Inferring Document Readability by Integrating Eye Movement Features

Capturing user’s emotional state is an emerging way for implicit relevance feedback in information retrieval (IR). Recently, EEG-based emotion recognition has drawn increasing attention. However, a key challenge is effective learning of useful features from EEG signals. In this paper, we present our on-going work on using Deep Belief Network (DBN) to automatically extract high-level features from raw EEG signals. Our preliminary experiment on the DEAP dataset shows that the learned features perform comparably to the use of manually generated features for emotion recognition

Multiple Solutions for a Class of -Laplacian Systems

We study the multiplicity of solutions for a class of Hamiltonian systems with the p(x)-Laplacian. Under suitable assumptions, we obtain a sequence of solutions associated with a sequence of positive energies going toward infinity

Will Jargon Use Increase or Decrease a Doctor\u27s Credibility? Exploring the Moderating Effects of eHealth Literacy and Question Type

Doctors are accustomed to using jargon to communicate in online medical communities, but is it actually effective? In this article, we propose two diametrically opposed mechanisms of jargon use that affect patients\u27 confidence in providers of online medical consultation services: The use of jargon affects competence-based confidence positively, but negatively on benevolence- and integrity-based trust. We take into account the moderating effects of eHealth literacy and question type to better comprehend the circumstances in which jargon use is at play. To test our conceptual model, we conduct a scenario experiment and then use a survey method to collect 203 valid questionnaires. Finally, we discuss our findings, their implications for theory and practise, and the study\u27s limitations