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Chunhua Ju
Department of Modern Business Research Center, Zhejiang Gongshang University, Hangzhou 310018, China

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Journal article
Published: 29 April 2020 in Sensors
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Although the Crowd-Sensing perception system brings great data value to people through the release and analysis of high-dimensional perception data, it causes great hidden danger to the privacy of participants in the meantime. Currently, various privacy protection methods based on differential privacy have been proposed, but most of them cannot simultaneously solve the complex attribute association problem between high-dimensional perception data and the privacy threat problems from untrustworthy servers. To address this problem, we put forward a local privacy protection based on Bayes network for high-dimensional perceptual data in this paper. This mechanism realizes the local data protection of the users at the very beginning, eliminates the possibility of other parties directly accessing the user’s original data, and fundamentally protects the user’s data privacy. During this process, after receiving the data of the user’s local privacy protection, the perception server recognizes the dimensional correlation of the high-dimensional data based on the Bayes network, divides the high-dimensional data attribute set into multiple relatively independent low-dimensional attribute sets, and then sequentially synthesizes the new dataset. It can effectively retain the attribute dimension correlation of the original perception data, and ensure that the synthetic dataset and the original dataset have as similar statistical characteristics as possible. To verify its effectiveness, we conduct a multitude of simulation experiments. Results have shown that the synthetic data of this mechanism under the effective local privacy protection has relatively high data utility.

ACS Style

Chunhua Ju; Qiuyang Gu; Gongxing Wu; Shuangzhu Zhang. Local Differential Privacy Protection of High-Dimensional Perceptual Data by the Refined Bayes Network. Sensors 2020, 20, 2516 .

AMA Style

Chunhua Ju, Qiuyang Gu, Gongxing Wu, Shuangzhu Zhang. Local Differential Privacy Protection of High-Dimensional Perceptual Data by the Refined Bayes Network. Sensors. 2020; 20 (9):2516.

Chicago/Turabian Style

Chunhua Ju; Qiuyang Gu; Gongxing Wu; Shuangzhu Zhang. 2020. "Local Differential Privacy Protection of High-Dimensional Perceptual Data by the Refined Bayes Network." Sensors 20, no. 9: 2516.

Journal article
Published: 09 April 2020 in Sustainability
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Existing literature tends to treat enterprises as a whole when measuring government intervention. However, in Chinese region-specific institutional development, ultimate control (i.e., local government) tends to control multiple enterprises. This paper considers the enterprises controlled by the same ultimate controller as a portfolio, which is used to measure government intervention by comparing the differences of the enterprises in the portfolio. This paper uses the data of Chinese listed local state-owned enterprises (LSOEs). and we assess whether local state ownership benefits or offsets LSOEs’ cross-border mergers and acquisitions (CBM & A) activities. We propose a new measurement of government intervention to explain the mechanisms through which government influences the cross-border mergers and acquisitions of local SOEs. The experimental results show that government intervention and region-specific marketization institutional development negatively moderate the effect of government internationalization subsidies and government intervention on CBM & A separately. However, government internationalization subsidies, government intervention, and region-specific marketization enhance the CBM & A effect of state ownership separately. This study explores the benefits of government involvement in local SOEs. The value of this paper is to provide a novel perspective, including the intermediary effect of government intervention and the market environment.

ACS Style

Qiuyang Gu; Chunhua Ju; Fuguang Bao. The Cross-Border Mergers and Acquisitions of Local State-Owned Enterprises: The Role of Home Country Government Involvement. Sustainability 2020, 12, 3020 .

AMA Style

Qiuyang Gu, Chunhua Ju, Fuguang Bao. The Cross-Border Mergers and Acquisitions of Local State-Owned Enterprises: The Role of Home Country Government Involvement. Sustainability. 2020; 12 (7):3020.

Chicago/Turabian Style

Qiuyang Gu; Chunhua Ju; Fuguang Bao. 2020. "The Cross-Border Mergers and Acquisitions of Local State-Owned Enterprises: The Role of Home Country Government Involvement." Sustainability 12, no. 7: 3020.

Journal article
Published: 12 March 2020 in Sustainability
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User influence has always been a major topic in the field of social networking. At present, most of the research focuses on three aspects: topological structure, social-behavioral dimension, and topic dimension and most of them ignore the difference between the audience. These models do not consider the impact of personality differences on user influences. To meet this need, this paper introduces the personality traits factor and proposes a user influence model which integrates personality traits (IPUIM) under a strong connection. The user influence measurement is constructed through the information dimension, structural dimension, and user behavioral dimension. The personality report of the user group is obtained by means of NEO-PI-R (The big five personality inventory, Chinese edition) and machine learning method, and it is integrated into the user influence model. The experiment proves that the model proposed in this paper has good accuracy and applicability in measuring user influence, and can effectively identify the key opinion leaders of different personality trait clusters.

ACS Style

Chunhua Ju; Qiuyang Gu; Yi Fang; Fuguang Bao. Research on User Influence Model Integrating Personality Traits under Strong Connection. Sustainability 2020, 12, 2217 .

AMA Style

Chunhua Ju, Qiuyang Gu, Yi Fang, Fuguang Bao. Research on User Influence Model Integrating Personality Traits under Strong Connection. Sustainability. 2020; 12 (6):2217.

Chicago/Turabian Style

Chunhua Ju; Qiuyang Gu; Yi Fang; Fuguang Bao. 2020. "Research on User Influence Model Integrating Personality Traits under Strong Connection." Sustainability 12, no. 6: 2217.

Article
Published: 06 March 2020 in Journal of Technology in Behavioral Science
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Based on the extended ECM-ISC continuous usage model and integration of the CAC model, a model that illustrates users’ continuous usage of online healthcare services is developed from the perspective of affect appeal by introducing satisfaction and worry variables. The factors influencing users’ continuous usage of online healthcare services in China are closely investigated in the paper. Based on the results of E-mail and Wen Juan Xing website online survey, the empirical analyses of the continuous usage of online healthcare services in China are conducted, utilizing SPSS and AMO. Through the screening of the questionnaires, 521 of the 600 questionnaires are valid. The main results of this study are as follows. Service quality, expectation confirmation degree, privacy concern, satisfaction degree, and continuous use intention are positively correlated with expectation confirmation degree, satisfaction degree, concern, and continuous use intention and continuous use behavior, respectively, while concern and continuous use intention are negatively correlated. It is found in this paper that both satisfaction and concern have a direct impact on users’ continuous use intention and continuous use behavior, and the effect of concern becomes increasingly prominent. Besides, service quality and privacy concern are the core variables affecting satisfaction degree and concern. Based on the above research results, this paper suggests to strengthen the security of user privacy information, and meanwhile, improve the quality of online doctors, which can promote users’continuous use intention and continuous use behavior, and thus further improve the efficiency of the use of health services online and quality medical resource allocation, as well as reduce hospital time and economic cost.

ACS Style

Chunhua Ju; Shuangzhu Zhang. Research on User’ Continuous Usage of Online Healthcare Services From the Perspective of Affect Appeal. Journal of Technology in Behavioral Science 2020, 5, 215 -225.

AMA Style

Chunhua Ju, Shuangzhu Zhang. Research on User’ Continuous Usage of Online Healthcare Services From the Perspective of Affect Appeal. Journal of Technology in Behavioral Science. 2020; 5 (3):215-225.

Chicago/Turabian Style

Chunhua Ju; Shuangzhu Zhang. 2020. "Research on User’ Continuous Usage of Online Healthcare Services From the Perspective of Affect Appeal." Journal of Technology in Behavioral Science 5, no. 3: 215-225.

Journal article
Published: 15 February 2020 in Sustainability
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Relationship of users in an online social network can be applied to promote personalized recommendation services. The measurement of relationship strength between user pairs is crucial to analyze the user relationship, which has been developed by many methods. An issue that has not been fully addressed is that the interaction behavior of individuals subjected to the activity field preference and interactive habits will affect interactive behavior. In this paper, the three-way representation of the activity field is given firstly, the contribution weight of the activity filed preferences is measured based on the interactions in the positive and boundary regions. Then, the interaction strength is calculated, integrating the contribution weight of the activity field preference and interactive habit. Finally, user relationship strength is calculated by fusing the interaction strength, common friend rate and similarity of feature attribute. The experimental results show that the proposed method can effectively improve the accuracy of relationship strength calculation.

ACS Style

Wanqiong Tao; Chunhua Ju; Chonghuan Xu. Research on Relationship Strength under Personalized Recommendation Service. Sustainability 2020, 12, 1459 .

AMA Style

Wanqiong Tao, Chunhua Ju, Chonghuan Xu. Research on Relationship Strength under Personalized Recommendation Service. Sustainability. 2020; 12 (4):1459.

Chicago/Turabian Style

Wanqiong Tao; Chunhua Ju; Chonghuan Xu. 2020. "Research on Relationship Strength under Personalized Recommendation Service." Sustainability 12, no. 4: 1459.