Mures : Un système de recommandation de musique

Pendant la dernière décennie nous avons vu une transformation incroyable du monde de la musique qui est passé des cassettes et disques compacts à la musique numérique en ligne. Avec l'explosion de la musique numérique, nous avons besoin de systèmes de recommandation de musique pour choisir les chansons susceptibles d’être appréciés à partir de ces énormes bases de données en ligne ou personnelles. Actuellement, la plupart des systèmes de recommandation de musique utilisent l’algorithme de filtrage collaboratif ou celui du filtrage à base de contenu. Dans ce mémoire, nous proposons un algorithme hybride et original qui combine le filtrage collaboratif avec le filtrage basé sur étiquetage, amélioré par la technique de filtrage basée sur le contexte d’utilisation afin de produire de meilleures recommandations. Notre approche suppose que les préférences de l'utilisateur changent selon le contexte d'utilisation. Par exemple, un utilisateur écoute un genre de musique en conduisant vers son travail, un autre type en voyageant avec la famille en vacances, un autre pendant une soirée romantique ou aux fêtes. De plus, si la sélection a été générée pour plus d'un utilisateur (voyage en famille, fête) le système proposera des chansons en fonction des préférences de tous ces utilisateurs. L'objectif principal de notre système est de recommander à l'utilisateur de la musique à partir de sa collection personnelle ou à partir de la collection du système, les nouveautés et les prochains concerts. Un autre objectif de notre système sera de collecter des données provenant de sources extérieures, en s'appuyant sur des techniques de crawling et sur les flux RSS pour offrir des informations reliées à la musique tels que: les nouveautés, les prochains concerts, les paroles et les artistes similaires. Nous essayerons d’unifier des ensembles de données disponibles gratuitement sur le Web tels que les habitudes d’écoute de Last.fm, la base de données de la musique de MusicBrainz et les étiquettes des MusicStrands afin d'obtenir des identificateurs uniques pour les chansons, les albums et les artistes.

Development of Hierarchical Clustering Techniques for Gridded Data from Mixed Data Sequences

Knowledge discovery in databases is the non-trivial process of identifying valid, novel potentially useful and ultimately understandable patterns from data. The term Data mining refers to the process which does the exploratory analysis on the data and builds some model on the data. To infer patterns from data, data mining involves different approaches like association rule mining, classification techniques or clustering techniques. Among the many data mining techniques, clustering plays a major role, since it helps to group the related data for assessing properties and drawing conclusions. Most of the clustering algorithms act on a dataset with uniform format, since the similarity or dissimilarity between the data points is a significant factor in finding out the clusters. If a dataset consists of mixed attributes, i.e. a combination of numerical and categorical variables, a preferred approach is to convert different formats into a uniform format. The research study explores the various techniques to convert the mixed data sets to a numerical equivalent, so as to make it equipped for applying the statistical and similar algorithms. The results of clustering mixed category data after conversion to numeric data type have been demonstrated using a crime data set. The thesis also proposes an extension to the well known algorithm for handling mixed data types, to deal with data sets having only categorical data. The proposed conversion has been validated on a data set corresponding to breast cancer. Moreover, another issue with the clustering process is the visualization of output. Different geometric techniques like scatter plot, or projection plots are available, but none of the techniques display the result projecting the whole database but rather demonstrate attribute-pair wise analysis

Capturing Emergent Semantics from Social Annotation Systems

The ongoing growth of the World Wide Web, catalyzed by the increasing possibility of ubiquitous access via a variety of devices, continues to strengthen its role as our prevalent information and commmunication medium. However, although tools like search engines facilitate retrieval, the task of finally making sense of Web content is still often left to human interpretation. The vision of supporting both humans and machines in such knowledge-based activities led to the development of different systems which allow to structure Web resources by metadata annotations. Interestingly, two major approaches which gained a considerable amount of attention are addressing the problem from nearly opposite directions: On the one hand, the idea of the Semantic Web suggests to formalize the knowledge within a particular domain by means of the "top-down" approach of defining ontologies. On the other hand, Social Annotation Systems as part of the so-called Web 2.0 movement implement a "bottom-up" style of categorization using arbitrary keywords. Experience as well as research in the characteristics of both systems has shown that their strengths and weaknesses seem to be inverse: While Social Annotation suffers from problems like, e. g., ambiguity or lack or precision, ontologies were especially designed to eliminate those. On the contrary, the latter suffer from a knowledge acquisition bottleneck, which is successfully overcome by the large user populations of Social Annotation Systems. Instead of being regarded as competing paradigms, the obvious potential synergies from a combination of both motivated approaches to "bridge the gap" between them. These were fostered by the evidence of emergent semantics, i. e., the self-organized evolution of implicit conceptual structures, within Social Annotation data. While several techniques to exploit the emergent patterns were proposed, a systematic analysis - especially regarding paradigms from the field of ontology learning - is still largely missing. This also includes a deeper understanding of the circumstances which affect the evolution processes. This work aims to address this gap by providing an in-depth study of methods and influencing factors to capture emergent semantics from Social Annotation Systems. We focus hereby on the acquisition of lexical semantics from the underlying networks of keywords, users and resources. Structured along different ontology learning tasks, we use a methodology of semantic grounding to characterize and evaluate the semantic relations captured by different methods. In all cases, our studies are based on datasets from several Social Annotation Systems. Specifically, we first analyze semantic relatedness among keywords, and identify measures which detect different notions of relatedness. These constitute the input of concept learning algorithms, which focus then on the discovery of synonymous and ambiguous keywords. Hereby, we assess the usefulness of various clustering techniques. As a prerequisite to induce hierarchical relationships, our next step is to study measures which quantify the level of generality of a particular keyword. We find that comparatively simple measures can approximate the generality information encoded in reference taxonomies. These insights are used to inform the final task, namely the creation of concept hierarchies. For this purpose, generality-based algorithms exhibit advantages compared to clustering approaches. In order to complement the identification of suitable methods to capture semantic structures, we analyze as a next step several factors which influence their emergence. Empirical evidence is provided that the amount of available data plays a crucial role for determining keyword meanings. From a different perspective, we examine pragmatic aspects by considering different annotation patterns among users. Based on a broad distinction between "categorizers" and "describers", we find that the latter produce more accurate results. This suggests a causal link between pragmatic and semantic aspects of keyword annotation. As a special kind of usage pattern, we then have a look at system abuse and spam. While observing a mixed picture, we suggest that an individual decision should be taken instead of disregarding spammers as a matter of principle. Finally, we discuss a set of applications which operationalize the results of our studies for enhancing both Social Annotation and semantic systems. These comprise on the one hand tools which foster the emergence of semantics, and on the one hand applications which exploit the socially induced relations to improve, e. g., searching, browsing, or user profiling facilities. In summary, the contributions of this work highlight viable methods and crucial aspects for designing enhanced knowledge-based services of a Social Semantic Web.

Dissociated Dipoles: Image representation via non-local comparisons

A fundamental question in visual neuroscience is how to represent image structure. The most common representational schemes rely on differential operators that compare adjacent image regions. While well-suited to encoding local relationships, such operators have significant drawbacks. Specifically, each filter's span is confounded with the size of its sub-fields, making it difficult to compare small regions across large distances. We find that such long-distance comparisons are more tolerant to common image transformations than purely local ones, suggesting they may provide a useful vocabulary for image encoding. . We introduce the "Dissociated Dipole," or "Sticks" operator, for encoding non-local image relationships. This operator de-couples filter span from sub-field size, enabling parametric movement between edge and region-based representation modes. We report on the perceptual plausibility of the operator, and the computational advantages of non-local encoding. Our results suggest that non-local encoding may be an effective scheme for representing image structure.

Towards Direct Policy Search Reinforcement Learning for Robot Control

This paper proposes a high-level reinforcement learning (RL) control system for solving the action selection problem of an autonomous robot. Although the dominant approach, when using RL, has been to apply value function based algorithms, the system here detailed is characterized by the use of direct policy search methods. Rather than approximating a value function, these methodologies approximate a policy using an independent function approximator with its own parameters, trying to maximize the future expected reward. The policy based algorithm presented in this paper is used for learning the internal state/action mapping of a behavior. In this preliminary work, we demonstrate its feasibility with simulated experiments using the underwater robot GARBI in a target reaching task

El MERCOSUR como vehículo estratégico de posicionamiento político regional. Caso de estudio: la política exterior de Brasil en el gobierno de Lula

La razón principal del protagonismo regional brasilero se deriva de su continuidad en la construcción de estrategias en política exterior. Fue precisamente esta continuidad, sustentada en una identidad nacional y una visión autónoma de inserción internacional, características propias de su herencia diplomática, lo que le permitió identificarse y ser identificado como un líder regional a través de uno de los mecanismos de integración más grandes en América Latina. Como resultado de la política exterior de Lula y su redireccionamiento hacia la región, Brasil logró impulsar y construir una región suramericana sustentada en un MERCOSUR. Un espacio de cooperación regido por unos intereses y valores compartidos en materia política, económica y cultural que le permitiera por un lado diversificar y expandir su economía y por el otro, un posicionamiento político reflejado en el UNASUR. Con base en lo anterior esta investigación busca responder a la pregunta ¿de qué manera el proyecto de integración MERCOSUR incidió en el posicionamiento político de Brasil en la región durante el gobierno de Lula? Para ello este trabajo se divide en tres partes. La primera explica la construcción de su política exterior hacia la región. La segunda parte busca analizar el rol que ha tenido Brasil en la evolución de MERCOSUR, toda vez que es por medio de este, que Brasil pudo afianzar un protagonismo regional y global. Por último, se explica el posicionamiento político regional brasilero teniendo en cuenta al MERCOSUR como un vehículo estratégico utilizado por Brasil para posicionarse políticamente en la región.

Las implicaciones geopolíticas de la construcción del Canal de Nicaragua para el vecindario del Caribe Occidental (2006-2014)

El presente estudio de caso, busca explicar cuáles son las posibles implicaciones e influencia de la construcción del Proyecto del Canal de Nicaragua en la geografía, la economía y la política exterior del Caribe Occidental. Esta investigación defiende que la construcción de este canal influirá en el largo plazo en la geopolítica de esta región, debido a la posibilidad de una competencia hasta hoy inexistente en la región entre dos canales interoceánicos, que puede llegar a afectar la disponibilidad de recursos naturales de la subregión, y asimismo, fortalecer la presencia asiática en América Latina; sin embargo, las consecuencias de este canal no pueden determinarse de manera específica. Para sustentar lo anterior, se realizará una revisión del proceso de construcción del canal de Panamá y del proyecto del de Nicaragua, para establecer un estudio de prospectiva de los escenarios posibles para la región del Caribe Occidental.

Establishing a community of practice of researchers, practitioners, policy-makers and communities to sustainably manage environmental health risks in Ecuador

The Sustainably Managing Environmental Health Risk in Ecuador project was launched in 2004 as a partnership linking a large Canadian university with leading Cuban and Mexican institutes to strengthen the capacities of four Ecuadorian universities for leading community-based learning and research in areas as diverse as pesticide poisoning, dengue control, water and sanitation, and disaster preparedness. By 2009, train-the-trainer project initiation involved 27 participatory action research Master’s theses in 15 communities where 1200 community learners participated in the implementation of associated interventions. This led to establishment of innovative Ecuadorian-led master’s and doctoral programs, and a Population Health Observatory on Collective Health, Environment and Society for the Andean region based at the Universidad Andina Simon Bolivar. Building on this network, numerous initiatives were begun, such as an internationally funded research project to strengthen dengue control in the coastal community of Machala, and establishment of a local community eco-health centre focusing on determinants of health near Cuenca. Alliances of academic and non-academic partners from the South and North provide a promising orientation for learning together about ways of addressing negative trends of development. Assessing the impacts and sustainability of such processes, however, requires longer term monitoring of results and related challenges.

Multimodal semantic-associative collateral labelling and indexing of still images

A novel framework for multimodal semantic-associative collateral image labelling, aiming at associating image regions with textual keywords, is described. Both the primary image and collateral textual modalities are exploited in a cooperative and complementary fashion. The collateral content and context based knowledge is used to bias the mapping from the low-level region-based visual primitives to the high-level visual concepts defined in a visual vocabulary. We introduce the notion of collateral context, which is represented as a co-occurrence matrix, of the visual keywords, A collaborative mapping scheme is devised using statistical methods like Gaussian distribution or Euclidean distance together with collateral content and context-driven inference mechanism. Finally, we use Self Organising Maps to examine the classification and retrieval effectiveness of the proposed high-level image feature vector model which is constructed based on the image labelling results.

Semantic-associative visual content labelling and retrieval: A multimodal approach

A novel framework referred to as collaterally confirmed labelling (CCL) is proposed, aiming at localising the visual semantics to regions of interest in images with textual keywords. Both the primary image and collateral textual modalities are exploited in a mutually co-referencing and complementary fashion. The collateral content and context-based knowledge is used to bias the mapping from the low-level region-based visual primitives to the high-level visual concepts defined in a visual vocabulary. We introduce the notion of collateral context, which is represented as a co-occurrence matrix of the visual keywords. A collaborative mapping scheme is devised using statistical methods like Gaussian distribution or Euclidean distance together with collateral content and context-driven inference mechanism. We introduce a novel high-level visual content descriptor that is devised for performing semantic-based image classification and retrieval. The proposed image feature vector model is fundamentally underpinned by the CCL framework. Two different high-level image feature vector models are developed based on the CCL labelling of results for the purposes of image data clustering and retrieval, respectively. A subset of the Corel image collection has been used for evaluating our proposed method. The experimental results to-date already indicate that the proposed semantic-based visual content descriptors outperform both traditional visual and textual image feature models. (C) 2007 Elsevier B.V. All rights reserved.

Detecting the number of signals using antenna array: a single threshold solution

A new approach is presented to identify the number of incoming signals in antenna array processing. The new method exploits the inherent properties existing in the noise eigenvalues of the covariance matrix of the array output. A single threshold has been established concerning information about the signal and noise strength, data length, and array size. When the subspace-based algorithms are adopted the computation cost of the signal number detector can almost be neglected. The performance of the threshold is robust against low SNR and short data length.

A policy critique of Stansted Airport's expansion to 25 million passengers per annum (MPPA)

In this paper we undertake a preliminary assessment of the regional planning and development implications of BAA Stansted Airport’s planning permission to grow to 25 million passengers per annum (mppa) by 2010. Our concern is not simply to consider the overall growth of the airport on the airport site itself but the nature and type of growth both on- and off-site. In this document we focus on the submitted planning permission documents and test them. The methodology we employed was to draw on published and unpublished numerical estimates of the airport’s growth – particularly including estimates produced by the airport owner, BAA, and their economic and planning consultants DTZ Pieda - and critically, and systematically analyse their figures. We adopted this approach because unless the figures which were employed in the initial calculations were correct then all of the subsequent projections which flow from them - and the polices which could then be based on them – could be flawed. The analysis is divided into two parts – firstly, are the growth forecasts correct?; and secondly, what do these forecasts actually mean in developmental terms? In effect, what we have done is to produce a critique of the existing body of evidence by questioning underpinning assumptions and then draw some preliminary conclusions for the region based on this analysis. A major focus of this report has been analyse the figures involved in the planning application to expand Stansted to 25mppa. Ironically, one of our key findings, that the local impact of Stansted’s proposed expansion in employment terms might well be less than was originally thought, might make it easier to gain the acceptance of the relevant local authorities involved to allow the development to take place. Our main overall findings are that the BAA projections over-estimate the local employment impact of the airport’s proposed growth and under-estimate its potential regional ‘transportation’ employment effect. These two findings are, of course, related to each other in important ways, and we also feel that they have potentially significant medium and long-term economic, competitiveness and planning policy implications for the East of England region

Impact of Laptev Sea flaw polynyas on the atmospheric boundary layer and ice production using idealized mesoscale simulations

The interaction between polynyas and the atmospheric boundary layer is examined in the Laptev Sea using the regional, non-hydrostatic Consortium for Small-scale Modelling (COSMO) atmosphere model. A thermodynamic sea-ice model is used to consider the response of sea-ice surface temperature to idealized atmospheric forcing. The idealized regimes represent atmospheric conditions that are typical for the Laptev Sea region. Cold wintertime conditions are investigated with sea-ice–ocean temperature differences of up to 40 K. The Laptev Sea flaw polynyas strongly modify the atmospheric boundary layer. Convectively mixed layers reach heights of up to 1200 m above the polynyas with temperature anomalies of more than 5 K. Horizontal transport of heat expands to areas more than 500 km downstream of the polynyas. Strong wind regimes lead to a more shallow mixed layer with strong near-surface modifications, while weaker wind regimes show a deeper, well-mixed convective boundary layer. Shallow mesoscale circulations occur in the vicinity of ice-free and thin-ice covered polynyas. They are forced by large turbulent and radiative heat fluxes from the surface of up to 789 W m−2, strong low-level thermally induced convergence and cold air flow from the orographic structure of the Taimyr Peninsula in the western Laptev Sea region. Based on the surface energy balance we derive potential sea-ice production rates between 8 and 25 cm d−1. These production rates are mainly determined by whether the polynyas are ice-free or covered by thin ice and by the wind strength.

Pre-Columbian ring ditch construction and land use on a “chocolate forest island” in the Bolivian Amazon

We present a palaeoecological investigation of pre-Columbian land use in the savannah “forest island” landscape of north-east Bolivian Amazonia. A 5700 year sediment core from La Luna Lake, located adjacent to the La Luna forest island site, was analysed for fossil pollen and charcoal. We aimed to determine the palaeoenvironmental context of pre-Columbian occupation on the site and assess the environmental impact of land use in the forest island region. Evidence for anthropogenic burning and Zea mays L. cultivation began ~2000 cal a BP, at a time when the island was covered by savannah, under drier-than-present climatic conditions. After ~1240 cal a BP burning declined and afforestation occurred. We show that construction of the ring ditch, which encircles the island, did not involve substantial deforestation. Previous estimates of pre-Columbian population size in this region, based upon labour required for forest clearance, should therefore be reconsidered. Despite the high density of economically useful plants, such as Theobroma cacao, in the modern forest, no direct pollen evidence for agroforestry was found. However, human occupation is shown to pre-date and span forest expansion on this site, suggesting that here, and in the wider forest island region, there is no truly pre-anthropogenic ‘pristine’ forest.

Noninvasive Evaluation of Oral Lesions Using Depth-sensitive Optical Spectroscopy

BACKGROUND: Optical spectroscopy is a noninvasive technique with potential applications for diagnosis of oral dysplasia and early cancer. In this study, we evaluated the diagnostic performance of a depth-sensitive optical spectroscopy (DSOS) system for distinguishing dysplasia and carcinoma from non-neoplastic oral mucosa. METHODS: Patients with oral lesions and volunteers without any oral abnormalities were recruited to participate. Autofluorescence and diffuse reflectance spectra of selected oral sites were measured using the DSOS system. A total of 424 oral sites in 124 subjects were measured and analyzed, including 154 sites in 60 patients with oral lesions and 270 sites in 64 normal volunteers. Measured optical spectra were used to develop computer-based algorithms to identify the presence of dysplasia or cancer. Sensitivity and specificity were calculated using a gold standard of histopathology for patient sites and clinical impression for normal volunteer sites. RESULTS: Differences in oral spectra were observed in: (1) neoplastic versus nonneoplastic sites, (2) keratinized versus nonkeratinized tissue, and (3) shallow versus deep depths within oral tissue. Algorithms based on spectra from 310 nonkeratinized anatomic sites (buccal, tongue, floor of mouth, and lip) yielded an area under the receiver operating characteristic curve of 0.96 in the training set and 0.93 in the validation set. CONCLUSIONS: The ability to selectively target epithelial and shallow stromal depth regions appeared to be diagnostically useful. For nonkeratinized oral sites, the sensitivity and specificity of this objective diagnostic technique were comparable to that of clinical diagnosis by expert observers. Thus, DSOS has potential to augment oral cancer screening efforts in community settings. Cancer 2009;115:1669-79. (C) 2009 American Cancer Society.