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Joseph Gogodze

complementarity problems, Comput. Optim. Appl., 7,1997, 3–25. [4] Bondarenko, A.S., Bortz, D.M., More, J.J., COPS: Large-scale nonlinearly constrained optimization problems, No. ANL/MCS-TM-237. Argonne National Lab., IL (US), 2000. [5] Bongartz, I., Conn, A.R., Gould, N.I.M., Saunders, M.A., Toint, P.L., A numerical comparison between the LANCELOT and MINOS packages for large-scale numerical optimization, Report 97/13, Namur University, 1997 [6] Brest, J., Greiner, S., Boskovic, B., Mernik, M., Zumer, V., Self-adapting control parameters in differential

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Michał Szczepanik and Ireneusz Jóźwiak

., Nandakumar K. (2011). Introducing to biometrics, Spinger. [12] Maltoni D., Maio D., Jain A.K., Prabhakar S. (2009) Handbook of Fingerprint Recognition , 2nd Edition, Springer [13] Pankanti S, Prabhakar S., Jain A.K. (2001) On the individuality of fingerprints, Proceedings of Computer Vision and Pattern Recognition (CVPR ). [14] Parziale G., Niel A. (2004) A fingerprint matching using minutiae triangulation. Proc. of ICBA . [15] Ratha N. K., Govindaraju V. (2007 ) Advances in Biometrics: Sensors, Algorithms

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Anna Kobusińska and Ching-Hsien Hsu

References [1] V. Mayer-Schnberger, and K. Cukier, Big Data: A Revolution That Will Transform How We Live, Work, and Think , Amazon, 2014. [2] N. Japkowicz, and J. Stefanowski (Eds.), Big Data Analysis: New Algorithms for a New Society , Studies in Big Data, 2016. [3] Jaroslav Pokorný, How to Store and Process Big Data: Are Today’s Databases Sufficient?, Proceedings of Computer Information Systems and Industrial Management - 13th IFIPTC8 International Conference , CISIM 2014, Ho Chi Minh City, Vietnam, November 5-7, 2014, pp.631–643. [4

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M. Borysiewicz, A. Wawrzynczak and P. Kopka

-68. [5] Gilks, W., S. Richardson, and D. Spiegelhalter, Markov Chain Monte Carlo in Practice . Chapman & Hall/CRC, 1996, 486. [6] Ivakhnenko, A.G., Group method of data Handling - A Rival of the Method of Stochastic Approximation, Soviel Automatic Control , 13 , 43-71, 1966. [7] Johannesson, G. et al., Sequential Monte-Carlo based framework for dynamic datadriven event reconstruction for atmospheric release., Proc. of the Joint Statistical Meeting, Minneapolis , MN, American Statistical Association and Cosponsors, 2005, 73

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Takayuki Nagoya

graph isomorphism, Technical Report CS-77-04, Computer Science Department, University of Waterloo, 1979 [5] Brandstädt A., Le V. B. and Spinrad J. P., Graph Classeses: A Survey , SIAM, 1999. [6] Colbourn G.J., On testing isomorphism of permutation graphs, Networks , 11, 1981, 13–21. [7] Hopcroft J. and Karp R. M., An n 5/2 algorithm for maximum matching in bipartite graphs, SIAM J. Comput. , 4, 1975, 225–231. [8] Köbler J. and Schöning U. and ToranJ., The Graph Isomorphism Problem: Its Structural Complexity , Birkhauser, 1993. [9

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Raghu Nandan Sengupta and Rakesh Kumar

Optimization, Frenk, H., Roos, C., Terlaky, T., Zhang, S. (Edited), Kluwer Academic Publishers , 2000b, 303–328. [11] Ben-Tal, A. and Nemirovski, A., Robust truss topology design via semi-definite programming. SIAM Journal on Optimization , 7 , 1997, 991-1016. [12] Ben-Tal, A. and Nemirovski, A., Robust convex optimization. Mathematics of Operations Research, 23 , 1998, 769-805. [13] Ben-Tal, A. and Nemirovski., A, Robust solutions to uncertain linear programs. Operations Research Letters , 25 , 1999, 1–13. [14] Ben-Tal, A. and Nemirovski, A

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Penny Sanchez, Gary Glonek and Andrew Metcalfe

References [1] G. E. P. Box and N. R Draper. Empirical Model Building and Response Surfaces. New York: J. Wiley & Sons., 1987. [2] G. E. P. Box, W. G. Hunter, and J. S. Hunter. Statistics for Experimenters. New York: J. Wiley & Sons., 1978. [3] G. E. P. Box and K. B. Wilson. On the experimental attainment of optimum conditions (with discussion). Journal of the Royal Statistical Society: Series B, 13(1):1{45, 1951. [4] P. Czyzak and A. Jaszkiewicz. Pareto simulated annealing-a

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Izabela Rejer

handicapped persons, Journal of Microcomputer Application , 16 , 1993, 293-299. [11] Pfurtscheller G., Lopes da Silva F. H., Event-related EEG/MEG synchronization and desynchronization: basic principles, Clinical Neurophysiology , 110 , 1999, 1842-1857 [12] Raudys S. J., Jain A. K., Small sample size effects in statistical pattern recognition: Recommendations for practitioners. IEEE Transactions on Pattern Analysis and Machine Intelligence , 13 , 3, 1991, 252-264. [13] Reed R. D., Marks II R. J., Neural Smithing

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Wojciech Wieczorek

Higuera, C.: Characteristic sets for polynomial grammatical inference. Mach. Learn. 27, 125-138, 1997. [13] de la Higuera, C.: A bibliographical study of grammatical inference. Pattern Recognit. 38, 1332-1348, 2005. [14] de la Higuera, C.: Grammatical Inference: Learning Automata and Grammars. Cambridge University Press, 2010. [15] Hopcroft, J.E., Motwani, R., Ullman, J.D.: Introduction to Automata Theory, Languages, and Computation, 2nd ed. Addison-Wesley, 2001. [16] Kieffer, J.C., Yang, E.: Grammar Based

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Pawel Wojciechowski, Wojciech Frohmberg, Michal Kierzynka, Piotr Zurkowski and Jacek Blazewicz

. thesis, Poznan University of Technology, Poznan, Poland , 2014. [10] Kuksa, P., Pavlovic, V., Efficient alignment-free DNA barcode analytics, BMC bioinformatics , 10 , 14, 2009, 1–18. [11] Langmead, B., Salzberg, S.L., Fast gapped-read alignment with Bowtie 2, Nat Methods , 9 , 4, 2013, 357–359. [12] Langmead, B., Trapnell, C., Pop, M., Salzberg, S.L., Ultrafast and memory-efficient alignment of short DNA sequences to the human genome, Genome Biology , 10 , 3, 2009, 1–10. [13] Liu, Y., Schröder, J., Schmidt, B., Musket: a multistage k