查看更多>>摘要：The parallel Hierarchical Dirichlet Process (pHDP) is an efficient topic model which explores the equivalence of the generation process between Hierarchical Dirichlet Process (HDP) and Gamma-Gamma-Poisson Process (G2PP), in order to achieve parallelism at the topic level. Unfortunately, pHDP loses the non-parametric feature of HDP, i.e., the number of topics in pHDP is predetermined and fixed. Furthermore, under the bootstrap structure of pHDP, the topic-indiscriminate words are of high probabilities to be assigned to different topics, resulting in poor qualities of the extracted topics. To achieve parallelism without sacrificing the non-parametric feature of HDP, in addition to improve the quality of extracted topics, we propose a parallel dynamic topic model by developing an adjustment mechanism of evolving topics and reducing the sampling probabilities of topic indiscriminate words. Both supervised and unsupervised experiments on benchmark data sets show the competitive performance of our model. (c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：In this paper, the problem of tracking control of networked control systems with false data injection attacks is studied. A new terminal integral adaptive sliding mode control method is proposed. Firstly, the mathematical model of networked control system with the actuator and sensor false data injection attacks is modeled. Secondly, an augmented state observer is designed in order to estimate the augmented states, in which, a discontinuous input term is designed to defenses the actuator attack, and the convergence of the estimation error is proved. Thirdly, the tracking output errors are introduced to design the terminal integral sliding mode control method. Furthermore, in order to reduce the oscillation induced by the sensor false data injection attack, the terminal integral adaptive sliding mode control algorithm using the estimation error as adaptive factor is structured, and the stability of the algorithm are proved by using Lyapunov theory. Finally, a numerical simulation and a practical experiment are executed to verify the superiority of proposed control strategies. (c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：Partitioning data into internally homogeneous parts is an important problem when mining in situ engineering data. In this paper, a polynomial regression-based fuzzy c-means (PRFCM) clustering algorithm that utilizes the functional relationships among the attributes of the input dataset is proposed. In this algorithm, a polynomial regression equation is taken as the center of each cluster instead of the cluster prototype used in conventional FCM, and the difference between a sample and a cluster prototype is defined as the distance between the actual value of one attribute and the corresponding predicted value provided by its own polynomial regression equation. An alternating optimization method is designed to optimize the new clustering objective function of the proposed algorithm. A series of experiments on synthetic and real-world datasets are conducted to evaluate the performance of the PR-FCM algorithm, which exhibits higher effectiveness and possesses more advantages than the original FCM algorithm. The PR-FCM algorithm is applied to tunnel boring machine (TBM) operation data from a TBM project in China. The experimental results show that the proposed algorithm can effectively cluster TBM operation data. (c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：A multi-party quantum key distribution protocol based on repetitive code is designed in this paper for the first time. First we encode the classical key sequence in accordance with this repetitive code. Then unitary transformation of the quantum state sequence corre-sponding to this encoded sequence is carried out by using the parameters from this (t, n) threshold protocol. Furthermore, we derive two thresholds for whether or not reserving the measured values of the received sequence, and extract the classical subkey sequence from the measured values conforming to these two threshold conditions. This protocol can authenticate the identity of the participant, resist the attack from the internal and external participants, and do not need the decoy state particles when testing the eaves-dropper, which is more efficient than the similar protocols, and also saves the quantum resources. (c) 2021 Published by Elsevier Inc.
查看更多>>摘要：In this paper, we investigate the distributed consensus problem for a class of nonlinear multi-agent systems. Firstly, the uniform quantizer with scaling function is considered in the communication channels to reduce the communication burden. Secondly, noting that the state' information of each agent cannot be directly obtained, we design a suitable observer to rebuild the states of the nonlinear systems with the help of dynamic gain function. Since the information of communication quantization and state observation is coupled to each other, it is necessary to jointly design uniform quantizer with scaling function and the observer. Based on this, a distributed control protocol is proposed, which leads to a distributed asymptotic consensus of overall nonlinear systems with either known or unknown Lipschitz constant through [log(2)(2R)]-bit information exchange between each pair of agents. Finally, to validity the feasibility of the control scheme, a simulation example is provided. (C) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：The forecasting of time series provides great convenience in our daily life. Studies of time series forecasting have been used in many fields such as financial models, weather, and traffic patterns. In this paper, we propose a model fusion-based time series forecasting to improve the forecasting accuracy and efficiency. We propose a time series forecasting scheme based on a multivariate grey model and uses artificial fish swarm algorithm to optimize the settings. We then propose two fusion models with the grey model-based schemes on two different perspectives: data decomposition, and weighted summation. We conduct evaluations based on real data series and compared them with other forecasting models. Results show that our model can achieve good prediction accuracy and efficiency, which can be used for time series forecasting in different scenarios. (c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：With the prevalence of online social media, users' social connections have been widely studied and utilized to enhance the performance of recommender systems. In this paper, we explore the use of hyperbolic geometry for social recommendation. We present the Hyperbolic Social Recommender (HSR), a novel social recommendation framework that utilizes hyperbolic geometry to boost the performance. With the help of hyperbolic space, HSR can learn high-quality user and item representations to better model user-item interaction and user-user social relations. Through extensive experiments on four real-world datasets, we show that our proposed HSR outperforms its Euclidean counterpart and state-of-the-art social recommenders in click-through rate prediction and top -K recommendation, demonstrating the effectiveness of social recommendation in the hyperbolic space. (c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：In group decision making (GDM) of organization management, information interaction usually occurs via the hierarchical social networks. In the hierarchical social networks, the relation and opinion exchange between decision makers and participants can influence the consensus reaching of three-way group decision. In this paper, we deeply explore the three-way group decision consensus reaching by constructing adjustment mechanism based on the hierarchical social networks. In the hierarchical social network, we firstly learn the attitudes of participants for the opinions of decision makers. Inspired by the thought of three-way decision, we successfully identify the valid followers, hesitant followers and invalid follower of decision makers from the participants with the aid of the trust and consensus information. According to the character of followers, we compute the real influence of decision makers. Then, we discuss the consensus model. In the adjustment process of GDM of social network, the inconsistent decision makers tend to adjust evaluation based on the evaluation of the most influential decision makers. Thus, we introduce the maximum closeness degree between the inconsistent decision maker and the most influential decision maker to construct the minimum adjustment consensus model. Besides, we extend the minimum adjustment consensus model by considering the various unit cost and limited adjustment budget. In light of the consensual loss information and Bayesian decision theory, we determine the classification rules of three-way group decision. Finally, we use an example of the medical equipment selection of purchasing department to elaborate and validate our proposed method. (c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：In many practical applications, the data are class imbalanced. Accordingly, it is very meaningful and valuable to investigate the classification of imbalanced data. In the framework of binary imbalanced data classification, the synthetic minority oversampling technique (SMOTE) is the best-known oversampling method. However, for each positive sample, SMOTE generates only k synthetic samples on the lines between the positive sam-ple and its k-nearest neighbors, resulting in three drawbacks: (1) SMOTE cannot effectively extend the training field of positive samples; (2) the generated positive samples lack diver-sity; (3) SMOTE does not accurately approximate the probability distribution of the posi-tive samples. Therefore, two binary imbalanced data classification methods named BIDC1 and BIDC2 based on diversity oversampling by generative models are proposed. The BIDC1 and BIDC2 conduct diversity oversampling using extreme learning machine autoencoder and generative adversarial network, respectively. Extensive experiments on 26 data sets are conducted to compare the two methods with 14 state-of-the-art methods using five metrics: F-measure, G-means, AUC-area, MMD-score, and Silhouette-score. The experimental results demonstrate that the two methods outperform the other 14 methods. (c) 2021 Elsevier Inc. All rights reserved.
查看更多>>摘要：In the constrained multi-objective optimization problems, the pursuit of feasibility could improve convergence but will lead to the loss of diversity. For optimization algorithm, balancing the weight between convergence and diversity dynamically is a challenge, especially in problems with low proportion of feasible regions. In this paper, a constrained multi-objective optimization algorithm is proposed based on a hybrid driven strategy to enhance both the feasibility and diversity performance of the approximate Pareto solutions. The proposed algorithm contains two archives, that one is driven by feasibility information and the other is driven by diversity information. A self-adaptive archive selection mechanism and a conditional tournament selection strategy are proposed to provide mating parent solutions according to the evolutionary stage. Moreover, in the update of the feasibility archive, an evolutionary direction prediction mechanism is proposed and adopted to improve the evolutionary efficiency. Compared to four other multi-objective algorithms on three benchmark suits of different types, the performance of the proposed algorithm is better than the peer algorithms, especially in large-infeasible-regions multi objective optimization problems. (c) 2021 Elsevier Inc. All rights reserved.
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