305,271 research outputs found
Petre alexandrescu & serban Papacostea (ed.), Il mar nero. Annali di archeologia e storia. V (2001-2003), 2006
No full textBaralis Alexandre. Petre alexandrescu & serban Papacostea (ed.), Il mar nero. Annali di archeologia e storia. V (2001-2003), 2006. In: L'antiquité classique, Tome 77, 2008. pp. 793-795
Petre alexandrescu & serban Papacostea (ed.), Il mar nero. Annali di archeologia e storia. V (2001-2003), 2006
No full textBaralis Alexandre. Petre alexandrescu & serban Papacostea (ed.), Il mar nero. Annali di archeologia e storia. V (2001-2003), 2006. In: L'antiquité classique, Tome 77, 2008. pp. 793-795
Bring Your Own Data to X-PLAIN
No full textExploring and understanding the motivations behind black-box model predictions is becoming essential in many different applications. X-PLAIN is an interactive tool that allows human-in-the-loop inspection of the reasons behind model predictions. Its support for the local analysis of individual predictions enables users to inspect the local behavior of different classifiers and compare the knowledge different classifiers are exploiting for their prediction. The interactive exploration of prediction explanation provides actionable insights for both trusting and validating model predictions and, in case of unexpected behaviors, for debugging and improving the model itself
Explaining black box models by means of local rules
No full textMany high performance machine learning methods produce black box models, which do not disclose their internal logic yielding the prediction. However, in many application domains understanding the motivation of a prediction is becoming a requisite to trust the prediction itself. We propose a novel rule-based method that explains the prediction of any classifier on a specific instance by analyzing the joint effect of feature subsets on the classifier prediction. The relevant subsets are identified by learning a local rule-based model in the neighborhood of the prediction to explain. While local rules give a qualitative insight of the local behavior, their relevance is quantified by using the concept of prediction difference. Preliminary experiments show that, despite the approximation introduced by the local model, the explanations provided by our method are effective in detecting the effects of attribute correlation. Our method is model-agnostic. Hence, experts can compare explanations and local behaviors of the predictions for the same instance made by different classifiers
I-prune: Item selection for associative classification
No full textAssociative classification is characterized by accurate models and high model generation time. Most time is spent in extracting and postprocessing a large set of irrelevant rules, which are eventually pruned.We propose I-prune, an item-pruning approach that selects uninteresting items by means of an interestingness measure and prunes them as soon as they are detected. Thus, the number of extracted rules is reduced and model generation time decreases correspondingly. A wide set of experiments on real and synthetic data sets has been performed to evaluate I-prune and select the appropriate interestingness measure. The experimental results show that I-prune allows a significant reduction in model generation time, while increasing (or at worst preserving) model accuracy. Experimental evaluation also points to the chi-square measure as the most effective interestingness measure for item pruning
Ottimizzazione nell'uso della risorsa geotermica superficiale nei centri urbani
No full textLa crescente domanda di energia rinnovabile nel campo del riscaldamento e raffrescamento degli edifici ha sostenuto un incremento della presenza di impianti geotermici a bassa entalpia. Nei grandi centri urbani italiani ed europei si concretizza dunque il rischio di interferenze tra installazioni che mettono a rischio l’investimento della loro realizzazione.
Nella nota, a partire dalle evidenze del caso studio della Città di Torino, sono discusse e analizzate le prospettive e i metodi per gestire queste problematiche nell’ottica di una pianificazione equa e razionale nell’uso della risorsa geotermica. L’integrazione tra i metodi analitici di valutazione del potenziale e la modellazione numerica termo-idraulica (T-H) delle effettive condizioni termiche del sottosuolo viene individuata come approccio privilegiato per l’ottimizzazione nell’utilizzo della geotermia superficiale alla scala urbana
Leveraging full-text article exploration for citation analysis
No full textScientific articles often include in-text citations quoting from external sources. When the cited source is an article, the citation context can be analyzed by exploring the article full-text. To quickly access the key information, researchers are often interested in identifying the sections of the cited article that are most pertinent to the text surrounding the citation in the citing article. This paper first performs a data-driven analysis of the correlation between the textual content of the sections of the cited article and the text snippet where the citation is placed. The results of the correlation analysis show that the title and abstract of the cited article are likely to include content highly similar to the citing snippet. However, the subsequent sections of the paper often include cited text snippets as well. Hence, there is a need to understand the extent to which an exploration of the full-text of the cited article would be beneficial to gain insights into the citing snippet, considering also the fact that the full-text access could be restricted. To this end, we then propose a classification approach to automatically predicting whether the cited snippets in the full-text of the paper contain a significant amount of new content beyond abstract and title. The proposed approach could support researchers in leveraging full-text article exploration for citation analysis. The experiments conducted on real scientific articles show promising results: the classifier has a 90% chance to correctly distinguish between the full-text exploration and only title and abstract cases
Learning from summaries: supporting e-learning activities by means of document summarization
No full textE-learning platforms allow users with different skills to explore large collections of electronic documents and annotate them with notes and highlights. Generating summaries of these document collections is potentially useful for gaining insights into teaching materials. However, most existing summarizers are general-purpose. Thus, they do not consider neither annotations nor user skills during the document summarization process. This paper studies the application of a state-of-the-art summarization system, namely the Itemset-based Summarizer (ItemSum), in an e-learning context. The summarizer produces an ordered sequence of key phrases extracted from a teaching document. The aim of this work is threefold: (i) Evaluate the usefulness of the generated summaries for supporting individual and collective learning activities in a real context, (ii) understand to what extent document highlights, annotations, and user skill levels can be used to drive the summarization process, and (iii) generate multiple summaries of the same document tailored to users with different skill levels. To accomplish Task (i), a crowd-sourcing experience of evaluation of the generated summaries was conducted by involving the students of a B.S. course given by a technical university. The results show that the automatically generated summaries reect, to a large extent, the students' expectations. Hence, they can be useful for supporting learning activities in university-level Computer Science courses. To address Task (ii), three extended versions of the ItemSum summarizer, driven by highlights, annotations, and user skill levels, respectively, have been proposed and their performance improvements with respect to the baseline version have been validated on benchmark documents. Finally, to accomplish Task (iii) multiple summaries of the same benchmark documents have been generated by considering only the annotations made by the users with a different skill level. The results conrm that the summary content reects the level of expertise of the targeted user
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