the growing era of technology due to its on-demand services for resource request. Despite of the enormous growth of cloud computing, there are many problems related to resource allocation in cloud that are still unaddressed. Current work for resource allocation strategy focuses on various methods to place Virtual Machine per appropriate requests. The current state of art focuses on the dynamic nature of the work load on cloud. But there is still scope of improvement in the resource allocation strategies that have been proposed in terms of well-balanced network even at the resource contention.
This study proposes a hybrid model composed of lease methodology and dynamic load balancing algorithm, with an attempt to overcome the problems of resource contention and starvation and a well-balanced network even at the input of varying loads. An attempt to increase the CPU utilization and throughput along with no request rejection is taken. The work also retains the lease options for its clients thus maintaining the anti-starvation for pre-emptible requests.

Testing is an important phase in system development to improve the quality, reliability, and performance of software systems. As more and more mobile applications are developed, testing and quality assurance are vital to developing successful products. Users are widely opening browsers on mobiles to interact with web pages. It is important to ensure that our web application will be compatible with mobile browsers and applications. There is a need for a framework that can be applied to test different mobile browsers and applications. This paper proposes a novel framework for automation testing of mobile applications using Appium tool. The novel framework uses automation testing to reduce effort and speed up the process of automated testing on a mobile device. Survey is conducted to validate the proposed framework.

According to the actual research results, combined with the teaching experience, this paper puts forward the reform method of the database course in police colleges. Based on the actual sector capacity requirement analysis, design of the five learning steps and four teaching scene, the task driven, project oriented, make students doing, learning by doing, students are required in the network security supervision work in the operation skill and ability.

Graphs play notable role in daily life. For instance, they are used in variety fields such as social networks, malware detection, and biological networks. Graph data processing performed to extract useful information is known as graph mining. A critical field of graph mining is graph containment problem, in which all graphs containing the query are returned by a graph query q. Scanning the whole database (graph query as a subgraph) for a query is a time consuming process. To improve query performance, an inverted index is constructed on the graph database and then the query is performed based on the query. The problem in this process is that when a graph is added to or removed from a database, the inverted index must be reconstructed. The present study proposes a method in which index updating is not needed upon a change in the database. This feature enables simultaneous inverted index updating and querying. The assessment results showed optimum and satisfactory performance of the proposed method.

Recommender systems have contributed to the success of personalized websites as they can automatically and efficiently select items or services adapted to the user's interest from huge datasets. However, these systems suffer of issues related to small number of evaluations; cold start system and data sparsity. Several approaches have been explored to find solutions to related issues. The advent of the Linked Open Data (LOD) initiative has spawned a wide range of open knowledge bases freely accessible on the Web. They provide a valuable source of information that can improve conventional recommender systems, if properly exploited. In this paper, we aim to demonstrate that adding semantic information from LOD enhance the effectiveness of traditional collaborative filtering. To evaluate the accuracy of the semantic approach, experiments on standard benchmark dataset was conducted. The obtained results indicate that the accuracy and quality of the recommendation are improved compared with existing approaches.