1,720,960 research outputs found
Advancing healthcare through data: the BETTER project's vision for distributed analytics
Full text linkIntroduction: Data-driven medicine is essential for enhancing the accessibility and quality of the healthcare system. The availability of data plays a crucial role in achieving this goal.
Methods: We propose implementing a robust data infrastructure of FAIRification and data fusion for clinical, genomic, and imaging data. This will be embedded within the framework of a distributed analytics platform for healthcare data analysis, utilizing the Personal Health Train paradigm.
Results: This infrastructure will ensure the findability, accessibility, interoperability, and reusability of data, metadata, and results among multiple medical centers participating in the BETTER Horizon Europe project. The project focuses on studying rare diseases, such as intellectual disability and inherited retinal dystrophies.
Conclusion: The anticipated impacts will benefit a wide range of healthcare practitioners and potentially influence health policymakers
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
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
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Representation and recognition of human actions in video
No full textPhDAutomated human action recognition plays a critical role in the development of human-machine
communication, by aiming for a more natural interaction between artificial intelligence and the
human society. Recent developments in technology have permitted a shift from a traditional
human action recognition performed in a well-constrained laboratory environment to realistic
unconstrained scenarios. This advancement has given rise to new problems and challenges still
not addressed by the available methods. Thus, the aim of this thesis is to study innovative approaches
that address the challenging problems of human action recognition from video captured
in unconstrained scenarios. To this end, novel action representations, feature selection methods,
fusion strategies and classification approaches are formulated.
More specifically, a novel interest points based action representation is firstly introduced, this
representation seeks to describe actions as clouds of interest points accumulated at different temporal
scales. The idea behind this method consists of extracting holistic features from the point
clouds and explicitly and globally describing the spatial and temporal action dynamic. Since
the proposed clouds of points representation exploits alternative and complementary information
compared to the conventional interest points-based methods, a more solid representation is then
obtained by fusing the two representations, adopting a Multiple Kernel Learning strategy. The
validity of the proposed approach in recognising action from a well-known benchmark dataset is
demonstrated as well as the superior performance achieved by fusing representations.
Since the proposed method appears limited by the presence of a dynamic background and fast
camera movements, a novel trajectory-based representation is formulated. Different from interest
points, trajectories can simultaneously retain motion and appearance information even in noisy
and crowded scenarios. Additionally, they can handle drastic camera movements and a robust
region of interest estimation. An equally important contribution is the proposed collaborative
feature selection performed to remove redundant and noisy components. In particular, a novel
feature selection method based on Multi-Class Delta Latent Dirichlet Allocation (MC-DLDA)
is introduced. Crucial, to enrich the final action representation, the trajectory representation is
adaptively fused with a conventional interest point representation. The proposed approach is
extensively validated on different datasets, and the reported performances are comparable with
the best state-of-the-art. The obtained results also confirm the fundamental contribution of both
collaborative feature selection and adaptive fusion.
Finally, the problem of realistic human action classification in very ambiguous scenarios is
taken into account. In these circumstances, standard feature selection methods and multi-class
classifiers appear inadequate due to: sparse training set, high intra-class variation and inter-class
similarity. Thus, both the feature selection and classification problems need to be redesigned.
The proposed idea is to iteratively decompose the classification task in subtasks and select the
optimal feature set and classifier in accordance with the subtask context. To this end, a cascaded
feature selection and action classification approach is introduced. The proposed cascade aims to
classify actions by exploiting as much information as possible, and at the same time trying to
simplify the multi-class classification in a cascade of binary separations. Specifically, instead of
separating multiple action classes simultaneously, the overall task is automatically divided into
easier binary sub-tasks. Experiments have been carried out using challenging public datasets;
the obtained results demonstrate that with identical action representation, the cascaded classifier
significantly outperforms standard multi-class classifiers
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Estimating counterparty credit risk fusing traditional and alternative data
Full text linkLAUREA MAGISTRALELa presente tesi propone una metodologia innovativa per la stima del rischio di credito dell'azienda, in particolare studia il rischio di controparte sfruttando un approccio guidato puramente dai dati combinato con dati alternativi.
Il rischio di controparte è un problema ben noto nell'ambito della finanza; in pratica, valuta il rischio che la controparte non rispetti i propri obblighi contrattuali.
Questo lavoro deriva da uno stage curriculare presso 3rdPLACE, un'azienda che offre soluzioni e servizi nel campo dell'intelligence applicata ai dati digitali, ed è stata commissionata da una società che supporta istituzioni finanziarie, grandi, medie e piccole imprese, compagnie assicurative, pubbliche amministrazioni e professionisti nella gestione efficace del credito.
Il progetto consiste nella creazione di un algoritmo di apprendimento automatico che consente di prevedere le aziende inadempienti. Il set di dati che ci hanno fornito sono composti da società italiane registrate presso la Camera di Commercio. Metà di essi hanno disponibile un bilancio finanziario, mentre per l'altra metà queste informazioni non sono disponibili.
All'interno di questo progetto l'obiettivo prevede lo sviluppo di un innovativo stimatore del rischio di credito progettato per lavorare su medie, piccole e piccolissime aziende italiane non quotate in borsa, utilizzando una metodologia puramente guidata dai dati con le tecnologie del machine learning.
A differenza della società quotata in cui sono disponibili informazioni chiare e trasparenti, in questo progetto affrontiamo anche una sfida in cui le informazioni sono scarse, non standardizzate.
Inoltre, è stato proposto di creare una nuova feature: il digital score. Esso misura la presenza, le prestazioni e l'efficacia dell'azienda sul web e la integra nel problema di classificazione iniziale di default.
Gli utenti finali di questo metodo innovativo potrebbero essere le agenzie di rating che si occupano di rischio finanziario, istituzioni finanziarie e banche.This thesis proposes an innovative methodology for estimating company’s credit risk, in specific it studies counterpart risk exploiting a data driven approach combined with alternative data.
Counterparty risk is a well know problem within the finance domain; practically, it evaluates the risk that the counterparty will not live up to its contractual obligation.
This work derives from a curricular internship at 3rdPLACE, which is a company that offers solutions and services in the field of intelligence applied to digital data, and it was commissioned by a company that supports financial institutions, large, medium and small businesses, insurance companies, public administrations and professionals in effective credit management.
The project consist in creating a machine learning algorithm that allows the prediction of companies default. The dataset they provided to us are composed by Italian companies registered at the national Companies House, half of them are available the balance sheet, while the other half we do not have this information.
Within this project the goal involves developing an innovative credit risk estimator designed to work on medium, small and very small Italian companies not quoted on exchange, using a methodology that is purely data-driven with the technologies of machine learning.
Differently from listed company where clear and transparent information is publicly available, in this project we tackle also a challenge where information is scarce, not standardized.
Furthermore it has been proposed to create a new feature, in addition to those provided by the client company: the digital score. It measures the company's presence, performance and effectiveness on the web and integrates it into the initial classification problem of default.
The end users of this innovative method could be the rating agencies that deal with financial risk, financial institutions and banks
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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