Computer Supported Cooperative Work and Social Computing : 15th CCF Conference, ChineseCSCW 2020, Shenzhen, China, November 7-9, 2020, Revised Selected Papers / by CCF Conference on Computer Supported Cooperative Work and Social Computing (15th : 2020 : Shenzhen, China), organiser 642953, Sun, Yuqing, editor 642954, Liu, Dongning, editor 642955, Liao, Hao, 1986-, editor 642956, Fan, Hongfei, editor 642957, Gao, Liping, editor 642958

Refining learning models in grammatical inference by Wang, Xiangrui

“…We introduce the use of recurrent neural networks (RNNs) and present a pruning learning method to avoid the exponential space costs. …”
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Robot programming using augmented reality by Chong, Jonathan Wun Shiung

“…The Piecewise Linear Parameterization (PLP) algorithm and a curve learning method based on Bayesian neural networks and reparameterization are proposed. …”
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A Non‐Intrusive Machine Learning Framework for Debiasing Long‐Time Coarse Resolution Climate Simulations and Quantifying Rare Events Statistics by Barthel Sorensen, B., Charalampopoulos, A., Zhang, S., Harrop, B. E., Leung, L. R., Sapsis, T. P.

“…Here, the scope is to formulate a learning method that allows for correction of dynamics and quantification of extreme events with longer return period than the training data. …”
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15.665B Power and Negotiation, Fall 2002 by Williams, Michele

“…You will learn and practice the technical skills and analytic frameworks that are necessary to negotiate successfully with peers from other top business schools, and you will learn methods for developing the powerful social capital you will need to rise in the executive ranks of any organization. …”
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Exploring disease axes as an alternative to distinct clusters for characterizing sepsis heterogeneity by Zhang, Zhongheng, Chen, Lin, Liu, Xiaoli, Yang, Jie, Huang, Jiajie, Yang, Qiling, Hu, Qichao, Jin, Ketao, Celi, Leo A., Hong, Yucai

“…The top-down transfer learning method (model trained on cohorts with greater severity was transferred to cohorts with lower severity score) had a higher NMI value than the bottom-up approach (median [Q1, Q3]: 0.64 [0.49, 0.78] vs. 0.23 [0.2, 0.31], p < 0.001). …”
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Hermes: Boosting the Performance of Machine-Learning-Based Intrusion Detection System through Geometric Feature Learning by Zhang, Chaoyu, Shi, Shanghao, Wang, Ning, Xu, Xiangxiang, Li, Shaoyu, Zheng, Lizhong, Marchany, Randy, Gardner, Mark, Hou, Y. Thomas, Lou, Wenjing

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Visual event recognition in videos by learning from web data by Duan, Lixin, Xu, Dong, Tsang, Ivor Wai-Hung, Luo, Jiebo

“…Second, we propose a new transfer learning method, referred to as Adaptive Multiple Kernel Learning (A-MKL), in order to 1) fuse the information from multiple pyramid levels and features (i.e., space-time features and static SIFT features) and 2) cope with the considerable variation in feature distributions between videos from two domains (i.e., web video domain and consumer video domain). …”
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Enhanced intrusion detection model based on principal component analysis and variable ensemble machine learning algorithm by John, Ayuba, Isnin, Ismail Fauzi, Madni, Syed Hamid Hussain, Muchtar, Farkhana

“…This paper proposes a variable ensemble machine learning method to solve the problem and achieve a low variance model with high accuracy and low false alarm. …”
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Speeding up deep neural network training with decoupled and analytic learning by Zhuang, Huiping

“…A fully decoupled learning method using delayed gradients (FDG) is first proposed which addresses all the three lockings. …”
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High cycle fatigue characterisation and modelling of 316L stainless steel processed by laser powder bed fusion by Zhang, Meng

“…Lastly, considering the numerous influencing factors arising from the process and the associated failure behaviours, a neuro-fuzzy-based machine learning method was applied to provide an effective unifying approach for high cycle fatigue life prediction. …”
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Topics in Bayesian machine learning for finance by Spears, T

“…Further, we estimate an approximation to epistemic uncertainty via a pseudo-Bayesian deep learning method. This work demonstrates the utility of the model output for deciding the relative allocation of risk capital across trades. …”

Learning-enabled decision-making for autonomous driving: framework and methodology by Huang, Zhiyu

“…The personalized cost learning method outperforms general cost modeling methods, leading to a more human-like driving experience. …”
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Conflict-free urban air mobility planning with an airspace-resource-centric approach by Dai, Wei

“…Motivated by the absence of a precise power consumption model that can be applied to multiple eVTOL aircraft types, we use the ensemble learning method to model the power consumption of eVTOL aircraft. …”
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Geometry guided supervised representation learning for classification by Li, Yue

“…However, the AE-based representation learning method, FAE-LG, is trained iteratively by using back-propagation (BP) that requires a significant amount of training time. …”
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Structured sparse representations for supervised and unsupervised learning by Zeng, Yijie

“…It is demonstrated that the proposed graph learning method, termed Adaptive Locality-constrained Clustering (ALC), generates more structured graph compared with predefined ones and provides better clustering performance on benchmark datasets. …”
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