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  • Economics  (3)
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  • Economics  (3)
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  • 1
    Online Resource
    Online Resource
    Consumer adoption of PDA phones in Taiwan
    Inderscience Publishers ; 2010
    In:  International Journal of Mobile Communications Vol. 8, No. 1 ( 2010), p. 1-
    Details
    In: International Journal of Mobile Communications, Inderscience Publishers, Vol. 8, No. 1 ( 2010), p. 1-
    Type of Medium: Online Resource
    ISSN: 1470-949X , 1741-5217
    URL: Article
    DOI: 10.1504/IJMC.2010.030517
    RVK:
    QA 10000
    Language: English
    Publisher: Inderscience Publishers
    Publication Date: 2010
    Location Call Number Limitation Availability
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Empirical on the Influencing Factors of Local Government’s Online Response
    Hindawi Limited ; 2022
    In:  Mobile Information Systems Vol. 2022 ( 2022-6-9), p. 1-8
    Details
    In: Mobile Information Systems, Hindawi Limited, Vol. 2022 ( 2022-6-9), p. 1-8
    Abstract: The development of local government online questioning is a practical exploration to promote the digital governance of government. Exploring the influencing factors of local government online questioning response can provide guidance for the governance practice of online questioning. Based on the TOE framework, this paper constructs an analysis model of the influencing factors of local government online questioning response. By using SPSS tools to conduct regression analysis on data from 230 questionnaire samples, the results show five factors: technical competence, high-level support, perceived benefits, public readiness, and public satisfaction to the responsiveness of local government were 0.019, 0.332, 0.265, 0.156, and 0.048, respectively. Therefore, the research conclusion that technical ability, high-level support, perceived benefits, public readiness, and public satisfaction, have a positive impact on local government’s response ability. The analysis of the influencing factors of government’s online in this paper improves the existing research to a certain extent and provides a new path and perspective for the development of network politics in China.
    Type of Medium: Online Resource
    ISSN: 1875-905X , 1574-017X
    URL: Article
    DOI: 10.1155/2022/8741336
    RVK:
    QA 10000
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2187808-0
    Location Call Number Limitation Availability
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    Online Resource
  • 3
    Online Resource
    Online Resource
    Android Malware Detection via Graph Representation Learning
    Hindawi Limited ; 2021
    In:  Mobile Information Systems Vol. 2021 ( 2021-6-4), p. 1-14
    Details
    In: Mobile Information Systems, Hindawi Limited, Vol. 2021 ( 2021-6-4), p. 1-14
    Abstract: With the widespread usage of Android smartphones in our daily lives, the Android platform has become an attractive target for malware authors. There is an urgent need for developing an automatic malware detection approach to prevent the spread of malware. The low code coverage and poor efficiency of the dynamic analysis limit the large-scale deployment of malware detection methods based on dynamic features. Therefore, researchers have proposed a plethora of detection approaches based on abundant static features to provide efficient malware detection. This paper explores the direction of Android malware detection based on graph representation learning. Without complex feature graph construction, we propose a new Android malware detection approach based on lightweight static analysis via the graph neural network (GNN). Instead of directly extracting Application Programming Interface (API) call information, we further analyze the source code of Android applications to extract high-level semantic information, which increases the barrier of evading detection. Particularly, we construct approximate call graphs from function invocation relationships within an Android application to represent this application and further extract intrafunction attributes, including required permission, security level, and Smali instructions’ semantic information via Word2Vec, to form the node attributes within graph structures. Then, we use the graph neural network to generate a vector representation of the application, and then malware detection is performed on this representation space. We conduct experiments on real-world application samples. The experimental results demonstrate that our approach implements high effective malware detection and outperforms state-of-the-art detection approaches.
    Type of Medium: Online Resource
    ISSN: 1875-905X , 1574-017X
    URL: Article
    DOI: 10.1155/2021/5538841
    RVK:
    QA 10000
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2021
    detail.hit.zdb_id: 2187808-0
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
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