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With the continuous accumulation of social network data, social recommendation has become a widely used recommendation method. Based on the theory of social relationship propagation, mining user relationships in social networks can alleviate the problems of data sparsity and the cold start of recommendation systems. Therefore, integrating social information into recommendation systems is of profound importance. We present an efficient network model for social recommendation. The model is based on the graph neural network. It unifies the attention mechanism and bidirectional LSTM into the same framework and uses a multilayer perceptron. In addition, an embedded propagation method is added to learn the neighbor influences of different depths and extract useful neighbor information for social relationship modeling. We use this method to solve the problem that the current research methods of social recommendation only extract the superficial level of social networks but ignore the importance of the relationship strength of the users at different levels in the recommendation. This model integrates social relationships into user and project interactions, not only capturing the weight of the relationship between different users but also considering the influence of neighbors at different levels on user preferences. Experiments on two public datasets demonstrate that the proposed model is superior to other benchmark methods with respect to mean absolute error and root mean square error and can effectively improve the quality of recommendations.

This paper proposes a deep neural network model (SDAE-BPR) based on Stack Denoising Auto-Encoder and Bayesian Personalized Ranking for the problem of accurate product recommendation. First, we use the Stack Denoising Auto-Encoder (SDAE) as the input of the item’s rating data and obtain the hidden features after encoding. Second, the Bayesian personalized Ranking (BPR) method is used to learn the hidden feature vector of the corresponding item. This model can avoid the influence of the sparseness of the matrix. Therefore, this model achieves the effect of more accurate recommendations of items. Third, to reduce the cost of model training, a unique pre-training and fine-tuning strategy is proposed in the deep neural network. Finally, based on the Movielens 20M dataset, the results of the SDAE-BPR, a traditional item-based collaborative filtering model and a user-based collaborative filtering model are compared. It is shown that the SDAE-BPR has higher accuracy. This method improves the accuracy of parameter estimation and the efficiency of model training.

Attribute reduction is one of the most important topics in rough set theory. In the classical rough sets, the method for attribute reduction is mainly to keep positive region, boundary region, and negative region unchanged. However, the three regions are no longer monotonic with respect to adding or deleting an attribute in decision-theoretic rough sets. In decision-theoretic rough set model, the decision regions are determined by using the Bayesian decision procedure, and decision-making should take consideration of the cost. In this paper, two attribute reduction methods based on minimum decision cost are proposed from the algebraic view and the information theory, respectively. First, significance of joint attributes is introduced to measure the classification ability of selected attribute subset to decision-making, which overcomes the disadvantage of only considering the significance of single attribute. By using significance of joint attributes, a heuristic method based on minimum decision cost for attribute reduction is presented. Second, conditional mutual information is proposed to evaluate the significance of attribute subset for decision-making in minimum cost attribute reduction. To decrease the computational complexity of the conditional mutual information, an approximate computation method is calculated from both maximum relevance and maximum significance. To evaluate the two proposed algorithms, extensive experiments are conducted on 10 University of California at Irvine data sets. We compare our proposed algorithms with several existing cost minimization attribute reduction algorithms. Experiment results show that our proposed algorithms have a superior performance in achieving the reduct. Copyright © 2016 John Wiley & Sons, Ltd.

This paper tackles a recent challenge in identifying culprit actors, who try to hide confidential payload with steganography, among many innocent actors in social media networks. The problem is called steganographer detection problem and is significantly different from the traditional stego detection problem that classifies an individual object as a cover or a stego. To solve the steganographer detection problem over large-scale social media networks, this paper proposes a method that uses high-order joint features and clustering ensembles. It employs 250-D features calculated from the high-order joint matrices of Discrete Cosine Transform (DCT) coefficients of JPEG images, which indicate the dependencies of image content. Furthermore, a number of hierarchical sub-clusterings trained by the features are integrated as a clustering ensemble based on the majority voting strategy, which is used to make optimal decisions on suspicious steganographers. Experimental results show that the proposed scheme is effective and efficient in identifying potential steganographers in large-scale social media networks, and has better performance when tested against the state-of-the-art steganographic methods.

The development of smart grid depends on increased deployment of two-way communication to improve its efficiency and reliability of power delivery. However, this additional dependency also expands the risk from pollution attacks, which consist of injecting malicious packets in the network. The pollution attacks are amplified by the network coding process, resulting in a greater damage to the state estimation and decision making. In this paper, we address this issue by designing an efficient authentication protocol, named EAPA, for the smart grid communication. The proposed scheme allows not only recipient nodes, but also intermediate nodes, to verify the integrity and origin of the packets received without having to decode, and thus detect and discard the malicious packets in transit that fail the verification. By this means, the pollution is canceled out before reaching the destinations. Security analysis demonstrates that the EAPA can be resilient to data/tag pollution attacks and replay attacks. Performance evaluation results show that our EAPA can reduce much more communication overhead than Wu’s scheme.

The classical rough set attribute reduction is mainly based on maintaining three unvarying regions: positive region, boundary region, and negative region. In the decision rough set model, the reduction procedure for adding or deleting an attribute is no longer monotonous, and as such the three regions will not be kept unchanged simultaneously. In decision theoretic rough set model, cost should be taken more consideration when making decisions. Therefore, in this paper, an attribute reduction algorithm based on minimizing the cost is proposed. To quantitatively evaluate the classification power of selected attribute subset in the decision making, mutual information is introduced. To avoid the strong correlation between selected attributes, the significance of an attribute is computed from both maximum relevance and maximum significance, which can ensure the selected attributes to have strong classification ability but weak correlation. Experiments show the effectiveness of our proposed method.

In power systems, load curve data is one of the most important datasets that are collected and retained by utilities. The quality of load curve data, however, is hard to guarantee since the data is subject to communication losses, meter malfunctions, and many other impacts. In this paper, a new approach to analyzing load curve data is presented. The method adopts a new view, termed \textit{portrait}, on the load curve data by analyzing the periodic patterns in the data and re-organizing the data for ease of analysis. Furthermore, we introduce algorithms to build the virtual portrait load curve data, and demonstrate its application on load curve data cleansing. Compared to existing regression-based methods, our method is much faster and more accurate for both small-scale and large-scale real-world datasets., Comment: 10 pages, 9 figures

With the distributed sensors that are deployed to monitor the urban environment in smart cities, the sensed data are overwhelming and beyond the scope of the wireless sensor networks (WSNs) capability. Due to the communication range limits of the sensors, most of these data will be outsourced and stored on some untrusted servers. Thus, how to maintain the data confidentiality and integrity, as well as source authentication and data query privacy of the outsourced data, is a challenging problem. In this paper, we propose a secure searchable encryption scheme, named SSE, for urban sensing and querying to address the problem. Specifically, our SSE constructs a secure hidden vector encryption-(HVE-) based rang query predicate. The sensed data can be stored on an untrusted server in encrypted form. A requester can obtain the correct ciphertexts when his authorized range query matches the HVE-based encryption predicate. With the help of the base station, the ciphertexts can be decrypted and data integrity can be verified; then, the requester can obtain the correct original data. Security analysis demonstrates that; in the SSE, only the authorized requesters can obtain the query results, while the data confidentiality and integrity and source authentication are also preserved.