Element weighted Kemeny distance for ranking data

Preference data are a particular type of ranking data that arise when several individuals express their preferences over a finite set of items. Within this framework, the main issue concerns the aggregation of the preferences to identify a compromise or a “consensus”, defined as the closest ranking (i.e. with the minimum distance or maximum correlation) to the whole set of preferences. Many approaches have been proposed, but they are not sensitive to the importance of items: i.e. changing the rank of a highly-relevant element should result in a higher penalty than changing the rank of a negligible one. The goal of this paper is to investigate the consensus between rankings taking into account the importance of items (element weights). For this purpose, we present: i) an element weighted rank correlation coefficient as an extension of the Emond and Mason’s one, and ii) an element weighted rank distance as an extension of the Kemeny distance. The one-to-one correspondence between the weighted distance and the rank correlation coefficient is analytically proved. Moreover, a procedure to obtain the consensus ranking among several individuals is described and its performance is studied both by simulation and by the application to real datasets

¿Dejar el hogar para mejorar? Movilidad ocupacional de inmigrantes cualificados: Algunas observaciones empíricas desde España, antes de la crisis económica actual

The purpose of this paper is to study the occupational mobility experienced following migration by migrants who are university graduates or postgraduates who completed their studies before their arrival in Spain. To do so, we compared the first job they obtained in Spain with the last one held before leaving their country of origin. We identify three different types of mobility: downward mobility, lateral mobility, and upward mobility. Finally, we used logistic regression models to identify the strongest predictors of the three types of mobility previously classified. The empirical analysis was carried out using data from the Spanish National Immigrant Survey (ENI) of 2007, including information about some 15 000 individuals. Our attention will be focused on the 2 425 skilled migrants from this survey.El objetivo de este artículo es estudiar la movilidad ocupacional experimentada por los inmigrantes que completaron sus estudios universitarios (de grado y postgrado) antes de su llegada a España. Para ello hemos comparado la primera ocupación que tuvieron en España con la última que tuvieron antes de dejar su país de origen. Hemos identificado tres tipos diferentes de movilidad: movilidad descendente; movilidad lateral y, finalmente, movilidad ascendente. Por último, hemos usado modelos de regresión logística para identificar los predictores más importantes de estos tres tipos de movilidad. El análisis empírico se hizo utilizando información aportada por la Encuesta Nacional de Inmigrantes (ENI) de 2007, que tiene información de unos 15 000 individuos. Nuestra atención se centra en 2 425 inmigrantes cualificado

GRID COMPUTING FOR COLLABORATIVE NETWORKS: A LITERATURE REVIEW

This paper describes the methodology and results of a literature review targeting the distinct interpretations of the Grid Computing paradigm within the context of Collaborative Networks. The review is based on the analysis of contributions published in selected scientific journals between 2002 and today. The analysis was performed taking into account the assumptions, scopes and solutions provided to approach the challenges for SMEs’ collaborative networks. The research questions driving this literature review have been the following: (1) How is the concept of Grid Computing associated with the concept of Collaborative Network? (2) How the Grid computing supports Collaborative Networks? (3) What are the business implications in Grid supported Collaborative Networks

Ranking coherence in Topic Models using Statistically Validated Networks

Probabilistic topic models have become one of the most widespread machine learning techniques in textual analysis. Topic discovering is an unsupervised process that does not guarantee the interpretability of its output. Hence, the automatic evaluation of topic coherence has attracted the interest of many researchers over the last decade, and it is an open research area. The present article offers a new quality evaluation method based on Statistically Validated Networks (SVNs). The proposed probabilistic approach consists of representing each topic as a weighted network of its most probable words. The presence of a link between each pair of words is assessed by statistically validating their co-occurrence in sentences against the null hypothesis of random co-occurrence. The proposed method allows one to distinguish between high-quality and low-quality topics, by making use of a battery of statistical tests. The statistically significant pairwise associations of words represented by the links in the SVN might reasonably be expected to be strictly related to the semantic coherence and interpretability of a topic. Therefore, the more connected the network, the more coherent the topic in question. We demonstrate the effectiveness of the method through an analysis of a real text corpus, which shows that the proposed measure is more correlated with human judgement than the state-of-the-art coherence measures

Supporting Policy Definition in the e-Health domain: a QCA based method

eHealth is broadly considered as a promising strategy to improve the economic sustainability and quality of the healthcare service provision in Europe. Nevertheless, despite the enthusiastic declarations of eHealth potential, the adoption of IT in health care has progressed very slowly. A critical factor, not deeply addressed in literature, is related to the process of prioritization of the eHealth solution to adopt, in presence of financial constrains, external and internal pressure from a wide range of heterogeneous stakeholders, and conflicting information on different technological solutions. In this paper we introduce a method supporting policy definition in the eHealth domain. This method is based on a qualitative comparative analysis (QCA) of best practices and previous experiences performed through the lens of an analytic framework whose dimensions and categories are well situated in the eHealth context. This method could support policy-makers in the identification of the properties and characteristics of innovative projects at European level and to analyze the gap between the international scenario and the local context in order to understand trends and dynamics of development, to evaluate the best opportunities for innovation and, therefore, to assign priorities for the next investments by respecting the constraints of available resources.eHealth is broadly considered as a promising strategy to improve the economic sustainability and quality of the healthcare service provision in Europe. Nevertheless, despite the enthusiastic declarations of eHealth potential, the adoption of IT in health care has progressed very slowly. A critical factor, not deeply addressed in literature, is related to the process of prioritization of the eHealth solution to adopt, in presence of financial constrains, external and internal pressure from a wide range of heterogeneous stakeholders, and conflicting information on different technological solutions. In this paper we introduce a method supporting policy definition in the eHealth domain. This method is based on a qualitative comparative analysis (QCA) of best practices and previous experiences performed through the lens of an analytic framework whose dimensions and categories are well situated in the eHealth context. This method could support policy-makers in the identification of the properties and characteristics of innovative projects at European level and to analyze the gap between the international scenario and the local context in order to understand trends and dynamics of development, to evaluate the best opportunities for innovation and, therefore, to assign priorities for the next investments by respecting the constraints of available resources.Uninvited Submission

Element weighted Kemeny distance for ranking data

Preference data are a particular type of ranking data that arise when n individuals express their preferences over a finite set of items. Within this framework, the main issue concerns the aggregation of the preferences to identify a compromise or a “consensus”, defined as the closest ranking (i.e. with the minimum distance or maximum correlation) to the whole set of preferences.  Many approaches have been proposed, but they are not sensitive to the importance of items: i.e.  changing the rank of a highly-relevant element should result in a higher penalty than changing the rank of a negligible one. The goal of this paper is to investigate the consensus between rankings taking into account the importance of items (element weights).  For this purpose, we present:  i) an element weighted rank correlation coefficient tau_ew as an extension of the Emond and Mason’s tau, and ii) an element weighted rank distance d_ew as an extension of the Kemeny distance d. The one-to-one correspondence between the weighted distance and the rank correlation coefficient is analytically proved. Moreover, a procedure to obtain the consensus ranking among n individuals is described and its performance is studied both by simulation and by the application to real datasets

Clustering alternatives in preference-approvals via novel pseudometrics

Preference-approval structures combine preference rankings and approval voting for declaring opinions over a set of alternatives. In this paper, we propose a new procedure for clustering alternatives in order to reduce the complexity of the preferenceapproval space and provide a more accessible interpretation of data. To that end, we present a new family of pseudometrics on the set of alternatives that take into account voters’ preferences via preference-approvals. To obtain clusters, we use the Ranked k-medoids (RKM) partitioning algorithm, which takes as input the similarities between pairs of alternatives based on the proposed pseudometrics. Finally, using non-metric multidimensional scaling, clusters are represented in 2-dimensional space

Cutaneous melanoma frequencies and seasonal trend in 20 years of observation of a population characterised by excessive sun exposure

Background: Cutaneous melanoma is an aggressive form of skin cancer. It has become an increasingly common neoplasm in the most developed countries, especially among individuals of European origin. Methods: Anonymous data of patients with cutaneous melanoma were collected from the diagnostic database of the University Hospital of Trieste from 1 January 1990 to 10 December 2013. Our study is based on a population which was constant over the period of observation; it was also well-defined and characterised by unrestrained sun exposure. Results: The number of cutaneous melanomas increased during the period of observation with a seasonality trend and gender related differences both for anatomical sites distribution and stage of the disease. Moreover, 6% of our cohort developed multiple melanomas. Conclusions: In a well-defined population devoted to excessive sun exposure the frequencies of skin melanomas roughly doubled from 1990 to 2013 following a seasonal trend. In that population, prevention efforts according to gender specific risk behaviour, as well as follow-up programmes both for evaluation of metastatic spreading and for early diagnosis of additional skin melanomas, are crucial due to gender specific differences and to the occurrence of multiple melanomas