Dynamic graph learning addresses graph data in which nodes and edges evolve over time. Traditional Graph Neural Networks （GNNs） typically rely on information from nodes and their neighbors， but in dynamic graph scenarios， models must capture real-time dynamic evolution information of nodes and edges while balancing global temporal dependencies and local structural details when facing large-scale data. To address these challenges， MTGN （Memory-based Temporal Graph Network）， a scalable dynamic graph representation learning model with neighborhood memory was proposed to realize efficient modeling via subgraph sampling and neighborhood memory mechanism. First， a parallel temporal sampler was used for efficient subgraph sampling. Second， a cache-based node neighborhood memory mechanism was introduced to maintain node historical status dynamically. Third， temporal relationships among dynamic neighbors were modeled through an attention mechanism adaptively， thereby generating more stable and robust dynamic graph representations. Finally， experiments were conducted on four real-world datasets. The results demonstrate that MTGN has excellent prediction accuracy in the link prediction tasks with the prediction accuracies of 99.38% and 74.46% on the MOOC and LASTFM datasets， respectively. In the dynamic node classification tasks， MTGN achieves prediction accuracies of 90.50% and 79.08% on the Wikipedia and Reddit datasets， respectively， showing significant performance improvements compared to the baseline methods. Additionally， MTGN has outstanding training efficiency， reducing training time by at least 86.9% and 93.6% on the Wikipedia and Reddit datasets， respectively， compared to the baseline methods， validating its scalability for large-scale dynamic graph tasks.
Due to high nesting of entities and the characteristics of long texts in the field of wind power， a nested Named Entity Recognition model based on Differential Boundary Enhancement （DBE-NER） was proposed. Firstly， a semantic encoder module was used to obtain feature representations fusing entity’s head and tail words， entity types， and relative distances， thereby enhancing the model’s ability to capture nested semantic features. Secondly， an efficient differential semantic encoding module was designed to solve the fuzziness problem of nested entity boundaries. Thirdly， a Grouped Dilated Attention Network （GDAN） was utilized to improve the model’s effectiveness in recognizing long-text entities， nested entities， and nested boundaries. Finally， the feature score matrix was input into a span decoder to obtain positions and categories of the entities. Experimental results indicate that the F1 score of DBE-NER is improved by 0.92% and 1.07% compared to those of DiFiNet （Differentiation and Filtration Network） and CNN-NER （Convolutional Neural Network for Named Entity Recognition） models on a manually annotated dataset from a large wind power energy enterprise — WPEF dataset， and the F1 scores of DBE-NER are also increased on various public datasets.
Sparse Matrix-Vector multiplication （SpMV） is an important numerical linear algebraic operation. The existing optimizations for SpMV suffer from issues such as incomplete consideration of preprocessing and communication time， lack of universality in storage structures. To address these issues， an adaptive optimization scheme for SpMV on heterogeneous platforms was proposed. In the proposed scheme， the Pearson correlation coefficients were utilized to determine highly correlated feature parameters， and two Gradient Boosting Decision Tree （GBDT） based algorithms eXtreme Gradient Boosting （XGBoost） and Light Gradient Boosting Machine （LightGBM） were employed to train prediction models to determine the optimal storage format for a certain sparse matrix. The use of grid searches to identify better model hyperparameters for model training resulted in both of those algorithms achieving more than 85% accuracy in selecting a more suitable storage structure. Furthermore， for sparse matrices with the HYBrid （HYB） storage format， the ELLPACK （ELL） and COOrdinate （COO） storage format parts in these metrices were computed on the GPU and CPU separately， establishing a CPU+GPU parallel hybrid computing mode. At the same time， hardware platforms were also selected for sparse matrices with small data sizes to improve computational speed. Experimental results demonstrate that the adaptive computing optimization achieves an average speedup of 1.4 compared to the Compressed Sparse Row （CSR） storage format in cuSPARSE library， and average speedup of 2.1 and 2.6 compared to the HYB and ELL storage formats， respectively.
With the advancement of technologies such as sensor networks and global positioning systems， the volume of meteorological data with both temporal and spatial characteristics has exploded， and the research on deep learning models for Spatiotemporal Sequence Forecasting （STSF） has developed rapidly. However， the traditional machine learning methods applied to weather forecasting for a long time have unsatisfactory effects in extracting the temporal correlations and spatial dependences of data， while the deep learning methods can extract features automatically through artificial neural networks to improve the accuracy of weather forecasting effectively， and have a very good effect in encoding long-term spatial information modeling. At the same time， the deep learning models driven by observational data and Numerical Weather Prediction （NWP） models based on physical theories are combined to build hybrid models with higher prediction accuracy and longer prediction time. Based on these， the application analysis and research progress of deep learning in the field of weather forecasting were reviewed. Firstly， the deep learning problems in the field of weather forecasting and the classical deep learning problems were compared and studied from three aspects： data format， problem model and evaluation metrics. Then， the development history and application status of deep learning in the field of weather forecasting were looked back， and the latest progress in combining deep learning technologies with NWP was summarized and analyzed. Finally， the future development directions and research focuses were prospected to provide a certain reference for future deep learning research in the field of weather forecasting.
To solve the problem of limited computing resources and storage space of edge nodes in the Edge Computing （EC） network， an Edge Computing and Service Offloading （ECSO） algorithm based on improved Deep Reinforcement Learning （DRL） was proposed to reduce node processing latency and improve service performance. Specifically， the problem of edge node service offloading was formulated as a resource-constrained Markov Decision Process （MDP）. Due to the difficulty of predicting the request state transfer probability of the edge node accurately， DRL algorithm was used to solve the problem. Considering that the state action space of edge node for caching services is too large， by defining new action behaviors to replace the original actions， the optimal action set was obtained according to the proposed action selection algorithm， so that the process of calculating the action behavior reward was improved， thereby reducing the size of the action space greatly， and improving the training efficiency and reward of the algorithm. Simulation results show that compared with the original Deep Q-Network （DQN） algorithm， Proximal Policy Optimization （PPO） algorithm and traditional Most Popular （MP） algorithm， the total reward value of the proposed ECSO algorithm is increased by 7.0%， 12.7% and 65.6%， respectively， and the latency of edge node service offloading is reduced by 13.0%， 18.8% and 66.4%， respectively， which verifies the effectiveness of the proposed ECSO algorithm and shows that the ECSO can effectively improve the offloading performance of edge computing services.
An important strategy for lightweighting a 3D model is to use the mesh simplification algorithm to reduce the number of triangular meshes on the model surface. The widely used edge collapse algorithm is more efficient and has better simplification effect than other mesh simplification algorithms， but some detailed geometric features may be damaged or lost during the simplification process of this algorithm. Therefore， the approximate curvature of curve and the average area of the first-order neighborhood triangle of the edge to be collapsed were added as penalty factors to optimize the edge collapse cost of the original algorithm. First， according to the definition of curve curvature in geometry， the calculation formula of the approximate curvature of curve was proposed. Then， in the calculation process of vertex normal vector， two stages - area weighting and interior angle weighting were used to modify the initial normal vector， thereby considering more abundant geometric information of the model. The performance of the optimized algorithm was verified by experiments. Compared with the classical Quadratic Error Metric （QEM） algorithm and the mesh simplification algorithm considering the angle error， the optimized algorithm has the maximum error reduced by 73.96% and 49.77% at least and respectively. Compared with the QEM algorithm， the optimized algorithm has the Hausdorff distance reduced by 17.69% at least. It can be seen that in the process of model lightweighting， the optimized algorithm can reduce the deformation of the model and better maintain its own detailed geometric features.
Floorplan design is an important step of house design. However， the existing automatic floorplan design methods lack the common constraints of considering user requirements and building boundaries. Thus， these methods suffer from unreasonable layout problems such as missing corners of generated room， severe occlusion between rooms and room getting out of the boundary. In order to solve the above problems， a building floorplan GBC-GAN （Graph Boundary Constrained-Generative Adversarial Network） was proposed based on user requirements and boundary constraints， and the proposed method consists of a constraint layout generator and a room relation discriminator. Firstly， the user-specified floorplan layout requirements （including the number and types of rooms and the adjacency relationship between houses） were transformed into a constraint relation graph structure， after that， the building boundary and constraint relation graph were encoded separately for feature fusion. Then， by adding the prediction module of bounding box， the constraint layout generator was used to convert the floorplan generation problem into a bounding box generation problem of each room object， and the geometric boundary optimization loss was used to solve the problems of severe occlusion between rooms and room getting out of the boundary. Finally， the room bounding box layout and the constraint relation graph were input into the room relation discriminator for training to generate the floorplan layout meeting the room objects and their relations. The Frechet Inception Distance （FID） and Structural Similarity Index Measure （SSIM） of the proposed method are improved by 4.39% and 2.3% compared with those of House-GAN on the large-scale real building dataset RPLAN. Experimental results show the proposed method improves the rationality and authenticity of the floorplan layout under different user requirements and boundary constraints.
Compared with ordinary networks， hypernetworks have complex tuple relationships， namely hyperedges. However， most existing network representation learning methods cannot capture the tuple relationships. To solve the above problem， a Heterogeneous hypernetwork Representation learning method with Hyperedge Constraint （HRHC） was proposed. Firstly， a method combining clique extension and star extension was introduced to transform the heterogeneous hypernetwork into the heterogeneous network. Then， the meta-path walk method that was aware of semantic relevance among the nodes was introduced to capture the semantic relationships among the heterogeneous nodes. Finally， the tuple relationships among the nodes were captured by means of the hyperedge constraint to obtain high-quality node representation vectors. Experimental results on three real-world datasets show that， for the link prediction task， the proposed method obtaines good results on drug， GPS and MovieLens datasets. For the hypernetwork reconstruction task， when the hyperedge reconstruction ratio is more than 0.6， the ACCuracy （ACC） of the proposed method is better than the suboptimal method Hyper2vec（biased 2nd order random walks in Hyper-networks）， and the average ACC of the proposed method outperforms the suboptimal method， that is heterogeneous hypernetwork representation learning method with hyperedge constraint based on incidence graph （HRHC-incidence graph） by 15.6 percentage points on GPS dataset.
The task of spatio-temporal sequence prediction has a wide range of applications in the fields such as transportation， meteorology and smart city. It is necessary to learn the spatio-temporal characteristics of different data with the combination of external factors such as precipitation and temperature when making station wind speed predictions， which is one of the main tasks in meteorological forecasting. The irregular distribution of meteorological stations and the inherent intermittency of the wind itself bring the challenge of achieving wind speed prediction with high accuracy. In order to consider the influence of multi-site spatial distribution on wind speed to obtain accurate and reliable prediction results， a Graph-based Dynamic Switch-Attention Network （Graph-DSAN） wind speed prediction model was proposed. Firstly， the distances between different sites were used to reconstruct the connection of them. Secondly， the process of local sampling was used to model adjacency matrices of different sampling sizes to achieve the aggregation and transmission of the information between neighbor nodes during the graph convolution process. Thirdly， the results of the graph convolution processed by Spatio-Temporal Position Encoding （STPE） were fed into the Dynamic Attention Encoder （DAE） and Switch-Attention Decoder （SAD） for dynamic attention computation to extract the spatio-temporal correlations. Finally， a multi-step prediction was formed by using autoregression. In experiments on wind speed prediction on 15 sites data in New York State， the designed model was compared with ConvLSTM， Graph Multi-Attention Network （GMAN）， Spatio-Temporal Graph Convolutional Network （STGCN）， Dynamic Switch-Attention Network （DSAN） and Spatial-Temporal Dynamic Network （STDN）. The results show that the Root Mean Square Error （RMSE） of 12 h prediction of Graph-DSAN model is reduced by 28.2%， 6.9%， 27.7%， 14.4% and 8.9% respectively， verifying the accuracy of Graph-DSAN in wind speed prediction.
In order to further improve the speed and precision of multi-scale object detection， and to solve the situations such as miss detection， wrong detection and repeated detection caused by small object detection， an object detection algorithm based on improved You Only Look Once v3 （YOLOv3） was proposed to realize automatic detection of multi-scale object. Firstly， the network structure was improved in the feature extraction network， and the attention mechanism was introduced into the spatial dimensions of residual module to pay attention to small objects. Then， Dense Convulutional Network （DenseNet） was used to fully integrate shallow information of the network， and the depthwise separable convolution was used to replace the normal convolution of the backbone network， thereby reducing the number of model parameters and improving the detection speed. In the feature fusion network， the bidirectional fusion of the shallow and deep features was realized through the bidirectional feature pyramid structure， and the 3-scale prediction was changed to 4-scale prediction， which improved the learning ability of multi-scale features. In terms of loss function， Generalized Intersection over Union （GIoU） was selected as the loss function， so that the precision of identifying objects was increased， and the object miss rate was reduced. Experimental results show that on Pascal VOC datasets， the mean Average Precision （mAP） of the improved YOLOv3 algorithm is as high as 83.26%， which is 5.89 percentage points higher than that of the original YOLOv3 algorithm， and the detection speed of the improved algorithm reaches 22.0 frame/s. Compared with the original YOLOv3 algorithm on Common Objects in COntext （COCO） dataset， the improved algorithm has the mAP improved by 3.28 percentage points. At the same time， in multi-scale object detection， the mAP of the algorithm has been improved， which verifies the effectiveness of the object detection algorithm based on the improved YOLOv3.
The existing public key encryption schemes with auxiliary inputs are impractical since they are only of Indistinguishability under Chosen Plaintext Attack (IND-CPA) security. This paper constructed a novel public-key encryption scheme resilient to auxiliary input leakage, which was based on CS '98 encryption scheme and Goldreich-Levin theorem over large field GF(q). The proposed scheme was based on Indistinguishability under Adaptive Chosen Ciphertext Attack (IND-CCA2) security, allowing an attacker to query decryption oracle with auxiliary input leakage when it tried to attack the challenge ciphertext. Compared with the BHHO (Boneh, Halevi, Hamburg, Ostrovsky) encryption scheme, the proposed scheme realizes the more strict IND-CCA2 security in spite of the encryption's and decryption's overhead being nearly doubled.
In order to improve the level of assessment of the credit risk of commercial bank credit management system based on data mining, the model of multiple decision trees by Choquet fuzzy integral fusion (MTCFF) was applied to the system. The basic idea was to mine the classified customer data by decision tree, form the different decision trees and rules according to different pruning degree, and detect unclassified customer data by different decision tree rules, and then nonlinearly combine the results from multiple decision trees by Choquet fuzzy integral to get the best decision. Using the German of the UCI dataset, the experimental results show that fusion of Choquet fuzzy integral is superior to the single decision tree in terms of classification accuracy, and it is also superior to other linear fusion methods. Choquet fuzzy integral is superior to Sugeno fuzzy integral.
The existing Identity-Based Online/Offline Encryption (IBOOE) schemes do not allow the attacker to choose the target identity adaptively, since they are only proven to be secure in the selective model. This paper introduced the online/offline technology into fully secure Identity-Based Encryption (IBE) schemes, and proposed a fully secure IBOOE scheme. Based on three static assumptions in composite order groups, this scheme was proven to be fully secure with the dual system encryption methodology. Compared with the famous IBOOE schemes, the proposed scheme not only greatly improves the efficiency of the online encryption, but also can meet the demands for complete safety in the practical systems.
In consideration of the problem that the traditional power Geographic Information System (GIS) has inabilities in storage, computing and scalability, cloud computing was applied into the power GIS field and a solution which used Hadoop cloud platform to store and manage the massive GIS data was proposed. After analyzing the characteristics of the power GIS data，a data storage strategy was proposed which combined relational database with non-relational database. Based on this strategy, the architecture of power GIS management based on Hadoop was presented, the data model and parallel data analysis based on MapReduce were designed. Finally, a quantity of experiments were carried out including spatial analysis and operation data queries in single-machine and cluster environment to compare and validate the performance. The experimental results show that the average time of data analysis and query declines over 30% after reaching certain amount of data. The proposed scheme has more obvious advantages to deal with large-scale data and has high efficiency and good feasibility.
