************************************* 16 of May, 14:00 Hours INESC Main Auditorium *************************************
Presenter: Hadi Fanaee Tork
Title: Event labeling combining ensemble detectors and background knowledge
Abstract: Event labeling is the process of marking events in unlabeled data. Traditionally, this is done
by involving one or more human experts through an expensive and time-consuming task. In this presentation we propose a new event labeling model relying on an ensemble of detectors and background knowledge. The target data are the usage log of a real bike sharing system. We first label events in the data and then evaluate the performance of the ensemble and individual detectors on the labeled data set using ROC analysis and static evaluation metrics in the absence and presence of background knowledge. The results show that when there is no access to human experts, the proposed approach can be an effective alternative for labeling events.
Paper: http://link.springer.com/article/10.1007/s13748-013-0040-3 Data set: http://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset
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Presenter: Mohammadreza Valizadeh
Title: Improving the Performance of Text Information Retrieval (IR) Systems
Abstract: This thesis focus on two major issues. One is re-ranking and the other is summarization. We have proposed a new method for re-ranking based on Query Sensitive similarity measure. After that, the
retrieved documents can be summarized. we have proposed several methods for summarizing multiple documents. one unsupervised (unsupervised Graph-based summarization) and 2 supervised  methods have been proposed (user-based method and Ensemble Method Combined with Actor-Object Relationship).