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Multi-objective soft subspace clustering in the composite kernel space Inform. Sci. (IF 5.91) Pub Date : 2021-02-12 Yuanrui Li; Qiuhong Zhao; Kaiping Luo
Conventional subspace clustering algorithms group the data samples by optimizing the objective function which aggregates different clustering criteria using the linear combination. However, the performance is sensitive to the user-defined coefficients. Besides, the widely used Euclidean distance metric falls short of handling the linear indivisible problems. Some composite kernel metrics are proposed
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Joint tracking of multiple quantiles through conditional quantiles Inform. Sci. (IF 5.91) Pub Date : 2021-02-20 Hugo Lewi Hammer; Anis Yazidi; Håvard Rue
The estimation of quantiles is one of the most fundamental data mining tasks. As most real-time data streams vary dynamically over time, there is a quest for adaptive quantile estimators. The most well-known type of adaptive quantile estimators is the incremental one which documents the state-of-the art performance in tracking quantiles. However, the absolute vast majority of incremental quantile estimators
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Delayed nonquadratic L2-stabilization of continuous-time nonlinear Takagi–Sugeno fuzzy models Inform. Sci. (IF 5.91) Pub Date : 2021-01-26 Rodrigo F. Araújo; Pedro H.S. Coutinho; Anh-Tu Nguyen; Reinaldo M. Palhares
This work deals with the design of fuzzy controllers for stabilization of continuous-time nonlinear systems subject to L2 disturbances, which are represented by nonlinear Takagi–Sugeno fuzzy models, i.e., Takagi–Sugeno fuzzy models with nonlinear consequents. A nonquadratic Lyapunov function is used to derive sufficient design conditions based on linear matrix inequality constraints as well as to reduce
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Sparse fuzzy two-dimensional discriminant local preserving projection (SF2DDLPP) for robust image feature extraction Inform. Sci. (IF 5.91) Pub Date : 2021-02-14 Minghua Wan; Xueyu Chen; Tianming Zhan; Chao Xu; Guowei Yang; Huiting Zhou
Recently, image feature extraction algorithms based on 2D discriminant local preserving projection (2DDLPP) algorithms have been successfully applied in many fields. The 2DDLPP can maintain the discrimination information of the local intrinsic manifold structure using two-dimensional image representation data. However, the 2DDLPP algorithm encounters the problem of the sensitivity of overlapping points
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Representing complex networks without connectivity via spectrum series Inform. Sci. (IF 5.91) Pub Date : 2021-02-02 Tongfeng Weng; Haiying Wang; Huijie Yang; Changgui Gu; Jie Zhang; Michael Small
We propose a new paradigm for describing complex networks in terms of the spectrum of the adjacency matrix and its submatrices. We show that a variety of basic node information, such as degree, clique, and subgraph centrality, can be calculated analytically. Moreover, we find that energy of spectrum series can uncover randomness and complexity of network structure. Interestingly, it presents an universal
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Robust Node Embedding against Graph Structural Perturbations Inform. Sci. (IF 5.91) Pub Date : 2021-03-04 Zhendong Zhao; Xiaojun Chen; Dakui Wang; Yuexin Xuan; Gang Xiong
Despite achieving superior performance for many graph-related tasks, recent works have shown that Graph Neural Networks (GNNs) are vulnerable to adversarial attacks on graph structures. In particular, by adding or removing a small number of carefully selected edges in a graph, an adversary can maliciously manipulate a GNNs-based classifier. The vulnerability to adversarial attacks poses numerous concerns
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Volatility GARCH models with the ordered weighted average (OWA) operators Inform. Sci. (IF 5.91) Pub Date : 2021-03-03 Martha Flores-Sosa; Ezequiel Avilés-Ochoa; José M. Merigó; Ronald R. Yager
Volatility is an important issue for companies, policy-makers, and researches. Autoregressive conditional heteroscedasticity (ARCH) and generalized ARCH (GARCH) models are frequently used to study volatility. However, forecasting efficiency tends to fail when complex data is used. This paper proposes the use of ordered weighted average (OWA) operators in combination with ordinary least squares (OLS)
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Sentiment analysis with genetic programming Inform. Sci. (IF 5.91) Pub Date : 2021-02-01 Airton Bordin Junior; Nádia Félix F. da Silva; Thierson Couto Rosa; Celso G.C. Junior
With the advent of online social networks, people became more eager to express and share their opinions and sentiment about all kinds of targets. The overwhelming amount of opinion texts soon attracted the interest of many entities (industry, e-commerce, celebrities, etc.) that were interested in analyzing the sentiment people express about what they produce or communicate. This interest has led to
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Lattice-valued overlap and quasi-overlap functions Inform. Sci. (IF 5.91) Pub Date : 2021-02-16 Rui Paiva; Regivan Santiago; Benjamín Bedregal; Eduardo Palmeira
As an important class of aggregation operators, the notion of overlap functions was first presented in 2009 in order to be considered for applications in image processing context. Later, many other researches arised bringing some variations of those functions for different purposes. Here, our main goal is defining overlap functions on lattices and discuss how a weakned version of it, named quasi-overlaps
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Modeling the dynamics of rumor diffusion over complex networks Inform. Sci. (IF 5.91) Pub Date : 2021-01-26 Linhe Zhu; Fan Yang; Gui Guan; Zhengdi Zhang
Rumor propagation on complex networks is rapidly affecting people’s life. As we all know, the regulatory control of rumors by regulators has a specific impact on the spread of rumors. Limited regulatory resources may saturate the regulatory level. Therefore, in this paper, we have introduced a saturation treatment function to model this phenomenon and further establish an SIS rumor propagation model
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Decrease and conquer-based parallel tensor factorization for diversity and real-time of multi-criteria recommendation Inform. Sci. (IF 5.91) Pub Date : 2021-02-12 Minsung Hong
In the field of recommender systems, diversity as the measure of recommendation quality has gained much attention recently. Unfortunately, many researchers have shown that it has a trade-off relation with accuracy. Meanwhile, tensor factorization has been used as a useful technique that considers multi-correlations between user-item-other factors directly. However, it generally suffers from the model
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Dynamical behaviors and control measures of rumor-spreading model in consideration of the infected media and time delay Inform. Sci. (IF 5.91) Pub Date : 2021-03-02 Yingying Cheng; Liang'an Huo; Laijun Zhao
With the development and the progress of science and technology, the spread of rumors presents new characteristics. The spreaders publish false and malicious rumors on public social networks media, and individuals visit these networks and share them with their friends through the friendship network, which provides a convenient hotbed for the indiscriminate spread of rumors. In this paper, an improved
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An Adaptive Polyploid Memetic Algorithm for Scheduling Trucks at a Cross-Docking Terminal Inform. Sci. (IF 5.91) Pub Date : 2021-03-02 Maxim A. Dulebenets
Many supply chain stakeholders rely on the cross-docking concept, according to which products delivered in specific transportation management units to the cross-docking terminal (CDT) undergo decomposition, sorting based on the end customer preferences, consolidation, and then transported to the final destinations. Scheduling of the inbound and outbound trucks for service at the CDT doors is considered
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Fuzzy Rule-Based Models: A Design with Prototype Relocation and Granular Generalization Inform. Sci. (IF 5.91) Pub Date : 2021-01-26 Yan Li; Chao Chen; Xingchen Hu; Jindong Qin; Yang Ma
Fuzzy rule-based models and the extension of classical fuzzy models have been widely used in many domains. From a holistic perspective, regardless of the design methods and rules adopted in a fuzzy model, the determination of fuzzy sets is a pivotal issue. In the proposed methods, instead of traditional data clustering with no directional tendency, we introduce an optimization algorithm that can adjust
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Average convergence rate of evolutionary algorithms in continuous optimization Inform. Sci. (IF 5.91) Pub Date : 2021-02-09 Yu Chen; Jun He
The average convergence rate (ACR) measures how fast the approximation error of an evolutionary algorithm converges to zero per generation. It is defined as the geometric average of the reduction rate of the approximation error over consecutive generations. This paper makes a theoretical analysis of the ACR in continuous optimization. The obtained results are summarized as follows. According to the
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Binary multi-layer classifier Inform. Sci. (IF 5.91) Pub Date : 2021-02-10 Huanze Zeng; Argon Chen
Binary decision trees (BDTs), where each node of the tree is split into two child nodes, are among the most popular classifiers. An alternative type of classification tree, namely, the multi-layer classifier (MLC), has been proposed to split the parent node into 1 or 2 classified child nodes and an unclassified child node at each layer. In contrast to the nodes in a BDT, only the unclassified node
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Jointly learning multi-instance hand-based biometric descriptor Inform. Sci. (IF 5.91) Pub Date : 2021-02-13 Lunke Fei; Bob Zhang; Chunwei Tian; Shaohua Teng; Jie Wen
Multibiometric recognition has become one of the most important solutions for enhancing overall personal recognition performance due to several inherent limitations of unimodal biometrics, such as nonuniversality and unacceptable reliability. However, most existing multibiometrics fuse completely different biometric traits based on addition schemes, which usually require several sensors and make the
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Deep model based on mode elimination and Fisher criterion combined with self-organizing map for visual multimodal chemical process monitoring Inform. Sci. (IF 5.91) Pub Date : 2021-02-02 Weipeng Lu; Xuefeng Yan
Multimode feature is widely adopted in complex continuous chemical processes to meet changes in market demand. However, conducting effective process monitoring in a multimode chemical process is challenging because data usually have multimodal distribution. In this study, DMF, a novel model based on a deep network, is proposed for learning new feature spaces; the multimodality of the mix data is eliminated
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Event-triggered adaptive fuzzy control for switched nonlinear systems with state constraints Inform. Sci. (IF 5.91) Pub Date : 2021-02-01 Yongchao Liu; Qidan Zhu; Ning Zhao
This paper studies the event-triggered adaptive fuzzy control (AFC) problem for switched nonlinear systems with state constraints. Fuzzy logic systems are explored to handle the lumped unknown dynamics. The barrier Lyapunov function is deployed to solve state constraints. In addition, event-triggered mechanism is incorporated into the backstepping framework to mitigate the communication burden. The
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Hip-DE: Historical population based mutation strategy in differential evolution with parameter adaptive mechanism Inform. Sci. (IF 5.91) Pub Date : 2021-02-01 Zhenyu Meng; Cheng Yang
Differential Evolution (DE) was a powerful population-based evolutionary algorithm for global optimization, and it achieved great success in both evolutionary computation competitions and engineering applications. Despite the excellent performance of the state-of-the-art DE variants, there are still two main weaknesses existing within them: one is the weakness in a given mutation strategy and the other
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Attributed community search based on effective scoring function and elastic greedy method Inform. Sci. (IF 5.91) Pub Date : 2021-02-09 Chunnan Wang; Hongzhi Wang; Hanxiao Chen; Daxin Li
In recent years, with the proliferation of rich attribute information available for entities in real-world networks and the increasing demand for more personalized community searches, attributed community search (ACS), an upgraded version of the community search problem, has attracted great attention from the both academic and industry areas. Some algorithms have been proposed to solve this novel research
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O3ERS: An explainable recommendation system with online learning, online recommendation, and online explanation Inform. Sci. (IF 5.91) Pub Date : 2021-01-26 Qianqiao Liang; Xiaolin Zheng; Yan Wang; Mengying Zhu
Explainable recommendation systems (ERSs) have attracted increasing attention from researchers, which generate high-quality recommendations with intuitive explanations to help users make appropriate decisions. However, most of the existing ERSs are designed with an offline setting, which can hardly adjust their models using the online feedback instantly for improved performance. To overcome the limitations
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Two-step hybrid collaborative filtering using deep variational Bayesian autoencoders Inform. Sci. (IF 5.91) Pub Date : 2021-02-10 Ravi Nahta; Yogesh Kumar Meena; Dinesh Gopalani; Ganpat Singh Chauhan
Existing recommender systems rely on user and item representations in a fixed continuous low-dimensional latent space. To predict ratings, they use only an implicit feedback matrix, whereas user and item side information is ignored. Furthermore, they use the same arbitrary priors for the user and item latent vectors, reducing the ability of the model to identify the actual latent vectors. Currently
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ma-CODE: A Multi-Phase Approach on Community Detection in Evolving Networks Inform. Sci. (IF 5.91) Pub Date : 2021-02-28 Keshab Nath; Ram Shanmugam; Vijayakumar Varadaranjan
Detecting communities or clusters in networks becomes a decisive issue in various interdisciplinary areas in recent years. Numerous methods are proposed to uncover community in networks, although the fundamental problem of most of the methods is the evolving nature of the networks and the presence of the imprecise number of communities. Since, real-world networks are scale-free networks and due to
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Image Robust Adaptive Steganography Adapted to Lossy Channels in Open Social Networks Inform. Sci. (IF 5.91) Pub Date : 2021-02-28 Yi Zhang; Xiangyang Luo; Jinwei Wang; Yanqing Guo; Fenlin Liu
Currently, the demand for covert communication in open social networks brings new opportunities and challenges to existing image steganography technology in terms of robustness and security. To this end, an image robust adaptive steganography is proposed with robustness against multiple image processing attacks and detection resistance. First, a robust embedding domain with theoretical foundation and
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Aggregation of triangle of distortion functions Inform. Sci. (IF 5.91) Pub Date : 2021-02-28 Ljubo Nedović; Endre Pap; Dorde Dragić
In this paper we introduce notions of distortion function and aggregated distortion function. Applying some extended aggregation function on the triangle of distortion functions, a new extended aggregation function is obtained. Properties of the aggregation function constructed in this way depend on the properties of applied aggregation function and the distortion functions used. Its properties as
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PA-Net: Learning Local Features Using by Pose Attention for Short-Term Person Re-identification Inform. Sci. (IF 5.91) Pub Date : 2021-02-27 Kai Wang; Shichao Dong; Nian Liu; Junhui Yang; Tao Li; Qinghua Hu
Person re-identification (Re-ID) is an important but challenging task in for video surveillance applications. In Re-ID tasks, pose is an extremely useful cue to identify a person, even from the back view. Therefore, pose-detection models may learn the features that are beneficial to the Re-ID task and improve the Re-ID performance by fusing the feature maps into the Re-ID model. Two key problems in
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A Novel Wrapper-Based Feature Subset Selection Method using Modified Binary Differential Evolution Algorithm Inform. Sci. (IF 5.91) Pub Date : 2021-02-27 Omid Tarkhaneh; Thanh Thi Nguyen; Samaneh Mazaheri
In classification problems, normally there exists a large number of features, but not all of them contributing to the improvement of classification performance. These redundant features make the classification problem time consuming and often result in poor performance. Feature selection methods have been proposed to reduce the number of features, minimize computational cost, and maximize classification
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Dimensional-Varying Integral Sliding Mode Controller Design for Uncertain Takagi-Sugeno Fuzzy Systems Inform. Sci. (IF 5.91) Pub Date : 2021-02-27 Jian Zhang; Wen-Jie Wu; Wen-Bo Xie; Chen Peng
An integral sliding mode control method for uncertain Takagi-Sugeno fuzzy systems is investigated in this paper. Considering the time-varying property of the fuzzy system control matrix, a dimensional-varying integral sliding mode controller is proposed. With a membership function piecewise linearization technique, the gain matrices of equivalent control law are derived. Then a dimension switching
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Fixed-Time Time-Varying Formation Tracking for Nonlinear Multi-Agent Systems under Event-Triggered Mechanism Inform. Sci. (IF 5.91) Pub Date : 2021-02-27 Yuliang Cai; Huaguang Zhang; Yingchun Wang; Juan Zhang; Qiang He
This study focuses on the fixed-time event-triggered time-varying formation tracking issue for a class of nonlinear multi-agent systems with multi-dimensional dynamics, uncertain disturbances and non-zero control input of leader. Firstly, a distributed fixed-time event-triggered control scheme is proposed such that the time-varying formation tracking can be achieved with intermittent controller updates
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Information Space of Multi-sensor Networks Inform. Sci. (IF 5.91) Pub Date : 2021-02-27 Mo Tao; Shaoping Wang; Hong Chen; Xingjian Wang
It is a challenging problem to explore the capability of multi-sensor networks due to the identity of the underlying information space across modalities. In this paper, the information space for multi-sensor networks is developed from information geometry. The relationship between information space and the performance of multi-sensor networks is investigated. Different sensor information obtained by
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Adaptive Discriminant Analysis for Semi-supervised Feature Selection Inform. Sci. (IF 5.91) Pub Date : 2021-02-27 Weichan Zhong; Xiaojun Chen; Feiping Nie; Joshua Zhexue Huang
As semi-supervised feature selection is becoming much more popular among researchers, many related methods have been proposed in recent years. However, many of these methods first compute a similarity matrix prior to feature selection, and the matrix is then fixed during the subsequent feature selection process. Clearly, the similarity matrix generated from the original dataset is susceptible to the
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On the minimality of some generating sets of the aggregation clone on a finite chain Inform. Sci. (IF 5.91) Pub Date : 2021-02-27 Radomír Halaš; Zbyněk Kurač; Jozef Pócs
Clone theory plays an important role in studying aggregation functions on bounded posets or bounded lattices. Several important classes of aggregation functions on a bounded lattice L form a clone, particularly the set of all aggregation functions on L, the so-called full aggregation clone on L. For any finite lattice L, this clone is known to be finitely generated and various generating sets and their
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ClEnDAE: A classifier based on ensembles with built-in dimensionality reduction through denoising autoencoders Inform. Sci. (IF 5.91) Pub Date : 2021-02-27 Francisco J. Pulgar; Francisco Charte; Antonio J. Rivera; María J. del Jesus
High dimensionality is an issue that affects most classification algorithms. This factor implies that the predictive performance of many traditional classifiers decreases considerably as the number of features increases. Therefore, there are numerous proposals that try to mitigate the effects of this issue. This study proposes ClEnDAE, a new classifier based on ensembles whose components incorporate
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A Data-Driven Structural Damage Detection Framework Based on Parallel Convolutional Neural Network and Bidirectional Gated Recurrent Unit Inform. Sci. (IF 5.91) Pub Date : 2021-02-27 Jianxi Yang; Fei Yang; Yingxin Zhou; Di Wang; Ren Li; Guiping Wang; Wangqiao Chen
With the extensive use of structural health monitoring technologies, vibration-based structural damage detection becomes a crucial task in both academic and industrial communities. Following the noteworthy trends of data-driven paradigms in recent years, some solutions have been released to identify, localize, and classify damages via deep neural networks. However, some deficiencies still exist for
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ILUNA: Single-pass Incremental Method for Uncertain Frequent Pattern Mining without False Positives Inform. Sci. (IF 5.91) Pub Date : 2021-02-27 Razieh Davashi
Nowadays, due to the mass production of uncertain data, numerous methods have been proposed for mining frequent patterns from uncertain data; however, none of them are proper for dynamic data environments. In many real-world applications, transactions are constantly being updated. After incremental updates, the validity of the uncertain patterns changes. The existing static algorithms to handle this
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Prescribed Performance Synchronization of Complex Dynamical Networks with Event-Based Communication Protocols Inform. Sci. (IF 5.91) Pub Date : 2021-02-27 Aili Fan; Junmin Li
The paper addresses the preset performance synchronization (PPS) issue for complex dynamical networks (CDNs)using an event-triggered communication mechanism and PPS scheme with event-based protocols is designed for the CDNs. The designed synchronization control strategy can reduce the overshoot in transient processes, and stabilize synchronous errors at the origin. Furthermore, by introducing event-based
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MSGP-LASSO: An improved multi-stage genetic programming model for streamflow prediction Inform. Sci. (IF 5.91) Pub Date : 2021-02-16 Ali Danandeh Mehr; Amir H. Gandomi
This paper presents the development and verification of a new multi-stage genetic programming (MSGP) technique, called MSGP-LASSO, which was applied for univariate streamflow forecasting in the Sedre River, an intermittent river in Turkey. The MSGP-LASSO is a practical and cost-neutral improvement over classic genetic programming (GP) that increases modelling accuracy, while decreasing its complexity
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Kronecker-decomposable robust probabilistic tensor discriminant analysis Inform. Sci. (IF 5.91) Pub Date : 2021-02-02 Fujiao Ju; Yanfeng Sun; Junbin Gao; Yongli Hu; Baocai Yin
As a generative model, probabilistic linear discriminant analysis (PLDA) has achieved good performance in supervised learning tasks. The model incorporates both within-individual and between-individual variation, and remaining unexplained data variation is assumed to follow Gaussian distribution. However, the assumption of Gaussian distribution makes the model sensitive to the presence of noise and
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An investigation of testing capacity for evaluating and modeling the spread of coronavirus disease Inform. Sci. (IF 5.91) Pub Date : 2021-02-16 Choujun Zhan; Jiaqi Chen; Haijun Zhang
Despite the consistent recommendation to scale-up the testing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), comprehensive analysis on determining the desirable testing capacity (TC) is limited. This study aims to investigate the daily TC and the percentage of positive cases over the tested population (PPCTP) to evaluate the novel coronavirus disease 2019 (COVID-19) trajectory phase
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Viewpoint Adaptation Learning with Cross-view Distance Metric for Robust Vehicle Re-Identification Inform. Sci. (IF 5.91) Pub Date : 2021-02-26 Qi Wang; Weidong Min; Qing Han; Ziyuan Yang; Xin Xiong; MengZhu; Haoyu Zhao
Many vehicle re-identification (Re-ID) problems require the robust recognition of vehicle instances across multiple viewpoints. Existing approaches for dealing with the vehicle re-ID problem are insufficiently robust because they cannot distinguish among vehicles of the same type nor recognize high-level representations in deep networks for identical vehicles with various views. To address these issues
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An oversampling algorithm combining SMOTE and k-means for imbalanced medical data Inform. Sci. (IF 5.91) Pub Date : 2021-02-26 Zhaozhao Xu; Derong Shen; Tiezheng Nie; Yue Kou; Nan Yin; Xi Han
The algorithm of C4.5 decision tree has the advantages of high classification accuracy, fast calculation speed and comprehensible classification rules, so it is widely used for medical data analysis. However, for imbalanced medical data, the classification accuracy of decision trees-based models is not ideal. Therefore, this paper proposes a cluster-based oversampling algorithm (KNSMOTE) combining
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Robust guaranteed cost control for uncertain discrete-time systems with state and input quantizations Inform. Sci. (IF 5.91) Pub Date : 2021-02-26 Qunxian Zheng; Haoling Chen; Shengyuan Xu
The robust guaranteed cost control problem for uncertain discrete-time systems with state and input quantizations has been studied in this paper. The polytope type uncertainties are considered in the plants. Different from previous related works, a novel guaranteed cost control strategy has been put forward in this paper. The novelty and challenge lie in that the quantized state and quantized input
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Full state constraints and command filtering-based adaptive fuzzy control for permanent magnet synchronous motor stochastic systems Inform. Sci. (IF 5.91) Pub Date : 2021-02-26 Qi Jiang; Jiapeng Liu; Jinpeng Yu; Chong Lin
In this article, an adaptive fuzzy control scheme based on command filtering is proposed for the position tracking control of permanent magnet synchronous motor (PMSM) stochastic system with full state constraints. Firstly, fuzzy logic systems are employed to approximate unknown stochastic nonlinear functions in PMSM stochastic system. Then, the barrier Lyapunov functions are constructed to ensure
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Evolutionary Continuous Constrained Optimization Using Random Direction Repair Inform. Sci. (IF 5.91) Pub Date : 2021-02-26 Peilan Xu; Wenjian Luo; Xin Lin; Yingying Qiao
To solve constrained optimization problems (COPs), it is crucial to guide the infeasible solution to a feasible region. Gradient-based repair (GR) is a successful repair strategy, where the forward difference is often used to estimate the gradient. However, GR has major deficiencies. First, it is difficult to deal with individuals falling into the local optima. Second, large amounts of fitness evaluations
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A nondominated selection procedure with partially consistent non-reciprocal probabilistic linguistic preference relations and its application in social donation channel selection under the COVID-19 outbreaks Inform. Sci. (IF 5.91) Pub Date : 2021-02-26 Lisheng Jiang; Huchang Liao
A non-reciprocal fuzzy preference relation (NrFPR) can express partial relations of alternatives, including indifference relations, preference relations and incomparability relations, but cannot depict linguistic preference intensities. A probabilistic linguistic preference relation (PLPR) can represent preference intensities in forms of probabilities and linguistic terms, but the incomparability relations
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A Multicriteria Group Decision-making Method Based on AIVIFSs, Z-numbers, and Trapezium Clouds Inform. Sci. (IF 5.91) Pub Date : 2021-02-26 Qianlei Jia; Jiayue Hu; Qizhi He; Weiguo Zhang; Ehab Safwat
Multicriteria group decision-making (MCGDM), with the strong uncertainty and randomness, has always been a hotspot in the world. The chief purpose of the paper is to address the problem with Atanassov’s interval-valued intuitionistic fuzzy sets (AIVIFSs), Z-numbers, and trapezium clouds. First, some related concepts and former operators of AIVIFSs, Z-numbers, and trapezium clouds are reviewed, meanwhile
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A highly effective hybrid evolutionary algorithm for the covering salesman problem Inform. Sci. (IF 5.91) Pub Date : 2021-02-26 Yongliang Lu; Una Benlic; Qinghua Wu
Covering salesman problem (CSP) is an extension of the popular traveling salesman problem (TSP) arising from a number of real-life applications. Given a set of vertices and a predetermined coverage radius associated with each vertex, the goal of CSP is to find a minimum cost Hamiltonian cycle across a subset of vertices, such that each unvisited vertex must be within the coverage radius of at lis NP-hard
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Empirical Risk Minimization for Dominance-based Rough Set Approaches Inform. Sci. (IF 5.91) Pub Date : 2021-02-26 Yoshifumi Kusunoki; Jerzy Błaszczyński; Masahiro Inuiguchi; Roman Słowiński
In this paper, we consider two parametric dominance-based rough set approaches (DRSA) proposed in the literature: variable precision DRSA (VP-DRSA) and variable consistency DRSA (VC-DRSA). They were introduced to cope with classification data encountered in practice for which the original definition of lower approximations is too restrictive. Both these extensions allow an augmentation of lower approximations
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A Turning Point-Based Offline Map Matching Algorithm for Urban Road Networks Inform. Sci. (IF 5.91) Pub Date : 2021-02-26 Dongqing Zhang; Yucheng Dong; Zhaoxia Guo
Offline map matching is a crucial step to facilitate many trajectory-based services in urban areas by finding vehicles’ travel paths from recorded and stored trajectory data. This paper proposes a novel turning point-based offline map matching algorithm, which introduces the concept of vehicle turning points to implement map matching piecewisely. The algorithm first separates the entire trajectory
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A Novel Prediction Model for the Inbound Passenger Flow of Urban Rail Transit Inform. Sci. (IF 5.91) Pub Date : 2021-02-26 Xin Yang; Qiuchi Xue; Xingxing Yang; Haodong Yin; Yunchao Qu; Xiang Li; Jianjun Wu
High-precision short-term inbound passenger flow prediction is of great significance to the daily crowd management and line rescheduling in urban rail systems. Although current models have been applied to prediction, most methods need optimization to meet refined passenger flow management demand. In order to predict the passenger flow, a novel Wave-LSTM model, based on long short-term memory network
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Learning Task-driving Affinity Matrix for Accurate Multi-view Clustering through Tensor Subspace Learning. Inform. Sci. (IF 5.91) Pub Date : 2021-02-26 Haiyan Wang; Guoqiang Han; Junyu Li; Bin Zhang; Jiazhou Chen; Yu Hu; Chu Han; Hongmin Cai
Multi-view clustering seeks an underlying partition of the data from multiple views. Organizing the data into a tensor and then learning a self-expressive latent one to exploit cross-view information has attracted much attention. Most of the recent works mainly focus on the tensor representation, but they fail to directly extract the task-driving affinity matrix for clustering. Such method is typically
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Adaptive Denoising Algorithm Using Peak Statistics-Based Thresholding and Novel Adaptive Complementary Ensemble Empirical Mode Decomposition Inform. Sci. (IF 5.91) Pub Date : 2021-02-26 Mengfei Hu; Shuqing Zhang; Wei Dong; Fengjiao Xu; Haitao Liu
This paper proposes an adaptive denoising methodology for noisy signals that employs a novel adaptive complementary ensemble empirical mode decomposition (NACEEMD) and a peak statistics (PS)-based thresholding technique. The key idea in this paper is the peak statistics (PS)-based thresholding technique,which breaks the traditional strategy with respect to selecting more accurate and more adaptive
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Outlier detection based on weighted neighbourhood information network for mixed-valued datasets Inform. Sci. (IF 5.91) Pub Date : 2021-02-26 Yu Wang; Yupeng Li
Outlier detection is of great importance in industry as unexpected errors or faults, abnormal behaviours or phenomena, etc. can occur due to a variety of human, system, and environmental reasons. To identify and analyse these rare items, events or observations can find either anomalies or novelties and, as a result, can help avoid potential unexpected consequences or improve industrial system performance
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The Detection of Low-rate DoS Attacks Using the SADBSCAN Algorithm Inform. Sci. (IF 5.91) Pub Date : 2021-02-26 Dan Tang; Siqi Zhang; Jingwen Chen; Xiyin Wang
Low-rate denial-of-service (DoS) attacks, which can exploit vulnerabilities in Internet protocols to deteriorate the quality of service, are variants of DoS attacks. It is challenging to identify low-rate DoS attacks using traditional DoS defence mechanisms due to their low attack rate and stealthy nature. Most of the existing attack detection techniques are based on statistical analysis and signal
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A Curvature-Segmentation-Based Minimum Time Algorithm for Autonomous Vehicle Velocity Planning Inform. Sci. (IF 5.91) Pub Date : 2021-02-26 Miao Wang; Qingshan Liu; Yanling Zheng
Velocity planning serves as an important issue in motion planing for autonomous vehicles. The presented paper proposes a novel velocity planning method with minimum moving time on the basis of path curvature which is accomplished in three steps. First, the assigned path is divided into some elementary parts based on the path curvature. Second, the velocity planning is transformed into an unconstrained
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Distributed predictor-based stabilization of interconnected systems with network induced delays Inform. Sci. (IF 5.91) Pub Date : 2021-02-26 Tito L. M. Santos; Taniel S. Franklin
This paper presents sufficient stability conditions for distributed prediction-based control of interconnected systems subject to network-induced time-varying delay. Due to the flexibility of the proposed criteria, continuous-time, sampled-data control and output-feedback problems can be handled by the same framework. A detuning procedure is combined with a distributed LQR design in order to ensure
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EOCD: An ensemble optimization approach for concept drift applications Inform. Sci. (IF 5.91) Pub Date : 2021-02-03 Antonino Feitosa Neto; Anne M.P. Canuto
Data streams applications generate a continuous stream of data in a high rate that it is not possible to store all data in available memory. Hence, it is important to apply techniques that are capable of learning concepts according to data presentation, taking into consideration available time, processing and memory resources. This paper presents a new ensemble-based approach to detect concept drift
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Document-level event causality identification via graph inference mechanism Inform. Sci. (IF 5.91) Pub Date : 2021-02-05 Kun Zhao; Donghong Ji; Fazhi He; Yijiang Liu; Yafeng Ren
Event causality identification is an important research task in natural language processing. Existing methods largely focus on identifying explicit causal relations, and give poor performance in implicit causalities, especially in the document level. In this paper, we formalize event causality identification as a graph-based edge prediction problem and propose a novel document-level context-based graph
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An efficient identity tracing scheme for blockchain-based systems Inform. Sci. (IF 5.91) Pub Date : 2021-02-05 Peili Li; Haixia Xu; Tianjun Ma
The enhancement of anonymity for blockchain users received much attention. However, in some cases tracing user’s identity is also very important, especially to expose illegal transactions. In this paper, we propose an identity tracing scheme and implement it using a simple and efficient proof method. In particular, we design a new signature scheme that is used to generate a user’s certificate, and
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