Abstract Previous studies examining attitudes towards performance heterogeneity have focused on among teachers. However, positive the school environment are also assumed to be conducive for students. The aim of this paper is examine students’ with a sample 784 5 th -grade Based three-component theory (Eagly & Chaiken, 1993), we investigated whether or negative. Furthermore, analyzed context...

In this poster, we present an approach to contextualized semantic image annotation as an optimization problem. Ontologies are used to capture general and contextual knowledge of the domain considered, and a genetic algorithm is applied to realize the final annotation. Experiments with images from the beach vacation domain demonstrate the performance of the proposed approach and illustrate the a...

The growing availability of data in electronic form, the expansion of the World Wide Web (WWW) and the accessibility of computational methods for large-scale data processing have allowed researchers in Information Retrieval (IR) to design systems which can effectively and efficiently constrain search within the boundaries given by context, thus transforming classical search into contextual sear...

Context-Aware Recommender System (CARS) models are trained on datasets of context-dependent user preferences (ratings and context information). Since the number of context-dependent preferences increases exponentially with the number of contextual factors, and certain contextual information is still hard to acquire automatically (e.g., the user’s mood or for whom the user is buying the searched...

We compare two learning algorithms for generating contextual assumptions in automated assume-guarantee reasoning. The CDNF algorithm implicitly represents contextual assumptions by a conjunction of DNF formulae, while the OBDD learning algorithm uses ordered binary decision diagrams as its representation. Using these learning algorithms, the performance of assume-guarantee reasoning is compared...

In this paper, we present a method to employ target-side syntactic contextual information in a Hierarchical Phrase-Based system. Our method uses Combinatory Categorial Grammar (CCG) to annotate training data with labels that represent the left and right syntactic context of target-side phrases. These labels are then used to assign labels to nonterminals in hierarchical rules. CCG-based contextu...

Contextual recommendation goes beyond traditional models by incorporating additional information. Context aware recommender systems (CARs) correspond to not only the user’s preference profile but also consider the given situation and context. However, the selection and incorporation of optimal contextual features in context aware recommender systems is always challenging. In this paper we evalu...

Recommender systems have been widely applied to assist user’s decision making by providing a list of personalized item recommendations. Context-aware recommender systems (CARS) additionally take context information into considering in the recommendation process, since user’s tastes on the items may vary from contexts to contexts. Several context-aware recommendation algorithms have been propose...

Recommender systems have been widely applied to assist user’s decision making by providing a list of personalized item recommendations. Context-aware recommender systems (CARS) additionally take context information into considering in the recommendation process, since user’s tastes on the items may vary from contexts to contexts. Several context-aware recommendation algorithms have been propose...

In this paper, we propose an activity localization method with contextual information of person relationships. Activity localization is a task to determine “who participates to an activity group”, such as detecting “walking in a group” or “talking in a group”. Usage of contextual information has been providing promising results in the previous activity recognition methods, however, the contextu...