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Information Fusion
Elsevier Science
Information Fusion

Elsevier Science

1566-2535

Information Fusion/Journal Information FusionEIISTPSCI
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    Multi-attentive hierarchical dense fusion net for fusion classification of hyperspectral and LiDAR data

    Wang, XianghaiFeng, YiningSong, RuoxiMu, Zhenhua...
    18页
    查看更多>>摘要：With recent advance in Earth Observation techniques, the availability of multi-sensor data acquired in the same geographical area has been increasing greatly, which makes it possible to jointly depict the underlying landcover phenomenon using different sensor data. In this paper, a novel multi-attentive hierarchical fusion net (MAHiDFNet) is proposed to realize the feature-level fusion and classification of hyperspectral image (HSI) with Light Detection and Ranging (LiDAR) data. More specifically, a triple branch HSI-LiDAR Convolutional Neural Network (CNN) backbone is first developed to simultaneously extract the spatial features, spectral features and elevation features of the land-cover objects. On this basis, hierarchical fusion strategy is adopted to fuse the oriented feature embeddings. In the shallow feature fusion stage, we propose a novel modality attention (MA) module to generate the modality integrated features. By fully considering the correlation and heterogeneity between different sensor data, feature interaction and integration is released by the proposed MA module. At the same time, self-attention modules are also adopted to highlight the modality specific features. In the deep feature fusion stage, the obtained modality specific features and modality integrated features are fused to construct the hierarchical feature fusion framework. Experiments on three real HSI-LiDAR datasets demonstrate the effectiveness of the proposed framework. The code will be public on https://github.com/SYFYN0317/-MAHiDFNet.
      原文链接:
    • NSTL
    • Elsevier

    Multi-sensor multi-rate fusion estimation for networked systems: Advances and perspectives

    Liu, HongjianShen, YuxuanWang, ZidongDong, Hongli...
    9页
    查看更多>>摘要：In industrial systems, the multi-rate sampling strategy has been widely used due to the advantage in balancing cost and performance as well as the psychical characteristics of the hardware. Accordingly, the analysis and synthesis problems of the multi-rate systems (MRSs) have received considerable research attentions owing to the significant engineering background. Among others, the state estimation problem, aims to estimate the system state based on the contaminated measurement signals, is one of the most important topics in the area of signal processing. In the past decades, plenty of research results have been obtained on the state estimation problems for MRSs. The intent of this survey is to provide a timely and systematic review with respect to the available state estimation algorithms for networked MRSs and the corresponding fusion methods. First, a general state-space model of the MRSs is given and the methods that transform the MRSs into single-rate ones are introduced. Then, the recent advances on the state estimation as well as fusion estimation problems for MRSs are discussed based on the performance indices used. Finally, some future research topics are given in the MRS state estimation problems.
      原文链接:
    • NSTL
    • Elsevier

    length Image fusion in the loop of high-level vision tasks: A semantic-aware real-time infrared and visible image fusion network

    Tang, LinfengYuan, JitengMa, Jiayi
    15页
    查看更多>>摘要：Infrared and visible image fusion aims to synthesize a single fused image that not only contains salient targets and abundant texture details but also facilitates high-level vision tasks. However, the existing fusion algorithms unilaterally focus on the visual quality and statistical metrics of fused images but ignore the demands of high-level vision tasks. To address these challenges, this paper bridges the gap between image fusion and high-level vision tasks and proposes a semantic-aware real-time image fusion network (SeAFusion). On the one hand, we cascade the image fusion module and semantic segmentation module and leverage the semantic loss to guide high-level semantic information to flow back to the image fusion module, which effectively boosts the performance of high-level vision tasks on fused images. On the other hand, we design a gradient residual dense block (GRDB) to enhance the description ability of the fusion network for fine-grained spatial details. Extensive comparative and generalization experiments demonstrate the superiority of our SeAFusion over state -of-the-art alternatives in terms of maintaining pixel intensity distribution and preserving texture detail. More importantly, the performance comparison of various fusion algorithms in task-driven evaluation reveals the natural advantages of our framework in facilitating high-level vision tasks. In addition, the superior running efficiency allows our algorithm to be effortlessly deployed as a real-time pre-processing module for high-level vision tasks.
      原文链接:
    • NSTL
    • Elsevier

    EPMDroid: Efficient and privacy-preserving malware detection based on SGX through data fusion

    Wei, WentaoWang, JieYan, ZhengDing, Wenxiu...
    15页
    查看更多>>摘要：Android has stood at a predominant position in mobile operating systems for many years. However, its popularity and openness make it a desirable target of malicious attackers. There is an increasing need for mobile malware detection. Existing analysis methods fall into two categories, i.e., static analysis and dynamic analysis. The dynamic analysis is more effective and timely than the static one, but it incurs a high computational overhead, thus cannot be deployed in resource-constrained mobile devices. Existing studies solve this issue by outsourcing malware detection to the cloud. However, the privacy of mobile app runtime data uploaded to the cloud is not well preserved during both detection model training and malware detection. Numerous efforts have been made to preserve privacy with cryptography, which suffers from high computational overhead and low flexibility. To address these issues, in this paper, we propose an Intel SGXempowered mobile malware detection scheme called EPMDroid. We also design a probabilistic data structure based on cuckoo filters, named CuckooTable, to effectively fuse features for detection and achieve high space efficiency. We conduct both theoretical analysis and real-world data based tests on EPMDroid performance. Experimental results show that EPMDroid can speed up malware detection by up to 43.8 times and save memory space by up to 3.7 times with the same accuracy, as compared to a baseline method.
      原文链接:
    • NSTL
    • Elsevier

    Applications of sketches in network traffic measurement: A survey

    Han, HuiJing, XuyangPedrycz, WitoldYan, Zheng...
    28页
    查看更多>>摘要：Accurate and timely network traffic measurement is essential for network status monitoring, network fault analysis, network intrusion detection, and network security management. With the rapid development of the network, massive network traffic brings severe challenges to network traffic measurement. However, existing measurement methods suffer from many limitations for effectively recording and accurately analyzing big volume traffic. Recently, sketches, a family of probabilistic data structures that employ hashing technology for summarizing traffic data, have been widely used to solve these problems. However, current literature still lacks a thorough review on sketch-based traffic measurement methods to offer a comprehensive insight on how to apply sketches for fulfilling various traffic measurement tasks. In this paper, we provide a detailed and comprehensive review on the applications of sketches in network traffic measurement. To this end, we classify the network traffic measurement tasks into four categories based on the target of traffic measurement, namely cardinality estimation, flow size estimation, change anomaly detection, and persistent spreader identification. First, we briefly introduce these four types of traffic measurement tasks and discuss the advantages of applying sketches. Then, we propose a series of requirements with regard to the applications of sketches in network traffic measurement. After that, we perform a fine-grained classification for each sketch-based measurement category according to the technologies applied on sketches. During the review, we evaluate the performance, advantages and disadvantages of current sketch-based traffic measurement methods based on the proposed requirements. Through the thorough review, we gain a number of valuable implications that can guide us to choose and design proper traffic measurement methods based on sketches. We also review a number of general sketches that are highly expected in modern network systems to simultaneously perform multiple traffic measurement tasks and discuss their performance based on the proposed requirements. Finally, through our serious review, we summarize a number of open issues and identify several promising research directions.
      原文链接:
    • NSTL
    • Elsevier

    Nonredundancy regularization based nonnegative matrix factorization with manifold learning for multiview data representation

    Li, YeCui, Guosheng
    13页
    查看更多>>摘要：In the real world, one object is usually described via multiple views or modalities. Many existing multiview clustering methods fuse the information of multiple views by learning a consensus representation. However, the feature learned in this manner is usually redundant and has neglected the distinctions among the different views. Addressing this issue, a method named nonredundancy regularization based nonnegative matrix factorization with manifold learning (NRRNMF-ML) is proposed in the paper. A novel nonredundancy regularizer defined with the Hilbert-Schmidt Independence Criterion (HSIC) is incorporated in the objective function of the proposed method. By minimizing this term, the redundant information among the multiple views can be effectively reduced and the distinct contributions of the different views can be encouraged. To further utilizing manifold structure information of the data, a manifold regularizer is also constructed and included in the objective function of the proposed method. For the proposed method, an iterative optimization strategy was designed to solve the problem; the corresponding proof is presented both theoretically and experimentally in this paper. Experimental results on five multiview data sets compared with several representative multiview clustering methods revealed the effectiveness of the proposed method.
      原文链接:
    • NSTL
    • Elsevier

    Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions

    Vos, WimFlerin, NinaCharbonnier, Jean-Paulvan Rikxoort, Eva...
    24页
    查看更多>>摘要：Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research.
      原文链接:
    • NSTL
    • Elsevier