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In view of the problem that the public data in the field of mine hoist fault is less and the fault knowledge is difficult to be effectively utilized, this paper proposes a method for constructing a mine hoist fault knowledge graph. This method firstly introduces a fault text classification process to deal with the information redundancy problem existing in the target corpus. Then it uses dictionary embedding BERT and BiLSTM-CRF combination for entity recognition, uses ERNIE for entity relation extraction, and stores the extracted triples in Neo4j graph database. On this basis, an intelligent question answering system based on mine hoist fault knowledge graph is realized. This knowledge graph can reveal the complex correlation between mine hoist faults, realize the root cause analysis of related faults, and provide support for mine hoist fault diagnosis. [ABSTRACT FROM AUTHOR]

The existing biomedical event trigger detection methods have the following defects: Redundant information unrelated to triggers are retained; potential correlations between entities and events are ignored; traditional methods are vulnerable to data scarcity. A biomedical event trigger detection based on two-stage question answering paradigm is proposed to address the above problems. In the event type identification phase, in order to exclude the interference of irrelevant information, the attention based on syntactic distance is allowed to capture more meaningful contextual features. In order to effectively utilize the potential features in the entities, the word-entity-event co-occurrence feature based on global statistics is used to guide event type aware attention to explore the strong relationship between words and events. In the trigger localization phase, the trigger index of the event in the sentence is answered according to the identified event type questions, thus leveraging the rich question answering database to achieve data enhancement. The results on the MLEE corpus show that the two-stage question answering paradigm, syntactic distance attention, and event type aware attention effectively improve the performance of the model, and the proposed model achieves 81.39% F1-score, outperforming other baseline models in terms of detailed results for multiple event types. [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.)

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.)

Copyright of Journal of Frontiers of Computer Science & Technology 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.)

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 Frontiers of Computer Science & Technology 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 Frontiers of Computer Science & Technology 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.)

Ciphertext-policy attribute-based encryption not only realizes the fine-grained access control of data in IoT system based on cloud storage, but also brings the problem of user and attribute revocation. However, in the existing access control schemes, the time-based schemes are difficult to achieve immediate revocation, and the third-party-based schemes usually require a large number of re-encrypted ciphertexts, the efficiency is low and the cost is large. Therefore, an efficient access control scheme supports immediate revocation of user and attribute based on RSA key management mechanism is proposed. The length of the keys and ciphertexts are fixed. With the help of fog nodes, user revocation is realized. At the same time, part of the encryption and decryption work is unloaded from the client to the nearby fog node, which reduces the computing burden of the client. The results of security analysis based on aMSE-DDH hypothesis show that the scheme can resist chosen-ciphertext attack. Theoretical analysis and experiments prove that the proposed scheme can provide efficient access control for application scenarios with frequent user and attribute changes and limited resources. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology 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.)

Location planning for electric taxi charging station based on the trajectory data to solve the problem of low utilization rate of electric vehicle charging piles and low profitability of charging stations is proposed. Firstly, the trajectory data characteristics of the taxi parking status are analyzed to get the taxi parking points that support the charging time of the vehicle. Secondly, it takes the map matching model to filter out the parking point on the road. Thirdly, the parking points in off-road areas are clustered, the area with lots of taxi parking point are obtained as the charging station location area. Finally, the time distribution of the taxi parking points is analyzed, and the number of points at the peak time of the points in the charging station area is used as reference to set the charging piles. The experimental data come from 4 416 taxis in Fuzhou, 16 locations are selected as charging stations, rationality analysis is made on the charging convenience of drivers and economy of charging station. The results show that the charging station contains 2 741 taxis, this means that more than 60% of taxis can be charged in a non-operating state. The total profit of the charging station during the depreciation period (six years)is 43.165 6 million that each charging station can make a profit of about two million. This method can not only provide car owners with convenience for charging, but also can effectively ensure the profitability of charging stations, it can provide effective suggestions for the construction and operation of electric taxi charging stations. [ABSTRACT FROM AUTHOR]

A spatial prevalent co-location pattern (SPCP) is a subset of spatial features whose instances frequently appear together in geographic space. Memory-based neighbor relationship materialization method to search for pattern's instances is efficient, but instance information is stored repeatedly. Graph database technology can efficiently model data with complex associations. Thus, it is possible to consider using the graph database technology to materialize neighbor relationships (i.e., to construct neighborhood graph), but directly transplanting existing mining methods cannot exert the advantages of graph traversal. To solve the above problem, this paper explores the graph databasebased approach to mine spatial prevalent co-location patterns. Firstly, the graph database is utilized to model the spatial instances and their neighbor relations, i.e., the instances and relations are stored in the graph database to construct the neighborhood graph. Then, a basic algorithm called subgraph (or clique) search is designed based on the graph database, using the way of clique search strategy to generate a pattern's table instance to obtain the participating instances, and avoid the inefficient combination or join operations in the traditional method. Considering the low efficiency of collecting participating instances by generating table instances, a participating instance verification algorithm is designed, including the filtering and verification phases. The filtering phase determines whether the features involved in the center instance's neighborhoods fully contain the features in the pattern, and the verification phase determines whether there is pattern instance containing the central instance. The participating instance verification algorithm determines as many participating instances as possible each time, thereby effectively reducing the search space and the number of clique searches. In addition, the correctness and completeness of the proposed algorithms are proven. Finally, extensive experiments are conducted on real and synthetic datasets to verify the efficiency and effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Attributed to its innate advantage in modeling relational information, graph data have been widely leveraged in various applications including social network, knowledge representation, etc. Compared with traditional relational database systems, primitive operators in graph data management, represented by subgraph matching, still observe space for further optimization. In a fully-fledged graph database system, in order to optimize the schedule of multiple subgraph matching tasks, it usually necessitates accurate estimation of the cost of every task, especially of the cardinality of matching results. However, current methods for cardinality estimation of subgraph matching fail to fully exploit the structural information in the graph, and moreover, it may potentially result in serious error accumulation. In this light, BoostCard is proposed to aggregate the local structural features via representing neighborhood information of nodes. Meanwhile, it utilizes statistical method to predict whether there is an edge between two nodes. And it adopts the Boosting strategy to compensate globally based on the acquired features of nodes. Hence, it can achieve intelligent cardinality estimation of subgraph matching, making itself a widely applicable framework for the task. In comparison with conventional statistic-based methods, BoostCard offers significant performance gain in the cardinality estimation of multi-node subgraph matching over real-life data sets. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology 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.)