查看更多>>摘要：In this paper, we propose a method for improving the multiplicative inconsistency of an intuitionistic fuzzy preference relation (IFPR) without computing any model to derive an underlying priority weight's vector with respect to alternatives. For this, a necessary and sufficient condition for the IFPR to be multiplicative consistent is proposed and proved. Based on it, a ratio-based deviation identifying matrix that takes an accurate measurement of deviation of every element in the IFPR is constructed. We prove that the greater the value of the deviation matrix is, the more inconsistent is its corresponding element in the IFPR and based on it, a convergent iterative algorithm of improving the multiplicative consistency is presented. The algorithm uses the fact that all the indeterminacy degrees of the IFPR are never changed in the revising process of multiplicative inconsistency and as a result, the most inconsistent elements are uniquely determined by suitable elements in the IFPR, respectively. The proposed method makes a great difference from the previous methods that derived the underlying priority weight's vector with respect to alternatives based on the given IFPR for improving consistency. A numerical example is provided to show the feasibility and efficiency of the proposed method. (c) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：Bike sharing systems (BSSs) have become an increasingly popular means of sustainable transportation, and have been implemented in many cities worldwide. Our approach con-tributes to the identification of abnormal patterns by applying real-time occupancy data from Paris. In particular, we propose a novel functional outlier detection algorithm based on a two-step approach: In the first stage, a clean dataset is obtained based on the com-bined effect of two extreme statistics calculated from random sampling; in the second stage, a multiple testing approach based on the clean dataset is proposed, in which the false discovery rate (FDR) control procedure is used to adaptively choose the thresholds for the hypothesis tests. Extensive numerical simulations were conducted to compare the outlier detection performance with those of other state-of-art methods. The proposed approach is then applied to the Paris Velib' bike sharing system dataset to identify abnormal patterns that are of particular interest to BSS operators for identifying system inefficiencies and update policies. (c) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：This paper focuses on the problem of event-triggered remote state estimation for cyberphysical systems (CPSs) under malicious denial-of-service (DoS) attacks. A remote estimator with intermittent observations is derived, and a novel event-triggered communication strategy is designed by transmitting observations to the remote estimator when the predicted estimation error covariance exceeds a given threshold. By constraining the upper bound of the total attack ratio, sufficient conditions to guarantee the almost sure stability of the proposed estimator are presented. Furthermore, the event-triggering parameter is designed to balance the estimation performance and the network resource utilization. In contrast to the previous studies on remote state estimation with random packet losses, the considered DoS attack case is more challenging since the probabilistic property of the packet loss process is unavailable to describe the attacks. Finally, the efficiency of the presented method is illustrated by simulation results.(c) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：As smart devices are becoming increasingly common in people's daily lives, privacy and security concerns make data collection expensive and limited, which further hinder the development of data-driven tasks. This paper studies how to better conduct private data trading via a novel generator method rather than direct trading of raw data. This new method facilitates more convenient data transactions by generator, protects the privacy of data owners and is satisfactory in terms of privacy compensation and query pricing. In detail, we propose RARIEA, a market framework for tRading privAte data geneRators based on GAN under renyI diffErential privAcy, which involves data owners, a data broker, and data consumers. To start, the broker employs the GAN training generator to augment the data to relieve the data shortage, introducing noise into its training process to preserve the owners' privacy. After that, the broker uses renyi differential privacy to quantify the privacy loss at the data item level during the GAN training process and compensates each owner according to their respective privacy policies. Finally, the data broker charges each of the data consumers for their queries, where the price is lower bounded by the total privacy compensation. We then evaluate the performance of RARIEA on classic data sets: MNIST, FashionMNIST, and CelebA. The analysis and simulation results reveal that the generator provided by RARIEA can not only meet the data consumers' demand for quantity and quality but also protect the owners' privacy. In addition, RARIEA not only allows finer control over data owner compensation, but also excels at controlling the data broker's revenue to improve market efficiency while ensuring fairness, balance, and monotonicity of pricing. (c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
查看更多>>摘要：The constrained stabilization problem of switched positive linear systems (SPLS) with bounded inputs and states is investigated via the set-theoretic framework of polyhedral copositive Lyapunov functions (PCLFs). It is shown that the existence of a common PCLF is proved to be necessary and sufficient for the stabilizability of an SPLS. As a primary contribution of this paper, we propose a PCLF-based approach for stabilization with a larger estimate of the domain of attraction for the constrained SPLSs. The analysis problems are converted into optimization problems whose constraints become linear matrix inequalities when a few variables are fixed. Finally, a turbofan engine model is employed to demonstrate the potential and effectiveness of the theoretical conclusions. (c) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：Asa hot research topic in network science, community detection has attracted much attention of scholars. In recent years, many methods have emerged to discover the underlying community structure in the network. However, most of these methods need to take the network topology information as prior knowledge that is not feasible in practical cases. When information diffusion occurs in the network, one can observe the cascade data in which nodes participate in the propagation process, which reflects the network's community structure to some extent. In this paper, we build a likelihood maximization model by utilizing the diffusion information and propose two different optimization algorithms to obtain community division of the network. Extensive experiments on various datasets show that our proposed methods achieve significant improvements in terms of accuracy, scalability, and efficiency of community detection compared with the existing state-ofthe-art methods. (c) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：Recognizing the subjectivity in human judgment and the limits to human cognition, fuzzy linguistic variables and uncertain linguistic variables are widely used to express the preference information of the decision experts in Multi-Attribute Group Decision Making (MAGDM). This paper develops an MAGDM method to solve a decision problem in which the attribute values are fuzzy numbers characterized by uncertain linguistic variables, that is, dual uncertain Z-numbers. The notion of the dual uncertain Z-number is first proposed based on the classical Z-number. Next, the cloud model is introduced, together with a new notion of the dual comprehensive cloud. A method of transforming the dual uncertain Z numbers into dual comprehensive clouds is proposed. Third, a new dual comprehensive cloud-weighted averaging operator (DCC-WA) is presented to aggregate the multiple dual comprehensive clouds. Fourth, a new dual comprehensive cloud-grey relational degree (DCC-GRD) is supplied, and a novel MAGDM method based on the DCC-GRD is proposed. Finally, a group decision making case of sustainable supplier selection is provided to validate the proposed method. A sensitivity analysis and comparison with several congeneric methods are conducted.(c) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：A novel joint image coding and reversible data hiding method for vector quantization (VQ) compressed images is proposed in this paper. Since the original VQ indices often exhibit uncorrelated, a rearrangement of them by considering their correlations may benefit the prediction performance so as to reduce the required bitrate. In this study, the tabu search algorithm is employed to rearrange codewords by fully exploiting their neighboring correlations, yielding a moepressible rearranged indices. By combining more highly-correlated indices of a to-be-predicted index into prediction, the improved linear regression method is then applied to achieve a sharper prediction-error histogram and less required additional information. After prediction, an adaptive run-length encoding method is presented to encode prediction errors, thereby eliminating unnecessary indicators. Experimental results demonstrate that the proposed method effectively reduces the bitrate of the compressed image while providing a comparable hiding capacity. (c) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：More training instances could lead to better classification accuracy. However, accuracy could also degrade if more training instances mean further noises and outliers. Additional training instances arguably need additional computational resources in future data mining operations. Instance selection algorithms identify subsets of training instances that could desirably increase accuracy or at least do not decrease accuracy significantly. There exist many instance selection algorithms, but no single algorithm, in general, dominates the others. Moreover, existing instance selection algorithms do not allow direct controlling of the instance selection rate. In this paper, we present a simple and generic cluster-oriented instance selection algorithm for classification problems. Our proposed algorithm runs an unsupervised K Means Clustering algorithm on the training instances and with a given selection rate, selects instances from the centers and the borders of the clusters. On 24 benchmark classification problems, when very similar percentages of instances are selected by various instance selection algorithms, K Nearest Neighbours classifiers achieve more than 2%-3% better accuracy when using instances selected by our proposed method than when using those selected by other state-of-the-art generic instance selection algorithms.(c) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：This study is concerned with the development of the axiomatic design (AD) method under an interval type-2 fuzzy (IT2F) environment and its application in evaluating blockchain deployment projects in supply chains. Blockchain is a transformative technology that has received significant attention recently. Blockchain technology can process various business transactions by offering a reliable and decentralized infrastructure. Supply chain management is an important application area of blockchains due to its desirable properties, including data security, extended visibility, product traceability, digitalization, and disintermediation. Since blockchain technologies are in their infancy, adopting them to supply chains requires proper design methodologies. Fuzzy AD offers valuable computational mechanisms to evaluate design options in the presence of functional requirements. However, extending AD to different fuzzy extensions is not an easy task, and area-based calculations hinder its widespread applicability. In this study, an IT2F-AD method is developed based on the concept of fuzzy subsethood. The potential of the fuzzy subsethood measure as the main computation engine within type-1 and IT2F-AD is demonstrated. Finally, an integrated multiple criteria decision-making (MCDM) model is proposed by using IT2F Best-Worst Method (IT2F-BWM) and IT2F-AD. The proposed model is used to prioritize blockchain deployment projects in a real-life case study. (c) 2022 Elsevier Inc. All rights reserved.
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