This study aimed to investigate the effectiveness of embodied learning (EL) approaches in classrooms to improve students' learning effectiveness. A drama-based learning strategy was used to investigate the gap in creating an authentic learning environment for classroom learners and facilitating them to be situationally embodied in the learning context and perform hands-on activities. A total of 76 undergraduate-level students were randomly selected for either the experimental group ($\mathrm{n}=31$) or the control group ($\mathrm{n}=44$). The experimental group used the learning system to immerse and situationally embodied in a drama-based virtual scenario for EL activities where real-time speech, objects and images, body motion and gesture recognition techniques were provided to them as self-evaluation techniques while performing, and the control group used immersion technologies to situationally embody in a virtual fictional context and the teacher and peers did evaluation after the activity. Results showed an improvement in learning effectiveness for both groups, but the approach used for the experimental group outperformed the control group. This study impacts that EL approaches focusing on situatedness can enhance learning effectiveness and provide a useful guide for educational practitioners. Educators can facilitate the effective transfer of knowledge and skills to learners through engaging and immersive learning experiences.

The aim of learning a language is to communicate with each other. In a drama that simulates problems taking place in real world, students can learn how to solve these problems via the communication and interaction with such a drama-based learning environment. On the other hand, besides oral interaction, visual interaction is essential as well. To address this issue, this study proposes a digital learning theater which combines itself with visual interaction learning forms and instant assessment. In this learning environment, students can learn a language by pictures and physical objects via visual interaction, and receive immediate feedback from the instant assessment of their learning performance provided by a virtual character. Consequently, students can realize whether their learning performance in the digital reality learning environment need to be improved. Furthermore, an experiment of the proposed learning environment was carried out in a Japanese language classroom for university students. The results suggest that students' learning performance is significantly improved.

Modern GPGPU's have enabled massively parallel computing with programmability that can exploit the highly parallel nature of LDPC decoding. Previous works customized the design on a GPGPU towards specific execution attributes of a particular LDPC decoding matrix. Supporting different LDPC decoding matrices requires either substantial rework on the current program, or a brand new parallel design. This paper proposes two unified designs that can achieve high performance for both regular and irregular LDPC decoding on a GPGPU. The first design introduces a node-based scheme with a versatile translation array mechanism that can efficiently handle the complex data access patterns of different LDPC decoding matrices. The second design proposes an edge-based parallel paradigm that uses more intuitive data layout. More edges than nodes in a Tanner graph also give the edge-based design higher computation parallelism when there are limited concurrent codewords. With the proposed unified designs, designers can be ignorant of the types of LDPC matrices and achieve high performance LDPC decoding. The experiments on a GTX 470 GPGPU have demonstrated up to 134.56x runtime improvement, when compared with designs on a high-end CPU. The maximum throughput can reach 80.25 Mbps. When compared with the previous customized designs, the proposed systematic designs can reach better performance while relieving the effort of customization.

A Chinese character embedded in different compound words may carry different meanings. In this paper, we aim at semantical clustering of a given family of morphologically related Chinese words. In Experiment 1, we employed linguistic features at the word, syntactic, semantic, and contextual levels in aggregated computational linguistics methods to handle the clustering task. In Experiment 2, we recruited adults and children to perform the clustering task. Experimental results indicate that our computational model achieved a similar level of performance as children.

Empirical mode decomposition (EMD) developed by Huang et al. is a nonlinear data analysis method for nonstationary real-valued time series. It has been applied extensively in many research areas. Recently, several generalized EMD methods for complex-valued data analysis was proposed. Since a plane closed curve comprises many two-dimensional (2D) space data points, one can imagine that the boundary points of a plane closed curve as a complex data sequence in the complex plane, and make use of the newly developed complex EMD (CEMD) to do further analysis. We have found that we can use CEMD to achieve boundary points noise-reduction of plane closed curves and perform shift-invariant, scale-invariant and rotation-invariant pattern recognition.