131,008 research outputs found
Pandemia e capitalismo del XXI secolo. Gestione securitaria della crisi tra individualizzazione neoliberista e ristrutturazione delle forze produttive
No full textIl contributo propone una riflessione sul Sars-Cov-2 quale epifenomeno del modo di produzione capitalistico ed in particolare della sua fase neoliberista. La pandemia, accelerando dinamiche e contraddizioni del capitale già preesistenti, è indubbiamente correlata agli aspetti più predatori del modo di produzione capitalistico, particolarmente nei confronti della natura e della biodiversità. Il saggio cerca anche di far riflettere su altri due aspetti emersi durante la pandemia: la gestione della pandemia, non solo in termini sanitari, è profondamente legata ai processi di individualizzazione, visti quali esiti delle trasformazioni sociali indotte dal neoliberismo; inoltre, il modo in cui si è affrontata l’emergenza sanitaria appare come un alibi per celare le ragioni della crisi economica capitalistica, precedente la pandemia, e la via per procedere ad una ulteriore accelerazione della ristrutturazione delle forze produttive, fenomeno ciclico e inevitabile del modo di produzione capitalistico, in vista di nuovi processi di accumulazione del capitale.The contribution proposes a reflection on Sars-Cov-2 as an epiphenomenon of the capitalist way of production and in particular of its neoliberal phase. The pandemic, accelerating pre-existing dynamics and capital contradictions, is undoubtedly related to the more predatory aspects of the capitalist way of production, particularly with regard to nature and biodiversity. The essay also tries to make us reflect on two other aspects that emerged during the pandemic: the management of the pandemic, not only in health terms, is deeply linked to the processes of individualization, seen as the results of the social transformations induced by neoliberalism; Moreover, the way in which the health emergency has been dealt with appears to be an alibi for concealing the reasons for the capitalist economic crisis, prior to the pandemic, and the way to proceed with a further acceleration of the restructuring of production forces, a cyclical and inevitable phenomenon of capitalist production, with a view to new processes of capital accumulation
Collective Reasoning over Shared Concepts for the Linguistic Atlas of Sicily
No full textIn this work, collective intelligence principles are applied in the context of the Linguistic Atlas of Sicily, an interdisciplinary research focusing on the study of the Italian language as it is spoken in Sicily, and its correlation with the Sicilian dialect and other regional varieties spoken in Sicily. The project has been developed over the past two decades and includes a complex information system supporting linguistic research; recently it has grown to allow research scientists to cooperate in an inte-grated environment to produce significant scientific advances in the field of ethnologic and sociolinguistic research. An interoperable infrastruc-ture was implemented and organized to allow exchange of information and knowledge between researchers providing tools and methodologies to allow collective reasoning over shared concepts. The project uses dif-ferent types of data (structured, unstructured, multimedia) that require tight data integration and interoperability. Additionally, the framework allows for data aggregation into shared concepts that can be exchanged between researchers and constitute a common knowledge base for the entire research community of the ALS
Ideologia (della globalizzazione neoliberista) dominio. A mò di prefazione
No full textpreface of the book "Sul filo rosso del tempo" by Alessandra Ciattin
A neural architecture for segmentation and modelling of range data
No full textA novel, two stage, neural architecture for the segmentation of range data and their modeling with undeformed superquadrics is presented. The system is composed by two distinct neural stages: a SOM is used to perform data segmentation, and, for each segment, a multi-layer feed-forward network performs model estimation. The topology preserving nature of the SOM algorithm makes this architecture suited to cluster data with respect to sudden curvature variations. The second stage is designed to model and compute the inside-outside function of an undeformed superquadric in whatever attitude, starting form the (x, y, z) data triples. The network has been trained using backpropagation, and the weights arrangement, after training, represents a robust estimate of the superquadric parameters. The modelling network is compared also with a second implementation, which estimates separately the parameters of the 2D superellipses generating the 3D model. The whole architectural design is general, it can be extended to other geometric primitives for part-based object recognition, and performs faster than classical model fitting techniques. Detailed explanation of the theoretical approach, along with some experiments with real data are reported
Dealing with preference uncertainty in contingent willingness to pay for a nature protection program: A new approach
No full textIn this paper, we propose an alternative preference uncertainty measurement approach where respondents have the option to indicate their willingness to pay (WTP) for a nature protection program either as exact values or intervals from a payment card, depending on whether they are uncertain about their valuation. On the basis of their responses, we then estimate their degree of uncertainty. New within this study is that the respondent's degree of uncertainty is "revealed", while it is "stated" in those using existing measurement methods. Three statistical models are used to explore the sources of respondent uncertainty. We also present a simple way of calculating the uncertainty adjusted mean WTP, and compare this to the one obtained from an interval regression. Our findings in terms of determinants of preference uncertainty are broadly consistent with a priori expectations. In addition, the uncertainty adjusted mean WTP is quite similar to the one derived from an interval regression. We conclude that our method is promising in accounting for preference uncertainty in WTP answers at little cost to interviewees in terms of time and cognitive effort, on the one hand, and without researcher assumptions regarding the interpretation of degrees of uncertainty reported by respondents, on the other. © 2013 Elsevier B.V
Conditioning Chat-GPT for Information Retrieval: The Unipa-GPT Case Study
No full textThis paper illustrates the architecture and training of Unipa-GPT, a Large Language Model based chatbot developed for assisting students in choosing a bachelor/master degree course at the University of Palermo. Unipa-GPT relies on gpt-3.5-turbo, it was presented in the context of the European Researchers' Night SHARPER event. In our experiments we adopted both the Retrieval Augmented Generation (RAG) approach and fine-tuning to develop the system. The whole architecture of Unipa-GPT is presented, both the RAG and the fine-tuned systems are compared, and a brief discussion on their performance is reported
UniQA: an Italian and English Question-Answering Data Set Based on Educational Documents
No full textIn this paper we introduce UniQA, a high-quality Question-Answering data set that comprehends more than 1k documents and nearly 14k QA pairs. UniQA has been generated in a semi-automated manner using the data retrieved from the website of the University of Palermo, covering information about the bachelor and master degree courses for the academic year 2024/2025. Data are both in Italian and English, thus making the data set suitable for QA and translation models. To assess the data, we propose a Retrieval Augmented Generation model based on Llama-3.1-instruct. UniQA can be found at https://github.com/CHILab1/UniQA
Automatic Generation of User Interfaces using the Set Description Language
No full textWe present a paradigm to generate automatically graphical user interfaces from a formal description of the data model following the well-known model-view-control paradigm. This paradigm provide complete separation between data model and interface description, setting the programmer free from the low-level aspects of programming interfaces, letting him take care of higher level aspects. The interface along with the data model is described by means of a formal language, the Set Description Language. We also describe the infrastructure based on this paradigm we implemented to generate graphical user interfaces for generic applications. Moreover, it can adapt the user interface of a program to the needs derived from the type of data managed by the user from time to time
Sex differences in human-directed social behavior in pet rabbits
No full textRabbits are common pets in many European countries, including Italy. However, little is known about their behavior and general welfare. The purpose of this study was to explore behaviors of Italian domestic rabbits as perceived by their owners and to investigate the effect of sex on these behaviors after puberty. A group of 308 adult does was compared with a group of 326 adult bucks. Owners were asked to complete an online questionnaire eliciting information on themselves, their rabbits, and whether the animals exhibited any of 16 common behaviors. Pearson χ2 test of independence in 2 × 2 contingency tables and binary logistic regressions were used to analyze the effects of sex on behavioral patterns while controlling for various owner- and rabbit-related variables. The odds of displaying owner-directed aggression and stranger-directed aggression were significantly greater for the does and neutered males than for the intact males. Bucks had a significantly higher likelihood of seeking contact with their owner. Our findings may provide insight into pet rabbit sex-related differences in behavior that may contribute to improving animal welfare and the rabbit companionship experience. These findings may have implications for future research aimed at studying behavioral disorders in pet rabbits
MeSH term explosion and author rank improve expert recommendations
No full textInformation overload is an often-cited phenomenon that reduces the productivity, efficiency and efficacy of scientists. One challenge for scientists is to find appropriate collaborators in their research. The literature describes various solutions to the problem of expertise location, but most current approaches do not appear to be very suitable for expert recommendations in biomedical research. In this study, we present the development and initial evaluation of a vector space model-based algorithm to calculate researcher similarity using four inputs: 1) MeSH terms of publications; 2) MeSH terms and author rank; 3) exploded MeSH terms; and 4) exploded MeSH terms and author rank. We developed and evaluated the algorithm using a data set of 17,525 authors and their 22,542 papers. On average, our algorithms correctly predicted 2.5 of the top 5/10 coauthors of individual scientists. Exploded MeSH and author rank outperformed all other algorithms in accuracy, followed closely by MeSH and author rank. Our results show that the accuracy of MeSH term-based matching can be enhanced with other metadata such as author rank
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