The initial cluster number of the K-means clustering algorithm is randomly determined, a large number of redundant features are contained in the original datasets, which will lead to the decrease of clustering accuracy, and Cuckoo Search (CS) algorithm has the disadvantages of low convergence speed and weak local search. To address these issues, a K-means clustering algorithm combined with Dynamic CS Feature Selection (DCFSK) was proposed. Firstly, an adaptive step size factor was designed during the Levy flight phase to improve the search speed and accuracy of the CS algorithm. Then, to adjust the balance between global search and local search, and accelerate the convergence of the CS algorithm, the discovery probability was dynamically adjusted. An Improved Dynamic CS algorithm (IDCS) was constructed, and then a Dynamic CS-based Feature Selection algorithm (DCFS) was built. Secondly, to improve the calculation accuracy of the traditional Euclidean distance, a weighted Euclidean distance was designed to simultaneously consider the contribution of samples and features to distance calculation. To determine the selection scheme of the optimal number of clusters, the weighted intra-cluster and inter-cluster distances were constructed based on the improved weighted Euclidean distance. Finally, to overcome the defect that the objective function of the traditional K-means clustering only considers the distance within the clusters and does not consider the distance between the clusters, a objective function based on the contour coefficient of median was proposed. Thus, a K-means clustering algorithm based on the adaptive cuckoo optimization feature selection was designed. Experimental results show that, on ten benchmark test functions, IDCS achieves the best metrics. Compared to algorithms such as K-means and DBSCAN (Density-Based Spatial Clustering of Applications with Noise), DCFSK achieves the best clustering effects on six synthetic datasets and six UCI datasets.
Accurate classification of massive user text comment data has important economic and social benefits. Nowadays, in most text classification methods, text encoding method is used directly before various classifiers, while the prompt information contained in the label text is ignored. To address the above issues, a pre-training model based Text and Label Information Fusion Classification model based on RoBERTa (Robustly optimized BERT pretraining approach) was proposed, namely TLIFC-RoBERTa. Firstly, a RoBERTa pre-training model was used to obtain the word vector. Then, the Siamese network structure was used to train the text and label vectors respectively, and the label information was mapped to the text through interactive attention, so as to integrate the label information into the model. Finally, an adaptive fusion layer was set to closely fuse the text representation with the label representation for classification. Experimental results on Today Headlines and THUCNews datasets show that compared with mainstream deep learning models such as RA-Labelatt (replacing static word vectors in Label-based attention improved model with word vectors trained by RoBERTa-wwm) and LEMC-RoBERTa (RoBERTa combined with Label-Embedding-based Multi-scale Convolution for text classification), the accuracy of TLIFC-RoBERTa is the highest, and it achieves the best classification performance in user comment datasets.
The operating cost of the port can be greatly reduced and economic benefits can be greatly improved by the automatic ship loading system, which is an important part of the smart port construction. Hatch recognition is the primary link in the automatic ship loading task, and its success rate and recognition accuracy are important guarantees for the smooth progress of subsequent tasks. Collected ship point cloud data is often missing due to issues such as the number and angle of the port lidars. In addition, the geometric information of the hatch cannot be expressed accurately by the collected point cloud data because there is often a large amount of material accumulation near the hatch. The recognition success rate of the existing algorithm is significantly reduced due to the frequent problems in the actual ship loading operation of the port mentioned above, which has a negative impact on the automatic ship loading operation. Therefore, it is urgent to improve the success rate of hatch recognition in the case of material interference or incomplete hatch data in the ship point cloud. A hatch recognition algorithm of bulk cargo ship based on incomplete point cloud normal filtering and compensation was proposed, by analyzing the ship structural features and point cloud data collected during the automatic ship loading process. Experiments were carried out to verify that the recognition success rate and recognition accuracy are improved compared with Miao’s and Li’s hatch recognition algorithms. The experimental results show that the proposed algorithm can not only filter out the material noise in the hatch, but also compensate for the missing data, which can effectively improve the hatch recognition effect.
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.
Aiming at the problems of limited attack rounds and high attack complexity of Blow-CAST-Fish (Blow-C.Adams S.Tavares-Fish) algorithm, a key recovery attack of Blow-CAST-Fish algorithm based on differential table was proposed. Firstly, after analyzing the collision of S-box, based on the collision of two S-boxes and a single S-box respectively, the 6-round and 12-round differential characteristics were constructed. Secondly, the differential tables of f3 were calculated, and three rounds were expanded based on the specific differential characteristic, thereby determining the relationship between ciphertext difference and the input and output differences of f3. Finally, the plaintexts meeting the conditions were selected to encrypt, the input and output differences of f3 were calculated according to the ciphertext difference, and the corresponding input and output pairs were found by querying the differential table, as a result, the subkeys were obtained. At the situation of two S-boxes collision, the proposed attack completed a differential attack of 9-round Blow-CAST-Fish algorithm, compared with the comparison attack, the number of attack rounds was increased by one, and the time complexity was reduced from 2107.9 to 274. At the situation of single S-box collision, the proposed attack completed a differential attack of 15-round Blow-CAST-Fish algorithm, compared with the comparison attack, although the number of attack rounds was reduced by one, the proportion of weak keys was increased from 2 - 52.4 to 2 - 42 and the data complexity was reduced from 254 to 247. The test results show that the attack based on differential table can increase the efficiency of attack based on the same differential characteristics.
Stock market is an essential element of financial market, therefore, the study on volatility of stock market plays a significant role in taking effective control of financial market risks and improving returns on investment. For this reason, it has attracted widespread attention from both academic circle and related industries. However, there are multiple influencing factors for stock market. Facing the multi-source and heterogeneous information in stock market, it is challenging to find how to mine and fuse multi-source and heterogeneous data of stock market efficiently. To fully explain the influence of different information and information interaction on the price changes in stock market, a graph neural network based on multi-attention mechanism was proposed to predict price fluctuation in stock market. First of all, the relationship dimension was introduced to construct heterogeneous subgraphs for the transaction data and news text of stock market, and multi-attention mechanism was adopted for fusion of the graph data. Then, the graph neural network Gated Recurrent Unit (GRU) was applied to perform graph classification. On this basis, prediction was made for the volatility of three important indexes: Shanghai Composite Index, Shanghai and Shenzhen 300 Index, Shenzhen Component Index. Experimental results show that from the perspective of heterogeneous information characteristics, compared with the transaction data of stock market, the news information of stock market has the lagged influence on stock volatility; from the perspective of heterogeneous information fusion, compared with algorithms such as Support Vector Machine (SVM), Random Forest (RF) and Multiple Kernel k-Means (MKKM) clustering, the proposed method has the prediction accuracy improved by 17.88 percentage points, 30.00 percentage points and 38.00 percentage points respectively; at the same time, the quantitative investment simulation was performed according to the model trading strategy.
Aiming at the problem that the current semantic segmentation algorithms are difficult to reach the balance between real-time reasoning and high-precision segmentation, a Squeezing and Refining Network (SRNet) was proposed to improve real-time performance of reasoning and accuracy of segmentation. Firstly, One-Dimensional (1D) dilated convolution and bottleneck-like structure unit were introduced into Squeezing and Refining (SR) unit, which greatly reduced the amount of calculation and the number of parameters of model. Secondly, the multi-scale Spatial Attention (SA) confusing module was introduced to make use of the spatial information of shallow layer features efficiently. Finally, the encoder was formed through stacking SR units, and two SA units were used to form the decoder. Simulation shows that SRNet obtains 68.3% Mean Intersection over Union (MIoU) on Cityscapes dataset with only 30 MB parameters and 8.8×109 FLoating-point Operation Per Second (FLOPS). Besides, the model reaches a forward reasoning speed of 12.6 Frames Per Second (FPS) with input pixel size of 512×1 024×3 on a single NVIDIA Titan RTX card. Experimental results imply that the designed lightweight model SRNet reaches a good balance between accurate segmentation and real-time reasoning, and is suitable for scenarios with limited computing power and power consumption.
Aiming at the difficulty in selecting stock valuation features and the lack of time series relational dimension features during the prediction of stock market volatility by intelligent algorithms such as Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) network, in order to accurately predict stock volatility and effectively prevent financial market risks, a new stock market volatility prediction method based on Improved Genetic Algorithm (IGA) and Graph Neural Network (GNN) named IGA-GNN was proposed. Firstly, the data of stock market trading index graph was constructed based on the time series relation between adjacent trading days. Secondly, the characteristics of evaluation indexes were used to improve Genetic Algorithm (GA) by optimizing crossover and mutation probabilities, thereby realizing the node feature selection. Then, the weight matrix of edge and node features of graph data was established. Finally, the GNN was used for the aggregation and classification of graph data nodes, and the stock market volatility prediction was realized. In the experiment stage, the studied number of total evaluation indexes of stock was 130, and 87 effective evaluation indexes were extracted from the above by IGA under GNN method, making the number of stock evaluation indexes reduced by 33.08%. The proposed IGA was applied to the intelligent algorithms for feature extraction. The obtained algorithms has the overall prediction accuracy improved by 7.38 percentage points compared with the intelligent algorithms without feature extraction. Compared with applying the traditional GA for feature extraction of the intelligent algorithms, applying the proposed IGA for feature extraction of the intelligent algorithms has the total training time shortened by 17.97%. Among them, the prediction accuracy of IGA-GNN method is the highest, which is 19.62 percentage points higher than that of GNN method without feature extraction. Compared with the GNN method applying the traditional GA for feature extraction, the IGA-GNN method has the training time shortened by 15.97% on average. Experimental results show that, the proposed method can effectively extract stock features and has good prediction effect.
The current service resources in Internet of Services (IoS) show a trend of refinement and specialization. Services with single function cannot meet the complex and changeable requirements of users. Service integrating and scheduling methods have become hot spots in the field of service computing. However, most existing service integrating and scheduling methods only consider the satisfaction of user requirements and do not consider the sustainability of the IoS ecosystem. In response to the above problems, a service integration method based on adaptive multi?objective reinforcement learning was proposed. In this method, a multi?objective optimization strategy was introduced into the framework of Asynchronous Advantage Actor?Critic (A3C) algorithm, so as to ensure the healthy development of the IoS ecosystem while satisfying user needs. The integrated weight of the multi?objective value was able to adjusted dynamically according to the regret value, which improved the imbalance of sub?objective values in multi?objective reinforcement learning. The service integration verification was carried out in a real large?scale service environment. Experimental results show that the proposed method is faster than traditional machine learning methods in large?scale service environment, and has a more balanced solution quality of each objective compared with Reinforcement Learning (RL) with fixed weights.
Microservice invocation link data is a type of important data generated in the daily operation of the microservice application system, which records a series of service invocation information corresponding to a user request in the microservice application in the form of link. Microservice invocation link data are generated at different microservice deployment nodes due to the distribution characteristic of the system, and the current collection methods for these distributed data include full collection and sampling collection. Full collection may bring large data transmission and data storage costs, while sampling collection may miss critical invocation data. Therefore, an event?driven and pipeline sampling based dynamic collection method for microservice invocation link data was proposed, and a microservice invocation link system that supports dynamic collection of invocation link data was designed and implemented based on the open?source software Zipkin. Firstly, the pipeline sampling was performed on the link data of different nodes that met the predefined event features, that is the same link data of all nodes were collected by the data collection server only when the event defined data was generated by a node; meanwhile, to address the problem of inconsistent data generation rates of different nodes, multi?threaded streaming data processing technology based on time window and data synchronization technology were used to realize the data collection and transmission of different nodes. Finally, considering the problem that the link data of each node arrives at the server in different sequential order, the synchronization and summary of the full link data were realized through the timing alignment method. Experimental results on the public microservice lrevocation ink dataset prove that compared to the full collection and sampling collection methods, the proposed method has higher accuracy and more efficient collection on link data containing specific events such as anomalies and slow responces.
Concerning that the federated chain lacks visualization methods to show the resource usage, health status, mutual relationship and consensus transaction process of each node, a Fabric consensus transaction Tracking method based on custom Log (FTL) was proposed. Firstly, Hyperledger Fabric, a typical federation framework, was used as the infrastructure to build the bottom layer of FTL. Then, the custom consensus transaction logs of the Fabric were collected and parsed by using the ELK (Elasticsearch, Logstash, Kibana) tool chain, and Spring Boot was used as the business logic processing framework. Finally, Graphin which focuses on graph analysis was utilized to realize the visualization of consensus trade trajectory. Experimental results show that compared with native Fabric applications, FTL Fabric?based application framework only experienced an 8.8% average performance decline after the implementation of visual tracking basis without significant latency, which can provide a more intelligent blockchain supervision solution for regulators.
The unique advantages of Named Data Networking (NDN) make it a candidate for the next generation of new internet architecture. Through the analysis of the communication principle of NDN and the comparison of it with the traditional Transmission Control Protocol/Internet Protocol (TCP/IP) architecture, the advantages of the new architecture were described. And on this basis, the key elements of this network architecture design were summarized and analyzed. In addition, in order to help researchers better understand this new network architecture, the successful applications of NDN after years of development were summed up. Following the mainstream technology, the support of NDN for cutting-edge blockchain technology was focused on. Based on this support, the research and development of the applications of NDN and blockchain technology were discussed and prospected.
For the efficient shape analysis of massive, heterogeneous and complex 3D models, an optimization method for 3D model shape based on optimal minimum spanning tree was proposed. Firstly, a model description based on 3D model Minimum Spanning Tree (3D-MST) was constructed. Secondly, local optimization was realized by topology and geometry detection and combination of bilateral filtering and entropy weight distribution, obtaining optimized MST representation of the model. Finally, the shape analysis and similarity detection of the model were realized by optimized Laplacian spectral characteristics and Thin Plate Spline (TPS). The experimental results show that the proposed method not only effectively preserves shape features of the model, but also effectively realizes sparse optimization representation of the complex model, improving the efficiency and robustness of geometric processing and shape retrieval.
Superword Level Parallelism (SLP) is a vector parallelism exploration approach for basic block. With loop unrolling, more parallel possibility can be explored. Simultaneously too much exploring paths are brought in. In order to solve the above problem, an optimized SLP method with subsection constraints was proposed. Redundant elimination on segmentation was used to obtain homogeneous segments. Inter-section exploring method based on SLP was used to restrain exploring paths and reduce the complexity of the algorithm. And finally pack adjustment was used to deal with the situation of overlap on memory access. The experimental results show that the vectorization capability of SLP is enhanced; for the test serial program, the average speedup of vectorized version is close to 2.
In order to deal with the channel fading in Underwater Wireless Sensor Networks (UWSN) changing randomly in time-space-frequency domain, underwater cooperative communication model with relays was proposed in this paper to improve reliability and obtain diversity gain of the communication system. Based on the new model, a relay selection algorithm for UWSN was proposed. The new relay selection algorithm used new evaluation criteria to select the best relay node by considering two indicators: channel gain and long delay. With the selected relay node, source node and relay nodes could adjust their sending power by the power allocation algorithm which was based on the principle of minimizing the bit error rate. In a typical scenario, by comparing with the traditional relay selecting algorithm and equal power allocation algorithm, the new algorithm reduces the delay by 16.7% and lowers bit error rate by 1.81dB.