Earticle

As the starting age of English education becomes younger around the world where they teach English as a foreign language, the debate on early English education is an unsettling issue as not only academic research, but also educational policy. To resolve the unsettled issues, a new approach is used: the big data and its analytics. To explore the pros and cons of the early English education, the study uses the analysis of research abstracts collected from scholars.google.com and www.kci.go.kr. It also analyzes the data posted on the discussion sites such as agora, daum café and naver café, plus daily interactions of the early English education using SNS. The study uses opinion mining technique using tools such as Sisense and WEKA to lay out the data and analyze them as basic data analytics. The results show that pro early English education is commonly co-occur with critical period, lateralization, ultimate attainment, universal grammar, fossili-zation, inhibition, acquisition process, bilingualism and exposition. Essays against early English education are related to no critical period, no authentic input, not effective, no universal grammar, national identity loss, self-identity loss and L2 interference. Other extraneous factors are based on practical problems such as social pressure, outcome pressure, political pressure, test reform and statistics.

Educational data mining has received considerable attention in the last few years. Many data mining techniques are proposed to extract the hidden knowledge from educational data. The extracted knowledge helps the institutions to improve their teaching methods and learning process. All these improvements lead to enhance the performance of the students and the overall educational outputs. In this paper, we propose a new student’s performance prediction model based on data mining techniques with new data attributes/features, which are called student’s behavioral features. These type of features are related to the learner’s interactivity with the e-learning management system. The performance of student’s predictive model is evaluated by set of classifiers, namely; Artificial Neural Network, Naïve Bayesian and Decision tree. In addition, we applied ensemble methods to improve the performance of these classifiers. We used Bagging, Boosting and Random Forest (RF), which are the common ensemble methods used in the literature. The obtained results reveal that there is a strong relationship between learner’s behaviors and their academic achievement. The accuracy of the proposed model using behavioral features achieved up to 22.1% improvement comparing to the results when removing such features and it achieved up to 25.8% accuracy improvement using ensemble methods. By testing the model using newcomer students, the achieved accuracy is more than 80%. This result proves the reliability of the proposed model.

Nowadays, the audiences' consumption attitudes, consumption patterns and consumer groups are all in the great changes, thus it is necessary to improve the film’s revenue by excellent script selecting, accurate market positioning, effective product marketing, and accurate forecasting of the box office. This paper introduced the application and benefit of big data in the film industry chain in terms of film making and investing, film publicity and distribution, film broadcasting and film audience, pointed out many challenges that big data encountered in China’s film industry and finally provided useful suggestions for the practitioners in the film industry of all aspects.

With the rapid development of modern information technology, especially the development of the Internet, network brings a vast amount of data and information. The development of information technology has changed the education and learning methods, greatly improving our work and study efficiency. In this paper, the author analyzes the college wushu teaching reform based on multimedia technology. Based on multimedia platform, this paper constructs the martial arts teaching activities under the background of modern technology. This system realizes the combination of multimedia technology and the martial arts curriculum structure, content, and resources; it changes the traditional classroom teaching mode, and will improve the students' enthusiasm in wushu teaching.

Increasing access to large-scale, high-dimensional and non-stationary streams in many real applications has made it necessary to design new dynamic classification algorithms. Most existing approaches for the textual stream classification are able to train the model relying on labeled data. However, only a limited number of instances can be labeled in a real streaming environment since large-scale data appear at a high speed. Therefore, it is useful to make unlabeled instances available for training and updating the ensemble models. In this paper, we present a new ensemble framework with partial labeled instances for learning from the textual stream. A new semi-supervised cluster-based classifier is proposed as the sub-classifier in our approach. In order to integrate these sub-classifiers, we propose an adaptive selection method. Empirical evaluation of textual streams reveals that our approach outperforms state-of-the-art stream classification algorithms.

Machine learning is ideal for exploiting the opportunities hidden in big data. It delivers on the promise of extracting value from big and disparate data sources with far less reliance on human direction. It is data driven and runs at machine scale. It is well suited to the complexity of dealing with disparate data sources and the huge variety of variables and amounts of data involved. And unlike traditional analysis, machine learning thrives on growing datasets. The more data fed into a machine learning system, the more it can learn and apply the results to higher quality insights. In this paper we propose a robust machine learning approach for dealing with large data set. Through experimental results, proposed method performs well on large data sets.

As the Internet is becoming a virtual platform for people’s sharing information and communicating daily, more and more people are connected together and the contact area between their personal privacy and the outside world turn to be unprecedented large. Therefore, the users of the Internet are faced with an enormous privacy threat. Anonymous P2P technology is a good way to solve the above problems. The ultimate goal of all anonymous P2P systems is to obtain the anonymity. Anonymity assessment on different anonymous P2P systems can be used to compare the amount of anonymity offered by different systems or to adjust the parameter setting of the same system according to different need, which is of great guiding significance to improve anonymous communication systems. In this paper, from the angle of relationship anonymity between the sender and the receiver, we presents the BIG-MIX anonymity method, taking the paths cross of different messages into consideration, to make anonymity assessment on anonymous P2P networks with the use of mapping. And with this method, this paper makes a quantitative analysis on the relationship anonymity of anonymous P2P networks under the mechanism of Threshold Mixes, Timed Mixes or Pool Mixes.

This paper presents a data-driven methodology to disambiguate a query by suggesting relevant subcategories within a specific domain. This is achieved by finding correlations between the user’s search history and the context of the current search keyword. We apply automatic categorization on each query to identify a list of categories which can describe the query given. To predict the categories of a user input query, we employed machine learning algorithms. We present the preliminary evaluation results and conclude with future work.

This article comprises a review of various sequential algorithms. The review represents the working nature of the learning methods. It also includes the methods such as Minimal Resource Allocation Network (MRAN), Extreme Learning Machine (ELM), Self-regulated Resource Allocation Network (SRAN) and Meta-Cognitive Neural Network (MCNN) for real –valued neural network. Projection Based Learning with Meta-Cognitive Radial Basis Function Network (PBL-McRBFN) for complex valued neural network. Finally about Meta-Cognitive Fuzzy Inference System (MCFIS) using the Neuro - Fuzzy inference system for learning. The previously said SRAN works on the basis of self – regulatory mechanism in order to reduce the huge loss error and to maximize the class – wise significance. The methods such as MCNN, PBL-McRBFN and MCFIS execute on the human learning strategies such as what – to-learn, when –to-learn and how –to – learn. This review helps to select the learning methods suitable for the data that is to be classified.

Database Reverse Engineering (DBRE) is an operation used to extract requirements from any system. The operation is implemented to facilitate the understanding of the system that has a little documentation about design and architecture. DBRE is a very important process used when database designers would like to expand the system or transition to the latest technology in DBRE fields. In the relational database model DBRE try to extract Entity Relationship Diagram (ERD) from relational database schema. Database designers find content of the data for a lot of attributes are not related with their names. In this paper, proposed methodology used to extract ERD from relational database schema with the attributes related with their names, both types of entities regular and weak entity, relationships and keys, which are found in the table that has extracted the relational database schema. The basic inputs of this approach are relational database schema that generated from database. The relational database schema used to extract the information about ERD. After that, obtain information that contain keywords help database designers to extract entities and their attributes semantics related with their names from relational database schemas. Then, Determine primary keys, foreign keys and constraints of the database system. In the final step, the ERD is successfully extracted