Online Language Teacher Skills and Roles in an Audio-Graphic Conferencing Classroom

Many institutions and individual teachers are moving from traditional face-to-face classrooms to online teaching. Traditional classroom language teachers need to understand why online teaching is different from classroom teaching before they acquire new skills and explore new pedagogies for online teaching. This study aims to identify the differences between teaching online and in face-to-face classrooms, and explore what new skills and roles beginner online language teachers need to develop in order to become successful language teachers in online classrooms. Audio-graphic conferencing classrooms are usually a basic form of online teaching and the starting point for many face-to-face teachers to move to online teaching. This study collects data from an OU-Live EAP tutorial in the Open University UK. Four critical incidents were selected from an online tutorial and analysed through multimodal discourse analysis based on the Model of Instructor Roles by Berge (2005) and the Skills Pyramid by Hampel and Stickler (2005). A video-stimulated recall interview was conducted to elicit the online tutor’s rationale for his actions in the four critical incidents. The major findings of the study include: (a) three major differences between teaching online and in face-to-face classrooms, including technical differences, lack of non-verbal cues, and multimodality in online learning environments; (b) two suggestions for the Skills Pyramid on ‘dealing with the possibilities and constraints of the system’ and ‘online socialization skill’ (Hampel and Stickler, 2005); and (c) two suggestions for the Model of Instructor Roles the on pedagogical role and the technical role of online language teachers (Berge, 1995). Recommendations for online teacher training and future research topics are presented in the end

TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series

This work summarizes two strategies for completing time-series (TS) tasks using today's language model (LLM): LLM-for-TS, design and train a fundamental large model for TS data; TS-for-LLM, enable the pre-trained LLM to handle TS data. Considering the insufficient data accumulation, limited resources, and semantic context requirements, this work focuses on TS-for-LLM methods, where we aim to activate LLM's ability for TS data by designing a TS embedding method suitable for LLM. The proposed method is named TEST. It first tokenizes TS, builds an encoder to embed them by instance-wise, feature-wise, and text-prototype-aligned contrast, and then creates prompts to make LLM more open to embeddings, and finally implements TS tasks. Experiments are carried out on TS classification and forecasting tasks using 8 LLMs with different structures and sizes. Although its results cannot significantly outperform the current SOTA models customized for TS tasks, by treating LLM as the pattern machine, it can endow LLM's ability to process TS data without compromising the language ability. This paper is intended to serve as a foundational work that will inspire further research.Comment: 10 pages, 6 figure

Digital piracy, creative productivity, and customer care effort: evidence from the digital publishing industry

We empirically investigate how writers’ output is affected by copyright piracy using data from a Chinese digital publishing platform. We identify two measurements of writers’ output—creative productivity and customer care—which are also affected by readers’ feedback through purchasing, tipping, and commenting. We take advantage of an exogenous event—the termination of a free personal storage service and search function by a leading Chinese cloud storage provider in June 2016—to causally identify the effects of the resulting reduced copyright piracy on writers’ efforts. Using a difference-in-differences modeling approach, we compare the changes in average writer behavior before and after the event across two groups of writers: (1) writers who have profit-sharing contracts with the platform and (2) those who do not. We find that after the termination, contracted writers increased their creative productivity efforts in terms of quantity without sac-rificing quality but reduced their customer care efforts. However, these effects are absent for noncontracted writers. Our study is among the first to provide empirical support for the positive effect of digital intellectual property rights infringement re-duction on creative productivity

Thick Cloud Removal of Remote Sensing Images Using Temporal Smoothness and Sparsity-Regularized Tensor Optimization

In remote sensing images, the presence of thick cloud accompanying cloud shadow is a high probability event, which can affect the quality of subsequent processing and limit the scenarios of application. Hence, removing the thick cloud and cloud shadow as well as recovering the cloud-contaminated pixels is indispensable to make good use of remote sensing images. In this paper, a novel thick cloud removal method for remote sensing images based on temporal smoothness and sparsity-regularized tensor optimization (TSSTO) is proposed. The basic idea of TSSTO is that the thick cloud and cloud shadow are not only sparse but also smooth along the horizontal and vertical direction in images while the clean images are smooth along the temporal direction between images. Therefore, the sparsity norm is used to boost the sparsity of the cloud and cloud shadow, and unidirectional total variation (UTV) regularizers are applied to ensure the unidirectional smoothness. This paper utilizes alternation direction method of multipliers to solve the presented model and generate the cloud and cloud shadow element as well as the clean element. The cloud and cloud shadow element is purified to get the cloud area and cloud shadow area. Then, the clean area of the original cloud-contaminated images is replaced to the corresponding area of the clean element. Finally, the reference image is selected to reconstruct details of the cloud area and cloud shadow area using the information cloning method. A series of experiments are conducted both on simulated and real cloud-contaminated images from different sensors and with different resolutions, and the results demonstrate the potential of the proposed TSSTO method for removing cloud and cloud shadow from both qualitative and quantitative viewpoints