Web search is among the most frequent online activities. In this context, widespread informational queries entail user intentions to obtain knowledge with respect to a particular topic or domain. To serve learning needs better, recent research in the field of interactive information retrieval has advocated the importance of moving beyond relevance ranking of search results and considering a user’s knowledge state within learning oriented search sessions. Prior work has investigated the use of supervised models to predict a user’s knowledge gain and knowledge state from user interactions during a search session. However, the characteristics of the resources that a user interacts with have neither been sufficiently explored, nor exploited in this task. In this work, we introduce a novel set of resource-centric features and demonstrate their capacity to significantly improve supervised models for the task of predicting knowledge gain and knowledge state of users in Web search sessions. We make important contributions, given that reliable training data for such tasks is sparse and costly to obtain. We introduce various feature selection strategies geared towards selecting a limited subset of effective and generalizable features.

Human Activity Classification with radar has made significant progress in the past few years. In this article, we propose a cyclostationarity-based approach in this field of application. Feature extraction, selection, and activity classification as it detects micro-Doppler is made starting from complex-valued cyclostationary statistical functions of the reflected radar signal. The human activity can be recognized with up to 92.6% with the real part, 95.4% with the imaginary part and 95.4% by the combination of real and imaginary part.