Configurable multi-perspective business process models

A configurable process model provides a consolidated view of a family of business processes. It promotes the reuse of proven practices by providing analysts with a generic modeling artifact from which to derive individual process models. Unfortunately, the scope of existing notations for configurable process modeling is restricted, thus hindering their applicability. Specifically, these notations focus on capturing tasks and control-flow dependencies, neglecting equally important ingredients of business processes such as data and resources. This research fills this gap by proposing a configurable process modeling notation incorporating features for capturing resources, data and physical objects involved in the performance of tasks. The proposal has been implemented in a toolset that assists analysts during the configuration phase and guarantees the correctness of the resulting process models. The approach has been validated by means of a case study from the film industry.     

A novel humid electronic nose combined with an electronic tongue for assessing deterioration of wine

We report herein the use of a combined system for the analysis of the spoilage of wine when in contact with air. The system consists of a potentiometric electronic tongue and a humid electronic nose. The potentiometric electronic tongue was built with thick-film serigraphic techniques using commercially available resistances and conductors for hybrid electronic circuits; i.e. Ag, Au, Cu, Ru, AgCl, and C. The humid electronic nose was designed in order to detect vapours that emanate from the wine and are apprehended by a moist environment. The humid nose was constructed using a piece of thin cloth sewn, damped with distilled water, forming five hollows of the right size to introduce the electrodes. In this particular case four electrodes were used for the humid electronic nose: a glass electrode, aluminium (Al), graphite and platinum (Pt) wires and an Ag-AgCl reference electrode. The humid electronic nose together with the potentiometric electronic tongue were used for the evaluation of the evolution in the course of time of wine samples. Additionally to the analysis performed by the tongue and nose, the spoilage of the wines was followed via a simple determination of the titratable (total) acidity.     

The organization of interaction design pattern languages alongside the design process

This work explores the possibility of taking the structural characteristics of approaches to interaction design as a basis for the organization of interaction design patterns. The Universal Model of the User Interface (Baxley, 2003) is seen as well suited to this; however, in order to cover the full range of interaction design patterns the model had to be extended slightly. Four existing collections of interaction design patterns have been selected for an analysis in which the patterns have been mapped onto the extended model. The conclusion from this analysis is that the use of the model supports the process of building a pattern language, because it is predictive and helps to complete the language. If several pattern writers were to adopt the model, a new level of synergy could be attained among these pattern efforts. A concluding vision would be that patterns could be transferred freely between pattern collections to make them as complete as possible.     

Combining Support Vector Machines and simulated annealing for stereovision matching with fish eye lenses in forest environments

We present a novel strategy for computing disparity maps from omni-directional stereo images obtained with fish-eye lenses in forest environments. At a first segmentation stage, the method identifies textures of interest to be either matched or discarded. Two of them are identified by applying the powerful Support Vector Machines approach. At a second stage, a stereovision matching process is designed based on the application of four stereovision matching constraints: epipolarity, similarity, uniqueness and smoothness. The epipolarity guides the process. The similarity and uniqueness are mapped once again through the Support Vector Machines, but under a different way to the previous case; after this an initial disparity map is obtained. This map is later filtered by applying the Discrete Simulated Annealing framework where the smoothness constraint is conveniently mapped. The combination of the segmentation and stereovision matching approaches makes the main contribution. The method is compared against the usage of simple features and combined similarity matching strategies.     

Strategies for collaborative writing and phases of knowledge construction in CSCL environments

Within the framework of research that describes the processes of collaborative knowledge construction in computer-supported collaborative learning (CSCL) environments, the present work has three objectives: (i) the identification of the strategies of six small groups of university students for the elaboration of written products in a CSCL environment; (ii) seek relations between the identified writing strategies and the processes and phases of collaborative knowledge construction in the groups; and (iii) relate these strategies and phases with the learning results obtained by the groups. We carried out a multiple-case study, with the analysis of four different didactic sequences, in two different virtual learning and teaching settings. In each setting, three student groups were studied, where each had to collaboratively develop between four and eight written products. For all the studied groups, the analysis enabled the identification of five types of strategies in the preparation of the elaboration of written products, and four types of phases of collaborative knowledge construction, which are interrelated and also connected with the grades that the groups obtained in each case.     

On the use of small training sets for neural network-based characterization of mixed pixels in remotely sensed hyperspectral images

In this work, neural network-based models involved in hyperspectral image spectra separation are considered. Focus is on how to select the most highly informative samples for effectively training the neural architecture. This issue is addressed here by several new algorithms for intelligent selection of training samples: (1) a border-training algorithm (BTA) which selects training samples located in the vicinity of the hyperplanes that can optimally separate the classes; (2) a mixed-signature algorithm (MSA) which selects the most spectrally mixed pixels in the hyperspectral data as training samples; and (3) a morphological-erosion algorithm (MEA) which incorporates spatial information (via mathematical morphology concepts) to select spectrally mixed training samples located in spatially homogeneous regions. These algorithms, along with other standard techniques based on orthogonal projections and a simple Maximin-distance algorithm, are used to train a multi-layer perceptron (MLP), selected in this work as a representative neural architecture for spectral mixture analysis. Experimental results are provided using both a database of nonlinear mixed spectra with absolute ground truth and a set of real hyperspectral images, collected at different altitudes by the digital airborne imaging spectrometer (DAIS 7915) and reflective optics system imaging spectrometer (ROSIS) operating simultaneously at multiple spatial resolutions.     

Forecasting peak air pollution levels using NARX models

Air pollution has a negative impact on human health. For this reason, it is important to correctly forecast over-threshold events to give timely warnings to the population. Nonlinear models of the nonlinear autoregressive with exogenous variable (NARX) class have been extensively used to forecast air pollution time series, mainly using artificial neural networks (NNs) to model the nonlinearities. This work discusses the possible advantages of using polynomial NARX instead, in combination with suitable model structure selection methods. Furthermore, a suitably weighted mean square error (MSE) (one-step-ahead prediction) cost function is used in the identification/learning process to enhance the model performance in peak estimation, which is the final purpose of this application. The proposed approach is applied to ground-level ozone concentration time series. An extended simulation analysis is provided to compare the two classes of models on a selected case study (Milan metropolitan area) and to investigate the effect of different weighting functions in the identification performance index. Results show that polynomial NARX are able to correctly reconstruct ozone concentrations, with performances similar to NN-based NARX models, but providing additional information, as, e.g., the best set of regressors to describe the studied phenomena. The simulation analysis also demonstrates the potential benefits of using the weighted cost function, especially in increasing the reliability in peak estimation.     

A Minimal Closed-form Solution for the Perspective Three Orthogonal Angles (P3oA) Problem: Application To Visual Odometry

We provide a simple closed-form solution to the Perspective three orthogonal angles (P3oA) problem: given the projection of three orthogonal lines in a calibrated camera, find their 3D directions. Upon this solution, an algorithm for the estimation of the camera relative rotation between two frames is proposed. The key idea is to detect triplets of orthogonal lines in a hypothesize-and-test framework and use all of them to compute the camera rotation in a robust way. This approach is suitable for human-made environments where numerous groups of orthogonal lines exist. We evaluate the numerical stability of the P3oA solution and the estimation of the relative rotation with synthetic and real data, comparing our results to other state-of-the-art approaches.     

Beyond social graphs: mining patterns underlying social interactions

This work aims at discovering and extracting relevant patterns underlying social interactions. To do so, some knowledge extracted from Facebook, a social networking site, is formalised by means of an Extended Social Graph, a data structure which goes beyond the original concept of a social graph by also incorporating information on interests. When the Extended Social Graph is built, state-of-the-art techniques are applied over it in order to discover communities. Once these social communities are found, statistical techniques will look for relevant patterns common to each of those, in such a way that each cluster of users is characterised by a set of common features. The resulting knowledge will be used to develop and evaluate a social recommender system, which aims at suggesting users in a social network with possible friends or interests.