66 Papers
113 Citations
Le Sun is an academic researcher from Nanjing University of Information Science and Technology. The author has contributed to research in topics: Computer science & Cloud computing. The author has an hindex of 13, co-authored 45 publications. Previous affiliations of Le Sun include Chinese Ministry of Education & University of Electronic Science and Technology of China.
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Papers
Cloud service selection
TL;DR: A survey of state-of-the-art Cloud service selection approaches, which are analyzed from the following five perspectives: decision-making techniques; data representation models; parameters and characteristics of Cloud services; contexts, purposes.
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Supervised Anomaly Detection in Uncertain Pseudoperiodic Data Streams
TL;DR: Wang et al. as discussed by the authors proposed a wavelet soft thresholding method to remove the noises or errors in data streams, and developed effective period pattern recognition and feature extraction techniques to improve the computational efficiency.
A framework for cardiac arrhythmia detection from IoT-based ECGs
TL;DR: This paper proposes a framework for arrhythmia detection from IoT-based ECGs and proposes two solutions for the heartbeat classification task, namely Dynamic Heartbeat Classification with Adjusted Features (DHCAF) and Multi-channel Heartbeat Convolution Neural Network (MCHCNN).
Cloud Service Description Model: An Extension of USDL for Cloud Services
TL;DR: This paper proposes a unified semantic Cloud Service Description Model (CSDM), which will be extended from the basic structure of USDL, by defining cloud-service-specific attributes and an additional module, named transaction module, will be defined, which models the rating system of cloud services from several aspects, such as risk, trust, and reputation.
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Few-Shot Class-Incremental Learning for Medical Time Series Classification.
TL;DR: The Meta self-Attention prototype incrementer (MAPIC) as discussed by the authors is a few-shot class-incremental learning (FSCIL) framework for medical time series classification, which is more challenging to learn due to its large intra-class variability.
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