990 resultados para Wikis (Computer science) -- TFC


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La present memòria es centra en l'anàlisi comparatiu de les diferents eines de software per a la gestió de projectes dins del mercat.

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We consider the numerical treatment of the optical flow problem by evaluating the performance of the trust region method versus the line search method. To the best of our knowledge, the trust region method is studied here for the first time for variational optical flow computation. Four different optical flow models are used to test the performance of the proposed algorithm combining linear and nonlinear data terms with quadratic and TV regularization. We show that trust region often performs better than line search; especially in the presence of non-linearity and non-convexity in the model.

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In this paper we present ClInt (Clinical Interview), a bilingual Spanish-Catalan spoken corpus that contains 15 hours of clinical interviews. It consists of audio files aligned with multiple-level transcriptions comprising orthographic, phonetic and morphological information, as well as linguistic and extralinguistic encoding. This is a previously non-existent resource for these languages and it offers a wide-ranging exploitation potential in a broad variety of disciplines such as Linguistics, Natural Language Processing and related fields.

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CoCo is a collaborative web interface for the compilation of linguistic resources. In this demo we are presenting one of its possible applications: paraphrase acquisition.

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In this article, we show how the use of state-of-the-art methods in computer science based on machine perception and learning allows the unobtrusive capture and automated analysis of interpersonal behavior in real time (social sensing). Given the high ecological validity of the behavioral sensing, the ease of behavioral-cue extraction for large groups over long observation periods in the field, the possibility of investigating completely new research questions, and the ability to provide people with immediate feedback on behavior, social sensing will fundamentally impact psychology.

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Este proyecto muestra la creación de una aplicación que permite trabajar con sistemas de almacenamiento en la nube de manera encriptada.

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El projecte realitzat procura una aproximació real al desenvolupament amb dispositius Android. La funció principal de l'aplicació desenvolupada és consultar les dades meteorològiques utilitzant un web service i l'actual posició GPS.

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Tietojenkäsittelyn pääkokoelma sijaitsee pääkirjastossa (Linnassa), jossa painettu yleis- ja käsikirjastokokoelma koostuu noin 4000 nimekkeestä monografioita (painettujen monografiasarjojen osat mukaan lukien). Tietojenkäsittely-kokoelmasta kartoitettiin neljä osa-aluetta. Näistä selvimmäksi painopistealaksi osoittautui ohjelmointi, ohjelmointikielet & atk-ohjelmat, joka käsitti noin 33 % nimekkeistä (1314). Muiden ryhmien osuudet olivat pienemmät: tietojärjestelmät, tiedonhallinta & tietoturva noin 18 % (727 nimekettä); tekoäly, tietämystekniikka & hahmontunnistus noin 16 % (629 nimekettä). Käsikirjaston karsitussa noin 100 nimekkeen kokoelmassa on runsaasti sanakirjoja ja erilaisia hakuteoksia kuten lähes täydellinen (44/45) Encyclopedia of computer science and technology ja myös e-muodossa oleva 3-osainen Handbook of information security. Painettuja lehtiä oli 6 nimekettä (IEEE Pervasive Computing, MikroPC, myös e-muodossa oleva Social Science Computer Review, Tekniikan näköalat, Tietokone ja Tietoyhteys). Sähkökirjoja kokoelmassa oli 466 nimekettä Ebrary: Information technology -tietokannassa, 24 nimekettä NetLibrary-tietokannassa, 3 nimekettä Taylor & Francis eBooks online -tietokannassa ja 2 nimekettä sähköisinä hakuteoksina (Encyclopedia of gender and information technology ja Encyclopedia of information science and technology) sekä 4964-osainen Lecture notes in computer science -monografiasarja. Verkkolehtiä kokoelmassa oli noin 300 nimekettä. Tietokantoja oli 4 kokotekstitietokantaa (ACM - Association for Computing Machinery, EBSCOhost Academic Search Premier, Elsevier ScienceDirect ja SpringerLink) sekä 2 viitetietokantaa (Computer + Info Systems (CSA) ja Web of Scence (ISI)).

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Aquest treball explica com una pime passa d'un entorn de servidors majoritàriament físic a un entorn virtual aprofitant la necessitat d'actualitzar la infraestructura. La plataforma de virtualització utilitzada serà VMware tant per a servidors com per a escriptoris.

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Clúster format per una màquina principal HEAD Node més 19 nodes de càlcul de la gama SGI13 Altix14 XE Servers and Clusters, unides en una topologia de màster subordinat, amb un total de 40 processadors Dual Core i aproximadament 160Gb de RAM.

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El objetivo del proyecto es mejorar el proceso de copias de seguridad tradicional utilizando redes WAN distribuidas de bajo rendimiento.

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In this paper we present a multi-stage classifier for magnetic resonance spectra of human brain tumours which is being developed as part of a decision support system for radiologists. The basic idea is to decompose a complex classification scheme into a sequence of classifiers, each specialising in different classes of tumours and trying to reproducepart of the WHO classification hierarchy. Each stage uses a particular set of classification features, which are selected using a combination of classical statistical analysis, splitting performance and previous knowledge.Classifiers with different behaviour are combined using a simple voting scheme in order to extract different error patterns: LDA, decision trees and the k-NN classifier. A special label named "unknown¿ is used when the outcomes of the different classifiers disagree. Cascading is alsoused to incorporate class distances computed using LDA into decision trees. Both cascading and voting are effective tools to improve classification accuracy. Experiments also show that it is possible to extract useful information from the classification process itself in order to helpusers (clinicians and radiologists) to make more accurate predictions and reduce the number of possible classification mistakes.

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In this paper, we propose a new supervised linearfeature extraction technique for multiclass classification problemsthat is specially suited to the nearest neighbor classifier (NN).The problem of finding the optimal linear projection matrix isdefined as a classification problem and the Adaboost algorithmis used to compute it in an iterative way. This strategy allowsthe introduction of a multitask learning (MTL) criterion in themethod and results in a solution that makes no assumptions aboutthe data distribution and that is specially appropriated to solvethe small sample size problem. The performance of the methodis illustrated by an application to the face recognition problem.The experiments show that the representation obtained followingthe multitask approach improves the classic feature extractionalgorithms when using the NN classifier, especially when we havea few examples from each class

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Peer-reviewed