The Use of Digital Storytelling in Bilingual/Multilingual Students\u27 Meaning-making: A Systematic Literature Review

This thesis is a systematic literature review of 24 empirical studies on using digital storytelling for bilingual/multilingual students’ meaning-making. Through thematic and scientific analyses, this review identifies the contextual backgrounds of the reviewed studies; for example, most of them were conducted in secondary schools in America. The strength analysis reveals that more than half of the papers lack sufficient details in analyzing and presenting data, and this may impact the trustworthiness of the claimed results. The reported uses of digital storytelling for bilingual/multilingual students include supporting learners as designers, promoting education equity, and incorporating multiliteracies pedagogy. The review also reported the pertaining benefits of digital storytelling, such as enhancing students’ identity investment, connecting learning domains, and supporting language learning. This thesis ends with implications for using digital storytelling as a pedagogy for diverse learners

Multi-Level Firing with Spiking DS-ResNet: Enabling Better and Deeper Directly-Trained Spiking Neural Networks

Spiking neural networks (SNNs) are bio-inspired neural networks with asynchronous discrete and sparse characteristics, which have increasingly manifested their superiority in low energy consumption. Recent research is devoted to utilizing spatio-temporal information to directly train SNNs by backpropagation. However, the binary and non-differentiable properties of spike activities force directly trained SNNs to suffer from serious gradient vanishing and network degradation, which greatly limits the performance of directly trained SNNs and prevents them from going deeper. In this paper, we propose a multi-level firing (MLF) method based on the existing spatio-temporal back propagation (STBP) method, and spiking dormant-suppressed residual network (spiking DS-ResNet). MLF enables more efficient gradient propagation and the incremental expression ability of the neurons. Spiking DS-ResNet can efficiently perform identity mapping of discrete spikes, as well as provide a more suitable connection for gradient propagation in deep SNNs. With the proposed method, our model achieves superior performances on a non-neuromorphic dataset and two neuromorphic datasets with much fewer trainable parameters and demonstrates the great ability to combat the gradient vanishing and degradation problem in deep SNNs.Comment: Accepted by the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI-22

CoLaDa: A Collaborative Label Denoising Framework for Cross-lingual Named Entity Recognition

Cross-lingual named entity recognition (NER) aims to train an NER system that generalizes well to a target language by leveraging labeled data in a given source language. Previous work alleviates the data scarcity problem by translating source-language labeled data or performing knowledge distillation on target-language unlabeled data. However, these methods may suffer from label noise due to the automatic labeling process. In this paper, we propose CoLaDa, a Collaborative Label Denoising Framework, to address this problem. Specifically, we first explore a model-collaboration-based denoising scheme that enables models trained on different data sources to collaboratively denoise pseudo labels used by each other. We then present an instance-collaboration-based strategy that considers the label consistency of each token's neighborhood in the representation space for denoising. Experiments on different benchmark datasets show that the proposed CoLaDa achieves superior results compared to previous methods, especially when generalizing to distant languages.Comment: ACL 2023. Our code is available at https://github.com/microsoft/vert-papers/tree/master/papers/CoLaD

Research on comment target extracting in Chinese online shopping platform

Purpose - This paper aims to extract the comment targets in Chinese online shopping platform. Design/methodology/approach - The authors first collect the comment texts, word segmentation, part-of-speech (POS) tagging and extracted feature words twice. Then they cluster the evaluation sentence and find the association rules between the evaluation words and the evaluation object. At the same time, they establish the association rule table. Finally, the authors can mine the evaluation object of comment sentence according to the evaluation word and the association rule table. At last, they obtain comment data from Taobao and demonstrate that the method proposed in this paper is effective by experiment. Findings - The extracting comment target method the authors proposed in this paper is effective. Research limitations/implications - First, the study object of extracting implicit features is review clauses, and not considering the context information, which may affect the accuracy of the feature excavation to a certain degree. Second, when extracting feature words, the low-frequency feature words are not considered, but some low-frequency feature words also contain effective information. Practical implications - Because of the mass online reviews data, reading every comment one by one is impossible. Therefore, it is important that research on handling product comments and present useful or interest comments for clients. Originality/value - The extracting comment target method the authors proposed in this paper is effective

Unveiling the multifaceted roles of microRNAs in extracellular vesicles derived from mesenchymal stem cells: implications in tumor progression and therapeutic interventions

Mesenchymal stem/stromal cells (MSCs) have the capacity to migrate to tumor sites in vivo and transmit paracrine signals by secreting extracellular vesicles (EVs) to regulate tumor biological behaviors. MSC-derived EVs (MSC-EVs) have similar tumor tropism and pro- or anti-tumorigenesis as their parental cells and exhibit superior properties in drug delivery. MSC-EVs can transfer microRNAs (miRNAs) to tumor cells, thereby manipulating multiple key cancer-related pathways, and further playing a vital role in the tumor growth, metastasis, drug resistance and other aspects. In addition, tumor cells can also influence the behaviors of MSCs in the tumor microenvironment (TME), orchestrating this regulatory process via miRNAs in EVs (EV-miRNAs). Clarifying the specific mechanism by which MSC-derived EV-miRNAs regulate tumor progression, as well as investigating the roles of EV-miRNAs in the TME will contribute to their applications in tumor pharmacotherapy. This article mainly reviews the multifaceted roles and mechanism of miRNAs in MSC-EVs affecting tumor progression, the crosstalk between MSCs and tumor cells caused by EV-miRNAs in the TME. Eventually, the clinical applications of miRNAs in MSC-EVs in tumor therapeutics are illustrated

A driving behavior model evaluation for UBI

Purpose – This paper aims to provide a driving behavior scoring model to decide the personalized automobile premium for each driver. Design/methodology/approach – Driving behavior scoring model. Findings – The driving behavior scoring model could effectively reflect the risk level of driver’s safe driving.Originality/value – A driving behavior scoring model for UBI

Innovation Input and Firm Value: Based on the Moderating Effect of Internal Control

Using the data of Chinese A-share listed firms with non-zero innovation investment between 2007 and 2017, this paper links the value relevance of innovation investment with internal control from the perspective of operating performance and market value, respectively. This paper empirically verifies that internal control significantly increases the value relevance of innovation input, that is, the better the internal control, the more innovation investment contributes to the operating performance and market value of a firm. Then, based on the potential mechanisms of alleviating agency problems and conducting better risk management, further investigation in this paper also indicates that internal control’s moderating effect on the value relevance of innovation input is more prominent for firms with relatively more severe agency problems and for expensed R&D expenditure which represents the part of innovation investment with higher uncertainty