1,721,049 research outputs found
Appuntamenti con l'educazione. Processi formativi, scuola e politica nella stampa periodica
No full textIl volume raccoglie 19 contributi che corrispondono ad altrettante ricerche tutte incentrate sulle caratteristiche, sui meccanismi e sulle implicazioni del rapporto tra educazione e politica come emergono dalla stampa periodica, nelle sue varie tipologie (quotidiano, settimanale, mensile, rotocalco, rivista di settore, testate on line ecc.). Nel suo complesso, questo lavoro risponde alla necessità di enulcleare possibili elementi generali e trasversali relativi alle cause e agli effetti di un certo modo di "trattare" l'universo formativo da parte della stampa periodica, al fine di offrire alcuni strumenti per leggere più in profondità, criticamente e consapevolmente, tali prodotti culturali e di fare emergere, direttamente o per contrasto, i tratti peculiari dell'educazione e della scuol
Probabilistic topic models for sequence data
No full textProbabilistic topic models are widely used in different contexts to uncover the hidden structure in large text corpora. One of the main (and perhaps strong) assumption of these models is that generative process follows a bag-of-words assumption, i.e. each token is independent from the previous one. We extend the popular Latent Dirichlet Allocation model by exploiting three different conditional Markovian assumptions: (i) the token generation depends on the current topic and on the previous token; (ii) the topic associated with each observation depends on topic associated with the previous one; (iii) the token generation depends on the current and previous topic. For each of these modeling assumptions we present a Gibbs Sampling procedure for parameter estimation. Experimental evaluation over real-word data shows the performance advantages, in terms of recall and precision, of the sequence-modeling approaches. © 2013 The Author(s)
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Barbieri (N. F.). — Le destin de l’enfant, son utilité pour le diagnostic en neuropsychiatrie infantile. Rev. Neur. Inf., 1962, n° 3-4, pp. 167-178
No full textBarbieri (N. F.). — Le destin de l’enfant, son utilité pour le diagnostic en neuropsychiatrie infantile. Rev. Neur. Inf., 1962, n° 3-4, pp. 167-178. In: Bulletin de psychologie, tome 20 n°257, 1967. p. 890
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Hierarchical latent factors for preference data
No full textIn this work we propose a probabilistic hierarchical generative approach for users' preference data, which is designed to overcome the limitation of current methodologies in Recommender Systems and thus to meet both prediction and recommendation accuracy. The Bayesian Hierarchical User Community Model (BH-UCM) focuses both on modeling the popularity of items and the distribution over item ratings. An extensive evaluation over two popular benchmark datasets shows that the combined modeling of item popularity and rating provides a powerful framework both for rating prediction and for the generation of accurate recommendation lists. Copyright (c) 2012 - Edizioni Libreria Progetto and the authors
Survival Factorization on Diffusion Networks
No full textIn this paper we propose a survival factorization framework that models information cascades by tying together social influence patterns, topical structure and temporal dynamics. This is achieved through the introduction of a latent space which encodes: (a) the relevance of a information cascade on a topic; (b) the topical authoritativeness and the susceptibility of each individual involved in the information cascade, and (c) temporal topical patterns. By exploiting the cumulative properties of the survival function and of the likelihood of the model on a given adoption log, which records the observed activation times of users and side-information for each cascade, we show that the inference phase is linear in the number of users and in the number of adoptions. The evaluation on both synthetic and real-world data shows the effectiveness of the model in detecting the interplay between topics and social influence patterns, which ultimately provides high accuracy in predicting users activation times. Code and data related to this chapter are available at: https://doi.org/10.6084/m9.figshare.5411341
A probabilistic hierarchical approach for pattern discovery in collaborative filtering data
No full textThis paper presents a hierarchical probabilistic approach to collaborative filtering which allows the discovery and analysis of both global patterns (i.e., tendency of some products of being 'universally appreciated') and local patterns (tendency of users within a community to express a common preference on the same group of items). We reformulate the collaborative filtering approach as a clustering problem in a high-dimensional setting, and propose a probabilistic approach to model the data. The core of our approach is a co-clustering strategy, arranged in a hierarchical fashion: first, user communities are discovered, and then the information provided by each user community is used to discover topics, grouping items into categories. The resulting probabilistic framework can be used for detecting interesting relationships between users and items within user communities. The experimental evaluation shows that the proposed model achieves a competitive prediction accuracy with respect to the state-of-art collaborative filtering approaches. Copyright © SIAM
A probabilistic hierarchical approach for pattern discovery in collaborative filtering data (Extended Abstract)
No full textThis paper presents a hierarchical probabilistic approach to collaborative filtering which allows the discovery and analysis of both global patterns (i.e., tendency of some products of being universally appreciated ) and local patterns (tendency of users within a community to express a common preference on the same group of items). The core of our approach is a probabilistic co-clustering strategy, arranged in a hierarchical fashion: first, user communities are discovered, and then the information provided by each user community is used to discover topics, grouping items into categories. The experimental evaluation shows that the proposed model achieves a competitive prediction accuracy with respect to the state-of-art collaborative filtering approaches
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