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Mobile cloud computing has emerged aiming at assisting mobile devices in processing computationally or data intensive tasks using cloud resources. This paper presents an optimization approach for utilizing cloud services for mobile client in mobile cloud, which considers the benefit of both mobile device users and cloud datacenters. The mobile cloud service provisioning optimization is conducted in parallel under the deadline, budget and energy expenditure constraint. Mobile cloud provider runs multiple VMs to execute the jobs for mobile device users, the cloud providers want to maximize the revenue and minimize theelectrical cost. The mobile device user gives the suitable payment to the cloud datacenter provider for available cloud resources for optimize the benefit. The paper proposes a distributed optimization algorithm for utilizing cloud services for mobile devices. The experiment is to test convergence of the proposed algorithm and also compare it with other related work. The experiments study the impacts of job arrival rate, deadline and mobility speeds on energy consumption ratio, execution success ratio, resource allocation efficiency and cost. The experiment shows that the proposed algorithm outperforms other related work in terms of some performance metrics such as allocation efficiency.

Poisson conditional autoregressive model of spatio-temporal data is proposed. Markov property and probabilistic characteristics of this model are presented. Algorithms for maximum likelihood estimation of the model parameters are constructed. Optimal forecasting statistic minimizing probability of forecast error is given. The “plug-in” principle based on ML-estimators is used for forecasting in the case of unknown parameters. The results of computer experiments on simulated and real medical data are presented.

A nonlinear substitution operation of bytes is the main strength factor of the Advanced Encryption Standard (AES) and other modern cipher systems. In this paper we have presented a new simple algorithm to generate key-dependent S-boxes and inverse S-boxes for block cipher systems. The quality of this algorithm was tested by using NIST tests, and changing only one bit of the secret key to generate new key-dependent S-boxes. The fact that the S-boxes are key-dependent and unknown is the main strength of the algorithm, since the linear and differential cryptanalysis require known S-boxes. In the second section of the paper, we analyze S-boxes. In the third section we describe the key-dependent S-boxes and inverse S-boxes generation algorithm. Afterwards, we experimentally investigate the quality of the generated key-dependent S-boxes. Comparison results suggest that the key-dependent S-boxes have good performance and can be applied to AES.

Classical evolutionary multi-objective optimization algorithms aim at finding an approximation of the entire set of Pareto optimal solutions. By considering the preferences of a decision maker within evolutionary multi-objective optimization algorithms, it is possible to focus the search only on those parts of the Pareto front that satisfy his/her preferences. In this paper, an extended preference-based evolutionary algorithm has been proposed for solving multi-objective optimization problems. Here, concepts from an interactive synchronous NIMBUS method are borrowed and combined with the R-NSGA-II algorithm. The proposed synchronous R-NSGA-II algorithm uses preference information provided by the decision maker to find only desirable solutions satisfying his/her preferences on the Pareto front. Several scalarizing functions are used simultaneously so the several sets of solutions are obtained from the same preference information. In this paper, the experimental-comparative investigation of the proposed synchronous R-NSGA-II and original R-NSGA-II has been carried out. The results obtained are promising.

In several areas like Global Optimization using branch-and-bound methods, the unit n-simplex is refined by bisecting the longest edge such that a binary search tree appears. This process generates simplices belonging to different shape classes. Having less simplex shapes facilitates the prediction of the further workload from a node in the binary tree, because the same shape leads to the same sub-tree. Irregular sub-simplices generated in the refinement process may have more than one longest edge when $\mathit{n}\ge 3$ . The question is how to choose the longest edge to be bisected such that the number of shape classes is as small as possible. We develop a Branch-and-Bound (B&B) algorithm to find the minimum number of classes in the refinement process. The developed B&B algorithm provides a minimum number of eight classes for a regular 3-simplex. Due to the high computational cost of solving this combinatorial problem, future research focuses on using high performance computing to derive the minimum number of shapes in higher dimensions.

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.