Adolescent anxiety is common, impairing and costly. Given the scale of adolescent anxiety and its impact, fresh innovations for therapy are in demand. Cognitive Bias Modification of Interpretations (CBM-I) studies of adults show that by training individuals to endorse benign interpretations of ambiguous situations can improve anxious mood-states particularly in response towards stress. While, t...

In this paper, we adopt two views, personal and impersonal views, and systematically employ them in both supervised and semi-supervised sentiment classification. Here, personal views consist of those sentences which directly express speaker’s feeling and preference towards a target object while impersonal views focus on statements towards a target object for evaluation. To obtain them, an unsup...

Numerous semi-supervised learning methods have been proposed to augment Multinomial Naive Bayes (MNB) using unlabeled documents, but their use in practice is often limited due to implementation difficulty, inconsistent prediction performance, or high computational cost. In this paper, we propose a new, very simple semi-supervised extension of MNB, called Semi-supervised Frequency Estimate (SFE)...

The blocking effect, canonical in the study of associative learning, is often explained as a failure of the blocked cue to become associated with the outcome. However, this perspective fails to explain recent findings that suggest learning about a blocked cue is superior to a different type of redundant cue. We report an experiment designed to test the proposal that blocking is not a failure of...

SCIENTIFIC FUNDAMENTALS A database can be seen as a model, i.e. as a simplified, abstract description, of an external reality. In the case of relational databases, one starts by choosing certain predicates of a prescribed arity. The schema of the database consists of this set of predicates, possibly attributes, which can be seen as names for the arguments of the predicates, together with an ind...

We present two methods for semiautomatic detection and correction of errors in textual databases. The first method (horizontal correction) aims at correcting inconsistent values within a database record, while the second (vertical correction) focuses on values which were entered in the wrong column. Both methods are data-driven and language-independent. We utilise supervised machine learning, b...

This paper addresses the scalar regression problem presenting a solution for optimizing the Huber loss in a general semi-supervised setting, which combines multi-view learning and manifold regularization. To this aim, we propose a principled algorithm to 1) avoid computationally expensive iterative solutions while 2) adapting the Huber loss threshold in a data-driven fashion and 3) actively bal...

We propose a new method for training iterative collective classifiers for labeling nodes in network data. The iterative classification algorithm (ICA) is a canonical method for incorporating relational information into classification. Yet, existing methods for training ICA models rely on the assumption that relational features reflect the true labels of the nodes. This unrealistic assumption in...