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  • 1
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    Entropy, Vol. 18, Pages 251: Maximum Entropy Learning with Deep Belief Networks (2016)
    MDPI Publishing
    In: Entropy
    Details
    Publication Date: 2016-07-09
    Description: Conventionally, the maximum likelihood (ML) criterion is applied to train a deep belief network (DBN). We present a maximum entropy (ME) learning algorithm for DBNs, designed specifically to handle limited training data. Maximizing only the entropy of parameters in the DBN allows more effective generalization capability, less bias towards data distributions, and robustness to over-fitting compared to ML learning. Results of text classification and object recognition tasks demonstrate ME-trained DBN outperforms ML-trained DBN when training data is limited.
    Electronic ISSN: 1099-4300
    Topics: Chemistry and Pharmacology , Physics
    Published by MDPI Publishing
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  • 2
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    Entropy, Vol. 17, Pages 3877-3897: A Colour Image Encryption Scheme Using Permutation-Substitution Based on Chaos (2015)
    MDPI Publishing
    In: Entropy
    Details
    Publication Date: 2015-06-10
    Description: An encryption scheme for colour images using a spatiotemporal chaotic system is proposed. Initially, we use the R, G and B components of a colour plain-image to form a matrix. Then the matrix is permutated by using zigzag path scrambling. The resultant matrix is then passed through a substitution process. Finally, the ciphered colour image is obtained from the confused matrix. Theoretical analysis and experimental results indicate that the proposed scheme is both secure and practical, which make it suitable for encrypting colour images of any size.
    Electronic ISSN: 1099-4300
    Topics: Chemistry and Pharmacology , Physics
    Published by MDPI Publishing
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  • 3
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    Symmetry, Vol. 10, Pages 319: CRCM: A New Combined Data Gathering and Energy Charging Model for WRSN (2018)
    MDPI Publishing
    In: Symmetry
    Details
    Publication Date: 2018-08-03
    Description: Symmetry, Vol. 10, Pages 319: CRCM: A New Combined Data Gathering and Energy Charging Model for WRSN Symmetry doi: 10.3390/sym10080319 Authors: Yuhou Wang Ying Dong Shiyuan Li Hao Wu Mengyao Cui With the development of wireless sensor networks (WSNs), the problem about how to increase the lifecycle of the WSNs is always a hot discussion point, and some researchers have devoted to the ‘energy saving’ to decrease the energy consumption of the sensor nodes by different algorithms. However, the fundamental technique is ‘energy acquiring’ for the battery which can solve the limited capacity problem. In this paper, we study the data gathering and energy charging by a mobile charger (MC) at the same time that most energy consumption can be saved by short communication distance. We have named this as the recharging model-combined recharging and collecting data model on-demand (CRCM). Firstly, the hexagon-based (HB) algorithm is proposed to sort all sensor nodes in the region to make data collecting and energy charging work at the same time. Then we consider both residual energy and geographic position (REGP) of the sensor node to calculate the priority of each cluster. Thirdly, the dynamic mobile charger (DMC) algorithm is proposed to calculate the number of MCs to make sure no sensor node will die in each charging queue. Finally, the simulations show that our REGP algorithm is better than Earliest Deadline First (EDF) and Nearest-Job-Next with Preemption (NJNP), and the DMC plays well when the number of sensor nodes increase.
    Electronic ISSN: 2073-8994
    Topics: Mathematics , Physics
    Published by MDPI Publishing
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  • 4
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    Entropy, Vol. 20, Pages 689: Evaluation of Sustainability Information Disclosure Based on Entropy (2018)
    MDPI Publishing
    In: Entropy
    Details
    Publication Date: 2018-09-11
    Description: Entropy, Vol. 20, Pages 689: Evaluation of Sustainability Information Disclosure Based on Entropy Entropy doi: 10.3390/e20090689 Authors: Ming Li Jialin Wang Ying Li Yingcheng Xu Disclosure of sustainability information is important for stockholders and governments. In order to evaluate the quality of sustainability information disclosure in heavily polluting industries, the quality of the disclosure is proposed to be evaluated from completeness, adequacy, relevance, reliability, normativeness and clarity aspects. The corresponding evaluation indicator system is constructed. Due to the ambiguity and complexity of the evaluation information, the intuitionistic fuzzy sets are applied to model the linguistic ratings. Entropy is used to derive the weight of experts, the object weight and the subject weight of the indicators. which are integrated when dealing with the evaluation information. The quality of sustainability information disclosure of seven representative companies in heavily polluting industries is evaluated. The importance of indicators and ranking of the companies are derived. Based on the evaluation results, the discussion and suggestions are also provided.
    Electronic ISSN: 1099-4300
    Topics: Chemistry and Pharmacology , Physics
    Published by MDPI Publishing
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  • 5
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    Future Internet, Vol. 9, Pages 22: A Method for Identifying the Mood States of Social Network Users Based on Cyber Psychometrics (2017)
    MDPI Publishing
    Details
    Publication Date: 2017-06-18
    Description: Analyzing people’s opinions, attitudes, sentiments, and emotions based on user-generated content (UGC) is feasible for identifying the psychological characteristics of social network users. However, most studies focus on identifying the sentiments carried in the micro-blogging text and there is no ideal calculation method for users’ real emotional states. In this study, the Profile of Mood State (POMS) is used to characterize users’ real mood states and a regression model is built based on cyber psychometrics and a multitask method. Features of users’ online behavior are selected through structured statistics and unstructured text. Results of the correlation analysis of different features demonstrate that users’ real mood states are not only characterized by the messages expressed through texts, but also correlate with statistical features of online behavior. The sentiment-related features in different timespans indicate different correlations with the real mood state. The comparison among various regression algorithms suggests that the multitask learning method outperforms other algorithms in root-mean-square error and error ratio. Therefore, this cyber psychometrics method based on multitask learning that integrates structural features and temporal emotional information could effectively obtain users’ real mood states and could be applied in further psychological measurements and predictions.
    Electronic ISSN: 1999-5903
    Topics: Computer Science
    Published by MDPI Publishing
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