Abstract
The ultimate goal of feature selection is to select the smallest subset of features that yields minimum generalization error from an original set of features. This effectively reduces the feature space, and thus the complexity of classifiers. Though several algorithms have been proposed, no single one outperforms all the other in all scenarios, and the problem is still an actively researched field. This paper proposes a new hybrid parallel approach to perform feature selection. The idea is to use a filter metric to reduce feature space, and then use an innovative wrapper method to search extensively for the best solution. The proposed strategy is implemented on a shared memory parallel environment to speedup the process. We evaluated its parallel performance using up to 32 cores and our results show 30 times gain in speed. To test the performance of feature selection we used five datasets from the well known NIPS challenge and were able to obtain an average score of 95.90% for all solutions.

Abstract
Nature and biological inspired search and optimization methods are simple and powerful tools that can be used to design classical industrial controllers. In this paper a particle swarm optimization (PSO) algorithm based technique is deployed to design proportional integrative and derivative controllers to fulfill minimum robustness constraints. PID robustness design using maximum sensitivity and complementary sensitivity values is re-addressed and formulated within a constrained PSO. Results are presented and analyzed regarding the control objective of load disturbance rejection and compared with other techniques.

Abstract
Determining the frequency of small subgraphs is an important graph mining primitive. One major class of algorithms for this task is based upon the enumeration of all sets of k connected nodes. These are known as network-centric algorithms. FAst Subgraph Enumeration (FaSE) is a exact algorithm for subgraph counting that contrasted with its past approaches by performing the isomorphism tests while doing the enumeration, encapsulating the topological information in a g-trie and thus largely reducing the number of required isomorphism tests. Our goal with this paper is to expand this approach by providing an approximate algorithm, which we called Rand-FaSE. It uses an unbiased sampling estimator for the number of subgraphs of each type, allowing an user to trade some accuracy for even faster execution times. We tested our algorithm on a set of representative complex networks, comparing it with the exact alternative, FaSE. We also do an extensive analysis by studying its accuracy and speed gains against previous sampling approaches. With all of this, we believe FaSE and Rand-FaSE pave the way for faster network-centric census algorithms.

Abstract

Abstract
In this paper we describe de application of a modeling framework, the so-called Conceptual Model Decomposing Value for the Customer (CMDVC), in a Footwear Industry case study, to ascertain the usefulness of this approach. The value networks were used to identify the participants, both tangible and intangible deliverables/endogenous and exogenous assets, and the analysis of their interactions as the indication for an adequate value proposition. The quantitative model of benefits and sacrifices, using the Fuzzy AHP method, enables the discussion of how the CMDVC can be applied and used in the enterprise environment and provided new relevant relations between perceived benefits (PBs).

Abstract
Nowadays, it is a high trend to increase the electricity production using Distributed Generation (DG) technology based on renewable energy resources (RERs) such as photovoltaics and wind power. If these systems are not properly controlled, their connection to the power grid can provoke many problems on the grid side. Therefore, considerations about power generation, safe running and grid connection must be done before connecting DG units into the power grid. This paper is mainly dealing with the grid connection of DG units to the power grids and compensation of the unbalanced loads. The proposed technique provides compensation for active, reactive, unbalanced, and harmonic current components of unbalanced loads. High performance of the proposed technique is obtained through Matlab/Simulink, guaranteeing balanced overall grid currents, injection of maximum available power from DG resources, power factor increase of the utility grid, and a reduction of total harmonic distortion (THD) of grid current, under the proposed operating conditions.