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.     

Stabilised finite element methods for a bending moment formulation of the Reissner-Mindlin plate model

This work presents new stabilised finite element methods for a bending moments formulation of the Reissner-Mindlin plate model. The introduction of the bending moment as an extra unknown leads to a new weak formulation, where the symmetry of this variable is imposed strongly in the space. This weak problem is proved to be well-posed, and stabilised Galerkin schemes for its discretisation are presented and analysed. The finite element methods are such that the bending moment tensor is sought in a finite element space constituted of piecewise linear continuos and symmetric tensors. Optimal error estimates are proved, and these findings are illustrated by representative numerical experiments.     

Global Atmospheric Profiles from Reanalysis Information (GAPRI): a new database for earth surface temperature retrieval

This paper presents the Global Atmospheric Profiles derived from Reanalysis Information (GAPRI) database, which was designed for earth surface temperature retrieval. GAPRI is a comprehensive compilation of selected atmospheric vertical profiles at global scale which can be used for radiative transfer simulation in order to obtain generalized algorithms to estimate land surface temperature (LST). GAPRI includes information on geopotential height, atmospheric pressure, air temperature, and relative humidity derived from the European Centre for Medium-Range Weather Forecasts Re-Analysis data from year 2011. The atmospheric profiles are structured for 29 vertical levels and extracted from a global spatial grid of about 0.75° × 0.75° latitude–longitude with a temporal resolution of 6 hours. The selection method is based in the extraction of clear sky profiles over different atmospheric weather conditions such as tropical, mid-latitude summer, subarctic, and arctic, while also considering sea and land areas and day- and night-time conditions. The GAPRI database was validated by comparing land and sea surface temperature values derived from it to those obtained using other existing atmospheric profile databases and in situ measurements. Moreover, GAPRI was also compared to previous radiosonde atmospheric profiles using simulated split-window algorithms. Results show good agreement between GAPRI and previous atmospheric databases, thus demonstrating the potential of GAPRI for studies related to forward simulations in the thermal infrared range. GAPRI is a freely available database that can be modified according to the user’s needs and local atmospheric conditions.     

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.     

Predicting teamwork results from social network analysis

Modelling students' behaviours has reached a status that can only be overcome by improving the ability of predicting the results on teamwork. Indeed, teamwork is an important piece on the learning process, but understanding their mechanisms and predicting the results achieved is far from being solved by traditional classifiers. In this paper, we address the problem of predicting teamwork results, and propose a recommender system that suggests new teams, in the context of a given curricular unit. Any student, who is looking for a team, may use the system; in particular, he may ask for the best team to join, either considering all available colleagues or just the set of his previous teammates. Our system makes use of social network analysis and classification methods as the algorithmic core of the decision‐making process. System evaluation is presented through a set of experimental results, which report the performance of social network analysis and classification algorithms over real datasets.     

Learning to rank academic experts in the DBLP dataset

Expert finding is an information retrieval task that is concerned with the search for the most knowledgeable people with respect to a specific topic, and the search is based on documents that describe people's activities. The task involves taking a user query as input and returning a list of people who are sorted by their level of expertise with respect to the user query. Despite recent interest in the area, the current state‐of‐the‐art techniques lack in principled approaches for optimally combining different sources of evidence. This article proposes two frameworks for combining multiple estimators of expertise. These estimators are derived from textual contents, from graph‐structure of the citation patterns for the community of experts and from profile information about the experts. More specifically, this article explores the use of supervised learning to rank methods, as well as rank aggregation approaches, for combining all of the estimators of expertise. Several supervised learning algorithms, which are representative of the pointwise, pairwise and listwise approaches, were tested, and various state‐of‐the‐art data fusion techniques were also explored for the rank aggregation framework. Experiments that were performed on a dataset of academic publications from the Computer Science domain attest the adequacy of the proposed approaches.     

On Weakly Smooth Uninorms on Finite Chain

In this paper, we characterize all weakly smooth uninorms (i.e., uninorms with smooth underlying operators) defined on a finite chain. It is proved that any such uninorm is determined by three unary functions and vice versa. As a by‐product, we obtain the characterizations of smooth t‐norms, smooth t‐conorms through additive generators and show that on a finite chain, there exists no counterpart of the class of uninorms continuous in ]0, 1[².     

Probabilistic consensus clustering using evidence accumulation

Machine Learning - Clustering ensemble methods produce a consensus partition of a set of data points by combining the results of a collection of base clustering algorithms. In the evidence...