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The rapid access of sensor technology provides both challenges and opportunities to authenticated spatiotemporal data. Authentication can be assured by developing related ontologies. Ontology explicitly specifies shared conceptualization and formal vocabularies. In this paper, we proposed an environmental spatio-temporal ontology (ESTO) using unified resource description framework (RDF) and Intelligent Environmental Knowledgebase (i-EKbase) recommendation system. Five different environmental data sources namely SILO, AWAP, ASRIS, CosmOz, and MODIS were considered to develop i-EKbase where knowledge was integrated. The recommendation system was founded on web based large scale dynamic data mining, contextual knowledge extraction, and integrated knowledge representation. The proposed ESTO was tested for optimization of the accessibility and usability issues related to big data sets and minimize the overall application costs. RDF representation made this ontology very flexible to publish on Linked Open Data Cloud environment.

Libraries have been established and always utilized in old fashion and modern school and academic environments in order to support educational programs. However, new instructional technologies have changed traditional library-based teaching models. Multimedia approach has shown successful in teaching different courses to different learners. However, there is little known how a combination of library and multimedia approach can be implemented and measured in teaching. This paper focuses on an innovative approach in which a blended library and multimedia model has been developed to teach geography concepts. It was implemented in teaching geography in a first grade class in an Iranian secondary school. The empirical results revealed the effectiveness of the blended model in learning and retention of geography.

It is a hotly research topic to eliminate irrelevant variables from counterexample, to make it easier to be understood. BFL algorithm is the most effective Counterexample minimization algorithm compared to all other approaches, but its run time overhead is very large due to one call to SAT solver per candidate variable to be eliminated. So we propose a faster counterexample minimization algorithm based on refutation analysis and incremental SAT. First, for every UNSAT instance of BFL, we perform refutation analysis to extract the set of variables that lead to UNSAT, all variables not belong to this set can be eliminated simultaneously. In this way, we can eliminate many variables with only one call to SAT solver. At the same time, we employ incremental SAT approach to share learned clauses between similar instances of BFL, to prevent overlapped state space from being searched repeatedly. Theoretic analysis and experiment result shows that, our approach can be 1 to 2 orders of magnitude faster than BFL, and still retain the minimization ability of BFL.