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The preview-based leader-following consensus control of distributed multi-agent systems (MAS) with a directed acyclic graph is investigated in this paper. The external disturbance considered in this paper is the step function that can be previewed, and the information of the leader can be obtained in advance. The leader-following consensus control of MAS is solved by using the state augmentation technique and the preview control method that transforms the consensus control problem into the finite horizon linear quadratic regulator (LQR) problem and the infinite horizon LQR problem. In the finite horizon, the minimum principle is utilized to design the optimal controller. In the infinite horizon, the standard infinite horizon LQR is applied to devise the corresponding controller. Assuming the original systems are stabilisable and detectable, the distributed consensus controller guarantees the asymptotic consensus of the preview MAS. The simulation illustrates the effectiveness of the distributed leader-following consensus controller designed in this paper.

Multi-attribute decision making (MADM) refers to a decision problem of choosing the best alternative or carrying out the ranking of alternatives under multiple attributes, and it is a key component of modern decision sciences. Three-way decisions (3WD) can effectively address MADM problems by reducing decision risks compared with traditional two-way decisions (2WD), and a decision model based on probabilistic dominance relations is presented in this paper. Particularly, we put forward a kind of 3WD-based MADM (3WD-MADM) with probabilistic dominance relations, where the two state sets are developed by virtue of probabilistic dominance classes. Afterwards, the independent innovation ability of high-tech enterprises is evaluated by using the proposed new method. At last, the newly proposed 3WD-MADM model in this paper is verified from different perspectives by comparative and experimental analyses.

Non-singleton fuzzification is used in rule-based fuzzy systems when the measurements that activate them are imperfect or uncertain or when their inputs are words. It models such measurements or words as fuzzy numbers or more general fuzzy sets so that, regardless of the cause of a measurement’s or word’s imperfections or uncertainties, they are treated within the framework of fuzzy sets and systems. Since 2011, there has been a resurgence of interest in both type-1 and interval type-2 non-singleton fuzzy systems. This paper removes a computational bottleneck associated with computing the firing level or firing interval for such fuzzy systems, by providing closed-form formulas for them when the involved fuzzy sets are trapezoidal or triangular, which are widely used fuzzy sets. This is done for both the minimum and product t-norms. It is also demonstrated that a non-singleton fuzzy system that uses the product t-norm has the potential to outperform a non-singleton fuzzy system that uses the minimum t-norm. The results in this paper greatly simplify non-singleton fuzzy systems, which should make them much more popular.

One-class classification is a machine learning problem, where training data has only one class. The objective is to determine if the input data is seen class or unseen class. Traditional deep learning algorithms are not suitable for this task since the algorithm can predict only class in training data. In this paper, the one-class classification algorithm using construction error of image transformation network (OCITN) is proposed. In particular, image transformation network (ITN) is introduced as a subtask, which transforms input image into one image, namely goal image. Moreover, the error of ITN, namely construction error (CE), is computed as a distance metric between the goal image and model output. ITN model is trained using only one-class images and is applied for testing images. Since the model is trained with only one-class images, the CE for one-class is small relative to other classes. Thus, one-class classification is made determining CE is large or small. The proposed method is experimented with using MNIST, Fashion MNIST, CIFAR10, CIFAR100, and Cat-vs-Dog datasets. OCITN shows good results where the goal image has high entropy. Additionally, the extension of OCITN, namely OCITNE, is implemented. This method shows the state of the art performance in MNIST (98.0), Fashion MNIST(95.6), and acceptable performance in CIFAR10(78.4). Furthermore, these methods provide high-speed processing, OCITN process 5291 images, and OCITNE 1261 images per second, 137 times and 33 times faster than state of the art. The source code used in this paper can be downloaded from: https://github.com/ToshiHayashi/OCITN .

International audience; This paper considers an epistemic interpretation of formal contexts, interpreting blank entries in the context matrix as absence of information, which is in agreement with the usual focus on the extraction of implications between attributes. After recalling non-classical connections induced by rough sets and possibility theory in formal concept analysis (FCA), and the standard theory of attribute implications in FCA, this paper presents the notion of disjunctive attribute implications, which reflect additional information that can be extracted from an epistemic context. We show that they can be computed like standard attribute implications from the complementary context. The paper also recalls the logic of classical attribute implications, relying on works pertaining to functional dependencies in database theory, and proposes a dual logic for disjunctive attribute implications. A method for extracting the latter kind of rules from a formal context is proposed, using a counterpart of pseudo-intents. Lastly, the paper outlines a generalization of both conjunctive and disjunctive attribute implications under the form of rules, with a conjunction of conditions in the body and a disjunction of conditions in the head, that hold in a formal context under the epistemic view.

International audience; Marketing messages are most effective if they reach the right customers. Deciding which customers to contact is an important task in campaign planning. The paper focuses on empirical targeting models. We argue that common practices to develop such models do not account sufficiently for business goals. To remedy this, we propose profit-conscious ensemble selection, a modeling framework that integrates statistical learning principles and business objectives in the form of campaign profit maximization. Studying the interplay between data-driven learning methods and their business value in real-world application contexts, the paper contributes to the emerging field of profit analytics and provides original insights how to implement profit analytics in marketing. The paper also estimates the degree to which profit-concious modeling adds to the bottom line. The results of a comprehensive empirical study confirm the business value of the proposed ensemble learning framework in that it recommends substantially more profitable target groups than several benchmarks.

Experton theory, a generalization of probabilistic set theory, that is of great usefulness to group decision analysis, was first proposed as a means of improving the processing and analysis of opinions issued by experts. This theory produces an information-fusion mathematical object , the experton, which can be used in predictive problems to justify decisions based on well-constructed reasoning. The aim of this paper is to present an aggregative method of several expertons, with the idea that some of the groups of experts involved in producing these expertons may have more influence than others in the decision-making process. In this article, we carry out an aggregation analysis of expertons, not experts, which culminates in the creation of a new mathematical object. This object, which is called the weighted average multiexperton, is coherent with an experton-type object created from a weighting of the initial data provided by all experts. Since the aggregation method presented has been devised to represent the decision-maker’s attitude regarding the importance of different groups of experts, this approach represents a new tool for dealing with group decision-making problems. Additionally, the study presents some properties of the new object. Finally, the paper ends with an application for business decision-making.

This paper focuses on investigates the leader-follower consensus problem for linear multi-agent systems (MASs). In order to reduce the frequency of system network communication, this paper first designs three kinds of triggering mechanisms, for the leader, the edges composed of the leader and informed followers, and the edges composed of uninformed followers, respectively. All agents in the MASs do not transmit information until the corresponding triggering mechanisms are satisfied, thus avoiding continuous communication and saving system resources. Then, two kinds of fully distributed edge-event-triggered adaptive protocols are designed to solve the leader-follower problem. The first state feedback control protocol is applicable to scenarios where all states of MASs are available. The second output feedback control protocol does not depend on the availability of the states of MASs. Moreover, under the designed triggering mechanisms and fully distributed edge-event-triggered adaptive consensus strategies, the MASs do not exhibit the Zeno behavior. Finally, two practical simulations are introduced to verify all the theoretical results obtained in this paper. Furthermore, in order to further demonstrate the advantages of the proposed methods, a comparative experiment is performed.

This paper mainly proposes a new method for clustering linear ordinal ranking (LOR) information by agglomerative hierarchical clustering (AHC) algorithm. Considering that the cores of the AHC algorithm for LOR clustering are the difference measure among different LORs and the aggregation of the individual LORs, we firstly systematically analyze the existing studies for LOR distance measure, based on which we extend the method to depict LORs. Subsequently, the corresponding new distance measure is proposed starting from the perspective of utilizing the rankings’ position information and relationship information together. In addition, we simplify the dominating index and dominated index-based aggregation method for LORs fusion. Further, we present a numerical case on online financial product recommendation to illustrate the usage of the algorithm and also try to provide a feasible way for online financial product recommendation. Then, we make some discussions on the proposed distance measure and the aggregation method under the framework of the AHC algorithm to show the features of the algorithm proposed in this paper.

Convolutional neural networks (CNN) have been widely used in various tasks, largely due to their ability to efficiently extract n-gram features for text analysis and document representation. In this paper, we intend to insight the CNN model regarding its capability on text analysis. Vanilla CNNs do have weaknesses when it comes to the representation and feature extraction. Duplicate filters are inevitable with vanilla CNNs, which reduces the discriminative power of the representations. In addition, the current pooling operations either limit the CNN to the local optimum (i.e., max pooling) or they do not consider the importance of all features (i.e., mean pooling). In this paper, we propose two modules for vanilla CNNs to overcome these shortcomings. The first equips the CNN with discriminative filters (distinct filters with maximised divergence) and the second provides the ability to comprehensively extract all salient features. Specifically, our model increases the discriminative power of the model by maximizing the distance between different filters, and a novel global pooling mechanism for feature extraction. Validation tests against state-of-the-art baselines on five benchmark classification datasets achieve the competitive performance of our proposed model. Furthermore, visualization on upgrade filters and pooling features verify our hypothesis that the proposed model can receive discriminative filters and salient features.

Proliferation of Industrial Internet has dramatically changed the way we live and work. It brings convenience to our society and sometimes requires real-time processing of dramatic data at the same time. However, traditional paradigm of computing on the center cloud can’t always meet such requirement, for the non-negligible time delay of data transmission and communication. Edge computing is a novel computing paradigm proposed to resolve such a problem. As a promising technology, it extends computing from cloud center to the edge of network. Edge computing has the advantage of low latency to achieve a shorter response time, as well as potential to address the concerns of energy consuming, bandwidth burden and security issue. In this paper, we give a survey about edge computing from the aspect of methodologies, application scenarios and its role in Industrial Internet. Some open issues of edge computing are also introduced in this paper. At the end of the manuscript, a discussion about future direction is proposed. The shallow network algorithms such as broad learning system (BLS), which have achieved great improvement in computing efficiency, show an optimistic outlook in this area. We propose our conceive about future applications when shallow network methods like BLS are applied in edge computing and hope the paper will inspire research in relative directions.

Although recurrent neural networks (RNNs) perfectly solve many difficult problems, their computational complexity significantly increases training time. Therefore, the primary problem with applying RNNs is to shorten the time needed to train and operate a network. An effective solution to this problem is to use parallel processing. In the paper, a particular approach for the Jordan network will be shown, however, the presented idea is applicable to other RNN structures. This type of network is characterized by natural parallelism, and in the paper, this feature is used to significantly accelerate the learning process. High-performance learning has been achieved using a novel parallel three-dimensional architecture. The presented solutions can be implemented in digital hardware.

Research shows that deep neural networks are vulnerable to adversarial examples due to the highly linear nature of deep neural networks (DNNs). Therefore, adversarial examples involve security of deep learning. However, there is a lack of research on the impact of adversarial examples on the biomedical segmentation model. Since a large part of medical image problems are segmentation problems, this paper analyzes the impact of adversarial examples on image segmentation models based on deep learning. We propose to fool the biomedical segmentation model and generate target segmentation masks with feature space perturbations and cross-entropy loss function. Different from the traditional gradient-based attack methods, which usually use the gradient of the final loss function, this paper adopts a Multi-scale Attack (MSA) method based on multi-scale gradients. Extensive experiments to attack U-Net on the ISIC skin lesion segmentation challenge dataset and the glaucoma optic disc segmentation dataset have proved that the predicted mask generated by this method has a high intersection over union(IoU) and pixel accuracy with the target mask. Besides, the L 2 and L ∞ distances between the adversarial and clean examples are reduced compared with the state-of-the-art method.

Identifying frequent item-sets is a popular data-mining task. It consists of finding sets of items frequently appearing in data. Yet, finding all frequent item-sets in large or dense datasets may be time-consuming, and a user may be interested merely in some specific item-sets rather than all of them. Recently, methods have been proposed for targeted item-set mining; that is to calculate the support of some item-sets of interest. Though this approach is often more suitable for real applications than traditional item-set mining approaches, performance remains an issue. To address that issue, this paper presents a novel algorithm for multitude-targeted mining, named Guided Frequent Pattern-Growth (GFP-Growth). The GFP-Growth algorithm is designed to quickly mine a given set of item-sets using a small amount of memory. This paper proves that GFP-Growth yields the exact frequency-counts for each item-set of interest. It further shows that GFP-Growth can boost the performance for several problems requiring item-set mining. We specifically study the problem of generating minority-class rules from imbalanced data and develop the Minority-Report Algorithm (MRA) that uses GFP-Growth to solve this problem efficiently. We prove several theoretical properties of MRA and present experimental results showing substantial performance gain.

In association rules mining from data that have numeric-valued attributes, automatically adjusting the attribute intervals at the time of the mining process without a preprocess is very critical for preventing data loss and attribute interactions. In this paper, differential evolution and sine cosine algorithm based novel hybrid multi-objective evolutionary optimization methods are proposed for rapidly and directly mining the reduced high-quality numerical association rules by simultaneously adjusting the relevant intervals of related attributes without finding the frequent itemsets. These algorithms perform a global search and find the high-quality rules set in only one execution by modeling the rule mining task as a multi-objective problem that simultaneously meets different conflicting metrics. The algorithms proposed in this paper ensure the discovered rules to have high confidence and support and to be comprehensible. The proposed methods automate the rule mining process by directly finding the minimum intervals for the attributes and eliminating the need for minimum confidence and minimum support determined beforehand for each data set. The performances of new algorithms proposed in this study were tested with those of the state-of-the-art algorithms. The results show superiority of the proposed methods on the data sets that contain fewer attributes and higher number of instances.

As a new type of network, the types of attack in the Internet of Vehicles (IoV) are constantly emerging and changing. Consequently, the machine learning based intrusion detection model has to update to cope with new attacks. However, existing machine learning based IoV intrusion detection schemes require large amounts of labeled data to complete model updates. For new attacks, the IoV cloud is also difficult to identify in time, which requires a lot of labor and time cost in IoV. To solve above issue, this paper employs transfer learning and proposes two model update schemes based on whether the IoV cloud can timely provide a small amount of labeled data for a new attack. The first one is the cloud-assisted update scheme where the IoV cloud can provide a small amount of data. And the second one is the local update scheme where the IoV cloud cannot provide any labeled data timely. In this paper, the local update scheme obtains pseudo label of the unlabeled data in new attacks via pre-classifies and uses the pseudo-labeled data for multiple rounds of transfer learning. Then the vehicle can complete the update without obtaining any labeled data through the IoV cloud. The experimental results show that compared with the existing method, our two schemes have improved the detection accuracy by at least 23%.

This paper tackles the observer-based finite-time H ∞ control problem for networked nonlinear semi-Markovian jump systems (SMJSs) with actuator fault, parameter uncertainties and partly unknown transition rates via an improved dynamic event-triggered scheme (DETS). In comparison with traditional dynamic event-triggered scheme, the observer-based improved dynamic event-triggered scheme which is not restricted by real system states and could save more network resources is firstly proposed in this paper. Then, a more general actuator fault model is considered and the reliable observer-based state feedback controller is designed. Sufficient conditions are proposed to guarantee the closed-loop system is stochastically finite-time bounded with a prescribed H ∞ disturbance attenuation level. Lastly, two simulation examples are given to affirm the validity of the results obtained in this paper.

Uncertain group decision making (UGDM) has been a hot research direction over the years. In this paper, according to VOS viewer and CiteSpace, we make a comprehensive bibliometric analysis of UGDM during the last four decades, namely from 1980 to 2019. After data preprocessing, 4,887 publications related to UGDM are obtained from the Web of Science (WoS). Considering numbers, citations and cooperation of publications, general analysis is explored on the basis of bibliometric indicators at the levels of countries/regions, institutions and authors. In order to investigate the development of UGDM, we present cooperation network in the whole period of time and the dynamic networks during the four sub-periods. After that, some deep researches are conducted by bibliometric analysis, such as burst detection analysis, co-occurrence analysis, timeline view analysis and bibliographic coupling analysis. Based on which, further discussions are provided, including current challenges and possible directions. Finally, some main findings are summarized from three aspects, and the advantages and disadvantages of methods in this paper are concluded. This paper explores the hot topics and development trends of UGDM, which offers an important reference for future research.

This paper aims at exploring the mean-square bounded synchronization of clustered stochastic systems under deception attack which is a ubiquitous phenomenon in cyber-physical network. Actually, when controllers in network send controlling signal to physical plants, controller-to-actuator channel may be injected with wrong signal by malicious hackers intending to destroy the system’s performance. To describe attacker’s behavior, some random variables are introduced to describe whether the cluster’s channel is attacked or not in the controlled moment. Meanwhile, it has been proved that pinning impulsive control is effective to offset both stochastic effect and attack effect, where the nodes with bigger error will be chosen to be pinned in impulsive instants. By applying Gronwall-Bellman inequality and Lyapunov method on stochastic network, several kinds of mean-square bounded synchronization criteria based on pinning impulsive strategy are derived in terms of algebraic conditions and some reasonable assumptions. Finally, some numerical simulations are presented to illustrate the effectiveness of the theoretical results in this paper.

In this paper, we propose a new fuzzy best-worst method (BWM) based on triangular fuzzy numbers for multi-criteria decision-making (MCDM). Aimed at the Best-to-Others vector and the Others-to-Worst vector in the form of triangular fuzzy numbers, this paper regards consistency equations as fuzzy equations. The derivation of optimal fuzzy weights of criteria is formulated as a fuzzy decision-making problem, where a mathematical programming model is constructed to derive optimal fuzzy weights of criteria to build a normalized triangular fuzzy weight vector. Then, we propose four linear programming models based on the obtained mathematical programming model for the optimistic decision maker, the pessimistic decision maker and the neutral decision maker, respectively. Through a proper selection of the values of tolerance parameters, each of the linear programming models certainly has a unique global optimal solution. Moreover, this paper proposes the concept of fuzzy consistency index and the concept of fuzzy consistency ratio. Several application examples are used to validate the proposed fuzzy BWM. The proposed fuzzy BWM provides us with a very useful way for MCDM in fuzzy environments.

Acute hypotensive episode (AHE) is a common serious postoperative complication in ICU, which may raise multiple system failure (especially of cardiac and respiratory kinds), and even cause death. Timely and effective clinical intervention is obviously vital to the saving of patients. AHE detection involves physiological time-series monitoring, processing and prediction technologies, which can offer insights to neuroscientists, biologists, and even provide support for clinicians. This paper presents a hybrid artificial intelligence model combined with CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise, a typical method for physiological signal decomposition), deep learning, multiple gene expression programming and fuzzy expert system for AHE detection. In this paper, the physiological signal is selected from a benchmark dataset, for example MIMIC-II (Multiparameter Intelligent Monitoring in Intensive Care II), which collects large scale real patients’ data for clinical research. In the hybrid model, a typical signal decomposition method is employed for AHE signal processing, and an autoencoder based deep neural network is established for feature extraction. Finally, a reliable and explainable classifier is presented by fusing gene expression programming and the fuzzy method. Experimental results based on real data set demonstrate that the proposed method outperforms state-of-the-art AHE detection methods by achieving the prediction accuracy of 88.14% in 2866 records.

This paper considers the problem of fuzzy logic adaptive fixed-time control for nonlinear switched system. Different from existing research results on fixed-time control, the system functions are completely uncertain in this paper. The approximation error will cause Lyapunov function not to be negative definite. To address this problem, a new criterion of fixed-time stability is developed at first. Based on this fixed-time theory, a fuzzy logic control method is proposed by using backstepping technique. The proposed adaptive laws in this paper do not satisfy a series of linear differential equations but nonlinear ones. Simulation results verify the feasibility of presented algorithms.

Multi-label classification is a machine learning task to construct a model for assigning an entity in the dataset to two or more class labels. In order to improve the performance of multi-label classification, a multi-objective feature selection algorithm has been proposed in this paper. Feature selection as a preprocessing task for Multi-label classification problems aims to choose a subset of relevant features. Selecting a small number of high-quality features decreases the computational cost and at the same time maximizes the classification performance. However extreme decreasing the number of features causes the failure of classification. As a result, feature selection has two conflicting objectives, namely, minimizing the classification error and minimizing the number of selected features. This paper proposes a multi-objective optimization algorithm to tackle the multi-label feature selection. The task is to find a set of solutions (a subset of features) in a sophisticated large-scale search space using a reference-based multi-objective optimization method. The proposed algorithm utilizes an opposition-based binary operator to generate more diverse solutions. Injection of extreme point of the Pareto-front is another component of the algorithm which aims to find feature subsets with less classification error. The proposed method is compared with two other existing methods on eight multi-label benchmark datasets. The experimental results show that the proposed method outperforms existing algorithms in terms of various multi-objective evaluation measures, such as Hyper-volume indicator, Pure diversity, Two-set coverage, and Pareto-front proportional contribution. The proposed method leads to get a set of well-distributed trade-off solutions which reach less classification error in comparing with competitors, even with the fewer number of features.

The growing demand of users for video streaming services has brought huge challenges to the streaming media service system. The cooperative edge-cloud computing architecture that combines cloud computing and edge computing can effectively reduce the burden on streaming media service systems. The streaming content caching under the cooperative edge-cloud computing architecture is important to improve the quality of user service. Based on this, this paper proposed a cache strategy based on user mobility and content popularity. In this cache strategy, the Markov model is used to describe the user mobility, the multiple linear regression model is used for content popularity prediction, and the content caching operation is performed according to the user mobility and the content popularity. To reduce the load of edge servers and further improve the quality of user service, this paper proposed a content placement strategy based on marginal gain. In this content placement strategy, a content placement problem is established by considering the content access latency and the content placement cost. Then a content placement algorithm based on marginal gain is proposed to solve the content placement problem. The content placement operation is performed by analyzing the marginal gain brought by placing the contents on edge servers to achieve the optimal performance of content placement. The experimental results show that the proposed strategies can effectively improve the performance in terms of the cache hit ratio, the content access latency, the storage cost and the remote data access cost.

Highlights • The minimum cost consensus model is proposed to coordinate the opinions of experts. • A new method of LSGDM clustering is proposed to improve CRP efficiency. • A consensus index CI reflecting the harmony degree between experts is devised. • A robust counterpart of robust optimization consensus is implemented., Recently, large-scale group decision making (LSGDM) in social network comes into being. In the practical consensus of LSGDM, the unit adjustment cost of experts is difficult to obtain and may be uncertain. Therefore, the purpose of this paper is to propose a consensus model based on robust optimization. This paper focuses on LSGDM, considering the social relationship between experts. In the presented model, an expert clustering method, combining trust degree and relationship strength, is used to classify experts with similar opinions into subgroups. A consensus index, reflecting the harmony degree between experts, is devised to measure the consensus level among experts. Then, a minimum cost model based on robust optimization is proposed to solve the robust optimization consensus problem. Subsequently, a detailed consensus feedback adjustment is presented. Finally, a case study and comparative analysis are provided to verify the validity and advantage of the proposed method.

This paper addresses the access control issue for the comprehensive and distributed Internet of Things (IoT) environments. The typical eXtensible Access Control Markup Language (XACML) which implements the sophisticated access conditions in XML files, is widely used to guarantee access control decisions for the distributed IoT environments. To the best of our knowledge, the typical XACML-based access control schemes never consider the authentication run-time parameters. Moreover, the access control schemes that are mainly based on the typical XACML cannot secure themselves against some kinds of attacks, such as the Masquerade attack. Also, those schemes are not secure in opposition to Man-in-the-Middle (MITM) attack. Therefore, this paper proposes an adaptive XACML scheme that extends the typical XACML by integrating an access code generation and verification schemes for heterogeneous distributed IoT environments. Our adaptive XACML scheme considers some sensitive authentication run-time parameters before authorizing the user. Moreover, our scheme is proven secure against the Masquerade attack and MITM attack through hashing the generated access code using Message Digest Algorithm-5 (MD5) and Secure Hash Algorithm-1 (SHA-1) Checksum Utility. The experimental analysis of many different configurations supports the efficacy and efficiency of our adaptive XACML. It also shows exceptional compatibility and performance with different implementations. The processing time comparison between our adaptive XACML and Typical XACML, has shown that there is a low time overhead when using our adaptive XACML. This processing time overhead is nothing compared to the extra features that have been achieved in excess of the typical XACML and the security against Masquerade and MITM attacks. Therefore, our adaptive XACML scheme has the capability to be applied in various distinct distributed environments not only IoT.

We in this paper study the current-state estimation problem of probabilistic Boolean networks with output observations. First, we propose the concepts of three fundamental categories of detectability in the context of PBNs based on the different purposes, which are periodic detectability, (periodic) k-detectability, and (periodic) d-detectability. Second, utilizing the semi-tensor product technique, we create a uniform methodology for the verification of all the aforementioned categories of detectability. Accordingly, several necessary and sufficient verification criteria are derived. All results obtained in this paper are numerically tractable since they avoid the graph-based symbolic manipulations. Finally, several examples modeled by the same PBN are provided to instantiate the correctness and effeteness of the obtained results.

Considering that most popular models solving aspect-level sentiment classification problems mainly focus on designing complicated neural networks to scale the importance of each word in the sentence, this paper addresses this problem from the view of semantic space. Motivated by the fact that the senses of a word can be sophisticatedly embedded into the semantic space using a distributed representation, this paper hypothesizes that each sense of a word can be represented by one or more specific dimensions, and thus the target of aspect-level sentiment classification can be simplified into searching the related dimensions for the aspects and sentiments concerned. Particularly, an Attention Vector (ATV) based on attention mechanism is designed for each aspect in terms of a specific task, which involves two sub-vectors, i.e., a Dimension Attention Vector (DATV) and a Sentiment Attention Vector (SATV). The DATV determines the significances of different dimensions based on their correlations with an aspect; and the SATV allocates weights for the attributes of words, which are decided by sentiment polarities and part-of-speech (PoS) tagging. Given a sub-dataset related to an aspect, the ATV will be optimized by an Artificial Bee Colony (ABC) algorithm with a Support Vector Machine (SVM) classifier, the objective of which is to maximize classification accuracy. Intrinsically, the DATV can reduce the ambiguity existing in polysemy, meanwhile, the SATV is an auxiliary means for the optimization of the DATV, which can help eliminate the misunderstandings caused by antonyms. Then, the optimized DATV will be applied on a Convolutional Neural Network (CNN) model via simply scaling the pretrained word embeddings as inputs (named as ATV-CNN model). Experimental results show that the ATV-CNN model can have substantial advantages when compared with state-of-the-art models.

The paper introduces a novel many-objective evolutionary method, with a diversity-based selection operator and aims to fill the “gaps” in the Pareto Front approximation and to increase its spread. It shows, that guiding the evolution process towards the least explored parts of a space increases overall diversity, but can also lead to increased convergence. A set of experiments is carried out on a many-objective Multi-Skill Resource-Constrained Project Scheduling Problem and a bi-objective Travelling Thief Problem. Both of those problems comprise two interwoven subproblems. Separate optimal solutions to the subproblems do not guarantee the optimal solution to the entire problem. Moreover, it shows that the existing diversity mechanism does not work well in combinatorial spaces, where the domain of each objective has a different size. The results indicate that a novel selection operator circumvents this issue. A set of Quality Measures is used to describe the desirable features of Pareto Front approximation: convergence, uniformity, and spread. The experiments are followed by a set of visualizations. Along with a set of Quality Measures, they give an insight into the characteristics of the method. The paper is concluded by a thorough theoretical analysis and possible directions for future work.

In the paper, a novel input-constrained H ∞ fault-tolerant control approach is developed by using sliding mode control technology and event-triggered reinforcement learning (RL) algorithm. To reduce or even eliminate the impacts of the time-varying actuator failures, a properly sliding mode control strategy is proposed for the controlled system, while the event-triggered H ∞ control scheme is established via RL algorithm for the equivalent sliding mode dynamics. By utilizing a single neural network (NN), the Hamilton–Jacobi–Bellman (HJB) equation can be solved approximately, thereby gaining time-triggered worst-case disturbance law, as well as event-triggered optimal control policy. Besides, it is unnecessary to given a initial stabilizing control input in the learning process of neural networks (NNs) in this paper. Moreover, the Lyapunov stability principle is applied to guarantee that the controlled system is uniformly ultimately bounded (UUB). Finally, to verify the feasibility and efficient performance of the developed approach, three simulations are carried out.

Periodic frequent patterns are sets of events or items that periodically appear in a sequence of events or transactions. Many algorithms have been designed to identify periodic frequent patterns in data. However, most assume that the periodic behavior of a pattern does not change much over time. To address this limitation, this paper proposes to discover a novel type of periodic patterns in a sequence of events or transactions, called Local Periodic Patterns (LPPs) which are patterns (sets of events) that have a periodic behavior in some non predefined time-intervals. A pattern is said to be a local periodic pattern if it appears regularly and continuously in some time-interval(s). Two novel measures are proposed to assess the periodicity and frequency of patterns in time-intervals. The maxSoPer (maximal period of spillovers) measure allows detecting time-intervals of variable lengths where a pattern is continuously periodic, while the minDur (minimal duration) measure ensures that those time-intervals have a minimum duration. To discover all LPPs, the paper presents three efficient algorithms. An experimental evaluation on real datasets shows that the proposed algorithms are efficient and can provide useful patterns that cannot be found using traditional periodic pattern mining algorithms.

In this paper, an improved cross coupled map lattice (CCML) which is derived from CCML is proposed. The experimental analysis of CCML shows that it exhibits weak or even lose chaotic behaviors in a large range of parameters. To enhance its chaotic performance, the new model changes the internal chaotic map of CCML from the logistics map to the tent map and introduces the module operations to limit the obtained value. The theoretical analysis and experimental results prove that the proposed model has a broad chaotic regime over an extensive range of system parameters, positive Lyapunov exponents, higher information entropies and lower mutual information values when compared to CCML, which are highly suitable for chaos-based image encryption. Moreover, this paper applies that model on a bit-level image encryption scheme. The algorithm consists of three main operations: the generation of secret keys, confusion and diffusion operations that can break the limitation of row and column. To confirm the novel algorithm's effectiveness and safety against the common types of attacks, many experiments are done and security analyses are discussed. It demonstrates the good chaos of the proposed model from the aspect of encryption performance.

In this paper, a chaotic image encryption algorithm based on the matrix semi-tensor product (STP) with a compound secret key is designed. First, a new scrambling method is designed. The pixels of the initial plaintext image are randomly divided into four blocks. The pixels in each block are then subjected to different numbers of rounds of Arnold transformation, and the four blocks are combined to generate a scrambled image. Then, a compound secret key is designed. A set of pseudosecret keys is given and filtered through a synchronously updating Boolean network to generate the real secret key. This secret key is used as the initial value of the mixed linear-nonlinear coupled map lattice (MLNCML) system to generate a chaotic sequence. Finally, the STP operation is applied to the chaotic sequences and the scrambled image to generate an encrypted image. Compared with other encryption algorithms, the algorithm proposed in this paper is more secure and effective, and it is also suitable for color image encryption.

There are three indispensable, yet contrasting requirements for a watermarking scheme: imperceptibility, robustness, and payload. Therefore, it is a challenge to obtain a tradeoff among above requirements in image watermark detection. In this paper, we propose a new statistical watermark decoder in discrete non-separable Shearlet transform (DNST) domain using singular values and Gaussian-Cauchy mixture-based vector hidden Markov tree (HMT). Our method can obtain great performance in imperceptibility, robustness and payload, and it is necessary for image copyright protection. We first perform DNST on the host image, and apply singular value decomposition (SVD) to the significant DNST domain high entropy blocks. We then embed the digital watermark into the DNST high entropy blocks by modifying the robust singular values. At the receiver, by combining the Gaussian-Cauchy mixture-based vector HMT and maximum likelihood (ML) decision, we propose a new blind image watermark decoder in DNST domain. Here, robust DNST domain singular values are firstly modeled by using Gaussian-Cauchy mixture-based vector HMT, where the Gaussian-Cauchy mixture marginal distribution and various strong dependencies of DNST domain singular values are incorporated. Then the statistical model parameters of Gaussian-Cauchy mixture-based vector HMT are estimated using parameter-expanded expectation–maximization (PXEM) approach. And finally, a blind image watermark decoder is developed using Gaussian-Cauchy mixture-based vector HMT and ML decision rule. The major contribution of this paper is the use of singular value, Gaussian-Cauchy mixture-based vector HMT and PXEM algorithms, which enhances the performance of watermarking scheme. Experimental results on some test images and comparison with well-known existing methods demonstrate the efficacy and superiority of the proposed method.

In this paper, we develop a novel multiple attribute decision making (MADM) method using the improved intuitionistic fuzzy weighted geometric (IIFWG) operator of intuitionistic fuzzy values (IFVs) proposed in this paper. First, we develop the IIFWG operator of IFVs to conquer the weak points of the existing operators of IFVs, where they have the drawbacks that their aggregated values are indeterminate in some situations. Based on the proposed IIFWG operator of IFVs, we present a MADM method to overcome the weak points of the existing MADM methods, which have the shortcomings that they obtain unreasonable ranking orders (ROs) of alternatives or they cannot discriminate the ROs of alternatives in some circumstances.

The Choquet integral model is mainly applied to describe non-additive multi-criteria decision-making (MCDM) problems. This paper considers the Choquet integral as a classifier which deals with complicated high-dimensional data. Although previously conducted studies have investigated the problem of classification using the Choquet integral and provided corresponding models, these models usually need to estimate a large number of fuzzy measure coefficients, which are not suitable when considering real situations. Sugeno et al. proposed the hierarchical Choquet integral (HCI) model to overcome this problem. However, the HCI model requires partition information of the criteria, which often cannot be obtained practically. This paper proposes two HCI models—shallow and deep models—by employing genetic algorithms (GAs) and neural networks (NNs) to automatically construct the structure of the HCI. The results of numerical experiments show that the proposed model outperforms the existing Naive Bayes, decision tree, and NN models.

The comprehensive logistics distribution center location selection (CLDCLS) problem is a multiattribute group decision-making (MAGDM) problem in which multiple commodity preference weights are considered. To better describe the preference information and expert evaluation information, this paper utilizes 2-dimensional linguistic (2DL) information to express the preference information of various commodities and the expert evaluation, which can represent not only the evaluation information of experts but also the reliability of the evaluation information. Additionally, for solving the CLDCLS problem, this paper puts forward improved operational rules a score function, a distance formula and a correlation coefficient measure. Based on the 2DL information and the improved operational rules, we propose a 2-dimensional linguistic similarity-degree-based clustering analysis method, the 2-dimensional linguistic partitioned Maclaurin symmetric mean (2DLPMSM) operator, and the 2-dimensional linguistic weighted partitioned Maclaurin symmetric mean (2DLWPMSM) operator. The corresponding properties and special cases are demonstrated. By using these proposed methods, this paper constructs a MAGDM solution framework for the CLDCLS problem. A practical case of the CLDCLS problem is presented to demonstrate the effectiveness, rationality, robustness and superior performance of the proposed method.

Bot detection is a fundamental and crucial task for tracing and mitigating cyber threats in the Internet. This paper aims to address two major limitations of current bot detection systems. First, existing flow-based bot detection approaches ignore structural information of botnets, which lead to false detection. Second, they cannot identify the interactive behavioral patterns among heterogeneous botnet objects. In this paper, we propose a novel bot detection framework, namely Bot-AHGCN, which models fine-grained network flow objects (e.g., IP, response) as a multi-attributed heterogeneous graph and transforms bot detection problem into a semi-supervised node classification task on the graph. Particularly, we first build a multi-attributed heterogeneous information network (AHIN) to model the interdependent relationships among botnet objects. Second, we present a weight-learning based node embedding method, which learns the interactive behavioral patterns among bots and integrates them into weighted similarity graphs. Finally, we perform graph convolution on the learned similarity graphs to characterize more comprehensive and discriminative features of bots, and feed them into a forward neural network to identify bots. The overall experimental results on two real-world datasets confirm that Bot-AHGCN outperforms the existing state-of-the-art approaches, and presents better interpretability by introducing meaningful meta-paths and meta-graphs.

This paper focuses on an adaptive synchronization for a class of uncertain chaotic systems (CSs) with time delay. The chaotic systems are represented by Takagi–Sugeno (T-S) fuzzy model. In order to estimate the unknown parameters of the uncertain chaotic systems, we use advanced adaptive laws with smooth projection operator to solve it. In addition, we take the adaptive law as a part of Lyapunov function to make the chaotic systems stable. Different from the previous study of adaptive synchronization of uncertain chaotic systems, this paper fully considers the effect of time delay on uncertain chaotic systems. Finally, by constructing a suitable Lyapunov function (LKF) and using linear matrix inequality (LMI) theory, we can obtain the stability condition of closed-loop system. Furthermore, the simulation cases in this paper illustrate the robust performance of the error system is guaranteed under the uncertain dynamic equation modeling.

User interactions are generally utilized to impose the hard constraint and estimate the label prior probability in the interactive image segmentation methods. The conventional interactive approaches cannot work well when the user inputs contain incorrect marks. The existing error tolerance methods mainly focus on the interaction itself and try to eliminate the influence of incorrect hard constraint, which however ignore the label prior estimation. This paper mainly focuses on solving the label prior estimation problem when error marks appear. The prior probability is generally defined as the matching degree between pixels and the cluster centers of marked pixels. Incorrect interaction leads to the formation of incorrect clusters. Therefore, a reliability learning model is constructed in this paper by 1) assigning smaller weighting factors to incorrect clusters, 2) assigning larger weighting factors to correct clusters with higher matching degree. Accurate label prior probability can be obtained by the weighted averaging. Then referring to the existing methods, an error-tolerant segmentation model is designed as a ratio energy function, which can both overcome the hard constraint and the label prior limitation with error marks. Experiments on the challenging Berkeley segmentation data set and Microsoft GrabCut database demonstrate the effectiveness of the proposed method.

Despite decades of intensive effort, the current solutions for efficiently searching high-dimensional data spaces are not entirely satisfactory. This paper proposes a more efficient global low-density locality sensitive hashing search algorithm (GLDH) based on the minimal cut hyperplane and ensemble learning. The innovation is that a novel global low-density hyperplane candidate set is constructed by the graph cut method, the minimum information gain method and random maximum entropy method are used to greedily select the hyperplane, and the ensemble learning method is used to query the global approximate nearest-neighbors data. This paper proves that the GLDH algorithm produces a low error hyperplane partition. The results of extensive experiments show that the proposed GLDH method performs better than the latest methods when using the same hash coding length for datasets from different fields.

Oblivious deterministic finite automata (DFA) evaluation is a powerful two-party primitive that has been widely used in cryptographic protocols. It enables two mutually distrusted participants to obliviously evaluate a DFA (provided by one party) on an input string (provided by the other party), while preserving the privacy of each party from the other one. However, this primitive is not flexible and powerful enough, in the sense that it only supports evaluation on input string that is provided by one participant. In this paper, we propose oblivious DFA evaluation on joint input (denoted as F ODFA JI ), a variant that extends the functionality of traditional oblivious DFA evaluation protocols. The new primitive enables two participants to collaboratively evaluate a DFA on a joint input, where the input is a combination of two input strings provided by both of the participants. To enable modularized instantiation, we first propose and instantiate F OT JC – oblivious transfer with joint choice – a functionality as modified oblivious transfer (OT), and then provide an efficient instantiation for F ODFA JI in F OT JC -hybrid model. Security proof demonstrates that the proposed protocol is secure under semi-honest model, and theoretical performance analysis shows that it achieves satisfactory efficiency and scalability. F ODFA JI is a basic as well as an important building block for high-level secure outsourced computing tasks. In this paper, we use secure outsourced pattern matching as a case study and show how it can be applied to construct such high-level protocols.

This paper visits the design of event-triggered static and dynamic feedback controllers for discrete-time linear systems. For the efficiency of energy, we make the first attempt to devise a switching event-triggered mechanism (ETM) which is characterized by a switching between the discrete-time sampled-data control and the common continuous ETM. In this sense, the event detector has a waiting time interval after sending a sampling signal. This method facilitates to enlarge the lower bound of inter-execution intervals so that the number of samplings can be reduced essentially. The issue of actuator saturation is also considered in this paper. By generalized sector condition, a switching Lyapunov functional is constructed to derive the sufficient conditions such that the discrete-time linear systems are local stable. Based on the stability conditions, the calculation methods are provided to solve both the desired control gains and the triggering parameters. Finally, numerical simulations are given to validate the effectiveness and superiority of the proposed method.