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This paper deals with the H∞ state estimation problem for a class of networked multi-rate time-varying systems with estimation error variance constraint. The redundant channel transmission scheme is employed to reduce the packet dropout rate and improve the quality of the data delivery. By utilizing the lifting technique, an augmented estimation error system is established with a uniform sampling rate. The objective of this paper is to design a time-varying state estimator such that, in the simultaneous presence of the asynchronous sampling, probabilistic packet dropouts as well as stochastic noises, the error dynamics of the state estimation satisfies both the prescribed H∞ performance requirement and the prescribed estimation error variance constraints. Through intensive stochastic analysis, sufficient conditions are established to ensure the existence of the desired estimator whose parameters are determined by solving a set of recursive linear matrix inequalities. A numerical example is presented to show the validity of the proposed estimation strategy.

Target tracking is a difficult estimation problem due to target motion uncertainty and measurement origin uncertainty. In this paper, we consider the target tracking problem in the presence of only target motion uncertainty. The traditional approaches to address this uncertainty, such as multiple model approaches, can suffer performance degradation when there is a model mismatch. The statistical accuracy of conventional model-based methods is also usually limited because of the measurement errors and insufficient data for the estimation. In this paper, deep neural network-based methods are proposed to handle target motion uncertainty due to their strong capability of fitting any mapping as long as there are sufficient training data. Specifically, a recurrent neural network-based structure is proposed to estimate the true states that is consistent with the sequential manner of target tracking. In addition, it is expected that better performance will be achieved due to access to true states during the training of the networks. We propose two networks that are based on different principles and are capable of real-time tracking. An approach to further reduce the computational load is also introduced. Simulation results show that the proposed methods can handle the target motion uncertainty well and provide better estimation accuracy.

Analysing the interpretability of a fuzzy system (either hierarchical or not) involves consideration of its semantic properties and evaluation of its structural complexity. The present paper concentrates on the semantic aspects of interpretability in hierarchical fuzzy systems. Complexity reduction is also considered, but from the perspective of its interaction with semantic preservation. In that sense, the paper shows that only the use of intermediate variables with meaning (interpretable variables) will transform the complexity reduction into a real improvement in interpretability. The paper formalises this idea by introducing the concept of semantically decouplable hierarchies. Under the assumption of semantic decouplability, it is shown that the interpretability of the overall hierarchical system can be directly obtained from that of its subsystems. Consequently, the paper defines and measures the interpretability of a semantically decouplable hierarchical fuzzy system as the aggregation of the interpretability of its subsystems. Finally, several options will be considered for this aggregation process.

Since Torra introduced the concept of hesitant fuzzy sets, it has seen wide applications in decision-making problems, cluster analysis, medical diagnosis, personnel appraisal, information retrieval and so on. And, nowadays, there arise many discussions related to hesitant fuzzy sets. In particular, recently, Hu extends the truth values set of hesitant fuzzy sets and hesitant fuzzy relations from the family of subsets of unit interval to the family of nonempty subsets of any linearly ordered set with an involutive negator and calls them hesitant sets and hesitant relations, respectively. This paper continues to consider this topic and mainly intends to give a systematical discussion to hesitant relations on their properties and potential applications in three-way decisions. First, this paper shows some novel properties of the operations on linearly ordered set with an involutive negator. Second, this paper investigates some vital properties of hesitant relations, especially for the compositions, projections, reflexivity, symmetry, *-transitivity and so on. Third, this paper proposes various types of ordered hesitant relations. Finally, this paper discusses some applications of hesitant relations in the constructions of decision evaluation functions in three-way decision spaces.

This paper proposes a novel top-down approach to rule-based fuzzy systems, one that begins with the product—an equation—and then addresses the unique features of the product, without requiring the reader to know anything about fuzzy sets and systems. The "products" are adaptive variable-structure basis function expansions , where "adaptive variable-structure" means that different subsets of its basis functions are active (non-zero) in different regions of the state space, something that occurs automatically by virtue of the structure of the basis functions, so that the products can be said to "adapt" to locations in the state space. These products are novel candidates for machine learning . Unique features of all products are: (1) Number of basis functions is no longer a variable and is established locally through type-1 or type-2 uncertainty partitioning of each variable; (2) Both coarse and fine sculpting of the state space are achieved, and are described in terms of first- and second-order partitions of the state space, respectively; and (3) Linguistic interpretability is obtained, which may be of value to an end-user. Learning about rule-based fuzzy systems can be greatly compressed by using the top-down approach that is described in this paper.

This paper explores the properties of subsethood measures, including boundary conditions, monotonicity, duality, and additivity. These properties can be used as candidates of axioms to define specific subsethood measures. The standard set theory introduces the set-inclusion relation with a qualitative nature, that is, a set is either a subset of the other or not. As a quantitative generalization, a subsethood measure considers the degree of inclusion. It should keep essential characteristics of the qualitative set-inclusion and satisfy generalized properties with the qualitative set-inclusion relation as a special case. A systematic study of the relationships between the qualitative and quantitative frameworks results in the four types of properties of subsethood measures presented in this paper. A combination of different types of properties helps in constructing an appropriate subsethood measure in real-world applications.

Similarity join is an essential operator in data processing, mining and analysis. However, it is resource intensive and time consuming, particularly when processing big data. There is also a need to ensure data confidentiality in similarity joins, as joining between two files may result in personal information disclosure . Based on these two considerations, this paper proposes a MapReduce-based similarity joins with differential privacy technology (hereafter, referred to as PSJoin). The proposed parallel algorithm is designed to achieve high efficiency, in terms of answering similarity join queries privately and effectively. Specifically, the use of PSJoin ensures the preservation of privacy during the similarity join process and in the published results. A new private global ordering approach is presented in this paper, which is designed to deal with potential disclosure issues during the process, and a differential private similarity function is provided for this algorithm. Findings from our evaluations using large-scale real-world datasets demonstrate that our method can effectively guarantee privacy with only minimal accuracy loss in similarity queries, while offering good scalability consistently.

The concept of SCN offers a fast framework with universal approximation guarantee for lifelong learning of non-stationary data streams. Its adaptive scope selection property enables for proper random generation of hidden unit parameters advancing conventional randomized approaches constrained with a fixed scope of random parameters. This paper proposes deep stacked stochastic configuration network (DSSCN) for continual learning of non-stationary data streams which contributes two major aspects: 1) DSSCN features a self-constructing methodology of deep stacked network structure where hidden unit and hidden layer are extracted automatically from continuously generated data streams; 2) the concept of SCN is developed to randomly assign inverse covariance matrix of multivariate Gaussian function in the hidden node addition step bypassing its computationally prohibitive tuning phase. Numerical evaluation and comparison with prominent data stream algorithms under two procedures: periodic hold-out and prequential test-then-train processes demonstrate the advantage of proposed methodology., This paper has been published in Information Sciences

In this paper, we present a multi-objective vehicle routing problem with flexible time windows (MOVRPFlexTW). In this problem, a fleet of vehicles can service a set of customers earlier and later than the required time with a given tolerance. This flexibility enables a logistics company to save distribution costs at the expense of customer satisfaction. This paper uses a direct interpretation of the vehicle routing problem with flexible time windows (VRPFlexTW) as a multi-objective problem, where the total distribution costs (including travel costs and fixed vehicle costs) are minimized and the overall customer satisfaction is maximized. We propose a solution strategy based on ant colony optimization and three mutation operators, which incorporates the concept of Pareto optimality for multi-objective optimization. The performance of the proposed approach was evaluated using the well-known benchmark Solomon’s problems. Our experimental results show that the suggested approach is effective, because it provides solutions that are comparative to the best known results in the literatures. Finally, we not only highlight the advantages of MOVRPFlexTW when compared with the single objective VRPFlexTW, but also illustrate its applicability and validation by analyzing a real case.

This paper is concerned with the problem of distributed Nash equilibrium computation in aggregative games. Note that the traditional computation algorithms are designed based on time-scheduled communication strategy, which may lead to high communication consumption of the whole network. To reduce the consumption, this paper proposes a novel distributed algorithm with an event-triggered mechanism, where the communication between any two agents is only carried out when an edge-based event condition is triggered. In the convergence analysis of the proposed algorithm, an important event-related error variable is firstly defined. Then, based on a zero-sum property of this event-related error, two key relations on the agents’ estimates in the proposed algorithm are provided. Further, by using these relations, it is proven that the agents’ estimates can achieve a Nash equilibrium under a proper event-triggering condition. Finally, examples on the demand response of power systems are presented to verify the theoretical findings.

To ensure security, image encryption algorithms generally include two stages: permutation and diffusion. The traditional image permutation algorithms include the sort-based permutation algorithm, Arnold-based permutation algorithm, Baker-based permutation algorithm and the cyclic shift permutation algorithm, etc. However, these algorithms have the disadvantages of either high time complexity or poor permutation performance. Therefore, in combination with cyclic shift and sorting, this paper proposes a permutation algorithm that can not only guarantees good permutation performance but also guarantee low time and space complexity. Most importantly, this paper proposes a parallel diffusion method. This method ensures the parallelism of diffusion to the utmost extent and achieves a qualitative improvement in efficiency over traditional streaming diffusion methods. Finally, combined with the proposed permutation and diffusion, the paper proposes a computational model for parallel image encryption algorithms.

This paper studies the design of neuro- adaptive tracking control schemes for non-integer order non-square systems subject to time-varying output constraints and input nonlinearities . It should first be stated that by employing the mean-value theorem, the original non-affine non-square system with actuator nonlinearities is converted into an equivalent affine square form. Neural networks , Barrier Lyapunov Functions and Nussbaum functions are then incorporated to overcome the difficulties raised by the uncertain nonlinear dynamics, output constraints and unknown control directions, respectively. In a further step, in order to systematically derive the control signals and updating laws, the Backstepping technique is applied. It is shown that by using the proposed adaptive controller , the semi-global asymptotic tracking and the boundedness of all variables in the closed-loop system are guaranteed without transgression of the constraints. The foremost contributions of this paper include: (1) by means of new lemmas and corollaries based on Caputo fractional derivative definitions, techniques and approaches related to the stability analysis and controllability of integer-order plants are extended to fractional-order non-square ones, and (2) the ‘explosion of complexity’ issue is fixed. Finally, simulation results are provided to reveal the effectiveness of the proposed control scheme.

Stochastic Configuration Networks (SCNs) can be incrementally constructed by using supervisory mechanisms on the selection of random weights and biases. Due to its ease in implementation, fast training and less human intervention, SCNs become increasingly popular for large-scale data analytics . This paper aims to further study the existing constraint condition used in building SCNs. Two new inequality constraints on random parameters assignment are presented, and a theoretical guidance for the key parameter selection in these constraints is given. The newly proposed inequality constraints enlarge the probability of the constraint holding, which implies a quicker learning process. Experimental results with comparisons indicate that the proposed constraints in this paper can greatly reduce the search time for constructing the hidden nodes.

The big data samples are important source for analytics. However, its relevant/irrelevant information, unspecified variables/scales, noise/null, and so forth impose huge challenges on the analysis of relevance, feature, cause, and evaluation. This paper proposes an evidential analytics to disclose buried information in big data samples. Technically, it models memberships composed of relevant preference and replaces data with these priors. Its operations include generating analytics baselines, reducing variables, identifying sparse features, and inducing rules by taking advantage of evidence. In illustration, a case study of semiconductor manufacturing in UCI secom is presented. It discloses relevant signals, key factors, variables’ thresholds, sparse characteristics, and causal effect of damages buried in normal samples. The contribution of this paper not only contains these achievements but provides priori data for inference.

In recent years, healthcare spending has risen and become a burden on governments especially in the US. One of the reasons for this increase is hospital readmissions. Decreasing the number of readmissions can reduce the healthcare spending. The first part of this paper examines state-of-the-art applications that aim to reduce the number of readmissions at hospitals. We categorize these applications into three different categories and highlight not only their benefits, but also their weaknesses. Moreover, we describe the limitations and challenges faced by these systems to demonstrate the possible future research paths. The second part of this paper presents novel algorithms to reduce the number of readmissions by applying the concept of personalization and actionable patterns. First, we start by extracting all possible procedure paths (course of treatments) for a given procedure. Then, we cluster patients according to the similarities in their diagnoses in order to increase the predictability of the next procedure. Finally, we present a novel algorithm that provides recommendations (actionable knowledge) to the physicians to put patients on a treatment path that would result in optimal reduction of the number of readmissions on average case.

DNA sequence reconstruction is a challenging research problem in the computational biology field. The evolution of the DNA is too complex to be characterized by a few parameters. Therefore, there is a need for a modeling approach for analyzing DNA patterns. In this paper, we proposed a novel framework for DNA pattern analysis. The proposed framework consists of two main stages. The first stage is for analyzing the DNA sequences evolution, whereas the other stage is for the reconstruction process. We utilized cellular automata (CA) rules for analyzing and predicting the DNA sequence. Then, a modified procedure for the reconstruction process is introduced, which is based on the Probabilistic Cellular Automata (PCA) integrated with Particle Swarm Optimization (PSO) algorithm. This integration makes the proposed framework more efficient and achieves optimum transition rules. Our innovated model leans on the hypothesis that mutations are probabilistic events. As a result, their evolution can be simulated as a PCA model. The main objective of this paper is to analyze various DNA sequences to predict the changes that occur in DNA during evolution (mutations). We used a similarity score as a fitness measure to detect symmetry relations, which is appropriate for numerous extremely long sequences. Results are given for the CpG-methylation-deamination processes, which are regions of DNA where a guanine nucleotide follows a cytosine nucleotide in the linear sequence of bases. The DNA evolution is handled as the evolved colored paradigms. Therefore, incorporating probabilistic components help to produce a tool capable of foretelling the likelihood of specific mutations. Besides, it shows their capabilities in dealing with complex relations.

Image quality assessment (IQA) measures predict the perceived quality of evaluated images, aiming to replace time-consuming human evaluation. This is particularly important for the automatic comparison of image processing techniques which often modify image content. Since the presence of noise highly affects the perception of images and only a few IQA measures address this type of distortion, a new no-reference IQA measure is proposed in this paper. In the introduced measure, two aspects of the Human Visual System (HVS) are considered. The sensitivity of the HVS to local image distortions is expressed by similarities between descriptors obtained with Speeded-Up Robust Features (SURF) technique. In order to improve the quality prediction, image patches for description are extracted from a gradient map of an input image. Noise also affects the visual saliency, since it decreases attention caused by image content. In the approach, visual saliency models of image patches are obtained and evaluated using adapted Visual Saliency-based Index (VSI). Experimental evaluation on popular IQA benchmarks reveals that the proposed measure outperforms the related state-of-the-art techniques . The applicability of the proposed method to an automatic selection of the best denoising algorithm is also discussed in the paper.

This paper focuses on heterogeneous group decision making (GDM) in the setting of incomplete preference relations, including incomplete fuzzy preference relations, incomplete multiplicative preference relations, incomplete additive linguistic preference relations, and incomplete multiplicative linguistic preference relations. Following the consistency-consensus optimization analysis, a new GDM method is proposed which can address inconsistent and incomplete preference relations. Firstly, we consider how to determine unknown information and how to obtain consistent preference relations. In contrast to the existing methods which rank the alternatives from fuzzy preference relations using the additive or multiplicative consistency analysis, this paper follows the consistency level to decide to use the additive or multiplicative consistency. Following the consensus analysis for each type of consistent preference relations, we calculate their comprehensively consistent preference relations and the priority vectors. After that, we build a consistency-based model for calculating the comprehensive priority vector. Furthermore, we offer an ordered position consensus-based method using the built optimization models. The proposed heterogeneous GDM method with incomplete preference relations provides us with a very useful way for dealing with GDM problems in heterogeneous environments.

The choice of the most promising algorithm to model and predict a particular phenomenon is one of the most prominent activities of the temporal data forecasting. Forecasting (or prediction), similarly to other data mining tasks , uses empirical evidence to select the most suitable model for a problem at hand since no modeling method can be considered as the best. However, according to our systematic literature review of the last decade, few scientific publications rigorously expose the benefits and limitations of the most popular algorithms for time series prediction. At the same time, there is a limited performance record of these models when applied to complex and highly nonlinear data. In this paper, we present one of the most extensive, impartial and comprehensible experimental evaluations ever done in the time series prediction field. From 95 datasets, we evaluate eleven predictors, seven parametric and four non-parametric, employing two multi-step-ahead projection strategies and four performance evaluation measures. We report many lessons learned and recommendations concerning the advantages, drawbacks, and the best conditions for the use of each model. The results show that SARIMA is the only statistical method able to outperform, but without a statistical difference, the following machine learning algorithms : ANN , SVM , and k NN-TSPI. However, such forecasting accuracy comes at the expense of a larger number of parameters. The evaluated datasets, as well detailed results achieved by different indexes as MSE , Theil’s U coefficient, POCID, and a recently-proposed multi-criteria performance measure are available online in our repository. Such repository is another contribution of this paper since other researchers can replicate our results and evaluate their methods more rigorously. The findings of this study will impact further research on this topic since they provide a broad insight into models selection, parameters setting, evaluation measures, and experimental setup.

Many real-world decision problems consider more than one objective. These objectives usually conflict with each other. In the problem environments, many data and parameters are usually uncertain at the time of planning. So multi-objective optimization combined with uncertainty is a challenging research topic. To deal with the basic search process in such problems, in this paper, a new approach has been proposed that combines multiple population based algorithms under a single algorithm structure. To deal with the uncertainty, a dynamic scenario-based approach is developed and integrated with the search process, in which the number of scenarios is dynamically set during the solution process. This is a new way of solving multi-objective optimization problems under uncertainty. To judge the performance of the proposed approach, we have solved a set of standard test problems without uncertainty and a number of practical problems with uncertainty. The practical problems are the well-known dynamic economic and emission dispatch problems, with different combinations of energy sources. The experimental studies demonstrated that the proposed approach performs better than other well-known algorithms compared in this paper.

Granular computing is an efficient and scalable computing method. Most of the existing granular computing-based classifiers treat the granules as a preliminary feature procession method, without revising the mathematical model and improving the main performance of the classifiers themselves. So far, only few methods, such as the G-svm and WLMSVM, have been combined with specific classifiers. Because of the complete symmetry of the ball and its simple mathematical expression, it is relatively easy to be combined with the other classifiers’ mathematical models. Therefore, this paper uses a ball to represent the grain, namely the granular ball, and not only the granular balls’ labels but also the distance between a pair of balls is defined. Based on that, this paper attempts to propose a new granular classifier framework by replacing the point inputs with the granular balls. We derive the basic model of both the granular ball support vector machine and granular ball k-nearest neighbor algorithm (GBkNN). In addition, the GBkNN is compared with the k-means tree based kNN, which is the most efficient and effective kNN as far as we known, on both public and artificial data sets. The Experimental results demonstrate the effectiveness and efficiency of the proposed framework.

This paper investigates the problem of stability and stabilization of a class of chaotic system through the use of sampled-data control. By employing a Takagi–Sugeno (T-S) fuzzy model to describe the chaotic system and using a time-dependent Lyapunov function, an exponential stability condition is derived for the resulting closed-loop systems with input saturation constraint. Based on this condition, a fuzzy sampled-data controller is designed to stabilize the systems under consideration. The results obtained in this paper are based on the actual characteristic of sampling model. They depend explicitly on both the upper and lower bounds of sampling intervals. The chaotic Lorenz system is considered and solved by using the proposed approach so as to demonstrate the benefits and the superiority of the proposed approach over existing methods.

This paper is concerned with the problem of finite-time stabilization for a class of high-order nonlinear systems with zero dynamic. As a significant feature, the systems considered suffer from both the unmeasurable dynamic uncertainties and inherent nonlinearities, including high-order and low-order nonlinear growth rates. On the basis of integral Lyapunov functions equipped with sign functions and the notion of input-to-state stability, a partial state feedback stabilizer is proposed to provide a faster finite-time state convergence compared to traditional finite-time one. The novelty of this paper lies in a distinct perspective to applying the concept of fast finite-time stability developed recently in partial state feedback control design, which has been previously regarded as a rather difficult problem.

This paper is concerned with the H∞ filtering problem for discrete-time T-S fuzzy systems with multiple time-varying delays. In order to take advantage of all possible information about the relationship between all the delay related terms and the current system states, an improved delays-dependent region partitioning method is first proposed. Then, by applying the Jensen’s inequality and reciprocally convex method to the terms resulted from the region partitioning, new sufficient conditions in terms of linear matrix inequalities (LMIs) are constructed to guarantee the desired performance of the augmented system. Moreover, it is shown that the design techniques presented in this paper are less conservatism than the existing methods. Finally, an example of the tunnel diode circuit is given to verify the effectiveness and advantages of this method.

Reasoning about the behavior of real-world systems and processes faces problems such as repeating events or subprocesses , evolving component behaviors, and indefinite time horizons. To date, existing representations of time and uncertainty have been unable to fully address such requirements. They often tradeoff assumptions of available knowledge against richness of representing time and semantics for uncertainty. This has made representations either overly simplistic or cumbersome to model with, or both. In this paper, we present a new theoretical representation for reasoning about time and uncertainty extending our ability to better address real-world systems. Called cost-based temporal reasoning, we believe it is simple to understand, is rigorously defined, and allows for strong semantics of time and uncertainty. This paper formally details the definitions and proofs about the capabilities of cost-based temporal reasoning.

This paper investigates the problem of designing a dissipativity-based asynchronous filter for a class of nonhomogeneous discrete-time Markovian jump fuzzy systems with uncertainties. Different from previous works, the proposed approach opens a possibility to tackle more realistic problems of handling both asynchronism and nonhomogeneity phenomena of plant and filter modes. To be more concrete, this paper proposes a novel relaxation method capable of handling triple-parameterized matrix inequalities in such a way that central constraints on multiple time-varying parameters can be sufficiently exploited. Especially, an attempt to utilize the conditional relation between two nonhomogeneous Markov chains is first made to reduce the conservatism of two-step relaxation process that has to employ mutually uncorrelated slack variables for plant and filter modes.

Given the explosive growth of online videos, it is becoming increasingly important to relieve the tedious work of browsing and managing the video content of interest. Video summarization aims at providing such a technique by transforming one or multiple videos into a compact one. However, conventional multi-video summarization methods often fail to produce satisfying results as they ignore the users’ search intents. To this end, this paper proposes a novel query-aware approach by formulating the multi-video summarization in a sparse coding framework, where the web images searched by a query are taken as the important preference information to reveal the query intent. To provide a user-friendly summarization, this paper also develops an event-keyframe presentation structure to present keyframes in groups of specific events related to the query by using an unsupervised multi-graph fusion method. Moreover, we release a new public dataset named MVS1K, which contains about 1000 videos from 10 queries and their video tags, manual annotations, and associated web images. Extensive experiments on the MVS1K and TVSum datasets demonstrate that our approaches produce competitively objective and subjective results. © 2018

Evolutionary methods are well-known tools used for solving hard computational problems. In this paper, we consider k-Shortest Steiner Trees (kSST) problem appearing in a diverse set of domains, e.g., multicast tree construction in communication networks in general, and optical networks in particular. The kSST is relatively new and has not been widely investigated in the literature. Thus, only a few algorithms have been proposed, each requiring significant resources amount and long execution times, partially following from the NP-hard nature of the problem. The kSST problem solution is a set of different trees, where each single tree can be easily represented using a genotype-encoding. However, encoding the tree set may be challenging, as each tree must be unique. Especially, in most applications the number of trees is large, resulting with the genotype containing high number of necessary genes. Thus, in this paper, we propose a transformation of the kSST problem into a dynamic problem, and when applied in the evolutionary method, a single individual encodes a single tree instead of a whole tree set. The results of extensive numerical experiments executed on two representative network topologies show that the proposed transformation improves the effectiveness of all considered state-of-the-art evolutionary methods.

This paper presents the unification and generalization of different evolving clustering methods based on Cauchy density. This can be done by introducing different inner matrix norms to obtain different functionalities of the algorithm. This unified approach with a general inner matrix norm is called eCauchy recursive clustering. The well-known possibilistic c-means clustering (PCM) can be seen as a special example of the proposed eCauchy algorithm. The main motivation of the proposed method is to solve and overcome the problems of modelling the nonlinear data streams in highly noisy environments with frequently appearing outliers. By introducing the different inner matrix into the density metric, the algorithm can be modified in different ways to deal with different clustering problems, from classical classification to the preprocessing for solving regression problems. The evolving nature of the algorithm and simple computation also make it appropriate for dealing with big-data problems. The described eCauchy algorithm needs just a few initial parameters such as minimal and maximal density. The algorithm incrementally changes the structure of the model based on the flow of samples from the data stream, more specifically it evolves the structure of the model during the operation by adding, merging, splitting and removing the clusters. This approach allows the identification of very different clusters in size and shape and is also quite insensitive to the outliers and significant noise. In the paper, the universality of the proposed algorithm is shown on various examples.

With the continued growth of interoperable software developed for Internet of Things (IoT), there is a growing demand to predict software defect at various testing and operational phases. This paper solves the software defect prediction problem by proposing a novel model with the help of a local tangent space alignment support vector machine (LTSA-SVM) algorithm. The model employs the SVM algorithm as the basic classifier of software defect distribution prediction model. Then, the model parameters are optimized by combining a grid search method and ten-fold cross validation. In the traditional dimensionality reduction algorithms, data loss caused by the poor attributes of data nonlinearity reduces the accuracy of SVM. Aiming at this problem, this paper uses a LTSA algorithm to extract the intrinsic structure of low-dimensional data and performs effective dimension reduction. The SVM algorithm is trained by the reduced dimension data. Finally, the feasibility of the prediction model is verified. Compared with the single SVM and the LLE-SVM prediction algorithm, the prediction model in this paper improves the prediction accuracy and F-measure by 1–4%.

This paper proposes an analytical model that captures the expected pairwise delivery delay in an opportunistic network when the source can generate multiple copies of the message. This model allows for the encounter rates among nodes to be heterogeneous, and it is completely independent of the forwarding scheme. The model is validated through simulation experiments performed with ns-3 and a multi-copy simulator specifically developed for this work. Along with the results, this paper also presents a discussion of the variability of the encounter rates over time and its influence on the accuracy of the proposed model.

© 2018 Elsevier Inc. An effective security strategy for Wireless Sensor Networks (WSNs) is imperative to counteract security threats. Meanwhile, energy consumption directly affects the network lifetime of a wireless sensor. Thus, an attempt to exploit a low-consumption Intrusion Detection System (IDS) to detect malicious attacks makes a lot of sense. Existing Intrusion Detection Systems can only detect specific attacks and their network lifetime is short due to their high energy consumption. For the purpose of reducing energy consumption and ensuring high efficiency, this paper proposes an intrusion detection model based on game theory and an autoregressive model. The paper not only improves the autoregressive theory model into a non-cooperative, complete-information, static game model, but also predicts attack pattern reliably. The proposed approach improves on previous approaches in two main ways: (1) it takes energy consumption of the intrusion detection process into account, and (2) it obtains the optimal defense strategy that balances the system's detection efficiency and energy consumption by analyzing the model's mixed Nash equilibrium solution. In the simulation experiment, the running time of the process is regarded as the main indicator of energy consumption of the system. The simulation results show that our proposed IDS not only effectively predicts the attack time and the next targeted cluster based on the game theory, but also reduces energy consumption.

With an exponential increase in the utilization of intellective appliances, meeting the energy demand of consumers by traditional power grids is a significant challenge. In integration, the amalgamation of electric vehicles, industrial Internet-of-Things (IoT), and smart communities with power grids has escalated the global energy demand. Consequently, the desideratum for a reliable energy supply and resilient energy ecosystem has incentivized the evolution of Smart Grids (SG). Such intelligent grids are equipped with autonomous controllers and advanced technologies like advanced metering infrastructure , smart sensors , and accounting management software. However, the existing demand response management and energy supply techniques are lagging behind in meeting the desired objectives in the SG ecosystem. In order to handle these challenges, a Demand Replication Optimization Scheme (DROpS) for the smart energy ecosystem is designed in this paper. In addition, a Multi-Leader Multi-Follower Stackelberg game is formulated in this paper to operate with the proposed scheme. However, the success of DROpS depends heavily on real-time communication between the consumers and the suppliers. Hence, dynamic and scalable network architecture is required to handle the seamless data generated by a sizably voluminous number of connected sensors, devices, and smart appliances deployed in the SG. For the successful operation of the architecture, a Software-Defined Networking (SDN)-enabled control scheme for flow management is additionally developed. In the proposed scheme, the SG ecosystem is divided into multiple zones such that the dedicated virtual SDN controllers are deployed for network resource utilization in an optimized manner. The proposed scheme is evaluated using a real smart home test bed and data traces from the Haryana power grid. The effectiveness of the proposed scheme is demonstrated in terms of significant gains observed for load variation and latency.

For a road networked system with stochastic travel demand, a two-stage model is proposed for period-dependent area traffic signal control. In this paper, a period-dependent mathematical program with equilibrium constraints (PMPEC) is presented to minimize total travel delay over successive time periods. For stochastic travel demand over multiple time periods, a period-dependent user equilibrium traffic assignment can be formulated as a variational inequality. Due to non-linearity of equilibrium constraints, a two-stage model is presented in this paper. In order to understand feasibility of the proposed model, numerical experiments using a real-data road network are conducted. The results indicate that the proposed model attains a promising system performance and exhibit computational advantage over existing alternatives.

Metaheuristic algorithms usually aim to solve nonconvex optimization problems in black-box and high-dimensional scenarios. Characterizing and understanding the properties of nonconvex problems is therefore important for effectively analyzing metaheuristic algorithms and their development, improvement and selection for problem solving. This paper establishes a novel analysis framework called nonconvex ratio analysis, which can characterize nonconvex continuous functions by measuring the degree of nonconvexity of a problem. This analysis uses two quantitative measures: the nonconvex ratio for global characterization and the local nonconvex ratio for detailed characterization. Midpoint convexity and Monte Carlo integral are important methods for constructing the measures. Furthermore, as a practical feature, we suggest a rapid characterization measure that uses the local nonconvex ratio and can characterize certain black-box high-dimensional functions using a much smaller sample. Throughout this paper, the effectiveness of the proposed measures is confirmed by numerical experiments using the COCO function set.

Advanced analytics such as event detection, pattern recognition, clustering, and classification with electroencephalogram (EEG) data often rely on extracted EEG features. Most of the existing EEG feature extraction approaches are hand-designed with expert knowledge or prior assumptions, which may lead to inferior analytical performances. In this paper, we develop a fully data-driven EEG feature extraction method by applying recurrent autoencoders on multivariate EEG signals. We use an Echo State Network (ESN) to encode EEG signals to EEG features, and then decode them to recover the original EEG signals. Therefore, we name our method feature extraction based on echo state network, or simply FE-ESN. We show that the well-known autoregression-based EEG feature extraction can be seen as a simplified variation of our FE-ESN method. We have conducted experiments on real-world EEG data to evaluate the effectiveness of FE-ESN for both classification tasks and clustering tasks. Experimental results demonstrate the superiority of FE-ESN over the state-of-the-art methods. This paper not only provides a novel EEG feature extraction method but also opens up a new way towards unsupervised EEG feature design.

In this paper a method for dynamically adjusting parameters in meta-heuristics based on interval type-2 fuzzy logic is proposed. Nowadays meta-heuristic algorithms have become a powerful choice in solving complex optimization problems. The gravitational search algorithm (GSA) based on the Newton laws of gravity and acceleration can be used to solve optimization problems achieving good results, however like in other optimization algorithms a critical issue is an appropriate adjustment of its parameters depending on the type of problem. In this paper the main contribution is a proposed method aimed at dynamic parameter adjustment in GSA with the help of type-2 fuzzy logic. Simulation results on benchmark problems show the advantages of the proposed approach.

In this paper we study the reachable region queries for objects moving in obstacle space. Loosely speaking, given any one of moving objects and many other information, we are asked to return the whole region in which each location has the non-zero probability to be reachable for the moving object. To solve this problem, this paper presents an efficient algorithm that runs in O(nlog n) time and consumes O(nlog n) space, where n is the number of vertices of obstacles. Moreover, this paper establishes the lower bound for the proposed problem, showing that our solution essentially is optimal in terms of random access memory (RAM) model. Empirical study based on both real and synthetic datasets demonstrates the effectiveness and efficiency of our solution.

A Top-k retrieval algorithm returns the k best answers of a query according to a given ranking. From a theoretical point of view, the solution of this query is straightforward if we do not take into consideration execution time. However, in many practical situations, execution time is a substantial issue. In such a case, the process of getting the top k in an optimal time becomes an interesting and non-trivial task. This paper presents an algorithm to retrieve the top-k associated to an arbitrary ranking function. The idea is to decompose the ranking function as a supremum of a certain set of functions where an efficient top-k retrieval procedure can be easily applied. Besides the theoretical presentation of the algorithm, the paper provides a set of experiments to validate the approach.

The idea that the brain is composed of logical gates similar to IP cores of today's computers were provided by McCulloch and Pitts in 1943. In this paper, six structures for the interaction of dynamic neurons have been proposed to create neural circuits with spike coding that operate similarly to Boolean gates. It was concluded that the network of the dynamic model of spiking neurons and synapses called spiking frequency gates (SFG) can emulate the operations of digital gates AND, OR, NOT, NOR, XOR, and NAND. Also, an attempt was made to construct complex spiking circuits like full-adder, multiplexer, and arithmetic-logic-unit using cascade connections of SFGs. Extending simple designs to more complex spiking circuits can continue in order to access sophisticated computing tools based on SFGs. SFGs are not limited to zero and one and respond to the continuous range of spike train frequencies. Therefore, the information coding of SFGs is more powerful than Boolean gates. This paper illustrates a novel Boolean computation platform in a neural-like manner, which can provide a potential and clear horizon for designing neural circuits with complex applications. We hope that our results will lead to a deeper comprehension of the brain's functionalities and the development of new systematic methods based on the proposed spiking approach of Boolean logic gates.

This paper presents an efficient use of the Ordered Fuzzy Numbers model in the description of processes undergoing dynamic changes. The purpose of this model is to provide a representation of inaccurate quantitative data, and is an alternative to the standard fuzzy numbers model proposed by Zadeh. Ordered Fuzzy Numbers have an additional feature—a direction. Giving them a new potential in applications to represent trends in imprecise values. This new feature is presented and explained via a simplified model of the volume of liquid in a reservoir. The environment is changing dynamically and is described by the changes of inflow and outflow. The proposed example explains how to interpret the direction of Ordered Fuzzy Numbers and how to understand the results of calculations and its influence. Additionally, this paper presents a comparison of the results for Ordered Fuzzy Numbers with the results of Zadeh’s classical fuzzy numbers applied to the same exemplary process.

This paper provides a new methodology for calibrating the fuzzy sets that are used in fsQCA, one that is based on clearly distinguishing between a linguistic variable and the linguistic terms for that variable, and that allows for uncertainties about those terms to be included in the calibration method. Each resulting fuzzy set, called an approximated reduced-information level 2 fuzzy set (RI L2 fuzzy set), is equivalent to a standard type-1 fuzzy set, but is for the linguistic variable, and, it has an S-shape, the kind of shape that is so widely used by fsQCA scholars, and is so important to fsQCA. This new calibration methodology is applied to Ragin's Breakdown of Democracy example, using new data provided by him, and demonstrates that his earlier solutions are also obtained using our approximated RI L2 fuzzy sets, something that should be reassuring to fsQCA scholars. Additionally, because the S-shaped membership functions are derived from footprints of uncertainty for all of the linguistic variable's terms, this paper shows how to obtain more precise statements of fsQCA causal combinations for their best instances, something that may be of added value to practitioners of fsQCA. Finally, we explain how different data-driven calibration robustness studies can be performed, something that may also be of great value to fsQCA practitioners.

The current paper investigates the distributed guaranteed-performance consensus design problems for high-order linear multiagent systems with leaderless and leader-follower structures, respectively. The information about the Laplacian matrix of the interaction topology or its minimum nonzero eigenvalue is usually required in existing works on the guaranteed-performance consensus, which means that their conclusions are not completely distributed. A new translation-adaptive strategy is proposed to realize the completely distributed guaranteed-performance consensus control by the structure feature of a complete graph in the current paper. For the leaderless case, an adaptive guaranteed-performance consensualization criterion is given in terms of Riccati inequalities and a regulation approach of the consensus control gain is presented by linear matrix inequalities . Extensions to the leader-follower cases are further investigated. Especially, the guaranteed-performance costs for leaderless and leader-follower cases are determined, respectively, which are associated with the intrinsic structure feature of the interaction topologies. Finally, two numerical examples are provided to demonstrate theoretical results.

Supervisory control of discrete-event systems (SCDES) is well developed to find a maximally permissive supervisor. As an extension to supervisory control theory, a new framework, state-tree structures (STS), has been deployed to manage the state explosion problem of SCDES. This paper aims to address this notorious issue of supervisory control with partial observation in the STS framework by state feedback control that calculates the controllers of the controllable-observable events only, which is realized by the following two steps. First, for a specification represented as a predicate, a supremal normal subpredicate that requires only the controllable-observable events enabled/disabled, is computed. Second, according to the new transition function constructed by the natural projection of the given STS, the supremal nonblocking, weakly controllable subpredicate is obtained from the supremal normal subpredicate. The proposed approach based on STS provides the possibility to supervise controllable events under partial observation in large-scale systems with the state explosion problem managed. An example with state size over 107 that leads to program crashes in SCDES can be solved in this paper. Moreover, in order to demonstrate the industrial applications of the contribution of this research, three examples are addressed.

With the development of Internet technology, the volume of data is increasing tremendously. To tackle with large-scale data, more and more applications choose to enlarge the storage capacity of users’ terminals with the help of cloud platforms. Before storing data to an untrusted cloud server, some measures should be adopted to guarantee the data security. However, the communication overhead will increase dramatically when users transmit files encrypted by a traditional encryption scheme. In this paper, we address the above problems by proposing a blockchain-based security architecture for distributed cloud storage, where users can divide their own files into encrypted data chunks, and upload those data chunks randomly into the P2P network nodes that provide free storage capacity. We customize a genetic algorithm to solve the file block replica placement problem between multiple users and multiple data centers in the distributed cloud storage environment. Numerical results show that the proposed architecture outperforms the traditional cloud storage architectures in terms of file security and network transmission delay. On average, the file loss rate based on the simulation assumptions utilized in this paper is close to 0% on our architecture while it’s nearly 100% and 71.66% on the architecture with single data center and the distributed architecture using genetic algorithm. Besides, with proposed scheme, the transmission delay on the proposed architecture is reduced by 39.28% and 76.47% on average on the user’s number and the number of file block replicas, respectively, in comparison to the architecture with single data center. Meanwhile, the transmission delay of file block replicas is also reduced by 41.36% on average than that on the distributed architecture using genetic algorithm.

Approximate nearest neighbor (ANN) search in high dimensional space is a fundamental operation in many applications. During the past decades, many indexing methods have been proposed to solve ANN search, including inverted index based methods, tree structures, locality sensitive hashing based methods and proximity graphs. In this paper, we explore lower bound techniques to speed up ANN search over the existing indexing methods. Since traditional lower bounds cannot reach a satisfying tradeoff between efficiency and tightness, we examine the concept of the existing lower bounds and define two new types of lower bounds, i.e., progressive lower bound (PLB) and statistical lower bound (SLB), which expand the concept of lower bound. Moreover, we propose a good instance of PLB, i.e. progressive partial distance (PPD). As the first attempt, we propose the idea of SLB and demonstrate asymmetric quantizer distance (AQD) as a good instance of SLB. Notably, AQD was only treated as an estimation of the distance in previous works, while we discover its functionality of being a lower bound in this paper. According to the extensive experiments on real data sets , we demonstrate that our lower bounds are able to obviously accelerate ANN search of the existing indexing methods, and our lower bounds outperform the existing lower bounds by a significant margin, due to their strong pruning powers. Especially, AQD speedups the state-of-the-art indexing method HNSW (Malkov and Yashunin, 2016) 1.9 times for a high recall 0.95. As to other indexing methods, the speedups of AQD are even higher, because they need to access more candidates than HNSW.

This paper considers the constrained consensus problem over multi-cluster networks. It is assumed that the agents’ states are constrained by different sets, where each constraint set is privately known by the corresponding agent. Within this framework, a hierarchical projection-based consensus algorithm is presented to solve the considered problem. Technically, the consensus analysis of the proposed algorithm consists of the following three aspects: First, by using the property of the projection operator, the limiting behaviors of the agents’ states generated by the algorithm are investigated. Then, based on the limiting behaviors, it is proven that the agents’ states in the whole network achieve a constrained consensus. Furthermore, by introducing an important auxiliary variable that relates to the agents’ states, the linear convergence of the proposed algorithm is proved. Compared with the existing results, this paper generalizes the constrained consensus methods under single-cluster networks to the multi-cluster ones. Finally, simulations are given to verify the theoretical results.

Belief rules extend traditional IF-THEN rules to represent vagueness, incompleteness, and nonlinear causal relationships by assigning belief degrees to singletons or the universe of all possible values that the consequents of rules can take. First, this paper extends belief rules by assigning belief degrees to the subsets of all possible values that the consequents of rules with interval-valued rule weights can take. Then, this paper proposes a Valuation-Based System (VBS) approach for the modeling and risk assessment of extended belief rule-based expert systems. Finally, the proposed VBS approach is applied to two use cases for evaluating the occurrence probabilities of accidents: one is a car equipped with Automated Speed Control (ASC) using values from experts, and the other is hazardous material (hazmat) transportation accidents using real statistical data.