Introducing requirements traceability support in model-driven development of web applications

In this work, we present an approach that introduces requirements traceability capabilities in the context of model-driven development of Web applications. This aspect allows us to define model-to-model transformations that not only provide a software artifact of lower abstraction (as model-to-model transformations usually do) but also to provide feedback about how they are applied. This feedback helps us to validate whether transformations are correctly applied. In particular, we present a model-to-model transformation that allows us to obtain navigational models of the Web engineering method OOWS from a requirements model. This transformation is defined as a set of mappings between these two models that have been implemented by means of graph transformations. The use of graph transformations allows us to develop a tool-supported strategy for applying mappings automatically. In addition, mechanisms for tracing requirements are also included in the definition of graph transformations. These mechanisms allow us to link each conceptual element to the requirements from which it is derived. In particular, we focus on tracing requirements throughout the navigational model, which describe the navigational structure of a Web application. To take advantage of these traceability mechanisms, we have developed a tool that obtains traceability reports after applying transformations. These reports help us to study aspects such as whether requirements are all supported, the impact of changing a requirement, or how requirements are modelled.     

Scientometric Study of the Progress and Development of e-Government Research During the Period 2000–2012

Many countries have implemented changes in public-sector management models, based on the strategic and intensive use of new information and communication technologies. From a critical standpoint, this paper analyzes and characterizes the contributions made by research in the field of e-government, identifying future areas of interest and potentially valuable methodologies. In addition, it compares research efforts focused on developing countries with those concerning developed economies, in order to identify research gaps and possibilities for improvement in the context of e-government research in developing countries. Diverse scientometric approaches are employed in this analysis of papers published by international journals listed in the SSCI index in the fields of Public Administration and of Information Science & Library Science. Our findings reveal the existence of various research gaps and highlight areas that should be addressed in future research, especially in developing countries. Indeed, the research approach to e-government remains immature, focusing on particular cases or dimensions, while little has been done to produce theories or models to clarify and explain the political processes of e-government. In addition, significant differences are found between the impact of scientific output and patterns of scientific production as regards developing and developed countries.     

Vision-based control for rigid body stabilization

This paper addresses the problem of stabilizing to a desired equilibrium point an eye-in-hand system, which consists of a single camera mounted on a rigid body free to move on . It is assumed that there is a collection of landmarks fixed in the environment and that the image coordinates of those landmarks are provided to the system by an on-board CCD camera. The proposed method addresses not only the problem of stabilization but also that of maintaining feature visibility along the system’s trajectory. The resulting solution consists of a feedback control law based on the current and desired image coordinates and reconstructed attitude and depth ratio information, which guarantees that (i) the desired equilibrium point is an almost global attractor; (ii) a set of necessary conditions for feature visibility holds throughout the system’s trajectories; and (iii) the image of a predefined feature point is kept inside the camera’s field of view.     

Robot arm reaching through neural inversions and reinforcement learning

We present a neural method that computes the inverse kinematics of any kind of robot manipulators, both redundant and non-redundant. Inverse kinematics solutions are obtained through the inversion of a neural network that has been previously trained to approximate the manipulator forward kinematics. The inversion provides difference vectors in the joint space from difference vectors in the workspace. Our differential inverse kinematics (DIV) approach can be viewed as a neural network implementation of the Jacobian transpose method for arm kinematic control that does not require previous knowledge of the arm forward kinematics. Redundancy can be exploited to obtain a special inverse kinematic solution that meets a particular constraint (e.g. joint limit avoidance) by inverting an additional neural network The usefulness of our DIV approach is further illustrated with sensor-based multilink manipulators that learn collision-free reaching motions in unknown environments. For this task, the neural controller has two modules: a reinforcement-based action generator (AG) and a DIV module that computes goal vectors in the joint space. The actions given by the AG are interpreted with regard to those goal vectors.     

3D posture estimation using geodesic distance maps

This paper presents a novel technique for three-dimensional (3D) human motion capture using a set of two non-calibrated cameras. The user’s five extremities (head, hands and feet) are extracted, labeled and tracked after silhouette segmentation. As they are the minimal number of points that can be used in order to enable whole body gestural interaction, we will henceforth refer to these features as crucial points. Features are subsequently labelled using 3D triangulation and inter-image tracking. The crucial point candidates are defined as the local maxima of the geodesic distance with respect to the center of gravity of the actor region that lie on the silhouette boundary. Due to its low computational complexity, the system can run at real-time paces on standard personal computers, with an average error rate range between 4% and 9% in realistic situations, depending on the context and segmentation quality.

Robot task plan representation by Petri nets: modelling, identification, analysis and execution

In this paper we introduce a framework to represent robot task plans based on Petri nets. Our approach enables modelling a robot task, analysing its qualitative and quantitative properties and using the Petri net representation for actual plan execution. The overall model is obtained from the composition of simple models, leading to a modular approach. Analysis is applied to a closed loop between the robot controller and the environment Petri net models. We focus here on the quantitative properties, captured by stochastic Petri net models. Furthermore, we introduce a method to identify the environment and action layer parameters of the stochastic Petri net models from real data, improving the significance of the model. The framework building blocks and a single-robot task model are detailed. Results of a case study with simulated soccer robots show the ability of the framework to provide a systematic modelling tool, and of determining, through well-known analysis methods for stochastic Petri nets, relevant properties of the task plan applied to a particular environment.     

An agent-based genetic algorithm for hybrid flowshops with sequence dependent setup times to minimise makespan

This paper deals with a variant of flowshop scheduling, namely, the hybrid or flexible flowshop with sequence dependent setup times. This type of flowshop is frequently used in the batch production industry and helps reduce the gap between research and operational use. This scheduling problem is NP-hard and solutions for large problems are based on non-exact methods. An improved genetic algorithm (GA) based on software agent design to minimise the makespan is presented. The paper proposes using an inherent characteristic of software agents to create a new perspective in GA design. To verify the developed metaheuristic, computational experiments are conducted on a well-known benchmark problem dataset. The experimental results show that the proposed metaheuristic outperforms some of the well-known methods and the state-of-art algorithms on the same benchmark problem dataset.