no code implementations • 1 Nov 2022 • Jun Zhan, Chengkun Wu, Canqun Yang, Qiucheng Miao, Xiandong Ma
In this paper, we propose a novel semi-supervised anomaly detection framework based on a heterogeneous feature network (HFN) for MTS, learning heterogeneous structure information from a mass of unlabeled time-series data to improve the accuracy of anomaly detection, and using attention coefficient to provide an explanation for the detected anomalies.
1 code implementation • 5 Mar 2020 • Peng Zhang, Jianbin Fang, Canqun Yang, Chun Huang, Tao Tang, Zheng Wang
This article presents an automatic approach to quickly derive a good solution for hardware resource partition and task granularity for task-based parallel applications on heterogeneous many-core architectures.
no code implementations • 8 Feb 2018 • Peng Zhang, Jianbin Fang, Tao Tang, Canqun Yang, Zheng Wang
In this paper, we present an automatic approach to determine the hardware resource partition and the task granularity for any given application, targeting the Intel XeonPhi architecture.
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