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Real-time Facial Expression Recognition via Dense & Squeeze-and-Excitation Blocks 实时面部表情识别通过密集&挤压和激励块
IF 6.6 2区 计算机科学 Q1 Computer Science
Human-Centric Computing and Information Sciences
Pub Date : 2022-01-01 DOI: 10.22967/HCIS.2022.12.039
Fan-Hsun Tseng, Yen-Pin Cheng, Yu Wang, Hung-Yue Suen
Due to the coronavirus disease 2019 (COVID-19) pandemic, traditional face-to-face courses have been transformed into online and e-learning courses. Although online courses provide flexible teaching and learning in terms of time and place, teachers cannot be fully aware of their students’ individual learning situation and emotional state. The cognition of learning emotion with facial expression recognition has been a vital issue in recent years. To achieve affective computing, the paper presented a fast recognition model for learning emotions through Dense Squeeze-and-Excitation Networks (DSENet), which rapidly recognizes students’ learning emotions, while the proposed real-time online feedback system notifies teacher instantaneously. Firstly, DSENet is trained and validated by an open dataset called Facial Expression Recognition 2013. Then, we collect students’ learning emotions from e-learning classes and apply transfer learning and data augmentation techniques to improve the testing accuracy. The proposed DSENet model and real-time online feedback system aim to realize effective e-learning for any teaching and learning environments, especially in the COVID-19 environment of late © This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
由于2019冠状病毒病(COVID-19)大流行，传统的面对面课程已转变为在线和电子学习课程。虽然网络课程在时间和地点上提供了灵活的教与学，但教师无法充分了解学生的个人学习情况和情绪状态。基于面部表情识别的学习情绪认知是近年来研究的热点问题。为了实现情感计算，本文提出了一种基于密集挤压激励网络(Dense Squeeze-and-Excitation Networks, DSENet)的学习情绪快速识别模型，该模型能够快速识别学生的学习情绪，同时提出的实时在线反馈系统能够即时通知教师。首先，DSENet通过一个名为“面部表情识别2013”的开放数据集进行训练和验证。然后，我们从在线学习课程中收集学生的学习情绪，并应用迁移学习和数据增强技术来提高测试的准确性。提出的DSENet模型和实时在线反馈系统旨在实现任何教学和学习环境下有效的电子学习，特别是在最近的COVID-19环境中©这是一篇基于知识共享署名非商业许可(http://creativecommons.org/licenses/by-nc/3.0/)的开放获取文章，该许可允许在任何媒介上不受限制的非商业使用，分发和复制，前提是正确引用原创作品。
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引用次数: 1
CoNavigator: A Framework of FCA-Based Novel Coronavirus COVID-19 Domain Knowledge Navigation 导航器:基于fca的新型冠状病毒COVID-19领域知识导航框架
IF 6.6 2区 计算机科学 Q1 Computer Science
Human-Centric Computing and Information Sciences
Pub Date : 2021-01-01 DOI: 10.22967/hcis.2021.11.006
F. Hao, D. Park
The rapidly increasing number of confirmed cases and extremely large amounts of medical resources about coronavirus disease 2019 (COVID-19) pandemic are enriching the COVID-19 domain knowledge bases. Undoubtedly, it is resulting in "information overload" and "information confusion" for users. Hence, how to achieve precision information navigation for COVID-19 related domain knowledge retrieval and personalized services, has become a huge challenge of this research. Due to the unique retrieval and navigation advantages of concept lattice, a framework of novel coronavirus COVID-19 domain knowledge navigation named CoNavigator, based on Formal Concept Analysis (FCA) theory is devised. It provides a promising approach for improving the retrieval efficiency from various novel coronavirus COVID-19 information resource platforms. Besides, some useful patterns are extracted and regarded as important evidence for an appropriate understanding of the pathological features of novel coronavirus COVID-19.
新型冠状病毒病(COVID-19)大流行的确诊病例数量迅速增加，医疗资源极其丰富，正在丰富COVID-19领域知识库。这无疑给用户带来了“信息超载”和“信息混乱”。因此，如何实现COVID-19相关领域知识检索和个性化服务的精准信息导航，成为本研究的巨大挑战。由于概念格独特的检索和导航优势，基于形式概念分析(FCA)理论设计了新型冠状病毒COVID-19领域知识导航框架connavigator。为提高各种新型冠状病毒信息资源平台的检索效率提供了一种有前景的途径。此外，我们还提取了一些有用的模式，作为正确理解新型冠状病毒COVID-19病理特征的重要证据。
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引用次数: 10
Multiple Kinect based system to monitor and analyze key performance indicators of physical training 基于多个Kinect的系统，用于监测和分析体能训练的关键性能指标
IF 6.6 2区 计算机科学 Q1 Computer Science
Human-Centric Computing and Information Sciences
Pub Date : 2020-12-01 DOI: 10.1186/s13673-020-00256-4
Karolis Ryselis, T. Petkus, T. Blažauskas, R. Maskeliūnas, Robertas Damaševičius
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引用次数: 46
An efficient attribute-based hierarchical data access control scheme in cloud computing 云计算中一种高效的基于属性的分层数据访问控制方案
IF 6.6 2区 计算机科学 Q1 Computer Science
Human-Centric Computing and Information Sciences
Pub Date : 2020-12-01 DOI: 10.1186/s13673-020-00255-5
Heng He, Liang-han Zheng, Peng Li, L. Deng, Li Huang, Xiang Chen
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引用次数: 20
A model-driven approach to ensure trust in the IoT 一种模型驱动的方法来确保对物联网的信任
IF 6.6 2区 计算机科学 Q1 Computer Science
Human-Centric Computing and Information Sciences
Pub Date : 2020-12-01 DOI: 10.1186/s13673-020-00257-3
Davide Ferraris, M. C. Gago, Javier López
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引用次数: 11
Social Internet of Things: vision, challenges, and trends 社交物联网:愿景、挑战和趋势
IF 6.6 2区 计算机科学 Q1 Computer Science
Human-Centric Computing and Information Sciences
Pub Date : 2020-12-01 DOI: 10.1186/s13673-020-00254-6
Mozhgan Malekshahi Rad, A. Rahmani, A. Sahafi, N. Qader
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引用次数: 49
The effect of eye movements and cultural factors on product color selection 眼球运动和文化因素对产品颜色选择的影响
IF 6.6 2区 计算机科学 Q1 Computer Science
Human-Centric Computing and Information Sciences
Pub Date : 2020-11-24 DOI: 10.1186/s13673-020-00249-3
Bo Wu, Yishui Zhu, Keping Yu, Shoji Nishimura, Qun Jin
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引用次数: 5
Collaborative behavior, performance and engagement with visual analytics tasks using mobile devices 使用移动设备进行可视化分析任务的协作行为、性能和参与度
IF 6.6 2区 计算机科学 Q1 Computer Science
Human-Centric Computing and Information Sciences
Pub Date : 2020-11-22 DOI: 10.1186/s13673-020-00253-7
Lei Chen, Hai-Ning Liang, Feiyu Lu, Konstantinos Papangelis, K. Man, Yong Yue
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引用次数: 10
Medical image processing with contextual style transfer 具有上下文风格迁移的医学图像处理
IF 6.6 2区 计算机科学 Q1 Computer Science
Human-Centric Computing and Information Sciences
Pub Date : 2020-11-10 DOI: 10.1186/s13673-020-00251-9
Yi Xu, Yan Li, B. Shin
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引用次数: 9
Newspaper article-based agent control in smart city simulations 智能城市仿真中基于报刊文章的代理控制
IF 6.6 2区 计算机科学 Q1 Computer Science
Human-Centric Computing and Information Sciences
Pub Date : 2020-11-03 DOI: 10.1186/s13673-020-00252-8
Euhee Kim, Sejun Jang, Shuyu Li, Yunsick Sung
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引用次数: 6
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