Three experiments tested whether the Implicit Association Test (IAT) reflects general knowledge, or whether it is sensitive to personally endorsed attitudes. In Experiment 1, participants learned to associate positive or negative attributes with two novel groups. Participants in one condition were told that the attributes accurately described the groups; in a second condition, they were told th...

Traditional methods for link prediction can be categorized into three main types: graph structure feature-based, latent feature-based, and explicit feature-based. Graph structure feature methods leverage some handcrafted node proximity scores, e.g., common neighbors, to estimate the likelihood of links. Latent feature methods rely on factorizing networks’ matrix representations to learn an embe...

Unsupervised learning of relative visual attributes is important because it is often infeasible for a human annotator to predefine and manually label all the relative attributes in large datasets. We propose a method for learning relative visual attributes given a set of images for each training class. The method is unsupervised in the sense that it does not require a set of predefined attribut...

Patients with prefrontal cortex lesions and controls were administered an implicit association task (IAT) that measured the degree of association between male and female names and their stereotypical attributes of strength and weakness. They also completed three questionnaires measuring their explicit judgment regarding gender-related stereotypical attributes. There were no between-group differ...

In Personal Learning Environments, personalized recommendations are used to support the activities of learners and deliver the suitable learning resources to learners. It models the dynamic multi preferences of learners using the multidimensional attributes of resources and learner ratings. By using the data mining technology the cold start and sparsity problems are eliminated. It also increase...

This study proposes a model for multimedia structured documents and concentrates on the impact of document structure on their attributes features. Traditional database attributes have implicit scopes in structured documents, and the key idea of our model is to make them explicit. So the main contribution resides in a novel deenition of the notion of attribute in structured document. These attri...

In this paper, the prediction of heart disease based on feature selection by using multilayer perceptron with back-propagation algorithm and k-nearest neighbor algorithm based on an explicit similarity measure with biomedical test values to diagnose heart disease is presented. The main motivation for this paper is to classify the heart disease with reduced number of attributes. We use the weigh...