Abstract-China has a long-standing problem in second-language education, that is, the lack of communicative learning opportunities. This study attempts to solve the problem by introducing mobile-assisted language learning with WeChat instant messaging. WeChat is one of the most popular instant messaging applications in China. It offers five advantages for education: multifunctionality, individuality, accessibility, interactivity, and affordability. 50 students participated in a one-semester program. They were divided into two groups. One group learned English with the assistance of mobile applications (WeChat Group), and the other learned English without assistance (Control Group). A pre-test and a post-test were given, and the scores were analyzed. The results showed that students in the WeChat group significantly improved in English proficiency. The results suggest that mobile-assisted language learning helps to create language immersion, which effectively motivates the learners further. Therefore, mobile-assisted language learning is promising in English learning for college students.

Abstract-With the increasing use of smartphones in China, WeChat has become one of the most popular social applications. With its powerful functions, WeChat not only implements social features for its users but also provides a new way to fulfill mobile learning (M-learning), which is a form of distance education with the use of mobile devices. After analyzing M-learning models and WeChat's features, this paper attempts to integrate WeChat's interactive functions to construct a WeChat teaching platform under the guidance of constructive theory. The WeChat teaching platform was constructed based on WeChat's multiple functions and with the support of wireless network technology. It can help to increase the interaction between students and teachers, because such interaction makes achieving ubiquitous learning for university students feasible. This empirical study proved that the new model is feasible and effective in facilitating interaction in translation teaching and in developing the students' translation competence.

Abstract:Auction is often applied in cognitive wireless networks due to its fairness properties and efficiency. To solve the allocation issues of cognitive wireless network in a multi-band spectrum, multi-item auction mechanism and models were discussed in depth. Multi-item highest price sealed auction was designed for cognitive wireless networks' multi-band spectrum allocation algorithm. This algorithm divided the spectrum allocation process into several stages which was along with low complexity. Experiments show that the algorithm improves the utilization of spectrum frequency, because it takes into account the spectrum owner's economic efficiency and the users' equity.

In many real-world applications, humangenerated data like images are often associated with several semantic topics simultaneously, called multi-label data, which poses a great challenge for classification in such scenarios. Since the topics are always not independent, it is very useful to respect the correlations among different topics for performing better classification on multi-label data. Hence, in this paper, we propose a novel method named Hypergraph Orthonormalized Partial Least Squares (HOPLS) for multi-label classification. It is fundamentally based on partial least squares with orthogonal constraints. Our approach takes into account the high-order relations among multiple labels through constructing a hypergraph, thus providing more discriminant information for training a promising multi-label classification model. Specifically, we consider such complex label relations via enforcing a regularization term on the objective function to control the model complexity and balance its contribution. Furthermore, we show that the optimal solution can be readily derived from solving a generalized eigenvalue problem. Experiments were carried out on several multilabel data sets to demonstrate the superiority of the proposed method.

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