OpenCOPI: middleware integration for Ubiquitous Computing

In this paper, we present OpenCOPI (Open COntext Platform Integration), a Service-Oriented Architecture-based middleware platform that supports the integration of services provided by distinct sources, ranging from services offered by simple systems to more complex services provided by context-provision middleware. OpenCOPI offers selection and composition mechanisms to, respectively, select and compose services provided by different sources, considering applications of both Quality of Service and Quality of Context requirements. It also offers an adaptation mechanism that enables to adapt the application execution due to service failures, service quality fluctuation and user mobility. OpenCOPI allows the definition of applications in a higher abstraction level by the specification of a semantic workflow that contains abstract activities. This paper illustrates the use of OpenCOPI in an application from the Gas & Oil Industry and it also shows the evaluation of the main mechanisms of OpenCOPI: the service selection, composition, adaptation and workflow execution.     

Frontline robots in tourism and hospitality: service enhancement or cost reduction?

Electronic Markets - Robots are being implemented in many frontline services, from waiter robots in restaurants to robotic concierges in hotels. A growing number of firms in hospitality and tourism...     

Twin-image suppression in digital in-line holography based on wave-front filtering

Pattern Analysis and Applications - Digital holography is an imaging process able to recreate three-dimensional representations of objects from recording pattern interference among distinct waves....     

The corrosion of mild steel in simulated SO2 containing atmospheres

Electrochemical tests were carried out on steel/steel multi-laminar cells under thin layers of sulphate solution simulating atmospheres with contents of 20, 50 and 75 μg m⁻³ of SO₂ and a relative humidity of 85%. Measurements of the current flowing between the two electrodes of the cell vs drying time and impedance measurements for different times were taken. Potentiodynamic cathodic polarization plots were also obtained for one concentration of contaminant at various drying times using three electrode multi-laminar cells. The results showed that the multi-laminar cells allowed the drying process of the thin electrolyte films to be followed. The use of electrochemical techniques gave information on the kinetics and mechanism of the corrosion process occurring during the drying period. Initially the process is controlled by oxygen diffusion through the liquid film, but for longer periods the control changes to the anodic reaction.     

Beyond multi-adjoint logic programming

In ‘multi-adjoint logic programming’, MALP in brief, each fuzzy logic program is associated with its own ‘multi-adjoint lattice’ for modelling truth degrees beyond the simpler case of true and false, where a large set of fuzzy connectives can be defined. On this wide repertoire, it is crucial to connect each implication symbol with a proper conjunction thus conforming constructs of the form (←_i, &_i) called ‘adjoint pairs’, whose use directly affects both declarative and operational semantics of the MALP framework. In this work, we firstly show how the strong dependence of adjoint pairs can be largely weakened for an interesting ‘sub-class’ of MALP programs. Then, we reason in a similar way till conceiving a ‘super-class’ of fuzzy logic programs beyond MALP, which definitively drops out the need for using adjoint pairs, since the new semantics behaviour relies on much more relaxed lattices than multi-adjoint ones.     

Extended Reflexive Ontologies for the Generation of Clinical Recommendations

Decision recommendations are a set of alternative options for clinical decisions (e.g., diagnosis, prognosis, treatment selection, follow-up, and prevention) that are provided to decision makers by knowledge-based Clinical Decision Support Systems (k-CDSS) as aids. We propose to follow a “reasoning over domain” approach for the generation of decision recommendations by gathering and inferring conclusions from production rules. In order to rationalize our approach, we present a specification that will sustain the logic models supported in the knowledge bases we use for persistence. We introduce first the underlying knowledge model and then the necessary extensions that will convey toward the solution of the reported needs. The starting point of our approach is the proposition of Reflexive Ontologies (RO). Here, we go a step further, proposing an extension of RO that includes the handling and reasoning that production rules provide. Our approach speeds up the recommendation generation process.     

Recommending multimedia visiting paths in cultural heritage applications

The valorization and promotion of worldwide Cultural Heritage by the adoption of Information and Communication Technologies represent nowadays some of the most important research issues with a large variety of potential applications. This challenge is particularly perceived in the Italian scenario, where the artistic patrimony is one of the most diverse and rich of the world, able to attract millions of visitors every year to monuments, archaeological sites and museums. In this paper, we present a general recommendation framework able to uniformly manage heterogeneous multimedia data coming from several web repositories and to provide context-aware recommendation techniques supporting intelligent multimedia services for the users—i.e. dynamic visiting paths for a given environment. Specific applications of our system within the cultural heritage domain are proposed by means of real case studies in the mobile environment related both to an outdoor and indoor scenario, together with some results on user’s satisfaction and system accuracy.     

3D Surface Reconstruction of Noisy Point Clouds Using Growing Neural Gas: 3D Object/Scene Reconstruction

With the advent of low-cost 3D sensors and 3D printers, scene and object 3D surface reconstruction has become an important research topic in the last years. In this work, we propose an automatic (unsupervised) method for 3D surface reconstruction from raw unorganized point clouds acquired using low-cost 3D sensors. We have modified the growing neural gas network, which is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation, to perform 3D surface reconstruction of different real-world objects and scenes. Some improvements have been made on the original algorithm considering colour and surface normal information of input data during the learning stage and creating complete triangular meshes instead of basic wire-frame representations. The proposed method is able to successfully create 3D faces online, whereas existing 3D reconstruction methods based on self-organizing maps required post-processing steps to close gaps and holes produced during the 3D reconstruction process. A set of quantitative and qualitative experiments were carried out to validate the proposed method. The method has been implemented and tested on real data, and has been found to be effective at reconstructing noisy point clouds obtained using low-cost 3D sensors.     

Structural design using multi-objective metaheuristics. Comparative study and application to a real-world problem

Many structural design problems in the field of civil engineering are naturally multi-criteria, i.e., they have several conflicting objectives that have to be optimized simultaneously. An example is when we aim to reduce the weight of a structure while enhancing its robustness. There is no a single solution to these types of problems, but rather a set of designs representing trade-offs among the conflicting objectives. This paper focuses on the application of multi-objective metaheuristics to solve two variants of a real-world structural design problem. The goal is to compare a representative set of state-of-the-art multi-objective metaheuristic algorithms aiming to provide civil engineers with hints as to what optimization techniques to use when facing similar problems as those selected in the study presented in this paper. Accordingly, our study reveals that MOCell, a cellular genetic algorithm, provides the best overall performance, while NSGA-II, the de facto standard multi-objective metaheuristic technique, also demonstrates a competitive behavior.