A self-stabilizing distributed algorithm for the bounded lattice domination problems under the distance-2 model

The domination problem is one of the fundamental graph problems, and there are many variations. In this article, we propose a new problem called the minus -domination problem where , and are integers such that , , and . The problem is to assign a value from for each vertex in a graph such that the local summation of values is greater than or equal to . We also propose a framework named the bounded lattice domination for a class of domination problems, including the minus -domination problem. Then, we present a self-stabilizing distributed algorithm under the distance-2 model for the bounded lattice domination. Here, self-stabilization is a class of fault-tolerant distributed algorithms that tolerate transient faults. The time complexity for convergence is $$, where $$ is the number of processes in a network if the cardinality of the domain of process values is finite and constant. Otherwise, the time complexity for convergence is $$.     

Novel admissibility and robust stabilization conditions for fractional-order singular systems with polytopic uncertainties

This paper considers the issues of the admissibility and robust stabilization of fractional-order singular systems with polytopic uncertainties and fractional-order and . Firstly, the novel admissibility conditions for nominal fractional-order singular systems are proposed with no conservatism and without any equalities or nonstrict inequalities. Secondly, the robust admissibility conditions for fractional-order singular systems with polytopic uncertainties are given based on the admissibility conditions for nominal fractional-order singular systems. Thirdly, to make the uncertain fractional-order singular systems robustly admissible, the methods of designing the static output feedback controllers are obtained with wider application scope, which are direct, concise, and more relaxed compared with the existing results. All the results are proposed in terms of linear matrix inequalities. Finally, three illustrative examples are given to demonstrate the effectiveness of the results.     

Linguistic Z-number fuzzy soft sets and its application on multiple attribute group decision making problems

In this study, the concept of linguistic Z-number fuzzy soft set ($\text{◂⋅▸}LZnFSS$) is proposed to describe multiple uncertainties in practical decision making problems. $\text{◂⋅▸}LZnFSS$ combines the concepts of fuzzy soft set, linguistic Z-number, and soft set, which could reflect both of the uncertainty in structure and the uncertainty in detailed evaluations. As an initial idea, the set operations on $\text{◂⋅▸}LZnFSSs$ are put forward, the properties of such operations are also discussed. With traditional soft set based decision procedure and fuzzy soft set based decision procedure, a novel linguistic Z-number fuzzy soft set based group decision procedure is developed to solve multiattribute group decision making with linguistic Z-numbers. Wherein an extended technique for order preference by similarity to ideal solution is also developed. Finally, a numerical example is shown to illustrate the practicality and effectiveness of the given method.     

An efficient framework for generating robust adversarial examples

Recent studies show that deep neural networks (DNNs) suffer adversarial examples. That is, attackers can mislead the output of a DNN by adding subtle perturbation to a benign input image. In addition, researchers propose new generation of technologies to produce robust adversarial examples. Robust adversarial examples can consistently fool DNN models under predefined hyperparameter space, which can break through some defenses against adversarial examples or even generate physical adversarial examples against real-world applications. Behind these achievements, expectation over transformation (EOT) algorithm plays as the backbone framework for generating robust adversarial examples. Though EOT framework is powerful, we know little about why such a framework can generate robust adversarial examples. To address this issue, we do the first work to explain the principle behind robust adversarial examples. Then, based on the findings, we point out that traditional EOT framework has a performance problem and propose an adaptive sampling algorithm to overcome such a problem. By modeling the sampling process as classic Coupon Collector Problem, we prove that our new framework reduces the cost from $O\text{◂()▸}\left(n\ast \log \mathrm{}\left(n\right)\right)$ to $O\left(n\right)$, where $n$ denotes the number of sampling points. Under the view of computational complexity, the algorithm is optimal for this problem. The experimental results show that our algorithm can save up to 23% overhead in average. This is significant for black-box attack, where the cost is charged by the amount of queries.     

Differential calculus of interval-valued q-rung orthopair fuzzy functions and their applications

The interval-valued q-rung orthopair fuzzy set (IVq-ROFS) provides an extension of Yager's q-rung orthopair fuzzy set (q-ROFS), where membership and nonmembership degrees are subsets of the closed interval $\left[0,1\right]$. In such a situation, it is more superior for decision makers to provide their judgments by intervals instead of crisp numbers due to the uncertainty and vagueness in real life. In this paper, we study the calculus theories of IVq-ROFS from the microscopic. In particular, we first introduce the elementary arithmetic of interval-valued q-rung orthopair fuzzy values (IVq-ROFVs), including addition, multiplication, and their inverse. They are the basis for analysis and calculation throughout the work. In addition, we discuss and prove in detail the operation properties and aggregation operators of IVq-ROFVs. Then, we introduce the concept of interval-valued q-rung orthopair fuzzy functions (IVq-ROFFs), which is the main research object of this paper. After that, we further discuss the continuity, derivatives and differentials of IVq-ROFFs. We also find that the derivatives of IVq-ROFFs are closely related to elasticity, which is an important concept in economics. Finally, we provide some application examples to verify the feasibility and effectiveness of the derived results.     

Efficient secure state estimation against sparse integrity attack for regular linear system

We consider the problem of estimating the state of a time-invariant linear Gaussian system in the presence of integrity attacks. The attacker can compromise $p$ out of $m$ sensors, the set of which is fixed over time and unknown to the system operator, and manipulate the measurements arbitrarily. Under the assumption that the system is regular and system matrix $A$ is non-singular, we propose a secure estimation scheme that is resilient to $p$-sparse attack as long as the system is $2p$-sparse detectable, which achieves the fundamental limit of secure dynamic estimation. In the absence of attack, the proposed estimation coincides with Kalman estimation with a certain probability that can be adjusted to trade-off between performance with and without attack. Furthermore, the detectability condition checking in the designing phase and the estimation computing in the online operating phase are both computationally efficient. Two numerical examples including the IEEE 68 bus test system are provided to corroborate the results and illustrate the performance of the proposed estimator.