Current local density-based anomaly detection methods are limited in that the local density estimation and the neighborhood density estimation are not accurate enough for complex and large databases, and the detection performance depends on the size parameter of the neighborhood. In this paper, we propose a new kernel function to estimate samples' local densities and propose a weighted neighborhood density estimation to increase the robustness to changes in the neighborhood size. We further propose a local kernel regression estimator and a hierarchical strategy for combining information from the multiple scale neighborhoods to refine anomaly factors of samples. We apply our general anomaly detection method to image saliency detection by regarding salient pixels in objects as anomalies to the background regions. Local density estimation in the visual feature space and kernel-based saliency score propagation in the image enable the assignment of similar saliency values to homogenous object regions. Experimental results on several benchmark datasets demonstrate that our anomaly detection methods overall outperform several state-of-art anomaly detection methods. The effectiveness of our image saliency detection method is validated by comparison with several state-of-art saliency detection methods.

Recently, recommender systems play a pivotal role in alleviating the problem of information overload. Latent factor models have been widely used for recommendation. Most existing latent factor models mainly utilize the interaction information between users and items, although some recently extended models utilize some auxiliary information to learn a unified latent factor for users and items. The unified latent factor only represents the characteristics of users and the properties of items from the aspect of purchase history. However, the characteristics of users and the properties of items may stem from different aspects, e.g., the brand-aspect and category-aspect of items. Moreover, the latent factor models usually use the shallow projection, which cannot capture the characteristics of users and items well. Deep neural network has shown tremendous potential to model the non-linearity relationship between users and items. It can be used to replace shallow projection to model the complex correlation between users and items. In this paper, we propose a Neural network based Aspect-level Collaborative Filtering model (NeuACF) to exploit different aspect latent factors. Through modelling the rich object properties and relations in recommender system as a heterogeneous information network, NeuACF first extracts different aspect-level similarity matrices of users and items, respectively, through different meta-paths, and then feeds an elaborately designed deep neural network with these matrices to learn aspect-level latent factors. Finally, the aspect-level latent factors are fused for the top-N recommendation. Moreover, to fuse information from different aspects more effectively, we further propose NeuACF++ to fuse aspect-level latent factors with self-attention mechanism. Extensive experiments on three real world datasets show that NeuACF and NeuACF++ significantly outperform both existing latent factor models and recent neural network models.

Change captioning is to describe the fine-grained change between a pair of images. The pseudo changes caused by viewpoint changes are the most typical distractors in this task, because they lead to the feature perturbation and shift for the same objects and thus overwhelm the real change representation. In this paper, we propose a viewpoint-adaptive representation disentanglement network to distinguish real and pseudo changes, and explicitly capture the features of change to generate accurate captions. Concretely, a position-embedded representation learning is devised to facilitate the model in adapting to viewpoint changes via mining the intrinsic properties of two image representations and modeling their position information. To learn a reliable change representation for decoding into a natural language sentence, an unchanged representation disentanglement is designed to identify and disentangle the unchanged features between the two position-embedded representations. Extensive experiments show that the proposed method achieves the state-of-the-art performance on the four public datasets. The code is available at https://github.com/tuyunbin/VARD.

Social networks are critical sources for event detection thanks to the characteristics of publicity and dissemination. Unfortunately, the randomness and semantic sparsity of the social network text bring significant challenges to the event detection task. In addition to text, time is another vital element in reflecting events since events are often followed for a while. Therefore, in this article, we propose a novel method named Multi-View Graph Attention Network (MVGAN) for event detection in social networks. It enriches event semantics through both neighbor aggregation and multi-view fusion in a heterogeneous social event graph. Specifically, we first construct a heterogeneous graph by adding the hashtag to associate the isolated short texts and describe events comprehensively. Then, we learn view-specific representations of events through graph convolutional networks from the perspectives of text semantics and time distribution, respectively. Finally, we design a hashtag-based multi-view graph attention mechanism to capture the intrinsic interaction across different views and integrate the feature representations to discover events. Extensive experiments on public benchmark datasets demonstrate that MVGAN performs favorably against many state-of-the-art social network event detection algorithms. It also proves that more meaningful signals can contribute to improving the event detection effect in social networks, such as published time and hashtags.

This note studies the resilient filtering problem for a class of discrete-time nonlinear complex networks. A novel resilient model is proposed by representing the variations of the filter gain matrix as a multiplicative noise term. By applying the variance-constrained approach to the coupled extended Kalman filter (EKF), an upper bound is derived for the estimation error covariance and such an upper bound is subsequently minimized to design the filter gain matrix at each sampling instant. A sufficient condition is established for the boundedness of the upper bound matrix which guarantees the boundedness of the estimation errors in the mean square sense. A numerical example involving tracking four mobile robots is provided to verify the effectiveness of the proposed filter.