Motivating English as a Foreign Language Teachers to Cultivate Intercultural Competence through an Online Module: An Instructional Design Project

Academic papers and online resourcesMost teaching pedagogies in English as a Foreign Language (EFL) classes focused on enhancing students’ linguistic skills rather than exploring how cultures or politics influenced the interpretation of the English language. To address the challenge, computer-mediated communication (CMC) tools were used to foster online intercultural communication. Attention, relevance, confidence, and satisfaction (ARCS) motivational design model and critical pedagogy were used in developing this online professional development module. To cultivate EFL teachers’ intercultural competence, this language and identity unit utilized multimedia resources to raise participants’ attention, news articles to relate their lived experiences, online forums to establish their confidence, and intercultural experiences to increase their satisfaction. Data was collected from 16 EFL teachers’ questionnaires, online comments, and interviews. It was found that task attractiveness and online environment were factors that motivated participants to become critically literate. Current research only reveals a partial view of motivation, and thus long-range research would be worthwhile to investigate how cultural dynamics within groups may influence online communication

Learning Deep Latent Spaces for Multi-Label Classification

Multi-label classification is a practical yet challenging task in machine learning related fields, since it requires the prediction of more than one label category for each input instance. We propose a novel deep neural networks (DNN) based model, Canonical Correlated AutoEncoder (C2AE), for solving this task. Aiming at better relating feature and label domain data for improved classification, we uniquely perform joint feature and label embedding by deriving a deep latent space, followed by the introduction of label-correlation sensitive loss function for recovering the predicted label outputs. Our C2AE is achieved by integrating the DNN architectures of canonical correlation analysis and autoencoder, which allows end-to-end learning and prediction with the ability to exploit label dependency. Moreover, our C2AE can be easily extended to address the learning problem with missing labels. Our experiments on multiple datasets with different scales confirm the effectiveness and robustness of our proposed method, which is shown to perform favorably against state-of-the-art methods for multi-label classification.Comment: published in AAAI-201

stereoacuity in processing near and far stimuli

Abstract The experiment compared stereoacuity with Chinese characters when they appeared at different visual field, depth, and time duration. Character in front of the horopter was presented in LoVF, which induces crossed retinal disparities (CRD). In contrast, character behind the horopter was presented in UVF, which induces uncrossed retinal disparities (URD). The results showed that males were superior to the information presented on the UVF, while females did not show significant bias. Moreover, males were more sensitive to the size constancy illusion in which a far thing appears larger (e.g., character behind the horopter) under short and long timescales, while females were sensitive to character in front of the horopter under long timescales. The results supported earlier claims that female brains were less lateralized than male brains, and two genders showed different strategies in processing the stereoscopic stimuli

Stereoacuity in processing near or far stimuli

Abstract The experiment compared stereoacuity with Chinese characters when they appeared at different visual field, depth, and time duration. Character in front of the horopter was presented in LoVF, which induces crossed retinal disparities (CRD). In contrast, character behind the horopter was presented in UVF, which induces uncrossed retinal disparities (URD). The results showed that males were superior to the information presented on the UVF, while females did not show significant bias. Moreover, males were more sensitive to the size constancy illusion in which a far thing appears larger (e.g., character behind the horopter) under short and long timescales, while females were sensitive to character in front of the horopter under long timescales. The results supported earlier claims that female brains were less lateralized than male brains, and two genders showed different strategies in processing the stereoscopic stimuli

Cloud-based Image Processing System with Priority-based Data Distribution Mechanism

[[abstract]]Most users process short tasks using MapReduce. In other words, most tasks handled by the Map and Reduce functions require low response time. Currently, quite few users use MapReduce for 2D to 3D image processing, which is highly complicated and requires long execution time. However, in our opinion, MapReduce is exactly suitable for processing applications of high complexity and high computation. This paper implements MapReduce on an integrated 2D to 3D multi-user system, in which Map is responsible for image processing procedures of high complexity and high computation, and Reduce is responsible for integrating the intermediate data processed by Map for the final output. Different from short tasks, when several users compete simultaneously to acquire data from MapReduce for 2D to 3D applications, data that waits to be processed by Map will be delayed by the current user and Reduce has to wait until the completion of all Map tasks to generate the final result. Therefore, a novel scheduling scheme, Dynamic Switch of Reduce Function (DSRF) Algorithm, is proposed in this paper for MapReduce to switch dynamically to the next task according to the achieved percentage of tasks and reduce the idle time of Reduce. By using Hadoop to implement our MapReduce platform, we compare the performance of traditional Hadoop with our proposed scheme. The experimental results reveal that our proposed scheduling scheme efficiently enhances MapReduce performance in running 2D to 3D applications.[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子

Learnable Mixed-precision and Dimension Reduction Co-design for Low-storage Activation

Recently, deep convolutional neural networks (CNNs) have achieved many eye-catching results. However, deploying CNNs on resource-constrained edge devices is constrained by limited memory bandwidth for transmitting large intermediated data during inference, i.e., activation. Existing research utilizes mixed-precision and dimension reduction to reduce computational complexity but pays less attention to its application for activation compression. To further exploit the redundancy in activation, we propose a learnable mixed-precision and dimension reduction co-design system, which separates channels into groups and allocates specific compression policies according to their importance. In addition, the proposed dynamic searching technique enlarges search space and finds out the optimal bit-width allocation automatically. Our experimental results show that the proposed methods improve 3.54%/1.27% in accuracy and save 0.18/2.02 bits per value over existing mixed-precision methods on ResNet18 and MobileNetv2, respectively