This correlational study examined the effects of motivation in the process of learning and its connections to the academic achievement of the students who study at the Basic Arabic Schools in China. The designed tool, which is a questionnaire, consists of 40 items and had been processed by statistical analysis to contrived psychometric properties and it had been achieved through validity and reliability. The study acknowledged the cumulative scores of academic achievement of students for the last academic year, which was considered as one of the variables of the current study and compared to motivation to learn in the questionnaire sample of 30 students as well as the sample of the final study which covered all 242 students in the basic Arabic schools of the 7th, 8th and 9th Grades. The study found that there was a significant correlation between motivation to learn and academic achievement of students. It was proved through correlation and regression analysis that there was a positive relationship between motivation and academic achievement and the ability to predict the academic achievement through motivation. It had been found that the most motivated students are the ones who achieve higher academic performance. There were significant differences in motivation according to the gender in which females scored the highest means. Besides, no significant differences found in grade variables.

The rapid development of Smart Cities and the Internet of Thinks (IoT) is largely dependent on data obtained through Wireless Sensor Networks (WSNs). The quality of data gathered from sensor nodes is influenced by abnormalities that happen due to different reasons including, malicious attacks, sensor malfunction or noise related to communication channel. Accordingly, outlier detection is an essential procedure to ensure the quality of data derived from WSNs. In the modern utilizations of WSNs, especially in online applications, the high detection rate for abnormal data is closely correlated with the time required to detect these data. This work presents an investigation of different outlier detection techniques and compares their performance in terms of accuracy, true positive rate, false positive rate, and the required detection time. The investigated algorithms include Particle Swarm Optimization (PSO), Deferential Evolution (DE), One Class Support Vector Machine (OCSVM), K-means clustering, combination of Contourlet Transform and OCSVM (CT-OCSVM), and combination of Discrete Wavelet Transform and OCSVM (DWT-OCSVM). Real datasets gathered from a WSN configured in a local lab are used for testing the techniques. Different types and values of outliers have been imposed in these datasets to accommodate the comparison requirements. The results show that there are some differences in the accuracy, detection rate, and false positive rate of the outlier detections, except K-means clustering which failed to detect outlier in some cases. The required detection time for both PSO and DE is very long as compared with the other techniques meanwhile, the CT-OCSVM and DWT-OCSVM required short time and also they can achieve high performance. On the other hand CT and DWT technique has the ability to compress its used dataset where in this paper, CT can extract much less number of coefficients as compared DWT. This makes CT-OCSVM more efficient to be utilized in detecting outliers in WSNS.

There is a growing body of attention to importance of document summarization in most NLP tasks. So far, full coverage information, coherence of output sentences and lack of similar sentences (non-redundancy) are the main challenges faced to many experiments in compacted summaries. Although some research has been carried out on compact summaries, there have been few empirical investigations into coherence of output sentences. The aim of this essay is to explore a comprehensive and useful methodology to generate coherent summaries. The methodological approach taken in this study is a mixed method based on most likely n-grams and word2vec algorithm to convert separated sentences into numeric and normalized matrices. This paper attempts to extract statistical properties from numeric matrices. Using a greedy approach, the most relevant sentences to main document subject are selected and placed in the output summary. The proposed greedy method is our backbone algorithm, which utilizes a repeatable algorithm, maximizes two features of conceptual coherence and subject matter diversity in the summary. Suggested approach compares its result to similar model Q_Network and shows the superiority of its algorithm in confronting with long text document.

The Information Technology system use visualization to represent data in different forms. Some new researches in this field working on extract Knowledge, rapid information retrieval from the graphical diagram. Therefore, data visualization now trends to use ontology approach to build a robust knowledge-based system. The proposed of this paper is to developed ontology approach and use software tools to improve visualized knowledge. Moreover, study data visualization subject in term of ontology and how to facilitate understanding it on the end user. Computer networks ontology adopted as a case study to prove the important of this approach. The Object Role Modeling (ORM) is a visualized notation used to build the prototype for ontology, and OntoGraf module in the Protégé tool used to build ontology.

In this paper, we propose a situation assessment model for video transmission network, which evaluates the security risk of video transmission network information from four aspects: front-end perception layer, transmission layer, application layer and other risks.We divide the video network security evaluation system into three layers, and use AHP to determine the weight of each index.The weights of these four aspects are calculated by analytic hierarchy process.The method proposed in this paper can provide an evaluation system and a calculation method for the safe operation of video transmission network and important systems, and can provide specialized information security services for the construction project of video transmission network.

One of the major concerns in teaching and learning programming concepts is the complexity of syntax and precision of semantics of programming languages. Traditional teaching methods are static and passive i.e. they do not engage students in an interactive manner thereby making it difficult for students to grasp the contents and instructors to convey the instruction. This obstacle even becomes challenging when programming courses are to be taught to beginners. To cope up with this challenge, this work has proposed and prototyped a system that is aimed to focus on students at their middle level of education. Multimedia technology i.e. videos have been used to plunge the students in an interactive environment where learning JavaScript programming becomes fun instead of a mind-burden. Visualization concepts have been incorporated to provide visual learning for variables, loops, control structures, functions etc. This application is dynamic in nature that is user can not only understand the programming concepts but can also run the codes using code panel. The designed system has been tested to ensure the functionality, performance and feedback from the targeted users as discussed in results section.