Image registration is a fundamental enabling technology in computer vision. Developing an accurate image registration algorithm will significantly improve the techniques for computer vision problems such as tracking, fusion, change detection, autonomous navigation. In this paper, our goal is to develop an algorithm that is robust, automatic, can perform multi-modality registration, reduces the ...

Latent Semantic Analysis (LSA) is a statistical technique for extracting semantic information from text corpora. LSA has been used with success to automatically grade student essays (Intelligent Essay Scoring), model human language learning, and model language comprehension. We examine how LSA may help to predict a reader’s interest in a selection of news articles, based on their reported inter...

In this paper, we propose deep learning architectures (FNN, CNN and LSTM) to forecast a regression model for time dependent data. These algorithm’s are designed to handle Floating Car Data (FCD) historic speeds to predict road traffic data. For this we aggregate the speeds into the network inputs in an innovative way. We compare the RMSE thus obtained with the results of a simpler physical mode...

The benefits of Free Space Optics (FSO) motivate to use it for future high data rate demanding communication applications. However, widespread growth of the technology has been hampered by reduced availability due to weather influences on the link. The fog has been analyzed as the most detrimental for FSO communication. There are some models that predict fog attenuation in terms of visibility. ...

Learning Factors Analysis (LFA), a form of student modeling, is used to predict whether a student can correctly answer a tutor question. Existing evaluations of LFA rely on metrics like the crossvalidated root mean squared error (RMSE). However, the LFA output can be a binary classification (the student will be correct or not), so we can use classification metrics, such as precision and recall,...

Since its introduction by Efron [1], the bootstrap has been the object of research in statistics. We proposed a new bootstrap procedure of indirect effect in mediation model which is resistant to outliers. The proposed approach was based on residual bootstrap which incorporated rescaled studentized residuals, namely the Rescaled Studentized Residual Bootstrap using Least Squares (ReSRB). The si...

This paper presents the corpus-driven approach in building the computational model of fundamental frequency, or F0, for Lithuanian language. The model was obtained by training the HMM-based speech synthesis system HTS on six hours of speech coming from multiple speakers. Several gender specific models, using different parameters and different contextual factors, were investigated. The models we...

in order to comparing geostatistics and artificial neural networks (ann) methods in predicting soil salinity and clay content this experiment was conducted in sistan region land. 121 soil samples were taken at the depth of 0-30 cm within the grid of 750 750 m and soil ec and clay percent were determined. 105 samples were used for training and 16 samples were used for test in both models. diffe...