To counteract community detection algorithms and thereby protect node privacy， community hiding methods have garnered more and more attention. However， current mainstream community hiding algorithms only focus on the network’s topological structure， neglecting the influence of node attributes on community structure， leading to bad performance on attribute networks. In response to these issues， an Attribute network Community hiding method based on Genetic algorithm （ACG） was proposed. In this method， network topological structure and node attributes were integrated， with the core of finding the optimal edge hiding strategy by optimizing a fitness function. In ACG， while minimizing hiding costs， maximizing modularity and attribute similarity was adopted as dual metric to select and perturb the set of edges with the greatest impact on community structure， thereby attacking community detection algorithms for attribute networks effectively. Experimental results demonstrate that without changing the total number of edges and attribute information， the proposed method counters mainstream attribute community detection methods effectively； compared with other community hiding methods， ACG has advantages in counteracting classic community detection algorithms on five attribute networks.
Dynamic searchable encryption has attracted wide attention due to its ability to add， delete， and search data on cloud servers. The existing dynamic searchable encryption schemes are usually constructed using highly secure cryptographic primitives， and multiple bilinear pairing operations need to be performed during scheme searching. In view of the large computational overhead of dynamic searchable encryption schemes when searching on servers， a Puncturable PseudoRandom Function （PPRF） was introduced into dynamic searchable encryption， and a dynamic searchable encryption scheme based on PPRF was designed and proposed. In this scheme， file identifiers did not need to be encrypted using symmetric encryption algorithms， and it is also not necessary to decrypt ciphertext to obtain file identifiers during server searches， and the client and the server were able to complete data search with only one interaction. At the same time， in the scheme， the key was marked when deleting keywords， the marked key was used to calculate the PPRF during search， and backward security was implemented with a forward-secure scheme， thereby ensuring security while improving search efficiency. According to security model of the dynamic searchable encryption scheme， the security of the scheme was verified. Simulation results show that compared with ROSE （RObust Searchable Encryption） scheme built on Key-Updatable Pseudorandom Function （KUPRF）， Janus++ scheme built on Symmetric Puncturable Encryption （SPE）， and Aura scheme built on Symmetric Revocable Encryption （SRE）， the proposed scheme has the average search time of each keyword reduced by 17%， 65%， and 58%， respectively. It can be seen that the proposed scheme is effective and feasible， and reduces the search cost of the server effectively， improves the search efficiency of the scheme， and increases the practicality of the scheme.
With the increasingly serious problem of global climate change， the goals of carbon peaking and carbon neutrality have been established in China. As logistics hubs and cargo distribution centers， the ports have highlighted carbon emission problem. Aiming at optimization problem of port operation scheduling， considering the key factors such as ship arrival time， cargo handling demand， quay crane operation capacity， and carbon emission cost， an optimization model of port operation scheduling was constructed for minimizing both carbon emission cost and terminal operating expense， and a port operation scheduling algorithm based on Enhanced NSGA-Ⅱ （Non-dominated Sorting Genetic Algorithm Ⅱ） （E-NSGA-Ⅱ） under the goals of carbon peaking and carbon neutrality was proposed. Firstly， the coding strategy， population initialization method and crossover and mutation operations of the algorithm were adjusted. Secondly， gene repair operators of infeasible solutions were designed， and adaptive crossover and mutation probability mechanisms were introduced. Experimental results show that compared with FCFS （First Come First Service） scheduling algorithm， the proposed algorithm reduces the total cost of model solving by 7.9%， the carbon emission cost by 19.7%， and the terminal operating expense by 6.5%. The above research results enrich the multi-objective optimization algorithm and port operation scheduling theory， and provide strong support for port enterprises to achieve green scheduling， reduce operating cost， and improve economic benefits.
Significant differences in object scale and aspect ratio in remote sensing images lead to difficult object detection in remote sensing images. Aiming at this characteristic of remote sensing image， in order to improve the precision of object detection in remote sensing images， EW-YOLO （Efficient Weighted-YOLO） was proposed by improving the YOLO framework. Firstly， the multi-level feature fusion structure was introduced in the feature fusion section， so that the dual-branch residual module was utilized to promote the fusion of features at different scales. And by the cascade of feature fusion modules and the cross-layer feature fusion design， the extraction capability of objects at different scales was improved， and the detection capability was further enhanced. Secondly， in the prediction section， the weighted detection head was proposed and Weighted Boxes Fusion （WBF） was introduced， so as to improve the detection precision of objects with different aspect ratios by weighting each candidate box using the confidence scores and generating prediction boxes by fusion. Finally， to address the issue of too large image size， an image resampling technique was proposed， which means that the images were sampled to appropriate sizes and joined into network training， solving the problem of low detection precision of large-size objects caused by cropping. Experimental results on DOTA dataset show that the detection mean Average Precision （mAP） of the proposed method is 77.47%， which is increased by 1.55 percentage points compared to that of the original YOLO framework based method. And compared with the current mainstream methods， the proposed method has superior performance. At the same time， the proposed method’s effectiveness is also verified on HRSC and UCAS-AOD datasets.
Although community detection can reveal underlying structural characteristics of the network and relationships between nodes deeply， it also raises privacy leakage issues. Community hiding methods can resist community detection algorithms effectively， thereby achieving privacy protection of network node information. However， most of the traditional community hiding methods only focus on privacy protection of a single target or community in the network， there is a lack of a method that can hide any target set. In order to solve the above problems， a Based on Permanence-loss Maximization for multiple target Nodes Hiding （BPMNH） method was proposed. In the method， the set of target nodes to be hidden was able to be configured freely， and permanence loss maximization scheme was provided according to the network scale adaptively， thereby achieving hiding of multiple target nodes in different communities with minimal network topology disturbance cost. On eight datasets such as Karate， the experimental results show that BPMNH is better than three baseline methods such as Modularity Based Attack （MBA） in terms of hiding effect， network structure and comprehensive deception effect， validating the superiority of the proposed method in multi-target node hiding.
In recent years， Deformable Convolutional Network （DCN） has been widely applied in fields such as image recognition and classification. However， research on the interpretability of this model is relatively limited， and its applicability lacks sufficient theoretical support. To address these issues， this paper proposed an interpretability study of DCN and its application in butterfly species recognition model. Firstly， deformable convolution was introduced to improve the VGG16， ResNet50， and DenseNet121 （Dense Convolutional Network121） classification models. Secondly， visualization methods such as deconvolution and Class Activation Mapping （CAM） were used to compare the feature extraction capabilities of deformable convolution and standard convolution. The results of ablation experiments show that deformable convolution performs better when used in the lower layers of the neural network and not continuously. Thirdly， the Saliency Removal （SR） method was proposed to uniformly evaluate the performance of CAM and the importance of activation features. By setting different removal thresholds and other perspectives， the objectivity of the evaluation is improved. Finally， based on the evaluation results， the FullGrad （Full Gradient-weighted） explanation model was used as the basis for the recognition judgment. Experimental results show that on the Archive_80 dataset， the accuracy of the proposed D_v2-DenseNet121 reaches 97.03%， which is 2.82 percentage points higher than that of DenseNet121 classification model. It can be seen that the introduction of deformable convolution endows the neural network model with the ability to extract invariant features and improves the accuracy of the classification model.
Both the Damped Least Squares （DLS） and Genetic Algorithm （GA） are applicable to automatic design of optical systems. Although DLS has a high search efficiency， it is susceptible to falling into local optima traps. Conversely， GA has strong global search capability in the parameter space of optical structures but weak local search capability. To address these challenges， a Correctable Reinforced Search GA （CRSGA） was proposed. Firstly， DLS was introduced after the GA crossover operation to enhance local search capability. Additionally， a correction strategy was introduced to rollback individuals with deteriorated fitness values before the next iteration， thereby achieving corrective evolutionary results. The improvement of two aspects to genetic algorithm enhanced strengths and compensated for weaknesses. Three typical optical system design experiments， including Double Gaussian （DG）， Reversed Telephoto （RT）， and Finite Conjugate Distance Imaging （FCDI）， were conducted to validate the effectiveness of CRSGA. CRSGA outperforms both DLS and GA， and its optimization outcomes are about 8.92%， 12.19%， and 9.39% respectively better than those of commercial optical design software Zemax DLS. In particularly， the optimization outcomes achieve a significant improvement， reaching 99.98%， 94.33%， and 88.45% respectively compared to the Zemax HAMMER algorithm. In conclusion， it is shown that the proposed algorithm is effective for optical system optimization and can be used for automatic optical system design.
The development of hot news events is very rich， and each stage of the development has its own unique narrative. With the development of events， a trend of hierarchical storyline evolution is presented. Aiming at the problem of poor interpretability and insufficient hierarchy of storyline in the existing storyline generation methods， a Hierarchical Storyline Generation Method （HSGM） for hot news events was proposed. First， an improved hotword algorithm was used to select the main seed events to construct the trunk. Second， the hotwords of branch events were selected to enhance the branch interpretability. Third， in the branch， a storyline coherence selection strategy fusing hotword relevance and dynamic time penalty was used to enhance the connection of parent-child events， so as to build hierarchical hotwords， and then a multi-level storyline was built. In addition， considering the incubation period of hot news events， a hatchery was added during the storyline construction process to solve the problem of neglecting the initial events due to insufficient hotness. Experimental results on two real self-constructed datasets show that in the event tracking process， compared with the methods based on singlePass and k-means respectively， HSGM has the F score increased by 4.51% and 6.41%， 20.71% and 13.01% respectively； in the storyline construction process， HSGM performs well in accuracy， comprehensibility and integrity on two self-constructed datasets compared with Story Forest and Story Graph.
Spatial-temporal co-occurrence patterns refer to the video object combinations with spatial-temporal correlations. In order to mine the spatial-temporal co-occurrence patterns meeting the query conditions from a huge volume of video data quickly， a spatial-temporal co-occurrence pattern mining algorithm with a triple-pruning matching strategy — Multi-Pruning Algorithm （MPA） was proposed. Firstly， the video objects were extracted in a structured way by the existing video object detection and tracking models. Secondly， the repeated occurred video objects extracted from a sequence of frames were stored and compressed， and an index of the objects was created. Finally， a spatial-temporal co-occurrence pattern mining algorithm based on the prefix tree was proposed to discover the spatial-temporal co-occurrence patterns that meet query conditions. Experimental results on real and synthetic datasets show that the proposed algorithm improves the efficiency by about 30% compared with Brute Force Algorithm （BFA）， and the greater the data volume， the more obvious the efficiency improvement. Therefore， the proposed algorithm can discover the spatial-temporal co-occurrence patterns satisfying the query conditions from a large volume of video data quickly.
Node feature representation was learned by Graph Convolutional Network （GCN） by deep graph matching models in the stage of node feature extraction. However， GCN was limited by the learning ability for node feature representation， affecting the distinguishability of node features， which causes poor measurement of node similarity， and leads to the loss of model matching accuracy. To solve the problem， a deep graph matching model based on self-attention network was proposed. In the stage of node feature extraction， a new self-attention network was used to learn node features. The principle of the network is improving the feature description of nodes by utilizing spatial encoder to learn the spatial structures of nodes， and using self-attention mechanism to learn the relations among all the nodes. In addition， in order to reduce the loss of accuracy caused by relaxed graph matching problem， the graph matching problem was modelled to an integer linear programming problem. At the same time， structural matching constraints were added to graph matching problem on the basis of node matching， and an efficient combinatorial optimization solver was introduced to calculate the local optimal solution of graph matching problem. Experimental results show that on PASCAL VOC dataset， compared with Permutation loss and Cross-graph Affinity based Graph Matching （PCA-GM）， the proposed model has the average matching precision on 20 classes of images increased by 14.8 percentage points， on Willow Object dataset， the proposed model has the average matching precision on 5 classes of images improved by 7.3 percentage points， and achieves the best results on object matching tasks such as bicycles and plants.
