Browsing by Author "Elejalde Sierra, Erick"
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Item Un algoritmo de descomposición de tareas para el reconocimiento de patrones de texto = A task decomposition algorithm for string matching.(Universidad de Concepción., 2014) Elejalde Sierra, Erick; Ferres, LeoPrevious attempts to parallelize string matching algorithms have focused on data decomposition; that is, splitting the text on the number of available cores. In this work, we demonstrate that it is possible to use task decomposition efficiently, using a multiprocessor model. We introduce the Task Decomposition String Matching algorithm, a new algorithm based on bit parallelism and finite automata theory. We also present a producer/consumer strategy which may be applied to the traditional algorithms as an alternative way to parallelize them. We run experiments that compare both our approaches against traditional algorithms (such as Boyer-Moore, KMP and Bitap) by varying the length of the pattern and the properties of alphabets, two critical variables that affect performance in string matching. The Domain Decomposition strategy proved to be very efficient for this problem. Although task decomposition is asymptotically optimal, it is practically affected by cache contingencies.Item Investigación computacional del "propaganda model" What the media do in the shadows: A computational investigation of the propaganda model(Universidad de Concepción., 2018) Elejalde Sierra, Erick; Ferres, Leo; Bollen, JohanThe Propaganda Model (PM) discussed in Manufacturing Consent is a theory in political economy that states that the mass media are channels through which governments and major power groups pass down certain ideologies and mold a general consent according to their own interests. According to the authors, every piece of news have gone through a set of filters that has ultimately yielded the source event as newsworthy. Current developments in communications, the digital availability of a huge amount of news on-line streaming from every corner of the world, together with our increasing capability to process all this information in a lot of different ways, give us the perfect environment to validate social theories using quantitative methods. In our work we take advantage of all these data to prove, empirically, the theory laid out in the PM. Previous work has had used machine learning and natural language processing techniques, but they have focused only on showing some leaning to a political party by a sample of the major news outlets. Here make a first attempt in the formalization of the model and the filters, and we help to provide and explanation on how the media works taking a computational approach. Results illustrate a measurable media bias, showing a marked favoritism of Chilean media for the ruling political parties. This favoritism becomes clearer as we empirically observe a shift in the position of the mass media when there is a change in government. Furthermore, results support the PM by showing that high levels of concentration characterizes the Chilean media landscape in terms of ownership and topical coverage. Our methods reveal which groups of outlets and ownership exert the greatest influence on news coverage and can be generalized to any nation’s news system. Our studies on the geographic news coverage also give indications of the presence of the second filter (advertising). Experiments on predicting