Showing papers in "Acta Cybernetica in 2022"
TL;DR: It is shown, that the complexity to verify a single strongly possible functional dependency is NP-complete in general, then some cases when verifying a single spFD can be done in polynomial time are introduced.
Abstract: Missing data is a large-scale challenge to research and investigate. It reduces the statistical power and produces negative consequences that may introduce selection bias on the data. Many approaches to handle this problem have been introduced. The main approaches suggested are either missing values to be ignored (removed) or imputed (filled in) with new values. This paper uses the second method. Possible worlds and possible and certain keyswere introduced in Köhler et.al., and by Levene et.al. Köhler and Link introduced certain functional dependencies (c-FD) as a natural complement to Lien's class of possible functional dependencies (p-FD). Weak and strong functional dependencies were studied by Levene and Loizou. We introduced the intermediate concept of strongly possible worlds that are obtained by imputing values already existing in the table in a preceding paper. This results in strongly possible keys (spKey's) and strongly possible functional dependencies (spFD's). We give a polynomial algorithm to verify a single spKey and show that in general, it is NP-complete to verify an arbitrary collection of spKeys. We give a graph-theoretical characterization of the validity of a given spFD X →sp Y.We show, that the complexity to verify a single strongly possible functional dependency is NP-complete in general, then we introduce some cases when verifying a single spFD can be done in polynomial time.As a step forward axiomatization of spFD's, the rules given for weak and strong functional dependencies are checked. Appropriate weakenings of those that are not sound for spFD's are listed. The interaction between spFD's and spKey's and certain keys is studied. Furthermore, a graph theoretical characterization of implication between singular attribute spFD's is given.
2 citations
TL;DR: In this paper , an Extended Kalman Filter (EKF) is applied to a system for which the dynamics are characterized by a discrete-time integrator disturbance model with additive Gaussian noise.
Abstract: Stability contractors, based on interval analysis, were introduced in recent work as a tool to verify stability domains for nonlinear dynamic systems. These contractors rely on the property that - in case of provable asymptotic stability - a certain domain in a multi-dimensional state space is mapped into its interior after a certain integration time for continuous-time processes or after a certain number of discretization steps in a discrete-time setting. However, a disadvantage of the use of axis-aligned interval boxes in such computations is the omnipresent wrapping effect. As shown in this contribution, the replacement of classical interval representations by ellipsoidal domain enclosures reduces this undesirable effect. It also helps to find suitable ratios for the edge lengths if interval-based domain representations are investigated. Moreover, ellipsoidal domains naturally represent the possible regions of attraction of asymptotically stable equilibrium points that can be analyzed with the help of quadratic Lyapunov functions, for which stability criteria can be cast into linear matrix inequality (LMI) constraints. For that reason, this paper further presents possible interfaces of ellipsoidal enclosure techniques with LMI approaches. This combination aims at the maximization of those domains that can be proven to be stable for a discrete-time range-only localization algorithm in robotics. There, an Extended Kalman Filter (EKF) is applied to a system for which the dynamics are characterized by a discrete-time integrator disturbance model with additive Gaussian noise. In this scenario, the measurement equations correspond to the distances between the object to be localized and beacons with known positions.
1 citations
TL;DR: In this paper , the authors present classes of semirings where these two approaches coincide, and show that they can be axiomatised in two different ways: by a map from an additively idempotent semiring into a boolean subalgebra of the semiring bounded by the additive and multiplicative unit of the semiiring, or by an endofunction on a semiring that induces a distributive lattice bounded by two units as its image.
Abstract: Domain operations on semirings have been axiomatised in two different ways: by a map from an additively idempotent semiring into a boolean subalgebra of the semiring bounded by the additive and multiplicative unit of the semiring, or by an endofunction on a semiring that induces a distributive lattice bounded by the two units as its image. This note presents classes of semirings where these approaches coincide.
1 citations
TL;DR: In this article , an interval observer-based approach is presented and visualized for a simplified battery model to quantify the arising time-domain truncation errors due to integrator resets.
Abstract: In recent years, fractional differential equations have received a significant increase in their use for modeling a wide range of engineering applications. In such cases, they are mostly employed to represent non-standard dynamics that involve long-term memory effects or to represent the dynamics of system models that are identified from measured frequency response data in which magnitude and phase variations are observed that could be captured either by low-order fractional models or high-order rational ones. Fractional models arise also when synthesizing CRONE (Commande Robuste d'Ordre Non Entier) and/or fractional PID controllers for rational or fractional systems. In all these applications, it is frequently required to transform the frequency domain representation into time domain. When doing so, it is necessary to carefully address the issue of the initialization of the pseudo state variables of the time domain system model. This issue is discussed in this article for the reinitialization of fractional integrators which arises among others when solving state estimation tasks for continuous-time systems with discrete-time measurements. To quantify the arising time-domain truncation errors due to integrator resets, a novel interval observer-based approach is presented and, finally, visualized for a simplified battery model.
1 citations
TL;DR: In this paper , the numerical construction of inverses for a class of rational functions was studied and two inverse algorithms were proposed, which can be used to simultaneously identify every zero of a rational function or polynomial.
Abstract: We consider the numerical construction of inverses for a class of rational functions. We propose two inverse algorithms, which can be used to simultaneously identify every zero of a rational function or polynomial. In the first case, we propose a generalization of an inverse algorithm based on our previous work and specify a class of rational functions, for which this generalized algorithm is applicable. In the second case, we provide a method to construct Blaschke-products, whose roots match the roots of a polynomial or a rational function. We also consider different iterative methods to numerically calculate the inverse points and discuss their properties.
1 citations
TL;DR: Corner I-patches as discussed by the authors represent a family of implicit surfaces defined by an arbitrary number of ribbons, and they can be used for design, volume rendering, or cell-based approximation of complex shapes.
Abstract: Free-form multi-sided surfaces are often defined by side interpolants (also called ribbons), requiring that the surface has to connect to them with a prescribed degree of smoothness. I-patches represent a family of implicit surfaces defined by an arbitrary number of ribbons. While in the case of parametric surfaces describing ribbons is a well-discussed problem, defining implicit ribbons is a different task.
In this paper, we introduce a new representation, corner I-patches, where implicit corner interpolants are blended together. Corner interpolants are usually simpler, lower-degree surfaces than ribbons. The shape of the patch depends on a handful of scalar parameters; constraining them ensures continuity between adjacent patches. Corner I-patches have several favorable properties that can be exploited for design, volume rendering, or cell-based approximation of complex shapes.
TL;DR: In this article , it is shown that the problem of computing the rotation distance between an arbitrary pair of trees, (S, T), can be efficiently reduced to a problem of finding the first step of a minimal length path, where there is no known first step.
Abstract: It is an open question whether there exists a polynomial-time algorithm for computing the rotation distances between pairs of extended ordered binary trees.The problem of computing the rotation distance between an arbitrary pair of trees, (S, T), can be efficiently reduced to the problem of computing the rotation distance between a difficult pair of trees (S', T'), where there is no known first step which is guaranteed to be the beginning of a minimal length path. Of interest, therefore, is how to sample such difficult pairs of trees of a fixed size. We show that it is possible to do so efficiently, and present such an algorithm that runs in time O(n4).
TL;DR: In this paper , the inverse epsilon distribution is proposed as an alternative to the inverse exponential distribution, which has many applications in reliability theory and biology and has been applied in many applications.
Abstract: This paper is devoted to a new flexible two-parameter lower-truncated distribution, which is based on the inversion of the so-called epsilon distribution. It is called the inverse epsilon distribution. In some senses, it can be viewed as an alternative to the inverse exponential distribution, which has many applications in reliability theory and biology. Diverse properties of the new lower-truncated distribution are derived including relations with existing distributions, hazard and reliability functions, survival and reverse hazard rate functions, stochastic ordering, quantile function with related skewness and kurtosis measures, and moments. A demonstrative survival times data example is used to show the applicability of the new model.
TL;DR: The authors generalized Syn'ya's result to a wider class of dense languages and non-primitive words, and obtained such a generalization for a wide class of languages with positive asymptotic density.
Abstract: In this paper, we are concerned with dense languages and non primitive words. A language L is said to be dense if any string can be found as a substring of element of L. In 2020, Ryoma Syn'ya proved that any regular language with positive asymptotic density always containsinfinitely many non-primitive words. Since positive asymptotic density implies density, it is natural to ask whether his result can be generalized for a wider class of dense languages. In this paper, we actually obtain such generalization.
TL;DR: In this article , interval-based verified approaches to solving ordinary differential equations can be extended to encompass temporally delayed state information in such a way that the evolution of future state trajectories depends not only on the current state as the initial condition but also on some previous state.
Abstract: Many dynamic system models in population dynamics, physics and control involve temporally delayed state information in such a way that the evolution of future state trajectories depends not only on the current state as the initial condition but also on some previous state. In technical systems, such phenomena result, for example, from mass transport of incompressible fluids through finitely long pipelines, the transport of combustible material such as coal in power plants via conveyor belts, or information processing delays. Under the assumption of continuous dynamics, the corresponding delays can be treated either as constant and fixed, as uncertain but bounded and fixed, or even as state-dependent. In this paper, we restrict the discussion to the first two classes and provide suggestions on how interval-based verified approaches to solving ordinary differential equations can be extended to encompass such delay differential equations. Three close-to-life examples illustrate the theory.
TL;DR: In this paper , a dynamic inventory control system described by a network model where the nodes are warehouses and the arcs represent production and distribution activities is considered, and the method of model predictive control is used to derive the control strategy.
Abstract: This paper is concerned with a dynamic inventory control system described by a network model where the nodes are warehouses and the arcs represent production and distribution activities. We assume that an uncertain demand may take any value in an assigned interval and we allow that the system is disturbed by noise inputs. These assumptions yield a model with a mix of interval and stochastic demand uncertainties. We use the method of model predictive control to derive the control strategy. To deal with interval uncertainty we use the interval analysis tools and act according to the interval analysis theory. The developed results are illustrated using a numerical example.
TL;DR: The widths of the iterates computed by these different methods: the naive iteration, methods based on the QRand SVD-factorization of A, and Lohner’s QR-Factorization method are compared.
Abstract: Affine iterations of the form xn+1=Axn+b converge, using real arithmetic, if the spectral radius of the matrix A is less than 1. However, substituting interval arithmetic to real arithmetic may lead to divergence of these iterations, in particular if the spectral radius of the absolute value of A is greater than 1. We will review different approaches to limit the overestimation of the iterates, when the components of the initial vector x(0) and b are intervals. We will compare, both theoretically and experimentally, the widths of the iterates computed by these different methods: the naive iteration, methods based on the QR- and SVD-factorization of A, and Lohner's QR-factorization method. The method based on the SVD-factorization is computationally less demanding and gives good results when the matrix is poorly scaled, it is superseded either by the naive iteration or by Lohner's method otherwise.
TL;DR: The paper proposes an ontology-based approach for facilitating and guiding the empirical evaluation throughout its various steps, and Hyperledger Fabric, an open-source blockchain platform by the Linux Foundation, is modelled and evaluated as a pilot example of the approach.
Abstract: The design and operation of modern software systems exhibit a shift towards virtualization, containerization and service-based orchestration. Performance capacity engineering and resource utilization tuning become priority requirements in such environments. Measurement-based performance evaluation is the cornerstone of capacity engineering and designing for performance. Moreover, the increasing complexity of systems necessitates rigorous performance analysis approaches. However, empirical performance analysis lacks sophisticated model-based support similar to the functional design of the system. The paper proposes an ontology-based approach for facilitating and guiding the empirical evaluation throughout its various steps. Hyperledger Fabric (HLF), an open-source blockchain platform by the Linux Foundation, is modelled and evaluated as a pilot example of the approach, using the standard TPC-C performance benchmark workload.
TL;DR: This paper designs an algorithm that provides social distancing methods that are efficient and have low impact on the economy and proposes several models for combinatorial optimization problems.
Abstract: Currently there are many attempts around the world to use computers, smartphones, tablets and other electronic devices in order to stop the spread of COVID-19. Most of these attempts focus on collecting information about infected people, in order to help healthy people avoid contact with them. However, social distancing decisions are still taken by the governments empirically. That is, the authorities do not have an automated tool to recommend which decisions to make in order to maximize social distancing and to minimize the impact for the economy.
In this paper we address the aforementioned problem and we design an algorithm that provides social distancing methods (i.e., what schools, shops, factories, etc. to close) that are efficient (i.e., that help reduce the spread of the virus) and have low impact on the economy.
On short: a) we propose several models (i.e., combinatorial optimization problems); b) we show some theoretical results regarding the computational complexity of the formulated problems; c) we give an algorithm for the most complex of the previously formulated problems; d) we implement and test our algorithm.
TL;DR: This paper derives mainly means of recognition for this interval matrices class, such as characterizations, necessary conditions and sufficient ones for BRπ-matrices, including some results regarding this class.
Abstract: In this paper we focus on generalizing BRπ-matrices into the interval setting, including some results regarding this class. There are two possible ways to generalize BRπ-matrices into the interval setting, but we will prove that, in a sense, they are one. We derive mainly means of recognition for this interval matrices class, such as characterizations, necessary conditions and sufficient ones. Next we will take a look at interval B-matrices and interval doubly B-matrices, which were introduced recently, and present their characterizations through reduction, as well as such characterization for BRπ-matrices.
TL;DR: In this study, a generator function-based mapping, called the generalized epsilon function is presented and it is demonstrated that the exponential function is an asymptotic generalized ePSilon function.
Abstract: It is well known that the exponential function plays an extremely important role in many areas of science. In this study, a generator function-based mapping, called the generalized epsilon function is presented. Next, we demonstrate that the exponential function is an asymptotic generalized epsilon function. Exploiting this result and the fact that this new function is generator function-dependent, it can be utilized as a very flexible alternative to the exponential function in a wide range of applications. We should add that if the generator is a rational function, then the generalized epsilon function is rational as well. In this case, the generalized epsilon function is computationally simple and it may be treated as an easy-to-compute alternative to the exponential function. In this paper, we briefly present two applications of this novel function: an approximation to the exponential probability distribution, and an alternative to the sigmoid function on a bounded domain.
TL;DR: In this article, the convergence theorems of the Galerkin finite element approximation for the second-order elliptic boundary value problems were studied and it was shown that the norm of the approximate operator converges to the corresponding norm for the inverse of a linear elliptic operator.
Abstract: This paper deals with convergence theorems of the Galerkin finite element approximation for the second-order elliptic boundary value problems. Under some quite general settings, we show not only the pointwise convergence but also prove that the norm of approximate operator converges to the corresponding norm for the inverse of a linear elliptic operator. Since the approximate norm estimates of linearized inverse operator play an essential role in the numerical verification method of solutions for non-linear elliptic problems, our result is also important in terms of guaranteeing its validity. Furthermore, the present method can also be applied to more general elliptic problems, e.g., biharmonic problems and so on.
TL;DR: In this paper , the authors argue for an approach in Java which is similar to C++ template construct and evaluate the runtime performance of instantiated code and present their tool which is able to use Java generics as templates.
Abstract: Type parametrization is an essential construct in modern programming languages. On one hand, Java offers generics, on the other hand, C++ offers templates for highly reusable code. The mechanism between these constructs differs and affects usage and runtime performance, as well. Java uses type erasure, C++ deals with instantiations.In this paper, we argue for an approach in Java which is similar to C++ template construct. We evaluate the runtime performance of instantiated code and we present our tool which is able to use Java generics as templates. This tool generates Java source code. We present how this approach improves the usage of Java generics.
TL;DR: In this article , an explainable AI-assisted permissioned blockchain framework named EA-POT was proposed for predicting potential defaulters' IP addresses, based on the suggestions levied by explainable artificial intelligence and the approval of IP authorizers to blockchain database.
Abstract: The culpable cybersecurity practices that threaten leading organizations are logically prone to establishing countermeasures, including HoneyPots, and bestow research innovations in various dimensions such as ML-enabled threat predictions. This article proposes an explainable AI-assisted permissioned blockchain framework named EA-POT for predicting potential defaulters' IP addresses. EA-POT registers the predicted defaulters based on the suggestions levied by explainable AI and the approval of IP authorizers to blockchain database to enhance immutability. Experiments were carried out at IoT Cloud Research laboratory using three prediction models such as Random Forest Modeling (RFM), Linear Regression Modeling (LRM), and Support Vector Machines (SVM); and, the observed experimental results for predicting the AWS HoneyPots were explored. The proposed EA-POT framework revealed the procedure to include interpretable knowledge while blacklisting IPs that reach HoneyPots.
TL;DR: In this article , the structural realizability of discrete time linear dynamical systems (LDSs) in fixed state space dimension is investigated, where the set of feasible state transition matrices associated to a Markov parameter sequence Y is convex.
Abstract: In this paper we investigate realizability of discrete time linear dynamical systems (LDSs) in fixed state space dimension. We examine whether there exist different Θ = (A,B,C,D) state space realizations of a given Markov parameter sequence Y with fixed B, C and D state space realization matrices. Full observation is assumed in terms of the invertibility of output mapping matrix C. We prove that the set of feasible state transition matrices associated to a Markov parameter sequence Y is convex, provided that the state space realization matrices B, C and D are known and fixed. Under the same conditions we also show that the set of feasible Metzler-type state transition matrices forms a convex subset. Regarding the set of Metzler-type state transition matrices we prove the existence of a structurally unique realization having maximal number of non-zero off-diagonal entries. Using an eigenvalue assignment procedure we propose linear programming based algorithms capable of computing different state space realizations. By using the convexity of the feasible set of Metzler-type state transition matrices and results from the theory of non-negative polynomial systems, we provide algorithms to determine structurally different realization. Computational examples are provided to illustrate structural non-uniqueness of network-based LDSs.