Els Molière de Joan Oliver

Joan Oliver admirava en la comèdia francesa clàssica l'elegància fluida del llenguatge, l'amenitat de l'anècdota i l'habilitat constructora de situacions i personatges, cosa que no era impediment, sinó al contrari, per aprofundir en el coneixement de la naturalesa humana. Oliver trobava en la comèdia francesa dels segles XVII i XVIII, i molt especialment en Molière, uns atractius dels quals mancava greument la tradició teatral catalana. Des d'aquesta perspectiva, l'escriptor va elaborar tres traduccions esplèndides, en vers, de tres comèdies de Molière, la primera de les quals va ser realitzada a l'exili de Santiago de Xile durant els anys quaranta del segle passat. Els epistolaris d'Oliver amb Xavier Benguerel i Josep Ferrater Mora ens permeten de resseguir l'esforç del traductor durant anys i el rigor i la sensibilitat que va esmerçar en les traduccions esmentades.In the French classical comedy, Joan Oliver admired the fluent elegance of language, the agreeableness of the anecdote and the constructive ability of situations and characters, which, far from being an obstacle, were useful to go deeper into the knowledge of human nature. In the French comedy of the XVII and XVIII centuries, and especially in Molière, Oliver could see some appeals which were seriously missing from the Catalan theatre tradition. From this perspective, the writer produced three magnificent translations, in verse, of three comedies written by Molière. The first of them was carried out during his exile in Santiago de Chile during the first forty years of the last century. The collected letters of Oliver with Xavier Benguerel and Josep Ferrater-Mora allow us to follow the effort the translator did for many years as well as the rigor and sensitivity he devoted to the aforementioned translations

On the role of pre and post-processing in environmental data mining

The quality of discovered knowledge is highly depending on data quality. Unfortunately real data use to contain noise, uncertainty, errors, redundancies or even irrelevant information. The more complex is the reality to be analyzed, the higher the risk of getting low quality data. Knowledge Discovery from Databases (KDD) offers a global framework to prepare data in the right form to perform correct analyses. On the other hand, the quality of decisions taken upon KDD results, depend not only on the quality of the results themselves, but on the capacity of the system to communicate those results in an understandable form. Environmental systems are particularly complex and environmental users particularly require clarity in their results. In this paper some details about how this can be achieved are provided. The role of the pre and post processing in the whole process of Knowledge Discovery in environmental systems is discussed

A survey on pre-processing techniques: relevant issues in the context of environmental data mining

One of the important issues related with all types of data analysis, either statistical data analysis, machine learning, data mining, data science or whatever form of data-driven modeling, is data quality. The more complex the reality to be analyzed is, the higher the risk of getting low quality data. Unfortunately real data often contain noise, uncertainty, errors, redundancies or even irrelevant information. Useless models will be obtained when built over incorrect or incomplete data. As a consequence, the quality of decisions made over these models, also depends on data quality. This is why pre-processing is one of the most critical steps of data analysis in any of its forms. However, pre-processing has not been properly systematized yet, and little research is focused on this. In this paper a survey on most popular pre-processing steps required in environmental data analysis is presented, together with a proposal to systematize it. Rather than providing technical details on specific pre-processing techniques, the paper focus on providing general ideas to a non-expert user, who, after reading them, can decide which one is the more suitable technique required to solve his/her problem.Peer ReviewedPostprint (author's final draft

A hybrid recommender system for industrial symbiotic networks

Various solutions enabling the realization of synergies in Industrial Symbiotic Networks have been proposed. However, incorporating intelligence into the platforms that these networks use, supporting the involved actors to automatically find possible candidates able to cover their needs, is still of high importance. Usually, the actors participating in these networks act based on previously predefined patterns, without taking into account all the possible alternatives, usually due to the difficulty of finding and properly evaluating them. Therefore, the recommendation of new matches that the users were not aware of is a big challenge, as companies many times are not willing to change their established workflows if they are not sure about the outcome. Thus, the ability of a platform to properly identify symbiotic alternatives that could provide both economic and environmental benefits for the companies, and the sector as a whole, is of high importance and delivering such recommendations is crucial. In this work, we propose a hybrid recommender system aiming to support users in properly filtering and identifying the symbiotic relationships that may provide them an improved performance. Several criteria are taken into account in order to generate, each time, the list of the most suitable solutions for the current user, at a given moment. In addition, the implemented system uses a graph-based similarity model in order to identify resource similarities while performing a hybrid case-based recommendation in order to find the optimal solutions according to more features than just the resources’ similarities.Peer ReviewedPostprint (published version