In [] we defined Inf-Datalog and characterized the fragments of Monadic inf-Datalog that have the same expressive power as Modal Logic (resp. CTL, alternation-free Modal μ-calculus and Modal μ-calculus). We study here the time and space complexity of evaluation of Monadic inf-Datalog programs on finite models. We deduce a new unified proof that model checking has 1. linear data and program comp...

Abstract. With the rapid development of remote sensing technology in last decade, different modalities data recorded via a variety sensors are now easily accessible. Different often provide complementary information and thus more detailed accurate Earth observation is possible by integrating their joint information. While change detection methods have been traditionally proposed for homogeneous...

Due to severe societal and environmental impacts, wildfire prediction using multi-modal sensing data has become a highly sought-after data-analytical tool by various stakeholders (such as state governments power utility companies) achieve more informed understanding of activities plan preventive measures. A desirable algorithm should precisely predict fire risk magnitude for location in real ti...

In this paper, a novel approach proposed for structural damage detection from limited number of sensors using extreme learning machine (ELM). As the number of sensors used to measure modal data is normally limited and usually are less than the number of DOFs in the finite element model, the model reduction approach should be used to match with incomplete measured mode shapes. The second-order a...

In this research, the modal parameters of a beam in free-free condition are extracted by performing different experiments in laboratory. For this purpose, two different techniques are employed. The first methodology is considered as a time domain method in Operational Modal Analysis. While the other one is frequency domain impact hammer test which is categorized as an Experimental Modal Analysi...

Cross-modal retrieval has drawn wide interest for retrieval across different modalities of data (such as text, image, video, audio and 3D model). However, existing methods based on deep neural network (DNN) often face the challenge of insufficient cross-modal training data, which limits the training effectiveness and easily leads to overfitting. Transfer learning is usually adopted for relievin...