TOP-DOWN AND BOTTOM-UP STRATEGY USE AMONG GOOD AND POOR READERS IN EFL READING COMPREHENSION

This study revealed the readers’ use of top-down and bottom-up strategies in EFL learning context in Taiwan. The participants, 111 undergraduates EFL learners, were classified into good and poor readers. Quantitative and qualitative data were collected through a questionnaire and interviews. The results showed that almost no difference was confirmed between good and poor readers in bottom-up and total strategy use, whereas it was found that good readers tended to use more top-down strategies than poor readers. It is suggested that both groups of readers use bottom-up strategies to a similar degree; however, the use of top-down strategies has helped good readers advance their level of reading comprehension.  Article visualizations

Recognition of Design Fixation via Body Language Using Computer Vision

From Hindawi via Jisc Publications RouterHistory: received 2020-12-21, publication-year 2021, rev-recd 2021-06-19, accepted 2021-08-12, archival-date 2021-08-27, pub-print 2021-08-27Publication status: PublishedFunder: National Natural Science Foundation of China; doi: http://dx.doi.org/10.13039/501100001809; Grant(s): 51905175Funder: Shanghai Pujiang Talent Program; Grant(s): 2019PJC021Funder: Ministry of Education of the People's Republic of China; doi: http://dx.doi.org/10.13039/501100002338; Grant(s): 202002SZ05Funder: Shanghai Soft Science Key Project; Grant(s): 21692196800The main objective of this study is to recognize design fixation accurately and effectively. First, we conducted an experiment to record the videos of design process and design sketches from 12 designers for 15 minutes. Then, we executed a video analysis of body language in designers, correlating body language to the presence of design fixation, as judged by a panel of six experts. We found that three body language types were significantly correlated to fixation. A two-step hybrid recognition model of design fixation based on body language was proposed. The first-step recognition model of body language using transfer learning based on a pretrained VGG-16 convolutional neural network was constructed. The average recognition rate achieved by the VGG-16 model was 92.03%. Then, the frames of recognized body language were used as input vectors to the second-step fixation classification model based on support vector machine (SVM). The average recognition rate for the fixation state achieved by the SVM model was 79.11%. The impact of the work could be that the fixation can be detected not only by the sketch outcomes but also by monitoring the movements, expressions, and gestures of designers, as it is happening by monitoring the movements, expressions, and gestures of designers

sEMG-Based Drawing Trace Reconstruction: A Novel Hybrid Algorithm Fusing Gene Expression Programming into Kalman Filter

How to reconstruct drawing and handwriting traces from surface electromyography (sEMG) signals accurately has attracted a number of researchers recently. An effective algorithm is crucial to reliable reconstruction. Previously, nonlinear regression methods have been utilized successfully to some extent. In the quest to improve the accuracy of transient myoelectric signal decoding, a novel hybrid algorithm KF-GEP fusing Gene Expression Programming (GEP) into Kalman Filter (KF) framework is proposed for sEMG-based drawing trace reconstruction. In this work, the KF-GEP was applied to reconstruct fourteen drawn shapes and ten numeric characters from sEMG signals across five participants. Then the reconstruction performance of KF-GEP, KF and GEP were compared. The experimental results show that the KF-GEP algorithm performs best because it combines the advantages of KF and GEP. The findings add to the literature on the muscle-computer interface and can be introduced to many practical fields