613 research outputs found
Comments on the Italian ‘Code for the digital administration’
No full textFranco Ruggieri provides an in-depth discussion of the new Code for the digital administration, recently adopted in Italy
Comments on the Italian ‘Code for the digital administration’
No full textFranco Ruggieri provides an in-depth discussion of the new Code for the digital administration, recently adopted in Italy
Security in digital data preservation
No full textFranco Ruggieri sets out the most recent guidelines and standards relating to preserve the secure preservation of digital objects. Index words: digital objects; preservation; standards; guidance; Ital
A technician’s views on the digital signature in Italy
No full textFranco Ruggieri considers the present take up of digital signatures in Italy and offers some personal views in relation to the use of digital signatures
Security in digital data preservation
No full textFranco Ruggieri sets out the most recent guidelines and standards relating to preserve the secure preservation of digital objects. Index words: digital objects; preservation; standards; guidance; Ital
A technician’s views on the digital signature in Italy
No full textFranco Ruggieri considers the present take up of digital signatures in Italy and offers some personal views in relation to the use of digital signatures
Spectrum of skeletal abnormalities in a complex malformation syndrome with "cutis tricolor" (Ruggieri-Happle syndrome)
No full textBackground: The term cutis tricolor describes the combination of congenital hyper- and hypopigmented skin lesions in close proximity to each other in a background of normal complexion. This phenomenon has been reported: (i) as a purely cutaneous trait; (ii) as a part of a complex malformation syndrome (Ruggieri-Happle syndrome - RHS); (iii) as a distinct type [cutis tricolor parvimaculata]; (iv) in association with other (e.g. vascular) skin disturbances. Objectives: To delineate the spectrum of skeletal defects in cutis tricolor. Methods: Retrospective and prospective analysis of skeletal surveys in 14 subjects (eight men; six women; aged 2-28 years) with cutis tricolor [4 purely cutaneous trait; 10 syndromic (RHS)]. Results: Bone abnormalities were recorded in 71.4% (10/14) of patients [100% (10/10) of cases with (other-than-skeletal) extra-cutaneous manifestations vs. null (0/4) in cases with purely cutaneous traits] and included overall small skull (n = 6); prognathism (n = 6); 'J'-shaped pituitary fossa (n = 1); absence of atlas posterior arch (n = 3); frontal bossing (n = 6); scoliosis (n = 9) with kyphosis (n = 6) and/or lordosis (n = 6); vertebral (n = 9) and ribs (n = 4) defects. Negative ZFHX1B gene analyses excluded overlaps with Mowat-Wilson syndrome. Conclusions: Cutis tricolor may be a marker of underlying skeletal involvement particularly in subjects with a complex syndromic (RHS) phenotype. © 2010 The Author(s)/Acta Pædiatrica © 2010 Foundation Acta Pædiatrica
Discrimination-aware data mining
No full textIn the context of civil rights law, discrimination refers to unfair or unequal treatment of people based on membership to a category or a minority, without regard to individual merit. Rules extracted from databases by data mining techniques, such as classification or association rules, when used for decision tasks such as benefit or credit approval, can be discriminatory, in the above sense. This deficiency of classification and association rules poses ethical and legal issues, as well as obstacles to practical application. In this paper, the notion of discriminatory classification rules is introduced and studied. Examples of potentially discriminatory attributes include gender, race, job, and age. A measure, termed -protection, of the discrimination power of a classification rule containing a discriminatory item is defined and its properties studied. We show that the introduced notion is non-trivial, in the sense that discriminatory rules can be derived from apparently safe ones under natural assumptions about background knowledge. Finally, we discuss how to check -protection and provide an empirical assessment on the German credit dataset
Fuhse and Donati on Relational Sociology: beyond the structural view of social networks
No full textThis paper aims to compare Pierpaolo Donati’s Relational Theory of
Society (also called Relational Sociology), and the theoretical proposal of Jan
Fuhse’s Relational Sociology. It focuses on two main issues: 1) Epistemology
– the «Relational Sociology» paradigm; 2) Ontology – what the social relation
consists of. Both perspectives (Donati and Fuhse) aim at describing the social
reality as a “relational construct”. The core issue and the real distinguishing
divergence point between these two theories is precisely the concept of
“relation”, then its application in methodological and empirical fields. Fuhse
starts with the network analysis sociological tradition (White) to reach a
relational “communicative” theory of society (Luhmann), whereas Donati
insists to build his sociological paradigm from the perspective of the “relation”
concept. The former reduces and considers networks as “communicative
events”, the latter does not renounce to analyse social facts as “relations”,
synthetizing structure, culture and agency in a wide “relational” social theory
Measuring Discrimination in Socially-Sensitive Decision Records
No full textDiscrimination in social sense (e.g., against minorities and disadvantaged groups) is the subject of many laws worldwide, and it has been extensively studied in the social and economic sciences. We tackle the problem of determining, given a dataset of historical decision records, a precise measure of the degree of discrimination suffered by a given group (e.g., an etnic minority) in a given context (e.g., a geographic area) with respect to the decision (e.g. credit denial). In our approach, this problem is rephrased in a classification rule based setting, and a collection of quantitative measures of discrimination is introduced, on the basis of existing norms and regulations. The measures are defined as functions of the contingency table of a classification rule, and their statistical significance is assessed, relying on a large body of statistical inference methods for proportions. Based on this basic method, we are then able to address the more general problems of: (1) unveiling all discriminatory decision patterns hidden in the historical data, combining discrimination analysis with association rule mining, (2) unveiling discrimination in classifiers that learn over training data biased by discriminatory decisions, and (3) in the case of rule-based classifiers, sanitizing discriminatory rules by correcting their confidence. Our approach is validated on the German credit dataset and on the CPAR classifier
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