Titlebook: Web and Big Data; 4th International Jo Xin Wang,Rui Zhang,Yang-Sae Moon Conference proceedings 2020 Springer Nature Switzerland AG 2020 art

后退

POWER

冷漠

Joint Learning-Based Anomaly Detection on KPI Datas for similarity-based model to timely improve the prior knowledge. Experiments on 13 public KPI datasets empirically confirm the superiority of our algorithm with a F1-Score improvement up to 20.5% on average.

invert

A Method for Decompensation Prediction in Emergency and Harsh Situations for prediction. The experimental results show that the Bi-LSTM-attention method, combined with eleven essential physiological variables, can be used to predict the decompensation of severe ICUs patients. The AUC-ROC can reach 0.8509. Furthermore, these eleven physiological variables can be easily m

Nonconformist

Parallel Variable-Length Motif Discovery in Time Series Using Subsequences Correlation to find time series motifs with variable lengths. We have conducted extensive experiments on public data sets, the results demonstrate that our method can efficiently find variable-length motifs in long time series.

separate

Improved Brain Segmentation Using Pixel Separation and Additional Segmentation Featuresrts in medical imaging to evaluate our segmentation results. We observe that our results are fairly close to the manual reference. Moreover, we observe that our model is 1.2.–2.6. faster than prior models. We conclude that our model is more efficient and accurate in practice for both infant and adul

Fatten

AXIOM

Evaluating Fault Tolerance of Distributed Stream Processing Systemsce performance. We carry out extensive experiments on two well-known open-sourced DSPSs. The results demonstrate performance gap of two systems, which is useful for choice and evolution of fault tolerance approaches.

HEDGE

LOCATE: Locally Anomalous Behavior Change Detection in Behavior Information Sequencevolving the locally anomalous behavior change in the BIS. Two real-world datasets were used to assess the performance of LOCATE. Experimental results demonstrated that LOCATE is effective in detecting locally anomalous behavior change.

性上瘾

Predicting Human Mobility with Self-attention and Feature Interaction in a more efficient and effective way. We evaluate MoveNet with three real-world mobility datasets, and show that MoveNet outperforms the state-of-the-art mobility predictor by around 10% in terms of accuracy, and simultaneously achieves faster convergence and over 4x training speedup.