Abstract. For the purpose of overcoming resource scarcity bottleneck in corpus-based translation knowledge acquisition research, this paper takes an approach of semi-automatically acquiring domain specific translation knowledge from the collection of bilingual news articles on WWW news sites. This paper presents results of applying standard co-occurrence frequency based techniques of estimating bilingual term correspondences from parallel corpora to relevant article pairs automatically collected from WWW news sites. The experimental evaluation results are very encouraging and it is proved that many useful bilingual term correspondences can be efficiently discovered with little human intervention from relevant article pairs on WWW news sites.
Flow-based networks such as OpenFlow-based networks have difficulty handling a large number of flows in a node due to the capacity limitation of search engine devices such as ternary content-addressable memory (TCAM). One typical solution of this problem would be to use MPLS-like tunneling, but this approach spoils the advantage of flow-by-flow path selection for load-balancing or QoS. We demonstrate a method named "Source Flow" that allows us to handle a huge amount of flows without changing the granularity of flows. By using our method, expensive and power consuming search engine devices can be removed from the core nodes, and the network can grow pretty scalable. In our demo, we construct a small network that consists of small number of OpenFlow switches, a single OpenFlow controller, and end-hosts. The hosts generate more than one million flows simultaneously and the flows are controlled on a per-flow-basis. All active flows are monitored and visualized on a user interface and the user interface allows audiences to confirm if our method is feasible and deployable.
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