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Creators/Authors contains: "Finn, Lisa"

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  1. ; ; ; ; (, Mexican Conference on Pattern Recognition)
    The trade on illegal goods and services, also known as illicit trade, is expected to drain 4.2 trillion dollars from the world economy and put 5.4 million jobs at risk by 2022. These estimates reflect the importance of combating illicit trade, as it poses a danger to individuals and undermines governments. To do so, however, we have to fi rst understand the factors that influence this type of trade. Therefore, we present in this article a method that uses node embeddings and clustering to compare a country based illicit supply network to other networks that represent other types of country relationships (e.g., free trade agreements, language). The results offer initial clues on the factors that might be driving the illicit trade between countries. 
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  2. ; ; ; ; (, International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems)
    The trade in illicit items, such as counterfeits, not only leads to the loss of large sums of private and public revenue, but also poses a danger to individuals, undermines governments, and--in the most extreme cases--fi nances criminal organizations. It is estimated that in 2013 trade in illicit items accounted for 2.5% of the global commerce. To combat illicit trade, it is necessary to understand its illicit supply networks. Therefore, we present in this article an approach that is able to find an optimal description of an illicit supply network using a series of Variable State Resolution-Markov Chains. The new method is applied to a real-world dataset stemming from the Global Product Authentication Service of Micro Focus International. The results show how an illicit supply network might be analyzed with the help of this method. 
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