Lianyong Qi
Qufu Normal University
250 Papers
558 Citations
Lianyong Qi is an academic researcher from Qufu Normal University. The author has contributed to research in topics: Computer science & Cloud computing. The author has an hindex of 36, co-authored 190 publications. Previous affiliations of Lianyong Qi include Nanjing University & Wuhan University.
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Papers
A Proof-of-Majority Consensus Protocol for Blockchain-enabled Collaboration Infrastructure of 5G Network Slice Brokers
Wenmin Lin,Xiaolong Xu,Lianyong Qi,Xuyun Zhang,Wanchun Dou,Mohammad Reza Khosravi +5 more
- 06 Oct 2020
TL;DR: A Proof-of-Majority (PoM) consensus protocol is designed to sort slice trading transactions before packing transaction blocks of the shared ledger to enhance the accountability of slice trading records.
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Temporal-Sparsity Aware Service Recommendation Method via Hybrid Collaborative Filtering Techniques
Shunmei Meng,Shunmei Meng,Qianmu Li,Shiping Chen,Shui Yu,Lianyong Qi,Wenmin Lin,Xiaolong Xu,Wanchun Dou +8 more
- 12 Nov 2018
TL;DR: A temporal-sparsity aware service recommendation method based on hybrid collaborative filtering techniques and a time-aware latent factor model based on a tensor decomposition model is applied to mine the temporal similarity between services.
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A QoS-aware exception handling method in scientific workflow execution
TL;DR: An exception handling method, named relaxingMe (constraints Relaxing Method, RelaxingMe) is proposed, which aims at relaxing the original QoS constraint values requested by the interrupted task node, in order to find a near‐to‐optimal candidate service to replace the unavailable one.
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TrCMP: A dependable app usage inference design for user behavior analysis through cyber-physical parameters
TL;DR: A design of a dependable app usage inference, named TrCMP is proposed to understand the inference in a mobile system through a combination of the cyber and physical system parameters, and to find the most effective weight values for each parameter.
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Towards effective learning for face super-resolution with shape and pose perturbations
TL;DR: This paper proposes to make use of the face-specific priors to enhance the performance of face super-resolution with the convolutional neural networks to impose perturbations on the low-dimensional space and generate face samples with novel appearance.
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