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Mining Similar Traces of Entities on Web

References 1. Yin, Z., L. Cao, J. Han et al. LPTA: A Probabilistic Model for Latent Periodic Topic Analysis. – In: Proc. of 11th International Conference on Data Mining (ICDM), 2011 IEEE, 2011, pp. 904-913. 2. Liu, C., K. Zhang, H. Xiong et al. Temporal Skeletonization on Sequential Data: Patterns, Categorization, and Visualization. – In: Proc. of 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2014, pp. 1336-1345. 3. Liu, Z., M. Huang, W. Zhou. Research on Event-Oriented Ontology Model. 2009, 36(11), pp

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Two-Dimensional l 1-Norm Minimization in SAR Image Reconstriction

References 1. Nicolas, J.-M., G. Vasile, M. Gay, F. Tupin, Em. Trouvé. SAR Processing in the Temporal Domain: Application to Direct Interferogram Generation and Mountain Glacier Monitoring Can. – J. Remote Sensing, Vol. 33 , 2007, No 1, pp. 52-59. 2. Leijen, V., F. R. Hanssen. Interferometric Radar Meteorology: Resolving the Acquisition Ambiguity. – In: CEOS SAR Workshop, Ulm Germany, 27-28 May 2004, pp. 6-14. 3. Colesanti, C., A. Ferretti, F. Novali, C. Prati, F. Rocca. SAR Monitoring of Progressive and Seasonal Ground Deformation Using the

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Structured Information Extraction from Medical Texts in Bulgarian

. - Journal of the American Medical Informatics Association, Vol. 15 , January/February 2008, No 1, 25-28. 14. Harkema, H., A. Setzer, R. Gaizauskas, M. Hepple, R. Power, J. Rogers. Mining and Modelling Temporal Clinical Data. - In: Proc. of 4th UK e-Science All Hands Meeting, Nottingham, UK, 2005. 15. Gaizauskas, R., M. Hepple, N. Davis, Y. Guo, H. Harkema, A. Roberts, I. Roberts. AMBIT: Acquiring Medical and Biological Information from Text. - In: S. J. Cox, Ed., Proc. of 2nd UK e-Science All Hands Meeting, Nottingham, UK, 2003

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Privacy-Preserving of Check-in Services in MSNS Based on a Bit Matrix

References 1. Gruteser, M., D. Grunwald. Anonymous Usage of Location-Based Services through Spatial and Temporal Cloaking. - In: Proc. of 1st International Conference on Mobile Systems, Applications and Services. ACM, 2003, pp. 31-42. 2. Xiao-Feng, H. U. O. Z. M. A Survey of Trajectory Privacy-Preserving Techniques. - Chinese Journal of Computers, Vol. 10, 2011. 3. Huo, Z., X. Meng, H. Hu, et al. You Can Walk Alone: Trajectory Privacy-Preserving through Significant Stays Protection. - In: Proc. of Database

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Classification of Mental Tasks from EEG Signals Using Spectral Analysis, PCA and SVM

References 1. Perez-Marcos, D., M. Solazzi, W. Steptoe, O. Oyekoya, A. Frisoli, T. Weyrich, A. Steed, F. Tecchia, M. Slater, M. V. Sanchez-Vives. A Fully-Immersive Set-Up for Remote Interaction and Neurorehabilitation Based on Virtual Body Ownership. – Frontiersin Neurology, Vol. 3 , 2012, No 110. 2. Vega, C. F., F. J. R. Fernández. Recognition of Mental Task with the Analysis of Long-Range Temporal Correlations on EEG Brain Oscillation. – In: Proc. of Biosignals and Biorobotics Conference (BRC’12), 2012, ISSNIP. 3. Golomb, M. R., B. C

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BRLO-Tree: A Data Structure Used for 3D GIS Dynamic Scene Rendering

and Geomechanical Survey During Tunnel Excavation. - Engineering Geology, Vol. 192, 2015, pp. 19-25. 4. Petrov, V. A. Spatial-Temporal Three-Dimensional GIS Modeling. - Automatic Documentation and Mathematical Linguistics, Vol. 49, 2015, No 1, pp. 21-26. 5. Mattausch, O., J. Bittner, M. Wimmer. CHC++: Coherent Hierarchical Culling Revisited. - Computer Graphics Forum, Vol. 27, 2008, No 2, pp. 221-230. 6. Bittner, J., M. Wimmer, H. Piringer, et al. Coherent Hierarchical Culling: Hardware Occlusion Queries Made Useful

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An Indirected Recommendation Model for Chinese Microblog

. - Pervasive and Mobile Computing, Vol. 11, 2014, pp. 106-131. 10. Li, H., J. Yan, H. Weihong, D. Zhaoyun. Mining User Interest in Microblogs witha User-Topic Model. - China Communications, Vol. 11, 2014, No 8, pp. 131-144. 11. Bosch, H., D. Thom, F. Heimer. Scatterblogs: Real-Time Monitoring of Microblog Messages through User-Guided Filtering. - IEEE Transactions on Visualization and Computer Graphics, Vol. 19, 2013, No 12, pp. 2022-2031. 12. Jeong, D.-H., M. Song. Time Gap Analysis by the Topic Model-Based Temporal Technique

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Ontology-based Image Representation/ Uz ontoloģiju bāzēta attēla reprezentācija/ Представление изображения на основе онтологии

://www.bioontology.org/. [Accessed Aug. 21, 2014]. [5] N. Magesh and P. Thangaraj, “Image Ontology Construction using Spatial and Temporal Relationships,” Life Science Journal, vol. 10, no. 3, pp. 1-9, 2013. [Online]. Available: Life Sciences, http://www.lifesciencesite.com/. [Accessed Sep. 27, 2014]. [6] R. O. Santos, “Ontology-Based Topological Representation of Remote Sensing Images,” International Journal of Remote Sensing, vol. 35, no. 1, pp. 16-28, 2014. [Online]. Available: Taylor & Francis Online, http://www.tandfonline.com/. [Accessed Sep. 28, 2014]. http

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Adaptation of Symmetric Positive Semi-Definite Matrices for the Analysis of Textured Images

, 2013, pp. 223-241. 17. Galerne, B., Y. Gousseau, J.-M. Morel. Micro-Texture Synthesis by Phase Randomization. – Image Process. Line, Vol. 1 , 2011 (Online). http://dx.doi.org/10.5201/ipol.2011.ggm_rpn 18. Köppel, M., X. Wang, D. Doshkov, T. Wiegand, P. Ndjiki-Nya. Depth Image-Based Rendering with Spatio-Temporally Consistent Texture Synthesis for 3-D Video with Global Motion. – In: 19th IEEE Int. Conf. Image Process. (ICIP’12), 2012, Orlando, FL, USA, pp. 2713-2716. 19. Paget, R., I. D. Longstaff. Texture Synthesis via a Non Causal Nonparametric

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A Study on Geographic Information Services Ontology and Its Applications

Geographical Information Point of View. O. Stock, Ed. Spatial and Temporal Reasoning. Netherlands, Springer, 1997, pp. 135-53. 5. Harvey, F. Designing for Interoperability: Overcoming Semantic Differences. M. Goodchild, M. Egenhofer, R. Fegeas et al., Eds. Interoperating Geographic Information Systems. Springer US. 1999, pp. 85-97. 6. Visser, U., H. Stuckenschmidt, G. Schuster et al. Ontologies for Geographic Information Processing. – Computers & Geosciences, Vol. 28 , 2002, No 1, pp. 103-117. 7. Wei, C. Using Ontology to Achieve the Semantic Integration

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