Informatica

The brokering of the best Cloud proposals that optimizes the application requirements allows to exploit the flexibility of the Cloud programming paradigm by a dynamically selection of the best SLA, which is available into the market. We present in this paper ascalable multi-users version of a Broker As A Service solution that uses the available resources of a distributed environment, and addresses related issues. The brokering problem is divided into simpler tasks, which are distributed among independent agents, whose population dynamically scales together the computing infrastructure, to support unforeseeable workloads produced by the interactions with large groups of users. The brokering model and its implementation, which adopts Cloud technologies itself, are described. Performance results and effectiveness of the first prototype implementation are discussed.

The fuzzy number is a special case of fuzzy set. As a generalization of the fuzzy number, trapezoidal intuitionistic fuzzy number (TrIFN) is a special intuitionistic fuzzy set defined on the real number set, which seems to suitably describe an ill-known quantity. The purpose of this paper is to propose a new method for solving the multi-attribute group decision making problems, in which the attribute values are TrIFNs and the attribute weight information are incomplete. The concepts, such as the weighted lower and upper possibility means, the weighted possibility means and variances of TIFNs, are introduced. Hereby, a new lexicographic method is developed to rank the TrIFNs. In the proposed method, the weights of experts are determined in terms of the voting model of intuitionistic fuzzy set. The attribute weights are objectively derived through constructing the bi-objective programming model, which is transformed into the single objective quadratic programming model to solve. The ranking order of alternatives is generated by the collective overall attribute values of alternatives. The stock selection example and comparison analyzes show the validity and applicability of the method proposed in this paper.

Wavelet analysis is a powerful tool with modern applications as diverse as: image processing, signal processing, data compression, data mining, speech recognition, computer graphics, etc. The aim of this paper is to introduce the concept of atomic decomposition of fuzzy normed linear spaces, which play a key role in the development of fuzzy wavelet theory. Atomic decompositions appeared in applications to signal processing and sampling theory among other areas.

First we give a general definition of fuzzy normed linear spaces and we obtain decomposition theorems for fuzzy norms into a family of semi-norms, within more general settings. The results are both for Bag–Samanta fuzzy norms and for Katsaras fuzzy norms. As a consequence, we obtain locally convex topologies induced by this types of fuzzy norms.

The results established in this paper, constitute a foundation for the development of fuzzy operator theory and fuzzy wavelet theory within this more general frame.

With respect to interval-valued hesitant fuzzy multi-attribute decision making, this study first presents a new ranking method for interval-valued hesitant fuzzy elements. In order to obtain the comprehensive values of alternatives, two induced generalized interval-valued hesitant fuzzy hybrid operators based on the Shapley function are defined, which globally consider the importance of elements and their ordered positions as well as reflect the interactions between them. If the weight information is incompletely known, models for the optimal weight vectors on the attribute set and on the ordered set are respectively established. Furthermore, an approach to interval-valued hesitant fuzzy multi-attribute decision making with incomplete weight information and interactive characteristics is developed. Finally, an illustrative example is provided to show the concrete application of the proposed procedure.

The paper summarizes the results of research on the modeling and implementation of advanced planning and scheduling (APS) systems done in recent twenty years. It discusses the concept of APS system – how it is thought of today – and highlights the modeling and implementation challenges with which the developers of such systems should cope. Some from these challenges were identified as a result of the study of scientific literature, others – through an in-depth analysis of the experience gained during the development of real-world APS system – a Production Efficiency Navigator (PEN system). The paper contributes to APS systems theory by proposing the concept of an ensemble of collaborating algorithms.

Clustering is one of the better known unsupervised learning methods with the aim of discovering structures in the data. This paper presents a distance-based Sweep-Hyperplane Clustering Algorithm (SHCA), which uses sweep-hyperplanes to quickly locate each point’s approximate nearest neighbourhood. Furthermore, a new distance-based dynamic model that is based on $2^{\mathit{N}}$-tree hierarchical space partitioning, extends SHCA’s capability for finding clusters that are not well-separated, with arbitrary shape and density. Experimental results on different synthetic and real multidimensional datasets that are large and noisy demonstrate the effectiveness of the proposed algorithm.

The present paper deals with building the text corpus for unit selection text-to-speech synthesis. During synthesis the target and concatenation costs are calculated and these costs are usually based on the prosodic and acoustic features of sounds. If the cost calculation is moved to the phonological level, it is possible to simulate unit selection synthesis without any real recordings; in this case text transcriptions are sufficient. We propose to use the cost calculated during the test data synthesis simulation to evaluate the text corpus quality. The greedy algorithm that maximizes coverage of certain phonetic units will be used to build the corpus. In this work the corpora optimized to cover phonetic units of different size and weight are evaluated.

Terminating procedure GS-LCK-PROC of the proof search in the sequent calculus GS-LCK of logic of correlated knowledge is presented in this paper. Also decidability of logic of correlated knowledge is proved, where GS-LCK-PROC is a decision procedure.

Reversible data hiding is a method that can guarantee that the cover image can be reconstructed correctly after the secret message has been extracted. Recently, some reversible data hiding schemes have concentrated on the VQ compression domain. In this paper, we present a new reversible data hiding scheme based on VQ and SMVQ techniques to enhance embedding capacity and compression rate. Experimental results show that our proposed scheme achieves higher embedding capacity and smaller average compression rate than some previous methods. Moreover, our proposed scheme maintains the high level of visual quality of the reconstructed image.