The problem of efficient collaboration of cross-domain fragmented resources under communication constraints is analyzed. Firstly, the concept of element-level resource allocation under communication constraints is proposed. Secondly, the distributed resource allocation framework, algorithm and models are put forward. Finally, the efficiency of traditional centralized and distributed resource allocation under different communication conditions is quantitatively analyzed through simulation. The simulation results show that with the increase of communication miss rate, the resource allocation efficiency of distributed element-level is significantly improved compared with that of centralized mode, with an increase of about$\mathbf{1 4 \%}$in typical scenarios.

Building detection in remote sensing images is significant to urban land planning, battlefield environment perception, and illegal building monitoring. Existing methods excel in detecting small-scale building areas but struggle with remote sensing images with diverse and multi-scale characteristics. To solve this problem, this paper proposes Two-stage Feature fusion Network(TFNet). Specifically, we propose dense short connection module to enable internal interaction between features from different layers, achieving intra-stage feature fusion. Moreover, the holistically-nested edge detection network is used to integrate the effective information between different layers to achieve inter-stage feature fusion. In addition, the adaptive fusion weight is introduced to make the model adaptively select the weights of varying levels of features. Experimental results demonstrate the effectiveness of TFNet in improving the detection performance of multi-scale buildings in remote sensing images.

Recently generative adversarial network (GAN) has been explored to multimodal template matching. Existing GAN-based multimodal template matching methods exploit the image generation to transform the multimodal template matching task as the unimodal one. However, image synthesis-based multimodal template matching methods relay on the generated image quality, which is unstable. Towards this end, this paper proposes a feature adversarial network, which maps different modal images into a common subspace and learns the correlation in the subspace. Specifically, the feature mapper is designed to map the multimodal features into intermediate features, and the modality discriminator is proposed to optimize the multimodal intermediate features until they are indistinguishable. Thus, an effective common feature subspace is generated for correlation learning. The experimental results on a public dataset demonstrate the superiority of the proposed method.

Computing the number ofk-hop paths is crucial for selecting services in social networks and analyzing graph data, for example, a service consumer require to evaluate the trustworthiness of a service provider along the social trust paths from a service consumer to the service provider, there are usually many social trust paths between two unconnected participants, people need to know the number of loop-freek-hop trust propogation paths; other applications include the similarity computation for services recommendation, information diffusion, etc. Previously, the number ofk-hop paths is roughly estimated by the elements in thekmultiplications of the network adjacency matrix. This method calculates much morek-hop paths than those actually exist, due to many paths with loops counted ask-hop paths, which may result in obvious errors in applications. Based on the idea of loops removing, accurate mathematical formulas for counting loop-free paths are obtained in this article for paths with five or less hops, an approximate method is provided for larger hops. Based on the proposed loop removing algorithm (LRA), the typical method for predicting trust between any two people in social networks is improved, the error rate is dramatically reduced; the traditional path based similarity indices are improved, which are much accurate than their antecedent counterparts; and a method for computing the spreading probability for information spreading between two unconnected vertices in the famous independent cascade (IC) model is also obtained. To reveal the effectiveness of the proposed LRA, this article also provide a traversal depth-first search algorithm (DFSA) for finding the true number ofk-hop loop-free paths.

Amongst the utilizations of Polarimetric Synthetic Aperture Radar (PoISAR) data, semi-supervised terrain classification is much in demand. Samples of the same category are distributed in multiple regions of a PolSAR image, resulting in differences in the feature distributions of samples of the same category located in different regions. In addition, some samples of confusable categories, and samples of different categories located near the edges, have small feature differences in a PolSAR image. To address this problem, we introduce a graph regular loss constructed from pseudo-labels to constrain the intra-class similarity and inter-class similarity of features, and improve the discriminative property of features. In addition, since the quality of pseudo-labels affects the optimization of the model by the graph regular loss, we introduce the idea of clustering into the teacher-student model to improve the quality of pseudo-labels. Experiments on real PolSAR data show that our proposed method achieves an excellent performance.