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Digital development has been vigorously promoted in China to drive a comprehensive and indepth change in production, life and governance through the construction of digital economy, digital society and digital government. It has put forward new and higher requirements for basic surveying and mapping products and services. Recently, the Ministry of Natural Resources of the People􀆳s Republic of China issued a notice to promote the construction of 3D realistic geospatial scene of China, aiming to develop new 3D geo-spatial information products which can describe and represent the real 3D spaces of industrial production, living environment and ecological development. The construction of such 3D realistic geospatial scene of China will lead to a 3D digital space which can be well connected with the real geo-spatial space through real-time inter-connection, and will provide a new generation of spatiotemporal information framework for the development of digital China. It is not only a major task for national and local basic surveying and map‐ ping agencies in the next few decades, but also a challenging scientific and technological initiative where several complex factors and technical problems remain to be solved. This paper analyzes the background and problems, discusses the development directions and general technical logic, and conductes the major technological path. [ABSTRACT FROM AUTHOR]

Homography estimation is a basic and important step in many computer vision tasks. Traditional homography estimation methods are based on feature point matching, which are difficult to work in weak texture images. Deep learning has been applied to homography estimation to improve its robustness, but the existing methods do not consider the multi-scale problem caused by object scale differences, resulting in limited accuracy. To solve the above problems, this paper proposes a multi-scale residual network for homography estimation. The network can extract the multi-scale feature of the image, and used the Multi-Scale Feature Fusion Module to effectively fuse the features. In addition, it further reduced the difficulty of network optimization by estimating the four-corner normalized offset. Experiments on MS-COCO dataset showed that the average corner error of this method was only 0.788 pixels, which achieved sub-pixel accuracy, and can maintain high accuracy in 99% of cases. Due to the comprehensive utilization of multi-scale features and easier to optimize, this method had significantly improved accuracy and stronger robustness. [ABSTRACT FROM AUTHOR]

To achieve high efficiency with limitation of energy consumption is a key problem in the system of UAVs. In existing UAV task allocation methods, the relevance between tasks and UAV cruise directions is neglected, which may further influence the energy consumption and delay of task accomplishment. In view of this, this paper proposed an UAV task allocation method based on the relevance analysis between tasks and cruise directions. This method includes two phases: task screening phase and conflict resolution phase based on consensus. In the first phase, this method selects out tasks without turning back for an UAV according to angles between the cruise direction of the UAV and directions of these tasks, then designs an algorithm to further select candidate tasks before interaction from the tasks without turning back according to their energy consumption utility parameters and time urgency parameters. In the second phase, this method solves tasks conflicts between UAVs after exchanging of their candidate tasks according to energy consumption utility parameters and time delay parameters of these tasks in different cruise directions of these UAVs. Simulation results verify that the proposed method can achieve lower average task energy consumption and average task delay. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aspect-level sentiment analysis aims to determine the sentiment polarity towards specific aspect in reviews. However, little research has been done on the influence of complex sentences on sentiment classification. Based on this, this paper proposed an aspect sentiment classification model based on Bert and Self-attention network with relative position. Firstly, it used the dynamic weighted sampling method to balance the rare contrastive sentences, so that the model can learn more contrastive sentence feature information. Then, it jointly trained the feature representations extracted by double-head self-attention network with relative position and the feature representations obtained by the Pre-trained model with absolute position. Finally, it used the label smoothing regularization technology to stabilize the model to identify the neutral samples. It tested this model on Sub Task 2 in SemEval 2014 task, and improved both accuracy and Macro-F1 indicators of the two datasets. The experimental results show that the effectiveness of the proposed model for contrastive sentences classification, and also yield improvements in the whole test set over other benchmark models. [ABSTRACT FROM AUTHOR]

Copyright of Commerce & Management Quarterly is the property of Chinese Association of Business & Management Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Few-shot image classification is a special task where the model learns to build correct concepts of categories from only a few examples. Due to the frequent occurrence of few-shot scenarios， it has aroused extensive research. However， most previous few-shot models process examples from different categories individually without considering inter-classes information. We propose a novel category-fusion network （CFN） to exploit the intra- and inter-classes information simultaneously by fusing the information of examples from different categories. The key part of CFN is the learning of a fusion map， that is， how to fuse the features in the sample to map out the network parameters. There is an important problem that whether the fusion mapping should change with different input examples. To explore this problem， we design three different modules：（1）the class-irrelevant module with a fixed mapping；（2）the semi-relevant module where the fusion mapping only depends on the target category whose knowledge is expected to be learned；（3）the fullyrelevant module where the fusion mapping totally depends on input examples. Our network can build the concept of a certain category by learning from examples of several categories， and generates a classifier with fused information. The experiments show the effectiveness of our network in few shot learning ，which obtains 60.03% in accuracy on the widely used MiniImage Net dataset. [ABSTRACT FROM AUTHOR]

Air traffic self-separation technology of the next generation air traffic management system is a cutting-edge direction recognized by the civil aviation community， which transfers partial or total separation maintenance tasks from ground to aircraft. The air traffic self-separation is implemented by using the distributed mechanism to facilitate flexibility and efficiency of flights. This paper reviews and analyzes the state-of-the-art and challenges of the development in this field. Three aspects of the research progress and trend are highlighted， including airborne flight situation awareness， airborne trajectory planning and controlling， and air traffic autonomous operation and control. Three trends of the technology are summarized， and directions for future research are identified. [ABSTRACT FROM AUTHOR]

Mutual information-based filtering feature selection algorithms are often limited to the metric of mutual information. In order to circumvent the limitations of adopting only mutual information, a distance metric-based algorithm RReliefF is introduced on the basis of mutual information to obtain better filtering criteria. RReliefF is used for the classification tasks to measure the relevance between features and labels. In addition, maximal information coefficient(MIC) is used to measure the redundancy between features and the relevance between features and labels. Finally, entropy weight method is applied to objectively weigh the MIC and RReliefF. On this basis, a filtering feature selection algorithm based on entropy weight method(FFSBEWM) is proposed. Comparing experiments carried out on 13 data sets show that the average classification accuracy and highest classification accuracy of the feature subsets selected by the proposed algorithm are higher than those of the comparison algorithms. [ABSTRACT FROM AUTHOR]

Copyright of Forensic Science & Technology is the property of Institute of Forensic Science, Ministry of Public Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

At present, most link-prediction based models on anomaly detection in social networks lack the ability to consider the influence of abnormal nodes evolution. With the limitation of network scale and complexity, the detection efficiency of traditional models is generally low. To address these issues, this paper proposed an anomaly detection method based on the spatial scale coarse granulation and weighting mechanism on abnormal nodes. Firstly, the method introduced a cohesive community discovery algorithm, Louvain algorithm, to coarsely granulate process to streamline network. Subsequently, it identified abnormal nodes with different evolution behaviors in the processed network following the quantification of abnormal evolution process. Finally, it applied the link prediction method combined with a weighting mechanism of abnormal nodes for final abnormal detection. Compared with different LinkEvent-based strategy adjustment algorithms and NESO_ED method on three real social network data sets VAST, Email-EU ( dept1 and dept2 ) and Enron, the proposed method outperforms other state-of-the-art methods, can take into account both stability and sensitivity on anomaly detection tasks and more reasonably describe the network evolution process. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhejiang University (Science Edition) is the property of Journal of Zhejiang University (Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The existing heterogeneous network embedding methods not only ignore heterogeneous edges in networks and their different effects on node embedding, but also do not consider the integration of network structure and node attributes. This paper proposed a novel heterogeneous network emhedding method with attributes information fusion, which named SHANE. It applied the sequence to sequence ( seq2seq) model to the sub-graphs divided according to edge types, seamlessly integrated the structure information and attributes information of nodes, and captured the high-order semantic information of nodes simultaneously. Experiments show that SHANE can achieve relatively significant results in link prediction tasks in two different types of datasets. [ABSTRACT FROM AUTHOR]

In psychophysical studies of discrimination, thresholds are commonly estimated by (nonparametric, fixed-stepsize) adaptive methods using a binary response format. However, it can sometimes be challenging for participants to give a dichotomous response. To resolve the issue, scholars have suggested adding more response categories to the algorithm (Kaernbach, 2001; Klein, 2001). However, Hsu and Chin (2014) argued that their extension has some limitations. Following Hsu and Chin (2014), we proposed an alternative framework to incorporate response confidence into existing adaptive methods for Yes/No tasks. In particular, we introduced the concept of a “cut-off,” thereby expanding the meaning of psychometric functions to psychometric surfaces. We performed simulations to investigate the feasibility of the framework. We considered three adaptive methods, namely Derman’s Up-Down (DUD) method (Derman, 1957), the Biased Coin Design (BCD) (Durham & Flournoy, 1995), and the Weighted Up-Down (WUD) method (Kaernbach, 1991). Setting 0.75 as the target probability for the threshold, we systematically manipulated the initial value, step size, cut-off, and number of trials to examine their impact on the threshold estimation performance of the three adaptive methods. Our simulation results showed that the proposed framework is applicable in general and that WUD and BCD performed slightly better than DUD. We also extended the framework to the Same/Different task, where the psychometric function is not monotonically increasing. We tackled this issue by providing mappings between the response confidence scales of the Same/Different task and the Yes/No task. Doing this enables us to obtain a transformed psychometric function that satisfies the monotonicity property, and thus the aforementioned framework can be readily applied to the Same/Different task. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper studies the task allocation problem of multiple aerial vehicles cooperative operation. Ftrstly, according to the characteristics of different aerial vehide types, the concept of task adaptablllty is proposed to describe the retative pros and cons of different aerial vehide types in different misiion scenarios. Secondly, based on the retaiion of aerial vehides between the same fleets or different fleets, a characteristic relation function is proposed to describe the impact of other aerial vehides on thetr own performance when performing the same task. Thtrdly, in response to the task requirements which multiple aerial vehicles complete together, a global proilt index coniidering the task adaptablilty and the charasterisiic relation of aerial fleets is deigned, and by solving the optimization problem, the multiple aerial vehides cooperative operation benefit is maximized. Finally, for the typical aerial vehides cooperative combat scenarios, by solving the above optimization problem, the task asiignment result is obtained. The resuls show that the proposed global proilt model is reasonable and can make full use of the advantages of multiple aerial vehides and the retaiion between members inside and outeide the fleet to increase the overall revenue. It has important iigniiicance for the assignment of aerial vehides cooperative operation tasks. [ABSTRACT FROM AUTHOR]

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to fully mine nodes' features and better integrate these features simultaneously in the heterogeneous information network, this paper proposed a AMF L& GRec Firstly, AMFL & GRec used the Leader Rank algorithm to extract the target node' global sequence, and used a meta-path-bas ed heterogeneous information network embedding model to extract the node' local sequence,and used the skip-gram model to learn the node' global and local features. And then it used the self-attention mechanism to learn the preference of the target nodes' local and global features to obtain the feature representation of the target node in a single meta-path. Secondly, it used the self-attention mechanism to fuse the representation of the same node under different meta-paths to obtain the final feature representation. Finally, it utilized a multi-layer perceptron to achieve the task of rating prediction. This paper conducted a large number of experiments on two real datasets. The experimental results verify that the AMFL & GRec algorithm can not only capture the micro(local) structure of densely connected nodes, but also capture the global structure of the node in the network, and finally obtain nodes' overall (local +global) characteristics. At the same time, the experimental results also prove that the AMFL & GRec's rating prediction performance is better than the baselines. It proves that in the heterogeneous information network utilizing the self-attention mechanism to consider the nodes' preferences for local and global features and meta-paths can improve the accuracy of rating prediction. [ABSTRACT FROM AUTHOR]

Since the prototype network cannot make full use of samples' semantic information, it 's difficult for model to fully excavate the transferable features in training data. As the result, the model underperforms when it is facing unfamiliar data in a new domain. To this end, this paper made improvements from three perspectives: model structure, encoding network, and metric network, and proposed a multiple-task prototypical network MTPN. In terms of model structure, on the basis of the prototype network ' s metric task, it added an auxiliary classification task to constrain the training target, which could improve the semantic feature extraction ability of the model. By using multi-task learning, model obtained a semantic representation that was more relevant to the auxiliary task. In order to improve feature transferability, this paper also proposed the LF-Transformer encoder which used hierarchical attention to fuse the underlying general encoding information. The metric network used the BiGRU-based class prototype generator to make the class prototype more representative and the distance measurement more accurate. Experiments show that MTPN achieves an accuracy of 91.62% in the sentiment classification task with few samples, which is 3.5 % higher than the existing best model. For samples in new field that have not appeared in training state, by using 5 references, the model can still obtain a classification accuracy of more than 90% on query samples. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In attribute network, the attribute information associated with nodes is essential to improve the performance of network embedding tasks. Nevertheless, network is a graph structure, in which nodes not only contain attribute information but also embrace the rich structural information. In order to make full use of the structural information, firstly, this paper defined influential node characteristics, spatial relationships, and constructed similarity matrix based on the definition of link prediction. Then it mapped correlation similarity vector associated with nodes in the binary group to the relationship space of the adjacency matrix, so as to maintain the node vector matrix structure information feature. Based on the definition of normalized graph Laplacian, it fused the attribute information and label feature and integrated the above three kinds of information into an optimization framework. Finally, it inferenced the projection matrix by calculating the local maximum value through a second order derivative. Experimental results indicate that the proposed algorithm can effectively utilize information of the adjacency structure with the binary group of nodes, and compared with other benchmark network embedding algorithms, it also can achieve better results on the node classification task. [ABSTRACT FROM AUTHOR]

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The traditional Encoder-Decoder model with attention mechanism has such problems as text redundancy, inconsistent representation and out of vocabulary (OOV) in the application of the summary task, resulting in low accuracy of the generated summary. The transformer model with ambeddable text location information was improved, pointer network was introduced to help with decoding, and take advantage of pointer network to generated text to generate summary. The effectiveness of the transformer model was verified on the LCSTS Chinese short text summary data set. The results show that the model outperforms the benchmark model by an average of two points in ROUGE scores, and the prominence of the generated content and the fluency of the language are significantly improved while ensuring the consistency of the summary with the input text. [ABSTRACT FROM AUTHOR]

Due to the limited computing resources and very different capabilities of the space-earth integration network, the processing power of complex tasks is not strong and important tasks fail to be processed. Therefore, a three-layer computational offloading overhead model that offloads tasks to local-backbone-edge access nodes was established and the optimal offloading strategy was formulated through the optimal offloading algorithm based on DQN ( deep Q-leaming network). Firstly, based on the characteristics of the three types of computing nodes( offloading sites) in the network, including the space-based backbone nodes, edge access nodes and ground-based backbone nodes, the expressions of the delay, energy consumption and the corresponding expressions of different offload sites were given. Then, based on this, the DQN algorithm was proposed to complete the low-delay, low-energy offloading process. Finally, simulation results show that the DQN algorithm can improve the speed of task execution, reduce the energy consumption of terminal equipment, and effectively improve the current situation of computing node resources in the network. [ABSTRACT FROM AUTHOR]

Intelligent question answering based on reading comprehension refers to letting computers read and comprehend texts like humans, extracts the text information and answers corresponding questions. The pre-training model RoBERTa-wwm-ext uses the extracted original fragments as the answers to the questions, but this method can’t solve the two situations that the answer fragments don’t exist in the original text or need to reply to the original text after summarizing. The pre-training model is used for generative model training, which can solve the problems that need to summarize the original text to a certain extent. Therefore, this paper improved the method of only using RoBERTa-wwm-ext model to extract answers. On this basis, it integrated the generative question answering model based on RAG model to answer questions that could not be handled by Roberta-wwm-ext and other extraction models. At the same time, this paper absorbed the advantages of PGN model, improved RAG model, and obtained RPGN sub model, which could make better use of reading and understanding articles to generate reasonable answers. Therefore, this paper proposed a fusion model of RPR（RAG, PGN, RoBERTa-wwm-ext）,which could be used to deal with both extractive question task and generative question answering task at the same time. [ABSTRACT FROM AUTHOR]

Recently, graph neural networks have received widespread attention due to their rich representation and reasoning capabilities. To best knowledge, current research mainly focuses on amending the convolutional strategy and network structure for higher performance, so the performance will be inevitably constrained by the limitations of the single model. Inspired by the idea of ​​ensemble learning, this paper innovatively proposed an ensemble learning framework for graph neural network (EL-GNN).Unlike regular text and images, graph data not only had features but also had rich topology information. Therefore, EL -GNN additionally supplemented the structure information during the ensemble stage rather than merely integrating the prediction results of independent classifiers. Besides, this paper further revised the ensemble strategy through reconstructing a feature-level similarity graph for subsequent assembling, which balances the feature and structure information on the basis of the assumptions of those nodes with the similar feature or easy reachability of high probability to share the same labels .The comprehensive experiments indicate that the proposed ensemble strategy achieves an impressive performance and EL-GNN is superior to other off-the-shelf models on the node classification task. The comprehensive experiments indicate that the proposed ensemble strategy achieves an impressive performance and EL-GNN is superior to other off-the-shelf models on the node classification task. The comprehensive experiments indicate that the proposed ensemble strategy achieves an impressive performance and EL-GNN is superior to other off-the-shelf models on the node classification task. [ABSTRACT FROM AUTHOR]

Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Considering that in the multi-robot patrol task, it is a common phenomenon that the importance of the nodes to be visited is different. In view of the fact that most patrolling algorithms don't consider the difference in the importance of nodes, the idle time of all nodes tends to be the same, resulting in important nodes access frequency ordinary nodes and excessive access problems. This paper proposed a distributed patrolling strategy based on nodes' importance to optimize node access frequency and reduce the global average idle time. The robot calculated the idle time and importance of surrounding nodes, online decided the target node, estimated the time of reaching the target node, and informed nearby companions of the target node and estimated time of arrival. It increased the importance of the edge node in order to prevent some nodes from being over-visited, when the access frequency of the edge node was lower than the minimum access frequency, so that the robot could visit the node as soon as possible. Finally, this paper analyzed the influence of factors on the completion of continuous patrolling tasks of robots through simulation on the number of robots, changing environment, the importance of nodes and other factors. The results show that the number of visits to the most important nodes has increased significantly. Moreover, under the premise of the same patrolling environment and number of robots, the algorithm has fewer outliers and smaller average idle time, and the performance of multi-robot continuous patrolling is better. [ABSTRACT FROM AUTHOR]

Traditional graph regularization methods use Euclidean distance to measure the similarity of sample space, and can not accurately preserve the neighborhood information of complex data sets, which easily lead to the degradation of the generalization performance of the model in complex shape data and non convex data sets. This paper proposed an improved graph regularization algorithm, which used isometric feature mapping to preserve the neighborhood information of the sample space and help the model learn manifold. The simultaneous used of KL constraints further smoothed the external structure of the data representation, thereby capturing more sparse and advanced feature representations. Experimental results on MNIST and YaleB datasets show that compared with several popular feature extraction algorithms, the proposed algorithm can extract more meaningful and robust features. It has advantages in classification and clustering tasks, and also has better anti-interference capability. [ABSTRACT FROM AUTHOR]

Joint extraction of entities and relations from unstructured text is an important task in information extraction. The existing methods have achieved considerable performance, but are still subject to some inherent limitations, such as error propagation, redundancy of relation prediction, inability to solve the problem of relations overlap, etc. For this reason, this paper proposed a joint entity relationship extraction model BSGB( BiLSTM + SDA-GAT + BiGCN) based on graph neural network. BSGB was a two-stage predicting process. The first stage of this model extended the semantic dependency analysis to the semantic dependency graph, and proposed a graph attention network to integrate the semantic dependency graph(SDA-GAT). By stacking BiLSTM and SDA-GAT, it extracted sentence sequence and local dependent features, and performed entity span detection and preliminary relationship prediction. In the second stage, it constructed the relation-weighted GCN, which further modeled the interaction between entities and relations, and completed the final extraction of entity-relationship triples. The experimental results on the NYT dataset show that the F1 value of this model reaches 67 .1 %, which is 5. 2% higher than the baseline model in this dataset, and the prediction of the overlap relation is also significantly improved. [ABSTRACT FROM AUTHOR]

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