In this day and age, the Internet has become an integral part of our lives. Question and answer (Q&A) networks like Stack Exchange have grown to become major sources of knowledge on the Internet. On the Internet, users devote time into looking for answers to questions they have. Sometimes, these users are able to find these answers and other times they do not, thus wasting their time. If these ...

The semantic technologies pose new challenge for the way in which we built and operate systems. They are tools used to represent significances, associations, theories, separated from data and code. Their goal is to create, to discover, to represent, to organize, to process, to manage, to ratiocinate, to represent, to share and use the significances and knowledge to fulfill the business, persona...

Adversarial examples have been shown to exist for a variety of deep learning architectures. Deep reinforcement learning has shown promising results on training agent policies directly on raw inputs such as image pixels. In this paper we present a novel study into adversarial attacks on deep reinforcement learning polices. We compare the effectiveness of the attacks using adversarial examples vs...

In order to obtain reliable accuracy estimates for automatic MOOC dropout predictors, it is important to train and test them in a manner consistent with how they will be used in practice. Yet most prior research on MOOC dropout prediction has measured test accuracy on the same course used for training the classifier, which can lead to overly optimistic accuracy estimates. In order to understand...

In Federated Learning (FL), models are as fragile centrally trained against adversarial examples. However, the robustness of federated learning remains largely unexplored. This paper casts light on challenge learning. To facilitate a better understanding vulnerability existing FL methods, we conduct comprehensive evaluations various attacks and training methods. Moreover, reveal negative impact...