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In the last decades, an increasing number of employers and job seekers have been relying on Web resources to get in touch and to find a job. If appropriately retrieved and analyzed, the huge number of job vacancies available today on on-line job portals can provide detailed and valuable information about the Web Labor Market dynamics and trends. In particular, this information can be useful to all actors, public and private, who play a role in the European Labor Market. This paper presents WoLMIS, a system aimed at collecting and automatically classifying multilingual Web job vacancies with respect to a standard taxonomy of occupations. The proposed system has been developed for the Cedefop European agency, which supports the development of European Vocational Education and Training (VET) policies and contributes to their implementation. In particular, WoLMIS allows analysts and Labor Market specialists to make sense of Labor Market dynamics and trends of several countries in Europe, by overcoming linguistic boundaries across national borders. A detailed experimental evaluation analysis is also provided for a set of about 2 million job vacancies, collected from a set of UK and Irish Web job sites from June to September 2015.

Many real world problems involve hybrid systems, subject to (continuous) physical effects and controlled by (discrete) digital equipments. Indeed, many efforts are being made to extend the current planning systems and modelling languages to support such kind of domains. However, hybrid systems often present also a nonlinear behaviour and planning with continuous nonlinear change that is still a challenging issue.In this paper we present the UPMurphi tool, a universal planner based on the discretise and validate approach that is capable of reasoning with mixed discrete/continuous domains, fully respecting the semantics of PDDL+. Given an initial discretisation, the hybrid system is discretised and given as input to UPMurphi, which performs universal planning on such an approximated model and checks the correctness of the results. If the validation fails, the approach is repeated by appropriately refining the discretisation.To show the effectiveness of our approach, the paper presents two real hybrid domains where universal planning has been successfully performed using the UPMurphi tool.

Online job portals collecting web vacancies have become important media for job demand and supply matching. They also represent a growing research area for the application of analytical methods to study the labour market using innovative data sources. This paper analyses Italian web job vacancies scraped from several types of Italian web job portals between June and September 2015. After describing how the occupations associated with each web vacancy (classification up to level 4) were identified and the related skills retrieved in texts using mixed supervised and unsupervised text mining approaches, we focused on job vacancies related to ICT and statistical positions. The principal aim of this paper is to describe these jobs in terms of the required skills that have emerged in the labour market from a demand perspective and to identify those skills that best distinguish statisticians from other ICT occupations. Hence, several machine learning techniques were used to assess those skills that best distinguish occupation codes from other job groups. After quality control and removal of duplications, the scraping collected more than 110,000 job advertisements: nearly 6,200 were classified as ICT or statistical positions (largely dominated by software developers). The data indicate that high‐level statisticians have superior and heterogeneous professional backgrounds, linked to theoretical statistics, where analytic skills are more relevant than computing skills. Many soft and management‐oriented skills were also called for, which are missing among lower level statisticians, who are restricted to more technical jobs oriented towards general computing and informatics.