Search

• The paper is the first batch that tries to inquiry the GPS of the PDIMNNs. Furthermore, unlike the previous literature [11,21,35] , the nonlinear substitution that converts the PDs system to a constant delays system is not used. Because the subsequent pro-cessing of the converted system is complicated, this paper directly performs on the original PDs system. • The paper employs the non-reduced-order method, which r-efrains the double-dimensional problem after reduced-order [3–5,23,24]. In practical applications, the method is more ponderable and si-gnificative for second-order scheme under controller. • In the paper, devise both the feedback controller and the adaptive controller for the first time to achieve the GPS of the PD-IMNNs. The advantages of the two types of controllers are compar-ed through numerical examples and simulations. The mathematical model is closer to reality, and selecting the appropriate controller in application can further reduce control expenses. This article probes into the global polynomial stabilization (GPS) of proportional delayed inertial memristive neural networks (PDIMNNs). Here, ruling out the reduced-order way, discuss the GPS of PDIMNNs under the second-order scheme directly. Firstly, a feedback controller is designed to make the system self-stabilizing. By designing suitable Lyapunov functional with adjustable parameters and combining with inequality techniques, two algebraic criteria are obtained to realize the GPS of the PDIMNNs. Owing to the conservatism caused by the ineluctable inequality scaling, it is worth noting that the controller gains are greater than the actual requirements. To further save control expenses, employing an adaptive controller to make the system stabilized. Finally, three numerical examples which sustain the usability of the obtained theoretical conclusions are shown. [ABSTRACT FROM AUTHOR]

To compute the similarity of scientific papers, text-based similarity measures, link-based similarity measures, and hybrid methods can be applied. The text-based and link-based similarity measures take into account only a single aspect of scientific papers, content or citations, respectively. The hybrid methods consider both content and citations; however, they do not carefully consider the relation between the content of a pair of papers involved in a citation relationship. In this paper, we propose a novel method, SimCC (similarity based on content and citations), that considers both aspects, content and citations, to compute the similarity of scientific papers. Unlike previous methods, SimCC effectively reflects both content and authority of scientific papers simultaneously in similarity computation by applying a new RA (relevance and authority) weighting scheme. Also, we propose an RA+R weighting scheme to consider the recency of papers and an RA+E weighting scheme to take into account the author expertise of papers in similarity computation. The effectiveness of our proposed method is demonstrated by extensive experiments on a real-world dataset of scientific papers. The results show that our method achieves more than 100% improvement in accuracy in comparison with previous methods. [ABSTRACT FROM AUTHOR]

• In this article, we present a three-phased framework to aid the multi-criteria decision analysis of portfolios in an online and interactive fashion. • This approach is based on the idea of enriching a classical mathematical technique (e.g. goal programming, ?-constraint, or compromise programming) by incorporating some concepts based on fuzzy relations taken from the European School of MCDA (Multi-Criteria Decision Analysis). • Experimental evidence on synthetic and real-world instances shows that our model computes quickly enough to be interactive and it is even capable of improving high quality solutions. • The contribution of this paper is in providing a guideline to build compromise programming models that follow the principles of ELECTRE III to prevent incomparability during the analysis. It aids the DM in identifying strictly preferred solutions. • In practical terms, the chief contribution of this approach is the level of confidence that the DM can feel in the final prescription. This paper introduces an interactive approach to support multi-criteria decision analysis of project portfolios. In high-scale strategic decision domains, scientific studies suggest that the Decision Maker (DM) can find help by using many-objective optimisation methods, which are supposed to provide values in the decision variables that generate high-quality solutions. Even so, DMs usually wish to explore the possibility of reaching some levels of benefits in some objectives. Consequently, they should repeatedly run the optimisation method. However, this approach cannot perform well – in an interactive way – for large instances under the presence of many objective functions. We present a mathematical model that is based on compromise programming and fuzzy outranking to aid DMs in analysing multi-criteria project portfolios on the fly. This approach allows relaxing the problem of rapidly optimising portfolios while preserving the beneficial properties of the DM's preferences expressed by outranking relations. Our model supports the decision analysis on two instance benchmarks: for the first one, a better compromise solution was generated 84% of the runs; for the second one, this ranged from 93% to 97%. Our model was also applied to a real-world problem involving social projects, obtaining satisfactory results. [ABSTRACT FROM AUTHOR]

The nature of the real-world problem is multi-modal and multidimensional. This paper proposes a novel metaheuristic algorithm based on social behaviors of people acquiring favorable information, which is the society-based metaheuristic optimization mechanism, called the Information-Decision Search Algorithm (IDSE), aiming to provide a new optimization technology for solving real-world optimization problems. This optimization technology proposes special searching mechanisms of delivery behavior, approaching behavior, inheritance behavior, mutation behavior, interaction, and learning behavior, establishing corresponding mathematical models to develop an efficient optimization framework for solving constrained optimization. The performance of the proposed algorithm and 10 state-of-the-art optimizers is evaluated on 46 benchmarks, including convergence, solution accuracy, robustness, diversity, significance, and the dimensional-scalability on CEC 2017 benchmarks (50 Dim and 100 Dim). The statistical results suggest, with the dimensionality of the problem variable increasing, the computing efficiency of the proposed optimization technology keeps on the highest level at all times. The low-rank feature for IDSE on 46 benchmarks emphasizes the selective priority in solving the same optimization problem. In addition, IDSE also considers 7 real-world engineering problems. The comparison results suggest that IDSE is superior to competitive algorithms in improving solution accuracy and reducing optimization costs, indicating the significant performance for solving constraint optimization. [ABSTRACT FROM AUTHOR]

Due to the growing complexity of multi-state deteriorating systems and the lack of data, information and knowledge, epistemic uncertainty cannot be ignored anymore. Although there already exist some reliability metrics to deal with epistemic uncertainty problems in multi-state deteriorating systems, the final results provided by these methods are always probability intervals, which will suffer from the interval extension challenge in the subsequent operation. In this paper, we propose a reliability analysis method for the multi-state deteriorating system based on belief reliability theory, which can provide more conservative estimations and avoid interval extension problems. To describe the state transition process of multi-state deteriorating systems under the effect of epistemic uncertainty, a mathematical model called uncertain state transition chain is proposed. Under the framework of uncertainty theory, a total uncertainty inequality is given to assist the calculation of belief reliability. Using the proposed model and inequality, an algorithm of belief reliability for multi-state deteriorating systems considering the uncertainty of inspection time is provided. As a numerical example, the calculating process, the evaluation result and some discussions of a specific deteriorating system are given at last. [ABSTRACT FROM AUTHOR]

• We propose a new fuzzy best-worst method (BWM) based on triangular fuzzy numbers. • We propose the concepts of fuzzy consistency index and fuzzy consistency ratio. • Four linear programming models are built to get optimal fuzzy weights, respectively. • We apply the proposed fuzzy BWM to deal with multi-criteria decision-making. • It gets a higher consistency than the ones of the existing fuzzy BWM and the BWM. 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. [ABSTRACT FROM AUTHOR]

• Minimum triangular norm (t-norm) leads to unrealizable fuzzy PID controllers. • Algebraic Product t-norm leads to realizable fuzzy PID controllers. • Two fuzzy PID controllers are derived using two different inference methods, namely, Larsen product and Mamdani minimum. • The mathematical models of the controllers are obtained using Minimum t-norm, Maximum triangular conorm, and Centre of Area (Centre of Gravity) defuzzification. In this paper, we show that Minimum triangular norm (Min t-norm) always leads to unrealizable fuzzy PID (FPID) controllers. To show this, mathematical models of two FPID controllers are obtained. Two fuzzy sets, Negative (N) and Positive (P), for fuzzification of each of the three input variables (error, change of error, and double change of error), and four fuzzy sets N, Negative Small (NS), Positive Small (PS), and P on the output variable (change of control effort) are considered. Min t-norm, Maximum (Max) s-norm, triangular membership functions, Larsen Product (LP)/ Mamdani Minimum (MM) inference, and Centre of Area (Centre of Gravity) (CoA (CoG)) defuzzification are used for mathematical modeling of the controllers. Then upon thoroughly analyzing the properties of the mathematical models of the controllers, we show that Min t-norm leads to unrealizable FPID controllers. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• The overall contour clarity of the image mainly depends on the edge distribution of the image. In our approach, the Laplace operator is used to sharpen the image, which can improve the quality of the image. • In the process of image style transfer, in order to reduce the iterative time. The image is first down-sampled to reduce the resolution, and then the generated image is super-resolution reconstructed. • Large number of evaluation experiments are conducted to show the effect of our proposed method. In deep learning, multi-robot systems intelligently cooperate to perform a complex task, and improve the performance of a single robot through communication, collaboration and sharing of information. Intelligent collaborative work makes the research and application of multi-robot systems develop rapidly. The traditional image style transfer algorithm mainly uses mathematical modeling to characterize the texture of the picture, and combines the content image with the style image to achieve the effect of style transfer. This style transfer algorithm ignores the edge distribution of the image, which makes the contour of the generated image blurred, and it takes a long time in the iterative process of style transfer, and the effect is poor. Therefore, this paper proposes a ceramic decoration pattern style migration algorithm based on the ESPCN model. The algorithm uses the Laplace operator to sharpen the image to highlight the edge distribution, and then uses downsampling to generate low-resolution images to reduce the iteration time of image style transfer. The final generated image uses ESPCN super-resolution reconstruction to reconstruct the low-resolution image. Converted to high-resolution images. The experimental results show that multiple image evaluation indicators show that the edge distribution of the generated image is clear, the iteration time is shortened, and the image quality and definition are improved. [ABSTRACT FROM AUTHOR]

• A fractional-order hydraulic turbine governing system is established. • A fuzzy control method using the frequency distributed model (FDM) is proposed. • By means of a new FDM transformation, the stability conditions are derived. • The gain matrices can be obtained by the linear matrix inequality technique. • The control method shows good robustness. The hydraulic turbine governing system (HTGS) is a core part of a hydropower station, and the dynamic characteristics of its transition process and stability under disturbances have been strong concerns for the stable operations of units. In this paper, a disturbance observer-based Takagi-Sugeno (T-S) fuzzy control (DOBFC) method using the frequency distributed model (FDM) is proposed to improve the anti-interference control performance of a delay fractional-order HTGS. First, a more practical mathematical model of a fractional-order HTGS considering both the mechanical time delay and an elastic water hammer is established, and then its fuzzy model is presented on the basis of the generalized T-S fuzzy rules. Second, the disturbance observer is constructed by utilizing the system state and disturbance information, and the output estimated value of the observer is input into the designed fuzzy state feedback controller to compensate for the effect of external disturbances on the system and achieve disturbance suppression. Third, by means of a new FDM transformation and the construction of a novel Lyapunov function, the stability condition and the parameter solving method of the controller are derived using the linear matrix inequality (LMI) technique. Finally, simulation results are given to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper deals with the optimal formation control problem of multi-agent systems under a prior unknown desired shapes. The objective of this paper is to accomplish a rolling optimal formation of multi-agent systems by incorporating trajectory tracking and energy saving control into a unified framework. First, according to the real-time desired position offset between each follower and the leader, the rolling optimization performance indexes are introduced. Then, a distributed rolling optimization algorithm is proposed to realize the desired formation of multi-agent systems under the minimum energy consumption of agents. Finally, several simulation examples are conducted to illustrate the effectiveness of the proposed design algorithm. [ABSTRACT FROM AUTHOR]

This paper is concerned with the admissibility analysis and stabilization problems for singular fuzzy discrete-time systems with time-varying delay. The novelty of this paper comes from the consideration of a new summation inequality which is less conservative than the usual Jensen inequality, the Abel-Lemma based inequality and the Seuret inequality. Based on the inequality, sufficient conditions are established to ensure the systems to be admissible. Moreover, the corresponding conditions for the existence of desired static output feedback controller gains are derived to guarantee that the closed-loop system is admissible. The conditions can be solved by a modified cone complementarity linearization (CCL) algorithm. Examples are given to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In order to cope with uncertainties in a platoon, this paper proposes a reconfigurable multi-agent architecture to address the platoon safety problem by handling two modes: the normal mode and the degraded mode. At this stage of research, the normal mode is characterized by the interaction between agents over a Vehicle-to-Vehicle (V2V) communication network while the degraded mode simply involves sensors for a local perception. The switching from the normal mode to the degraded one is triggered when the communication quality is considered not fully reliable. A PID (Proportional Integral Derivative) controller is proposed to regulate the inter-vehicle distance and orientation. Two models are proposed in this paper: in the first one, the management operations such as splitting and joining are set up while the second is mainly modeled to assess the implemented controller quality. In this paper, the safety of a platoon is represented by the quality of tracking and the inter-vehicle distance. The mentioned features are assessed for both modes through a formal verification using the Uppaal software. We prove the efficiency of the proposed platoon model for several situations such as merging, following or leaving the platoon by verifying different properties using the model checking. The evaluation of the second model, simulated by the Webots software, proves the impact of the number of vehicles on the platoon performance and the vehicle tracking quality. We conclude that when the platoon reaches a certain number of vehicles, the safety criterion is no more reliable. [ABSTRACT FROM AUTHOR]

Pareto optimality is the fundamental construct employed to determine whether a given solution to a multi-criteria mathematical optimization model is preferred to another solution. In this paper we describe an approach (Pattern Efficient Set Algorithm – PESA) to generating a pattern-efficient set of non-dominated vectors to a multi-objective optimization problem. Our approach incorporates an optimization model designed to yield certain non-dominated vectors that can fill gaps between already generated non-dominated vectors, providing a way to deal with the adjacency of generated non-dominated vectors and to quantify the gaps between them. We also propose a pseudo-randomized variant of PESA (rPESA) that randomly generates hypothetical bounds for the objective functions and uses them in the optimization model. To test our approach we selected ten problems from the literature, including bi-objective, 3-objective, 5-objective and 10-objective test instances with non-convex, disconnected or continuous Pareto. The inverted generational distance (IGD) and the hyper-volume (HV) are used as performance metrics to measure the quality of the obtained approximations. We also present graphically the numerical results from applying our method. [ABSTRACT FROM AUTHOR]

In this paper we propose a new method, Return Random Walk, for link prediction to infer new intra-class edges while minimizing the amount of inter-class noise, and we show how to exploit it in an unsupervised densifier method, Dirichlet densification, which can be used to increase the edge density in undirected graphs, setting so that commute times can be better estimated by state-of-the-art methods. Moreover, this approach allows us to predict new intra-class links by exploring vertex similarities analyzing the mathematical relationship between the Cheeger constant and the minimization of the spectral gap, and the meaningful estimation of the commute distances. Our experiments show a significant improvement as inter-class filtering with respect to the state-of-the-art of link prediction, providing a weighting matrix denser and more clustered than others link predictors, and preconditioned the input graph to subsequent tasks of pattern recognition. [ABSTRACT FROM AUTHOR]

Under the idea to develop a mathematical model for three-way decisions, the aim of the paper is to study trice-valued fuzzy sets, i.e., mappings from a set to a structure called a trice. A trice is a triple semilattice satisfying roundabout absorption laws, suitable for representing multi-dimensional orders, which appear in complex movements in a plane or in a space. Our approach is cutworthy, namely, we investigate cuts of such fuzzy sets and prove theorems of decomposition and synthesis. This new notion provides a possibility to capture vague triangular situations. Therefore, a motivation for our research is to provide a new algebraic and order-theoretic model for three-way decisions, as this topic has been introduced recently for solving particular human problems and for information processing. [ABSTRACT FROM AUTHOR]

The principle of justifiable granularity (PoJG) balances coverage and specificity to optimize information granularity. Although numerous studies have successfully formed information granules (IGs) using this principle, the stability of these IGs rarely receives attention. This paper analyzes the stability of such an IG's modal, performance and upper or lower bound. First, we define some concepts to quantify the stability. Then, by the use of the binomial distribution, the central limit theorem and the union bound, we prove some theorems, which rely on several reasonable hypotheses and reveal the relations between the data size and the stability of the IG's modal, performance and upper or lower bound. Furthermore, we put forward an algorithm built on the theorems to generate stable IGs by building data that have the proper size. Finally, we analyze its time complexity, applications and limitations. Experiments indicate the reliability of this algorithm when it is applied to several probability distributions and real datasets with a large scale of evidence. [ABSTRACT FROM AUTHOR]

The relaxation of monotonicity requirements is a trend in the theory of aggregation functions. In the recent literature, we can find several relaxed forms of monotonicity, such as weak, directional, cone, ordered directional and strengthened directional monotonicity. All these forms of monotonicity are global properties in the sense that they are imposed for all the points in the domain of a function. In this work, we introduce a local notion of monotonicity called pointwise directional monotonicity, or directional monotonicity at a point. Based on this concept, we characterize all the previously defined notions of monotonicity and, in the final part of the paper, we present some geometric aspects of the global weaker forms of monotonicity, stressing their relations and singularities. [ABSTRACT FROM AUTHOR]

With the growing trend of making vehicles smarter, the idea of utilizing vehicles as the infrastructures for communication and computation has triggered great interest. There have been increasingly efforts integrating the connected vehicles into a cloud computing system, but the performance of such system is restricted by its high latency. To solve this problem, a new computing paradigm, fog computing has been proposed to better exploit potential computing resources of connected vehicles with a collaborative multitude of end-user clients or near-user edge devices [8]. The fog computing differs from cloud computing by its proximity to end users, dense geographical distribution and support for mobility. However, current studies on fog computing based vehicular system mainly focus on its reliability and security issues instead of investigating realistic scenarios. To the best of our knowledge, this paper is the first to propose vehicular fog computing by studying its capabilities using realistic data acquired from tens of thousands of taxis in Beijing, China. A mathematical model is developed for vehicular fog computing, based on which we can make prediction of potential computing capacity of a vehicular fog and analyze the impact of communication range on the capacity. Then we present temporal and spatial distribution of potential computation capacity of vehicular fog computing in a city-wide scale. Our study quantitatively reveals the capabilities of vehicular fog computing at different scales, which offers insightful guidelines for the related system and protocol designs in the future. [ABSTRACT FROM AUTHOR]

In the last decade, several new construction methods of fuzzy implication functions from one or more given ones have been proposed. Following this line of research, in our paper some construction methods based on polynomial functions of three variables are presented. Concretely, these methods provide a (possibly new) fuzzy implication function from a given one and a quadratic polynomial function. A complete characterization of those quadratic functions adequate to obtain a fuzzy implication function through this strategy is presented. Moreover, the invariant fuzzy implication functions with respect to this method are determined. Finally, some quadratic construction methods preserving some specific additional properties are analysed. [ABSTRACT FROM AUTHOR]

• A framework has been developed for extracting the most consistent bits from iris features (IrisCodes). • The proposed model is based on the incorporation of some novel features such as use of iris masks and optimized scoring based on 1D cluster formation. • The results obtained on four benchmark iris databases (CASIAv3 Interval, IITD, MMU2, and CASIAv4 Thousand) demonstrate significant improvements over the baseline EER (%). Iris-based biometric systems are widely considered as one of the most accurate forms for authenticating individual identities. Features from an iris image are commonly represented as a sequence of bits, known as IrisCodes. The work in this paper focuses on locating and subsequently extracting the most consistent bit-locations from these binary iris features. We achieve this objective by initially constructing a Matching-Code vector from some specifically designated training IrisCodes, and subsequently forming a series of 1D clusters in them. Every cluster element is then assigned a score in the range [ 0 − 1 ] on the basis of two cluster properties - the size of the cluster it belongs to and its distance from the center of the cluster. We term this cumulative score as the Significance Index S (b) for a cluster element b. Finally, we select those locations which correspond to the highest scores for every IrisCode. We have tested our approach for four benchmark iris databases (CASIAv3-Interval, CASIAv4-Thousand, IIT Delhi and MMU2) while varying the number of extracted bit-locations from 50 to 300. Our empirical results exhibit significant improvements over the baseline results regarding both the consistency of the extracted bit-locations, as well as the overall performance of the resulting biometric system. [ABSTRACT FROM AUTHOR]

Clustering analysis is a fundamental technique in machine learning, which is also widely used in information granulation. Multiple clustering systems granulate a data set into multiple granular structures. Therefore, clustering ensemble can serve as an important branch of multigranulation information fusion. Many approaches have been proposed to solve the clustering ensemble problem. This paper focuses on the direct approaches which involve two steps: finding cluster correspondence and utilizing a fusion strategy to produce a final result. The existing direct approaches mainly discuss the process of finding cluster correspondence, while the fusing process is simply done by voting. In this paper, we mainly focus on the fusing process and propose a Dempster-Shafer evidence theory-based clustering ensemble algorithm. The advantage of the algorithm is that the information of an object’s surrounding cluster structure is taken into consideration by using its neighbors to describe it. First, we find neighbors of each object and generate its label probability outputs in every base partition. Second, these label probability outputs are integrated based on DS theory. Theoretically, our method is superior to other voting methods. Besides, several experiments show that the proposed algorithm is statistically better than seven other clustering ensemble methods. [ABSTRACT FROM AUTHOR]

In this paper, we study the model-checking problem of linear-time properties in multi-valued systems. Safety properties, invariant properties, liveness properties, persistence and dual-persistence properties in multi-valued logic systems are introduced. Some algorithms related to the above multi-valued linear-time properties are discussed. The verification of multi-valued regular safety properties and multi-valued ω -regular properties using lattice-valued automata are thoroughly studied. Since the law of non-contradiction (i.e., a ∧ ¬ a = 0 ) and the law of excluded-middle (i.e., a ∨ ¬ a = 1 ) do not hold in multi-valued logic, the linear-time properties introduced in this paper have new forms compared to those in classical logic. Compared to those classical model-checking methods, our methods to multi-valued model checking are accordingly more direct: We give an algorithm for showing TS ⊧ P for a model TS and a linear-time property P , which proceeds by directly checking the inclusion Traces ( TS ) ⊆ P instead of T r a c e s ( T S ) ∩ ¬ P = ∅ . A new form of multi-valued model checking with membership degree is also introduced. In particular, we show that multi-valued model checking can be reduced to classical model checking. The related verification algorithms are also presented. Some illustrative examples and a case study are also provided. [ABSTRACT FROM AUTHOR]

Rough set theory, as a new mathematical tool to handle uncertainty decision making problems, was used to study conflict analysis decision making in late90′s, and then the Pawlak conflict analysis model was established. The approach of rough set provides a new perspective and also gives an effective tool to deal with conflict analysis decision making, both theoretically and practically. In this paper, we propose a new rough set model of conflict analysis: a conflict analysis decision model based on rough set theory over two universes. It is a natural extension of the Pawlak conflict analysis model. Subsequently, we define the conflict matrix and a consistent disagreement matrix on the basis of the model proposed in this paper. Then we can find the intrinsic reasons for conflict and attain a feasible strategy for a conflict situation. Furthermore, we define a positive alliance matrix and a negative alliance matrix for a conflict situation. These can help us analyze the opinions of different agents in the conflict situation. Moreover, we develop a matrix approach for a conflict analysis model based on rough set theory over two universes, which provides a convenient way to analyze and solve the conflict situation. Comparing to the Pawlak conflict analysis model, our proposed model not only could reveal the core causes for a conflict situation but also can find a possible optimal feasible consensus strategy to solve the conflict situation which satisfies the agents as much as possible. Finally, we illustrate the idea and the basic principles established in this paper by analyzing a conflict decision making scenario. [ABSTRACT FROM AUTHOR]

Highlights • We prove some properties of the Pari Mutuel Model as a coherent lower (and upper) probability. • We show that a Pari Mutuel Model is a particular case of a probability interval. • We give the maximal number of extreme points of the credal set associated with a Pari Mutuel Model and we give the form of those extreme points. • We give sufficient and necessary conditions for the Pari Mutuel Model to induce a belief function. • We show how to merge information given in terms of Pari Mutuel Models. Abstract The pari-mutuel model is a betting scheme that has its origins in horse racing, and that has been applied in a number of contexts, mostly economics. In this paper, we consider the set of probability measures compatible with a pari-mutuel model, characterize its extreme points, and investigate the properties of the associated lower and upper probabilities. We show that the pari-mutuel model can be embedded within the theory of probability intervals, and prove necessary and sufficient conditions for it to be a belief function or a minitive measure. In addition, we also investigate the combination of different pari-mutuel models and their definition on product spaces. [ABSTRACT FROM AUTHOR]

This paper presents a stochastic self-triggered model predictive control (MPC) scheme for linear systems with additive uncertainty, and with the states and inputs being subject to chance constraints. In the proposed control scheme, the succeeding sampling time instant and current control inputs are computed online by solving a formulated optimization problem. The chance constraints are reformulated into a deterministic fashion by leveraging the Cantelli’s inequality. Under few mild assumptions, the online computational complexity of the proposed control scheme is similar to that of a nominal self-triggered MPC. Furthermore, initial constraints are incorporated into the MPC problem to guarantee the recursive feasibility of the scheme, and the stability conditions of the system have been developed. Finally, numerical examples are provided to illustrate the achievable performance of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper studies the global leader-following consensus problem for a group of discrete-time neutrally stable linear systems subject to actuator saturation. An event-triggered linear feedback law, either of the state feedback type or the output feedback type, is constructed for each follower agent and an event-triggering strategy is designed for updating these control laws. These event-triggered control laws are shown to achieve global leader-following consensus when the communication topology among the follower agents is strongly connected and detailed balanced and the leader is a neighbor of at least one follower agent. Simulation results illustrate the theoretical validity. [ABSTRACT FROM AUTHOR]

Association rule mining is a popular data mining task, which has important in many domains. Because the task of association rule mining is very time consuming, evolutionary and swarm based algorithms have been designed to find approximate solutions. However, these approaches still have long execution times, especially when applied on dense and big databases, or when low minsup and minconf threshold values are used. Moreover, these approaches suffer from the lack of diversity in the rules presented to the user. To address these drawbacks of previous algorithms, this paper proposes an efficient parallel algorithm named CGPUGA. It is a genetic algorithm that runs on clusters of GPUs to efficiently discover diversified association rules. It benefits from cluster computing to generate rules. Then, to evaluate rules, which is the most time consuming task, the designed algorithm relies on the massively parallel GPU threads. Furthermore, to deal with the issue of rule quality, the search space of rules is partitioned into several regions assigned to different workers, and rules found by each workers are the merged to ensure diversification. The designed approach has been empirically compared with state-of-the-art algorithms using small, medium, large and big datasets. Results reveal that CGPUGA is 600 times faster than the sequential version of the algorithm for big datasets. Moreover, it outperforms state-of-the-art high performance computing based association rule mining algorithms for real big datasets such as Pokec, Webdocs and Wikilinks. In terms of rule quality, results show that the designed CGPUGA algorithm provides rules of higher quality compared to the state-of-the-art NIGGAR, MSP-MPSO and MPGA algorithms for diversified association rule mining. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of leader-follower consensus for a class of nonlinear multi-agent systems with Lipschitz-type dynamics and a directed communication topology. First, in order to reduce communication frequency among followers, an output-based event-triggered control strategy is proposed for leader-following consensus. Second, neural-network-based observers are designed to reconstruct those immeasurable information of follower agents. Third, instead of continuously monitoring follower agents’ measurements, a distributed self-triggered control strategy is put forward, which is based only on each agent’s local estimation and its previous triggered instants. Furthermore, it is proved theoretically that the consensus tracking errors are ultimately bounded and the Zeno behavior is definitely excluded. Finally, simulation results are given to show the effectiveness and advantage of the proposed results. [ABSTRACT FROM AUTHOR]

Consensus in group decision making requires discussion and deliberation between the group members with the aim to reach a decision that reflects the opinions of every group member in order for it to be acceptable by everyone. Traditionally, the consensus reaching problem is theoretically modelled as a multi stage negotiation process, i.e. an iterative process with a number of negotiation rounds, which ends when the consensus level achieved reaches a minimum required threshold value. In real world decision situations, both the consensus process environment and specific parameters of the theoretical model can change during the negotiation period. Consequently, there is a need for developing dynamic consensus process models to represent effectively and realistically the dynamic nature of the group decision making problem. Indeed, over the past few years, static consensus models have given way to new dynamic approaches in order to manage parameter variability or to adapt to environment changes. This paper presents a systematic literature review on the recent evolution of consensus reaching models under dynamic environments and critically analyse their advantages and limitations. [ABSTRACT FROM AUTHOR]

In this paper we analyze the notion of a finite mean from an axiomatic point of view. We discuss several axiomatic alternatives, with the aim of establishing a universal definition reconciling all of them and exploring theoretical links to some branches of Mathematics as well as to multidisciplinary applications. [ABSTRACT FROM AUTHOR]

In a coupled network system, information can be sourced and disseminated in many ways. In this study, we investigate the diffusion effect of information in a coupled social network environment, using two new node states to augment information dissemination. Using an improved SIR model, this paper posits that a coupled network comprises two categories of nodes, independent spreaders and cross-network spreaders, which participate in the process of information spreading. The independent spreaders act as a source for information, leading to a higher spreading probability and a wider spreading scope. The cross-network spreaders transfer the spreading onto another network. Our simulation result using synthetic data suggests that the environment of the coupled social network affects the diffusion of information and information diffusion has a prolonged relaxation time due to these nodes. Further, we analyze the data collected from Weibo and WeChat of an actual news event to visualize the information spread process in the cross-network dissemination case of public opinion, and show that our improved SIR model provides a good fit to explain the data. [ABSTRACT FROM AUTHOR]

In this paper, an extended ranking method for fuzzy numbers, which is a synthesis of fuzzy targets and the Dempster–Shafer Theory (DST) of evidence, is devised. The use of fuzzy targets to reflect human viewpoints in fuzzy ranking is not new. However, different fuzzy targets can lead to contradictory fuzzy ranking results; making it difficult to reach a final decision. In this paper, the results from different viewpoints are treated as different sources of evidence, and Murphy's combination rule is used to aggregate the fuzzy ranking results. DST allows fuzzy numbers to be compared and ranked while preserving their uncertain and imprecise characteristics. In addition, a hybrid method consisting of fuzzy targets and DST with the Transferable Belief Model is formulated, which fulfils a number of important ordering properties. A series of empirical experiments with benchmark examples has been conducted and the experimental results clearly indicate the usefulness of the proposed method. [ABSTRACT FROM AUTHOR]

In current years, Biogeography-Based Optimization (BBO), a novel Evolutionary Algorithm (EA), has drawn a lot of attention due to its dramatic performance. In our previous work, BBO’s migration models for single-objective problem (SOP) have been investigated to reveal their effects to algorithm’s performance. However to date, there is a few investigation about migration models for Multi-Objective Problems (MOPs) which are common in practice though more difficult. To make BBO competitive in dealing with MOPs, migration models of BBO are explored and exploited in this paper. One contribution of our work is that we propose Multi-objective BBO (MOBBO). By comparing MOBBO with other popular MOEAs, this algorithm is competent to handle MOPs. Besides, we present and compare six principal migration models. In comparisons, we find that Trapezoidal Migration Model performs well for MOPs, while its performance is inferior to other migration models for SOPs. Besides, Quadratic Migration Model’s performance for MOPs is worse, while it has a good performance to solve SOPs. These demonstrate that the conclusion to evaluate migration models for SOPs does not hold for MOPs, so another contribution in this paper is that we reevaluate migration models for MOPs in an empirical way, which is helpful to design migration models for MOBBO. [ABSTRACT FROM AUTHOR]

Many real-life problems can be described as unbalanced, where the number of instances belonging to one of the classes is much larger than the numbers in other classes. Examples are spam detection, credit card fraud detection or medical diagnosis. Ensembles of classifiers have acquired popularity in this kind of problems for their ability to obtain better results than individual classifiers. The most commonly used techniques by those ensembles especially designed to deal with imbalanced problems are for example Re-weighting, Oversampling and Undersampling. Other techniques, originally intended to increase the ensemble diversity, have not been systematically studied for their effect on imbalanced problems. Among these are Random Oracles, Disturbing Neighbors, Random Feature Weights or Rotation Forest. This paper presents an overview and an experimental study of various ensemble-based methods for imbalanced problems, the methods have been tested in its original form and in conjunction with several diversity-increasing techniques, using 84 imbalanced data sets from two well known repositories. This paper shows that these diversity-increasing techniques significantly improve the performance of ensemble methods for imbalanced problems and provides some ideas about when it is more convenient to use these diversifying techniques. [ABSTRACT FROM AUTHOR]

In this paper the comparison of a proposed neural network with generalized type-2 fuzzy weights (NNGT2FW) with respect to the monolithic neural network (NN) and the neural network with interval type-2 fuzzy weights (NNIT2FW) is presented. Generalized type-2 fuzzy inference systems are used to obtain the generalized type-2 fuzzy weights and are designed by a strategy of increasing and decreasing an epsilon variable for obtaining the different sizes of the footprint of uncertainty (FOU) for the generalized membership functions. The proposed method is based on recent approaches that handle weight adaptation using type-1 and type-2 fuzzy logic. The approach is applied to the prediction of the Mackey–Glass time series, and results are shown to outperform the results produced by other neural models. Gaussian noise was applied to the test data of the Mackey–Glass time series for finding out which of the presented methods in this paper shows better performance and tolerance to noise. [ABSTRACT FROM AUTHOR]

Assessment and evaluation methodologies as well as combinations of them, for modelling of Human–Robot Interaction (HRI), are reviewed extensively and thoroughly in this paper. However, based on the types of robots and the kinds of interactions involved in the modelling of HRI, we concentrate just on the assistive social robot types. A comprehensive review has been done on each of these extensive evaluation and assessment methodologies applied for testing the usability of assistive social robots, user acceptance towards robots and robot acceptance in terms of behavioural adaptation during the HRI. The evaluation methodologies are reviewed based on the primary and non-primary basis, while the assessment methodologies are reviewed based on the type(s) of modelling approaches. We then discussed the weaknesses, strengths and uniqueness of each type of the past research work done on the evaluation and assessment methodologies. Comparison and contrast tables are also illustrated. Lastly, this paper provides our recommended directions, new vision, as well as our inspirations and new insights for future researches by highlighting the key areas for enhancing each of the past evaluation and assessment methodologies so that a better modelling approach for HRI can be achieved. Contributions of this review paper are also discussed thoroughly. [ABSTRACT FROM AUTHOR]

It is well known that one of the basic advantages of evolutionary programming is in deciding the most suitable place for breeding offspring and finding the route toward the global minimum. To reach this goal, the algorithm needs to estimate the coordinates of the global minimum and then steer the new point toward it. In this paper, the estimation of the global minimum is calculated by weighted mean coordination of individuals (WMP), and then a road is mapped between the coordinates of the parents and the WMP. In the proposed method, fuzzy logic is used for deciding on the road and the best coordination to breed offspring. The proposed algorithm is tested on 65 well-known cost functions and is compared with five algorithms inside the EP family. In the next section of the paper, the algorithm is tested on the high-dimensional problem of modeling ozone layer data, which includes almost 26,000 unknown parameters. The results demonstrate the capability of the proposed method in having acceptable speed and accuracy. [ABSTRACT FROM AUTHOR]

This paper proposes using the opposition-based learning (OBL) strategy in the shuffled frog leaping (SFL). The SFL divides a population into several memeplexes and then improves each memeplex in an evolutionary process. The OBL by comparing the fitness of an individual to its opposite and retaining the fitter one in the population accelerates search process. The objective of this paper is to introduce new versions of the SFL which employ on one hand the OBL to accelerate the SFL without making premature convergence and on the other hand use the OBL strategy to diversify search moves of SFL. Four versions of SFL algorithm are proposed by incorporating the OBL and the SFL. All algorithms similarly use the opposition-based population initialization to achieve fitter initial individuals and their difference is in applying opposition-based generation jumping. Experiments are performed on parameter identification problems. The obtained results demonstrate that incorporating the opposition-based strategy and SFL performed in a proper way is a good idea to enhance performance of SFL. Two versions of opposition-based SFL outperform their pure competitor i.e., SFL in terms of all aspects on all problems but two other versions of SFL obtained a worse performance than the pure SFL. Also some performance comparisons of the proposed algorithms with some other algorithms reported in the literature confirm a significantly better performance of our proposed algorithms. Also in final part of the comparison study, a comparison of the proposed algorithm in this study in respect to other algorithms on CEC05 functions demonstrates a completely comparable performance of proposed algorithm. [ABSTRACT FROM AUTHOR]

Identity-based broadcast encryption (IBBE) is an important type of broadcast encryption. In the paper, we put forward the formal definition and security model of IBBE with leakage-resilience. Furthermore, we propose an IBBE scheme with continuous leakage-resilience. The presented scheme is resilient to private key leakage. By refreshing the private key, our scheme can resist continuous private key leakage. Based on dual system encryption technique, we prove the security of the proposed scheme under subgroup decisional assumptions. We give the specific performance analysis of leakage-resilience. The relative leakage ratio can amount to 1/3. [ABSTRACT FROM AUTHOR]

Collaborative Filtering (CF) is one of the most successful recommendation techniques. Recently, implicit trust-based recommendation approaches have emerged that incorporate implicit trust information into CF in order to improve recommendation performance. Previous implicit trust models assume that all users have the same perception of ratings. However, although all users employ members of the same rating domain (e.g. ratings on a 1–5 scale), each individual has his own interpretations about a rating domain in order to express his preferences. Thus, it is reasonable that a user's rating vector has some degree of uncertainty, depending upon the rating usage trend of that user. In this paper, we present a new approach for confidence modeling in the context of recommender systems. The idea of this modeling is that confidence in a particular user depends not only on the trust in the opinions of that user but also on the certainty of these opinions. Based on this idea, we propose a new Confidence-Based Recommendation (CBR) approach. This approach employs four different confidence models that derive the users’ and items’ confidence values from both local and global perspectives. Experimental results on real-world data sets demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Image classification methods often use class-level information without considering the distinctive character of each image. Images of the same class may have varied appearances. Besides, visually similar images may not be semantically correlated. To solve these problems, in this paper, we propose a novel image classification method by automatically learning the image-level hierarchical structure (ILHS) using both visual and semantic similarities. We try to generate new representations by exploring both visual and semantic similarities of images. Images are clustered hierarchically to explore their correlations. We then use them for image representations. The diversity of image classes within each cluster is used to re-weight visual similarities. The re-weighted similarities are aggregated to generate new image representations. We conduct image classification experiments on the Caltech-256 dataset, the PASCAL VOC 2007 dataset and the PASCAL VOC 2012 dataset. Experimental results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In group decision making (GDM) process, consensus is a fundamental problem. The information provided by the experts in a GDM problem is usually expressed as preferences, and the linguistic preference relation (LPR) is one of the most frequently used structures to model the experts’ preferences. As a new type of LPR, the probabilistic linguistic preference relation (PLPR) not only allows the experts to provide more than one linguistic term about linguistic variables, but also reflects different importance degrees of the possible preference values. This paper focuses on the consensus reaching process for GDM with PLPRs. An index for measuring the consensus degree is defined first. Then, as for the expert group with unacceptable consensus degree, a consensus improving method is introduced based on the consistency and consensus criteria. Moreover, the whole GDM process is introduced based on the aggregation operators for the probabilistic linguistic term sets (PLTSs). Finally, an application case about medical information presented on search engine is discussed. [ABSTRACT FROM AUTHOR]

The Cops and Robber problem is a game played between two adversary groups, in which one group (cops) attempts to capture the other group (robber). In this paper, the optimal capture time problem is addressed, which seeks the best motion strategy from the viewpoint of the cops for capturing the robber within the minimum time or number of steps, and a new algorithm based on linguistic geometry (LG) is proposed. LG is widely used for solving two-player abstract board games and wargaming problems by breaking the system (problem) into several subsystems, in each of which the playing elements and their paths (called ‘trajectories’) are identified, and a network consisting of these trajectories is constructed. A solution is identified by searching the subsystems by the alpha-beta pruning method. Also, to provide a lower bound for the robber's capture time, we have enhanced the alpha-beta pruning method by incorporating a new admissible heuristic more informed than existing heuristics. As a result, the branching factor of the search is decreased, and the problem is solved more efficiently. For experimentation, several Cops and Robber problems on regular and holed square grids were solved by the LG-based method, and the results were compared with the Reverse Minimax A* (RMA*) algorithm. According to the results, the presented algorithm requires far less computational time and memory than the RMA* method (in fact, approximately 95.5% and 97.5% are saved on average, respectively), and near-optimal solutions were obtained. Also, it was observed that while increasing the number of cops severely affects the computational time and required memory of the RMA*, it has insignificant effects on our algorithm. [ABSTRACT FROM AUTHOR]

The artificial bee colony (ABC) algorithm is a new branch of evolutionary algorithms (EAs) that is inspired by the collective foraging behavior of real honey bee colonies. Due to its foraging model and its solution search equation, ABC generally performs well in exploration but badly in exploitation. To address this concerning issue and obtain a good balance between exploration and exploitation, in this paper, we mainly introduce into the ABC an adaptive method for the population size (AMPS). AMPS is inspired by the natural principle that the size of a population is affected by the availability of food resources. When food resources are abundant, a population tends to expand; otherwise, a decrease in the availability of food resources leads to a shrinkage in the population size. Specifically, when the algorithm performs well in exploration, AMPS will shrink the population to enhance exploitation by periodically removing some inferior solutions that have low success rates. In contrast, AMPS will enlarge the population to improve exploration by introducing some reserved solutions. Furthermore, to make AMPS perform better, we design a new solution search equation for employed bees and onlooker bees. Moreover, we also improve the probability model of the onlooker bees. By embedding our three proposed algorithmic components into ABC, we propose a novel ABC variant, called APABC. To demonstrate the performance of APABC, we compare APABC with some state-of-the-art ABC variants and some other non-ABC methods on 22 scalable benchmark functions and 30 CEC2014 test functions. The simulation results show that APABC is better than or at least competitive with the competitors in terms of its solution quality, robustness and convergence speed. [ABSTRACT FROM AUTHOR]

The problem of attitude stabilizing control for underactuated spacecraft is addressed in this paper. Previously in the literature, a quaternion-based kinematic control law with a singularity has been presented to stabilize the attitude of an axially symmetric spacecraft using two independent control inputs for the case of zero spin rate. This method, however, may require a significant amount of control effort, especially for initial conditions close to the singular manifold. To reduce the control effort, a novel control law is proposed for the kinematic system of an axially symmetric underactuated spacecraft. The control inputs are small and bounded even if the initial conditions are close to the singular manifold. Then, a control law for the complete spacecraft system (both kinematics and dynamics) is designed. The control law is subsequently extended to solve the problem of attitude stabilizing control for an underactuated nonsymmetric spacecraft. The stability of the closed-loop system is guaranteed through a Lyapunov-based approach together with a corollary of Barbalat’s Lemma. The performance of the control laws derived here is demonstrated by numerical simulations. [ABSTRACT FROM AUTHOR]

This paper investigates consistency and uncertainty measurements as well as acceptability checking for triangular fuzzy multiplicative preference relations (TFMPRs). A geometric consistency index is proposed to measure the consistency degree of a TFMPR, and thresholds of acceptable consistency are discussed for TFMPRs. Uncertainty indices are introduced to measure indeterminacy degrees of triangular fuzzy judgments and of TFMPRs and a notion of acceptable TFMPRs are put forward by considering both acceptable consistency and acceptable uncertainty in TFMPRs. We also explore the elicitation of triangular fuzzy weights from acceptable TFMPRs and the aggregation of local triangular fuzzy weights for solving hierarchical multi-criteria decision-making problems. A geometric mean-based equation and a logarithmic least square model are developed to elicit normalized acceptable triangular fuzzy multiplicative weights from an acceptable TFMPR. An equation and a linear program are established to transform triangular fuzzy multiplicative weights into triangular fuzzy additive weights. A geometric weighting-based equation and a pair of linear programs are devised to generate global priority fuzzy weights from local triangular fuzzy multiplicative weights. Three numerical examples, including a hierarchical multi-criteria decision-making problem, are used to illustrate how to apply the developed framework, and comparative studies are provided to show its availability and advantages. [ABSTRACT FROM AUTHOR]

In the recent years, Searchable Symmetric Encryption (SSE) has become one of the hottest topic in cloud-computing area because of its availability and flexibility, and there are a series of SSE schemes were proposed. The adversary considered in these schemes is either honest-but-curious or malicious. However, in the real world, there also exists such adversary who has stronger power. Namely, he/she may coerce some one of the participants to disclose some communication information. The existing SSE algorithms cannot against this adversary under the premise of confidentiality. To solve this problem, in this paper we firstly introduce coercer into SSE scheme. According to the role that the coercer plays, two situations are considered. In the first situation, the adversary is an inside coercer. In the second situation, the adversary is an outside coercer. The inside coercer means the server, while the outside coercer is such person that outside of the data owner, the user and the server. In view of the ability that the coercer has, we firstly construct the deniable SSE model (i.e., Den-SSE). Then we define the property what the Den-SSE should satisfy. By using the Deniable Encryption idea, we propose a scheme that can against the outside coercer, and two scheme that can resist inside coercer. At last, we prove our schemes can meet the property that we define. [ABSTRACT FROM AUTHOR]

Most businesses focus on the profits. For example, supermarkets often analyze sale activities to investigate which products bring the most revenue, as well as find out customer trends based on their carts. To achieve this, a number of studies have examined high utility itemsets (HUI). Traditional association rule mining algorithms only generate a set of highly frequent rules, but these rules do not provide useful answers for what the high utility association rules are. Therefore, Sahoo et al. (2015) proposed an approach to generate utility-based non-redundant high utility association rules and a method for reconstructing all high utility association rules. This approach includes three phases: (1) mining high utility closed itemsets (HUCI) and generators; (2) generating high utility generic basic (HGB) association rules; and (3) mining all high utility association rules based on HGB. The third phase of this approach consumes more time when the HGB list is large and each rule in HGB has many items in both antecedent and consequent. To overcome this limitation, in this paper, we propose an algorithm for mining high utility association rules using a lattice. Our approach has two phases: (1) building a high utility itemsets lattice (HUIL) from a set of high utility itemsets; and (2) mining all high utility association rules (HARs) from the HUIL. The experimental results show that mining HARs using HUIL is more efficient than mining HARs from HGB (which is generated from HUCI and generators) in terms of runtime and memory usage. [ABSTRACT FROM AUTHOR]

Abstract: Formal models can be useful in computer graphics as a conceptual framework supporting representation systems. This allows to formally derive properties and algorithms and proof their correctness and validity. This paper describes a formal model based on a geometric algebra. This algebra has been used to obtain specific representation systems and study their equivalence. The representation systems derived in a natural way from this model are based on simplicial coverings and can be applied to non-manifold solids and to solids with holes. Representations have been developed for polyhedral and free-form solids. Algorithms described and proved include boolean operations and representation conversion. The paper covers the three abstraction levels: theoretical model, representations and derived algorithms. As a practical application an experimental modeller for free-form solid has been developed (ESC-MOD system: “Extended Simplicial Chains MOdeller”). [Copyright &y& Elsevier]