Titlebook: Neural Information Processing; 27th International C Haiqin Yang,Kitsuchart Pasupa,Irwin King Conference proceedings 2020 Springer Nature Sw

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Xiaoyuan Hu,Qing Xu,Yuejun Guoprojects, the number of changes that need to be integrated, and consequently the number of comments triggered during MCRs could be overwhelming. Therefore, there is a need for quickly recognizing which comments are concerning issues that need prompt attention to guide the focus of the code authors,

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Amina Ben Meriem,Lobna Hlaoua,Lotfi Ben Romdhaneprojects, the number of changes that need to be integrated, and consequently the number of comments triggered during MCRs could be overwhelming. Therefore, there is a need for quickly recognizing which comments are concerning issues that need prompt attention to guide the focus of the code authors,

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A Hybrid Representation of Word Images for Keyword Spottingcabulary (OOV) is frequently occurred in keyword spotting. Therefore, the problem of OOV keyword spotting is a challenging task. In this paper, a hybrid representation approach of word images has been presented to accomplish the aim of OOV keyword spotting. To be specific, a sequence to sequence mod

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A Simple and Novel Method to Predict the Hospital Energy Use Based on Machine Learning: A Case Studyutilization and great variability of usage characteristic. With the development of machine learning techniques, it can offer opportunities for predicting the energy consumptions in hospital. With a case hospital building in Norway, through analyzing the characteristic of this building, this paper fo

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maculated

Clustering Ensemble Selection with Analytic Hierarchy Process. The significance of base clustering is quantified by the average or weighted average of multiple evaluation indexes. However, there exist two limitations in these methods. First, the evaluation of base clusterings in the form of linear combination of multiple indexes lacks the structural analysis

Libido

Deep Learning for In-Vehicle Intrusion Detection Systemur safety directly. In this work, we propose a Deep CAN intrusion detection system framework. We introduce a multivariate time series representation for asynchronous CAN data which enhances the temporal modelling of deep learning architectures for anomaly detection. We study different deep learning