9,215 research outputs found

    On Web User Tracking: How Third-Party Http Requests Track Users' Browsing Patterns for Personalised Advertising

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    On today's Web, users trade access to their private data for content and services. Advertising sustains the business model of many websites and applications. Efficient and successful advertising relies on predicting users' actions and tastes to suggest a range of products to buy. It follows that, while surfing the Web users leave traces regarding their identity in the form of activity patterns and unstructured data. We analyse how advertising networks build user footprints and how the suggested advertising reacts to changes in the user behaviour.Comment: arXiv admin note: substantial text overlap with arXiv:1605.0653

    Analyzing urban sprawl patterns through fractal geometry: the case of Istanbul metropolitan area

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    Over the last decade, there has been a rapid increase in the amount of literature on the measurement of urban sprawl. Density gradients, sprawl indexes which are based on a series of measurable indicators and certain simulation techniques are some quantitative approaches used in previous studies. Recently, fractal analysis has been used in analyzing urban areas and a fractal theory of cities has been proposed. This study attempts to measure urban sprawl using a sprawl index and analyses urban form through fractal analysis for characterizing urban sprawl in Istanbul which has not been measured or characterized yet. In this study, measures of sprawl were calculated at each neighborhood level and then integrated within sprawl index through “density” and “proximity” factors. This identifies the pattern of urban sprawl during six periods from 1975 to 2005, and then the urban form of Istanbul is quantified through fractal analysis in given periods in the context of sprawl dynamics. Our findings suggest that the fractal dimension of urban form is positively correlated with the urban sprawl index score when urban growth pattern is more likely “concentrated”. However, a negative relationship has been observed between fractal dimension and sprawl index score when the urban growth pattern changes from the concentrated to the semi-linear form

    Graph Summarization

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    The continuous and rapid growth of highly interconnected datasets, which are both voluminous and complex, calls for the development of adequate processing and analytical techniques. One method for condensing and simplifying such datasets is graph summarization. It denotes a series of application-specific algorithms designed to transform graphs into more compact representations while preserving structural patterns, query answers, or specific property distributions. As this problem is common to several areas studying graph topologies, different approaches, such as clustering, compression, sampling, or influence detection, have been proposed, primarily based on statistical and optimization methods. The focus of our chapter is to pinpoint the main graph summarization methods, but especially to focus on the most recent approaches and novel research trends on this topic, not yet covered by previous surveys.Comment: To appear in the Encyclopedia of Big Data Technologie

    A hierarchical Bayesian model for predicting ecological interactions using scaled evolutionary relationships

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    Identifying undocumented or potential future interactions among species is a challenge facing modern ecologists. Recent link prediction methods rely on trait data, however large species interaction databases are typically sparse and covariates are limited to only a fraction of species. On the other hand, evolutionary relationships, encoded as phylogenetic trees, can act as proxies for underlying traits and historical patterns of parasite sharing among hosts. We show that using a network-based conditional model, phylogenetic information provides strong predictive power in a recently published global database of host-parasite interactions. By scaling the phylogeny using an evolutionary model, our method allows for biological interpretation often missing from latent variable models. To further improve on the phylogeny-only model, we combine a hierarchical Bayesian latent score framework for bipartite graphs that accounts for the number of interactions per species with the host dependence informed by phylogeny. Combining the two information sources yields significant improvement in predictive accuracy over each of the submodels alone. As many interaction networks are constructed from presence-only data, we extend the model by integrating a correction mechanism for missing interactions, which proves valuable in reducing uncertainty in unobserved interactions.Comment: To appear in the Annals of Applied Statistic

    Towards a Universal Wordnet by Learning from Combined Evidenc

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    Lexical databases are invaluable sources of knowledge about words and their meanings, with numerous applications in areas like NLP, IR, and AI. We propose a methodology for the automatic construction of a large-scale multilingual lexical database where words of many languages are hierarchically organized in terms of their meanings and their semantic relations to other words. This resource is bootstrapped from WordNet, a well-known English-language resource. Our approach extends WordNet with around 1.5 million meaning links for 800,000 words in over 200 languages, drawing on evidence extracted from a variety of resources including existing (monolingual) wordnets, (mostly bilingual) translation dictionaries, and parallel corpora. Graph-based scoring functions and statistical learning techniques are used to iteratively integrate this information and build an output graph. Experiments show that this wordnet has a high level of precision and coverage, and that it can be useful in applied tasks such as cross-lingual text classification
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