Recursos de colección

UPCommons - E-prints UPC Universitat Politècnica de Catalunya (83.371 recursos)

E-prints UPC cobreix dues finalitats: per una banda, és el dipòsit institucional de la UPC que recull els articles de revista, les comunicacions de congrés i els reports de recerca generats en les activitats de recerca del personal docent i investigador de la universitat; per l'altra, és una eina que permet accelerar la producció científica, allotjant versions de documents prèvies a la publicació en una revista o a les actes d’un congrés.

Reports de recerca

Mostrando recursos 1 - 20 de 45

  1. Adaptive clock with useful jitter

    Cortadella Fortuny, Jordi; Lavagno, Luciano; López Muñoz, Pedro; Lupon Navazo, Marc; Moreno Vega, Alberto; Roca Pérez, Antoni; Sapatnekar, Sachin S.
    The growing variability in nanoelectronic devices due to uncertainties from the manufacturing process and environmental conditions (power supply, temperature, aging) requires increasing design guardbands, forcing circuits to work with conservative clock frequencies. Various schemes for clock generation based on ring oscillators have been proposed with the goal to mitigate the power and performance losses attributable to variability. However, there has been no systematic analysis to quantify the benefits of such schemes.This paper presents and analyzes an Adaptive Clocking scheme with Useful Jitter (ACUJ) that uses variability as an opportunity to reduce power by adapting the clock frequency to the varying...

  2. On the complexity of exchanging

    Molinero Albareda, Xavier; Olsen, Martin; Serna Iglesias, María José
    We analyze the computational complexity of the problem of deciding whether, for a given simple game, there exists the possibility of rearranging the participants in a set of j given losing coalitions into a set of j winning coalitions. We also look at the problem of turning winning coalitions into losing coalitions. We analyze the problem when the simple game is represented by a list of wining, losing, minimal winning or maximal loosing coalitions.

  3. Experiments on document level machine translation

    Martínez Garcia, Eva; España Bonet, Cristina; Márquez Villodre, Luís
    Most of the current SMT systems work at sentence level. They translate a text assuming that sentences are independent, but, when one looks at a well formed document, it is clear that there exist many inter sentence relations. There is much contextual information that, unfortunately, is lost when translating sentences in an independent way. We want to improve translation coherence and cohesion using document level information. So, we are interested in develop new strategies to take advantage of context information to achieve our goal. For example, we want to approach this challenge developing postprocesses in order to try to fix a...

  4. Wikicardi : hacia la extracción de oraciones paralelas de Wikipedia

    Boldoba Trapote, Josu; Barrón Cedeño, Luis Alberto; España Bonet, Cristina
    Uno de los objetivos del proyecto Tacardi (TIN2012-38523-C02-00) consiste en extraer oraciones paralelas de corpus comparables para enriquecer y adaptar traductores automáticos. En esta investigación usamos un subconjunto de Wikipedia como corpus comparable. En este reporte se describen nuestros avances con respecto a la extracción de fragmentos paralelos de Wikipedia. Primero, discutimos cómo hemos definido los tres dominios de interés -ciencia, informática y deporte-, en el marco de la enciclopedia y cómo hemos extraído los textos y demás datos necesarios para la caracterización de los artículos en las distintas lenguas. Después discutimos brevemente los modelos que usaremos para identificar oraciones...

  5. Extracting user spatio-temporal profiles from location based social networks

    Béjar Alonso, Javier
    Location Based Social Networks (LBSN) like Twitter or Instagram are a good source for user spatio-temporal behavior. These social network provide a low rate sampling of user's location information during large intervals of time that can be used to discover complex behaviors, including mobility profiles, points of interest or unusual events. This information is important for different domains like mobility route planning, touristic recommendation systems or city planning. Other approaches have used the data from LSBN to categorize areas of a city depending on the categories of the places that people visit or to discover user behavioral patterns from their...

  6. Mining frequent spatio-temporal patterns from location based social networks

    Béjar Alonso, Javier
    Location Based Social Networks (LBSN) like Twitter or Instagram are a good source for user spatio-temporal behavior. These social network provide a low rate sampling of user's location information during large intervals of time that can be used to discover complex behaviors, including frequent routes, points of interest or unusual events. This information is important for different domains like route planning, touristic recommendation systems or city planning. Other approaches have used the data from LSBN to categorize areas of a city depending on the categories of the places that people visit or to discover user behavioral patterns from their visits. The aim...

  7. Strategies and algorithms for clustering large datasets: a review

    Béjar Alonso, Javier
    The exploratory nature of data analysis and data mining makes clustering one of the most usual tasks in these kind of projects. More frequently these projects come from many different application areas like biology, text analysis, signal analysis, etc that involve larger and larger datasets in the number of examples and the number of attributes. Classical methods for clustering data like K-means or hierarchical clustering are beginning to reach its maximum capability to cope with this increase of dataset size. The limitation for these algorithms come either from the need of storing all the data in memory or because of...

  8. K-means vs Mini Batch K-means: a comparison

    Béjar Alonso, Javier
    Mini Batch K-means (cite{Sculley2010}) has been proposed as an alternative to the K-means algorithm for clustering massive datasets. The advantage of this algorithm is to reduce the computational cost by not using all the dataset each iteration but a subsample of a fixed size. This strategy reduces the number of distance computations per iteration at the cost of lower cluster quality. The purpose of this paper is to perform empirical experiments using artificial datasets with controlled characteristics to assess how much cluster quality is lost when applying this algorithm. The goal is to obtain some guidelines about what are the...

  9. Unsupervised feature selection by means of external validity indices

    Béjar Alonso, Javier
    Feature selection for unsupervised data is a difficult task because a reference partition is not available to evaluate the relevance of the features. Recently, different proposals of methods for consensus clustering have used external validity indices to assess the agreement among partitions obtained by clustering algorithms with different parameter values. Theses indices are independent of the characteristics of the attributes describing the data, the way the partitions are represented or the shape of the clusters. This independence allows to use these measures to assess the similarity of partitions with different subsets of attributes. As for supervised feature selection, the goal...

  10. Event-based real-time decomposed conformance analysis

    vanden Broucke, Seppe; Muñoz Gama, Jorge; Carmona Vargas, Josep; Baesens, Bart; Vanthienen, Jan
    Process mining deals with the extraction of knowledge from event logs. One important task within this research field is denoted as conformance checking, which aims to diagnose deviations and discrepancies between modeled behavior and real-life, observed behavior. Conformance checking techniques still face some challenges, among which scalability, timeliness and traceability issues. In this paper, we propose a novel conformance analysis methodology to support the real-time monitoring of event-based data streams, which is shown to be more efficient than related approaches and able to localize deviations in a more fine-grained manner. Our developed approach can be directly applied in business process...

  11. Deliverable 6.1 Infrastructure for Extractive Summarization

    Saggion, Horacio; Padró, Lluís; Fuentes Fort, Maria
    SKATER Internal Report: software of infrastructure for extractive Summarization (work carried out until December 2013)

  12. Spectral learning of transducers over continuous sequences

    Recasens, Adria; Quattoni, Ariadna Julieta
    In this paper we present a spectral algorithm for learning weighted nite state transducers (WFSTs) over paired input-output sequences, where the input is continuous and the output discrete. WFSTs are an important tool for modeling paired input-output sequences and have numerous applications in real-world problems. Recently, Balle et al (2011) proposed a spectral method for learning WFSTs that overcomes some of the well known limitations of gradient-based or EM optimizations which can be computationally expensive and su er from local optima issues. Their algorithm can model distributions where both inputs and outputs are sequences from a discrete alphabet. However, many real world...

  13. Specialization in i* strategic rationale diagrams

    López Cuesta, Lidia; Franch Gutiérrez, Javier; Marco Gómez, Jordi
    The specialization relationship is offered by the i* modeling language through the is-a construct defined over actors (a subactor is-a superactor). Although the overall meaning of this construct is highly intuitive, its semantics of strategic rationale (SR) diagrams is not defined. In this report we provide a formal definition of the specialization relationship at the level of i* SR diagrams. We root our proposal over existing work in conceptual modeling in general, and object-orientation in particular. Also, we use the results of a survey conducted in the i* community that provides some hints about what i* modelers expect from specialization....

  14. Weighted games without a unique minimal representation in integers

    Freixas Bosch, Josep; Molinero Albareda, Xavier
    Recerca de jocs amb mínim número de jugadors sense representacions enteres mínimes o mínimes normalitzades

  15. Non-functional requirements in software architecture practice

    Ameller, David; Ayala Martínez, Claudia Patricia; Cabot Sagrera, Jordi; Franch Gutiérrez, Javier
    Dealing with non-functional requirements (NFRs) has posed a challenge onto software engineers for many years. Over the years, many methods and techniques have been proposed to improve their elicitation, documentation, and validation. Knowing more about the state of the practice on these topics may benefit both practitioners’ and researchers’ daily work. A few empirical studies have been conducted in the past, but none under the perspective of software architects, in spite of the great influence that NFRs have on daily architects’ practices. This paper presents some of the findings of an empirical study based on 13 interviews with software architects....

  16. Unsupervised ensemble minority clustering

    González Pellicer, Edgar; Turmo Borras, Jorge
    Cluster a alysis lies at the core of most unsupervised learning tasks. However, the majority of clustering algorithms depend on the all-in assumption, in which all objects belong to some cluster, and perform poorly on minority clustering tasks, in which a small fraction of signal data stands against a majority of noise. The approaches proposed so far for minority clustering are supervised: they require the number and distribution of the foreground and background clusters. In supervised learning and all-in clustering, combination methods have been successfully applied to obtain distribution-free learners, even from the output of weak individual algorithms. In this report, we present...

  17. WeSSQoS: a configurable SOA system for quality-aware web service selection

    Cabrera Bejar, Oscar; Oriol Hilari, Marc; Franch Gutiérrez, Javier; López Cuesta, Lidia; Marco Gómez, Jordi; Fragoso, Olivia; Santaolaya, René
    Web Services (WS) have become one the most used technologies nowadays in software systems. Among the challenges when integrating WS in a given system, requirements-driven selection occupies a prominent place. A comprehensive selection process needs to check compliance of Non-Functional Requirements (NFR), which can be assessed by analysing WS Quality of Service (QoS). In this paper, we describe the WeSSQoS system that aims at ranking available WS based on the comparison of their QoS and the stated NFRs. WeSSQoS is designed as an open service-oriented architecture that hosts a configurable portfolio of normalization and ranking algorithms that can be selected by the engineer...

  18. Frequent sets, sequences and taxonomies: new efficient algorithmic proposals

    Baixeries i Juvillà, Jaume; Casas Garriga, Gemma; Balcázar Navarro, José Luis
    We describe efficient algorithmic proposals to approach three fundamental problems in data mining: association rules, episodes in sequences, and generalized association rules over hierarchical taxonomies. The association rule discovery problem aims at identifying frequent itemsets in a database and then forming conditional implication rules among them. For this association task, we will introduce a new algorithmic proposal to reduce substantially the number of processed transactions. The resulting algorithm, called Ready-and-Go, is used to discover frequent sets efficiently. Then, for the discovery of patterns in sequences of events in ordered collections of data, we propose to apply the appropiate variant of...

  19. Word sense ranking based on semantic similarity and graph entropy

    Sousa Lopes, João; Álvarez Napagao, Sergio; Vázquez Salceda, Javier
    In this paper we propose a system for the recommendation of tagged pictures obtained from the Web. The system, driven by user feedback, executes an abductive reasoning (based on WordNet synset semantic relations) that is able to iteratively lead to new concepts which progressively represent the cognitive creative user state. Furthermore we design a selection mechanism to pick the most relevant abductive inferences by mixing a topological graph analysis together with a semantic similitude measure.

  20. USE: a multi-agent user-driven recommendation system for semantic knowledge extraction

    Lopes, João Sousa; Álvarez Napagao, Sergio; Confalonieri, Roberto; Vázquez Salceda, Javier
    Semiotics is a field where research on Computer Science methodologies has focused, mainly concerning Syntax and Semantics. These methodologies, however, are lacking of some flexibility for the continuously evolving web community, in which the knowledge is classified with tags rather than with ontologies. In this paper we propose a multi-agent system for the recommendation of tagged pictures obtained from mainstream Web applications. The agents in this system execute a hybrid reasoning based on WordNet and Markov chains that is able, driven by user feedback, to iteratively disambiguate the semantics of the picture tags and thus to generate knowledge from the,...

Aviso de cookies: Usamos cookies propias y de terceros para mejorar nuestros servicios, para análisis estadístico y para mostrarle publicidad. Si continua navegando consideramos que acepta su uso en los términos establecidos en la Política de cookies.