Supporting multiple perspectives in feature-based configuration

Feature diagrams have become commonplace in software product line engineering as a means to document variability early in the life cycle. Over the years, their application has also been extended to assist stakeholders in the configuration of software products. However, existing feature-based configuration techniques offer little support for tailoring configuration views to the profiles of the various stakeholders. In this paper, we propose a lightweight, yet formal and flexible, mechanism to leverage multidimensional separation of concerns in feature-based configuration. We propose a technique to specify concerns in feature diagrams and to generate automatically concern-specific configuration views. Three alternative visualisations are proposed. Our contributions are motivated and illustrated through excerpts from a real web-based meeting management application which was also used for a preliminary evaluation. We also report on the progress made in the development of a tool supporting multi-view feature-based configuration.     

Is Deep-Learning and Natural Language Processing Transcending the Financial Forecasting? Investigation Through Lens of News Analytic Process

Computational Economics - This study tries to unravel the stock market prediction puzzle using the textual analytic with the help of natural language processing (NLP) techniques and Deep-learning...     

A novel composite model approach to improve software quality prediction

Context:How can quality of software systems be predicted before deployment? In attempting to answer this question, prediction models are advocated in several studies. The performance of such models drops dramatically, with very low accuracy, when they are used in new software development environments or in new circumstances.ObjectiveThe main objective of this work is to circumvent the model generalizability problem. We propose a new approach that substitutes traditional ways of building prediction models which use historical data and machine learning techniques.MethodIn this paper, existing models are decision trees built to predict module fault-proneness within the NASA Critical Mission Software. A genetic algorithm is developed to combine and adapt expertise extracted from existing models in order to derive a “composite” model that performs accurately in a given context of software development. Experimental evaluation of the approach is carried out in three different software development circumstances.ResultsThe results show that derived prediction models work more accurately not only for a particular state of a software organization but also for evolving and modified ones.ConclusionOur approach is considered suitable for software data nature and at the same time superior to model selection and data combination approaches. It is then concluded that learning from existing software models (i.e., software expertise) has two immediate advantages; circumventing model generalizability and alleviating the lack of data in software-engineering.     

Design and optimization of LNA topologies with image rejection filters

An important problem in designing RFIC in CMOS technology is the parasitic elements of passive and active devices that complicate design calculations. This article presents three LNA topologies including cascode, folded cascade, and differential cascode and then introduces image rejection filters for low‐side and high‐side injection. Then, a new method for design and optimization of the circuits based on a Pareto‐based multiobjective genetic algorithm is proposed. A set of optimum device values and dimensions that best match design specifications are obtained. The optimization method is layout aware, parasitic aware, and simulation based. Circuit simulations are carried out based on TSMC 0.18 μm CMOS technology by using Hspice. © 2010 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2010.     

Shape optimized design of microwave dielectric resonators by level‐set and topology gradient methods

The finite element method is coupled with the topology gradient (TG) and level‐set (LS) methods for optimizing the shape of microwave components using a computer‐aided design model. On the one hand, the LS approach is based on the classical shape derivative; while on the other hand, the TG method is precisely designed for introducing new perturbations in the optimization domain. These two approaches, which consist in minimizing a cost function related to the component behavior, are first described. Regarding given electrical specifications, these techniques are applied to optimize the distribution of ceramic parts of a dual‐mode resonator in order to improve its behavior. The optimized dielectric resonators result in a wide spurious‐free stop band. A comparison between classical and optimized dual mode resonator is presented. Theoretical results are then validated by careful measurements. © 2009 Wiley Periodicals, Inc. Int J RF and Microwave CAE 2010.     

Implementation of speed controller for rotary hydraulic motor based on LS-SVM

The performance of a model-based control system depends strongly on the accuracy of the process model used. LS-SVM is a powerful method for modeling nonlinear systems. The main objective of this paper is to implement a conventional controller based on LS-SVM model for hydraulic motor. An off-line model is first identified based on LS-SVM, then via simulation tests the parameters of the discrete PI-Controller and its velocity-form are obtained then the controller parameters are applied experimentally for the hydraulic motor as a speed controller. The system performance has been evaluated; results show good performance over a wide range of operating conditions and load disturbances.     

Fast,effective heuristics for the 0–1 multi-dimensional knapsack problem

The objective of the multi-dimensional knapsack problem (MKP) is to find a subset of items with maximum value that satisfies a number of knapsack constraints. Solution methods for MKP, both heuristic and exact, have been researched for several decades. This paper introduces several fast and effective heuristics for MKP that are based on solving the LP relaxation of the problem. Improving procedures are proposed to strengthen the results of these heuristics. Additionally, the heuristics are run with appropriate deterministic or randomly generated constraints imposed on the linear relaxation that allow generating a number of good solutions. All algorithms are tested experimentally on a widely used set of benchmark problem instances to show that they compare favourably with the best-performing heuristics available in the literature.     

Universal integral controllers for minimum-phase nonlinear systems

We consider a single-input-single-output (SISO) nonlinear system that has a well-defined normal form with asymptotically stable zero dynamics. Using only knowledge of the relative degree and the sign of the high-frequency gain, we design an output feedback integral controller that asymptotically regulates the output to a bounded time-varying reference signal with a constant limit. We give regional as well as semi-global results. We also show that, for relative degree one and two systems, the proposed integral controller reduces to the classical PI and PID controllers, respectively     

Effect of Cd Addition on Superconducting Fluctuations and Mechanical Properties of Bi1.82Pb0.36Sr2Ca2Cd x Cu3O y System

A study was made of the effect of Cd additions on the superconducting and mechanical properties of Bi_1.82Pb_0.36Sr₂Ca₂ Cd_xCu₃O_y (x = 0.0, 0.15, 0.25, 0.35 and 0.55). Characterization of the Cd-samples using XRD, DTA, and SEM techniques, has confirmed that remarkably formation of low-T _c phase (2212) by the addition of Cd up to 0.35. High-resolution electrical resistivity ρ (T) data on the composition of Cd = 0.35 have been taken for investigating critically the superconducting fluctuations. Using the Aslamazov and Larkin (AL) and Lawrence and Doniach (LD) models of excess conductivity. Excess conductivity analysis shows that this composition (Cd = 0.35) is 2D in the temperature range 137.8–163.7 K and a 3D one below 137.8 K. Thus, a crossover from 2D to 3D is observed at 137.8 K. Sample microhardness and density are greatly improved by Cd-additions (0.35). This trend is probably due to the intercalation of cadmium between superconducting grains in compositions may provide a plastic-flow region that allows relaxation of undesirable stresses resulting from the grain anisotropy of superconductors.