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1.
Smart wheelchairs based on brain–computer interface (BCI) have been widely utilized recently to address certain mobility problems for people with disability. In this paper, we present SmartRolling, an intuitive human–machine interaction approach for the direct control of robotic wheelchair that jointly leverages EEG signals and motion sensing techniques. Specifically, SmartRolling offers two wheelchair-actuation modes for users with different physical conditions: (1) head motion only — people who are severely disabled but able to do basic tasks using eyes and head, and (2) head and hands motion — in addition to type 1, people who can use functioning hands/arms for extra tasks. The system issues operation commands by recognizing different EEG patterns elicited by motor execution (ME) tasks including eye blink, jaw clench, and fist open/close, while at the same time estimates users’ steering intentions based on their facing direction by leveraging inertial measurements and computer vision techniques. The experiment results demonstrate that the proposed system is robust and effective to meets the individual’s needs and has great potential to promote better health.  相似文献   
2.
李乖乖 《天津教育》2021,(6):183-184
许多小学数学教师已然认识到了开展小学数学阅读活动和制定科学的教学方法,对提高学生的计算能力和思维能力的重要作用。所以,教师在教学过程中要积极探索科学的教学方法培养学生的有效阅读能力,这样学生才能够更好地理解和学习数学知识,提高学生的解题思维能力和学习有效性。以下便对数学阅读活动的开展和深度学习课堂的构建进行分析和探究。  相似文献   
3.
Recently, models that based on Transformer (Vaswani et al., 2017) have yielded superior results in many sequence modeling tasks. The ability of Transformer to capture long-range dependencies and interactions makes it possible to apply it in the field of portfolio management (PM). However, the built-in quadratic complexity of the Transformer prevents its direct application to the PM task. To solve this problem, in this paper, we propose a deep reinforcement learning-based PM framework called LSRE-CAAN, with two important components: a long sequence representations extractor and a cross-asset attention network. Direct Policy Gradient is used to solve the sequential decision problem in the PM process. We conduct numerical experiments in three aspects using four different cryptocurrency datasets, and the empirical results show that our framework is more effective than both traditional and state-of-the-art (SOTA) online portfolio strategies, achieving a 6x return on the best dataset. In terms of risk metrics, our framework has an average volatility risk of 0.46 and an average maximum drawdown risk of 0.27 across the four datasets, both of which are lower than the vast majority of SOTA strategies. In addition, while the vast majority of SOTA strategies maintain a poor turnover rate of approximately greater than 50% on average, our framework enjoys a relatively low turnover rate on all datasets, efficiency analysis illustrates that our framework no longer has the quadratic dependency limitation.  相似文献   
4.
Partnership in higher education emphasises an active role for students in both teaching and learning. This pedagogical culture is likely to make students assessment literate and engage them in deep learning. In this study, Iranian students experiencing learning-by-teaching (LbT) in private language institutes were interviewed to compare their perceptions toward assessment and learning with their counterparts without this experience. Findings show that LbT fosters students’ assessment literacy and deep learning. Results also reveal that by teaching other students, quasi-teachers promote a broader understanding of assessment and grade practices in comparison to other students. Unlike their counterparts, quasi-teachers de-emphasised grades and showed a greater focus on learning. Moreover, explaining the materials to other students provided them with a deeper cognitive process resulting in deeper learning. These results underscore the perceived importance of partnership in higher education.  相似文献   
5.
《Journal of Informetrics》2019,13(2):485-499
With the growing number of published scientific papers world-wide, the need to evaluation and quality assessment methods for research papers is increasing. Scientific fields such as scientometrics, informetrics, and bibliometrics establish quantified analysis methods and measurements for evaluating scientific papers. In this area, an important problem is to predict the future influence of a published paper. Particularly, early discrimination between influential papers and insignificant papers may find important applications. In this regard, one of the most important metrics is the number of citations to the paper, since this metric is widely utilized in the evaluation of scientific publications and moreover, it serves as the basis for many other metrics such as h-index. In this paper, we propose a novel method for predicting long-term citations of a paper based on the number of its citations in the first few years after publication. In order to train a citation count prediction model, we employed artificial neural network which is a powerful machine learning tool with recently growing applications in many domains including image and text processing. The empirical experiments show that our proposed method outperforms state-of-the-art methods with respect to the prediction accuracy in both yearly and total prediction of the number of citations.  相似文献   
6.
Dimensions is a partly free scholarly database launched by Digital Science in January 2018. Dimensions includes journal articles and citation counts, making it a potential new source of impact data. This article explores the value of Dimensions from an impact assessment perspective with an examination of Food Science research 2008–2018 and a random sample of 10,000 Scopus articles from 2012. The results include high correlations between citation counts from Scopus and Dimensions (0.96 by narrow field in 2012) as well as similar average counts. Almost all Scopus articles with DOIs were found in Dimensions (97% in 2012). Thus, the scholarly database component of Dimensions seems to be a plausible alternative to Scopus and the Web of Science for general citation analyses and for citation data in support of some types of research evaluations.  相似文献   
7.
Information and Communication Technology (ICT) has improved education widely in China, transforming traditional teaching into an interactive one. It is important to build a new evaluation model to measure teaching efficiency in an “ICT-enabled classroom”. This study designed an evaluation model named “TPOCME Deep classroom” through ongoing five iterations. It includes six dimensions, which are higher-order Thinking, classroom Participation, Openness of educational system, Cooperative learning, Meaningful learning and Effectiveness of technology use (named TPOCME). This model helps educational researchers and teachers gain a comprehensive understanding of ICT in education.  相似文献   
8.
[目的/意义]基于文本挖掘技术自动发现更具代表性的文献内容主题词,通过定位主题词在章节中的具体位置,并基于可视化技术进行主题标引,帮助读者直观高效发现文献主题间的潜在关系。[方法/过程]基于文本挖掘技术深入文献内容层挖掘主题词,并利用可视化工具直观呈现所获信息,在此基础上尝试构建可视化主题自动标引系统,并在格萨尔领域的多个主题中对该系统的自动标引效果进行验证。[结果/结论]研究结果显示,该标引方法在格萨尔领域实现了文献内容级的可视化主题自动标引,快速精准地定位到章节、段落和句子。标引相关信息获取过程直观可视,并且具有交互性,可提升用户体验和参与度。文章以《英雄格萨尔》为例完成系统验证,但该标引方法技术本身无领域限定,可应用于其他领域的文献。  相似文献   
9.
[目的/意义] 从用户角度出发,研究基于用户自然标注的TF-IDF辅助标引算法。[方法/过程] 首先以核心期刊论文中作者标注的关键词和分类号为源数据,通过对关键词词频进行统计,使用TF-IDF算法构建用户标注词表、形成标引知识库,然后通过IK Analyzer分词软件对待标引的科技项目数据进行切词和停用词处理,进而使用TF-IDF算法和位置加权算法提取科技项目数据的特征词,最终实现对科技项目数据进行关键词和分类的同步标引。[结果/结论] 实验结果表明,机标关键词与人标关键词的相似比在60%以上的科技项目数据占总数的68.1%,机标分类号与人标分类号前三位一致的占总数的83.9%,结果表明基于用户自然标注数据并采用TF-IDF算法在关键词和分类标引方面是可行的。  相似文献   
10.
Quickly and accurately summarizing representative opinions is a key step for assessing microblog sentiments. The Ortony-Clore-Collins (OCC) model of emotion can offer a rule-based emotion export mechanism. In this paper, we propose an OCC model and a Convolutional Neural Network (CNN) based opinion summarization method for Chinese microblogging systems. We test the proposed method using real world microblog data. We then compare the accuracy of manual sentiment annotation to the accuracy using our OCC-based sentiment classification rule library. Experimental results from analyzing three real-world microblog datasets demonstrate the efficacy of our proposed method. Our study highlights the potential of combining emotion cognition with deep learning in sentiment analysis of social media data.  相似文献   
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