Fang Shen

Xidian University · School of Artificial Intelligence

Reconstructing the structure of complex networks from time series is beneficial to understanding and controlling the collective dynamics of networked systems. Existing network reconstruction algorithms can only deal with one network reconstruction problem at one time. However, real-world applications typically have multiple network reconstruction t...

The problem of reconstructing nonlinear and complex dynamical systems from available data or time series is prominent in many fields including engineering, physical, computer, biological, and social sciences. Many methods have been proposed to address this problem and their performance is satisfactory. However, none of them can reconstruct network...

The problem of inferring nonlinear and complex dynamical systems from available data is prominent in many fields including engineering, biological, social, physical, and computer sciences. Many evolutionary algorithm (EA) based network reconstruction methods have been proposed to address this problem, but they ignore several useful information of n...

The semi-supervised video anomaly detection assumes that only normal video clips are available for training. Therefore, the intuitive idea is either to learn a dictionary by sparse coding or to train encoding-decoding neural networks by minimizing the reconstruction errors. For the former, the optimization of sparse coefficients is extremely time-c...

Fuzzy cognitive maps (FCMs) have been successfully applied to time series forecasting. However, it still remains challenging to handle multivariate long nonstationary time series, such as EEG data, which may change rapidly and have patterns of trend. To overcome this limitation, in this article, we propose a fast prediction model to deal with multi...

Fuzzy cognitive map (FCM) is an effective tool for modeling and simulating complex dynamic systems. Research on the problem of learning FCM from available time series is outstanding. Many batch FCM learning methods have been proposed to address this issue and the performance of these methods is satisfactory. However, these batch-learning methods ar...

Learning large-scale fuzzy cognitive maps (FCMs) with the sparse attribute automatically from time series without prior knowledge remains a challenging problem. Most existing automated learning methods were applied to learn small-scale FCMs and the learned FCMs are much denser than the maps constructed by human experts. Learning FCMs is the procedu...

In real-world applications, there exist multiple fuzzy cognitive maps (FCMs) learning tasks with similar attributes that have to be optimized simultaneously, however, all existing algorithms were designed to learn single FCM without considering the valuable patterns that can share with each other. For the purpose of making use of similar structure...

How to build a generic deep one-class (DeepOC) model to solve one-class classification problems for anomaly detection, such as anomalous event detection in complex scenes? The characteristics of existing one-class labels lead to a dilemma: it is hard to directly use a multiple classifier based on deep neural networks to solve one-class classificati...

The babyhood is a very important stage in the human growth process. Thus learning the facial expression characteristics of infants is of great significance to nursing care for infants. Infants' faces are significantly different from adults' faces, like eyebrows, eyes, nose, cheeks, skin texture, etc. Therefore, the facial expression recognition mod...

The Local Binary Pattern (LBP) is a widely used descriptor in facial expression recognition due to its efficiency and effectiveness. However, existing facial expression recognition methods based on LBP either ignore different kinds of information, such as details and the contour of faces, or rely on the division of face images, such as dividing the...