Word embeddings and CNN (convolutional neural networks) architecture are crucial ingredients of sentiment analysis. However, sentiment and lexicon embeddings are rarely used and CNN is incompetent to ...capture global features of sentence. To this end, semantic embeddings, sentiment embeddings and lexicon embeddings are applied for texts encoding, and three different attentions including attention vector, LSTM (long short term memory) attention and attentive pooling are integrated with CNN model in this paper. Additionally, a word and its context are explored to disambiguate the meaning of the word for rich input representation. To improve the performance of three different attention CNN models, CCR (cross-modality consistent regression) and transfer learning are presented. It is worth noticing that CCR and transfer learning are used in textual sentiment analysis for the first time. Finally, some experiments on two different datasets demonstrate that the proposed attention CNN models achieve the best or the next-best results against the existing state-of-the-art models.
In this paper, a hybrid protocol which can control multi-agent systems to achieve “leader-following consensus” is proposed, the control protocol is periodic and is composed of continuous control and ...impulse control. Considering the power limitation and the time error of the actual industrial production equipments, the impulse saturation effects and the impulse time window are introduced. In this paper, the multi-agent system is modeled, one of the agents is designated as the leader, and the rest of the agents are the followers. Then the error system equations of the multi-agent system is derived. The stability of the error system is analyzed by constructing the Lyapunov function. Theorem 1 and theorem 2 are obtained as the criteria of state consensus of the multi-agent system. Finally, a numerical experiment is carried out to verify the reliability and efficiency of the conclusions are given in this paper. Because it takes into account the impulse time window and impulse saturation effects, which is more in line with the actual industrial production.
This paper mainly studies the consensus of a new multi-agent system which can heuristically construct the communication network topology. Firstly, we presented some preparatory knowledge and ...constructed a dynamic model for the new multi-agent system. Secondly, we studied the control protocol. Inspired by the laws of nature, we designed some strategies for constructing the communication network topology heuristically, and these strategies were used in the control protocol. At the same time, in order to save the cost of communication and control, the impulsive control mechanism was also used in the control protocol. Thirdly, based on the dynamic model and the control protocol, we derived a state error system. Further, we used Lyapunov stability theory to analyze the state error system and obtained two theorems that enable the new multi-agent system to achieve consensus. Finally, we compared the proposed model with several related studies. Meanwhile, we also applied the results we obtained into the real scenario of symmetric secure communication to verify their practicability and efficiency.
This paper proposes an adaptive relay selection method that exploits the social network and establishes a physical domain and social domain-based model named the Social-based device-to-device (D2D) ...relay selection model to address relay selection failure caused by the diversity of users cooperation willingness and the instability of communication links due to human mobility. Under the premise of tolerable interference caused to cellular users, the proposed approach synthetically considers the desired physical-relation factor and the social-interaction factor for the relay user. In particular, the reliability of the D2D link is obtained by considering the transmission stability of the link, assessed by the user encounter history and cooperation willingness characterized by user social distance, which is set as the criterion of relay selection, combined with the physical channel condition. Simulation results show that compared with the traditional socially blind method, the proposed method can increase the probability of relay selection success and reduce the burden of the cellular network to improve the system performance.
This paper mainly aims to explore the propagation behaviors of computer virus over complex networks under the combined effects of network topology and removable storage media. To this end, a ...dynamical model is established and analyzed theoretically, including the outbreak threshold, equilibria and their global dynamics. It is found from the systematic analysis that the virus will die out or persist in the network depending on the outbreak threshold, which is associated with the network topology and removable storage media. Moreover, a set of well-designed experiments are performed over the Oregon routing network to verify the proposed model and its theoretical analysis results. Experimental results show that the predicted results of the model are in good agreement with the simulation results over the above network, and the removable storage media have a great effect on viral spread. To this regard, some preventive measures against virus infection are recommended.
This paper is dedicated to investigating the application of blockchain in eHealth systems. Unlike the previous work, patients here play a supervisory role and allow medical institutions to legally ...use their medical data without prior authorization but own the right to manage the medical data. This concept is able to protect the rights and interests of patients without affecting the normal diagnosis and research work of medical institutions. On this basis, a blockchain-based access control scheme for eHealth systems is proposed. To encourage patients to actively share their medical data, an incentive mechanism is also included. Finally, a case study on Ethereum is given to illustrate the feasibility and practicality of the proposed method.
This paper aims to effectively solve the problem of the influence maximization in social networks. For this purpose, an influence maximization method that can identify influential nodes via the ...community structure and the influence distribution difference is proposed. Firstly, the network embedding-based community detection approach is developed, by which the social network is divided into several high-quality communities. Secondly, the solution of influence maximization is composed of the candidate stage and the greedy stage. The candidate stage is to select candidate nodes from the interior and the boundary of each community using a heuristic algorithm, and the greedy stage is to determine seed nodes with the largest marginal influence increment from the candidate set through the sub-modular property-based Greedy algorithm. Finally, experimental results demonstrate the superiority of the proposed method compared with existing methods, from which one can further find that our work can achieve a good tradeoff between the influence spread and the running time.
Clustering analysis of massive data in wireless multimedia sensor networks (WMSN) has become a hot topic. However, most data clustering algorithms have difficulty in obtaining latent nonlinear ...correlations of data features, resulting in a low clustering accuracy. In addition, it is difficult to extract features from missing or corrupted data, so incomplete data are widely used in practical work. In this paper, the optimally designed variational autoencoder networks is proposed for extracting features of incomplete data and using high-order fuzzy c-means algorithm (HOFCM) to improve cluster performance of incomplete data. Specifically, the feature extraction model is improved by using variational autoencoder to learn the feature of incomplete data. To capture nonlinear correlations in different heterogeneous data patterns, tensor based fuzzy c-means algorithm is used to cluster low-dimensional features. The tensor distance is used as the distance measure to capture the unknown correlations of data as much as possible. Finally, in the case that the clustering results are obtained, the missing data can be restored by using the low-dimensional features. Experiments on real datasets show that the proposed algorithm not only can improve the clustering performance of incomplete data effectively, but also can fill in missing features and get better data reconstruction results.
The traditional beam selection algorithms determine the optimal beam direction by feeding back the perfect channel state information (CSI) in a millimeter wave (mmWave) massive Multiple-Input ...Multiple-Output (MIMO) system. Popular beam selection algorithms mostly focus on the methods of feedback and exhaustive search. In order to reduce the extra computational complexity coming from the redundant feedback and exhaustive search, a position fingerprint (PFP)-based mmWave multi-cell beam selection scheme is proposed in this paper. In the proposed scheme, the best beam identity (ID) and the strongest interference beam IDs from adjacent cells of each fingerprint spot are stored in a fingerprint database (FPDB), then the optimal beam and the strongest interference beams can be determined by matching the current PFP of the user equipment (UE) with the PFP in the FPDB instead of exhaustive search, and the orthogonal codes are also allocated to the optimal beam and the strongest interference beams. Simulation results show that the proposed PFP-based beam selection scheme can reduce the computational complexity and inter-cell interference and produce less feedback, and the system sum-rate for the mmWave heterogeneous networks is also improved.
This paper aims to explore coevolution of emotional contagion and behavior for microblog sentiment analysis. Accordingly, a deep learning architecture (denoted as MSA-UITC) is proposed for the target ...microblog. Firstly, the coevolution of emotional contagion and behavior is described by the tie strength between microblogs, that is, with the spread of emotional contagion, user behavior such as emotional expression will be affected. Then, based on user interaction and the correlation with target microblog, the Hawkes process is adopted to quantify the tie strength between microblogs so as to build the corresponding weighted network. Secondly, in the weighted network, the Deepwalk algorithm is used to build the sequence representation of microblogs which are similar to the target microblog. Next, a CNN-BiLSTM-Attention network (the convolutional neural network and bidirectional long short-term memory network with a multihead attention mechanism) is designed to analyze the sentiment analysis of target and similar microblogs. Finally, the experimental results on two real Twitter datasets demonstrate that the proposed MSA-UITC has advanced performance compared with the existing state-of-the-art methods.
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