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Exam paper generation is an indispensable part of teaching. Existing methods focus on the use of question extraction algorithms with labels for each question provided. Obviously, manual labeling is inefficient and cannot avoid label bias. Furthermore, the quality of the exam papers generated by the existing methods is not guaranteed. To address these problems, we propose a novel approach to generating exam papers based on prediction of exam performance. As such, we update the quality of the initially generated questions one by using dynamic programming, as well as in batches by using genetic algorithms. We performed the prediction task by using Deep Knowledge Tracing. Our approach considered the skill weight, difficulty, and distribution of exam scores. By comparisons, experimental results indicate that our approach performed better than the two baselines. Furthermore, it can generate exam papers with adaptive difficulties closely to the expected levels, and the related student exam scores will be guaranteed to be relatively reasonable distribution. In addition, our approach was evaluated in a real learning scenarios and shows advantages.

This paper presents a new Reinforcement Learning (RL)-based control approach that uses the Policy Iteration (PI) and a metaheuristic Grey Wolf Optimizer (GWO) algorithm to train the Neural Networks (NNs). Due to an efficient tradeoff to exploration and exploitation, the GWO algorithm shows good results in NN training and solving complex optimization problems. The proposed approach is compared to the classical PI RL-based control approach using the Gradient Descent (GD) algorithm, and with the RL-based control approach which uses the metaheuristic Particle Swarm Optimization (PSO) algorithm. The experiments are conducted using a nonlinear servo system laboratory equipment. Each approach evaluated on how well it solves the optimal reference tracking control for an experimental servo system position control system. The policy NNs specific to all three approaches are implemented as state feedback with integrator controllers to remove the steady-state control errors and thus ensure the convergence of the objective function. Because of the random nature of metaheuristic algorithms, the experiments for GWO and PSO algorithms are run multiple times and the results are averaged before the conclusions are presented. The experimental results shows that for the control objective considered in this paper, the GWO algorithm represents a better solution compared to GD and PSO algorithms.

Aiming at the security issues during the multi-types data storage and data transmission in autonomous Internet of Things (IoT) systems, this paper proposes an AI algorithm for data enhanced encryption used in the ends and the intermediate nodes of IoTs. The algorithm in this paper first constructs a three-dimensional Arnold transformation matrix for data unit value encryption in the end of IoTs, and designs a quantum logic intelligent mapping that effectively diffuses the encrypted data units to reduce the linear correlation of the image data and to improve the security performance of IoT edge data. Furthermore, the algorithm designs an AI access strategy for scrambling sequence nodes and builds a random-access route for the elements of the scrambling sequence which can reduce the calculation cost and improve the operating efficiency of IoT system in the ends and intermediate nodes. Finally, the data shared matrix is used to share the encrypted data to achieve the (k, n) threshold strategy. Experimental results prove that the algorithm has high plaintext and key sensitivity and can effectively resist brute force attacks, statistical analysis and differential attacks. The algorithm in this paper provides an AI solution for data security encryption in the ends and the intermediate nodes of autonomous IoT systems.

In this paper, the cluster synchronization problem of a heterogeneous second-order leader-following multi-agent system with nonlinear dynamics, actuator faults, and integral quadratic constraints (IQCs) under a directed topology with a directed spanning tree is investigated. Based on the local topology information, two adaptive fault-tolerant pinning control strategies with fixed and adaptive pinning gains are proposed to guarantee cluster synchronization in finite time. An adaptive input compensation is developed to attenuate the adverse effects of actuator faults. It is worth mentioning that just one parameter needs to be estimated for each agent in this compensation, which implies that the strategies designed in this paper can effectively reduce the computational cost. Furthermore, the use of the pinning control method instead of the fully equipped control method makes the strategies more cost-effective for large-scale multi-agent systems. Finally, numerical simulation examples are introduced to demonstrate the effectiveness and advantages of the proposed strategies.

This paper aims to develop a new axiomatic design-based mathematical programming method for heterogeneous multi-criteria group decision making (HMCGDM) problems with linguistic fuzzy truth degrees (LFTDs). The main contributions of this paper are summarized in five aspects: (1) The information content definitions for six types of fuzzy numbers are initially provided according to axiomatic design . (2) Considering the authority of experts on different criteria and group consensus, a bi-objective programming model is constructed to derive experts’ weights by maximizing individual deviation and minimizing group discordance. (3) Each alternative is assessed on the basis of its information content to a fuzzy positive ideal solution. Information content is firstly used to define the linguistic fuzzy consistency and inconsistency indices. (4) A bi-objective linguistic fuzzy mathematic programming model is built to determine the criteria weights, which considers consistency and inconsistency indices simultaneously. This model can be dexterously transformed into a crisp linear programming model for resolution by the linguistic scale function. (5) The group information content of each alternative to fuzzy positive ideal solution is calculated to determine the ranking order of alternatives. Finally, an example of blockchain service provider selection is given to validate the proposed method.

Network science is a widely studied subject and graph theory has a major role in it. This paper studies connectivity status of vertices in a fuzzy graph. This paper adopt connectivity status to build up the status sequence related to a fuzzy graph. Results on connectivity status and status sequence of different structures are obtained. On the basis of connectivity status, vertices of a fuzzy graph can be classified into connectivity status enhancing vertices, connectivity status reducing vertices and connectivity status neutral vertices. Connectivity status analysis of vertices is also carried out. Algorithms related to these concepts are provided and an application related to bandwidth allocation problem in networking is proposed.

In many applications, like database management systems , is very useful to have an expression to compute the cardinality of cartesian product of k sets without repeated elements; we designate this problem as T ( k ) . The value of | T ( k ) | is upper-bounded by the multiplication of cardinalities of the sets. As long as we have searched, it has not been reported a general expression to compute T ( k ) using cardinalities of the intersections of sets, this is the main topic of this paper. Given three sets with indices { 0 , 1 , 2 } , C i is the cardinality of one set, C i , j ( i j ) and C i , j , l ( i j l ) are respectively the cardinalities of the intersections of 2 and 3 sets, then the searched formulas for T ( k ) are: T ( 1 ) = C 0 ; T ( 2 ) = C 0 C 1 - C 0 , 1 ; T ( 3 ) = C 0 C 1 C 2 - ( C 0 , 1 C 2 + C 0 , 2 C 1 + C 1 , 2 C 0 ) + 2 C 0 , 1 , 2 . In this paper, we prove formulas for computing T ( k ) and its specialization when a set is contained in the next sets. For this purpose, we will use concepts like partitions of the integer k in v parts, Bell numbers, Stirling numbers of the first kind and Stirling numbers of the second kind. Additionally, we present a complexity analysis for the computation of T ( k ) .

This paper proposes a new tent-multi dynamic piecewise coupled mapping lattice (TMDPCML). Through the comprehensive analysis of the performance test results, TMDPCML system increases the correlation dynamics of spatiotemporal behavior and improves the efficiency of energy diffusion between lattices. Moreover, TMDPCML system has larger parameter space, better chaos and outstanding cryptographic characteristics. Therefore, this paper proposes a privacy image encryption algorithm combined with TMDPCML system. The application of TMDPCML system in private images encryption further proves that TMDPCML system has good chaotic behavior and meets the requirements of cryptography.

Nowadays, more scholars and practitioners are committed to improving and applying type-2 fuzzy sets (T2FSs) in various domains because of the stronger ability to handle the uncertainty of fuzzy complex systems. To explore the status and internal laws of development of this field, this paper presents the development overview based on the bibliometric analysis, then the dynamic evolution of main topics and the knowledge diffusion trajectory are also displayed on the basis of the strategy diagram and main path analysis (MPA). From 1997 to 2019, 1749 documents are retrieved from Web of Science (WoS) repository for the analysis. The results show that there are four stable collaborative communities existing in the countries/regions’ collaborative network and the collaboration is affected by geographical factors to some degree. Three main evolution paths of hot topics are also presented in this paper and the multi-criteria decision-making (MCDM) has gradually developed into the moto theme. Furthermore, articles appearing on the main path mainly focus on the research of basic concept and framework, self-optimization and applications in various domains of T2FSs. In general, this paper provides a new landscape in the longitudinal research based on the development overview, thematic evolution and the knowledge diffusion trajectory.

In this paper, a novel approach of supervised feature selection is proposed based on the principle of dense subgraph discovery. To exploit dense subgraph discovery for the purpose of feature selection, the dataset is initially mapped to an equivalent weighted graph notation by considering the set of all features as its vertex set and the mutual dependency between each pair of features as the weight of the corresponding edge. The proposed feature selection algorithm proceeds in a two-phase manner. In the first stage, a dense sub-graph is first discovered so that the features within it become maximally non-redundant among each other and the averaged class relevance as well as averaged standard deviation of all these features are obtained as maximal as possible. In this regard, a novel induced degree is also defined for each feature by incorporating the aforesaid three important objectives of feature selection. In this phase, a modified version of an existing approximation algorithm is also used to find dense subgraph module. Finally, in the second stage, a floating forward–backward search is performed on the dense subgraph so obtained to reveal a better feature subset. In both stages, an existing version of the normalized mutual information score is employed to compute both the class relevance and redundancy. The main contribution of this paper is proposing a feature selection strategy by which the reduced features have the characteristics like maximal average class relevance, minimal average pairwise redundancy, and good discriminating power. The experimental results demonstrate that the proposed approach is competent with several conventional as well as state-of-art algorithms of supervised feature selection.

The influence of each entity in a network is a crucial index of the network information dissemination. Greedy influence maximization algorithms suffer from time efficiency and scalability issues. In contrast, heuristic influence maximization algorithms improve efficiency, but they cannot guarantee accurate results. Considering this, this paper proposes a Gaussian propagation model based on the social networks. Multi-dimensional space modeling is constructed by offset, motif, and degree dimensions for propagation simulation. This space’s circumstances are controlled by some influence diffusion parameters. An influence maximization algorithm is proposed under this model, and this paper uses an improved CELF algorithm to accelerate the influence maximization algorithm. Further, the paper evaluates the effectiveness of the influence maximization algorithm based on the Gaussian propagation model supported by theoretical proofs. Extensive experiments are conducted to compare the effectiveness and efficiency of a series of influence maximization algorithms. The results of the experiments demonstrate that the proposed algorithm shows significant improvement in both effectiveness and efficiency.

This paper addresses the distributed H ∞ state estimation problem for a class of sensor networks with switching characteristics, where the switchings of parameters are presumed to obey persistent dwell-time switching mechanism rather than dwell time or average dwell-time ones in the discrete-time context. For the purpose of tracking the unavailable state of the target plant, a sensor network is formed by employing multiple sensor nodes distributed in space and worked cooperatively under a specific connection topology. The intention of the paper mainly centers on deriving some sufficient criteria for the addressed model to achieve the exponential mean-square stability with a prescribed H ∞ performance, and the estimator gains corresponding to differently constructed estimators are further solved by means of the convex optimization method. Finally, the validity of the proposed approach is illustrated by a numerical example.

This paper focuses on improving the accuracy of image matching by eliminating the residual mismatches in the matching results of standard RANSAC. Based on pixel shift clustering and RANSAC algorithms, a matching optimization algorithm called pixel shift clustering RANSAC, PSC-RANSAC in short, is proposed in this paper. Firstly, the pixel shift model of space point from two perspectives are established by parallax principle and camera projection model. Then, based on the established pixel shift model, density peaks clustering (DPC) algorithm is used to select the mismatches out to enhance the accuracy of image matching. Meanwhile the comparisons among PSC-RANSAC, standard RANSAC, progressive sample consensus and graph-cut RANSAC show that PSC-RANSAC can more effectively and robustly eliminate the residual mismatches in initial matching results. The proposed method provides an effective tool for optimization on image matching.

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 thresholds. The NACEEMD algorithm is proposed to decompose the noisy signal into a series of intrinsic mode functions (IMFs). At the same time, NACEEMD is also used to verify the applicability of the PS-based thresholding technique in different decomposition algorithms. The PS-based threshold is used to remove the noise inherent in noise-dominant IMFs, and the denoised signal is reconstructed by combining the denoised noise-dominant IMFs and the signal-dominant IMFs. This paper uses a various of simulated signals in various noisy environments for experiments, the experimental results indicate that the proposed algorithm outperforms traditional threshold denoising methodologies in terms of signal-to-noise ratio, root mean square error, and percent root distortion. Moreover, through real ECG signal and multi-sensor data fusion experiments, the application of the proposed algorithm in the field of engineering is explored and expanded.

Surrogate-assisted evolutionary algorithms (SAEAs), which use efficient surrogate models or meta-models to approximate the fitness function in evolutionary algorithms (EAs), are effective and popular methods for solving computationally expensive optimization problems. During the past decades, a number of SAEAs have been proposed by combining different surrogate models and EAs. This paper dedicates to providing a more systematical review and comprehensive empirical study of surrogate models used in single-objective SAEAs. A new taxonomy of surrogate models in SAEAs for single-objective optimization is introduced in this paper. Surrogate models are classified into two major categories: absolute fitness models, which directly approximate the fitness function values of candidate solutions, and relative fitness models, which estimates the relative rank or preference of candidates rather than their fitness values. Then, the characteristics of different models are analyzed and compared by conducting a series of experiments in terms of time complexity (execution time), model accuracy, parameter influence, and the overall performance when used in EAs. The empirical results are helpful for researchers to select suitable surrogate models when designing SAEAs. Open research questions and future work are discussed at the end of the paper.

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 constructions have been presented in recent papers. The aim of this paper is to extend previous results concerning generating sets of aggregation clones on finite chains. Namely, the objective is to discuss the minimality of certain generating bases, the so-called ( χ , ⊕ ) -generating sets.

Velocity planning serves as an important issue in motion planning 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 optimization problem by assuming the velocity of vehicle to be a specific cubic polynomial on every elementary part to avoid a sudden acceleration in path switching. Finally, we use a modified projection particle swarm optimization (PPSO) algorithm to obtain the time-optimal velocity profile . The proposed method can generate a smooth time-optimal velocity profile while considering all possible relevant constraints. Three examples are provided on different types of path to demonstrate that the final velocity profile is efficient to avoid the sudden acceleration change. Furthermore, the modified PPSO algorithm in this paper is used to solve the optimization problem with high dimensional variables when its upper bound is known, which can not be achieved by the general PPSO algorithm.

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.

To deal with multicriteria decision making (MCDM) problems with interaction criteria, the Choquet integral (CI) is one of effective tools. This paper first proposes the reverse Choquet integral (RCI), which defines the importance of the ordered elements in an opposite principle to the CI. To show the principle of the RCI, we offer its concrete expression in view of the Mobius representation by which one can clearly see the difference and the relationship between the CI and the RCI. Then, we propose the “bi-direction Choquet integral” (BDCI), which is a convex combination of the CI and the RCI. To get the interactions of ordered coalitions comprehensively, this paper further proposes the generalized Shapley bi-direction Choquet integral (GSBDCI). Furthermore, the hybrid generalized Shapley bi-direction Choquet integral (HGSBDCI) is proposed, which defines the importance of ordered positions and the criteria with interactions simultaneously. With respect to these types of CIs, their exponent forms are also discussed. Finally, we use an application case to show the utilization of the proposed new CIs for MCDM. The proposed new Choquet integrals provide us a very useful way to deal with MCDM problems.

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.

As a popular dimensionality-reduction technique, nonnegative matrix factorization (NMF) has been widely researched since it is consistent with human cognitive processes in the psychology and physiology. This paper presents a novel NMF framework, called robust distribution-based NMF (RDNMF), to learn the robustly discriminative representations for data. In this RDNMF, a Kullback–Leibler divergence to measure the similarity between the data and representations is introduced, which fully preserves the geometrical structure of data. Meanwhile, this RDNMF employs the l 2 , 1 -norm loss to reduce the influence of noise and outliers. This paper further proposes a semi-supervised RDNMF (SRDNMF) by enforcing the representations of labeled points in the same class to be aligned on the same axis. The proposed RDNMF and SRDNMF are solved by the modified multiplicative update rules. Clustering experiments on seven benchmark datasets demonstrate the effectiveness of our methods in comparison to other state-of-the-art methods.

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 this paper, we study first order interval-valued differential equations with the boundary value condition x(0)=αx(T). By constructing proper operators, some sufficient conditions for the existence of solutions are provided for α∈R⧹{0,1}, corresponding to non-periodic boundary value problems. At the end of this paper, some examples are shown to illustrate the theorems proved.

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.

A Boolean function in n variables is 2-rotation symmetric if it is invariant under even powers of ρ ( x 1 , … , x n ) = ( x 2 , … , x n , x 1 ) , but not under the first power (ordinary rotation symmetry); we call such a function a 2-function. A 2-function is called monomial rotation symmetric (MRS) if it is generated by applying powers of ρ 2 to a single monomial. If the quartic MRS 2-function in 2 n variables has a monomial x 1 x q x r x s , then we use the notation 2 - ( 1 , q , r , s ) 2 n for the function. A detailed theory of equivalence of quartic MRS 2-functions in 2 n variables was given in a 2020 paper by Cusick, Cheon and Dougan. This theory divides naturally into two classes, called mf 1 and mf 2 in the paper. Next to describing the equivalence classes, the second major problem is giving details of the linear recursions that the Hamming weights for any sequence of functions 2 - ( 1 , q , r , s ) 2 n (with q r s , say), n = s , s + 1 , … can be shown to satisfy. This problem was solved for the mf 1 case only in the 2020 paper. In this paper the problem for the mf 2 case is solved, using new ideas about short functions, as defined in this paper. These short functions are also of independent interest, and further results about them are in the paper.

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.

In this paper, the problem of exponential dissipation stability of T-S fuzzy system with state quantization is studied by using non-fragile sampled-data control. First, A Lyapunov-Krasovskii function containing all sampled-data and quantization information is constructed. Second, the better results can be obtained by using the integral inequality and the Newton Leibniz formula. In addition, a dissipative controller for non-fragile sampled-data is designed. Finally, The reasonable examples illustrate the significant improvement and value of this paper.

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.