Abstract
3D object detection and recognition is increasingly used for manipulation and navigation tasks in service robots. It involves segmenting the objects present in a scene, estimating a feature descriptor for the object view and, finally, recognizing the object view by comparing it to the known object categories. This paper presents an efficient approach capable of learning and recognizing object categories in an interactive and open-ended manner. In this paper, "open-ended" implies that the set of object categories to be learned is not known in advance. The training instances are extracted from on-line experiences of a robot, and thus become gradually available over time, rather than at the beginning of the learning process. This paper focuses on two state-of-the-art questions: (1) How to automatically detect, conceptualize and recognize objects in 3D scenes in an open-ended manner? (2) How to acquire and use high-level knowledge obtained from the interaction with human users, namely when they provide category labels, in order to improve the system performance? This approach starts with a pre-processing step to remove irrelevant data and prepare a suitable point cloud for the subsequent processing. Clustering is then applied to detect object candidates, and object views are described based on a 3D shape descriptor called spin-image. Finally, a nearest-neighbor classification rule is used to predict the categories of the detected objects. A leave-one-out cross validation algorithm is used to compute precision and recall, in a classical off-line evaluation setting, for different system parameters. Also, an on-line evaluation protocol is used to assess the performance of the system in an open-ended setting. Results show that the proposed system is able to interact with human users, learning new object categories continuously over time.

Abstract
In this paper, an indirect approach to the dual-mode adaptive robust controller (DMARC) is proposed, which combines the typical transient and robustness properties of variable structure systems with a smooth control signal in steady state, typical of conventional adaptive controllers, as model reference adaptive controller. The aim of this indirect version, here named indirect DMARC, is to provide a more intuitive controller design, based on physical plant parameters, as resistances, inertia moments, capacitances, and so on, maintaining DMARC properties. In this paper, a stability analysis for the proposed controller and simulations to an unstable second-order plant will be presented. Copyright (c) 2013 John Wiley & Sons, Ltd.

Abstract

Abstract
Websites quality is strategically important for organizations and for their clients' satisfaction. The rapid growth of web applications increases the need to evaluate web applications quantitatively and qualitatively. When you attempt to evaluate websites, many different evaluation methods can be used. This paper analyzes and presents different quality models in order to perceive which is the most appropriate, depending on the situation.

Abstract
This paper reviews the main characteristics of cloud computing, where they are exposed their main components and ways of use. In addition to the technological review that is done, is also carried out a critical analysis of their potential and challenges in the context of SMEs. To understand how cloud computing can lead to a powerful ally of SMEs in the context of organizational competitiveness in a world where the role of information systems for a long time proved decisive, it is a reflection that the SMEs, whose core business is not technology, need to carry out.

Abstract
Technologies for contextualization and Recognition of people and situations have made great progresses in the last years. Every time we use the Internet, pass by car in motorways, enter inside a commercial shopping, even when we go on street, there are in-build space mechanisms capable of getting our personal data video surveillance, our position through the tracking cell phones position or even when we post a message on some Social Network and we have GPS coordinates associated. Basically we share where we are, what we are doing and with time, we reveal our pattern behaviours. But what we get in return by sharing all this Information? Very few, but it could be a lot. That's the start point of this paper, that review the state of art of contextualization and recognition technologies, discuss is business and social potential and finishes with the presentation of an architecture for supporting a user-centered approach for the definition of a rules-engine capable of translating the users' interests and provide enterprises a source of useful information.