Mask is considered as an important prior for fusion, which could selectively enhance specific regions to generate ideal fused images. However, masks used in the existing methods exhibit limitations in the precise representation of targets, and more importantly, these masks are generated from a single modality, which restricts the effective integration of multi-modal information. To address this issue, we propose a competitive mask-guidance fusion method for infrared and visible images. A multi-modal semantic-sensitive mask selection network is proposed to generate complementary-mask maps, which organically integrate advantageous target regions of different modalities by competitively comparing the qualities of masks. In this network, a pseudosiamese architecture is designed to obtain respective target masks, and specifically, a spatial-aligned-based feature aggregation module is devised to produce high-quality pseudo-labels which are served as references for the generation of the complementary-mask maps. Furthermore, we propose a bidirectional-collaboration region fusion strategy, which enhances the expression of advantageous target regions from each modality inforeground while suppressing the contribution of corresponding regions from the other modality in background. Compared to methods on public datasets, the results show that our method significantly enhances the description of semantic-sensitive targets in fused images, including the saliency and the integrity of structural information. Code are available athttps://github.com/xbsj-cool/MSCRFusion.

• Graph Neural Networks (GNN) in building and civil infrastructure operation and management (OM) data and process enhancement. • Domain-specific feature engineering, architecture and hyperparameter optimisations. • Integration of explainable Artificial Intelligence (AI) techniques. • Future implementation aspects for building and civil infrastructure OM. This systematic review, conducted within the PRISMA framework, investigates the disruptive capabilities of Graph Neural Networks (GNNs) in optimising Operations and Maintenance (OM) practices within the building and civil infrastructure domain. Addressing 5 research questions and encompassing 111 studies from 2014 to 2024, our study identifies the multifaceted applications of GNNs across different project stages from data enhancement to operational scenario enhancement. When considering integrated Facilities Management (FM) approaches, GNNs are employed for data enhancement purposes, leveraging techniques such as semantic enrichment of Building Information Modelling (BIM), various data imputation scenarios, and semantic segmentation of point clouds to enhance data quality and completeness. Operational scenarios involve the utilisation of GNN algorithms for anomaly detection, fault classification, system optimisation, and forecasting. Methodological optimisations crucial for GNN feasibility include feature engineering, architecture optimisation to balance complexity and overfitting risk, and the integration of Explainable Artificial Intelligence (XAI) methods to enhance model validity and trust. Physical principles integration through Physics-Informed Graph Neural Networks (PIGNNs) further enhances model explainability and validation. Future research directions focus on data interoperability enhancement, scalability improvements, and explainability enhancements. Automated graph generation and labelling, heterogeneous GNN models, supporting algorithms such as Long Short-Term Memory (LSTM) and reinforcement learning are proposed to overcome analysis limitations. Specific workflows targeting building performance-based semantic enrichment, building systems data imputation, and interdependency prediction are proposed in future directions. The review highlights the symbiotic relationship between GNN-based analysis and digital twin data analysis, emphasising the suitability of GNNs in addressing the demands of digital twin data analysis in the building and civil infrastructure domain.

In order to achieve requirements such as fast search of flow entries and mask matching, OpenFlow hardware switches usually use TCAM to store flow entries. Limited by the capacity of TCAM, the current commercial OpenFlow switches can only support hundreds of thousands of flow entries, which makes SDN network using OpenFlow hardware switches vulnerable to the threat of flow table overflow attack. Among them, low-rate DoS (LDoS) attack against table overflow poses a serious threat to SDN networks due to its high attack efficiency and concealed flow, and it is also difficult to detect. In this regard, this paper analyzed two types of LDoS attack flow against table overflow and proposed an attack detection and defense mechanism named SAIA (Small-flow Analysis and Inport-flow Analysis) through the design of table overflow prediction and flow entries deletion strategy. Experiments conducted through the SDN network environment showed that SAIA can effectively detect and suppress LDoS attack flows in the flow table in large-scale network conditions and verified that the deployment of SAIA is lightweight. At the same time, SAIA implemented the flow entry deletion strategy based on LRU when the flow table overflows in a nonattack situation, which further enhances the stability of the network.

Fingerprint minutiae are prevalently used in fingerprint recognition systems. The extraction of fingerprint minutiae is heavily affected by the quality of fingerprint images. This leads to the incorporation of a fingerprint enhancement module in fingerprint recognition systems to make the system robust with respect to the quality of input fingerprint images. Most of existing enhancement methods suffer from two main kinds of defects: (1) time consuming and thus unusable in time critical applications; and (2) blocky and directional effects in the enhanced image. This paper proposes an improved fingerprint enhancement scheme based on the Gabor filter tuning its frequency to the average frequency of the input image and changing its shape from square to circle and dynamically adjusting the filter's size based on the average frequency. This scheme can enhance the fingerprint image rapidly and overcome the blocky and directional effects and does improve the performance of minutiae detection.