The Effectiveness Of Motion Graphic In Learning Chinese Character Stroke Order Through Cognitive Load Theory Assessment

This research aimed to investigate the effects of CCSOMG on students' learning performance and cognitive load. The Chinese character strokes are numerous and complex. Memorising the Chinese character stroke order is a major challenge for non-native language learners. To this end, this research developed and evaluated six CCSOMG for non-native language learners to remember the Chinese character stroke order. The six CCSOMG were developed by adopting the cognitive load theory as the important theoretical grounding and the ADDIE model as the instructional design framework and implemented in the language classes at the USM School of Languages, Literacies, and Translation. A proper experimental design was used to conduct this study. Forty participants were randomly divided into the experimental group (CCSOMG method, N=20) and the control group (traditional teaching method, N=20). Statistical analysis was performed on the learning performance of the two groups using independent samples T-test, and the results showed a significant difference in the scores for the two groups. The learning performance of the experimental group was better than that of the control group. Then the cognitive load of the two groups of students was measured with the subjective rating scale. Statistical analysis of the mental effort and material difficulty of the two groups using the Mann-Whitney U test showed a significant difference in scores between the two groups. The learners who used CCSOMG exerted much less mental effort and felt much less material difficulty than those who used the traditional method

The Instructional Design of Chinese Characters’ Stroke Order Motion Graphics Based on Cognitive Load Theory

This research aims to develop stroke order motion graphics for Chinese characters to solve the problem of memorising Chinese characters’ stroke order in the learning process. This research adopted cognitive load theory and the ADDIE model as an instructional design process guide. Herbart’s four-stage teaching method is used as a guide for the motion graphics presentation module. Based on the characteristics of Malaysian students who learn Chinese as their second language, motion graphics for Chinese characters’ stroke order learning were developed. The expert evaluation was conducted to identify problems, and modifications were done to improve the created prototype. A total of six Chinese characters’ stroke order motion graphics have been successfully developed. The result shows that cognitive load theory provides an effective solution for developing Chinese characters’ stroke order motion graphics. The ADDIE model also offered a significant direction for the instructional design process. In addition, to be more effective in Chinese character stroke order teaching, interface design must consider the relevant teaching effects of cognitive load theory. However, making the prototype in advance can avoid large-scale modifications in the later process. The successful development of the Chinese characters’ stroke order motion graphics allows teaching Chinese character stroke order in Malaysia to be carried out more effectively

Statistical and Biological Evaluation of Different Gene Set Analysis Methods

AbstractGene-set analysis (GSA) methods have been widely used in microarray data analysis. Owing to the unusual characteristics of microarray data, such as multi-dimension, small sample size and complicated relationship between genes, no generally accepted methods have been used to detect differentially expressed gene sets (DEGs) up to now. Our group assessed the statistical performance of some commonly used methods through Monte Carlo simulation combined with the analysis of real-world microarray data sets. Not only did we discover a few novel features of GSA methods during experiences, but also we find that some GSA methods are effective only if genes were assumed to be independent. And we also detected that model-based methods (GlobalTest and PCOT2) performed well when analyzing our simulated data sets in which the inter-gene correlation structure was incorporated into each gene set separately for more reasonable. Through analysis of real-world microarray data, we found GlobalTest is more effective. Then we concluded that GlobalTest is a more effective gene set analysis method, and recommended using it with microarray data analysis

Semantic-Constraint Matching Transformer for Weakly Supervised Object Localization

Weakly supervised object localization (WSOL) strives to learn to localize objects with only image-level supervision. Due to the local receptive fields generated by convolution operations, previous CNN-based methods suffer from partial activation issues, concentrating on the object's discriminative part instead of the entire entity scope. Benefiting from the capability of the self-attention mechanism to acquire long-range feature dependencies, Vision Transformer has been recently applied to alleviate the local activation drawbacks. However, since the transformer lacks the inductive localization bias that are inherent in CNNs, it may cause a divergent activation problem resulting in an uncertain distinction between foreground and background. In this work, we proposed a novel Semantic-Constraint Matching Network (SCMN) via a transformer to converge on the divergent activation. Specifically, we first propose a local patch shuffle strategy to construct the image pairs, disrupting local patches while guaranteeing global consistency. The paired images that contain the common object in spatial are then fed into the Siamese network encoder. We further design a semantic-constraint matching module, which aims to mine the co-object part by matching the coarse class activation maps (CAMs) extracted from the pair images, thus implicitly guiding and calibrating the transformer network to alleviate the divergent activation. Extensive experimental results conducted on two challenging benchmarks, including CUB-200-2011 and ILSVRC datasets show that our method can achieve the new state-of-the-art performance and outperform the previous method by a large margin

Linear MIMO Precoders Design for Finite Alphabet Inputs via Model-Free Training

This paper investigates a novel method for designing linear precoders with finite alphabet inputs based on autoencoders (AE) without the knowledge of the channel model. By model-free training of the autoencoder in a multiple-input multiple-output (MIMO) system, the proposed method can effectively solve the optimization problem to design the precoders that maximize the mutual information between the channel inputs and outputs, when only the input-output information of the channel can be observed. Specifically, the proposed method regards the receiver and the precoder as two independent parameterized functions in the AE and alternately trains them using the exact and approximated gradient, respectively. Compared with previous precoders design methods, it alleviates the limitation of requiring the explicit channel model to be known. Simulation results show that the proposed method works as well as those methods under known channel models in terms of maximizing the mutual information and reducing the bit error rate.Comment: Accepted by GLOBECOM 202