Studying the emergence of a new social representation: Changes in thinking about nanotechnologies in early 21st-century Italy

We investigated the emergence of a new social representation (SR) of a techno\u2010scientific innovation\u2014nanotechnologies\u2014among the Italian public. We reviewed how nanotechnologies entered parliamentary debates and the media agenda in the early third millennium. We conducted cross\u2010sectional surveys in 2006 (N = 246) and 2011 (N = 486) to examine the emerging SR of nanotechnologies. We sought to observe processes of anchoring and objectification \u2018in action\u2019, by analyzing roles of (i) social groups, and (ii) neighboring SRs of science and of technology, over time. Several changes from 2006 to 2011 were identified: From a \u2018descriptive\u2019 to an \u2018evaluative\u2019 approach; from a \u2018neutral\u2019 to a \u2018controversial\u2019 issue; from a \u2018concrete\u2019 to an \u2018abstract\u2019 object; and from a \u2018technological\u2019 to a \u2018scientific\u2019 phenomenon. We conclude that nanotechnologies finally became \u2018relevant enough\u2019 by 2011 to be considered a proper object of SR, and an emerging SR can be observed

Analyze Large Multidimensional Datasets Using Algebraic Topology

This paper presents an efficient algorithm to extract knowledge from high-dimensionality, high- complexity datasets using algebraic topology, namely simplicial complexes. Based on concept of isomorphism of relations, our method turn a relational table into a geometric object (a simplicial complex is a polyhedron). So, conceptually association rule searching is turned into a geometric traversal problem. By leveraging on the core concepts behind Simplicial Complex, we use a new technique (in computer science) that improves the performance over existing methods and uses far less memory. It was designed and developed with a strong emphasis on scalability, reliability, and extensibility. This paper also investigate the possibility of Hadoop integration and the challenges that come with the framework

A kernel-based framework for learning graded relations from data

Driven by a large number of potential applications in areas like bioinformatics, information retrieval and social network analysis, the problem setting of inferring relations between pairs of data objects has recently been investigated quite intensively in the machine learning community. To this end, current approaches typically consider datasets containing crisp relations, so that standard classification methods can be adopted. However, relations between objects like similarities and preferences are often expressed in a graded manner in real-world applications. A general kernel-based framework for learning relations from data is introduced here. It extends existing approaches because both crisp and graded relations are considered, and it unifies existing approaches because different types of graded relations can be modeled, including symmetric and reciprocal relations. This framework establishes important links between recent developments in fuzzy set theory and machine learning. Its usefulness is demonstrated through various experiments on synthetic and real-world data.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

A generic model of dyadic social relationships

We introduce a model of dyadic social interactions and establish its correspondence with relational models theory (RMT), a theory of human social relationships. RMT posits four elementary models of relationships governing human interactions, singly or in combination: Communal Sharing, Authority Ranking, Equality Matching, and Market Pricing. To these are added the limiting cases of asocial and null interactions, whereby people do not coordinate with reference to any shared principle. Our model is rooted in the observation that each individual in a dyadic interaction can do either the same thing as the other individual, a different thing or nothing at all. To represent these three possibilities, we consider two individuals that can each act in one out of three ways toward the other: perform a social action X or Y, or alternatively do nothing. We demonstrate that the relationships generated by this model aggregate into six exhaustive and disjoint categories. We propose that four of these categories match the four relational models, while the remaining two correspond to the asocial and null interactions defined in RMT. We generalize our results to the presence of N social actions. We infer that the four relational models form an exhaustive set of all possible dyadic relationships based on social coordination. Hence, we contribute to RMT by offering an answer to the question of why there could exist just four relational models. In addition, we discuss how to use our representation to analyze data sets of dyadic social interactions, and how social actions may be valued and matched by the agents