233 research outputs found

    Motivating Students to Talk: TED Conference in University-Based Chinese Language Classrooms

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    This paper presents an innovative teaching method that uses TED conference to stimulate and support learners of Chinese in improving their speaking skills. In non-target language countries, students are often reluctant to use Chinese in real life. This study overviews a case where TED conferences were integrated into a university-based Chinese language course in New Zealand. It also discusses the major findings emerging from implementing this innovative pedagogy as well as the recommendations for future research. While motivating students to use Chinese in public setting, the approach has also enhanced critical thinking skills and co-learning in language classrooms. This paper ends by providing food for thought on designing engaging pedagogy in building and sustaining a knowledge-sharing culture in advanced Chinese courses in universities

    Motivating Students to Talk: TED Conference in University-Based Chinese Language Classrooms

    Get PDF
    This paper presents an innovative teaching method that uses TED conference to stimulate and support learners of Chinese in improving their speaking skills. In non-target language countries, students are often reluctant to use Chinese in real life. This study overviews a case where TED conferences were integrated into a university-based Chinese language course in New Zealand. It also discusses the major findings emerging from implementing this innovative pedagogy as well as the recommendations for future research. While motivating students to use Chinese in public setting, the approach has also enhanced critical thinking skills and co-learning in language classrooms. This paper ends by providing food for thought on designing engaging pedagogy in building and sustaining a knowledge-sharing culture in advanced Chinese courses in universities

    Vestibule et cadre de référence

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    Super-resolution of Ray-tracing Channel Simulation via Attention Mechanism based Deep Learning Model

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    As an emerging approach, deep learning plays an increasingly influential role in channel modeling. Traditional ray tracing (RT) methods of channel modeling tend to be inefficient and expensive. In this paper, we present a super-resolution (SR) model for channel characteristics. Residual connection and attention mechanism are applied to this convolutional neural network (CNN) model. Experiments prove that the proposed model can reduce the noise interference generated in the SR process and solve the problem of low efficiency of RT. The mean absolute error of our channel SR model on the PL achieves the effect of 2.82 dB with scale factor 2, the same accuracy as RT took only 52\% of the time in theory. Compared with vision transformer (ViT), the proposed model also demonstrates less running time and computing cost in SR of channel characteristics

    Multispecies Coevolution Particle Swarm Optimization Based on Previous Search History

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    A hybrid coevolution particle swarm optimization algorithm with dynamic multispecies strategy based on K-means clustering and nonrevisit strategy based on Binary Space Partitioning fitness tree (called MCPSO-PSH) is proposed. Previous search history memorized into the Binary Space Partitioning fitness tree can effectively restrain the individuals’ revisit phenomenon. The whole population is partitioned into several subspecies and cooperative coevolution is realized by an information communication mechanism between subspecies, which can enhance the global search ability of particles and avoid premature convergence to local optimum. To demonstrate the power of the method, comparisons between the proposed algorithm and state-of-the-art algorithms are grouped into two categories: 10 basic benchmark functions (10-dimensional and 30-dimensional), 10 CEC2005 benchmark functions (30-dimensional), and a real-world problem (multilevel image segmentation problems). Experimental results show that MCPSO-PSH displays a competitive performance compared to the other swarm-based or evolutionary algorithms in terms of solution accuracy and statistical tests

    FeatureBooster: Boosting Feature Descriptors with a Lightweight Neural Network

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    We introduce a lightweight network to improve descriptors of keypoints within the same image. The network takes the original descriptors and the geometric properties of keypoints as the input, and uses an MLP-based self-boosting stage and a Transformer-based cross-boosting stage to enhance the descriptors. The enhanced descriptors can be either real-valued or binary ones. We use the proposed network to boost both hand-crafted (ORB, SIFT) and the state-of-the-art learning-based descriptors (SuperPoint, ALIKE) and evaluate them on image matching, visual localization, and structure-from-motion tasks. The results show that our method significantly improves the performance of each task, particularly in challenging cases such as large illumination changes or repetitive patterns. Our method requires only 3.2ms on desktop GPU and 27ms on embedded GPU to process 2000 features, which is fast enough to be applied to a practical system.Comment: 14 pages, 8 figures, 5 table

    Lightweight AE and HASH in a Single Round Function

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    To deal with message streams, which is required by many symmetric cryptographic functionalities (MAC, AE, HASH), we propose a lightweight round function called Thin Sponge. We give a framework to construct all these functionalities (MAC, AE, and HASH) using the same Thin Sponge round function. Besides the common security assumptions behind traditional symmetric algorithms, the security of our schemes depends on the hardness of problems to find collisions of some states. We give a class of constructions of Thin Sponge, which is improvement of the round function of Trivium and ACORN. We give simple criteria for determining parameters. According to these criteria, we give an example, which achieves all functionalities in a single round function and hence can be realized by the same hardware. Our algorithm is also efficient in software
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