Informatica

In current paper a problem of classification of T-distributed random field observation into one of two populations specified by common scaling function is considered. The ML and LS estimators of the mean parameters are plugged into the linear discriminant function. The closed form expressions for the Bayes error rate and the actual error rate associated with the aforementioned discriminant functions are derived. This is the extension of one for the Gaussian case. The actual error rates are used to evaluate and compare the performance of the plug-in discriminant function by means of Monte Carlo study.

The nonlinear stochastic programming problem involving CVaR in the objective and constraints is considered. Solving the latter problem in a framework of bi-level stochastic programming, the extended Lagrangian is introduced and the related KKT conditions are derived. Next, the sequential simulation-based approach has been developed to solve stochastic problems with CVaR by finite sequences of Monte Carlo samples. The approach considered is grounded by the rule for iterative regulation of the Monte Carlo sample size and the stochastic termination procedure, taking into account the stochastic model risk. The rule is introduced to regulate the size of the Monte Carlo sample inversely proportionally to the square of the stochastic gradient norm allows us to solve stochastic nonlinear problems in a rational way and ensures the convergence. The proposed termination procedure enables us to test the KKT conditions in a statistical way and to evaluate the confidence intervals of the objective and constraint functions in a statistical way as well. The results of the Monte Carlo simulation with test functions and solution of the practice sample of trade-offs of gas purchases, storage and service reliability, illustrate the convergence of the approach considered as well as the ability to solve in a rational way the nonlinear stochastic programming problems handling CVaR in the objective and constraints, with an admissible accuracy, treated in a statistical manner.

Visual appearance can be phenomenologically modeled through an integral equation, known as reflectance equation. It describes the surface radiance which depends on the interaction between incident light field and surface Bidirectional Reflectance Distribution Function (BRDF). Being defined on the Cartesian product of the incident and outgoing hemispheres, hemispherical basis is the natural way to represent surface BRDFs. Nonetheless, due to their compactness in the frequency space, spherical harmonics have been extensively used for this purpose. Addressing the geometrical compliance of hemispherical basis, this paper proposes a Cartesian product of the hemispherical harmonics to provide a compact representation of plausible BRDFs, while satisfying the Helmholtz reciprocity property. We provide an analytical analysis and experimental justification that our basis provides better approximation accuracy when compared to similar bases in literature.

The choice of natural image prior decides the quality of restored image. Recently successful algorithms exploit heavy-tailed gradient distribution as image prior to restore latent image with piecewise smooth regions. However, these prior assumed also remove the mid-frequency component such as textural details regions while they preserve sharp edges. That because gradient profile in fractal-like texture do not have sparse characteristic.

Fuzzy C-Means (FCM) algorithm is one of the commonly preferred fuzzy algorithms for image segmentation applications. Even though FCM algorithm is sufficiently accurate, it suffers from the computational complexity problem which prevents the usage of FCM in real-time applications. In this work, this convergence problem is tackled through the proposed Modified FCM (MFCM) algorithm. In this algorithm, several clusters among the input data are formed based on similarity measures and one representative data from each cluster is used for FCM algorithm. Hence, this methodology minimizes the convergence time period requirement of the conventional FCM algorithm to higher extent. This proposed approach is experimented on Magnetic Resonance (MR) brain tumor images. Experimental results suggest promising results for the MFCM algorithm in terms of the performance measures.

A multitude of heuristic stochastic optimization algorithms have been described in literature to obtain good solutions of the box-constrained global optimization problem often with a limit on the number of used function evaluations. In the larger question of which algorithms behave well on which type of instances, our focus is here on the benchmarking of the behavior of algorithms by applying experiments on test instances. We argue that a good minimum performance benchmark is due to pure random search; i.e. algorithms should do better. We introduce the concept of the cumulative distribution function of the record value as a measure with the benchmark of pure random search and the idea of algorithms being dominated by others. The concepts are illustrated using frequently used algorithms.

Certificateless public-key systems (CL-PKS) were introduced to simultaneously solve two critical problems in public-key systems. One is the key escrow problem in ID-based public-key systems and the other is to eliminate the presence of certificates in conventional public-key systems. In the last decade, several certificateless signature (CLS) schemes have been proposed in the random oracle model. These CLS schemes possess existential unforgeability against adaptive chosen-message attacks, and only few of them possess strong unforgeability. A CLS scheme with strong unforgeability plays an important role in the construction of certificateless cryptographic schemes. Unfortunately, all the existing CLS schemes in the standard model (without random oracles) have been shown insecure to provide existential unforgeability under a generally adopted security model. In the article, we propose a strongly secure CLS scheme in the standard model under the generally adopted security model. Our scheme possesses not only existential unforgeability but also strong unforgeability, and turns out to be the first strongly secure CLS scheme in the standard model. Under the collision resistant hash (CRH) and computational Diffie–Hellman (CDH) assumptions, we prove that our CLS scheme possesses strong unforgeability against both Type I (outsiders) and Type II (key generation center) adversaries.

Fair input/output (or I/O) automata are a state-machine model for specifying and verifying reactive and concurrent systems. For the verification purposes, one is usually interested only in the sequences of interactions fair I/O automata offer to their environment. These sequences are called fair traces. The usual approach to the verification consists in proving fair trace inclusion between fair I/O automata. This paper presents a simple approach to the specification of fair traces and shows how to establish a fair trace inclusion relation for a pair of fair I/O automata by using the temporal logic of actions.

Interval-valued intuitionistic fuzzy numbers (IVIFNs) characterized by a membership function and a non-membership function with values that are intervals, have strong ability to handle imprecise and ambiguous information in real-world applications. This paper proposes an integrated maximizing consistency and multi-choice goal programming (MCGP) approach to handle hybrid multi-criteria group decision making problems based on IVIFNs. Firstly, the hybrid decision information (including crisp numbers, intervals, intuitionistic fuzzy numbers and linguistic variables) are normalized into the IVIFNs. Then, an ordinal consistency index and a cardinal consistency index are proposed to measure the consistency between the individual opinion and the group opinion, respectively. And an optimal model based on maximizing consistency is constructed to derive the weights of experts. Afterwards, the comprehensive ratings and the ranking values of alternatives are obtained by the hybrid weighted aggregation operator and the proposed ratio function of IVIFNs, respectively. Furthermore, a MCGP model based on the ranking values is constructed to identify the optimal alternatives and their optimum quantities. At length, an illustrative case is provided to verify the proposed approach.