892 resultados para Pure word deafness
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In light of the growing international competition among states and globally operating companies for limited natural resources, export restrictions on raw materials have become a popular means for governments to strive for various goals, including industrial development, natural resource conservation and environmental protection. For instance, China as a major supplier of many raw materials has been using its powerful position to both economic and political ends. The European Union (EU), alongside economic heavyweights such as the US, Japan and Mexico, launched two high-profile cases against such export restrictions by China at the WTO in 2009 and 2012. Against this background, this paper analyses the EU’s motivations in the initiation of trade disputes on export restrictions at WTO, particularly focusing on the two cases with China. It argues that the EU's WTO complaints against export restrictions on raw materials are to a large extent motivated by its economic and systemic interests rather than political interests. The EU is more likely to launch a WTO complaint, the stronger the potential and actual impact on its economy, the more ambiguous the WTO rules and the stronger the internal or external lobbying by member states or companies. This argumentation is based on the analysis of pertinent factors such as the economic impact, the ambiguity of WTO law on export restrictions and the pressure by individual member states on the EU as well as the role of joint complaints at the WTO and political considerations influencing the EU’s decision-making process.
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Internet traffic classification is a relevant and mature research field, anyway of growing importance and with still open technical challenges, also due to the pervasive presence of Internet-connected devices into everyday life. We claim the need for innovative traffic classification solutions capable of being lightweight, of adopting a domain-based approach, of not only concentrating on application-level protocol categorization but also classifying Internet traffic by subject. To this purpose, this paper originally proposes a classification solution that leverages domain name information extracted from IPFIX summaries, DNS logs, and DHCP leases, with the possibility to be applied to any kind of traffic. Our proposed solution is based on an extension of Word2vec unsupervised learning techniques running on a specialized Apache Spark cluster. In particular, learning techniques are leveraged to generate word-embeddings from a mixed dataset composed by domain names and natural language corpuses in a lightweight way and with general applicability. The paper also reports lessons learnt from our implementation and deployment experience that demonstrates that our solution can process 5500 IPFIX summaries per second on an Apache Spark cluster with 1 slave instance in Amazon EC2 at a cost of $ 3860 year. Reported experimental results about Precision, Recall, F-Measure, Accuracy, and Cohen's Kappa show the feasibility and effectiveness of the proposal. The experiments prove that words contained in domain names do have a relation with the kind of traffic directed towards them, therefore using specifically trained word embeddings we are able to classify them in customizable categories. We also show that training word embeddings on larger natural language corpuses leads improvements in terms of precision up to 180%.
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where Jones School is now (1959)
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Includes index.
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Mode of access: Internet.
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Mode of access: Internet.
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Mode of access: Internet.
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Includes bibliographies.
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Bibliography: p. 6.