The change in appearance is a great challenge for cloth-changing person re-identification. Existing methods tackle this challenge by learning the shape features of the human body, however, these features are easily affected by the human pose or camera perspective. Thus, the ability to learn and extract the invariant features of person in varying conditions is crucial to overcome the above challenge. To address the issue of invariant feature extraction for cloth-changing person re-identification, a Pose-Guided Attention Learning (PGAL) framework is proposed in this paper. First, we introduce the human pose estimation network to remove the background effects and align the fine-grained key points features of human body. Then, to fully exploit the available appearance information, we develop a Feature Enhancement Module (FEM) that improves the feature representation of non-key point regions of human body through the Multi-Head Self Attention. Finally, in order to adaptively learn the invariant features of the person, we construct an Attention Learning Module (ALM) to achieve automatic selection of multi-granularity features by utilizing three different loss functions. Comparing with current popular methods on four cloth-changing person Re-ID datasets, the experimental results show the superiority of our method.

Unattributed social networks are more complicated, and it tends not to determine the best division by over-optimizing a theoretical measure for unsupervised algorithms. Nowadays, communities strongly overlap due to the fact that people strongly interact, which makes community detection even more challenging. The paper develops a new algorithm by rearranging ‘indivisible’ blocks (RaidB). In RaidB, we first initialize ‘indivisible’ blocks by disjoint k-clique blocks in a network, and then these blocks are rearranged by moving nodes from one block to another based on maximizing modularity to uncover non-overlapping communities. For identifying overlapping communities, the above blocks are further rearranged, i.e., each block is subdivided and expanded to determine sub-blocks by introducing a dynamic linear threshold (DLT) model for influence interpenetration, and we finally determine a division from these sub-blocks with the minimum size that can cover the network. We compare RaidB with the existing state of the art methods for non-overlapping and overlapping community detection. The results show that RaidB tends to achieve better performance especially on sparse networks with unobvious communities and networks with strongly overlapping communities.

In this paper, we propose a level-aware progressive network (LAP-Net) for single image dehazing. Unlike previous multi-stage algorithms that generally learn in a coarse-to-fine fashion, each stage of LAP-Net learns different levels of haze with different supervision. Then the network can progressively learn the gradually aggravating haze. With this design, each stage can focus on a region with specific haze level and restore clear details. To effectively fuse the results of varying haze levels at different stages, we develop an adaptive integration strategy to yield the final dehazed image. This strategy is achieved by a hierarchical integration scheme, which is in cooperation with the memory network and the domain knowledge of dehazing to highlight the best-restored regions of each stage. Extensive experiments on both real-world images and two dehazing benchmarks validate the effectiveness of our proposed method.

Machine learning is among the most popular methods in designing unknown and variant malware detection algorithms. However, most of the existing methods take a single type of features to build binary classifiers. In practice, these methods have limited ability in depicting malware characteristics and the binary classification suffers from inadequate sampling of benign samples and extremely imbalanced training samples when detecting malware. In this paper, we present a malware detection Framework based on ENsemble One-Class Learning, namely FENOC. It uses hybrid features at different semantic layers to ensure a comprehensive insight of the program to be analyzed. We construct the malware detector by a novel learning algorithm called Cost-sensitive Twin One-class Classifier (CosTOC), which uses a pair of one-class classifiers to describe malware and benign programs respectively. CosTOC is more flexible and robust in comparison to conventional binary classifiers when training samples are extremely imbalanced or the benign programs are inadequately sampled. Finally, random subspace method and clustering-based ensemble method are developed to enhance the generalization ability of CosTOC. Experimental results show that FENOC gives a comparative detection rate and a lower false positive rate than many other binary classification algorithms, especially when the detector are trained with imbalanced data, or evaluated in terms of false positive rate.

In a learning process, features play a fundamental role. In this paper, we propose a Boosting-based feature selection algorithm called BoostFS. It extends AdaBoost which is designed for classification problems to feature selection. BoostFS maintains a distribution over training samples which is initialized from the uniform distribution. In each iteration, a decision stump is trained under the sample distribution and then the sample distribution is adjusted so that it is orthogonal to the classification results of all the generated stumps. Because a decision stump can also be regarded as one selected feature, BoostFS is capable to select a subset of features that are irrelevant to each other as much as possible. Experimental results on synthetic datasets, five UCI datasets and a real malware detection dataset all show that the features selected by BoostFS help to improve learning algorithms in classification problems, especially when the original feature set contains redundant features.