Yining Quan

Xidian University

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i-10 index

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Co-Authors

Qiguang Miao

Xidian University

Peng Gang Sun

Xidian University

Jianfeng Song

Xidian University

Xunlian Wu

Xidian University

Ruyi Liu

Peking University

Xing Tang

Nanjing Institute of Industry Technology

Yunan Li

Ministry of Education of the People's Republic of China

Yutao Qi

Guangzhou Huali College

Jiachen Liu

University of Shanghai for Science and Technology

Han Zhang

Shenzhen University

Cooperative Institutions

Xidian University

Chinese Academy of Sciences

Ministry of Education of the People's Republic of China

University of Hong Kong

Huazhong University of Science and Technology

First Affiliated Hospital of Shihezi University Medical College

Sichuan University

Shanghai Artificial Intelligence Laboratory

South China University of Technology

Zhejiang University

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An improved secure access control model in Operating System

Journal of Xidian University (2006)

Yining Quan Yupu Hu

A mandatory secure access control model named SOSACM of Operating System that is based on the multi-level security policy is put forward.Its confidetiality inherits the BLP model,and its definition of integrity is on the basis of Biba model.But in fact,the simple conjunct of BLP and Biba models will make some legal object not accessible because the directions of information flow in the BLP model and integrity in the Biba model are opposite.In the model,a trusted level strategy that makes the combination of confidentiality and integrity property tight has been developed,which should ensure that subjects can access objects legally.The model will be beneficial to its application to constructing secure Operating Systems in future.

Mandatory access control

Discretionary access control

Information flow

Source

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Citations (1)

DLTD: Dynamic linear threshold model with decay for influence spread

Xunlian Wu Peng Gang Sun Yining Quan Qiguang Miao

<p>Influence propagates more widely over social networks, and strongly affects people’s thoughts, opinions, even behaviors in human livings. Influence spread is a more complicated problem that makes the modeling of this process even more challenging. The linear threshold (LT) model provides a simple way to describe the problem. However, this model neglects the decay of individuals’ influence as time lapses. This paper develops a dynamic linear threshold model with decay (DLTD) in which influence decays based on three strategies: (1) random decay, (2) linear decay, and (3) nonlinear decay. The results on real-world datasets show that compared with the LT model, the DLTD tends to be more accurate especially the decay with a linear function, and more concentrated to converge to the ground-truth as the size of seed set increases. We also observe that our model is more stable to the networks’ structures. Furthermore, an interesting fifinding indicates that the balance between community structures’ internal links and external links is more effective for influence spread.</p>

Threshold model

Distance decay

10.36227/techrxiv.20106149.v3

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Citations (0)

Filtering LiDAR data based on adjacent triangle of triangulated irregular network

Multimedia Tools and Applications (2016)

Yining Quan Jianfeng Song Xue Guo Qiguang Miao Yun Yang

Triangulated irregular network

Elevation (ballistics)

Ranging

Data set

10.1007/s11042-016-3465-4

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Citations (13)

Utopian point based decomposition for multi-objective optimization problems with complicated Pareto fronts

Applied Soft Computing (2017)

Yutao Qi Qingsai Zhang Xiaoliang Ma Yining Quan Qiguang Miao

Benchmark (surveying)

Reciprocal

10.1016/j.asoc.2017.08.036

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Citations (29)

A Novel Email Virus Propagation Model with Local Group

Qiguang Miao Xing Tang Yining Quan

Several email virus models have been proposed in previous papers. Usually, users in these models are homogeneous and symmetric which fail to model the real situation accurately. In this paper, a novel model with local group is presented. In real email system, users will usually group their contacts into various kinds of groups. Moreover, users have different levels of security awareness according to different closeness with certain group. To model the condition, we propose the propagation model with local group. In our model, a user is modeled as an autonomy agent who is able to deal with email and group its ego network. Based on the local group algorithm, open probability is carefully defined overcoming the disadvantage of homogeneous model. Applying our model on benchmark networks, we use the space-time diagrams for a small network and curve graph for real email network to verify our model. Simulations indicate that users will not be infected until his close friend group mostly being infected, showing that the group provides buffer for virus spreading. On the other hand, in accordance with the local group action in reality, the propagation is largely deferred compared with the homogeneous model.

Epidemic model

Closeness

10.1109/uic-atc-scalcom.2014.99

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Citations (1)

DLTD: Dynamic linear threshold model with decay for influence spread

Xunlian Wu Anqi Zhang Peng Gang Sun Yining Quan Qiguang Miao

Influence propagates more widely over social networks, and strongly affects people’s thoughts, opinions, even behaviors in human livings. Influence spread is a more complicated problem that makes the modeling of this process even more challenging. The linear threshold (LT) model provides a simple way to describe the problem. However, this model neglects the decay of individuals’ influence as time lapses. This paper develops a dynamic linear threshold model with decay (DLTD) in which influence decays based on three strategies: (1) random decay, (2) linear decay, and (3) nonlinear decay. The results on real-world datasets show that compared with the LT model, the DLTD tends to be more accurate especially the decay with a linear function, and more concentrated to converge to the ground-truth as the size of seed set increases. We also observe that our model is more stable to the networks’ structures. Furthermore, an interesting fifinding indicates that the balance between community structures’ internal links and external links is more effective for influence spread.

Threshold model

Distance decay

10.36227/techrxiv.20106149.v4

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Citations (0)

A Decomposition-Ensemble Learning Model Based on LSTM Neural Network for Daily Reservoir Inflow Forecasting

Water Resources Management (2019)

Yutao Qi Zhanao Zhou Lingling Yang Yining Quan Qiguang Miao

Inflow

Data pre-processing

10.1007/s11269-019-02345-1

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Citations (48)

Fast structural ensemble for One-Class Classification

Pattern Recognition Letters (2016)

Jiachen Liu Qiguang Miao Yanan Sun Jianfeng Song Yining Quan

Ensemble Learning

10.1016/j.patrec.2016.06.028

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Citations (17)

Refined probability distribution module for fine-grained visual categorization

Neurocomputing (2022)

Peipei Zhao Qiguang Miao Hongsheng Li Ruyi Liu Yining Quan

Similarity (geometry)

10.1016/j.neucom.2022.10.004

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Citations (3)

A Combinatorial Solution to Point Symbol Recognition

Sensors (2018)

Yining Quan Yuanyuan Shi Qiguang Miao Yutao Qi

Recent work has shown that recognizing point symbols is an essential task in the field of map digitization. For the identification of symbols, it is generally necessary to compare the symbols with a specific criterion and find the most similar one with each known symbol one by one. Most of the works can only identify a single symbol, a small number of works are to deal with multiple symbols simultaneously with a low recognition accuracy. Given the two deficiencies, this paper proposes a deep transfer learning architecture, where the task is to learn a symbol classifier with AlexNet. For the insufficient dataset, we develop a method for transfer learning that uses a MNIST dataset to pretrain the model, which makes up for the problem of small training dataset and enhances the generalization of the model. Before the recognition process, preprocessing the point symbols in the map to coarse screening out the areas suspected of point symbols. We show a significant improvement over using point symbol images to keep a high performance in being able to deal with many more categories of symbols simultaneously.

MNIST database

Symbol (formal)

Digitization

Transfer of learning

10.3390/s18103403

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Citations (4)